Causality, instruments and

global health policyRodrigo Moreno-Serra

Department of Economics, University of Sheffield

r.a.morenoserra@sheffield.ac.uk 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 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

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.

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

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

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

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