Health · indicator 37
Catastrophic health spending (health-coverage proxy)
Modi aheadRahul India
40.71
synthetic 2022
Modi India
20.40
actual 2022
Difference
-50%
vs counterfactual
Placebo rank
1/18
p = 0.056
What this measures
Catastrophic health spending (health-coverage proxy) is the share of people spending more than a tenth of the household budget on health in a year. A falling number is an improvement.
India stood at 53.20 in 2013, before the two paths separate. By 2022 Modi’s current India had fallen to 20.40, while Rahul’s India — the blend of comparison countries that tracked India before 2014 — reached 40.71.
That leaves Modi’s current India 50% lower than Rahul’s India — better than where comparable economies ended up. Modi’s current India ranks 1/18 against placebo countries (p = 0.056), so the gap is larger than chance usually produces.
Both paths, 1984–2022
Weights fitted on 2003-2013 only; the counterfactual is projected across 2014-2022.
Divergence
Modi India minus Rahul India, in % of population.
What Rahul’s India is made of
Donor weights, solved on pre-2014 data. 17 countries in the pool; those not listed carry zero weight.
- China51.5%
- Côte d'Ivoire35.9%
- Bolivia9.6%
- Bangladesh3.0%
Method & reliability
- Measure
- catastrophic_health_pct
- Source type
- Cross-country proxy
- Estimation window
- 2003-2013
- Projection window
- 2014-2022
- Donor pool
- 17 countries
- Pre-2014 fit (RMSPE)
- 2.7% of pre-period mean
- Placebo rank
- 1/18
- Standardised p-value
- 0.056
- BH-adjusted q across tier 1
- 0.224
The q-value applies a Benjamini–Hochberg correction across all 20 indicators with a counterfactual. We report p as the primary figure because each indicator is treated as a separate question rather than one family of hypotheses — but no result on this site survives q ≤ 0.10, and a reader who prefers the family view should read the p-values accordingly.
Households covered by health insurance has no cross-country annual panel. Financial protection — the outcome health coverage is designed to produce — is measured instead.
How this was calculated
Nothing here is chosen by hand. The comparison countries are not picked for being like India — they are whatever mix best reproduces India’s own path before 2014 on this particular measure.
1Describe India before 2014
India's catastrophic health spending (health-coverage proxy) is summarised over 2003-2013 — the value at five evenly spaced years starting 2003, plus three background controls averaged across the whole window: GDP per capita, urbanisation and life expectancy.
2Find the blend that matches it
Each of the 17 comparison countries is described the same way. An optimiser then searches for the weighted mix of them whose pre-2014 path sits closest to India's. Weights cannot be negative and must sum to 100%, so the result is always a real combination of real countries — never an extrapolation.
3Check the match is good enough
The blend misses India's actual pre-2014 path by 2.7% on average. That is close enough to treat the projection as meaningful.
4Project it forward and compare
The weights are frozen and applied to 2014-2022, years the optimiser never saw. That projection is Rahul's India. The difference against what actually happened is the gap shown above.
5Test it against the other countries
The whole procedure is re-run pretending each comparison country was the one treated in 2014. India's break ranks 1/18 against those placebos, giving p = 0.056 — larger than chance would usually produce.
Why China?
China carries 51% of the weight here — not because it resembles India economically or politically, but because its catastrophic health spending (health-coverage proxy) moved like India’s did between 2003 and 2013. On 15 of the other 27 indicators on this site it carries no weight at all; where it does appear, the largest is services exports at 61%. The blend is re-solved from scratch for every indicator, which is why the same country can dominate one estimate and be absent from the next.
4 of 17 comparison countries carry any weight at all. The rest are assigned zero by the optimiser.
Related measures
Other series bearing on the same question, each with its own counterfactual.
pre-2014 fit 8.8% · rank 18/36 · p = 0.50
- Rahul
- 30.86
- Modi
- 39.02
- Difference
- +27%
pre-2014 fit 1.4%
- Rahul
- 46.58
- Modi
- 40.20
- Difference
- -14%
The counterfactual is 'India without the 2014 structural break', estimated from comparator countries. It is not a forecast of any individual's policies. Sources: World Bank WDI API (2026-07-13); IRENA via Our World in Data.