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Better Determine Causes of Morale Problems Would Assist in Targeting 
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United States Government Accountability Office: 
GAO: 

Report to Congressional Requesters: 

September 2012: 

Department of Homeland Security: 

Taking Further Action to Better Determine Causes of Morale Problems 
Would Assist in Targeting Action Plans: 

GAO-12-940: 

GAO Highlights: 

Highlights of GAO-12-940, a report to congressional requesters. 

Why GAO Did This Study: 

DHS is the third largest cabinet-level department in the federal 
government, employing more than 200,000 staff in a broad range of 
jobs. Since it began operations in 2003, DHS employees have reported 
having low job satisfaction. DHS employee concerns about job 
satisfaction are one example of the challenges the department faces 
implementing its missions. GAO has designated the implementation and 
transformation of DHS as a high risk area, including its management of 
human capital, because it represents an enormous and complex 
undertaking that will require time to achieve in an effective and 
efficient manner. GAO was asked to examine: (1) how DHS’s employee 
morale compared with that of other federal employees, and (2) the 
extent to which DHS and selected components have determined the root 
causes of employee morale, and developed action plans to improve 
morale. To address these objectives, GAO analyzed survey evaluations, 
focus group reports, and DHS and component action planning documents, 
and interviewed officials from DHS and four components, selected based 
on workforce size, among other things. 

What GAO Found: 

Department of Homeland Security (DHS) employees reported having lower 
average morale than the average for the rest of the federal 
government, but morale varied across components and employee groups 
within the department. Data from the 2011 Office of Personnel 
Management (OPM) Federal Employee Viewpoint Survey (FEVS)—-a tool that 
measures employees’ perceptions of whether and to what extent 
conditions characterizing successful organizations are present in 
their agencies—-showed that DHS employees had 4.5 percentage points 
lower job satisfaction and 7.0 percentage points lower engagement in 
their work overall. Engagement is the extent to which employees are 
immersed in their work and spending extra effort on job performance. 
Moreover, within most demographic groups available for comparison, DHS 
employees scored lower on average satisfaction and engagement than the 
average for the rest of the federal government. For example, within 
most pay categories DHS employees reported lower satisfaction and 
engagement than non-DHS employees in the same pay groups. Levels of 
satisfaction and engagement varied across components, with some 
components reporting scores above the non-DHS averages. Several 
components with lower morale, such as Transportation Security 
Administration (TSA) and Immigration and Customs Enforcement (ICE), 
made up a substantial share of FEVS respondents at DHS, and accounted 
for a significant portion of the overall difference between the 
department and other agencies. In addition, components that were 
created with the department or shortly thereafter tended to have lower 
morale than components that previously existed. Job satisfaction and 
engagement varied within components as well. For example, employees in 
TSA’s Federal Security Director staff reported higher satisfaction (by 
13 percentage points) and engagement (by 14 percentage points) than TSA’
s airport security screeners. 

DHS has taken steps to determine the root causes of employee morale 
problems and implemented corrective actions, but it could strengthen 
its survey analyses and metrics for action plan success. To understand 
morale problems, DHS and selected components took steps, such as 
implementing an exit survey and routinely analyzing FEVS results. 
Components GAO selected for review—-ICE, TSA, the Coast Guard, and 
Customs and Border Protection—-conducted varying levels of analyses 
regarding the root causes of morale to understand leading issues that 
may relate to morale. DHS and the selected components planned actions 
to improve FEVS scores based on analyses of survey results, but GAO 
found that these efforts could be enhanced. Specifically, 2011 DHS-
wide survey analyses did not include evaluations of demographic group 
differences on morale-related issues, the Coast Guard did not perform 
benchmarking analyses, and it was not evident from documentation the 
extent to which DHS and its components used root cause analyses in 
their action planning. Without these elements, DHS risks not being 
able to address the underlying concerns of its varied employee 
population. In addition, GAO found that despite having broad 
performance metrics in place to track and assess DHS employee morale 
on an agency-wide level, DHS does not have specific metrics within the 
action plans that are consistently clear and measurable. As a result, 
DHS’s ability to assess its efforts to address employee morale 
problems and determine if changes should be made to ensure progress 
toward achieving its goals is limited. 

What GAO Recommends: 

GAO recommends that DHS examine its root cause analysis efforts and 
add the following, where absent: comparisons of demographic groups, 
benchmarking, and linkage of root cause findings to action plans; and 
establish clear and measurable metrics of action plan success. DHS 
concurred with our recommendations. 

View [hyperlink, http://www.gao.gov/products/GAO-12-940]. For more 
information, contact David C. Maurer at (202) 512-9627 or 
maurerd@gao.gov. 

[End of section] 

Contents: 

Letter: 

Background: 

DHS Employees Reported Lower Morale than the Rest of the Federal 
Government, but Morale Varied across DHS Components and Employee 
Groups: 

DHS Took Steps to Determine Root Causes of Morale Problems and 
Implemented Corrective Actions, but Could Strengthen Its Efforts: 

Conclusion: 

Recommendations for Executive Action: 

Agency Comments and Our Evaluation: 

Appendix I: Statistical Analysis of Employee Morale at Department of 
Homeland Security and Other Agencies: 

Appendix II: Scope and Methodology: 

Appendix III: DHS and Selected Component Steps Taken to Determine Root 
Causes of Morale Problems: 

Appendix IV: Selected Components' Data Sources for Evaluating Morale, 
Other than the Federal Employee Viewpoint Survey: 

Appendix V: Comments from the Department of Homeland Security: 

Appendix VI: Comments from the U.S. Office of Personnel Management: 

Appendix VII: GAO Contact and Staff Acknowledgments: 

Tables: 

Table 1: 2011 FEVS Job Satisfaction and Engagement Scores by DHS 
Component (Sorted by Job Satisfaction Index Score): 

Table 2: DHS-wide and Component Action Plan Goals and Examples of Low-
Scoring FEVS Topics Addressed through such Goals: 

Table 3: DHS-Wide and Selected Components' Action Planning Steps: 

Table 4: Examples of DHS-Wide and Selected Components' Measures of 
Success: 

Table 5: DHS and Non-DHS Employee Engagement Index by Demographic 
Group for the 2011 FEVS: 

Table 6: Model Estimates of Employee Engagement Index at DHS and Other 
Agencies Using the 2011 FEVS: 

Table 7: OPM Employee Morale Index by DHS Component and Offices Using 
the 2011 FEVS: 

Table 8: Morale at Preexisting and Recently Created Components of DHS 
Using the 2011 FEVS: 

Table 9: Model Estimates of the Difference in Engagement and Job 
Satisfaction between Employees in DHS Components and Non-DHS Agencies 
Based on the 2011 FEVS: 

Figures: 

Figure 1: Percentage of Satisfied DHS Employees Compared with 
Governmentwide Averages, 2006, 2008, 2010, and 2011: 

Figure 2: OPM Job Satisfaction and Employee Engagement Indexes in the 
2011 FEVS, for Selected Categories of DHS and Non-DHS Employees: 

Figure 3: Satisfaction and Engagement by TSA Employee Group, 2011: 

Figure 4: The Extent to Which OCHCO, TSA, CBP, ICE, and the Coast 
Guard Incorporated Recommended Factors in Analyzing 2011 FEVS Results: 

Figure 5: OPM's Six Steps for Action Planning to Improve FEVS Scores: 

Figure 6: Engagement Index Scores by Supervisory Status and Tenure, 
for DHS and Non-DHS Employees: 

Figure 7: Satisfaction Index Scores by Supervisory Status and Tenure, 
for DHS and Non-DHS Employees: 

Figure 8: DHS's 2011 Component Comparison Based on Four HCAAF Indexes: 

Figure 9: DHS HCAAF Scores since 2006: 

Abbreviations: 

AES: Annual Employee Survey: 

CBP: U.S. Customs and Border Protection: 

CIS: U.S. Citizenship and Immigration Services: 

DHS: Department of Homeland Security: 

EE: employee engagement: 

EEESC: Employee Engagement Executive Steering Committee: 

FEMA: Federal Emergency Management Agency: 

FEVS: Federal Employee Viewpoint Survey: 

FLETC: Federal Law Enforcement Training Center: 

FOCS: Federal Organizational Climate Survey: 

GS: General Schedule: 

HCAAF: Human Capital Assessment and Accountability Framework: 

I&A: Intelligence and Analysis: 

ICE: Immigration and Customs Enforcement: 

JS: job satisfaction: 

MGMT: Management Directorate: 

MVP: Most Valuable Perspective: 

NPPD: National Protection and Programs Directorate: 

OAS: Organizational Assessment Survey: 

OCHCO: Office of the Chief Human Capital Officer: 

OIG: Office of the Inspector General: 

OPM: Office of Personnel Management: 

OS: Office of the Secretary: 

S&T: Science and Technology: 

TSA: Transportation Security Administration: 

USSS: U.S. Secret Service: 

[End of section] 

United States Government Accountability Office: 
Washington, DC 20548: 

September 28, 2012: 

The Honorable Susan M. Collins: 
Ranking Member: 
Committee on Homeland Security and Governmental Affairs: 
United States Senate: 

The Honorable Michael T. McCaul: 
Chairman: 
The Honorable William R. Keating: 
Ranking Member: 
Committee on Homeland Security, Subcommittee on Oversight, 
Investigations, and Management: 
House of Representatives: 

The Department of Homeland Security (DHS) is the third largest cabinet-
level department in the federal government, employing more than 
200,000 staff in a broad range of jobs, including aviation and border 
security, emergency response, cybersecurity analysis, and chemical 
facility inspection. The DHS workforce is situated throughout the 
nation, carrying out activities in support of DHS's mission to (1) 
prevent terrorism and enhance security, (2) secure and manage the 
nation's borders, (3) enforce and administer immigration laws, (4) 
safeguard and secure cyberspace, (5) ensure resilience from disasters, 
and (6) provide essential support to national and economic security. 

Since it began operations in 2003, DHS employees have reported having 
low job satisfaction. In 2011, for example, DHS's scores on the Office 
of Personnel Management (OPM) Federal Employee Viewpoint Survey (FEVS)-
-a tool that measures employees' perceptions of whether and to what 
extent conditions characterizing successful organizations are present 
in their agency--and the Partnership for Public Service's (the 
Partnership) rankings of the Best Places to Work in the federal 
government, were generally low.[Footnote 1] 

DHS employee concerns about job satisfaction are one example of the 
challenges the department faces in implementing its missions. In 
January 2003, we designated the implementation and transformation of 
DHS as high risk, including its management of human capital, because 
it represented an enormous and complex undertaking that would require 
time to achieve in an effective and efficient manner, and it has 
remained on our high-risk list since that time.[Footnote 2] Improving 
human capital management is a DHS priority, reflected through several 
DHS-wide strategy documents. In June 2012, DHS provided us with its 
updated Integrated Strategy for High Risk Management (Integrated 
Strategy), which identified activities to improve employee job 
satisfaction scores, among other things. In addition, DHS has issued 
various other strategies and plans for its human capital activities 
and functions, such as a human capital strategic plan for fiscal years 
2009 through 2013,[Footnote 3] and a workforce strategy for fiscal 
years 2011 through 2016, which contains the department's workforce 
goals, objectives, and performance measures for human capital 
management.[Footnote 4] 

We have previously reported that successful organizations empower and 
involve their employees to gain insights about operations from a 
frontline perspective, increase their understanding and acceptance of 
organizational goals and objectives, and improve motivation and 
morale.[Footnote 5] In light of the critical nature of DHS's mission 
to protect the security and economy of our nation and the importance 
of attracting and retaining engaged and satisfied DHS employees to 
perform its work, you asked us to assess DHS's efforts to address 
employee morale.[Footnote 6] Thus, this report addresses the following 
questions: 

* How does DHS's employee morale compare with that of other federal 
government employees? 

* To what extent have DHS and selected components determined the root 
causes of employee morale and developed action plans to improve morale? 

To address these questions, we analyzed survey evaluations for the 
2011 FEVS, focus group reports from 2007, and DHS and selected 
component 2011 action planning documents and compared the documents 
with OPM and Partnership guidance. We also interviewed officials from 
DHS's Office of the Chief Human Capital Officer (OCHCO), and human 
capital officials from four components--U.S. Customs and Border 
Protection (CBP), U.S. Immigration and Customs Enforcement (ICE), 
Transportation Security Administration (TSA), and U.S. Coast Guard 
(Coast Guard). We selected these four DHS components based on their 
workforce size and how their 2011 Job Satisfaction and Engagement 
Index scores compared with the non-DHS average.[Footnote 7] The 
components selected had scores both above, below, and similar to the 
average. In addition, we interviewed representatives of employee 
groups within the four selected components to gather employee 
perspectives on drivers of morale. Details of the selected component 
index scores, and their statistical significance, are reported in 
appendix I. 

To compare DHS's employee morale with that of other federal government 
employees, we analyzed the 2011 FEVS results and reviewed OPM survey 
results issued since 2004, the first full year in which survey data 
are available. During the course of our analysis, we interviewed 
knowledgeable agency officials, reviewed relevant documentation, 
tested data for errors, and determined that the FEVS data are 
sufficiently reliable for the purposes of this report. As part of this 
analysis, we compared 2011 DHS and non-DHS job satisfaction and 
engagement score results by several categories of employees, such as 
supervisory status, pay grade, and age. We also compared satisfaction 
and engagement scores within the selected components by employee 
group, where possible. For example, within TSA, we compared 
satisfaction and engagement scores reported by Transportation Security 
Officers, Federal Security Director staff, headquarters staff, and 
Federal Air Marshals. 

To determine the extent to which DHS and the selected components 
identified the root causes of employee morale and developed action 
plans for improvements, we reviewed agency analysis results, 
interviewed agency human capital officials and representatives of 
employee groups, and evaluated action plans for improving morale. We 
also compared DHS and selected components' morale root cause analyses 
and related action plans with available guidance for such efforts. 

We conducted this performance audit from October 2011 through 
September 2012 in accordance with generally accepted government 
auditing standards. Those standards require that we plan and perform 
the audit to obtain sufficient, appropriate evidence to provide a 
reasonable basis for our findings and conclusions based on our audit 
objectives. We believe that the evidence obtained provides a 
reasonable basis for our findings and conclusions based on our audit 
objectives. Appendix II contains more detailed information about our 
scope and methodology. 

Background: 

Federal employees are routinely surveyed through OPM's administration 
of the FEVS, which is administered to collect data on federal 
employees' perceptions about how effectively agencies are managing 
their workforces. The FEVS is a tool that measures employees' 
perceptions of whether, and to what extent, conditions that 
characterize successful organizations are present in their agencies, 
according to OPM.[Footnote 8] This survey was administered for the 
first time in 2002 and then repeated in 2004, 2006, 2008, 2010, 2011, 
and April through June 2012.[Footnote 9] The survey provides general 
indicators of how well the federal government is managing its human 
resources management systems. It also serves as a tool for OPM to 
assess individual agencies and their progress on strategic management 
of human capital, and gives senior managers employee perspectives on 
agency management. Specifically, the survey includes categories of 
questions asking employees for their perspectives on their work 
experience, work unit, agency, supervisor, leadership, and 
satisfaction. OPM intends for agency managers to use the findings to 
develop policies and action plans for improving agency performance. In 
2011, OPM provided a summary of FEVS findings to DHS. In that report, 
OPM summarized DHS's survey results relative to governmentwide 
averages and provided positive and negative response levels for each 
survey question. Also included in the report was action planning 
guidance for using FEVS results to improve human capital management. 

To guide governmentwide efforts to support agency mission results with 
human capital strategies and in response to the Chief Human Capital 
Officers Act of 2002,[Footnote 10] OPM created the Human Capital 
Assessment and Accountability Framework (HCAAF). Agencies are 
evaluated by OPM on their progress in meeting HCAAF standards in areas 
such as talent management, which is focused on agencies having quality 
people with the appropriate competencies in mission-critical 
activities. The FEVS job satisfaction index is one of the metrics used 
by OPM to assess whether agencies are effectively managing the talent 
management system. The FEVS provides one source of information for 
evaluating success on other HCAAF standards as well by measuring 
responses to groups of FEVS questions for four indices. The four index 
measures are: Leadership and Knowledge Management; Results-Oriented 
Performance Culture; Talent Management; and Job Satisfaction. In 
addition, in 2011, OPM added an index to measure employee engagement, 
which OPM defines as the extent to which an employee is immersed in 
the content of the job and energized to spend extra effort in job 
performance. 

DHS's OCHCO is responsible for implementing policies and programs to 
recruit, hire, train and retain DHS's workforce. As the department-
wide unit responsible for human capital issues within DHS, OCHCO 
provides OPM with a DHS-wide action plan every other year, with the 
next plan due in January 2013. OCHCO also provides guidance and 
oversight to the DHS components related to morale issues. For example, 
OCHCO provides a survey analysis and action planning tool that the 
components must use in response to FEVS results to develop action 
plans for improving employees' positive scores.[Footnote 11] These 
plans are to state objectives and identify actions to be taken in 
response to survey results. OCHCO also has provided oversight by 
reviewing and providing feedback on component action plans. 

DHS Employees Reported Lower Morale than the Rest of the Federal 
Government, but Morale Varied across DHS Components and Employee 
Groups: 

Data from the 2011 FEVS show that DHS employees have lower average 
levels of job satisfaction and engagement overall and across most 
demographic groups available for comparison, such as pay grade, when 
compared with the average for the rest of the federal government. 
Levels of satisfaction and engagement vary across components, with 
some components reporting satisfaction or engagement above the average 
for the rest of the government. Similarly, these measures of morale 
vary within components as well, with some employee groups reporting 
higher morale than other groups within the same component. 

DHS Employees as a Whole Reported Lower Satisfaction and Engagement 
According to Several Measures: 

As shown in figure 1, DHS employees generally reported improvements in 
job satisfaction index levels since 2006 that narrowed the gap between 
DHS and the governmentwide average.[Footnote 12] However, employees 
continue to indicate less satisfaction than the governmentwide 
average.[Footnote 13] Partnership analysis of FEVS data also indicates 
consistent levels of low employee satisfaction relative to other 
federal agencies. Similar to its 2011 ranking, 31st of 33 federal 
agencies, the Partnership ranked DHS 28th of 32 in 2010, 28th of 30 in 
2009, and 29th of 30 in 2007 in the Best Places to Work ranking on 
overall scores for employee satisfaction and commitment.[Footnote 14] 

Figure 1: Percentage of Satisfied DHS Employees Compared with 
Governmentwide Averages, 2006, 2008, 2010, and 2011: 

[Refer to PDF for image: vertical bar graph] 

Year: 2006; 
DHS: 59%; 
Governmentwide: 66%. 

Year: 2008; 
DHS: 63%; 
Governmentwide: 67%. 

Year: 2010; 
DHS: 65%; 
Governmentwide: 69%. 

Year: 2011; 
DHS: 64%; 
Governmentwide: 68%. 

Source: U.S. Office of Personnel Management. 

Note: Because the FEVS was not administered each year, the job 
satisfaction index and DHS versus governmentwide averages are 
available only for 2006, 2008, 2010, and 2011. 

[End of figure] 

Our analyses of 2011 FEVS results also indicate that average DHS-wide 
employee satisfaction and engagement scores were consistently lower 
when compared with average non-DHS employee scores in the same 
demographic groups. As shown in figure 2, comparisons of DHS with non-
DHS employees by supervisory status, pay group, and tenure indicate 
that satisfaction and engagement are lower across many of the DHS 
groups where statistically significant differences are evident. 
[Footnote 15] For example, across pay categories DHS satisfaction and 
engagement were lower than the scores for the same non-DHS employee 
pay groups, with the exception of senior executives, senior leaders, 
employees with less than 1 year of tenure, and General Schedule pay 
grades 1-6.[Footnote 16] Similarly, job satisfaction and engagement 
scores for DHS management and non-management employees were lower than 
for the same non-DHS employee groups. 

Figure 2: OPM Job Satisfaction and Employee Engagement Indexes in the 
2011 FEVS, for Selected Categories of DHS and Non-DHS Employees: 

[Refer to PDF for image: table] 

All employees: 
Job Satisfaction Index[A] - DHS: 64.0; 
Job Satisfaction Index - Non-DHS: 
Job Satisfaction Index - Difference: -4.5[D]; 
Employee Engagement Index[B] - DHS: 60.1; 
Employee Engagement Index - Non-DHS: 67.1; 
Employee Engagement Index - Difference: -7.0[D]; 

Supervisory status: Management/supervisor; 
Job Satisfaction Index[A] - DHS: 71.2; 
Job Satisfaction Index - Non-DHS: 76.1; 
Job Satisfaction Index - Difference: -4.9[D]; 
Employee Engagement Index[B] - DHS: 67.0; 
Employee Engagement Index - Non-DHS: 74.1; 
Employee Engagement Index - Difference: -7.2[D]; 

Supervisory status: Non-management/non-supervisor; 
Job Satisfaction Index[A] - DHS: 62.1; 
Job Satisfaction Index - Non-DHS: 67.0; 
Job Satisfaction Index - Difference: -4.9[D]; 
Employee Engagement Index[B] - DHS: 58.2; 
Employee Engagement Index - Non-DHS: 65.7; 
Employee Engagement Index - Difference: -7.5[D]; 

Pay group: Fed Wage System; 
Job Satisfaction Index[A] - DHS: 59.2; 
Job Satisfaction Index - Non-DHS: 67.1; 
Job Satisfaction Index - Difference: -7.0; 
Employee Engagement Index[B] - DHS: 57.3; 
Employee Engagement Index - Non-DHS: 61.6; 
Employee Engagement Index - Difference: -4.3[D]; 

Pay group: General Schedule 1-6; 
Job Satisfaction Index[A] - DHS: 60.7; 
Job Satisfaction Index - Non-DHS: 62.9; 
Job Satisfaction Index - Difference: -2.2; 
Employee Engagement Index[B] - DHS: 61.4; 
Employee Engagement Index - Non-DHS: 65.6; 
Employee Engagement Index - Difference: -4.2; 

Pay group: General Schedule 7-12; 
Job Satisfaction Index[A] - DHS: 66.0; 
Job Satisfaction Index - Non-DHS: 67.9; 
Job Satisfaction Index - Difference: -1.9[D]; 
Employee Engagement Index[B] - DHS: 61.3; 
Employee Engagement Index - Non-DHS: 66.8; 
Employee Engagement Index - Difference: -5.5[D]; 

Pay group: General Schedule 13-15; 
Job Satisfaction Index[A] - DHS: 67.7; 
Job Satisfaction Index - Non-DHS: 72.4; 
Job Satisfaction Index - Difference: -4.7[D]; 
Employee Engagement Index[B] - DHS: 63.0; 
Employee Engagement Index - Non-DHS: 70.5; 
Employee Engagement Index - Difference: -7.5[D]; 

Pay group: Senior Executive Service; 
Job Satisfaction Index[A] - DHS: 81.5; 
Job Satisfaction Index - Non-DHS: 81.2; 
Job Satisfaction Index - Difference: 0.4; 
Employee Engagement Index[B] - DHS: 79.8; 
Employee Engagement Index - Non-DHS: 82.7; 
Employee Engagement Index - Difference: -3.0; 

Pay group: Senior Leader; 
Job Satisfaction Index[A] - DHS: 71.3; 
Job Satisfaction Index - Non-DHS: 72.6; 
Job Satisfaction Index - Difference: -1.3; 
Employee Engagement Index[B] - DHS: 66.2; 
Employee Engagement Index - Non-DHS: 70.5; 
Employee Engagement Index - Difference: -4.4; 

Pay group: Other[C]; 
Job Satisfaction Index[A] - DHS: 55.5; 
Job Satisfaction Index - Non-DHS: 68.9; 
Job Satisfaction Index - Difference: -13.4[D]; 
Employee Engagement Index[B] - DHS: 53.1; 
Employee Engagement Index - Non-DHS: 65.6; 
Employee Engagement Index - Difference: -12.5[D]; 

Agency Tenure: Less than 1 Year; 
Job Satisfaction Index[A] - DHS: 72.9; 
Job Satisfaction Index - Non-DHS: ; 
Job Satisfaction Index - Difference: ; 
Employee Engagement Index[B] - DHS: 75.6; 
Employee Engagement Index - Non-DHS: 76.0; 
Employee Engagement Index - Difference: -0.4; 

Agency Tenure: 1-3 Years; 
Job Satisfaction Index[A] - DHS: 66.2; 
Job Satisfaction Index - Non-DHS: 68.4; 
Job Satisfaction Index - Difference: -2.2[D]; 
Employee Engagement Index[B] - DHS: 65.2; 
Employee Engagement Index - Non-DHS: 69.7; 
Employee Engagement Index - Difference: -4.6[D]; 

Agency Tenure: 4-5 years; 
Job Satisfaction Index[A] - DHS: 62.5; 
Job Satisfaction Index - Non-DHS: 67.7; 
Job Satisfaction Index - Difference: -5.1[D]; 
Employee Engagement Index[B] - DHS: 58.4; 
Employee Engagement Index - Non-DHS: 66.0; 
Employee Engagement Index - Difference: -7.7[D]; 

Agency Tenure: 6-10 years; 
Job Satisfaction Index[A] - DHS: 61.2; 
Job Satisfaction Index - Non-DHS: 67.9; 
Job Satisfaction Index - Difference: -6.7[D]; 
Employee Engagement Index[B] - DHS: 56.0; 
Employee Engagement Index - Non-DHS: 65.8; 
Employee Engagement Index - Difference: -9.9[D]; 

Agency Tenure: 11-20 years; 
Job Satisfaction Index[A] - DHS: 66.6; 
Job Satisfaction Index - Non-DHS: 68.1; 
Job Satisfaction Index - Difference: -1.5[D]; 
Employee Engagement Index[B] - DHS: 61.3; 
Employee Engagement Index - Non-DHS: 65.1; 
Employee Engagement Index - Difference: -3.8[D]; 

Agency Tenure: 20 + years; 
Job Satisfaction Index[A] - DHS: 65.2; 
Job Satisfaction Index - Non-DHS: 69.7; 
Job Satisfaction Index - Difference: 4.5[D]; 
Employee Engagement Index[B] - DHS: 60.3; 
Employee Engagement Index - Non-DHS: 67.2; 
Employee Engagement Index - Difference: -6.9[D]; 

Source: GAO analysis of 2011 FEVS data. 

Note: Estimates of job satisfaction and employee engagement have a 
margin of error at the 95 percent confidence level of no more than 
plus or minus 5.1 percentage points, except for employees in the 
Senior Leader pay group. Because statistical significance is a 
function of two things--the size of the difference and the size of the 
sampled groups being compared--the biggest differences are not always 
the differences that are significant. 

[A] The Job Satisfaction index, composed of seven Federal Employee 
Viewpoint Survey (FEVS) questions, indicates the extent to which 
employees are satisfied with their jobs and various aspects thereof. 

[B] The Engagement index, composed of 15 FEVS questions, indicates the 
extent to which employees are immersed in the content of the job and 
energized to spend extra effort in job performance. 

[C] The "Other" pay group may include employees who are not part of 
the General Schedule system, such as those in pay-band systems. 

[D] Denotes statistically significant differences between DHS and non-
DHS employees that are distinguishable from zero at the 0.05 level. 

[End of figure] 

Employee Satisfaction and Engagement Vary across and within DHS 
Components: 

Satisfaction and engagement variation across components. As shown in 
table 1, the 2011 FEVS data indicate that satisfaction and engagement 
levels vary across DHS components. For example, TSA is 11.6 percentage 
points below the non-DHS average on the job satisfaction index, but 
other large components, such as CBP and the Coast Guard, are 1 and 1.5 
percentage points more satisfied than the average for the rest of the 
government, respectively. Three other components--the Inspector 
General, the Federal Law Enforcement Training Center, and the U.S. 
Secret Service--also score above the non-DHS engagement averages on 
the job satisfaction or engagement indexes. Morale varies widely 
across all components, with the job satisfaction index ranging from 
56.9 to 71.6 percent and the employee engagement index ranging from 
53.2 to 70.6 percent. 

Table 1: 2011 FEVS Job Satisfaction and Engagement Scores by DHS 
Component (Sorted by Job Satisfaction Index Score): 

Federal Law Enforcement Training Center; 
Job Satisfaction index: 71.6*; 
Employee Engagement index: 66.2. 

Inspector General; 
Job Satisfaction index: 70.8; 
Employee Engagement index: 70.5*. 

U.S. Coast Guard; 
Job Satisfaction index: 70.0; 
Employee Engagement index: 70.6*. 

U.S. Customs and Border Protection; 
Job Satisfaction index: 69.5; 
Employee Engagement index: 62.9*. 

U.S. Secret Service; 
Job Satisfaction index: 69.4; 
Employee Engagement index: 68.1. 

Non-DHS government employees; 
Job Satisfaction index: 68.5; 
Employee Engagement index: 67.1. 

U.S. Citizenship and Immigration Service; 
Job Satisfaction index: 66.6*; 
Employee Engagement index: 64.0*. 

Under Secretary for Management; 
Job Satisfaction index: 65.7*; 
Employee Engagement index: 65.7. 

DHS, no sub-agency; 
Job Satisfaction index: 65.2; 
Employee Engagement index: 60.5*. 

DHS (overall); 
Job Satisfaction index: 64.0*; 
Employee Engagement index: 60.1*. 

Federal Emergency Management Agency; 
Job Satisfaction index: 63.2*; 
Employee Engagement index: 58.0*. 

Office of the Secretary; 
Job Satisfaction index: 63.0*; 
Employee Engagement index: 63.6*. 

National Policy and Programs Directorate; 
Job Satisfaction index: 61.9*; 
Employee Engagement index: 57.7*. 

U.S. Immigration and Customs Enforcement; 
Job Satisfaction index: 60.6*; 
Employee Engagement index: 58.0*. 

Science and Technology Directorate; 
Job Satisfaction index: 59.7*; 
Employee Engagement index: 55.7*. 

Office of Intelligence and Analysis; 
Job Satisfaction index: 57.6*; 
Employee Engagement index: 53.2*. 

Transportation Security Administration; 
Job Satisfaction index: 56.9*; 
Employee Engagement index: 54.4*. 

Source: GAO analysis of 2011 FEVS data. 

Note: Estimates of job satisfaction and employee engagement have a 95 
percent margin of error of no more than plus or minus 6.3 percentage 
points. Asterisks indicate estimates that are distinguishable from the 
non-DHS estimate at the 0.05 level. 

[End of table] 

Satisfaction and engagement variation within components. Within the 
selected components reviewed--the Coast Guard, ICE, CBP, and TSA--
satisfaction and engagement varied across workgroups as well. For 
example, as shown in figure 3, responses by TSA employee groups varied 
widely, with the screening workforce reporting significantly lower 
satisfaction and engagement scores.[Footnote 17] 

Figure 3: Satisfaction and Engagement by TSA Employee Group, 2011: 

[Refer to PDF for image: horizontal bar graph] 

Satisfaction and engagement scores: 

TSA Job Satisfaction (JS) Index: 
Federal Security Director staff: 67.8%; 
Headquarters staff: 63.5%; 
Federal Air Marshals: 58.4%; 
Screeners: 53.6%. 

TSA Employee Engagement (EE) Index: 
Federal Security Director staff: 64.8%; 
Headquarters staff: 61.5%; 
Federal Air Marshals: 52.9%; 
Screeners: 50.9%. 

Source: GAO analysis of 2011 FEVS data. 

Note: Estimates of job satisfaction and employee engagement have a 95 
percent margin of error of no more than plus or minus 4.4 percentage 
points. 

[End of figure] 

Employee group scores showed variability in the three other selected 
DHS components as well.[Footnote 18] For example: 

* CBP. Border Patrol employees were 8 percentage points more satisfied 
and 12 percentage points more engaged than CBP field operations 
employees.[Footnote 19] 

* Coast Guard. Satisfaction and engagement levels exceeded the non-DHS 
average for some civilian employee groups, such as those under the 
Chief of Staff for Mission Support, but some groups were substantially 
higher. These groups include Districts 13 and 14, which were 18.6 and 
10.3 percentage points more satisfied than non-DHS employees, 
respectively.[Footnote 20] 

* ICE. Homeland security investigators and immigration enforcement 
employees were less satisfied and engaged than many other employee 
groups in DHS and the average for the rest of the government.[Footnote 
21] Homeland security investigators were 5.5 percentage points lower 
on satisfaction and 8.2 percentage points lower on engagement than the 
non-DHS averages. Enforcement and Removal employees were also less 
satisfied and engaged, with scores 12.7 percentage points below on 
satisfaction and 14.4 percentage points lower on engagement than the 
non-DHS averages. 

In addition to variation in satisfaction and engagement levels across 
employee groups, representatives of DHS employee groups we interviewed 
identified a range of issues that may be creating lower satisfaction 
rates among employees.[Footnote 22] These examples highlight the 
variety of issues that can lead to morale problems and may be unique 
to particular DHS components.[Footnote 23] For example: 

* A TSA screener union representative described TSA's performance 
assessment system as a key driver of morale problems among passenger 
screeners. According to the union representative, if a screener fails 
a portion of the annual examination more than three times, the 
screener will be terminated. The union representative explained that 
failing three times is possible, even for highly effective screeners 
who may not be effective under testing conditions because of anxiety 
about the examination, resulting in a significant burden on the 
screeners and, therefore, the performance assessment will result in 
lower morale. 

* ICE homeland security investigators who participated in a focus 
group we held cited frustrations with frequent turnover in regional 
leadership positions, which they stated negatively affects employee 
morale. Unequal resource allocations across investigative groups were 
also described as leading to lower morale among investigators. 

* A union representative for CBP's field operations employees 
described staffing shortages at ports of entry, temporary assignments 
to the southwest border that affect work-life balance, and management 
resistance to employee telework arrangements, among other things, as 
resulting in employee morale problems. 

* Border Patrol union representatives cited uncertainties in overtime 
pay policy, living conditions at small, temporary shelters for Border 
Patrol agents deployed in an area, and inflexible employee work 
scheduling practices, among other things, as creating morale problems 
among Border Patrol agents. 

Coast Guard civilian officials who participated in a focus group we 
held, on the other hand, provided examples of drivers of high levels 
of morale within the Coast Guard. The officials described a Coast 
Guard culture of mission focus that has led to high morale among 
civilian Coast Guard employees. For example, the officials stated that 
a sense of making a difference in maritime security and safety through 
work activities such as vessel inspections, contingency planning for 
natural disasters, and training Coast Guard employees results in 
employees who are engaged and satisfied with their jobs. The officials 
also described a Coast Guard cultural focus on team cohesion and 
shared successes, among both military and civilian Coast Guard 
personnel, both of which are recognized by Coast Guard leadership, 
according to the officials. For example, the officials stated that 
Coast Guard leadership designates a portion of awards for team 
successes, rather than individual achievement, which the officials we 
interviewed found more satisfying than individual awards. 

A statistical analysis of 2011 FEVS and employee demographic data we 
conducted suggested several other explanations for differences in 
morale[Footnote 24] between DHS and non-DHS agencies: 

* Several of the DHS components with lower morale, such as TSA and 
ICE, make up a substantial share of FEVS respondents at DHS, as shown 
in appendix I, table 7. Those components have more influence on the 
agency's overall morale score than smaller components--many of which 
have higher average morale scores. Consequently, the gap between DHS 
and the rest of the government in employee morale is driven primarily 
by the scores of a few large components. 

* DHS is not more likely than other agencies to employ the types of 
staff who tend to have lower morale across all agencies, as shown in 
appendix I, table 6. Instead, employees in the various groups we 
analyzed had lower morale at DHS than the same types of employees at 
other agencies. This suggests that the gap may be explained by factors 
unique to DHS, such as management practices and the nature of the 
agency's work, or by differences among employees we could not analyze. 
[Footnote 25] 

* DHS employees who joined the department since its creation tend to 
have lower morale than employees who joined the department as part of 
preexisting components, as shown in appendix I, table 8. DHS employees 
who started working for the agency between 1 and 10 years ago are less 
engaged than employees with similar tenures and demographics at other 
agencies. In addition, several of the least engaged components, such 
as the Intelligence and Analysis and Science and Technology divisions, 
as shown in appendix I, table 7, were created with the department or 
subsequently, rather than being added from elsewhere in the federal 
government. 

The variation of factors that can result in morale problems as 
suggested by these examples, as well as the variation in levels of 
satisfaction and engagement among employee groups, underscores the 
importance of looking beyond survey scores to understand where 
problems, such as low employee satisfaction, are taking place within 
the organization, and to identify and address the causes of these 
problems. 

Appendix I provides comparisons for additional demographic groups, 
including age, component tenure, and location, and summarizes our 
statistical analysis that examines the relationships between each of 
these factors and satisfaction and engagement, holding constant each 
of the other factors. The appendix also provides a more detailed list 
of satisfaction and engagement estimates for components and offices, 
in some cases holding constant demographic differences among employees. 

DHS Took Steps to Determine Root Causes of Morale Problems and 
Implemented Corrective Actions, but Could Strengthen Its Efforts: 

DHS and the selected components have taken steps to understand morale 
problems, such as holding focus groups, implementing an exit survey, 
and routinely analyzing FEVS results. On the basis of FEVS results, 
DHS and the selected components planned actions to improve FEVS 
scores. However, we found that DHS could enhance its survey analysis 
and monitoring of action plan results. In addition, according to DHS's 
Integrated Strategy for addressing the implementing and transforming 
high risk area, DHS has begun implementing activities to address 
morale but has not yet improved DHS's scores on OPM's job satisfaction 
index or its ranking on the Partnership's Best Places to Work in the 
Federal Government. 

DHS and Selected Components' Have Taken Steps to Understand Morale 
Problems: 

DHS's OCHCO has taken several steps to understand morale problems DHS-
wide. Specifically, since 2007, OCHCO: 

* Conducted focus groups DHS-wide in 2007 to determine employee 
concerns related to morale, which identified employee concerns in 
areas of leadership, communication, empowerment, and resources. 

* Performed statistical analysis in 2008 to identify workplace factors 
that drove employee job satisfaction, finding that the DHS mission and 
supervisor support, among other things, drove employee job 
satisfaction. 

* Initiated an exit survey, first administered DHS-wide in 2011, to 
understand why employees chose to leave their position. The survey 
found lack of quality supervision and advancement opportunities were 
the top reasons for leaving.[Footnote 26] 

* Analyzed 2011 FEVS results, among other things, showing where lower 
scores on HCAAF indices were concentrated among several components--
Intelligence and Analysis, TSA, ICE, National Protection and Programs 
Directorate, and the Federal Emergency Management Agency (FEMA). 

* Launched an Employee Engagement Executive Steering Committee (EEESC) 
in January 2012 that will identify action items for improving employee 
engagement by September 2012, according to OCHCO officials. 

The selected components also evaluated FEVS results to identify morale 
problems and considered additional information sources. For example: 

* TSA convened a corporate action planning team in March 2011, as part 
of its response to FEVS results, which relied on data sources such as 
the TSA-administered exit survey, employee advisory groups, and an 
online employee suggestion tool, to gain perspectives on systemic 
challenge areas and to develop plans to address morale, according to 
TSA officials. TSA's action plan for improving morale, based on these 
sources, was completed in July 2012. 

* ICE considered results of a Federal Organizational Climate Survey 
(FOCS), last completed in March 2012, and held focus groups to gauge 
the extent to which employees view ICE as having an organizational 
culture that promotes diversity. 

* CBP launched a quarterly online employee survey in 2009 to solicit 
opinions on one specific topic per quarter, such as use of career 
development resources and how the resources contributed to employees' 
professional growth at CBP. 

* The Coast Guard relied on an Organizational Assessment Survey (OAS), 
last administered by OPM in 2010, to understand employee morale. The 
OAS solicits opinions on a range of topics, including job 
satisfaction, leadership, training, innovation, and use of resources. 
It included civilian and military Coast Guard personnel, but is not 
administered governmentwide so comparisons between the Coast Guard and 
other federal employees are limited to organizations that may use the 
OAS, according to Coast Guard officials. 

Appendix III provides more detailed descriptions of DHS's steps to 
address morale problems and selected components' 2011 FEVS analysis 
methods and findings. Appendix IV provides additional information on 
the selected components' data sources beyond FEVS for evaluating root 
causes of morale, including a summary of results and how the 
information was used by the components. 

DHS and Selected Components Conducted Limited FEVS Analyses: 

For the 2011 FEVS, DHS and the selected components completed varying 
levels of analyses to determine the root causes of low morale. 
However, DHS and the selected components conducted limited analysis in 
several areas that is not consistent with OPM and Partnership guidance 
that lays out useful factors for evaluating root causes of morale 
problems through FEVS analysis, as shown in figure 4. 

Figure 4: The Extent to Which OCHCO, TSA, CBP, ICE, and the Coast 
Guard Incorporated Recommended Factors in Analyzing 2011 FEVS Results: 

[Refer to PDF for image: table] 

Component: Office of the Chief Human Capital Officer (DHS-wide); 
Demographic group comparisons? No; 
Benchmarking against similar organizations?[A] Yes; 
Linkage of root causes with action plans?[B] Partial. 

Component: Transportation Security Administration; 
Demographic group comparisons? Yes; 
Benchmarking against similar organizations?[A] Yes; 
Linkage of root causes with action plans?[B] Partial. 

Component: U.S. Customs and Border Protection; 
Demographic group comparisons? Yes; 
Benchmarking against similar organizations?[A] Partial; 
Linkage of root causes with action plans?[B] Partial. 

Component: U.S. Immigration and Customs Enforcement; 
Demographic group comparisons? No; 
Benchmarking against similar organizations?[A] Yes; 
Linkage of root causes with action plans?[B] Partial. 

Component: U.S. Coast Guard; 
Demographic group comparisons? No; 
Benchmarking against similar organizations?[A] No; 
Linkage of root causes with action plans?[B] Partial. 

Source: GAO analysis of 2011 FEVS analysis summary documentation 
provided by OHS OCHCO, TSA, ICE, CBP, and the Coast Guard. 

Note: 

Demographic comparisons. In its guidance to federal agencies for 
improving employee job satisfaction, the Partnership for Public 
Service advises, among other things, determining demographic group 
differences. According to the Partnership for Public Service, 
demographic group analysis shows where there may be gaps in 
satisfaction at an agency or subcomponent (i.e., perhaps some 
demographic groups report lower satisfaction than others). For the 
purposes of this report, demographic group is used to describe any 
common characteristic among employees, such as pay grade, supervisory 
status, or work group. 

Benchmarking. Benchmarking agency survey results against those of 
similar organizations can provide a point of reference for 
improvements, according to the Partnership for Public Service. OPM's 
management report to DHS for the 2011 FEVS also suggests, as part of 
action planning, comparing agency results with governmentwide results 
and noting which survey questions and Human Capital Assessment and 
Accountability Framework indexes scored lowest relative to the 
governmentwide averages. 

Linking analysis results with action plans. Both OPM and the 
Partnership for Public Service action planning guidance list analyzing 
survey results as a first step to developing action plans to address 
employee concerns. According to OPM's guidance, the data analysis may 
include reviewing FEVS results and following up on survey findings 
with focus groups to clarify reasons for low scores. OPM's guidance 
then calls for agencies to translate issues uncovered through data 
analysis into a set of action plan goals. Similarly, the Partnership 
for Public Service advises agencies to develop action plan approaches 
to improve employee satisfaction, based on issues the data identify, 
while considering the organization's mission, culture, available time, 
and resources. 

[A] CBP partially benchmarked its FEVS results because it compared 
results with governmentwide and DHS averages, but not with those for 
similar organizations for the 2011 FEVS. CBP officials stated that few 
agencies within the United States both use FEVS and have occupations 
similar to those of CBP. 

[B] OCHCO and the selected components partially linked root causes 
with action plans because low-scoring questions were listed on action 
plans as the reason why actions were chosen. However, additional root 
cause analysis findings, such as those listed in appendix IV, were not 
included in the action plan documentation. 

[End of figure] 

Usage of the three factors described in figure 4 varied across DHS-
wide and component-level 2011 FEVS analyses we reviewed. In some 
instances, the factors were partially or not used. For example: 

* Demographic group comparisons. According to our reviews of OCHCO's 
analyses, OCHCO's DHS-wide analyses did not include evaluations of 
demographic group differences on morale-related issues for the 2011 
FEVS. According to OCHCO officials, DHS's Office of Civil Rights and 
Civil Liberties reviews survey results to identify diversity issues 
that may be reflected in the survey, and OCHCO officials considered 
these results when developing one of the current (as of August 2012) 
DHS action plans to create policies that identify barriers to 
diversity. In 2007 and 2009, years in which DHS administered the 
Annual Employee Survey (AES), demographic comparisons were made. For 
example, on the basis of 2009 AES data, DHS found no significant 
demographic differences other than supervisors' positive responses to 
questions were generally higher than those of non-supervisors and 
differences among pay grade levels. Because OPM now administers the 
survey each year, DHS is not able to make significant demographic 
group comparisons because of the format of the data provided by OPM, 
according to OCHCO officials. However, we obtained FEVS data from OPM 
that allowed us to make demographic group comparisons. For example, we 
compared DHS and non-DHS employee satisfaction and engagement scores 
across available demographic groups and found that both satisfaction 
and engagement were generally lower for DHS employees, which is 
summarized in appendix I, table 5. 

For the DHS component analyses we reviewed, TSA and CBP conducted some 
demographic analysis. For example, TSA compared screeners, Federal 
Security Director staff, Federal Air Marshals, and headquarters staff 
on each FEVS dimension (e.g., work experiences, supervisor/leader, 
satisfaction, and work/life). As a result, TSA was able to identify 
screeners as having survey scores below those of other TSA employee 
groups. CBP also compared race, ethnicity, gender, and program office 
scores. CBP found that no significant differences were present in the 
positive responses to the 2011 FEVS core questions when comparing 
race, ethnicity and gender, and found that Border Patrol employees 
reported higher job satisfaction than field operations employees (74 
versus 66 percent on the job satisfaction index). In contrast, the 
Coast Guard did not conduct analysis in addition to data that was 
provided by DHS OCHCO. Because OCHCO's data did not include 
demographic information for the 2011 FEVS, Coast Guard did not make 
demographic group comparisons.[Footnote 27] ICE and CBP officials 
stated that they did not have access to 2011 FEVS data files necessary 
to conduct more detailed demographic comparisons. However, as shown in 
appendix I, we were able to make various demographic comparisons based 
on a more detailed data file provided by OPM, which is similar to a 
file that OPM makes available to agencies and the public.[Footnote 28] 

* Benchmarking against similar organizations. TSA benchmarked its FEVS 
results against results from similar organizations, by comparing 
results with CBP, and OCHCO's DHS-wide analysis highlighted 
Partnership rankings data, showing DHS's position relative to the 
positions of other federal agencies as a Best Place to Work. 
Similarly, ICE benchmarked its FEVS results overall and for program 
offices, such as homeland security investigators, against other DHS 
components, including the U.S. Secret Service and CBP. For the 2011 
FEVS, CBP performed more limited benchmarking, by comparing FEVS 
results with governmentwide averages. According to CBP officials, when 
analyzing annual employee surveys prior to 2011, CBP benchmarked its 
results against agencies with high positive FEVS scores, such as the 
Social Security Administration, the Federal Bureau of Investigation, 
the Internal Revenue Service, and the Nuclear Regulatory Commission. 
CBP is in the initial planning phase of a larger benchmarking project 
that would benchmark CBP against foreign immigration, customs, and 
agriculture inspection agencies, such as the Canadian Border Services 
Agency and the Australian Customs and Border Protection Service. If 
approved, this benchmarking project is expected to occur in fiscal 
year 2013, according to CBP officials. The Coast Guard did not perform 
FEVS benchmarking analysis, according to the documentation we 
reviewed, but did make OAS-based comparisons between the Coast Guard 
and other organizations that use the OAS, according to Coast Guard 
officials. 

* Linkage of root causes with action plans. For both DHS-wide and 
selected component action plans, FEVS questions with low scores were 
linked with action plan areas. For example, in the DHS-wide action 
plan, low scores on employee satisfaction with opportunities to get a 
better job in the organization were linked to action plan items for 
enhancing employee retention. However, the extent to which DHS and the 
components used root causes found through other analyses to inform 
their action plans, such as quarterly exit survey results or 
additional internal component surveys, was not evident in action plan 
documentation (see appendix IV for a description of these additional 
root cause analyses). For example, 

- OCHCO's DHS-wide action plan was last updated based on 2010 FEVS 
data and therefore did not rely on data from the DHS 2011 exit survey, 
since those results were not published until January 2012. Similarly, 
the EEESC was launched in January 2012 and therefore its efforts are 
not yet documented in DHS-wide action planning documents. According to 
OCHCO officials, the 2010 DHS-wide action plan includes consideration 
of results from OCHCO's 2008 statistical analysis identifying key 
drivers of job satisfaction and results from the 2007 focus groups. 
However, linkage to items in the DHS-wide action plan to these results 
is not clearly identified because a new action plan template OPM 
introduced in 2010 did not provide an area to identify the linkage 
between each action and the driver, according to OCHCO officials. In 
addition, DHS's September 2009 action plan indicates consideration of 
the 2008 key driver analysis and 2007 focus group effort that led to a 
focus on leadership effectiveness initiatives. 

- According to CBP and TSA officials, data from other root cause 
analysis efforts are not explicitly documented in action plans 
developed in response to FEVS results because DHS has not included 
linkage of other root cause analysis efforts to actions items in the 
FEVS action planning templates used by the components. TSA officials 
also stated that other root cause efforts (see appendix IV) were used 
to develop TSA's July 2012 action plan update. However, the July 2012 
plan did not include linkage of root cause findings other than FEVS 
results, such as exit survey results, to action plan items. 

- ICE officials stated that results from other root cause efforts, 
such as its FOCS, have not yet been considered in FEVS-based action 
planning but that ICE plans to do so in future efforts to address 
morale. 

- The Coast Guard uses information from its OAS as part of a process 
separate from FEVS-based action planning for addressing morale, so OAS 
results are not linked to FEVS-based action plans. 

OCHCO and component human capital officials described several reasons 
for the variation in root cause analysis of FEVS results. OCHCO 
officials described resource constraints and leadership changes within 
the OCHCO position as resulting in a lack of continuity in root cause 
analysis efforts. For example, one OCHCO official stated that because 
of resource constraints, OCHCO has focused more efforts on workforce 
planning than on morale problem analysis since 2009. ICE human capital 
officials stated that ICE's human capital services were provided via a 
contract with CBP until 2010, when the human capital function became 
an independently funded part of the ICE organization. Only since 
moving to its current position within ICE has the human capital office 
been able to devote more resources to addressing morale issues, 
according to the officials. CBP human capital officials stated that 
for assessing morale issues, CBP uses both quantitative and 
qualitative information. However, according to the officials, 
qualitative evidence is preferable over quantitative survey analysis 
because focus groups and open-ended surveys, such as the Most Valuable 
Perspective online survey, allow CBP to better understand the issues 
affecting employees. Because of CBP human capital officials' 
preference for qualitative information, CBP has not emphasized 
extensive quantitative analysis of survey results, such as statistical 
analysis that may determine underlying causes of morale problems. 

Without a complete understanding of which issues are driving low 
employee morale, DHS risks not being able to effectively address the 
underlying concerns of its varied employee population. Emphasis on 
survey analysis that includes demographic group comparisons, 
benchmarking against similar organizations, and linkage of other 
analysis efforts outside of FEVS within action plan documentation 
could assist DHS in better addressing its employee morale problems. 

DHS and Its Components Completed Action Plans: 

DHS and the selected components routinely update their action plans to 
address employee survey results in accordance with the Office of 
Management and Budget's budget guidance; the DHS-wide plan is updated 
every two years, and components update their plans at least annually. 
[Footnote 29] According to OPM's guide for using FEVS results, action 
planning involves, among other things, identifying goals and actions 
for improving low-scoring FEVS satisfaction topics such as reviewing 
survey results to determine steps to be taken to improve how the 
agency manages its workforce. DHS-wide and component action plan goals 
and examples of low-scoring FEVS satisfaction topics are listed in 
table 2. 

Table 2: DHS-wide and Component Action Plan Goals and Examples of Low-
Scoring FEVS Topics Addressed through such Goals: 

DHS unit: DHS-wide; 
Summary of action plan goals to address FEVS results: Enhance 
leadership, recruitment, employee retention, and DHS unification; 
Example of low-scoring FEVS satisfaction topics addressed by action 
plan goal: Opportunity to get a better job in the organization. 

DHS unit: TSA; 
Summary of action plan goals to address FEVS results: Launch a 
corporate action planning team to study employee issues and develop 
recommendations, enhance employee performance management, and improve 
TSA communication mechanisms; 
Example of low-scoring FEVS satisfaction topics addressed by action 
plan goal: Discussions with supervisors about performance. 

DHS unit: ICE; 
Summary of action plan goals to address FEVS results: Advance telework 
opportunities, increase communication between employees and 
management, and develop an awards handbook for distribution to 
employees; 
Example of low-scoring FEVS satisfaction topics addressed by action 
plan goal: Physical conditions that allow employees to do their job 
well. 

DHS unit: CBP; 
Summary of action plan goals to address FEVS results: Develop action 
plans within CBP program offices to address results, enhance 
communication between management and employees, create career and 
leadership development opportunities, replace pass/fail performance 
appraisal with multi-leveled performance management system, implement 
training improvements, and maintain an existing virtual focus group to 
enable upward feedback to senior leaders; 
Example of low-scoring FEVS satisfaction topics addressed by action 
plan goal: Policies and practices of senior leaders. 

DHS unit: Coast Guard; 
Summary of action plan goals to address FEVS results: Improve 
communication with employees and training options; 
Example of low-scoring FEVS satisfaction topics addressed by action 
plan goal: Information received from management. 

Source: GAO analysis of DHS-wide, TSA, Coast Guard, CBP, and ICE FEVS 
action plans based on FEVS results. 

[End of table] 

As part of DHS's efforts to address our high-risk designation of 
implementing and transforming DHS, DHS described a plan for improving 
employee morale in its Integrated Strategy for High Risk Management 
(Integrated Strategy). In June 2012, DHS provided us with its updated 
Integrated Strategy, which summarized the status of the department's 
activities for addressing its implementation and transformation high-
risk designation. In the Integrated Strategy, DHS identified 
activities to improve employee job satisfaction scores, among other 
things. The status of the activities included ongoing analysis of the 
2011 FEVS results, launch of the EEESC to address DHS scores on the 
HCAAF indexes, ongoing coordination between the OCHCO and components 
to develop action plans in response to the 2011 FEVS results, and 
launch of an online employee survey in the first quarter of fiscal 
year 2013. 

Within the Integrated Strategy action plan for improving job 
satisfaction scores, DHS reported that three of six efforts were 
hindered by a lack of resources.[Footnote 30] For example, resources 
are a constraining factor for DHS's Office of the Chief Human Capital 
Officer to consult with components in developing action plans in 
response to 2011 FEVS results. Similarly, resources are a constraining 
factor to deploy online focus discussions on job satisfaction-related 
issues. 

DHS and Selected Components Generally Followed OPM's Six Steps for 
Effective Action Planning but Do Not Have Effective Metrics for 
Monitoring Efforts: 

According to our review of the action plans created in response to the 
FEVS and interviews with agency officials, DHS and the selected 
components generally incorporated the six action planning steps 
suggested by OPM, but the agency does not have effective metrics to 
support its efforts related to monitoring.[Footnote 31] (See figure 5.) 

Figure 5: OPM's Six Steps for Action Planning to Improve FEVS Scores: 

[Refer to PDF for image: illustration] 

FEVS action planning steps: 

1. Identify the issues: Review the survey results and conduct follow-
up activities needed to clarify their meaning. Communicate the results 
to employees and describe the issues the agency plans to address.  

2. Set goals: Develop goals for improvement. 

3. Identify staff and budget resources: Assemble a team and evaluate 
the time and resources available to you. 

4. Develop the action plan: Break down the goals into actions to be 
accomplished, assign them to responsible parties, and seek necessary 
approvals for the action plan. 
    
5. Implement the action plan: Publicize the plan within the agency and 
launch the plan that will help meet the agency's goals. 

6. Monitor and evaluate the results of the implementation: Monitor 
progress and evaluate outcomes of the action plan. Provide regular 
feedback on progress and outcomes to managers and employees. 

Source U.S. Office of Personnel Management. 

[End of figure] 

We found that, in general, DHS and its components are implementing the 
six steps for action planning as demonstrated in table 3 below. 

Table 3: DHS-Wide and Selected Components' Action Planning Steps: 

Action Planning Step: 1. Identify issues; 
DHS and Component Response: All the components and OCHCO evaluated the 
FEVS results by, at a minimum, determining high and low positive 
response levels to survey questions; 
Examples of What Has Been Done: For example, CBP's action plan 
identified the FEVS questions that pertained to the job satisfaction 
dimension along with the positive response results for each of the 
survey questions. 

Action Planning Step: 2. Set goals; 
DHS and Component Response: All of the components and OCHCO developed 
broad goals to improve FEVS scores; 
Examples of What Has Been Done: For example, TSA's action plan 
included a goal of enhancing overall communication and innovation. 

Action Planning Step: 3. Identify staff; 
DHS and Component Response: All of the components and OCHCO identified 
personnel responsible for their action plan; 
Examples of What Has Been Done: For example, TSA's action plan 
described a cross-functional team that included representatives from 
existing councils and from all levels and organizational units to 
address an action item. 

Action Planning Step: 4. Develop plan; 
DHS and Component Response: All of the components and OCHCO developed 
plans that identified actions to be taken to accomplish their goals; 
Examples of What Has Been Done: For example, ICE's action plan for 
increasing communication included utilizing ICE broadcast 
announcements for more tangible and employee-related purposes. 

Action Planning Step: 5. Implement plan; 
DHS and Component Response: All of the components and OCHCO set target 
dates and in some cases indicated whether an action item was completed 
or on-going; 
Examples of What Has Been Done: For example, Coast Guard's action plan 
listed the redesign of the civilian website by December 20, 2012. 

Action Planning Step: 6. Monitor results; 
DHS and Component Response: All of the components and OCHCO 
established measures of success, providing the status of success or 
completion dates; 
Examples of What Has Been Done: For example, OCHCO's action plan 
included a goal to retain an engaged workforce. The three measures of 
success listed for this goal are (1) improve DHS ranking on the 
Partnership for Public Service Best Places to Work, (2) reduce 
attrition rates, and (3) achieve an HCAAF index positive response 
average of 58 percent on the 2011 FEVS. 

Source: GAO analysis of DHS-wide, TSA, ICE, CBP, and Coast Guard 
action plans and U.S. Office of Personnel Management action planning 
guidance. 

[End of table] 

Although we found that OCHCO and the four selected components are 
generally taking actions to execute the sixth step--monitor and 
evaluate the results of implementation--they have not established 
effective measures of the agency's achievement of action plan goals. 
For example, in the DHS-wide action plan, one of the four goals is to 
build an effective, mission focused, diverse and inspiring cadre of 
leaders. A measure of success listed for this goal is that progress 
will be measured against the 2011 HCAAF index. For this measure, it is 
not clear which HCAAF index will be assessed and it does not include a 
target for improvement--such as a percent increase in satisfaction--by 
which DHS can benchmark its results. The measures of success within 
the DHS-wide and selected component action plans could be improved by 
including additional attributes of successful metrics. Specifically, 
in our prior work, we identified attributes of successful metrics that 
allow agencies to better determine whether they are meeting their 
goals while holding agency staff accountable for improving 
performance.[Footnote 32] Three attributes relevant to the action 
plans are: 

* linkage--determines whether there is a relationship between the 
performance measure and the goals; 

* clarity--determines whether the performance measures are clearly 
stated; and: 

* measurable target--determines whether, performance measures have 
quantifiable, numerical targets or other measurable values, where 
appropriate. 

In general, DHS and component measures satisfied the linkage attribute 
but did not address the clarity and measurable targets attributes. We 
compared DHS and the four components measures of success to the three 
attributes and found that all 54 measures of success incorporated the 
linkage attribute, 12 of the 54 measures of success did not address 
the clarity attribute, and 29 of the 54 measures of success did not 
address the measurable target attribute. 

As shown in table 4 below, we found that these measures demonstrate 
linkage because they align with the action plan goals. However, we 
determined that the measures demonstrate neither clarity nor a 
measurable target. Specifically, the measures do not demonstrate 
clarity because they do not provide enough detail to clearly state the 
metric used to measure success. They also do not demonstrate a 
measurable target because they do not list quantitative goals or 
provide a qualitative predictor of a desired outcome, which would 
allow the agency to better determine the extent to which they were 
making progress toward achieving their goals. 

Table 4: Examples of DHS-Wide and Selected Components' Measures of 
Success: 

DHS unit: DHS-wide; 
Goal: Recruit a highly qualified and diverse workforce; 
Measure of Success: Recruitment of a highly qualified and diverse 
workforce; 
Examples of Actions to Achieve Goal: 
* Streamline the hiring process to increase applicant and manager 
satisfaction; 
* Improve manager involvement; 
* Hold managers accountable for hiring process involvement through 
performance evaluations; 
* Streamline all DHS job opportunity announcements; 
GAO Assessment: 
Clarity: The measure lacks key information that would make it more 
clear--namely, what constitutes a highly qualified workforce and how 
should "diverse workforce" be interpreted; 
Measurable Target: The measure does not list quantifiable or other 
measurable values to help determine when the goal has been reached. 
For example, the measure does not provide a target number of employees 
against which DHS can benchmark its results. 

DHS unit: TSA; 
Goal: Performance Management Enhancement; 
Measure of Success: Programs launched and systems deployed; 
Examples of Actions to Achieve Goal: 
* Provide status updates to the employee performance management 
program; 
* Provide training and resources to ensure managers and supervisors 
use fair, objective, and consistent merit-based principles during 
performance evaluations; 
GAO Assessment: 
Clarity: The measure lacks key information that would make it more 
clear--namely, which programs and systems TSA is to launch; 
Measurable Target: The measure does not list quantifiable or other 
measurable values to help determine when the goal has been reached. 
For example, the measure does not provide a target number of programs 
against which TSA can benchmark its results. 

DHS unit: CBP; 
Goal: Continue to Implement Communication Strategies for Disseminating 
Information from Management to Employees via Technology; 
Measure of Success: Increase awareness of survey results to employees; 
Examples of Actions to Achieve Goal: 
* Initiate Division Director communiqués; 
* Hold a series of town hall meetings across program offices; 
* Launch a virtual town hall meeting to address employee concerns; 
* Launch a series of town hall meetings in various geographic 
locations to address issues identified through the FEVS; 
GAO Assessment: 
Clarity: The measure lacks key information that would make it more 
clear--namely, what topics in the survey results are targeted, which 
employees should be included, and how "increase awareness" should be 
interpreted; 
Measurable Target: The measure does not list quantifiable or other 
measurable values to help determine when the goal has been reached. 
For example, the measure does not provide a target number of employees 
or geographic locations against which CBP can benchmark its results 
nor does it provide a target measurement for "increase awareness." 

DHS unit: ICE; 
Goal: Increasing Communication; 
Measure of Success: Survey employees; 
Examples of Actions to Achieve Goal: 
* Increase engagement with union representatives; 
* Development of labor management forums within ICE; 
GAO Assessment: 
Clarity: The measure lacks key information that would make it more 
clear--namely, what issues are to be surveyed and which employees 
should be included; 
Measurable Target: The measure does not list quantifiable or other 
measurable values to help determine when the goal has been reached. 
For example, the measure does not provide a target number of employee 
survey responses against which ICE can benchmark its results. 

DHS unit: Coast Guard; 
Goal: Training and Development; 
Measure of Success: Develop e-learning course for new employees; 
Examples of Actions to Achieve Goal: 
* Enhance guidance provided to employees describing their role in 
individual development plans development; 
GAO Assessment: 
Clarity: The measure lacks key information that would make it more 
clear--namely, what is the course content or the specific training 
being provided through the e-learning course; 
Measurable Target: The measure does not list quantifiable or other 
measurable values to help determine when the goal has been reached. 
For example, the measure does not provide a target number of new 
employees who will receive the training against which the Coast Guard 
can benchmark its results. 

Source: GAO analysis of DHS-wide, TSA, CBP, ICE and Coast Guard action 
plans. 

[End of table] 

Officials provided several reasons why their measures of success may 
fall short of the attributes for successful metrics. According to 
OCHCO officials, OCHCO considers accomplishment of an action item step 
as a success and relies on the measures of success listed in its 
action plan as a metric for whether the action plan items were 
implemented. OCHCO considers whether positive responses to survey 
questions noted in the action plan improve over time as the outcome 
measure for whether action plans are effective. However, as part of 
its oversight and feedback on component action plans, OCHCO does not 
monitor or evaluate measures of success for action planning and 
therefore is not in a position to determine whether the measures 
reflect improvement. CBP officials stated that they monitor the change 
in FEVS results overall as the intent of the action planning is to 
improve their scores on the HCAAF indexes. Coast Guard officials 
stated that they rely on qualitative feedback from employees on action 
plan items, such as improved training and website updates, to measure 
action plan performance. TSA officials stated they assess action plan 
results by tracking completion dates for action items and updating 
OCHCO on results at least semi-annually, and ICE officials have stated 
they have not yet fully developed monitoring efforts to evaluate job 
satisfaction action planning because the human capital office received 
funding in the summer of 2011 to implement human capital programs. 

We acknowledge that positive responses in survey results and positive 
employee feedback are good indicators that action planning is working. 
However, until DHS and its components begin to see positive results, 
it is important for them to (1) understand whether they are 
successfully implementing the individual steps of their action plans 
and (2) make any necessary changes to improve on them. By not having 
specific metrics within the action plans that are clear and 
measurable, it will be more difficult for DHS to assess its efforts to 
address employee morale problems, as well as determine if changes 
should be made to ensure progress toward achieving its goals. 
Furthermore, effective measures are key to DHS's action plan as it is 
part of a process that informs the Office of Management and Budget and 
OPM of DHS efforts to address survey results. According to an OPM 
official responsible for federal action planning to improve morale, 
DHS should carefully consider, for each action step, what success 
means to the agency, such as increased employee engagement targets. 
The official said that when success is defined, it should not only be 
clear and measurable, but should also take into account as many of the 
different demographic groups evaluated as possible. 

DHS and the Selected Components Consulted Best Practices: 

DHS and the selected components have initiated efforts to determine 
how other entities approach employee morale issues. DHS officials 
stated they have started to review and implement what they consider to 
be best practices for improving employee morale, such as the following: 

* DHS working group--OCHCO leads a survey engagement team that holds 
monthly meetings during which action planning efforts from across the 
different components are shared and discussed. Representatives from 
other federal agencies such as the National Aeronautics and Space 
Administration and the Federal Aviation Administration have also 
attended these meetings and presented their action plans for 
addressing survey results. 

* Idea Factory--a TSA web-based tool adopted by DHS that empowers 
employees to develop, rate, and improve innovative ideas for programs, 
processes, and technologies. According to a DHS assessment, the Under 
Secretary for Management plans to use this tool for internal DHS 
employee communication so as to promote greater job satisfaction and 
enhance organization effectiveness.[Footnote 33] 

Component officials we interviewed also stated they have started to 
review, implement, and share what they consider to be best practices 
for improving morale. For example: 

* ICE officials stated they consult with other agencies and DHS 
components, such as the U.S. Marshal's Service, when addressing morale 
challenges and developing policies and programs. For example, the U.S. 
Marshal's Service has a critical incident response program for 
employees encountering a traumatic event and ICE is exploring adopting 
a similar program. 

* TSA officials stated that they reached out to Marriott Corporation, 
CBP, and the National Aeronautics and Space Administration to identify 
actions for increasing employee rewards and employee confidence in 
leadership. 

* CBP officials stated they have established several ongoing working 
groups that routinely meet and share human capital best practices 
within the agency. One of these working groups has conducted 
benchmarking work with high-FEVS-scoring federal agencies such as the 
Social Security Administration, the U.S. Secret Service, the Federal 
Bureau of Investigation, the Internal Revenue Service and the Nuclear 
Regulatory Commission. 

* Coast Guard officials stated they share human capital best practices 
that may improve job satisfaction with other DHS components such as 
(1) their performance appraisal system which was adopted, in part, DHS-
wide; (2) their automated cash award process with FEMA; and (3) Coast 
Guard training to supervisors with both DHS headquarters officials and 
FEMA. 

Conclusion: 

Given the critical nature of DHS's mission to protect the security and 
economy of our nation, it is important that DHS employees are 
satisfied with their jobs so that DHS can retain and attract the 
talent required to complete its work. Employee survey data indicate 
that when compared to other federal employees, many DHS employees 
report being dissatisfied and not engaged with their jobs. It is 
imperative that DHS understand what is driving employee morale 
problems and address those problems through targeted actions that 
address employees' underlying concerns. DHS has made efforts to 
understand morale issues across the department, but those efforts 
could be improved. Specifically, given the annual employee survey data 
available through the FEVS, DHS and its components could improve their 
efforts to determine root causes of morale problems by comparing 
demographic groups, benchmarking against similar organizations, and 
linking root cause findings to action plans. Uncovering root causes of 
morale problems could help identify appropriate actions to take in 
efforts to improve morale. In addition, DHS has established 
performance measures for its action plans to improve morale, but 
incorporating attributes such as improved clarity and measurable 
targets could better position DHS to determine whether its action 
plans are effective. Without doing so, DHS will have a more difficult 
time determining whether it is achieving its goals. 

Recommendations for Executive Action: 

To strengthen DHS's evaluation and planning process for addressing 
employee morale, we recommend that the Secretary of Homeland Security 
direct OCHCO and component human capital officials to take the 
following two actions: 

* examine their root cause analysis efforts and, where absent, add the 
following: comparisons of demographic groups, benchmarking against 
similar organizations, and linkage of root cause findings to action 
plans; and: 

* establish metrics of success within the action plans that are clear 
and measurable. 

Agency Comments and Our Evaluation: 

We requested comments on a draft of this report from DHS. On September 
25, 2012, DHS provided written comments, which are reprinted in 
appendix V, and provided technical comments, which we incorporated as 
appropriate. DHS concurred with our two recommendations and described 
actions planned to address them. Specifically: 

* DHS stated that it will ensure that department-wide and component 
action plans are tied to root causes and that the department will 
conduct benchmarking against other organizations. DHS also stated that 
its ability to conduct demographic analysis is limited due to the data 
set OPM makes available to federal agencies. However, according to 
OPM, DHS has access to the data necessary for conducting analysis 
similar to our comparison of demographic groups. 

* DHS stated it will review action plans to ensure that each action is 
clear and measurable. 

We also requested comments on a draft of this report from OPM. On 
September 18, 2012, OPM provided a written response, which is 
reprinted in appendix VI. OPM's letter indicated that it reviewed the 
draft report and had no comments. 

As agreed with your offices, unless you publicly announce the contents 
of this report earlier, we plan no further distribution until 30 days 
from the report date. At that time, we will send copies to the 
Secretary of Homeland Security, the U.S. Office of Personnel 
Management, and interested congressional committees. The report also 
will be available at no charge on GAO's website at [hyperlink, 
http://www.gao.gov]. 

If you or your staff have any questions about this report please 
contact me at (202) 512-9627 or maurerd@gao.gov. Contact points for 
our Offices of Congressional Relations and Public Affairs may be found 
on the last page of this report. GAO staff who made major 
contributions to this report are listed in appendix VII. 

Signed by: 

David C. Maurer: 
Director, Homeland Security and Justice Issues: 

[End of section] 

Appendix I: Statistical Analysis of Employee Morale at Department of 
Homeland Security and Other Agencies: 

We conducted a statistical analysis of the 2011 Federal Employee 
Viewpoint Survey (FEVS) to assess employee morale at the Department of 
Homeland Security (DHS). Our analysis addressed two specific 
questions. First, how does morale at DHS and its components compare 
with morale at other agencies, holding constant demographic 
differences among employees? Second, to what extent is the morale gap 
between DHS and other agencies explained by differences in the 
demographic composition of the DHS workforce versus other unique 
characteristics of the agency or unmeasured demographic factors? 

This appendix explains the value of statistical analysis for 
understanding the employee morale gap, describes the data and methods 
we used, and provides additional details about our findings, which are 
summarized in the body of the report. In sum: 

* DHS employees with the same demographic profiles (measured by FEVS) 
were about 7 percentage points less engaged and 6 points less 
satisfied than non-DHS employees. 

* Demographic differences (measured by FEVS) between DHS and other 
agencies are unlikely to explain the overall morale gap. Unique 
features of DHS (or unmeasured demographics) are more likely to be 
responsible. 

* DHS middle managers and employees with 1 to 10 years of tenure at 
their components--those hired after the department's creation--have 
lower morale than similar employees at other departments. 

* Morale varies widely across DHS components, and some have similar 
morale as non-DHS agencies. Individual offices can strongly influence 
the morale gap at the component level. 

* The morale gap is smaller for DHS components that existed before the 
department was created. 

Understanding the Morale Gap at DHS: 

The morale gap between DHS and other agencies may be due to unique 
issues within DHS or common issues faced by all agencies in similar 
circumstances. Unique issues might include developing an agency-wide 
culture, the decisions and composition of senior leaders, and the 
inherent uniqueness of homeland security programs. Common 
characteristics might include having many law enforcement and front-
line customer service occupations, and having employees dispersed 
among many headquarters and field offices. 

Determining whether unique or shared issues account for the overall 
morale gap is important for understanding the cause of the problem. If 
morale at DHS was not uniquely low, compared with morale at agencies 
with similar demographics and programs, the agency might learn from 
peer agencies facing similar challenges. Alternatively, if morale was 
lower at DHS for reasons unique to the agency, DHS might put more 
emphasis on understanding its own particular challenges. 
Distinguishing among these possible explanations can help develop a 
solution that is narrowly tailored to the problem. 

Our analysis focused on one group of shared circumstances that might 
explain the morale gap: employee demographics. If DHS were more likely 
to employ the types of workers who tend to have lower morale across 
all agencies of the government, the composition of the workforce might 
account for the gap to a greater extent than factors specific to DHS. 
In other words, morale at DHS may be no worse than at other agencies 
among demographically equivalent employees. Our analysis focused on a 
limited number of demographic differences, such as location and age, 
but attitudinal differences about pay, benefits, supervision, 
training, mentoring, and other human capital issues could be assessed 
in a similar way. 

We also considered how large of a morale gap there was between 
employees in various DHS components and work groups and non-DHS 
employees. The gap at the department level can mask groups of 
employees with higher or lower morale. Disaggregating morale into 
small work groups identifies areas of DHS in which morale may be high 
or low, and thus provides sufficiently detailed data for focused 
solutions to the problem. 

Any analysis of morale in employee surveys is limited by the fact that 
associations among the variables of interest may not represent cause-
and-effect relationships. Nevertheless, a limited observational 
analysis remains useful for evaluating human capital programs. Since 
federal agencies cannot easily conduct high-quality randomized 
controlled trials of various approaches to managing their employees, 
the use of observational methods is common, often in the form of 
quantitative survey analyses or qualitative interviews and focus 
groups. We have previously found that a pragmatic approach to 
answering necessary policy questions, using the best methods and data 
that are feasible, is widely supported by academic experts and 
practitioners in policy analysis.[Footnote 34] Moreover, statistical 
theory has shown that observational methods can estimate cause-and-
effect relationships in certain conditions.[Footnote 35] 

Associations between morale and demographic characteristics are useful 
for understanding the operation of human capital programs, when 
interpreted cautiously and in the context of all the available 
evidence. Our analysis here describes patterns across the demographic 
groups identified in the 2011 FEVS and determines whether the 
aggregate differences between DHS and other agencies persists among 
demographically similar employees. We make no causal interpretations 
of these relationships, and our approach is only one that might be 
valid and useful. 

The 2011 Federal Employee Viewpoint Survey: 

The Office of Personnel Management (OPM) provided us with a version of 
the 2011 FEVS that included more detailed demographic and 
organizational data than the file it released to the public. 
Specifically, our file contained the same variables as the public file 
but identified more detailed groups of employees. The 2011 survey 
included responses from 266,376 full-time, permanent federal 
employees, working for agencies that, according to OPM, constituted 97 
percent of the executive branch workforce. OPM sampled employees 
within strata formed by supervisory status and organizational subgroup 
(e.g., component and work group).[Footnote 36] This produced generally 
large sample sizes even for many small work groups within components, 
which allowed us to analyze morale among small groups of employees 
with an acceptable degree of precision. 

We focused on two types of variables in the FEVS: (1) employee 
demographics and (2) OPM's Employee Engagement and Job Satisfaction 
indexes. A series of questions at the end of the survey collected the 
demographic data, rather than preexisting administrative records. OPM 
reported independently developing and validating the engagement 
indexes using factor-analytic procedures, which are common 
psychometric statistical methods. The survey items that made up each 
index used five-point, Likert-type scales, with "agree/disagree," 
"satisfied/dissatisfied," or "good/poor" response options.[Footnote 37] 

We used weights provided by OPM to calculate estimates and sampling 
variances for all analyses. The weights were the product of the 
unequal sampling probabilities across strata and non-response and post-
stratification adjustments. Because some strata had relatively small 
population sizes--one-quarter with 18 employees or fewer--we corrected 
for finite populations. 

Morale Differences Between DHS Employees and Employees at Other 
Agencies: 

One explanation for lower morale at DHS is that its employees could be 
members of demographic groups that typically have lower morale across 
all agencies. If this is true, the cause of morale problems and their 
solutions might focus less on factors that are unique to DHS and more 
on approaches that apply to any agency with a similar workforce. 

Table 5 provides basic evidence to help assess the demographic 
explanation. The table presents the average OPM Engagement Index for 
several demographic groups in the 2011 FEVS. If engagement problems at 
DHS were isolated to particular subgroups of employees, the morale gap 
should vary widely across those subgroups. In fact, engagement at DHS 
is lower (or statistically indistinguishable from zero) than at other 
agencies in each demographic subgroup we analyzed, and the gap 
relative to DHS does not vary by large amounts across most subgroups. 
However, the gap is somewhat larger among employees who were in 
certain subgroups, such as those who had 4 to 10 years of experience 
with their components and who worked outside of headquarters. 

Table 5: DHS and Non-DHS Employee Engagement Index by Demographic 
Group for the 2011 FEVS: 

All employees; 
OPM Employee Engagement Index - Non-DHS: 67.1; 
95% Margin of Error (+/-): 0.3; 
OPM Employee Engagement Index - DHS: 60.1; 
95% margin of error (+/-): 0.7; 
OPM Employee Engagement Index - difference: -7.0*; 
95% margin of error (+/-): 0.7. 

Supervisory status: Non-management; 
OPM Employee Engagement Index - Non-DHS: 65.7; 
95% Margin of Error (+/-): 0.3; 
OPM Employee Engagement Index - DHS: 58.2; 
95% margin of error (+/-): 0.8; 
OPM Employee Engagement Index - difference: -7.5*; 
95% margin of error (+/-): 0.9. 

Supervisory status: Management; 
OPM Employee Engagement Index - Non-DHS: 74.1; 
95% Margin of Error (+/-): 0.4; 
OPM Employee Engagement Index - DHS: 67.0; 
95% margin of error (+/-): 1.1; 
OPM Employee Engagement Index - difference: -7.2*; 
95% margin of error (+/-): 1.2. 

Pay group: Federal Wage System; 
OPM Employee Engagement Index - Non-DHS: 61.6; 
95% Margin of Error (+/-): 1.3; 
OPM Employee Engagement Index - DHS: 57.3; 
95% margin of error (+/-): 3.7; 
OPM Employee Engagement Index - difference: -4.3*; 
95% margin of error (+/-): 3.9. 

Pay group: GS 1-6; 
OPM Employee Engagement Index - Non-DHS: 65.6; 
95% Margin of Error (+/-): 1.0; 
OPM Employee Engagement Index - DHS: 61.4; 
95% margin of error (+/-): 5.1; 
OPM Employee Engagement Index - difference: -4.2; 
95% margin of error (+/-): 5.2. 

Pay group: GS 7-12; 
OPM Employee Engagement Index - Non-DHS: 66.8; 
95% Margin of Error (+/-): 0.4; 
OPM Employee Engagement Index - DHS: 61.3; 
95% margin of error (+/-): 1.1; 
OPM Employee Engagement Index - difference: -5.5*; 
95% margin of error (+/-): 1.2. 

Pay group: GS 13-15; 
OPM Employee Engagement Index - Non-DHS: 70.5; 
95% Margin of Error (+/-): 0.4; 
OPM Employee Engagement Index - DHS: 63.0; 
95% margin of error (+/-): 1.2; 
OPM Employee Engagement Index - difference: -7.5*; 
95% margin of error (+/-): 1.2. 

Pay group: Senior Executive Service; 
OPM Employee Engagement Index - Non-DHS: 82.7; 
95% Margin of Error (+/-): 1.2; 
OPM Employee Engagement Index - DHS: 79.8; 
95% margin of error (+/-): 3.0; 
OPM Employee Engagement Index - difference: -3.0; 
95% margin of error (+/-): 3.2. 

Pay group: SL; 
OPM Employee Engagement Index - Non-DHS: 70.5; 
95% Margin of Error (+/-): 3.7; 
OPM Employee Engagement Index - DHS: 66.2; 
95% margin of error (+/-): 16.5; 
OPM Employee Engagement Index - difference: -4.4; 
95% margin of error (+/-): 16.9. 

Pay group: Other[A]; 
OPM Employee Engagement Index - Non-DHS: 65.6; 
95% Margin of Error (+/-): 1.1; 
OPM Employee Engagement Index - DHS: 53.1; 
95% margin of error (+/-): 1.4; 
OPM Employee Engagement Index - difference: - 12.5*; 
95% margin of error (+/-): 1.8. 

Agency tenure: Less than 1 year; 
OPM Employee Engagement Index - Non-DHS: 76.0; 
95% Margin of Error (+/-): 1.3; 
OPM Employee Engagement Index - DHS: 75.6; 
95% margin of error (+/-): 4.2; 
OPM Employee Engagement Index - difference: -0.4; 
95% margin of error (+/-): 4.4. 

Agency tenure: 1-3 years; 
OPM Employee Engagement Index - Non-DHS: 69.7; 
95% Margin of Error (+/-): 0.6; 
OPM Employee Engagement Index - DHS: 65.2; 
95% margin of error (+/-): 1.6; 
OPM Employee Engagement Index - difference: -4.6*; 
95% margin of error (+/-): 1.7. 

Agency tenure: 4-5 years; 
OPM Employee Engagement Index - Non-DHS: 66.0; 
95% Margin of Error (+/-): 0.9; 
OPM Employee Engagement Index - DHS: 58.4; 
95% margin of error (+/-): 1.8; 
OPM Employee Engagement Index - difference: -7.7*; 
95% margin of error (+/-): 2.0. 

Agency tenure: 6-10 years; 
OPM Employee Engagement Index - Non-DHS: 65.8; 
95% Margin of Error (+/-): 0.6; 
OPM Employee Engagement Index - DHS: 56.0; 
95% margin of error (+/-): 1.1; 
OPM Employee Engagement Index - difference: -9.9*; 
95% margin of error (+/-): 1.2. 

Agency tenure: 11-20 years; 
OPM Employee Engagement Index - Non-DHS: 65.1; 
95% Margin of Error (+/-): 0.6; 
OPM Employee Engagement Index - DHS: 61.3; 
95% margin of error (+/-): 1.7; 
OPM Employee Engagement Index - difference: -3.8*; 
95% margin of error (+/-): 1.8. 

Agency tenure: 20+ years; 
OPM Employee Engagement Index - Non-DHS: 67.2; 
95% Margin of Error (+/-): 0.5; 
OPM Employee Engagement Index - DHS: 60.3; 
95% margin of error (+/-): 2.3; 
OPM Employee Engagement Index - difference: -6.9*; 
95% margin of error (+/-): 2.3. 

Location: Headquarters; 
OPM Employee Engagement Index - Non-DHS: 68.5; 
95% Margin of Error (+/-): 0.4; 
OPM Employee Engagement Index - DHS: 64.9; 
95% margin of error (+/-): 1.5; 
OPM Employee Engagement Index - difference: -3.5*; 
95% margin of error (+/-): 1.5. 

Location: Field; 
OPM Employee Engagement Index - Non-DHS: 66.5; 
95% Margin of Error (+/-): 0.3; 
OPM Employee Engagement Index - DHS: 59.0; 
95% margin of error (+/-): 0.8; 
OPM Employee Engagement Index - difference: -7.5*; 
95% margin of error (+/-): 0.8. 

Age: Under 26; 
OPM Employee Engagement Index - Non-DHS: 74.6; 
95% Margin of Error (+/-): 2.0; 
OPM Employee Engagement Index - DHS: 66.7; 
95% margin of error (+/-): 4.8; 
OPM Employee Engagement Index - difference: -7.9*; 
95% margin of error (+/-): 5.2. 

Age: 26-29; 
OPM Employee Engagement Index - Non-DHS: 70.6; 
95% Margin of Error (+/-): 1.4; 
OPM Employee Engagement Index - DHS: 58.9; 
95% margin of error (+/-): 3.2; 
OPM Employee Engagement Index - difference: - 11.7*; 
95% margin of error (+/-): 3.5. 

Age: 30-39; 
OPM Employee Engagement Index - Non-DHS: 67.9; 
95% Margin of Error (+/-): 0.7; 
OPM Employee Engagement Index - DHS: 60.5; 
95% margin of error (+/-): 1.4; 
OPM Employee Engagement Index - difference: -7.4*; 
95% margin of error (+/-): 1.5. 

Age: 40-49; 
OPM Employee Engagement Index - Non-DHS: 67.2; 
95% Margin of Error (+/-): 0.5; 
OPM Employee Engagement Index - DHS: 60.6; 
95% margin of error (+/-): 1.2; 
OPM Employee Engagement Index - difference: -6.6*; 
95% margin of error (+/-): 1.3. 

Age: 50-59; 
OPM Employee Engagement Index - Non-DHS: 66.2; 
95% Margin of Error (+/-): 0.4; 
OPM Employee Engagement Index - DHS: 58.9; 
95% margin of error (+/-): 1.3; 
OPM Employee Engagement Index - difference: -7.3*; 
95% margin of error (+/-): 1.3. 

Age: 60+; 
OPM Employee Engagement Index - Non-DHS: 67.8; 
95% Margin of Error (+/-): 0.7; 
OPM Employee Engagement Index - DHS: 58.1; 
95% margin of error (+/-): 2.2; 
OPM Employee Engagement Index - difference: -9.8*; 
95% margin of error (+/-): 2.3. 

Race: Hispanic; 
OPM Employee Engagement Index - Non-DHS: 66.8; 
95% Margin of Error (+/-): 1.1; 
OPM Employee Engagement Index - DHS: 63.7; 
95% margin of error (+/-): 1.7; 
OPM Employee Engagement Index - difference: -3.1*; 
95% margin of error (+/-): 2.0. 

Race: American Indian/Alaska Native; 
OPM Employee Engagement Index - Non- DHS: 60.1; 
95% Margin of Error (+/-): 1.8; 
OPM Employee Engagement Index - DHS: 52.7; 
95% margin of error (+/-): 7.6; 
OPM Employee Engagement Index - difference: -7.3; 
95% margin of error (+/-): 7.8. 

Race: Asian; 
OPM Employee Engagement Index - Non-DHS: 72.4; 
95% Margin of Error (+/-): 1.2; 
OPM Employee Engagement Index - DHS: 65.0; 
95% margin of error (+/-): 3.4; 
OPM Employee Engagement Index - difference: -7.4*; 
95% margin of error (+/-): 3.6. 

Race: Black; 
OPM Employee Engagement Index - Non-DHS: 68.2; 
95% Margin of Error (+/-): 0.7; 
OPM Employee Engagement Index - DHS: 60.8; 
95% margin of error (+/-): 2.0; 
OPM Employee Engagement Index - difference: -7.4*; 
95% margin of error (+/-): 2.1. 

Race: Hawaiian/Pacific Islander; 
OPM Employee Engagement Index - Non-DHS: 65.4; 
95% Margin of Error (+/-): 4.1; 
OPM Employee Engagement Index - DHS: 56.8; 
95% margin of error (+/-): 6.6; 
OPM Employee Engagement Index - difference: -8.5*; 
95% margin of error (+/-): 7.8. 

Race: White; 
OPM Employee Engagement Index - Non-DHS: 67.5; 
95% Margin of Error (+/-): 0.3; 
OPM Employee Engagement Index - DHS: 58.5; 
95% margin of error (+/-): 0.9; 
OPM Employee Engagement Index - difference: -9.0*; 
95% margin of error (+/-): 0.9. 

Race: Two or more races; 
OPM Employee Engagement Index - Non-DHS: 62.3; 
95% Margin of Error (+/-): 1.9; 
OPM Employee Engagement Index - DHS: 56.3; 
95% margin of error (+/-): 4.0; 
OPM Employee Engagement Index - difference: -6.0*; 
95% margin of error (+/-): 4.4. 

Source: GAO analysis of 2011 FEVS data. 

Note: Asterisks denote differences that are statistically 
distinguishable from zero at the 0.05 level. 

[A] Because respondents to the 2011 FEVS reported their own pay 
groups, the "other" category may have included workers in various 
groups other than the GS system. At DHS, this group may have included 
Transportation Security Administration airport security screeners. 

[End of table] 

Multivariate Analysis: 

We developed several statistical models to further assess the 
demographic explanation. These models held constant the demographic 
profiles of DHS and non-DHS employees, in order to isolate the portion 
of the morale gap that was specifically due to non-demographic 
factors. The models allowed us to compare morale at DHS and other 
agencies among employees who were in the same demographic groups, as 
measured by the FEVS. 

To avoid methodological complications with modeling latent variables, 
we created a binary measure that identified whether a respondent was 
engaged or satisfied on each item in the respective scales. Our 
measure equaled 1 if the respondent gave positive answers (4 or 5) to 
each item in the index and 0 if the respondent gave neutral or 
negative responses (1,2, or 3) to at least one item. Collapsing the 
scale loses some information, since morale and satisfaction are 
continuous, latent variables. However, a collapsed measure provides 
some degree of comparability between OPM's aggregate indices and our 
individual-level analysis, since the OPM's indices also collapse the 
scale. The differences among agencies and subgroups of employees are 
generally similar using either our measure or OPM's.[Footnote 38] 

We focused on the associations between broad measures of morale and 
fixed demographic characteristics available in the 2011 FEVS. Fixed 
demographics and broad measures of satisfaction are not subject to 
artificially high correlations that a survey's design can produce 
among attitudinal measures. 

The models took the following form: 

[Refer to PDF for image: formulas] 

E(Morale ij | DHS j, Demog ij)= A (~DHS j + Demog ij B) (1); 

E(Morale ij | DHS j, Demog ij)= A (~DHS j + DHS j x Demog ij Bd +Demog 
ijBg) (2). 

[End of formulas] 

Morale ij indicates whether employee i at agency j was engaged or 
satisfied, using the binary measure we calculated from the survey 
items that make up the OPM indexes (see above). DHS j indicates 
whether the employee worked for DHS, Demog ij is a vector of 
demographic indicators (listed in table 6), A is the logistic 
function, and ~ and B are vectors of coefficients that estimate how 
morale varied among employees in different demographic groups. We 
included all demographic factors measured by the FEVS that plausibly 
could have predicted morale and were clearly causally prior to morale. 
We excluded pay group, however, because of its high correlation with 
supervisory status. Model 2 allows DHS and non-DHS employees in the 
same demographic groups to have different levels of morale, as 
described by Bd and Bg. We estimated each model using cluster-robust 
maximum likelihood methods, with 365 agency clusters (e.g., 
Transportation Security Administration [TSA]). 

Our multivariate analysis found that DHS employees remained an average 
of 6.4 percentage points less engaged (+/-3.2) (see table 6) and 5.5 
points less satisfied (+/-2.2) (not shown) on our scales than 
employees at other agencies who had the same age, office location, 
race, sex, supervisory status, and tenure. This suggests that measured 
demographic differences between employees at DHS and other agencies do 
not fully explain the morale gap. Instead, factors that are intrinsic 
to DHS, such as culture or management practices, or demographic 
factors not measured by FEVS, such as education or occupation, are 
likely to be responsible. 

Decomposing the Morale Gap: 

We can further explore the roles of demographics and unique DHS 
characteristics by performing an Oaxaca decomposition of the results 
of model 2, in order to compare DHS with other agencies.[Footnote 39] 
Oaxaca decomposition can assess whether the overall morale gap is 
explained by the demographic characteristics of DHS employees, or 
whether it is explained by lower morale among DHS employees in the 
same demographic groups. In other words, does DHS employ an unusually 
large number of workers who tend to have low morale across all 
agencies, or do workers with the same backgrounds have uniquely lower 
morale at DHS? 

Table 6: Model Estimates of Employee Engagement Index at DHS and Other 
Agencies Using the 2011 FEVS: 

All employees; 
DHS: Percentage engaged (GAO measure): 20.7%; 
DHS: Standard error: 1.5; 
Other Agencies: Percentage engaged (GAO measure): 27.1%; 
Other Agencies: Standard error: 0.5. 

Gap between DHS and other agencies; 
DHS: Percentage engaged (GAO measure): -6.3%[A]. 

Portion of gap due to the demographic composition of DHS; 
DHS: Percentage engaged (GAO measure): 0.1%. 

Portion of gap due to unique differences between DHS and non-DHS 
employees with similar demographic profiles; 
DHS: Percentage engaged (GAO measure): -6.4%. 

Race: Hispanic; 
DHS: Percentage engaged (GAO measure): 27.0%; 
DHS: Standard error: 1.1; 
Other Agencies: Percentage engaged (GAO measure): 29.0%; 
Other Agencies: Standard error: 0.7. 

Race: American Indian/Alaska Native; 
DHS: Percentage engaged (GAO measure): 13.2%; 
DHS: Standard error: 3.7; 
Other Agencies: Percentage engaged (GAO measure): 21.3%; 
Other Agencies: Standard error: 0.8. 

Race: Asian; 
DHS: Percentage engaged (GAO measure): 29.0%; 
DHS: Standard error: 3.5; 
Other Agencies: Percentage engaged (GAO measure): 34.4%; 
Other Agencies: Standard error: 0.8. 

Race: Black; 
DHS: Percentage engaged (GAO measure): 23.7%; 
DHS: Standard error: 2.0; 
Other Agencies: Percentage engaged (GAO measure): 27.8%; 
Other Agencies: Standard error: 0.5. 

Race: Hawaiian/Pacific Islander; 
DHS: Percentage engaged (GAO measure): 23.1%; 
DHS: Standard error: 2.5; 
Other Agencies: Percentage engaged (GAO measure): 28.4%; 
Other Agencies: Standard error: 1.3. 

Race: White; 
DHS: Percentage engaged (GAO measure): 19.0%; 
DHS: Standard error: 1.5; 
Other Agencies: Percentage engaged (GAO measure): 26.7%; 
Other Agencies: Standard error: 0.5. 

Race: Two or more races; 
DHS: Percentage engaged (GAO measure): 18.3%; 
DHS: Standard error: 3.0; 
Other Agencies: Percentage engaged (GAO measure): 20.7%; 
Other Agencies: Standard error: 0.8. 

Years worked for component: less than 1; 
DHS: Percentage engaged (GAO measure): 34.6%; 
DHS: Standard error: 2.5; 
Other Agencies: Percentage engaged (GAO measure): 38.1%; 
Other Agencies: Standard error: 0.9. 

Years worked for component: less than 1-3; 
DHS: Percentage engaged (GAO measure): 22.8%; 
DHS: Standard error: 2.2; 
Other Agencies: Percentage engaged (GAO measure): 30.5%; 
Other Agencies: Standard error: 0.6. 

Years worked for component: less than 4-5; 
DHS: Percentage engaged (GAO measure): 20.0%; 
DHS: Standard error: 1.8; 
Other Agencies: Percentage engaged (GAO measure): 27.3%; 
Other Agencies: Standard error: 0.7. 

Years worked for component: less than 6-10; 
DHS: Percentage engaged (GAO measure): 17.6%; 
DHS: Standard error: 1.6; 
Other Agencies: Percentage engaged (GAO measure): 25.7%; 
Other Agencies: Standard error: 0.5. 

Years worked for component: less than 11-20; 
DHS: Percentage engaged (GAO measure): 19.3%; 
DHS: Standard error: 2.2; 
Other Agencies: Percentage engaged (GAO measure): 24.7%; 
Other Agencies: Standard error: 0.5. 

Years worked for component: less than 20+; 
DHS: Percentage engaged (GAO measure): 21.2%; 
DHS: Standard error: 3.0; 
Other Agencies: Percentage engaged (GAO measure): 26.6%; 
Other Agencies: Standard error: 0.6. 

Sex: Male; 
DHS: Percentage engaged (GAO measure): 20.6%; 
DHS: Standard error: 1.6; 
Other Agencies: Percentage engaged (GAO measure): 27.1%; 
Other Agencies: Standard error: 0.5. 

Sex: Female; 
DHS: Percentage engaged (GAO measure): 20.9%; 
DHS: Standard error: 1.5; 
Other Agencies: Percentage engaged (GAO measure): 27.2%; 
Other Agencies: Standard error: 0.5. 

Supervisory status: Non-supervisor; 
DHS: Percentage engaged (GAO measure): 18.2%; 
DHS: Standard error: 1.5; 
Other Agencies: Percentage engaged (GAO measure): 23.3%; 
Other Agencies: Standard error: 0.5. 

Supervisory status: Team leader; 
DHS: Percentage engaged (GAO measure): 17.8%; 
DHS: Standard error: 1.7; 
Other Agencies: Percentage engaged (GAO measure): 26.3%; 
Other Agencies: Standard error: 0.6. 

Supervisory status: Supervisor; 
DHS: Percentage engaged (GAO measure): 23.3%; 
DHS: Standard error: 1.7; 
Other Agencies: Percentage engaged (GAO measure): 31.7%; 
Other Agencies: Standard error: 0.6. 

Supervisory status: Manager; 
DHS: Percentage engaged (GAO measure): 31.7%; 
DHS: Standard error: 2.5; 
Other Agencies: Percentage engaged (GAO measure): 40.5%; 
Other Agencies: Standard error: 0.7. 

Supervisory status: Executive; 
DHS: Percentage engaged (GAO measure): 47.3%; 
DHS: Standard error: 2.8; 
Other Agencies: Percentage engaged (GAO measure): 54.2%; 
Other Agencies: Standard error: 1.3. 

Age: under 26; 
DHS: Percentage engaged (GAO measure): 20.0%; 
DHS: Standard error: 2.9; 
Other Agencies: Percentage engaged (GAO measure): 28.6%; 
Other Agencies: Standard error: 1.1. 

Age: 26-29; 
DHS: Percentage engaged (GAO measure): 16.7%; 
DHS: Standard error: 2.6; 
Other Agencies: Percentage engaged (GAO measure): 24.9%; 
Other Agencies: Standard error: 0.9. 

Age: 30-39; 
DHS: Percentage engaged (GAO measure): 19.3%; 
DHS: Standard error: 1.6; 
Other Agencies: Percentage engaged (GAO measure): 26.3%; 
Other Agencies: Standard error: 0.6. 

Age: 40-49; 
DHS: Percentage engaged (GAO measure): 20.8%; 
DHS: Standard error: 1.9; 
Other Agencies: Percentage engaged (GAO measure): 27.4%; 
Other Agencies: Standard error: 0.5. 

Age: 50-59; 
DHS: Percentage engaged (GAO measure): 21.0%; 
DHS: Standard error: 1.7; 
Other Agencies: Percentage engaged (GAO measure): 26.7%; 
Other Agencies: Standard error: 0.5. 

Age: 60+; 
DHS: Percentage engaged (GAO measure): 22.8%; 
DHS: Standard error: 1.6; 
Other Agencies: Percentage engaged (GAO measure): 29.3%; 
Other Agencies: Standard error: 0.6. 

Location: Headquarters; 
DHS: Percentage engaged (GAO measure): 21.4%; 
DHS: Standard error: 1.6; 
Other Agencies: Percentage engaged (GAO measure): 27.8%; 
Other Agencies: Standard error: 0.6. 

Location: Field; 
DHS: Percentage engaged (GAO measure): 20.2%; 
DHS: Standard error: 1.9; 
Other Agencies: Percentage engaged (GAO measure): 26.6%; 
Other Agencies: Standard error: 0.6. 

Source GAO analysis of U.S. Office of Personnel Management 2011 FEVS 
data. 

Note: We created a unique measure of whether each employee in the 2011 
FEVS was engaged for the purposes of statistical analysis and 
describing differences among groups of employees. Because our measure 
is defined differently than the OPM Employee Engagement Index, the 
measures are not comparable. Our measure counts an employee as engaged 
if he or she gave positive answers to each item in the OPM index (see 
text). Engagement statistics in this table are the in-sample 
percentage of employees who are predicted by model 2 to be engaged on 
GAO's measure. 

[A] Gap estimates and their decomposition are estimated using Oaxaca 
methods applied to model 2. 

[End of table] 

As shown in table 6, the model suggests that the demographic profile 
of DHS employees (measured by FEVS) tends to slightly increase their 
engagement and reduce the gap compared with employees at other 
agencies. The demographic characteristics we can observe in FEVS 
reduce the overall gaps in the proportion engaged and satisfied on our 
scales by 0.1 and 1.0 percentage points, respectively.[Footnote 40] 
Instead, the morale gap is better explained by unique differences in 
morale between DHS and other agencies among demographically similar 
employees. Such intrinsic differences increase the gaps in the 
proportion engaged and satisfied by 6.4 and 5.5 percentage points, 
respectively. If the demographic profile of the DHS workforce did not 
change, but DHS could achieve the same levels of morale as other 
agencies from the same types of employees, our model predicts that DHS 
employees would not have lower morale than employees at other agencies. 

DHS employees with lower-level positions and component tenure were 
among those with lower morale, relative to employees in other 
agencies. As shown in figures 6 and 7, our measures of engagement and 
satisfaction generally increased with seniority and decreased with 
tenure, among employees at DHS and other agencies. At DHS, however, 
morale increased more slowly as employees gained more seniority, and 
it declined more quickly as they spent more time at the agency. For 
example, the average newly hired employee at DHS and similar employees 
at other agencies had statistically indistinguishable levels of 
engagement. By their sixth years, however, satisfaction for the DHS 
employee declined to an average of 18 percentage points, whereas 
satisfaction for the non-DHS employees declined to an average of only 
26 percentage points. A similar pattern exists with respect to 
supervisory status (see figures 6 and 7). These patterns are 
particularly important for explaining the overall morale gap, because 
DHS had about 30 percent more supervisors and about twice as many 
people with 6 to 10 years of component tenure (as a share of all 
employees), compared with people at other agencies (according to 
FEVS).[Footnote 41] 

Figure 6: Engagement Index Scores by Supervisory Status and Tenure, 
for DHS and Non-DHS Employees: 

[Refer to PDF for image: 2 multiple line graphs] 

Percent engaged (GAO measure): 

Non-Supervisor: 
DHS: 18.2%; 
Non-DHS: 23.3%. 

Team Leader: 
DHS: 17.8%; 
Non-DHS: 26.3%. 

Supervisor: 
DHS: 23.3%; 
Non-DHS: 31.7%. 

Manager: 
DHS: 31.8%; 
Non-DHS: 40.5%. 

Executive: 
DHS: 47.3% 
Non-DHS: 54.2%. 

Time worked at component: less than 1 year; 
DHS: 34.6%; 
Non-DHS: 38.1%. 

Time worked at component: 1-3 years; 
DHS: 22.8%; 
Non-DHS: 30.6%. 

Time worked at component: 4-5 years; 
DHS: 20.0%; 
Non-DHS: 27.3%. 

Time worked at component: 6-10 years; 
DHS: 17.6%; 
Non-DHS: 25.7%. 

Time worked at component: 11-20 years;
DHS: 19.3%; 
Non-DHS: 24.7%. 

Time worked at component: 20+ years; 
DHS: 21.2%; 
Non-DHS: 26.6%. 

Source: GAO analysis of 2011 FEVS data. 

[End of figure] 

Figure 7: Satisfaction Index Scores by Supervisory Status and Tenure, 
for DHS and Non-DHS Employees: 

[Refer to PDF for image: 2 multiple line graphs] 

Percent engaged (GAO measure): 

Non-Supervisor: 
DHS: 119.7%; 
Non-DHS: 24.0%. 

Team Leader: 
DHS: 19.8%; 
Non-DHS: 27.5%. 

Supervisor: 
DHS: 27.7%; 
Non-DHS: 34.5%. 

Manager: 
DHS: 34.8%; 
Non-DHS: 42.9%. 

Executive: 
DHS: 43.8% 
Non-DHS: 48.3%. 

Time worked at component: less than 1 year; 
DHS: 32.9%; 
Non-DHS: 32.0%. 

Time worked at component: 1-3 years; 
DHS: 25.2%; 
Non-DHS: 28.6%. 

Time worked at component: 4-5 years; 
DHS: 22.6%; 
Non-DHS: 27.7%. 

Time worked at component: 6-10 years; 
DHS: 19.2%; 
Non-DHS: 27.3%. 

Time worked at component: 11-20 years;
DHS: 22.8%; 
Non-DHS: 27.2%. 

Time worked at component: 20+ years; 
DHS: 22.5%; 
Non-DHS: 29.3%. 

Source: GAO analysis of 2011 FEVS data. 

[End of figure] 

Morale Differences within DHS Components and Work Groups: 

Low employee morale is not a uniform problem throughout DHS. As shown 
in table 7, engagement varies widely across components within the 
department, with employees in some components not being significantly 
different from the average employee at non-DHS agencies. These 
components include the U.S. Coast Guard (Coast Guard), Federal Law 
Enforcement Training Center (FLETC), Management Directorate (MGMT), 
and U.S. Secret Service (USSS). Job satisfaction at these components 
also matches or exceeds that found at other agencies (not shown in 
table 7). 

DHS has a number of components whose employees have substantially 
lower morale than employees at other agencies and elsewhere in the 
department. The large share of DHS employees working in these 
components accounts for the overall morale gap between DHS and other 
agencies. 

Components with lower morale include Federal Emergency Management 
Agency (FEMA), Immigration and Customs Enforcement (ICE), Intelligence 
and Analysis (IA), National Protection and Programs Directorate 
(NPPD), Science and Technology (ST), and the TSA. The engagement 
scores of these components range from 9.1 to 13.9 percentage points 
lower than the average score for non-DHS agencies (see table 7). As a 
group, these components make up 46 percent of the employees 
interviewed for the FEVS. Consequently, the components with 
substantially lower morale have a large influence on the gap relative 
to the rest of the government, despite the fact that morale at many 
smaller DHS components is no worse. 

Morale at some of the less engaged and satisfied components is, in 
turn, strongly influenced by particular employee workgroups (see table 
7). For example, the average engagement at TSA is 12.8 percentage 
points (apart from rounding) lower than at non-DHS agencies. Within 
TSA, however, the collectively large groups of air marshal, law 
enforcement, and screening workers account for much of the overall 
difference. A similar pattern applies to the enforcement, removal, and 
homeland security investigation staffs at ICE, the field operations 
staff at CBP, and the Federal Protective Service. Such variation 
within components further suggests that the morale gap is isolated to 
particular areas within DHS that account for a large proportion of its 
workforce. 

At other components, morale is more uniformly lower across most 
offices. Average engagement at all work groups within FEMA is 5.8 to 
17.7 percentage points lower than the non-DHS average, with the 
exception of two regional offices and the offices of the Administrator 
and Chief of Staff. The components of ST and IA also have more 
consistently low morale across work groups. 

Table 7: OPM Employee Morale Index by DHS Component and Offices Using 
the 2011 FEVS: 

DHS component: 

Non-DHS; 
OPM Employee Engagement (EE) Index: 67.1; 
95% margin of error (+/-): 0.3; 
EE Index minus DHS average: [Empty]; 
95% margin of error (+/-): [Empty]; 
EE Index minus non-DHS average: 0.0; 
95% margin of error (+/-): 0.0; 
N: 250,870. 

Citizenship and Immigration Services; 
OPM Employee Engagement (EE) Index: 64.0; 
95% margin of error (+/-): 2.0; 
EE Index minus DHS average: [Empty]; 
95% margin of error (+/-): [Empty]; 
EE Index minus non-DHS average: -3.2*; 
95% margin of error (+/-): 2.0; 
N: 1,303. 

CBP; 
OPM Employee Engagement (EE) Index: 62.9; 
95% margin of error (+/-): 1.3; 
EE Index minus DHS average: [Empty]; 
95% margin of error (+/-): [Empty]; 
EE Index minus non-DHS average: -4.3*; 
95% margin of error (+/-): 1.3; 
N: 3,057. 

Coast Guard; 
OPM Employee Engagement (EE) Index: 70.6; 
95% margin of error (+/-): 2.5; 
EE Index minus DHS average: [Empty]; 
95% margin of error (+/-): [Empty]; 
EE Index minus non-DHS average: 3.5*; 
95% margin of error (+/-): 2.5; 
N: 863. 

FEMA; 
OPM Employee Engagement (EE) Index: 58.0; 
95% margin of error (+/-): 2.3; 
EE Index minus DHS average: [Empty]; 
95% margin of error (+/-): [Empty]; 
EE Index minus non-DHS average: -9.1*; 
95% margin of error (+/-): 2.3; 
N: 972. 

FLETC; 
OPM Employee Engagement (EE) Index: 66.2; 
95% margin of error (+/-): 1.9; 
EE Index minus DHS average: [Empty]; 
95% margin of error (+/-): [Empty]; 
EE Index minus non-DHS average: -1.0; 
95% margin of error (+/-): 1.9; 
N: 611. 

ICE; 
OPM Employee Engagement (EE) Index: 58.0; 
95% margin of error (+/-): 2.5; 
EE Index minus DHS average: [Empty]; 
95% margin of error (+/-): [Empty]; 
EE Index minus non-DHS average: -9.1*; 
95% margin of error (+/-): 2.5; 
N: 1,313. 

Intel and Analysis; 
OPM Employee Engagement (EE) Index: 53.2; 
95% margin of error (+/-): 4.0; 
EE Index minus DHS average: [Empty]; 
95% margin of error (+/-): [Empty]; 
EE Index minus non-DHS average: -13.9*; 
95% margin of error (+/-): 4.0; 
N: 150. 

IG; 
OPM Employee Engagement (EE) Index: 70.5; 
95% margin of error (+/-): 3.3; 
EE Index minus DHS average: [Empty]; 
95% margin of error (+/-): [Empty]; 
EE Index minus non-DHS average: 3.3; 
95% margin of error (+/-): 3.3; 
N: 307. 

MGMT; 
OPM Employee Engagement (EE) Index: 65.7; 
95% margin of error (+/-): 2.1; 
EE Index minus DHS average: [Empty]; 
95% margin of error (+/-): [Empty]; 
EE Index minus non-DHS average: -1.4; 
95% margin of error (+/-): 2.2; 
N: 628. 

NPPD; 
OPM Employee Engagement (EE) Index: 57.7; 
95% margin of error (+/-): 2.1; 
EE Index minus DHS average: [Empty]; 
95% margin of error (+/-): [Empty]; 
EE Index minus non-DHS average: -9.4*; 
95% margin of error (+/-): 2.2; 
N: 771. 

Science and Technology; 
OPM Employee Engagement (EE) Index: 55.7; 
95% margin of error (+/-): 2.8; 
EE Index minus DHS average: [Empty]; 
95% margin of error (+/-): [Empty]; 
EE Index minus non-DHS average: -11.5*; 
95% margin of error (+/-): 2.8; 
N: 258. 

USSS; 
OPM Employee Engagement (EE) Index: 68.1; 
95% margin of error (+/-): 2.4; 
EE Index minus DHS average: [Empty]; 
95% margin of error (+/-): [Empty]; 
EE Index minus non-DHS average: 1.0; 
95% margin of error (+/-): 2.4; 
N: 959. 

Office of Secretary; 
OPM Employee Engagement (EE) Index: 63.6; 
95% margin of error (+/-): 2.5; 
EE Index minus DHS average: [Empty]; 
95% margin of error (+/-): [Empty]; 
EE Index minus non-DHS average: -3.6*; 
95% margin of error (+/-): 2.6; 
N: 409. 

TSA; 
OPM Employee Engagement (EE) Index: 54.4; 
95% margin of error (+/-): 1.1; 
EE Index minus DHS average: [Empty]; 
95% margin of error (+/-): [Empty]; 
EE Index minus non-DHS average: -12.8*; 
95% margin of error (+/-): 1.2; 
N: 3,701. 

DHS, no sub-agency; 
OPM Employee Engagement (EE) Index: 60.5; 
95% margin of error (+/-): 6.3; 
EE Index minus DHS average: [Empty]; 
95% margin of error (+/-): [Empty]; 
EE Index minus non-DHS average: -6.6*; 
95% margin of error (+/-): 6.3; 
N: 204. 

TSA: 

Non-DHS; 
OPM Employee Engagement (EE) Index: 67.1; 
95% margin of error (+/-): 0.3; 
EE Index minus DHS average: 4.4; 
95% margin of error (+/-): 0.9; 
EE Index minus non-DHS average: 0.0; 
95% margin of error (+/-): 0.0; 
N: 250,870. 

Other DHS; 
OPM Employee Engagement (EE) Index: 62.7; 
95% margin of error (+/-): 0.8; 
EE Index minus DHS average: 0.0; 
95% margin of error (+/-): 0.0; 
EE Index minus non-DHS average: -4.4*; 
95% margin of error (+/-): 0.9; 
N: 11,805. 

Headquarters staff; 
OPM Employee Engagement (EE) Index: 61.5; 
95% margin of error (+/-): 4.4; 
EE Index minus DHS average: -1.2; 
95% margin of error (+/-): 4.5; 
EE Index minus non-DHS average: -5.6*; 
95% margin of error (+/-): 4.4; 
N: 279. 

Law enforcement/air marshal; 
OPM Employee Engagement (EE) Index: 52.9; 
95% margin of error (+/-): 4.4; 
EE Index minus DHS average: -9.8; 
95% margin of error (+/-): 4.4; 
EE Index minus non-DHS average: -14.2*; 
95% margin of error (+/-): 4.4; 
N: 246. 

Federal security director staff; 
OPM Employee Engagement (EE) Index: 64.8; 
95% margin of error (+/-): 2.6; 
EE Index minus DHS average: 2.1; 
95% margin of error (+/-): 2.7; 
EE Index minus non-DHS average: -2.3; 
95% margin of error (+/-): 2.6; 
N: 630. 

Screeners; 
OPM Employee Engagement (EE) Index: 50.9; 
95% margin of error (+/-): 1.4; 
EE Index minus DHS average: -11.8; 
95% margin of error (+/-): 1.6; 
EE Index minus non-DHS average: -16.2*; 
95% margin of error (+/-): 1.4; 
N: 2,273. 

ICE: 

Non-DHS; 
OPM Employee Engagement (EE) Index: 67.1; 
95% margin of error (+/-): 0.3; 
EE Index minus DHS average: 6.7; 
95% margin of error (+/-): 0.7; 
EE Index minus non-DHS average: 0.0; 
95% margin of error (+/-): 0.0; 
N: 250,870. 

Other DHS; 
OPM Employee Engagement (EE) Index: 60.4; 
95% margin of error (+/-): 0.7; 
EE Index minus DHS average: 0.0; 
95% margin of error (+/-): 0.0; 
EE Index minus non-DHS average: -6.7*; 
95% margin of error (+/-): 0.7; 
N: 14,193. 

Director; 
OPM Employee Engagement (EE) Index: 66.3; 
95% margin of error (+/-): 23.4; 
EE Index minus DHS average: 5.9; 
95% margin of error (+/-): 23.4; 
EE Index minus non-DHS average: -0.9; 
95% margin of error (+/-): 23.4; 
N: 10. 

Acquisitions; 
OPM Employee Engagement (EE) Index: 41.2; 
95% margin of error (+/-): 31.0; 
EE Index minus DHS average: -19.2; 
95% margin of error (+/-): 31.0; 
EE Index minus non-DHS average: -26.0; 
95% margin of error (+/-): 31.0; 
N: 13. 

CFO; 
OPM Employee Engagement (EE) Index: 68.3; 
95% margin of error (+/-): 12.6; 
EE Index minus DHS average: 7.9; 
95% margin of error (+/-): 12.6; 
EE Index minus non-DHS average: 1.2; 
95% margin of error (+/-): 12.6; 
N: 33. 

CIO; 
OPM Employee Engagement (EE) Index: 69.8; 
95% margin of error (+/-): 14.1; 
EE Index minus DHS average: 9.4; 
95% margin of error (+/-): 14.1; 
EE Index minus non-DHS average: 2.6; 
95% margin of error (+/-): 14.1; 
N: 29. 

Enforcement and removal operations; 
OPM Employee Engagement (EE) Index: 52.8; 
95% margin of error (+/-): 3.8; 
EE Index minus DHS average: -7.6; 
95% margin of error (+/-): 3.9; 
EE Index minus non-DHS average: -14.4*; 
95% margin of error (+/-): 3.8; 
N: 491. 

Human capital; 
OPM Employee Engagement (EE) Index: 51.2; 
95% margin of error (+/-): 24.3; 
EE Index minus DHS average: -9.2; 
95% margin of error (+/-): 24.3; 
EE Index minus non-DHS average: -16.0; 
95% margin of error (+/-): 24.3; 
N: 11. 

Intelligence; 
OPM Employee Engagement (EE) Index: 59.1; 
95% margin of error (+/-): 16.7; 
EE Index minus DHS average: -1.3; 
95% margin of error (+/-): 16.7; 
EE Index minus non-DHS average: -8.1; 
95% margin of error (+/-): 16.7; 
N: 27. 

International affairs; 
OPM Employee Engagement (EE) Index: 57.5; 
95% margin of error (+/-): 11.0; 
EE Index minus DHS average: -2.9; 
95% margin of error (+/-): 11.0; 
EE Index minus non-DHS average: -9.6; 
95% margin of error (+/-): 11.0; 
N: 21. 

Homeland security investigation; 
OPM Employee Engagement (EE) Index: 59.0; 
95% margin of error (+/-): 4.1; 
EE Index minus DHS average: -1.4; 
95% margin of error (+/-): 4.2; 
EE Index minus non-DHS average: -8.2*; 
95% margin of error (+/-): 4.1; 
N: 433. 

Principle legal advisor; 
OPM Employee Engagement (EE) Index: 69.4; 
95% margin of error (+/-): 7.7; 
EE Index minus DHS average: 9.0; 
95% margin of error (+/-): 7.8; 
EE Index minus non-DHS average: 2.2; 
95% margin of error (+/-): 7.7; 
N: 62. 

Professional responsibility; 
OPM Employee Engagement (EE) Index: 66.6; 
95% margin of error (+/-): 12.4; 
EE Index minus DHS average: 6.2; 
95% margin of error (+/-): 12.4; 
EE Index minus non-DHS average: -0.6; 
95% margin of error (+/-): 12.4; 
N: 40. 

Other; 
OPM Employee Engagement (EE) Index: 77.2; 
95% margin of error (+/-): 20.1; 
EE Index minus DHS average: 16.8; 
95% margin of error (+/-): 20.1; 
EE Index minus non-DHS average: 10.1; 
95% margin of error (+/-): 20.1; 
N: 13. 

Coast Guard: 

Non-DHS; 
OPM Employee Engagement (EE) Index: 67.1; 
95% margin of error (+/-): 0.3; 
EE Index minus DHS average: 7.6; 
95% margin of error (+/-): 0.7; 
EE Index minus non-DHS average: 0.0; 
95% margin of error (+/-): 0.0; 
N: 250,870. 

Other DHS; 
OPM Employee Engagement (EE) Index: 59.6; 
95% margin of error (+/-): 0.7; 
EE Index minus DHS average: 0.0; 
95% margin of error (+/-): 0.0; 
EE Index minus non-DHS average: -7.6*; 
95% margin of error (+/-): 0.7; 
N: 14,643. 

Vice commandant; 
OPM Employee Engagement (EE) Index: 71.9; 
95% margin of error (+/-): 18.1; 
EE Index minus DHS average: 12.3; 
95% margin of error (+/-): 18.1; 
EE Index minus non-DHS average: 4.7; 
95% margin of error (+/-): 18.1; 
N: 12. 

Chief of staff, mission support; 
OPM Employee Engagement (EE) Index: 77.8; 
95% margin of error (+/-): 8.9; 
EE Index minus DHS average: 18.2; 
95% margin of error (+/-): 9.0; 
EE Index minus non-DHS average: 10.6*; 
95% margin of error (+/-): 8.9; 
N: 66. 

Deputy commandant for operations; 
OPM Employee Engagement (EE) Index: 89.1; 
95% margin of error (+/-): 5.1; 
EE Index minus DHS average: 29.6; 
95% margin of error (+/-): 5.2; 
EE Index minus non-DHS average: 22.0*; 
95% margin of error (+/-): 5.1; 
N: 15. 

Force readiness command; 
OPM Employee Engagement (EE) Index: 67.4; 
95% margin of error (+/-): 17.8; 
EE Index minus DHS average: 7.9; 
95% margin of error (+/-): 17.8; 
EE Index minus non-DHS average: 0.3; 
95% margin of error (+/-): 17.8; 
N: 13. 

Marine safety security stewardship; 
OPM Employee Engagement (EE) Index: 68.8; 
95% margin of error (+/-): 14.5; 
EE Index minus DHS average: 9.2; 
95% margin of error (+/-): 14.5; 
EE Index minus non-DHS average: 1.6; 
95% margin of error (+/-): 14.5; 
N: 21. 

Engineering and logistics; 
OPM Employee Engagement (EE) Index: 69.9; 
95% margin of error (+/-): 9.8; 
EE Index minus DHS average: 10.3; 
95% margin of error (+/-): 9.9; 
EE Index minus non-DHS average: 2.8; 
95% margin of error (+/-): 9.8; 
N: 20. 

Command, control, communications, computers, and IT; 
OPM Employee Engagement (EE) Index: 79.3; 
95% margin of error (+/-): 18.7; 
EE Index minus DHS average: 19.8; 
95% margin of error (+/-): 18.7; 
EE Index minus non-DHS average: 12.2; 
95% margin of error (+/-): 18.7; 
N: 13. 

Atlantic; 
OPM Employee Engagement (EE) Index: 72.6; 
95% margin of error (+/-): 10.0; 
EE Index minus DHS average: 13.0; 
95% margin of error (+/-): 10.1; 
EE Index minus non-DHS average: 5.4; 
95% margin of error (+/-): 10.0; 
N: 44. 

Pacific; 
OPM Employee Engagement (EE) Index: 61.0; 
95% margin of error (+/-): 14.5; 
EE Index minus DHS average: 1.4; 
95% margin of error (+/-): 14.5; 
EE Index minus non-DHS average: -6.1; 
95% margin of error (+/-): 14.5; 
N: 19. 

Districts 1; 
OPM Employee Engagement (EE) Index: 76.7; 
95% margin of error (+/-): 8.7; 
EE Index minus DHS average: 17.1; 
95% margin of error (+/-): 8.8; 
EE Index minus non-DHS average: 9.6*; 
95% margin of error (+/-): 8.7; 
N: 46. 

Districts 5; 
OPM Employee Engagement (EE) Index: 69.5; 
95% margin of error (+/-): 8.8; 
EE Index minus DHS average: 9.9; 
95% margin of error (+/-): 8.9; 
EE Index minus non-DHS average: 2.3; 
95% margin of error (+/-): 8.8; 
N: 69. 

Districts 7; 
OPM Employee Engagement (EE) Index: 66.8; 
95% margin of error (+/-): 12.2; 
EE Index minus DHS average: 7.2; 
95% margin of error (+/-): 12.2; 
EE Index minus non-DHS average: -0.3; 
95% margin of error (+/-): 12.2; 
N: 41. 

Districts 8; 
OPM Employee Engagement (EE) Index: 71.8; 
95% margin of error (+/-): 8.7; 
EE Index minus DHS average: 12.2; 
95% margin of error (+/-): 8.7; 
EE Index minus non-DHS average: 4.7; 
95% margin of error (+/-): 8.7; 
N: 45. 

Districts 9; 
OPM Employee Engagement (EE) Index: 71.8; 
95% margin of error (+/-): 9.9; 
EE Index minus DHS average: 12.2; 
95% margin of error (+/-): 10.0; 
EE Index minus non-DHS average: 4.7; 
95% margin of error (+/-): 9.9; 
N: 19. 

Districts 11; 
OPM Employee Engagement (EE) Index: 64.3; 
95% margin of error (+/-): 14.2; 
EE Index minus DHS average: 4.7; 
95% margin of error (+/-): 14.2; 
EE Index minus non-DHS average: -2.9; 
95% margin of error (+/-): 14.2; 
N: 35. 

Districts 13; 
OPM Employee Engagement (EE) Index: 78.5; 
95% margin of error (+/-): 17.4; 
EE Index minus DHS average: 18.9; 
95% margin of error (+/-): 17.4; 
EE Index minus non-DHS average: 11.4; 
95% margin of error (+/-): 17.4; 
N: 18. 

Districts 14; 
OPM Employee Engagement (EE) Index: 80.2; 
95% margin of error (+/-): 19.4; 
EE Index minus DHS average: 20.6; 
95% margin of error (+/-): 19.4; 
EE Index minus non-DHS average: 13.0; 
95% margin of error (+/-): 19.4; 
N: 12. 

Districts 17; 
OPM Employee Engagement (EE) Index: 68.7; 
95% margin of error (+/-): 14.5; 
EE Index minus DHS average: 9.1; 
95% margin of error (+/-): 14.5; 
EE Index minus non-DHS average: 1.6; 
95% margin of error (+/-): 14.5; 
N: 22. 

Other; 
OPM Employee Engagement (EE) Index: 69.2; 
95% margin of error (+/-): 4.6; 
EE Index minus DHS average: 9.6; 
95% margin of error (+/-): 4.6; 
EE Index minus non-DHS average: 2.0; 
95% margin of error (+/-): 4.6; 
N: 270. 

CBP: 

Non-DHS; 
OPM Employee Engagement (EE) Index: 67.1; 
95% margin of error (+/-): 0.3; 
EE Index minus DHS average: 8.5; 
95% margin of error (+/-): 0.8; 
EE Index minus non-DHS average: 0.0; 
95% margin of error (+/-): 0.0; 
N: 250,870. 

Other DHS; 
OPM Employee Engagement (EE) Index: 58.6; 
95% margin of error (+/-): 0.8; 
EE Index minus DHS average: 0.0; 
95% margin of error (+/-): 0.0; 
EE Index minus non-DHS average: -8.5*; 
95% margin of error (+/-): 0.8; 
N: 12,449. 

Office of the commissioner; 
OPM Employee Engagement (EE) Index: 73.2; 
95% margin of error (+/-): 18.4; 
EE Index minus DHS average: 14.5; 
95% margin of error (+/-): 18.5; 
EE Index minus non-DHS average: 6.0; 
95% margin of error (+/-): 18.4; 
N: 15. 

Chief Counsel; 
OPM Employee Engagement (EE) Index: 58.3; 
95% margin of error (+/-): 15.9; 
EE Index minus DHS average: -0.3; 
95% margin of error (+/-): 15.9; 
EE Index minus non-DHS average: -8.8; 
95% margin of error (+/-): 15.9; 
N: 29. 

Human resources management; 
OPM Employee Engagement (EE) Index: 73.3; 
95% margin of error (+/-): 11.5; 
EE Index minus DHS average: 14.7; 
95% margin of error (+/-): 11.6; 
EE Index minus non-DHS average: 6.2; 
95% margin of error (+/-): 11.5; 
N: 36. 

Border Patrol; 
OPM Employee Engagement (EE) Index: 69.2; 
95% margin of error (+/-): 2.2; 
EE Index minus DHS average: 10.6; 
95% margin of error (+/-): 2.3; 
EE Index minus non-DHS average: 2.1; 
95% margin of error (+/-): 2.2; 
N: 861. 

International trade; 
OPM Employee Engagement (EE) Index: 66.1; 
95% margin of error (+/-): 7.4; 
EE Index minus DHS average: 7.5; 
95% margin of error (+/-): 7.4; 
EE Index minus non-DHS average: -1.1; 
95% margin of error (+/-): 7.4; 
N: 80. 

Internal affairs; 
OPM Employee Engagement (EE) Index: 73.0; 
95% margin of error (+/-): 13.0; 
EE Index minus DHS average: 14.4; 
95% margin of error (+/-): 13.0; 
EE Index minus non-DHS average: 5.9; 
95% margin of error (+/-): 13.0; 
N: 36. 

Field operations; 
OPM Employee Engagement (EE) Index: 57.5; 
95% margin of error (+/-): 1.9; 
EE Index minus DHS average: -1.1; 
95% margin of error (+/-): 2.0; 
EE Index minus non-DHS average: -9.6*; 
95% margin of error (+/-): 1.9; 
N: 1,326. 

Administration and public affairs; 
OPM Employee Engagement (EE) Index: 61.5; 
95% margin of error (+/-): 8.4; 
EE Index minus DHS average: 2.9; 
95% margin of error (+/-): 8.4; 
EE Index minus non-DHS average: -5.6; 
95% margin of error (+/-): 8.4; 
N: 69. 

Information and Technology; 
OPM Employee Engagement (EE) Index: 66.9; 
95% margin of error (+/-): 6.8; 
EE Index minus DHS average: 8.3; 
95% margin of error (+/-): 6.8; 
EE Index minus non-DHS average: -0.3; 
95% margin of error (+/-): 6.8; 
N: 98. 

Training and development; 
OPM Employee Engagement (EE) Index: 59.4; 
95% margin of error (+/-): 9.1; 
EE Index minus DHS average: 0.8; 
95% margin of error (+/-): 9.1; 
EE Index minus non-DHS average: -7.7; 
95% margin of error (+/-): 9.1; 
N: 47. 

Internal Affairs; 
OPM Employee Engagement (EE) Index: 59.2; 
95% margin of error (+/-): 19.6; 
EE Index minus DHS average: 0.6; 
95% margin of error (+/-): 19.6; 
EE Index minus non-DHS average: -7.9; 
95% margin of error (+/-): 19.6; 
N: 16. 

Air and marine; 
OPM Employee Engagement (EE) Index: 58.2; 
95% margin of error (+/-): 6.6; 
EE Index minus DHS average: -0.5; 
95% margin of error (+/-): 6.7; 
EE Index minus non-DHS average: -9.0*; 
95% margin of error (+/-): 6.6; 
N: 103. 

Intelligence and operations coordination; 
OPM Employee Engagement (EE) Index: 60.9; 
95% margin of error (+/-): 20.3; 
EE Index minus DHS average: 2.3; 
95% margin of error (+/-): 20.4; 
EE Index minus non-DHS average: -6.2; 
95% margin of error (+/-): 20.3; 
N: 16. 

Technology, innovation, and acquisition; 
OPM Employee Engagement (EE) Index: 80.1; 
95% margin of error (+/-): 12.4; 
EE Index minus DHS average: 21.5; 
95% margin of error (+/-): 12.4; 
EE Index minus non-DHS average: 13.0*; 
95% margin of error (+/-): 12.4; 
N: 23. 

Source: GAO analysis of U.S. Office of Personnel Management 2011 FEVS 
data. 

Note: Asterisks denote differences that are distinguishable from zero 
at the 0.05 level. Offices listed within the components are described 
as they are identified in OPM's 2011 FEVS data files provided to GAO. 

[End of table] 

Comparison of Morale between Employees in Preexisting Components and 
Components Created with DHS: 

One explanation for why morale varies across components focuses on the 
length of time each organization has existed. Components that existed 
prior to the creation of DHS may have had more time to develop 
successful cultures and management practices than components that 
policymakers created with the department in 2003. As a result, the 
preexisting components may have better morale today than components 
with less mature cultures and practices. 

To assess this explanation, we analyzed morale among two groups of 
components, divided according to whether the component was established 
with the creation of DHS or existed previously (see table 8). We 
considered three components to be preexisting--FLETC, USSS, and the 
Coast Guard--and the rest to be newly created. Because TSA was created 
about 2 years before DHS, we included it with components that were 
created with DHS. 

Our analysis shows that employees at the more recently created 
components were less engaged and satisfied on average than employees 
at the preexisting components and at non-DHS agencies. For the 
preexisting components, engagement was about 2.2 percentage points 
higher than at the rest of the government, and the difference in 
satisfaction was small (less than 1.4 percentage points). In contrast, 
engagement and satisfaction at the more recently created components 
were about 8 and 5.1 percentage points lower than at the rest of the 
government, respectively. 

Table 8: Morale at Preexisting and Recently Created Components of DHS 
Using the 2011 FEVS: 

Non-DHS; 
OPM Job Satisfaction Index: 68.5; 
Margin of error (+/-): 0.2; 
OPM Employee Engagement Index: 67.1; 
Margin of error (+/-): 0.3. 

Preexisting components; 
OPM Job Satisfaction Index: 69.9; 
Margin of error (+/-): 1.4; 
OPM Employee Engagement Index: 69.3; 
Margin of error (+/-): 1.6. 

Components created with DHS; 
OPM Job Satisfaction Index: 63.4; 
Margin of error (+/-): 0.6; 
OPM Employee Engagement Index: 59.2; 
Margin of error (+/-): 0.7. 

Source: GAO analysis of OPM 2011 FEVS data. 

Note: Preexisting components include FLETC, USSS, and Coast Guard, 
with all others classified as being created with DHS. 

[End of table] 

Multivariate Analysis: 

We developed a statistical model to confirm whether the differences 
among components persist, holding constant demographic differences 
among their employees. In an alternative version of model 1 above, we 
replaced DHS j with a vector of variables indicating whether the 
employee worked for DHS components or at an agency other than DHS. All 
other parts of the model were identical. 

The model estimates generally confirmed the differences in engagement 
between non-DHS and DHS component employees in the raw data (see table 
9), with two exceptions. The model estimated that, holding constant 
demographic differences, employees in the Management Directorate and 
Office of the Secretary were 6.9 and 7.7 percentage points less 
engaged on average than employees in non-DHS agencies. This suggests 
that the engagement gap for employees in these offices is more similar 
to the gap at other offices, holding constant the demographic 
differences among offices measured by FEVS. The model estimated that 
differences in satisfaction between the components and non-DHS 
agencies were generally similar to such differences in engagement (see 
table 9). 

The fact that differences among components remained, even among 
demographically equivalent employees, suggests that either unmeasured 
demographic variables or intrinsic characteristics of the components 
are responsible for the differences in morale. 

Table 9: Model Estimates of the Difference in Engagement and Job 
Satisfaction between Employees in DHS Components and Non-DHS Agencies 
Based on the 2011 FEVS: 

DHS component: CIS; 
Difference in engagement (GAO measure): -5.6%; 
Standard error: 0.5; 
Difference in satisfaction (GAO measure): -3.5%; 
Standard error: 0.5. 

DHS component: CBP; 
Difference in engagement (GAO measure): -6.1%; 
Standard error: 0.5; 
Difference in satisfaction (GAO measure): -2.5%; 
Standard error: 0.6. 

DHS component: Coast Guard; 
Difference in engagement (GAO measure): 2.6%; 
Standard error: 0.5; 
Difference in satisfaction (GAO measure): -3.3%; 
Standard error: 0.5. 

DHS component: FEMA; 
Difference in engagement (GAO measure): -9.9%; 
Standard error: 0.5; 
Difference in satisfaction (GAO measure): -5.7%; 
Standard error: 0.4. 

DHS component: FLETC; 
Difference in engagement (GAO measure): 0.5%; 
Standard error: 0.6; 
Difference in satisfaction (GAO measure): 6.8%; 
Standard error: 0.5. 

DHS component: ICE; 
Difference in engagement (GAO measure): -9.8%; 
Standard error: 0.6; 
Difference in satisfaction (GAO measure): -8.4%; 
Standard error: 0.5. 

DHS component: I&A; 
Difference in engagement (GAO measure): -13.9%; 
Standard error: 0.5; 
Difference in satisfaction (GAO measure): -10.6%; 
Standard error: 0.5. 

DHS component: IG; 
Difference in engagement (GAO measure): 6.4%; 
Standard error: 0.9; 
Difference in satisfaction (GAO measure): 4.2%; 
Standard error: 0.8. 

DHS component: MGMT; 
Difference in engagement (GAO measure): -6.9%; 
Standard error: 0.6; 
Difference in satisfaction (GAO measure): -3.0%; 
Standard error: 0.6. 

DHS component: NPPD; 
Difference in engagement (GAO measure): -11.3%; 
Standard error: 0.5; 
Difference in satisfaction (GAO measure): -9.0%; 
Standard error: 0.5. 

DHS component: S&T; 
Difference in engagement (GAO measure): -15.7%; 
Standard error: 0.6; 
Difference in satisfaction (GAO measure): -9.6%; 
Standard error: 0.8. 

DHS component: USSS; 
Difference in engagement (GAO measure): 3.6%; 
Standard error: 0.6; 
Difference in satisfaction (GAO measure): 0.1%; 
Standard error: 0.5. 

DHS component: Office of Secretary; 
Difference in engagement (GAO measure): -7.7%; 
Standard error: 0.6; 
Difference in satisfaction (GAO measure): -8.5%; 
Standard error: 0.7. 

DHS component: TSA; 
Difference in engagement (GAO measure): -12.4%; 
Standard error: 0.5; 
Difference in satisfaction (GAO measure): -12.2%; 
Standard error: 0.4. 

DHS component: DHS, no component; 
Difference in engagement (GAO measure): 7.5%; 
Standard error: 0.3; 
Difference in satisfaction (GAO measure): 3.3%; 
Standard error: 1.4. 

DHS component: Components existing prior to DHS creation; 
Difference in engagement (GAO measure): 2.5%; 
Standard error: 0.8; 
Difference in satisfaction (GAO measure): 0.5%; 
Standard error: 2.0. 

DHS component: Components created with DHS (plus TSA); 
Difference in engagement (GAO measure): -8.8%; 
Standard error: 1.4; 
Difference in satisfaction (GAO measure): -6.8%; 
Standard error: 1.9. 

Source: GAO analysis of 2011 FEVS data. 

Note: Engagement statistics are the in-sample proportion of employees 
who are predicted by model 1 to be engaged or satisfied on GAO's 
measure. 

[End of table] 

Opportunities for Additional Analysis: 

Our analysis discussed in this appendix has a narrow scope: assessing 
whether demographic differences among employees explain the morale 
differences across DHS and non-DHS employees. Consequently, DHS or 
others could expand and improve upon our findings. 

Future work could examine whether attitudinal differences among 
employees at DHS and other agencies explain the overall morale gap, in 
addition to demographic differences. The 2011 FEVS measures employee 
attitudes about pay, benefits, health and safety hazards, training, 
supervisors, and other issues that could vary meaningfully between 
employees at DHS and other agencies and, therefore, explain why DHS 
has lower morale. One might include these factors in a decomposition 
similar to the one we performed in this appendix. This could further 
assess how factors unique to DHS and factors that are common across 
all agencies explain the overall morale gap. 

A broader attitudinal analysis likely would require the use of more 
sophisticated statistical methods for estimating the values of and 
relationships among latent variables. The broad measures of morale we 
analyze in this appendix, such as the OPM Employee Engagement index, 
are made up of responses to questions on smaller dimensions, such as 
leadership and supervision. To avoid simply replicating the 
correlations that were used to create the indexes, latent variable 
models could be useful to examine the relationships among these 
concepts and compare morale on latent scales between DHS and non-DHS 
agencies. This was beyond the scope of our work. 

[End of section] 

Appendix II: Scope and Methodology: 

The objectives for this report were to evaluate (1) how DHS employee 
morale compares with that of other federal government employees and 
(2) to what extent DHS and its selected components determined the root 
causes of employee morale and developed action plans to improve morale. 

To address our objectives, we evaluated both DHS-wide efforts and 
efforts at four selected components to address employee morale--CBP, 
ICE, TSA, and the Coast Guard. We selected the four DHS components 
based on their workforce size and how their 2011 job satisfaction and 
engagement index scores[Footnote 42] compare with the non-DHS average. 
[Footnote 43] The components selected had scores both above, below, 
and similar to the average: TSA--below average on both indexes, 
constituting 25 percent of the DHS workforce; ICE--below average on 
both indexes, accounting for 9 percent of the DHS workforce; CBP--at 
the non-DHS average for satisfaction and below on engagement, 
representing 27 percent of the DHS workforce; and the civilian portion 
of the Coast Guard--at the non-DHS average for satisfaction and above 
on engagement, composing 4 percent of the DHS workforce.[Footnote 44] 
Together these components represent 65 percent of DHS's workforce. 

To evaluate how DHS's employee morale compares with that of other 
federal government employees, we analyzed employee responses to the 
2011 FEVS. We determined that the 2011 FEVS data were reliable for the 
purposes of our report, based on interviews with OPM staff, review and 
analysis of technical documentation of its design and administration, 
and electronic testing. We used two measures created by OPM--the 
employee job satisfaction and engagement indexes--to describe morale 
across the federal government and within DHS. We calculated these 
measures for various demographic groups, DHS components, and work 
groups, in order to compare morale at DHS and other agencies among 
employees who were demographically similar, in part using statistical 
models. Appendix I describes our methods and findings in more detail. 
In addition, we interviewed employee groups about morale to identify 
examples of what issues may drive high and low morale within DHS. We 
selected the employee groups based on the size of the employee group 
within each selected component, ensuring we met with employees from 
employee groups that composed significant proportions of FEVS 
respondents, such as screeners from TSA (61 percent of TSA 
respondents) and homeland security investigators from ICE (33 percent 
of ICE respondents). The comments received from these interviews are 
not generalizable to entire groups of component employees, but provide 
insights into the differing issues that can drive morale. 

To determine the extent to which DHS and the selected components 
identified the root causes of employee morale and developed action 
plans for improvements, we reviewed analysis results, interviewed 
agency human capital officials and representatives of employee groups, 
and evaluated action plans for improving morale. To identify criteria 
for determining effective root cause analysis using survey data, we 
reviewed both OPM and Partnership for Public Service guidance for 
action planning based on annual employee survey results. On the basis 
of these guidance documents, we identified factors that should be 
considered in employee survey analysis that attempts to understand 
morale problems, such as use of demographic group comparisons, 
benchmarking results against results at similar organizations, and the 
linking results of root cause analyses to action planning efforts. We 
evaluated documents summarizing DHS-wide and selected component root 
cause analyses of the 2011 FEVS to determine whether the factors we 
identified were included in the analyses. In addition, we interviewed 
DHS officials who conducted the analyses in order to fully understand 
root cause analysis efforts. To identify criteria for determining 
agency action plans we reviewed OPM guidance for using FEVS results 
and previous GAO work indicating agencies' success in measuring 
performance. On the basis of these guidance documents, we identified 
OPM's six steps that should be considered in developing action plans 
and identified three attributes that were relevant for measuring 
action plan performance--linkage, clarity, and measurable target. We 
compared the action plans with these criteria to determine whether 
these items were included in the action plans. In addition, we 
interviewed DHS and component officials to identify efforts to 
leverage best practices for improving morale. 

We conducted this performance audit from October 2011 through 
September 2012 in accordance with generally accepted government 
auditing standards. Those standards require that we plan and perform 
the audit to obtain sufficient, appropriate evidence to provide a 
reasonable basis for our findings and conclusions based on our audit 
objectives. We believe that the evidence obtained provides a 
reasonable basis for our findings and conclusions based on our audit 
objectives. 

[End of section] 

Appendix III: DHS and Selected Component Steps Taken to Determine Root 
Causes of Morale Problems: 

DHS Efforts to Determine Root Causes of Morale Problems: 

Since 2007 DHS's Office of the Chief Human Capital Officer (OCHCO) has 
completed several efforts to determine root causes of morale DHS-wide. 

One Face at the Border: 

For operations at ports of entry, in September 2003 CBP issued its 
plan for consolidating the inspection functions formerly performed by 
separate inspectors from the three legacy agencies--customs inspectors 
from U.S. Customs, immigration inspectors and Border Patrol from the 
former Immigration and Naturalization Service, and the agriculture 
border inspectors from the Department of Agriculture's Animal and 
Plant Health Inspection Service. The plan, referred to as "One Face at 
the Border," called for unifying and integrating the legacy inspectors 
into two new positions--a CBP officer and a CBP agricultural 
specialist. The new CBP officer would serve as the frontline officer 
responsible for carrying out the priority anti-terrorism mission as 
well as the traditional customs and immigration inspection functions 
while also identifying and referring goods in need of a more extensive 
agricultural inspection to the agricultural specialist. CBP 
anticipated that having a well-trained and well-integrated workforce 
that could carry out the complete range of inspection functions 
involving the processing of individuals and goods would allow it to 
utilize its inspection resources more effectively and enable it to 
better target potentially high-risk travelers. Together, CBP 
envisioned the result to be more effective inspections and enhanced 
security at ports of entry while also accelerating the processing of 
legitimate trade and travel. 

Focus groups. In 2007 OCHCO conducted focus groups to determine 
employee concerns related to employee morale. DHS's focus group effort 
probed for insights into four areas--(1) leadership, (2) 
communication, (3) empowerment, and (4) resources--and highlighted 
concerns raised by focus group participants in each of those areas. 
For example, within the leadership area, OCHCO's focus group analysis 
found that the Customs and Immigration reorganization was a topic 
discussed by many of the U.S. Customs and Border Protection (CBP), 
U.S. Immigration and Customs Enforcement (ICE), and Citizenship and 
Immigration Services (CIS) personnel, especially what they felt was a 
lack of mission understanding on the part of their managers. According 
to the analysis, non-supervisory participants expressed 
dissatisfaction with the combination of three types of inspection 
functions to present "one face at the border." 

[Text box: One Face at the Border: 
For operations at ports of entry, in September 2003 CBP issued its 
plan for consolidating the inspection functions formerly performed by 
separate inspectors from the three legacy agencies—customs inspectors 
from U.S. Customs, immigration inspectors and Border Patrol from the 
former Immigration and Naturalization Service, and the agriculture 
border inspectors from the Department of Agriculture's Animal and
Plant Health Inspection Service. The plan, referred to as "One Face at 
the Border," called for unifying and integrating the legacy inspectors 
into two new positions—a CBP officer and a CBP agricultural 
specialist. The new CBP officer would serve as the frontline officer 
responsible for carrying out the priority anti-terrorism mission as 
well as the traditional customs and immigration inspection functions 
while also identifying and referring goods in need of a more extensive 
agricultural inspection to the agricultural specialist. CBP 
anticipated that having a well-trained and well-integrated workforce 
that could carry out the complete range of inspection functions 
involving the processing of individuals and goods would allow it to 
utilize its inspection resources more effectively and enable it to 
better target potentially high-risk travelers. Together, CBP 
envisioned the result to be more effective inspections and enhanced 
security at ports of entry while also accelerating the processing of 
legitimate trade and travel. End of text box] 

Focus group results were distributed to DHS components for 
consideration in action planning efforts, according to OCHCO 
officials. CBP, CIS, TSA, the Federal Emergency Management Agency 
(FEMA), and the Federal Law Enforcement Training Center each addressed 
at least one of the focus group results relating to leadership, 
communication, empowerment, or resources in subsequent action plans, 
according to OCHCO officials. 

Statistical analysis. In 2008 OCHCO performed statistical analysis of 
Federal Employee Viewpoint Survey (FEVS) data, beyond examining high-
and low-scoring questions, in an effort to determine what workplace 
factors drove employee job satisfaction. Specifically, the analysis 
involved isolating which sets of FEVS questions most affect employee 
job satisfaction. The analysis found that five work areas identified 
in FEVS questions drive employee job satisfaction: (1) performance and 
rewards, (2) supervisor support, (3) physical conditions and safety, 
(4) senior leadership effectiveness, and (5) the DHS mission. 
According to OCHCO officials, DHS components were encouraged to 
conduct follow-up discussions at the lowest possible organizational 
level based on component survey scores in each of the five work areas. 
However, OCHCO officials stated that they are not aware of any results 
of this effort because OCHCO did not track or follow-up with the 
components on the effect of key driver discussions that may have 
occurred. In addition, increased emphasis on supervisor performance 
management training was also implemented as a result of the analysis, 
according to OCHCO officials. 

Exit survey. In 2011, DHS began administering an exit survey to 
understand why employees choose to leave their DHS positions. 
Specifically, according to OCHCO officials, the DHS exit survey was 
designed to determine where departing employees were moving both 
inside and outside of DHS, to identify barriers related to diversity, 
to identify reasons that veterans may be leaving DHS, and to capture 
feedback from interns. The 2011 exit survey found, among other things, 
that 27 percent of departing employees who responded to the exit 
survey were staying within DHS or moving to a different position, and 
an additional 12 percent of respondents were retiring. Lack of quality 
supervision and advancement opportunities were the top reasons 
responding employees indicated for leaving their positions.[Footnote 
45] Exit survey results are shared with DHS components on a quarterly 
and annual basis. 

2011 FEVS analysis. For the 2011 FEVS, DHS's OCHCO evaluated the 
results by comparing Human Capital Assessment and Accountability 
Framework (HCAAF) index results by component. The analysis showed 
where the lowest index scores were concentrated. As shown in figure 8, 
lower scores across the indexes were concentrated among several 
components, including Intelligence and Analysis, Transportation and 
Security Administration (TSA), ICE, National Protection and Programs 
Directorate, and FEMA. 

Figure 8: DHS's 2011 Component Comparison Based on Four HCAAF Indexes: 

[Refer to PDF for image: table] 

Office of the Inspector General: 
Leadership and knowledge management: 69; 
Results-oriented performance Culture: 61; 
Talent management: 66; 
Job satisfaction: 71. 

Federal Law Enforcement Training Center: 
Leadership and knowledge management: 63; 
Results-oriented performance Culture: 58; 
Talent management: 63; 
Job satisfaction: 72. 

U.S. Coast Guard: 
Leadership and knowledge management: 66; 
Results-oriented performance Culture: 58; 
Talent management: 60; 
Job satisfaction: 70. 

U.S. Secret Service: 
Leadership and knowledge management: 64; 
Results-oriented performance Culture: 56; 
Talent management: 60; 
Job satisfaction: 69. 

Management Directorate: 
Leadership and knowledge management: 60; 
Results-oriented performance Culture: 56; 
Talent management: 61; 
Job satisfaction: 66. 

U.S Citizenship and Immigration Services: 
Leadership and knowledge management: 60; 
Results-oriented performance Culture: 52; 
Talent management: 54; 
Job satisfaction: 67. 

U.S. Customs and Border Protection: 
Leadership and knowledge management: 58; 
Results-oriented performance Culture: 51; 
Talent management: 55; 
Job satisfaction: 69. 

Office of the Secretary: 
Leadership and knowledge management: 57; 
Results-oriented performance Culture: 54; 
Talent management: 54; 
Job satisfaction: 63. 

Undersecretary for Science and Technology: 
Leadership and knowledge management: 51; 
Results-oriented performance Culture: 51; 
Talent management: 56; 
Job satisfaction: 60. 

Federal Emergency Management Agency: 
Leadership and knowledge management: 53; 
Results-oriented performance Culture: 49; 
Talent management: 51; 
Job satisfaction: 63. 

National Protection and Programs Directorate: 
Leadership and knowledge management: 51; 
Results-oriented performance Culture: 51; 
Talent management: 50; 
Job satisfaction: 62. 

Immigration and Customers Enforcement: 
Leadership and knowledge management: 52; 
Results-oriented performance Culture: 48; 
Talent management: 49; 
Job satisfaction: 61. 

Transportation Security Administration: 
Leadership and knowledge management: 48; 
Results-oriented performance Culture: 42; 
Talent management: 50; 
Job satisfaction: 57. 

Undersecretary for Intelligence and Analysis: 
Leadership and knowledge management: 45; 
Results-oriented performance Culture: 47; 
Talent management: 47; 
Job satisfaction: 58. 

Govemmentwide (average score): 
Leadership and knowledge management: 62; 
Results-oriented performance Culture: 54; 
Talent management: 60; 
Job satisfaction: 68. 

Department of Homeland Security (average score): 
Leadership and knowledge management: 55; 
Results-oriented performance Culture: 48; 
Talent management: 53; 
Job satisfaction: 64. 

Source: DHS analysis of 2011 FEVS data. 

[End of figure] 

The analysis also determined how DHS's scores on the four indexes 
trended over time and compared with governmentwide averages. As shown 
in figure 9, DHS-wide scores have generally trended upward over time, 
but continue to lag behind governmentwide averages for each index. 

Figure 9: DHS HCAAF Scores since 2006: 

[Refer to PDF for image: vertical bar graph] 

Positive responses: 

Leadership and knowledge management: 
2006, Department of Homeland Security: 47%; 
2008, Department of Homeland Security: 53%; 
2010, Department of Homeland Security: 55%; 
2010: Governmentwide: 61%; 
2011, Department of Homeland Security: 55%; 
2011: Governmentwide: 62%. 

Results-oriented performance culture: 
2006, Department of Homeland Security: 43%; 
2008, Department of Homeland Security: 47%; 
2010, Department of Homeland Security: 49%; 
2010: Governmentwide: 54%; 
2011, Department of Homeland Security: 48%; 
2011: Governmentwide: 54%. 

Talent management: 
2006, Department of Homeland Security: 49%; 
2008, Department of Homeland Security: 54%; 
2010, Department of Homeland Security: 54%; 
2010: Governmentwide: 60%; 
2011, Department of Homeland Security: 53%; 
2011: Governmentwide: 60%. 

Job satisfaction: 
2006, Department of Homeland Security: 59%; 
2008, Department of Homeland Security: 63%; 
2010, Department of Homeland Security: 65%; 
2010: Governmentwide: 69%; 
2011, Department of Homeland Security: 64%; 
2011: Governmentwide: 68%. 

Source: DHS analysis of 2011 FEVS data. 

[End of figure] 

Employee Engagement Executive Steering Committee (EEESC). In January 
2012 the DHS Secretary directed all component heads to take steps to 
improve employee engagement through launch of the EEESC. According to 
OCHCO officials, the EEESC was launched in response to congressional 
concerns about DHS employee morale and the Partnership for Public 
Service results showing DHS's low placement on the list of Best Places 
to Work. The EEESC is charged with serving as the DHS corporate body 
responsible for identifying DHS-wide initiatives to improve employee 
engagement, oversee the efforts of each DHS component to address 
employee engagement, and provide periodic reports to the Under 
Secretary for Management, Deputy Secretary, and Secretary on DHS-wide 
efforts to improve employee morale and engagement. Specifically, the 
Secretary made the following directives to component heads: 

* develop and assume responsibility for employee engagement 
improvement plans, 

* identify and assign specific responsibilities for improved employee 
engagement to component senior executive performance objectives, 

* identify and assign a senior accountable official to serve on the 
EEESC, 

* conduct town hall meetings with employees, 

* attend a Labor-Management Forum meeting, and: 

* provide monthly reports on actions planned and progress made to the 
Office of the Chief Human Capital Officer. 

As of August 2012, each of the Secretary's directives were completed, 
with the exception of assigning responsibilities for improved employee 
engagement to Senior Executive performance objectives, which DHS plans 
to implement in October 2012 as part of the next senior executive 
performance period. The EEESC met in February 2012, and component 
representatives shared their latest action plans and discussed issues 
of joint concern. In preparation for the 2012 FEVS, the EEESC released 
a memorandum from the Secretary describing the responsibilities of the 
EEESC, highlighting department actions, and encouraging employee 
participation in the FEVS, which began in April 2012. The EEESC also 
agreed that a corresponding message should be released from component 
heads outlining specific component actions taken in response to past 
survey results and encouraging participation in the next survey. In an 
April 2012 EEESC meeting, the Partnership for Public Service provided 
a briefing describing the Best Places to Work in the Federal 
Government rankings and best practices across the government for 
improving morale scores. The EEESC members also discussed methods for 
improving the response rates for the upcoming survey and engaged in an 
action planning exercise designed to help identify actions for 
department-wide deployment, according to OCHCO officials. As of August 
2012, EEESC action items were in development and had not been 
finalized. According to OCHCO officials, the EEESC plans to decide on 
action items by September 2012, but a projected date for full 
implementation has yet to be established because the actions have not 
been decided upon. 

Components Have Also Conducted Some Root Cause Analyses Using FEVS 
Results: 

In addition to the DHS-wide efforts, the components we selected for 
review--ICE, TSA, the U.S. Coast Guard (Coast Guard), and CBP--
conducted varying levels of analyses regarding the root causes of 
morale issues to inform agency action planning efforts. The selected 
components each analyzed FEVS data to understand leading issues that 
may relate to morale, but the results indicated where job satisfaction 
problem areas may exist and do not identify the causes of 
dissatisfaction within employee groups. A discussion of the four 
selected components' 2011 FEVS analysis and results are described 
below. 

TSA. In its analysis of the 2011 FEVS, TSA focused on areas of concern 
across groups, such as pay and performance appraisal concerns, and 
also looked for insight on which employee groups within TSA may be 
more dissatisfied with their jobs than others by comparing employee 
group scores on satisfaction-related questions. TSA compared its 
results with CBP results, as well as against DHS and governmentwide 
results. When comparing CBP and TSA scores, TSA found that the 
greatest differences in scores were on questions related to 
satisfaction with pay and whether performance appraisals were a fair 
reflection of performance. TSA scored 40 percentage points lower on 
pay satisfaction and 25 percentage points lower on performance 
appraisal satisfaction. In comparing TSA results with DHS and 
governmentwide results, TSA found that TSA was below the averages for 
all FEVS dimensions.[Footnote 46] TSA also evaluated FEVS results 
across employee groups by comparing dimension scores for headquarters 
staff, the Federal Air Marshals, Federal Security Director staff, and 
the screening workforce. TSA found that the screening workforce scored 
at or below scores for all other groups across all of the dimensions. 

ICE. In its analysis of the 2011 FEVS, ICE analyzed the results by 
identifying ICE's FEVS questions with the top positive and negative 
responses. ICE found that its top strength was employees' willingness 
to put in the extra effort to get a job done. ICE's top negative 
result was employees' perceptions that pay raises did not depend on 
how well employees perform their jobs. ICE also sorted the primary low-
scoring results into action planning themes, such as leadership, 
empowerment, and work-life balance. ICE found, among other things, 
that employee views on the fairness of its performance appraisals were 
above DHS's average but that views on employee preparation for 
potential security threats were lower. When comparing ICE's results 
with average governmentwide figures, ICE found, among other things, 
that ICE was lower on all of the HCAAF indexes, including job 
satisfaction. According to ICE human capital officials, future root 
cause analysis plans for the 2012 FEVS are to benchmark FEVS scores 
with those of similar law enforcement agencies such as the Drug 
Enforcement Agency; Federal Bureau of Investigation; Federal Law 
Enforcement Training Center; United States Secret Service; Alcohol, 
Tobacco and Firearms, and the U.S. Marshals. 

CBP. In its analysis of the 2011 FEVS, CBP focused its analysis on 
trends since 2006. For example, the analysis showed that CBP increased 
its scores by 5 or more percentage points for 36 of the 39 core FEVS 
questions. CBP highlighted its greatest increases in HCAAF areas, such 
as results-oriented performance, which showed a 21 percent improvement 
over 2006 responses to the question--my performance appraisal is a 
fair reflection of my performance. The analysis also identified areas 
in greatest need of improvement, which showed progress since 2006 but 
continued low scores, such as questions on dealing with poor 
performers who cannot or will not improve (28 percent positive), 
promotions based on merit (28 percent positive) and differences in 
performance are recognized (34 percent positive).[Footnote 47] 

Coast Guard. In its review of high and low 2011 FEVS responses, the 
Coast Guard identified employee responses to two questions that 
warranted action planning items--(1) How satisfied are you with the 
information you receive from management on what's going on in your 
organization (53 percent positive) and (2) My training needs are 
assessed (51 percent positive).[Footnote 48] The Coast Guard officials 
did not provide any additional FEVS analyses that were used to inform 
action planning. 

[End of section] 

Appendix IV: Selected Components' Data Sources for Evaluating Morale, 
Other than the Federal Employee Viewpoint Survey: 

Component: U.S. Immigration and Customs Enforcement; 
Data source: DHS exit survey; 
Purpose: Identify why employees leave the agency and where they are 
going; 
Summary of results and how used: The number of exit survey respondents 
from ICE was too low to identify any results and have not been used to 
address morale as of June 2012, according to ICE officials. 

Component: U.S. Immigration and Customs Enforcement; 
Data source: Federal Organizational Climate Survey (FOCS) and focus 
groups; 
Purpose: Last conducted in March 2012, the FOCS is a data-gathering 
tool for addressing the extent to which employees perceive their 
organizational culture as one that incorporates mutual respect, 
acceptance, teamwork, and productivity among individuals who are 
diverse in the dimensions of human differences. Additionally, ICE 
conducts focus groups and individual one-on-one interview sessions to 
obtain clarifying information pertaining to the FOCS results and 
written comments; 
Summary of results and how used: The survey showed low employee 
perceptions of ICE as an organization where people trust and care for 
each other, relative to the federal average, according to ICE 
officials. The results from the FOCS and feedback from the focus 
groups and individual one-on-one interview sessions are provided to 
ICE program offices with recommended strategies to improve the program 
office's organizational climate. 

Component: U.S. Customs and Border Protection; 
Data source: Focus groups; 
Purpose: Conducted in 2007, focus groups were launched in response to 
the 2006 annual employee survey results, which showed CBP below DHS 
and governmentwide averages; 
Summary of results and how used: The focus groups identified 
employees' perceived problems in specific work environment areas, such 
as leaders lacking supervisory or communication skills; 
Among other things, the issues identified by focus group participants 
allowed CBP to develop action plans that addressed these issues, 
according to CBP officials. 

Component: U.S. Customs and Border Protection; 
Data source: Most Valuable Perspective online survey (MVP); 
Purpose: Launched in 2009, this survey was implemented to solicit 
employee opinions on one topic per quarter as a mechanism for 
gathering further insights on FEVS results. The MVP was implemented as 
a continuation of the CBP focus groups completed in 2007; 
Summary of results and how used: In the July 2012 MVP, which solicited 
employee preferences for future CBP webcasts to employees, employees 
suggested retirement planning and financial management as their top 
two preferences. CBP's action plan planning process in response to 
FEVS results includes consideration of MVP results, according to CBP 
officials. 

Component: Coast Guard; 
Data source: U.S. Office of Personnel Management Organizational 
Assessment Survey (OAS); 
Purpose: Beginning in 2002, in order to provide the granularity, 
detail, and reliability needed to ensure the best organizational 
value, the Coast Guard adopted the OAS as its primary personnel 
attitude survey, according to Coast Guard officials. The OAS is 
administered to military (active and reserve) and civilian personnel 
biennially; 
Summary of results and how used: OPM's report to the Coast Guard on 
the 2010 OAS results identified seven strong organizational areas 
(diversity, teamwork, work environment, leadership and quality, 
communication, employee involvement and supervision) and three areas 
for improvement (innovation, use of resources, and 
rewards/recognition); 
Coast Guard unit commanders and headquarters program managers use the 
OAS to support overall Coast Guard improvement. This improvement is 
achieved by feeding results of the OAS to Coast Guard Unit Commanders 
and Program Managers who then use OAS results in conjunction with 
other information as part of routine unit and program leadership and 
management. 

Component: Transportation Security Administration; 
Data source: TSA exit survey; 
Purpose: Identify why employees leave the agency, launched in 2005; 
Summary of results and how used: Top reasons for leaving overall were 
personal reasons, career advancement, management, schedule, and pay. 
Each quarterly report includes actions managers should take to reduce 
turnover. A real-time reporting system is also available for each 
airport and office within TSA so managers can gain access to their 
results and use them to reduce turnover and make improvements, 
according to DHS officials; 
Results from the exit survey were also used by TSA officials in 
updating TSA's action plan, according to TSA officials. However, the 
July 2012 action plan did not link exit survey findings to action 
items. 

Component: Transportation Security Administration; 
Data source: Idea Factory; 
Purpose: An online tool for gathering employee suggestions for agency 
improvement. Each week, approximately 4,000 TSA employees log on to 
rate, comment, or search, or to submit ideas of their own. The Idea 
Factory team reviews all submissions and uses Idea Factory challenges 
to implement solutions to issues; 
Summary of results and how used: Results were not available for our 
evaluation. 

Component: Transportation Security Administration; 
Data source: National Advisory Council; 
Purpose: Two-year detail for TSA field representatives to collect and 
address workplace environment issues; 
Summary of results and how used: Results were not available for our 
evaluation. 

Component: Transportation Security Administration; 
Data source: DHS Ombudsman; 
Purpose: Provides informal problem resolution services with the 
mission of promoting fair and equitable treatment in matters involving 
TSA, according to TSA officials. The Ombudsman assists customers by 
identifying options, making referrals, explaining policies and 
procedures, coaching individuals on how to constructively deal with 
problems, facilitating dialogue, and mediating disputes; 
Summary of results and how used: Results were not available for our 
evaluation. 

Component: Transportation Security Administration; 
Data source: Employee Advisory Committee; 
Purpose: Each airport and TSA headquarters has an employee advisory 
council made up of elected members who work on understanding and 
addressing a variety of workplace issues; 
Summary of results and how used: Results were not available for our 
evaluation. 

Source: GAO analysis of interviews with agency officials and documents 
provided by DHS. 

[End of table] 

[End of section] 

Appendix V: Comments from the Department of Homeland Security: 

U.S. Department of Homeland Security: 
Washington, DC 20528: 

September 25, 2012: 

Mr. David C. Maurer: 
Director, Homeland Security and Justice: 
U.S. Government Accountability Office: 
441 0 Street, NW: 
Washington, DC 20548: 

Re: Draft Report GA0-12-940, "Department of Homeland Security: Taking
Further Action to Better Determine Causes of Morale Problems Would 
Assist in Targeting Action Plans" 

Dear Mr. Maurer: 

Thank you for the opportunity to review and comment on this draft 
report. The U.S. Department of Homeland Security (DHS) appreciates the 
U.S. Government Accountability Office's (GAO's) work in conducting its 
review and issuing this report. 

The Department is pleased to note GAO's recognition of many of the 
ongoing efforts underway across DHS to improve morale and engagement. 
Specifically, since 2006, DHS has, at both the Department and 
Component levels, hosted focus groups, performed key driver analyses, 
and conducted pulse surveys. In addition, the Department has 
implemented, at the Department level, a DHS Exit Survey and instituted 
a DHS Survey Analysis, Reporting and Action Planning Tool to assist 
Components with survey analysis and action planning. 

The Employee Engagement Executive Steering Committee, created by the 
Secretary last February, also now guides departmental engagement and 
action planning efforts. The Department appreciates GAO's recognition 
that some of its Components have morale rankings at or above the 
government average, a fact that is often overlooked by some. However, 
the Department also recognizes that more work remains to achieve the 
scores we want. 

The draft report contained two recommendations with which the 
Department concurs. Specifically, GAO recommended that the Secretary 
of Homeland Security direct the Office of the Chief Human Capital 
Officer (OCHCO) and Component human capital officials to: 

Recommendation 1: Examine their root cause analysis efforts and, where 
absent, add the following: comparisons of demographic groups, 
benchmarking against similar organizations, and linkage of root cause 
findings to action plans. 

Response: Concur. DHS is committed to improving employee engagement 
and morale. OCHCO will review the Department's action plan and lead 
Components in a review of their action plan activities to ensure that 
all are tied, where appropriate, to root causes. The Department will 
conduct benchmarking against other agencies and organizations to the 
extent possible given the unique missions of the Components. Regarding 
demographic analysis, the Office of Personnel and Management provided 
GAO with a data set that is not released to other agencies, which 
allowed GAO to perform a level of analysis that other agencies cannot. 
Without access to lower level demographic information, the ability to 
conduct meaningful analysis is limited. 

Recommendation 2: Establish metrics of success with the action plans 
that are clear and measurable. 

Response: Concur. Together with our Components, OCHCO will review 
action plans to ensure that each action is clear and measurable. 

Again, thank you for the opportunity to review and comment on this 
draft report. Technical comments were provided under separate cover. 
Please feel free to contact me if you have any questions. We look 
forward to working with you in the future. 

Sincerely, 

Signed by: 

Jim H. Crumpacker: 
Director: 
Departmental GAO-OIG Liaison Office: 

[End of section] 

Appendix VI: Comments from the U.S. Office of Personnel Management: 

United States Office of Personnel Management: 
Employee Services: 
Washington, DC 20415: 

September 17, 2012: 

Mr. David C. Maurer: 
Director, Homeland Security and Justice: 
U.S. Government Accountability Office: 
441 G Street, NW: 
Washington, DC 20548: 

Dear Mr. Maurer: 

The U. S. Office of Personnel Management (OPM) has reviewed the U.S. 
Government Accountability Office's draft report on the U.S. Department 
of Homeland Security's "Taking Further Action to Better Determine 
Causes of Morale Problems Would Assist in Targeting Action Plans". OPM 
has no further recommendations at this time. 

We appreciate the opportunity to review the draft report. 

Sincerely, 

Signed by: 

Angela Bailey: 
Associate Director: 
Employee Services and Chief Human Capital Officer: 
Office of Personnel Management: 

[End of section] 

Appendix VII GAO Contact and Staff Acknowledgments: 

GAO Contact: 

David C. Maurer, (202) 512-9627 or maurerd@gao.gov: 

Staff Acknowledgments: 

In addition to the contact named above, Dawn Locke (Assistant 
Director), Sandra Burrell (Assistant Director), Lydia Araya, Ben 
Atwater, Tracey King, Kirsten Lauber, Jean Orland, Jessica Orr, and 
Jeff Tessin made key contributions to this report. 

[End of section] 

Footnotes: 

[1] OPM, the central human resources agency for the federal 
government, has conducted the FEVS every year since 2010. Prior to 
2010, OPM conducted the survey during even numbered years, beginning 
in 2004. The most recent survey sample of 2011 included employees from 
29 major federal agencies, as well as 54 independent federal 
organizations. The survey results represent a snapshot in time of the 
perceptions of the federal workforce. In 2012 the FEVS will be 
implemented as a census, rather than a sample-based survey, in an 
effort to gather the opinions of the entire federal workforce. 
According to the Partnership, the Best Places to Work ranking is based 
on employee responses to the following three FEVS assessment items: 
(1) I recommend my organization as a good place to work. (2) 
Considering everything, how satisfied are you with your job? (3) 
Considering everything, how satisfied are you with your organization? 

[2] In determining whether a government program is high risk, we 
consider whether it involves national significance, a management 
function that is key to performance and accountability, or whether 
there is an inherent or systematic problem, among other things. Our 
prior work has identified four high-risk areas for which DHS has 
primary or significant responsibilities: (1) Implementing and 
Transforming DHS, (2) The National Flood Insurance Program, (3) 
Protecting the Federal Government's Information Systems and the 
Nation's Critical Infrastructure, and (4) Establishing Effective 
Mechanisms for Sharing Terrorism-Related Information to Protect the 
Homeland. GAO, Department of Homeland Security: Progress Made in 
Implementation and Transformation of Management Functions, but More 
Work Remains, [hyperlink, http://www.gao.gov/products/GAO-10-911T] 
(Washington, D.C.: Sept. 30, 2010). 

[3] DHS, Human Capital Strategic Plan, Fiscal Years 2009-2013 
(Washington, D.C.). 

[4] DHS, Workforce Strategy for Fiscal Year 2011-2016 (Washington, 
D.C.). 

[5] GAO, High-Risk Series: Strategic Human Capital Management, 
[hyperlink, http://www.gao.gov/products/GAO-03-120] (Washington, D.C.: 
January 2003). 

[6] For the purposes of this report, we define employee morale as 
being characterized by job satisfaction and employee engagement, both 
of which are measured in OPM'S FEVS. The job satisfaction index, 
composed of seven FEVS questions such as "my work gives me a feeling 
of personal accomplishment," indicates the extent to which employees 
are satisfied with their jobs and various aspects thereof. The 
Engagement Index, composed of 15 FEVS questions, indicates the extent 
to which employees are immersed in the content of the job and 
energized to spend extra effort in job performance. 

[7] Throughout this report, non-DHS refers to all federal employee 
FEVS responses outside of DHS. 

[8] OPM, 2011 Federal Employee Viewpoint Survey, Empowering Employees, 
Inspiring Change, Department of Homeland Security, Agency Management 
Report. (Washington, D.C.). 

[9] Data for the 2012 FEVS will not be available until November 2012 
and thus are not relied upon for this report. 

[10] Pub. L. No. 107-295, § 1304, 116 Stat. 2315, 2289 (2002) 
(codified at 5 U.S.C. § 1103(c)). 

[11] OCHCO's authority for requiring components to use the action 
planning tool is based on DHS's Human Capital Line of Business 
Integration and Management Directive, issued in 2004. 

[12] Two thousand six is the first year in which job satisfaction 
index data were made available and can be compared between DHS and the 
rest of the federal government. 

[13] For the purposes of our report, we list governmentwide averages 
in some instances, which include DHS. In other instances where we were 
able to make statistical adjustments, we report non-DHS averages, 
which exclude DHS. 

[14] Partnership for Public Service and the Institute for the Study of 
Public Policy Implementation at the American University School of 
Public Affairs, The Best Places to Work in the Federal Government. The 
Partnership for Public Service's ranking cited here is composed of 
large agencies, defined as agencies with more than 2,000 full-time 
permanent employees. 

[15] Because statistical significance is a function of two things--the 
size of the difference and the size of the sampled groups being 
compared--the biggest differences are not always the differences that 
are significant. 

[16] The differences between DHS and non-DHS senior leader, General 
Schedule 1-6, and less than 1 year of tenure satisfaction and 
engagement were not statistically significant. 

[17] For the purposes of this report we define employee groups as the 
different occupational groups that exist within DHS components, such 
as TSA's Federal Air Marshals, screeners, and federal security 
director staff. 

[18] All differences between pairs of groups in this paragraph are 
distinguishable from zero at the 0.05 level. 

[19] A border patrol agent is involved in detection, prevention and 
apprehension of terrorists, undocumented aliens and smugglers of 
aliens at or near the land border. A CBP field operations officer is 
responsible for, among other things, determining the nationality and 
identity of each applicant for admission to the United States and for 
preventing the entry of ineligible aliens, including criminals, 
terrorists, and drug traffickers. 

[20] The Coast Guard is organized into two major commands that are 
responsible for overall mission performance: the Pacific area that 
includes District 13 which covers the Pacific Northwest area, and 
District 14 which covers the Hawaii and Guam region. 

[21] Homeland security investigators investigate crime, human rights 
violations and human smuggling, smuggling of narcotics, weapons and 
other types of contraband, financial crimes, cybercrime, and export 
enforcement issues while enforcement removal operations employees work 
to enforce the nation's immigration laws by ensuring the removal of 
aliens who pose a threat to national security or public safety. 

[22] For employee groups that had union representation, we interviewed 
union representatives who identified employee group perspectives on 
employee morale. For those groups without union representation, we 
convened focus groups of employees to discuss employee perspectives on 
morale. 

[23] The examples provided by agency officials are the observations of 
individual employees and are not representative of all employee 
opinions. 

[24] We used measures of job satisfaction and employee engagement as 
indicators of morale in our analysis. 

[25] We used a statistical method called "Oaxaca decomposition" to 
divide the overall difference in morale between DHS and non-DHS 
agencies into two parts: the part explained by employee 
characteristics present in the FEVS (i.e. supervisory status, employee 
tenure, age and location), and the part explained by how those 
characteristics affect morale. This let us assess whether the morale 
gap is explained by available employee characteristics or by how those 
characteristics affect morale differently at DHS because of unique 
characteristics of the agency such as management practices or program 
goals. The method cannot identify specific unique characteristics that 
are responsible, however. Appendix I describes this analysis in more 
detail. 

[26] Results from DHS' Exit Survey should be interpreted with caution. 
Due to the method in which it is administered, the survey's response 
rate in 2011 was quite low, close to 40 percent. It is likely that a 
higher response rate would have produced somewhat different results. 

[27] In the 2010 OAS report to the Coast Guard, OPM made demographic 
comparisons among several employee groups within the Coast Guard, 
including work groups (such as ship versus shore-based employees), 
race and ethnicity, and gender, and comparisons between civilian and 
military ranks. However, the results of the OAS analysis are not 
included in the Coast Guard's FEVS-based action planning 
documentation. See appendix IV for a description of how the OAS is 
used by the Coast Guard. 

[28] Our file contained the same variables as the public release file, 
but it identified smaller demographic groups and work units. 

[29] The selected component action plans we reviewed were updated as 
of January 2012. Subsequent updates to the plans, due to OCHCO in July 
2012, were not included in our evaluation. 

[30] The six efforts are: (1) OPM releases 2011 survey results; DHS 
issues employee communications and conducts analysis; (2) CHCO leads 
an Enterprise-wide Executive Steering Committee to develop a 
deliberate way forward for addressing FEVS key indices; (3) OCHCO 
works with components to develop action plans responding to 2011 FEVS 
results; (4) On-line focus discussion survey deploys across 
Department; (5) CHCO provides components with feedback on action 
plans; and (6) Updated component and headquarter action plans are due. 
The three efforts that are hindered by a lack of resources are: OCHCO 
working with components to develop action plans, on-line focus 
discussions, and CHCO providing components with feedback. 

[31] The most recent DHS-wide action plan update was based on 2010 
FEVS results. According to OCHCO officials, the next update to the 
action plan will be released in January 2013. 

[32] GAO, Tax Administration: IRS Needs to Further Refine Its Tax 
Filing Season Performance Measures, [hyperlink, 
http://www.gao.gov/products/GAO-03-143] (Washington, D.C.: Nov. 22, 
2002). Of the nine attributes of successful metrics listed in this 
report, we determined that linkage, clarity and measurable target are 
relevant to this evaluation. The six attributes that we did not 
evaluate are: objectivity, reliability, core program activities, 
balance, governmentwide priorities, and limited overlap. We did not 
include these six attributes because they were not relevant to 
employee morale action planning efforts. 

[33] DHS, Privacy Impact Assessment for the Idea Factory, January 21, 
2010. 

[34] See [hyperlink, http://www.gao.gov/products/GAO-10-30], "Program 
Evaluation: A Variety of Rigorous Methods Can Help Identify Effective 
Interventions," 20-31. 

[35] See, for example, Donald Rubin, "The Use of Matched Sampling and 
Regression Adjustment to Remove Bias in Observational Studies," 
Biometrics 29 (1973): 185-203, and Paul Rosenbaum, Observational 
Studies, New York: Springer-Verlag,1995. 

[36] OPM refers to these organizational subgroups as "bureaus" and 
"offices." 

[37] For a detailed description of the questions that made up the OPM 
indexes, see OPM, 2011 Federal Employee Viewpoint Survey, Empowering 
Employees, Inspiring Change, Department of Homeland Security, Agency 
Management Report. (Washington, D.C.), 21 and 43. 

[38] We discuss options for a more sophisticated analysis of 
engagement and satisfaction on latent, continuous scales at the end of 
this appendix. 

[39] Oaxaca decomposition is a method of disaggregating an average 
difference between two groups into (1) the part due to the differences 
in the values of the variables that determine the outcome of interest 
and (2) the part due to differences in the partial relationships 
between the predictor variables and the outcome. See Ronald Oaxaca, 
"Male-Female Wage Differentials in Urban Labor Markets," International 
Economic Review 14 (1973). 693-709 

[40] We omitted results for job satisfaction from table 6 to conserve 
space, but they generally resemble the results for engagement. 

[41] DHS employees may have answered the 2011 FEVS question on tenure 
differently, depending on whether they worked for a component that 
existed before the department was created. Specifically, employees who 
reported more than 10 years of service may have interpreted the 
question to include their service prior to the creation of DHS. 

[42] The job satisfaction index, composed of seven FEVS questions such 
as "my work gives me a feeling of personal accomplishment," indicates 
the extent to which employees are satisfied with their jobs and 
various aspects thereof. The Engagement Index, composed of 15 FEVS 
questions, indicates the extent to which employees are immersed in the 
content of the job and energized to spend extra effort in job 
performance. 

[43] Throughout this report, non-DHS refers to all federal employee 
FEVS responses outside of DHS. 

[44] The FEVS does not survey military personnel. Therefore, for the 
purposes of our review, our focus at the Coast Guard was on morale-
related concerns for the Coast Guard's civilian workforce, which 
included 8,342 employees in 2011. The Coast Guard's civilian workforce 
is responsible for supporting the Coast Guard mission through over 200 
different types of professional and trade fields, such as engineering, 
information technology, administration, and electrical work. 

[45] Results from DHS' exit survey should be interpreted with caution. 
Because of the method in which the survey is administered, its 
response rate in 2011 was quite low, close to 40 percent. It is likely 
that a higher response rate would have produced somewhat different 
results. 

[46] The FEVS includes questions grouped into the following 
dimensions: work experiences, supervisor/team leader, agency, work 
unit, leadership, satisfaction, and work/life. 

[47] CBP noted its three FEVS scores as low, but CBP's scores are not 
substantially lower than the governmentwide scores. In the 2011 FEVS, 
the governmentwide average for the three questions were: dealing with 
poor performers who cannot or will not improve (31 percent positive), 
promotions based on merit (36 percent positive) and differences in 
performance are recognized (36 percent positive). 

[48] The Coast Guard's scores on these questions are not substantially 
different from the governmentwide averages (51 percent positive on 
information satisfaction with information received and 54 percent 
positive on training needs). However, according to an OCHCO official 
who monitored the Coast Guard's action planning in previous years, 
these questions were addressed in the Coast Guard's action plan 
because they have an impact on other low-scoring items and were 
important for employee satisfaction. 

[End of section] 

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