The Role of Leadership in Performance Management

1. The Role of Leadership in Performance Management Donald P. Moynihan, La Follette School of Public Affairs, University of Wisconsin-Madison Presentation to Chicago Federal Leadership Forum

2. Have you encountered? • Strategic planning • Performance measures • Performance contracts • Pay for performance

3. The role of leadership • During my 20 years in the private sector as a CEO and advisor to CEOs, I found that leadership, measurement, and a motivated workforce create the foundation for good performance. I am confident that the same is true in government • Jeff Zients – Chief Performance Officer, 2009

4. Outline • Defining terms • Era of governance by performance management • From Bush to Obama • How do we use performance systems? • What fosters use of performance data? • Summary points

5. Defining terms

6. Performance management • A system that generates performance information through strategic planning and performance measurement routines, and connects this information to decision venues,

7. Performance regimes • Performance tools create unprecedented pressure on public actors to perform, in a context where performance is defined by quantitative indicators

8. Purposes of Performance Information • Promote: How can I convince political actors, stakeholders and the public that my agency is doing a good job? • Celebrate: What accomplishments are worthy of the important ritual of celebrating success? • Learn: What is what working or not working? • Improve: What exactly should who do differently to improve performance?

9. Purposes of Performance Information • Evaluate: how well is my agency performing? • Control: how can I ensure that my subordinates are doing the right thing? • Budget: on what program’s, people, or projects should my agency spend the public’s money? • Motivate: how can I motivate employees and collaborators to improve performance?

10. ERA of governance by performance management

11. Era of Governance by Performance Management • The rise of a doctrine • Not new, but more influential than before • Must justify actions in terms of outputs and outcomes • Basis for holding new structural forms accountable

12. Doctrinal logic for change

13. Government Performance and Results Act 1993 • Mandated: • 5 year strategic plans, updated every 3 years • Specific goals and objectives • Annual performance reviews and plans

14. From Bush to Obama

15. Bush approach • Presidents Management Agenda “everyone agrees that scarce federal resources should be allocated to programs that deliver results” • Wanted to integrate performance data into budget process

16. Congressional Justifications • Center around performance goals • Pushback from Appropriations Committees • Veteran’s Administration told; “to refrain from incorporating ‘performance-based’ budget documents”; later told: “If the Department wishes to continue the wasteful practice of submitting a budget structure that will not serve the needs of the Congress, the Congress has little choice but to reject that structure and continue providing appropriations that serve its purposes.” • Two budgets required

17. Congressional Justifications • Department of Transportation told: “agencies are directed to refrain from including substantial amounts of performance data within the budget justifications themselves, and to instead revert to the traditional funding information previously provided. Performance-related information may be submitted under separate cover.” • Negative consequences were promised for agencies that ignored this directive: “If the Office of Management and Budget or individual agencies do not heed the Committee’s direction, the Committee will assume that individual budget offices have excess resources that can be applied to other, more critical missions.”

18. Program Assessment Rating Tool (PART) • 5 year summary by OMB of evidence on program performance for 1016 programs • 18 percent are Effective • 31 percent are Moderately Effective • 29 percent are Adequate • 3 percent are Ineffective • 19 percent are Results Not Demonstrated

19. PART as Evidence-based Dialogue • Third-party program review with a clear opinion • Greater emphasis on performance • The standard of proof for program performance can only be satisfied by positive evidence of results • The burden of proof for performance rests on agencies • Entire programs are evaluated on a regular basis • The routine nature of PART creates an incentive to engage

20. Obama: A Pragmatic approach • “The question we ask today is not whether our government is too big or too small, but whether it works -- whether it helps families find jobs at a decent wage, care they can afford, a retirement that is dignified. Where the answer is yes, we intend to move forward. Where the answer is no, programs will end. And those of us who manage the public's dollars will be held to account, to spend wisely, reform bad habits, and do our business in the light of day, because only then can we restore the vital trust between a people and their government”

21. Example: Pedometer challenge! • Voluntary • Belief that transparent performance numbers will change behavior, create a sense of competition and raise performance

22. Early evidence on Obama • Performance measurement will be important • “The President is creating a focused team within the White House that will work with agency leaders and the OMB to improve the results and outcomes for Federal Government programs while eliminating waste and inefficiency” • Chief performance officer • Continue to maintain agency level performance positions

23. What happens to PART? • Not clear • Criticized as ideological, as too broad, as a data collection exercise • Analysis remains in place, but new PARTs have not started • OMB have offered agencies funds for better evaluations

24. New emphasis on leadership • Focusing leaders on what matters – key goals • Accelerating results – Performance Improvement Council; data driven meetings • Style: focused collaboration

25. New focus on information use • Will be a central aspect of the Obama administration’s performance initiatives • Jeff Zients: “The ultimate test of our performance management efforts is whether or not the information is used” • Shelly Metzenbaum: “the key performance management challenge facing the Obama administration is to use—not just produce—performance goals and measures”

26. How do we use performance systems?

27. Why care about use? • For reforms to succeed, implies that data is used • Provides a tractable means of studying the impact of results-based reform • Public organizations have devoted significant time and resources into creating routines to collect and disseminate data • Almost no attention to creating routines of use • How do you use performance data?

28. Types of responses: 4 Ps • Passive • Perverse • Political • Purposeful

29. Passive use of data • Passive: • Do the minimum to comply with requirements • Do not actually use data • Correlated with cynicism about reforms

30. Perverse use of data • Effort Substitution: Reducing effort on non-measured dimensions • Cherry picking/Cream-skimming: Focusing effort on subgroups of clients most likely to provide greatest impact on performance measures while effectively denying services to others. • Measure selection: Selecting metrics or data to measure that will offer the most favorable portrayal of a service • Hiding numbers: Declining to present performance measures that may exist

31. Perverse use of data • Output distortion: Manipulating measurement processes to improve measured performance. • Ratchet effects: Curbing productivity in one time period to avoid the setting of more challenging targets in another. • Churning: Frequently adopting different targets or measures to prevent comparison across time. • Cheating: Simply making up numbers, though rare, does occur.

32. Responding to perversity • Add new/additional measures • Change existing measures • Rely/cultivate intrinsic norms to limit misbehavior • Avoid high-powered incentives

33. Political uses of data • Process of selecting measures means shaping a program narrative • “Understand that measuring policy is not a science. It is an art. It is words, and pictures and numbers. And you create impressions, beliefs, understandings and persuasions.”

34. Political uses of data • Data tells us what happened • Program officials still need to interpret and explain: • why performance did or did not occur; • the context of performance; • how implementation occurred; • an understanding of outside influences on performance; and • how to choose which program measure is a priority. • Exploit ambiguity and subjectivity of data

35. Political: Ambiguity of 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

36. Political: Subjectivity of data • Actors will select and interpret performance information consistent with institutional values and purposes

37. Evidence of Ambiguity in PART • Ambiguity of terms: • E.g.: Program purpose, quality evaluation, ambitious, having made progress • How to interpret results? Multiple logics from experiment: • Argue that ratings are unreliable • Cut poorly managed programs • Raise funding for programs with positive assessments • Parity: Raise funding because program with similiar assessment received more • Delay cuts because progress being made • Clear relationship between resources, need and program delivery • Stakeholder and congressional views

38. Evidence of Subjectivity with PART • OMB using PART to expand influence in performance management/policy • OMB can define programs, goals, measures, agency responsibility • Disagreement with agencies/Congress on meaning/relevance of PART • Experimental evidence: • UW students significantly more likely to disagree with OMB, and to argue for higher assessments and resources

39. Implications for Decisionmaking • Performance information use reflects political process, does not replace it • Performance information use does not lead to clarity • Ability to structure dialogue tied to power

40. Purposeful use of data • Use data to improve program performance • Goal-based learning • efficiency improvements • better targeting of resources • more informed strategic decisions, • tying indicators to rewards/sanctions in contract arrangements

41. Purposeful use of data • Use of performance information for problem-solving more likely to occur in intra-institutional settings • Reduces competing interpretations • Problem of neglect • rarely do anything with information

42. Learning forums • Routines specifically focused on solution-seeking, where actors collectively examine information, consider its significance and decide how it will affect future action • What measures are useful for agency officials? • What other ways can we encourage learning forums?

43. What fosters performance information use?

44. The Right Context • Simple function that is easy to measure • Clear link between measures of actions, and measures of outcomes • One-dimensional – relatively few measures that do not conflict with one another • Stakeholder support – clear agreement about purpose

45. Other factors • Learning forums • Mission-based culture/supportive culture • Resources • Administrative stability • Administrative capacity

46. Quantitative approach • 3 studies using survey-based data • Self-reported performance information use • Results from Moynihan and Pandey (in press) and Moynihan, Wright and Pandey (2009; 2010)

47. Study 1: Ordinal regression of reported performance information use for decisions

48. Intrinsic vs. extrinsic motivation • Sense of public service motivation mattered • Possibility of extrinsic reward did not create an incentive to use data • Implication: performance information use as extra role behavior

49. Organizational factors • Information availability • Supply-side approach • Use increases with better information, and when information is tied to management systems

50. Organizational factors • Demand side approach • Culture matters • Previous work focuses on whether culture welcomed performance management reforms • What about broader measures of culture? • Developmental culture (adaptability, readiness, growth) • Flexibility – unlikely to use data if cannot apply insights