Health and Health Care Delivery in Developing Countries

Health and Health Care Delivery in Developing Countries Michael Kremer Economics 1386 Fall 2006

The Determinants of Mortality • Cutler, Deaton, Llenas-Muney (2005) • Life expectancy increased 30% last century • Hunter-gatherers: 25 years • England 1700: 37 years • England 1820-1870: 41 years • UK today: 77 years • South Asia 2004: 63 years (WB WDI, 2006) • Vietnam today: higher life expectancy (69 years) than US in 1900 (47 years) but US in 1900 had 10 times higher per capita income than Vietnam today (Kremer 2002) • Sub-Saharan Africa 2004: 46 (WB WDI, 2006) • Why? Possible explanations: • Improved nutrition • Public health • Urbanization • Vaccination • Medical treatments • Early life factors

The Determinants of Mortality (II) • Still - differences between rich and poor countries • Progress in health until HIV/AIDS • Correlation between per capita income and mortality • Correlation within countries: richer, better educated people live longer. Why? • Unclear • Access to medical care • Resources for non-health care • Differences in health-related behaviors • Social structures, stress and health • Authors: • Scientific advance and technical progress • Less on direct causality of income

Health Care Delivery in Rural Rajasthan • Banerjee, Deaton and Duflo (2004) • Survey of 100 hamlets in Udaipur, collaboration with Seva Mandir (NGO) and Vidhya Bhavan (schools group) • Village survey, facility/traditional healer survey, weekly visit to facilities, and household/individual survey • Very poor region • Adult literacy: men 46%, women 11% • 21% of households have electricity

Health Care Delivery in Rural Rajasthan (II) • Poor health status • >50% of men and women anemic • 93% men and 88% women below US BMI cut-off for low nutrition • High self-reported disease symptoms • Health facility use • Adults visit 0.51 times per month • More for wealthier households • 0.12 to public facilities, 0.11 to bhopas, 0.28 to private facilities • 7% of household budget spent on health

Health Care Delivery in Rural Rajasthan (III) • Public Health Care Facilities • Official policy: one subcentre with one nurse per 3000 people, open 6 days per week, 6 hours per day, free care • 3600 people per subcentre. One primary health centre (PHC) with 5.8 medical personnel and 1.5 doctors per 48,000 people • 45% absenteeism in subcentre, 35% in larger PHCs • Subcentres closed 56% of time, unpredictable • 32% given injection, 6% given drip, 3% given test • 75% report that visit made them feel better

Health Care Delivery in Rural Rajasthan (IV) • Private Care • Poor training as doctors 41% have no medical degree, 18% have no medical training, 17% did not graduate from high school • 68% given injection, 12% given drip, 3% test • 81% report that visit made them feel better • Almost comparable costs even though public supposed to be free: • Public: Rs 71 • Private: Rs 84 • Bhopa: Rs 61 • Reported satisfaction compared with poor health outcomes suggests need for state involvement

Do Conditional Cash Transfers Improve Child Health? • Gertler (2004) on PROGRESA • Requirements for cash transfer: • Clinic visits for infants, young children, pregnant/lactating women • Vaccinations for infants • Yearly clinic checkups for all family members • Meetings on health, hygiene, nutrition for adults • Illness rate of treatment children 39.5% lower than control group • Treatment children almost 1 cm taller • Treatment children 25.5% less likely to be anemic

Contracting for Health:Evidence from Cambodia Elizabeth King Michael Kremer Benjamin Loevinsohn Brad Schwartz Indu Bhushan Erik Bloom David Clingingsmith Rathavuth Hong

Contracting in Cambodia • Many developing countries have mix of salaried government and private fee for service provision. Lots of problems: • Weak incentives of government providers (high absence rates) • Glucose drip problem: misalignment of private providers’ incentives with • patients’ interest (asymmetric information) • public health (inadequate attention to externalities from infectious disease, drug resistance) • Lack of risk sharing • In 1999, Cambodia tried experiment of tendering contracts for management of health services in certain districts • Monitored performance against 8 targeted outcomes • Increased public funding (offset by decreased private expenditure) • District level contracting could potentially: • Tie incentives to public health objectives • Allow benchmark competititon • Pool risks with limited adverse selection (in rural areas) • But could also lead to diversion of effort from important non-targeted activities.

Estimating program impact: • Call for bids in 8 districts, randomly chosen from 12 candidates • Acceptable bids in only some districts • IV with random assignment • Small number of districts • Clustering • Randomization inference • Average effects

Main results • Delivery of targeted services improved • Non-targeted services were unchanged • Some evidence of health improvement • Management of health centers improved. • Patients shifted to public providers • Effect on total spending (public and private) was neutral to negative.

Previous work • Keller and Schwartz (2001) • Bhushan, Keller, and Schwartz (2002) • Schwartz and Bhushan (2004) • Based on 2001 survey of districts where program actually implemented

Overview • Background on project and context • Model of health care provision • Empirical methods • Health services results • Targeted outcomes • Non-targeted outcomes • Health outcomes • Choice of provider • Health facility management • Consumer perception of care quality • Public and private health spending • Conclusion

Cambodian health context • Political background • 1975-1979 Khmer Rouge • 1979-1993 Vietnamese-backed regime • 1993 Elections; adoption of market economy • 1998 End of fighting • Health care system • Government health worker salary ~85% GDP/cap; politicization of civil service, governance issues • Boom in private medical practice after ‘93: • government staff moonlighting • drug sellers get about 33% of curative visits in 1997 • Traditional understanding of health, disease • Spending high; health service coverage poor • Project runs 1999-2003 • Huge improvements in health over study period. Health center construction.

Contracting Project I • Covered 11% of population • Responsible for Minimum Package of Activities • Targets on improvement of child and maternal health service coverage. • Prevention oriented

Two program variants • Contracting in (CI) • Management authority, but can’t hire/fire, procure outside government • Operating budget through government • Contracting out (CO) • Full control of staffing--hire and fire • Full control of procurement • Operating budget through ADB/WB loans

Bidding • Fixed price per capita bids; increased public spending • Technical criteria and price • 3 districts got no technically acceptable bids

HR practices under contracting I • Example: Peareng district, contracting in (CI) • Facilities signed annual contracts with NGO, workers 3-mo subcontracts. Private practice banned. • Staff motivation viewed as key problem • Additional payment on top of government salary • Fixed supplement, attendance, facility performance • Staff incentives based on targeted outcomes, patient satisfaction, quality of care, and no fraud

HR practices under contracting II • All contractors chose to use some expatriates. • Between 0.5 and 3.0 expatriate staff per contracted district. • Local staff between 90 and 150 per district.

HR practices under contracting III • Additional compensation for workers in all treated districts • Two officially banned private practice, three allowed it • Contractor compensation choices • Contracting in (CI): Base salary plus performance bonus, no provision for firing • Contracting out (CO): high fixed salaries, with possibility of firing non-performers • CO attracted some staff from outside district, outside government service

Randomization procedure • Three provinces with 3, 4, and 5 treatment-eligible districts respectively • Randomly assign CI, CO, and comparison district within each province. • Remaining 3 districts randomized in capital • Each district had equal probability of being CI, CO, comparison

Data • Baseline household survey in 1997, follow-up in 2003; facility survey in 2003 • 30 randomly selected villages in each of 12 districts; 7-14 households per village randomly chosen in each survey year • Household census, recent illnesses and treatment, program outcomes • Follow-up included health service quality module

Targeted outcomes at baseline

Randomization quality • Will see baselines as go through each outcome • Of baseline levels for 22 outcomes: • three significant for each of CI and CO under clustering • one significant for each of CI and CO under randomization inference

Holmstrom and Milgrom (1991)Model of health service provision • Suppose targeted and non-targeted outcomes produced by exerting various kinds of costly effort • Suppose only targeted outcome T is contractable; linear compensation contract in T • Increasing incentives for T may lead to more or less NT, depending on whether effort types relevant for T and NT are complements or substitutes • Either plausible

Econometric Issues • Selection into treatment • CO: 4 districts tendered, 3 awarded • CI: 4 districts tendered, 2 awarded • Previous analysis based on actual treatment status, not initial assignment • Perhaps NGOs focused bids on districts where gains would be easiest • Cluster-level intervention • Clustering, randomization inference • Family-level effects

Empirical method I • District-level intervention with individual outcomes • Randomly-assigned eligibility an instrument for actual treatment. • TOT for outcome k: • Instruments:

Empirical method II • District-level intervention with individual outcomes • Need to account for district level shocks • Clustering may over-reject null with small number of clusters • Randomization inference • Create full set placebo random assignments using actual randomization process. (Rosenbaum 2002) • Generate placebo treatment effect for each member of the set. • Use distribution of placebo treatment effects as test distribution. • Low power: imposes no structure on error

Randomization Inference

Empirical method III • Average effect size (AES) for family of K outcomes • Kling, Katz, Leibman, and Sonbanmatsu (2003), O’Brien (1984) • Joint estimation of TOT for K outcomes • Aggregate to get common unit of observation v • VCM estimates cross-equation correlation of effects • AES is the average treatment effect measured in standard deviation units. • We use the standard deviation of the change in outcome for comparison group.

Change in District Averages I

Change in District Averages II

TOT for changes in targeted outcomes Notes: IV regressions including province X year fixed effects. Average effects are differential increases caused by treatment in units of baseline comparison-group standard deviations. Standard errors presented in parentheses are corrected for clustering at the district level. Stars indicate significance under clustering: * at 10%; ** at 5%; *** at 1%. P-values for treatment effects computed by randomization inference.

Robustness check: wealth controls Notes: All IV regressions in Panel B include province X year fixed effects and wealth controls. Standard errors presented in parentheses are corrected for clustering at the district level. Stars indicate significance under clustering: * at 10%; ** at 5%; *** at 1%. P-values for treatment effects computed by randomization inference.

TOT for non-contracted outcomes Notes: All regressions include province X year fixed effects. Standard errors presented in parentheses are corrected for clustering at the district level. Stars indicate significance under clustering: * at 10%; ** at 5%; *** at 1%. P-values for treatment effects computed by randomization inference. Treatment effects are in bold.

TOT for final health outcomes Notes: All regressions include province X year fixed effects. Standard errors presented in parentheses are corrected for clustering at the district level. Stars indicate significance under clustering: * at 10%; ** at 5%; *** at 1%. P-values for treatment effects computed by randomization inference. Treatment effects are in bold.

TOT for changes in care-seeking outcomes Notes: IV regressions with provinceXyear effects. Standard errors presented in parentheses are corrected for clustering at the district level. Stars indicate significance under clustering: * at 10%; ** at 5%; *** at 1%. P-values for treatment effects computed by randomization inference. Average effect codes drug seller and traditional healer visits as negative and qualified private and public provider visits as positive.

TOT for health center management I Notes: All columns are IV regressions in levels with province fixed effects. Standard errors presented in parentheses are corrected for clustering at the district level. Stars indicate significance under clustering: * at 10%; ** at 5%; *** at 1%. P-values for treatment effects computed by randomization inference.

TOT for health center management II Notes: All columns are IV regressions in levels with province fixed effects. Standard errors presented in parentheses are corrected for clustering at the district level. Stars indicate significance under clustering: * at 10%; ** at 5%; *** at 1%. P-values for treatment effects computed by randomization inference.

Health center open with patients All scheduled staff present Child delivery service available User fees clearly posted Number of supervisor visits Number of outreach trips Index of equipment installed and functional Index of drugs and other supplies available All childhood immunizations available AES for 11 health center management outcomes Notes: Standard errors presented in parentheses are corrected for clustering at the district level. Stars indicate significance under clustering: * at 10%; ** at 5%; *** at 1%. P-values for treatment effects computed by randomization inference.

TOT for consumer perception of quality Notes: All regressions include province X year fixed effects. Standard errors presented in parentheses are corrected for clustering at the district level. Stars indicate significance under clustering: * at 10%; ** at 5%; *** at 1%. P-values for treatment effects computed by randomization inference. Treatment effects are in bold.

Health and Health Care Delivery in Developing Countries