Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Pay-for-Performance and Distributional Effects in Tanzania: A Supply-side Assessment - Peter Binyaruka

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

Related Books

Free with a 30 day trial from Scribd

See all

Related Audiobooks

Free with a 30 day trial from Scribd

See all
  • Be the first to comment

  • Be the first to like this

Pay-for-Performance and Distributional Effects in Tanzania: A Supply-side Assessment - Peter Binyaruka

  1. 1. Pay-for-Performance and Distributional Effects in Tanzania: A Supply-side Assessment Peter Binyaruka Nov, 2015
  2. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 12. 
 
 
 
 Thank you..!!

×