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Causality, instruments and
global health policyRodrigo Moreno-Serra
Department of Economics, University of Sheffield
[email protected] London, 01 July 2015
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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
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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?
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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
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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
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• 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
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• IV estimation in two stepsSecond 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)• 2SLS estimation to obtain consistent
Our econometric approach
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IV estimation: Second step
Note: Bold entries indicate coefficients statistically significant at the 10% level of confidence or below.
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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.
Government
health spending per capita
VHI health spending
per capita
OOP health spending
per capita
OOP health spending (share of total)
Immunization coverage rate
Under-five mortality rate (-) 7.9 per 1,000 No effect. No effect. No effect. Negative significant effect not robust.
Female mortality rate (adult) (-) 1.6 per 1,000 No effect. (-) 4.4 per 1,000 (+) 11.6 per 1,000 (-) 8.5 per 1,000
Male mortality rate (adult) (-) 1.3 per 1,000 No effect. (-) 2.9 per 1,000 (+) 13.6 per 1,000 (-) 6.8 per 1,000
For a 10% increase in:
• Larger public spending effects on under-five mortality for low & middle-income countries (x1.5)
Main IV results: Summary
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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?
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Additional slides
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Descriptive statistics
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IV estimation: First step(under-five mortality)
Note: Instruments are CO2 emissions and number of battle-related deaths.
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First step IV: Just-identified models without weaker instrument
Panel C: Just-identified model with stronger instrument (weaker
instrument included as covariate)
Government health
spending OOP health spending VHI health spending Immunization coverage
IV-2SLS IV-2SLS IV-2SLS IV-2SLS
(9) (10) (11) (12)
Under-five mortality rate 0.700 0.052 0.073 0.009 [0.055] [0.370] [0.279] [0.840]
CO2 emissions
Conflict deaths 0.0018 0.0002 0.0001 0.0011 (0.213) (0.178) (0.475) (0.000)
Country fixed effects Yes Yes Yes YesYear fixed effects Yes Yes Yes Yes
Single instrument (stronger) CO2 emissions CO2 emissions CO2 emissions CO2 emissionsFirst stage under-identification LM test (p-value) 0.079 0.079 0.079 0.079Number of countries 153 153 153 153
Observations 1,398 1,397 1,397 1,398
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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 0.132
Lives saved 451
Years of life saved 30,443Spending per life saved $72,042
Marginal cost of saving a year of life (under-five)
$1,067