This study used an innovative method, best-worst scaling, to identify the criteria valued by policy-maker to be most and least important in deciding on user fee abolition/reduction in Africa.

1. How do policy makers decide on
user fee abolition?
Best-worst scaling analysis of decision-making on user
fee abolition in Sub-Saharan Africa
Presenter: Manuela De Allegri
Team: Aleksandra Torbica, Valery Ridde,
Danielle Belemsaga, Antonieta Medina-Lara

2. Background
• Several countries in Africa have abolished
user fees ...
• ... largely to advance progress towards MDGs
• User fee abolition (especially in West Africa)
has largely revolved around maternal care
services
• But specific interventions differ substantially
across countries

3. Background (cont.)
• Impact of user fee abolition on service
utilisation and household health care spending
is undoubful

4. An illustration from Burkina Faso
100 4000
90 3500
80 3000
70 2500
60 2000
50 1500
40 1000
% 2006 2007 2008 2009 2010 2011
XOF
% facility based delivery
% paid for delivery (when facility based delivery)
Average amount paid for facility based delivery (in XOF)
User fee reduction for facility-based delivery: Jan 1st, 2007
Given 12 months recall period, utilisation data always refer to the year
prior the one indicated on the table 4

5. Background (cont.)
• Impact of user fee abolition on service
utilisation and household health care spending
is undoubful
• Yet the implementation process suffers from a
number of difficulties: low communication &
low understanding among providers; low
prepardness of the system; poor
reimbursement rate

6. Research question
Looking at difficulties on implementation process
& at variety of interventions, one is left to wonder:
How do policy makers decide
on user fee abolition?

7. Research question
Looking at difficulties on implementation process
& at variety of interventions, one is left to wonder:
How do policy makers decide
on user fee abolition?
What criteria guide policy makers‘ decisions
on user fee abolition?

8. Our study objective
To identify a methodology that would allow us to
quantify the importance/the weight that different
criteria play in the decision making process on
user fee abolition

9. Why did we care to do that?
• Lack of information to this regard
• Only existing accounts are based on
qualitative retrospective policy analysis
• Important to understand how decisions are
made in a context of scarse resources &
limited capacity
• Our challenge: try out something with very
limited prior application

10. 10

11. Original study design
Exploratory study design
QUAL QUAN QUAL
Focus Group Discrete choice FGD & Individual
Discussions (FGD) experiment Interviews
Explanatory study design
Focus: user fee abolition for maternal care services
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12. Actual study design
Focus: user fee abolition for maternal care services
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13. What is best worst scaling?
• An approach based in random utility theory to
identify preferences
• Respondents are asked to identify the best
and the worst item from a given list

14. What is best worst scaling?
• An approach based in random utility theory to
identify preferences
• Respondents are asked to identify the best
and the worst item from a given list
• Represents some sort of compromise
between DCE and ranking excercises
• Cognitively easier than a DCE and better
suited to evaluate absolute utility of single
items

15. Need to start qualitatively
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16. Our qualitative study component
• May/Oct 2011: four FGD among policy makers
concerned with user fee abolition in Africa
• Three independent inductive analyses of
qualitative data (MDA, VR, DB)
• Results led to identification of 11 criteria
• Additional triangulation through systematic
comparison with existing literature
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17. The 11 criteria
International pressure
Donor support
Political commitment
Financial sustainability
Equity
Increase in service utilisation
Institutional capacity
Quality of care
Impact on health
Cost effectiveness
Burden of disease
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18. The 11 criteria in the actual experiment
International pressure • A Balance Incomplete Block
Design (BIBD) was used to
Donor support
produce random combinations
Political commitment of the 11 criteria
Financial sustainability • Half the sample was presented
Equity with 11 sets of 5 criteria and
Increase in service utilisation half the sample with 11 sets of
Institutional capacity 6 criteria
Quality of care • On each set, respondents were
asked to identify the most
Impact on health
important and the least
Cost effectiveness important criteria guiding
Burden of disease decisions on user fee abolition
in maternal care 18

19. An illustration from the actual questionnaire
Scenario 9 Most important Least Important
Impact on health
Financial sustainability
International pressure
Quality of care
Equity
Burden of disease
Scenario 10 Most important Least Important
Institutional capacity
Political commitment
Increase in service utilisation
Financial sustainability
Burden of disease
Equity
Scenario 11 Most important Least Important
Donor money
Burden of disease
Cost effectiveness
Impact on health
Financial sustainability
Increase in service utilisation
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20. The quantitative phase
• Nov 2011: BWS sets embedded in a self-administered
survey completed by 38 respondents
• Analysis of preferences assigns the most valued
principles a „1‟ and the least valued principles „-1‟ in
each set
• With each item appearing max six times in each
block, preferences were analyzed over a cardinal utility
scale bounded by -6 and +6
• Descriptive statistics, log-linear regression, and ordered
logit model (clustered by individual & block)
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21. Results: descriptive statistics
Criteria Total counts Total MOST Total LEAST MOST- Mean 95% Confidence interval
(individuals * IMPORTANT IMPORTANT LEAST individual
scenarios) (% of total) (% of total) score
Political commitment 418 69 (16.5) 9 (2.2) 60 1.579 [1.409 to 1.748]
Impact on health 418 66 (15.8) 9 (2.2) 57 1.500 [1.304 to1.696]
Burden of disease 418 48 (11.5) 11 (2.6) 37 0.974 [0.792 to 1.155]
Increase in service utilization 418 54(12.9) 18 (4.3) 36 0.947 [0.803 to 1.093]
Financial sustainability 418 52 (12.4) 18 (4.3) 34 0.895 [0.693 to 1.097]
Equity 418 49 (11.7) 21 (5.0) 28 0.737 [0.551 to 0.922]
Quality of care 418 31 (7.4) 16 (3.8) 15 0.395 [0.254 to0.535]
Cost effectiveness 418 23 (5.5) 36 (8.6) -13 -0.342 [-0.498 to-0.185]
Institutional capacity 418 13 (3.1) 52 (12.4) -39 -1.026 [-1.194 to -0.859]
Donor money 418 8 (1.90 105 (25.1) -97 -2.556 [-2.743 to -2.362]
International pressure 418 5 (1.2) 123 (29.4) -118 -3.105 [-3.304 to -2.907]
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22. Results: analytical models
Criteria Regression analysis* Cluster Ordered Logit**
Estimated SE RANK Estimated SE 95% Confidence Interval RANK
Coefficent Coefficient
Political Commitment 0.94 0.13 1 -2.82 0.25 [-3.30 to -2.33] 1
Impact on Health 0.92 0.13 2 -2.69 0.25 [-3.18 to -2.20] 2
Equity 0.37 0.13 5 -2.64 0.31 [-3.24 to -2.04] 3
Increase Service Utilisation 0.47 0.13 3 -2.55 0.27 [-3.08 to -2.02] 4
Financial Sustainability 0.46 0.13 4 -2.49 0.27 [-3.01 to -1.96] 5
Quality of Care 0.26 0.13 6 -2.2 0.25 [-2.70 to -1.71] 6
Burden of Disease 3.28 0.04 7 -2.14 0.24 [-2.61 to -1.68] 7
Cost Effectiveness -0.30 0.13 8 -1.28 0.26 [-1.78 to -0.78] 8
Institutional Capacity -0.76 0.13 9 -1.12 0.26 [-1.63 to -0.62] 9
Donor Money -1.36 0.13 10 -0.21 0.24 [-0.69 to 0.27] 10
International Pressure*** -1.68 0.13 11 11
*The natural logarithm of the answer
** Analysis was clustered by individual and scenario.
*** Reference category for multi-level ordered logit; the remaining variables were coded as dummy variables
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23. Study limitations
• Low number of respondents
• Impossibility to interview “ultimate decision
makers”
• Influence of historical knowledge on
respondents‟ answers
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24. Conclusions
• Results confirm historical observation on role of
political commitment & limited donor influence
• Low value attributed to role of institutional
capacity reveals dangerous underlying
assumption that “once the political decision is
made, the implementation will follow”
• Low value attributed to equity largely reflects
discourse on user fee abolition and their
contexts (MDGs)
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25. Acknolwedgements
The study was funded by the Africa Initiative
Research Grant of the Centre for International
Governance Innovation
Special thanks to: Pinaki Dey, Olivier Kalmus,
Afua Asante-Poku & to all the members of the
Community of Practice on Financial Access
(Harmonisation for Health in Africa)
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