This presentation was given at the pay-for-performance workshop in Tanzania, November 2015.

1. Pay-for-Performance and Distributional Effects in
Tanzania: A Supply-side Assessment
Peter Binyaruka
Nov, 2015

2. Introduction
Background
• P4P is expected to close the gap between poorly
performing and well performing facilities
• But the gap could also widen if better performing or
better off facilities are better able to meet the targets
What we know
No evidence from low and middle income countries.
Studies in high income countries (US, UK) found:
• Performance was positively associated with facility
economic status, workforce resources, facility size, etc.
• Decreasing trend in performance inequality

3. Objectives
Our study had 2 objectives:
1. To assess how the P4P payouts are distributed across
health facilities in Pwani region
• By socio-economic status
• Identify contributing factors to any inequity in payouts
2. To examine whether there was any difference in the
effect of P4P on service utilization outcomes between
facilities
• Based on analysis of household and facility data from Pwani
region and control sites before and 13 months after the
intervention.

4. Bonus Payouts
Made every 6 months (in USD)
Total % staff % facility
improvement
Hospitals 6,790 60 (RCH)
30 (non-RCH)
10
Upgraded health centres 4,380 75 25
Health centres 3,220 75 25
Dispensaries 820 75 25
Health worker incentive + 10% of salary. Extra 0.30 USD per capita

5. Methods
Definition of outcome variables:
(1) Facility payouts –payout score
-% of payout received relative to potential payouts if all targets achieved
by 100%
-Since there is a maximum potential amount by level of care
-Score for each payment cycle [1-7] …[2011 -2014]
-Data source: P4P implementing agency (CHAI)
(2) Utilization outcomes –service coverage (%)
• Facility-based deliveries (FBD)
• Antimalarial provision during pregnancy (IPT2)
-Data source: from household surveys (2012 -2013) cycles
1-4

6. Methods: facility characteristics considered
Facility characteristics (predictors) “Better-off” “Worse-off”
Facility socio-economic status (SES) Higher SES Lower SES
Ownership Public owned Non-public
Level of care Hospital & HC Dispensary
Insurance scheme (CHF) With CHF Without CHF
Infrastructure With clean water, electricity Without
Drug stock-out (binary for 50% of 9 drugs) Lower stock-out Higher stock-out
Equipment availability (binary for 50% of 19 items) Higher availability Lower availability
Baseline coverage group level (5 groups by P4P design) Higher level Lower level
• Predictors were measured at baseline
• Grouping based on: capacity to enhance demand, resource capacity, organization and size
• Expecting better-off to have higher payouts and more utilization effects than worse-off

7. Statistical Analysis
Distribution of facility payouts (benefits)
• Bivariate analysis [Payout score vs. Facility SES]
✓ Equity gap, ratio and concentration indices
• Decomposition analysis –on significant inequality
Distribution of utilization effects
• Difference-in-difference (DD) regression analysis
✓ Sub-group analysis [Better vs. Worse-off]
✓ Full sample analysis [Interaction term]

8. RESULTS (1): Payout scores by facility SES (inequalities)
“Pro-rich pay outs, but improves over time”
Payout scores †
All Facility SES Equity Conc. Index
(CI)
Mean [SD] Higher Lower Gap Ratio
(1) (2) (3) (4) (5) (6)
CYCLE 1 (%) 50.1 [19.4] 53.8 46.5 7.3 (0.157) 1.16 0.045
CYCLE 2 (%) 50.3 [19.1] 57.2 43.3 13.9 (0.000) 1.32 0.087***
CYCLE 3 (%) 64.6 [18.8] 69.5 59.8 9.7 (0.015) 1.16 0.036*
CYCLE 4 (%) 67.5 [19.5] 68.5 66.5 2.0 (0.414) 1.01 0.006
CYCLE 5 (%) 74.5 [18.5] 75.1 74.0 1.1 (0.829) 1.01 0.006
CYCLE 6 (%) 69.6 [20.1] 73.5 65.8 7.7 (0.154) 1.12 0.035
CYCLE 7 (%) 77.7 [16.3] 78.2 77.2 1.0 (0.871) 1.01 0.006
Average score cycles (1-3) (%) 54.9 [13.7] 60.8 51.7 9.1 (0.068) 1.18 0.054**
Average score cycles (1-7) (%) 64.7 [11.7] 67.7 61.8 5.9 (0.071) 1.09 0.027*
Notes: † =(Actual/Potential) x 100%, where potential is the payout amount entitled to be given for reaching 100% of pre-defined target; p-values in parentheses in equity gap are from
t-test; SD=Standard Deviation; Gap=Higher-Lower; Ratio=Higher/Lower

9. RESULTS (2): Decomposition of Inequality in Payouts
(during first 3 cycles)
Predictors
Conc. Index -CI
(P-value)
OLS –regression
(P-value)
Elasticity Contribution
CI %
(1) (2) (3) (4) (5)
Facility in lower SES -0.493 (0.000) -5.9 (0.310) -0.054 0.028 49.8%
Dispensary level -0.130 (0.014) -4.4 (0.160) -0.056 0.007 13.7%
Baseline coverage 0.029 (0.556) 3.8 (0.000) 0.188 0.005 10.2%
Public owned -0.050 (0.041) -5.5 (0.250) -0.083 0.004 7.8%
Facility with CHF 0.028 (0.495) 6.4 (0.275) 0.091 0.003 4.8%
Higher Drug stock-out -0.026 (0.137) -5.2 (0.190) -0.042 0.001 2.0%
Higher Equipment 0.030 (0.386) 2.4 (0.600) 0.028 0.001 1.6%
Infrastructure available 0.153 (0.011) -0.1 (0.980) -0.001 -0.000 -0.3%
Residual 0.005 10.4%
Total N=75
R-squared=40.9
0.054** 100.0%
Note: Drug stock-out includes nine drugs (antimalarial and delivery drugs); Equipment is binary (high and low availability); infrastructure is availability
of both electricity and clean water; OLS estimates using bootstrapping approach in data clustering; same pattern in % contribution if other predictors are
added –distance (3.5%), patient-staff ratio (-9.0%) and intrinsic motivation (0.02%).

10. RESULTS (3): Distribution of service utilization effects
Outcome variable/
Grouping variable
Baseline level Difference in differences, effect
Intervention arm
(1)
Comparison arm
(2)
Better-off
(3)
Worse-off
(4)
Differential
(5)
Better Worse Gap Better Worse Gap N N
Facility based
delivery
Facility SES 90.6 81.4 9.2 (0.004) 89.1 82.6 6.5
(0.018)
2886 3.6* 2861 10.0*** -6.4 (0.141)
Ownership 84.6 85.2 -0.6 (0.868) 86.6 87.9 -1.3
(0.716)
4716 9.1*** 1031 4.9 4.2 (0.228)
Level of care 89.4 82.7 6.7 (0.134) 90.9 85.0 5.9
(0.020)
1712 5.4 4035 8.9*** -3.5 (0.584)
CHF 84.9 84.1 0.8 (0.795) 88.9 84.1 4.8
(0.059)
3833 7.6*** 1914 7.0* 0.6 (0.911)
Infrastructure 87.2 81.7 5.5 (0.118) 88.3 85.2 3.1 (0.223) 3077 7.0** 2670 9.0*** -2.0 (0.545)
Drug stock out 85.9 83.7 2.2 (0.532) 87.3 85.9 1.4 (0.561) 3095 5.2** 2652 11.0*** -5.8 (0.205)
Equipment 88.0 78.5 9.5 (0.032) 86.4 87.4 -1.0
(0.710)
2192 13.0*** 3555 5.6** 7.4 (0.164)
IPT2
Facility SES 45.6 51.6 -6.0 (0.100) 54.6 60.6 -6.0
(0.074)
2430 9.8** 2329 13.0*** -3.2 (0.659)
Ownership 50.2 46.3 3.9 (0.531) 57.6 53.0 4.6 (0.313) 3904 10.0*** 855 11.0 -0.9 (0.916)
Level of care 42.4 52.7 -10.3
(0.009)
60.5 54.9 5.6 (0.124) 1450 14.0** 3309 8.3** 5.7 (0.250)
CHF 49.5 49.8 -0.3 (0.950) 60.4 52.2 8.2
(0.023)
3162 12.0*** 1597 9.2 2.8 (0.656)
Infrastructure 47.4 52.4 -4.9 (0.159) 57.9 55.2 2.7 (0.453) 2609 11.0*** 2150 10.0** 1.0 (0.761)
Drug stock out 47.0 52.8 -5.8 (0.107) 55.5 57.9 -2.4
(0.499)
2539 11.0*** 2220 8.6** 2.4 (0.628)
Equipment 48.2 52.3 -4.3 (0.254) 55.8 57.9 -2.1
(0.552)
3000 11.0*** 1759 8.3 2.7 (0.608)
Notes: P-values in parentheses; Better-off facilities are those in high SES, public, non-dispensary, with CHF and infrastructure, low drug stock-out and
high equipment availability; the coefficient is the estimated intervention effect controlling for a year dummy, facility-fixed effects, individual-level and
household characteristics; * p<0.10, ** p<0.05, *** p<0.01;

11. Conclusions
Distribution of facility payouts [payout score]
• Payout scores were increasing over time among all facilities
• Payouts were initially pro-rich, but improved over time
• Main contributors to initial inequality in payouts (decomposition)
▪ Facility SES, level of care, baseline level of performance,
ownership
Distribution of utilization effects [deliveries & IPT2]
• No evidence of differential effects, but indication of:
▪ Stronger effect on lower SES facilities (both outcomes)
▪ Stronger effect on deliveries among dispensaries
▪ Stronger effect on IPT among better resourced facilities

12.
Thank you..!!