Workshop on Using Contribution Analysis to Address Cause-Effect Questions

1. Workshop onUsing Contribution Analysis to Address Cause-Effect Questions Danish Evaluation Society Conference Kolding, September 2008 John Mayne, Advisor on Public Sector Performance john.mayne@rogers.com

2. Workshop Objectives • Understand the need to address attribution • Understand how contribution analysis can help • Have enough information to undertake a contribution analysis on your own

3. Outline • Dealing with attribution • Contribution analysis • Working a case • Levels of contribution analysis • Conclusions

4. The challenge • Attribution for outcomes always a challenge • Strong evaluations (such as RCTs) not always available or possible • A credible performance story needs to address attribution • Sensible accountability needs to address attribution • Complexity significantly complicates the issue • What can be done?

5. The idea • Based on the theory of change of the program, • Buttressed by evidence validating the theory of change, • Reinforced by examination of other influencing factors, • Contribution analysis builds a reasonably credible case about the difference the program is making

6. The typical context • A program has been funded to achieve intended results • The results have occurred, perhaps more or less • It is recognized that several factors likely ‘caused’ the results • Need to know what was the program’s role in this

7. Two measurement problems • Measuring outcomes • Linking outcomes to actions (activities and outputs), i.e. attribution • Are we making a difference with our actions?

8. Attribution • Outcomes not controlled; are always other factors at play • Conclusive causal links don’t exist • Are trying to understand better the influence you are having on intended outcomes • Need to understand the theory of the program, to establish plausible association • Something like contribution analysis can help

9. The need to say something • Many evaluations and most public reporting are silent on attribution • Credibility greatly weakened as a result • In evaluations, in performance reporting and in accountability, something be said about attribution

10. Proving Causality • The gold standard debate (RCTs et al) • Intense debate underway, especially in development impact evaluation • Some challenge on RCTs (e.g. Scriven) • Does appear if RCTs have limited applicability • Then what do we do?

11. Proving Causality • AEA and EES: many methods capable of demonstrating scientific rigour • Methodological appropriateness for given evaluation questions • Causal analysis: auto mechanic, air crashes, forensic work, doctors—Scriven’s Modus Operandi approach

12. Theory-based evaluation • Reconstructing the theory of the program • Assess/test the credibility of the micro-steps in the theory (links in the results chain) • Developing & confirming the results achieved by the program

13. Contribution analysis: the theory • There is a postulated theory of change • The activities of the program were implemented • The theory of change is supported by evidence • Other influencing factors have been assessed & accounted for Therefore • The program very likely made a contribution

14. Steps in Contribution Analysis 1. Set out the attribution problem to be addressed 2. Develop the postulated theory of change 3. Gather the existing evidence on the ToC 4. Assemble & assess the contribution story 5. Seek out additional evidence 6. Revise & strengthen the contribution story 7. Develop the complex contribution story

15. 1. Set out the attribution problem • Acknowledge the need to address attribution • Scope the attribution problem • What is really being asked • What level of confidence is needed? • Explore the contribution expected • What are the other influencing factors? • How plausible is a contribution?

16. Cause-Effect Questions Traditional attribution questions • Has the program caused the outcome? • How much of the outcome is caused by the program? Contribution questions • Has the program made a difference? • How much of a difference?

17. Cause-Effect Questions Management questions • Is it reasonable to conclude that the program made a difference? • What conditions are needed to make this type of program succeed? • Why has the program failed?

18. Step 1 building an evaluation office contribution story • Evaluation aim is to ‘make a difference’ (an outcome) • e.g., improvements in management and reporting, more cost-effective public service, enhanced accountability, etc. • Evaluation products (outputs): • Evaluations and evaluation reports • Advice and assistance

19. 2. Develop the ToC and Risks to It • Build the postulated results chain and ToC • Identify roles played by other influencing factors • Identify the risks to the assumptions • Determine how contested the ToC is

20. A results chain Examples negotiating, consulting, inspecting, drafting legislation activities (how the program carries out its work) Examples checks delivered, advice given, people processed, information provided, reports produced outputs (goods and services produced by the program) Immediate outcomes (the first level effects of the outputs) Examples actions taken by the recipients, or behaviour changes External Factors Results Examples satisfied users, jobs found, equitable treatment, illegal entries stopped, better decisions made intermediate outcomes (the benefits and changes resulting from the outputs) end outcomes (the final or long-term consequences) Examples environment improved, stronger economy, safer streets, energy saved

21. Results chain links Examples negotiating, consulting, inspecting, drafting legislation activities (how the program carries out its work) Examples checks delivered, advice given, people processed, information provided, reports produced outputs (goods and services produced by the program) Why will these immediate outcomes come about? Immediate outcomes (the first level effects of the outputs) Examples actions taken by the recipients, or behaviour changes External Factors Results Examples satisfied users, jobs found, equitable treatment, illegal entries stopped, better decisions made intermediate outcomes (the benefits and changes resulting from the outputs) end outcomes (the final or long-term consequences) Examples environment improved, stronger economy, safer streets, energy saved

22. Reduction in smoking Anti-smoking campaign Theories of change • A results chain with embedded assumptions and risks identified • An explanation of why the results chain is expected to work; what has to happen Assumptions: target is reached, message is heard, message is convincing, no other major influences at work Risks: target not reached, poor message, peer pressure very strong

23. Figure 1 Enhancing Management Capacity in Agricultural Research Organizations (AROs) Theory of Change: Assumptions and Risks Results Chain More effective, efficient and relevant agricultural programs Assumptions: Better management will result in more effective, efficient and relevant agricultural programs. Risks: New approaches do not deliver (great plans but poor delivery); resources cut backs affect PM&E first; weak utilization of evaluative information. final outcomes (impacts) (impacts Strengthened management of agriculture research Assumptions: The new planning, monitoring and evaluation approaches will enhance the capacity of the AROs to better manage their resources. Risks: Management becomes too complicated; PM&E systems become a burden; information overload; evidence not really valued for managing intermediate outcomes Institutionalization of integrated PM&E systems and strategic management principles Assumptions: Over time and with continued participatory assistance, AROs will integrate these new approaches into how they do business. The projects activities complement other influencing factors. Risks: Trial efforts do not demonstrate their worth; pressures for greater accountability dissipate; PM&E systems sidelined. Enhanced planning processes, evaluation systems,monitoring systems, and professional PM&E capacities immediate outcomes Assumptions: Intended target audience received the outputs. With hands on, participatory assistance and training, AROs will try enhanced planning, monitoring and evaluation approaches. Risks: Intended reach not met; training and information not convincing enough for AROs to make the investment; only partially adopted to show interest to donors. information training and workshops facilitation of organizational change outputs Adapted from Horton, Mackay, Anderson and Dupleich (2000).

24. Theory one: Classification The quality of particular aspects of health care can be monitored and measured to provide valid and reliable rankings of comparative performance Theory two: Disclosure Information on the comparative performance and the identity of the respective parties is disclosed and publicised through public media Theory four: Response Parties subject to the public notification measures will react to the sanctions in order to maintain position or improve performance Theory three: Sanction Members of the broader health community act on the disclosure in order to influence subsequent performance of named parties Figure 2 An initial ‘theory map’ of the public disclosure of health care information Theory seven: Measure manipulation Response may be made to the measurement rather than its consequences with attempts to outmanoeuvre the monitoring apparatus Theory three a, b, c, d Alternative sanctions The sanction mounted on the basis of differential performance operate through: a) ‘regulation’ b) ‘consumer choice’ c) ‘purchasing decisions’ d) ‘shaming’ Theory five: Ratings Resistance The authority of the performance measures can be undermined by the agents of those measured claiming that the data are invalid and unreliable Theory six: Rival Framing The ‘expert framing’ assumed in the performance measure is distorted through the application of the media’s ‘dominant frames’ From Pawson et al. (2005)

25. Office has credibility and evidence Theory of Change for an Evaluation Office Step 2 Results Chain • Evaluation Studies • participation • Evaluation Reports • findings & conclusions • recommendations Advice Outputs Enhanced value of evaluative thinking better informed management acceptance of recommendations & advice Immediate Outcomes Changes not planned anyway Better designed programs Better data for evaluations Intermediate Outcomes implementation of recommendations & advice Recommendations work managers’ & organisation initiatives Other influencing factors better management practices Recommendations work • More effective programs • informed decision-making • productive operations • cost-effective programs Better benefits to citizens Our contribution story line Final Outcomes

26. 3. Gather existing evidence • Assess the logical robustness of the ToC • Gather available evidence on • Results • Assumptions • Other influencing factors

27. 4. Assemble and assess the contribution story • Set out the contribution story • Assess its strengths and weaknesses • Refine the ToC

28. Theory of change analysis • Need to identify which of the links in the results chain have the weakest evidence • Some may be supported by prior research • Some may be well accepted • But some may be a large leap of faith, or the subject of debate • With limited resources, these contested links are where effort should be focused

29. 5. Seek out additional evidence • Determine what is needed • Gather new evidence

30. Strengthening Techniques • Refine the results chain and/or gather additional results data • Survey knowledgeable others involved • Track program variations and their impacts (time, location, strength) • Undertake case studies • Identify relevant research or evaluation • Use multiple lines of evidence • Do a focused mini-evaluation

31. The Agr Research Orgs evaluation • CA done: • Theory of change developed • Other influencing factors recognized • The theory of change was revised based on lessons learned • CA that could have been done: • A more CA structured approach • More analysis of other factors • More attention to the risks faced

32. 6. Revise and strengthen the contribution story • Build the more credible contribution story • Reassess its strengths and weaknesses • Revisit step 5

33. A CA Case Study Patton (2008). Advocacy Impact Evaluation. JMDE, 5(9): 1-10. • Collaboration of agencies spent over $2M on a campaign to influence a Supreme Court decision • Evaluation Issue: Did it work? • Conclusion: the campaign contributed significantly to the Court’s decision

34. Features • Was a stealth campaign • Evaluation used Scriven’s General Elimination Method, or the modus operandi approach. • Undertook considerable document review and interviews, an in-depth case study which served as the evidence for the evaluation

35. Cause-effect • Attribution vs contribution • Attribution concepts don’t work well in complex settings • Contribution analysis identifies likely influences • Case examined 2 alternative possible influences

36. Levels of contribution analysis • Minimalist contribution analysis • Contribution analysis of direct influence • Contribution analysis of indirect influence

37. Minimalist CA • Develop the theory of change • Confirm that the expected outputs were delivered then, • Based on the strength of the theory of change, conclude the program made a contribution

38. Other influencing factors • Literature and knowledgeable others can identify the possible other factors • Reflecting on the theory of change may provide some insight on their plausibility • Prior evaluation/research may provide insight • Relative size compared to the program intervention can be examined • Knowledgeable others will have views on the relative importance of other factors

39. CA of direct influence • Minimalist CA, plus • Verifying the expected direct outcomes occurred • Confirming the assumptions associated with the direct outcomes • Accounting for other influencing factors

40. CA of indirect influence • CA of direct influence, plus • Verifying theintermediate and final outcomes occurred • Confirming the assumptions associated with these indirect outcomes • Accounting for other influencing factors

41. A credible contribution statement • Description of program context and other influencing factors • A plausible theory of change • Confirmed program activities, outputs and outcomes • CA findings: evidence supporting the ToC and assessment of other influencing factors • Discussion of the quality of evidence

42. When is CA useful? • Program is not experimental • Funding is based on a theory of change • Program has been in place for some time • No real scope for varying the intervention(s)

43. Contribution analysis Builds evidence on • Immediate/intermediate outcomes, the behavioural changes • Links in the results chain • Other influencing factors at play • Other explanations for observed outcomes Contribution Evaluation