EXPERIENCES WITH PERFORMANCE
MANAGEMENT IN THE UNITED STATES
PRESENTATION TOWARDS A MORE RESULT ORIENTED
FLEMISH PUBLIC SECTOR, JANUARY 10, 2014
Donald P.
Moynihan
PART I: OVERVIEW
OVERVIEW
US Experience - background
Errors in understanding performance
management
Expectations about implementation
The politics of performance management
Lessons: how do we encourage purposeful
use?
BACKGROUND
NATIONAL GOVERNMENT-WIDE
CHANGES
Government Performance and Results Act
- GPRA (1993-2010)
Program Assessment Rating Tool (20022008)
GPRA Modernization Act (2010-)
State level variations on these models
DOCTRINAL LOGIC FOR CHANGE
20 YEARS OF LEARNING?
Some lessons on how it went
 Partly from study of topic
 Reflected in some policy changes, especially GPRA
Modernization Act
EXPECTATIONS ABOUT
IMPLEMENTATION
IS THE IDEA OF PERFORMANCE
MANAGEMENT RUNNING OUT OF STEAM?
OECD 2012 survey
Seems to be less use of performance data
than in past
Performance targets not consequential
General sense of disappointment: we have
systems in place, have not delivered
desired results
EXPECTATIONS PROBLEM
We define performance systems by the
benefits we hope will occur (more rational
budgeting, more efficient management)
The gap between our aspirations and the
observed effects of these rules are usually
large, resulting in disappointment
More grounded and accurate description:
performance systems are a set of formal
rules that seek to disrupt strongly embedded
social routines
CONFUSION: ADOPTION VS.
IMPLEMENTATION
Speak of governments doing performance
management
 What do we mean?
 Rules about measuring and disseminating data
INATTENTION TO THE USE OF DATA
 Performance data by itself does not do much
 Implementation of performance management means
using the data
 Why focus on performance information use?
 Difficult to connect public actions to outcomes
 Intermediate measure of effectiveness – performance
information use
 Without it, good things we want don’t happen
 There are different types of use
THE FOUR T YPES OF USE
Passive – minimal compliance with
procedural requirements
Purposeful –improve key goals and efficiency
Political – advocate for programs
Perverse – behave in ways detrimental to
goals (goal displacement and gaming)
EFFECT OF PERFORMANCE REFORMS
 Can observe if agencies
comply with requirements
(passive use), but not other
types of use
 Performance systems
encourage passive use, not
purposeful
THE POLITICS OF
PERFORMANCE
MANAGEMENT
APOLITICAL PERFORMANCE REFORMS?
Performance data associated with neutrality
Offers objective account of the world, and will
engender consensus
Reduces the role of politics by offering an
alternative basis to make arguments
This is part of political appeal
Has implications for adoption and
implementation
POLITICS OF ADOPTION
Elected officials motivated by symbolic values
 Primary focus on adopting information reporting
requirements, not broader change
ACTUAL PATTERN OF CHANGE
ONE BASIC REASON FOR CONFUSION
We fail to understand the nature of
performance data
We assume data are
 Comprehensive
 Objective
 Indicative of actual performance
 Consistently understood
 Prompts a consensus
THE AMBIGUIT Y OF PERFORMANCE DATA
Examine same programs but disagree on data
Agree on data but disagree on meaning
Agree on meaning, but not on next action
steps/resources
Not clear on how data links to budget
decisions
THE SUBJECTIVIT Y OF PERFORMANCE DATA
Actors will select and interpret performance
information consistent with institutional
values and purposes
Greater contesting of performance data and
less potential for solution seeking in forums
featuring actors with competing beliefs
IMPLICATIONS: POLITICAL USE
 Performance data
 is socially constructed by individuals subject to personal
biases, institutional beliefs, and partisan preferences
 has qualities of ambiguity and subjectivity
 These qualities make performance management
likely to operate as part of political process, not as
alternative to it
EVIDENCE OF ADVOCACY
 “Spinning” (Hood 2006)
 Claim credit when things go well, deny responsibility when
things do not
 Advocacy by agents seeks to avoid blame and
respond to “negativity bias”
 disproportionate citizen dissatisfaction with missed target
(James 2011)
 political officials pay more attention to high and low
performers (Nielsen and Baekgaard 2013)
 more bureaucratic explanations of failed performance
(Charbonneau and Bellavance 2012)
STAKEHOLDERS
 Political support for agency associated with performance
information use (Moynihan and Pandey 2010)
 May worry less about blame, freedom to experiment
 Belief that stakeholders care about performance or
performance measures associated with bureaucratic use
(Moynihan and Pandey 2010)
 More performance information use when:
stakeholders perceived as more influential, more in conflict, and
when there is more networking with stakeholders (Askim, Johnsen,
and Christophersen 2008; Moynihan and Hawes 2012)
PRINCIPAL AGENT ARGUMENT
Assumption: Use performance data to
reduce information advantage that
agencies have over center & elected
officials
Reality:
Some evidence of partisan biases in
implementation
As long as agencies play role in defining,
collecting, and disseminating information,
they retain information asymmetry
AN EXAMPLE: PROGRAM ASSESSMENT
RATING TOOL (PART)
• Bush-era questionnaire used by Office of
Management and Budget to rank programs
from ineffective to effective
Four sections: program purpose and design,
strategic planning, program management, and
program results/accountability
Burden of proof on agencies
Almost all federal programs evaluated
HOW MIGHT POLITICS AFFECT PART
IMPLEMENTATION?
 Ostensibly neutral reforms may serve—or may be
seen as serving—political ends:
Partisan reformers may implement reforms differently if
programs/agencies are ideologically divergent
Managers of ideologically divergent programs may perceive
bias (whether or not a reform effort is biased against their
programs)
WAS PART POLITICAL?
 Designed to be good government, politically neutral
reform, and qualitative studies do not report overt
partisanship, but…
 More liberal agencies and programs get lower scores
(Gallo and Lewis 2012; Gilmour and Lewis 2006)
 PART scores only related to President’s budget
proposals for liberal programs (Gilmour and Lewis
2006)
DID POLITICS AFFECT RESPONSE TO PART?
 Liberal agencies, though smaller, had significantly
higher PARTs completed
 Two types of effort:
 Observable: self-reported effort in completing PART –
higher for managers in liberal agencies (Lavertu, Lewis
and Moynihan 2013)
 Discretionary: performance information use – lower
for managers in liberal agencies (Lavertu and Moynihan
2012)
WHY WOULD PART IMPOSE A GREATER
ADMINISTRATIVE BURDEN ON LIBERAL
AGENCIES?
 Liberal agencies likely concerned about making their
programs look as good as possible, given preference
divergence
 Potentially greater scrutiny of liberal programs,
requiring more costly agency data collection and
reporting
LESSONS: HOW DO WE
ENCOURAGE
PURPOSEFUL USE
WHEN DOES PERVERSE USE OCCUR?
 Goal displacement – e.g. cream-skimming
 Data manipulation – including outright cheating
 Becomes more likely when
 Data is self-reported
 Task is complex and hard to measure
 High-powered incentives attached to measures
 Especially in contracting
 Job-training programs, tuition programs
 Policymakers have imperfect knowledge of perversity, amend
contracts after problems occur
NEXT GENERATION PERFORMANCE SYSTEM?
GPRA MODERNIZATION ACT OF 2010
 Quarterly performance reviews
 Goal leaders
 Chief operating officers/performance
improvement officers
 High-priority goals
 Cross-agency priority goals
 For summary, see Moynihan 2013
CONTINUING CHALLENGE: HOW TO MAKE USE
OF PERFORMANCE DATA
 Create learning forums: routine discussions of
performance data with supervisors/peers associated
with use (Moynihan and Lavertu 2012)
 GPRA Modernization Act: quarterly performance
reviews
 Not just routines, also learning culture
 Tolerates error
 Rewards innovation
 Brings together multiple perspectives
 Gives discretion to users
 Tradeoff between learning and accountability
 Accountability evokes defensive reactions and gaming
LOOK FOR ACTIONABLE DATA
You might want to measure everything but you
can’t manage everything
Problem with PART – equal attention to all
goals
Modernization Act: focus on important targets,
areas of opportunity (high priority goals,
cross-agency priority goals)
FOSTER GOAL CLARIT Y
Clear goals increase performance information
use (Moynihan and Pandey 2010); may not be
easy if:
 Service has many different aspects
 Tension between:
 Few enough measures to generate attention
 Enough measures to avoid encouraging workers to
ignore unmeasured aspects
APPEAL TO ALTRUISM
Appeal to altruistic motivations, not extrinsic
reward (Moynihan, Wright and Pandey 2012)
 Select goals that motivate
 Clear line of sight between goals and actions
 Celebrate achievement
 Connect to beneficiaries
INTEGRATE PROGRAM EVALUATION AND
PERFORMANCE MANAGEMENT
 Performance data tells you if a measure moved up or
down, evaluations tell you what affects performance
 Discussion of evaluations should be incorporated
into performance management
 Assign evaluation funding for new policies
 Example: Washington State Institute for Public Policy
provides meta-analyses of research on different
policies, and provides return-on-investment
estimates to policymakers
INDUCE LEADERSHIP COMMITMENT
Leadership commitment associated with use
(Dull 2009; Moynihan and Lavertu 2012)
How do you create commitment?
 Reputation: public commitments and responsibility
(high priority goals)
 Create leadership positions with oversight for
performance (COOs, PIOs, goal leaders)
 Select leaders based on ability to manage
performance
CONCLUSION
 Welcome your feedback and questions
 Performance Information Project:
 http://www.lafollette.wisc.edu /publi cser vi ce /per formance/ index.html
 dmoynihan@lafollette.wisc.edu
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