Causality, instruments and global health policy Rodrigo Moreno-Serra Department of Economics, University of Sheffield London, 01 July 2015

The Universal Health Coverage (UHC) debate Repeated calls for expansions in health system coverage (e.g., WHO 2010; Lancet 2010; UN 2012) UHC (WHO 2010) : access to needed health services of sufficient quality to be effective, without financial hardship

The Universal Health Coverage (UHC) debate So progress towards UHC requires: Higher prominence of pooled (pre-paid) health spending Enhanced (effective) access to care Argued to be linked to better population health, but where is the cross-country evidence?

Aims and empirical concerns Research question: Do higher pooled health spending and broader access to care lead to better population outcomes? Potential endogeneity of system coverage measures - unobserved cross-country heterogeneity - reverse causality or simultaneity

Our econometric approach Start with a basic panel data model that allows for country-specific unobserved effects (1) where: y = health outcome (mortality rates) C = vector of coverage indicators (pooled spending, immunisation coverage) Changes in indicators: fixed-effects estimation But limited ability to deal with endogeneity of coverage: simultaneity

Instrumental variables (IV) estimation in two steps First step: IV estimation of (reverse) causal effect of mortality on health system coverage (2) Need valid/relevant instruments for mortality: CO2 emissions per capita Number of battle-related deaths GMM estimation to obtain consistent for each coverage indicator Our econometric approach

IV estimation in two steps Second step: IV estimation of the causal effect of system coverage on mortality Construct adjusted series of coverage indicators (3) Use as instrument for corresponding coverage indicator in equation (1)equation (1) 2SLS estimation to obtain consistent Our econometric approach

IV estimation: Second step Note: Bold entries indicate coefficients statistically significant at the 10% level of confidence or below.

Notes: Elasticities relative to the observed average in the data. Models estimated through two-stage least squares. VHI = private voluntary health insurance; OOP = private out-of-pocket. Incremental effects expressed in deaths per 1,000. No effect = no statistically significant effect is found in the baseline model. Significant effect not robust = a statistically significant effect is found in the baseline model but not across robustness tests. Larger public spending effects on under-five mortality for low & middle-income countries (x1.5) Main IV results: Summary

Conclusions Broader health coverage (access, financial protection) improves population health Additional health funds lead to larger health gains if pooled and pre-paid, rather than spent out-of- pocket Increased reliance on pooled pre-payment leads to population health gains Results are averages: particular country stories?

Additional slides

Descriptive statistics

IV estimation: First step (under-five mortality) Note: Instruments are CO2 emissions and number of battle-related deaths.

First step IV: Just-identified models without weaker instrument Panel C: Just-identified model with stronger instrument (weaker instrument included as covariate) Government health spendingOOP health spendingVHI health spendingImmunization coverage IV-2SLS (9)(10)(11)(12) Under-five mortality rate [0.055][0.370][0.279][0.840] CO2 emissions Conflict deaths (0.213)(0.178)(0.475)(0.000) Country fixed effects Yes Year fixed effects Yes Single instrument (stronger) CO2 emissions First stage under-identification LM test (p-value) Number of countries 153 Observations 1,3981,397 1,398

Are the estimated magnitudes important? For an extra $1 PuHE per capita… Average country Total additional spending $32.5 million Deaths per 1,000 averted Lives saved451 Years of life saved30,443 Spending per life saved $72,042 Marginal cost of saving a year of life (under-five) $1,067