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Infrastructure · indicator 16

Operational airports

Modi ahead

Rahul India

1.1m

synthetic 2023

Modi India

1.2m

actual 2023

Difference

+12%

vs counterfactual

Placebo rank

14/30

p = 0.47

What this measures

Operational airports is take-offs by registered carriers, standing in for how much of the country aviation reaches. A rising number is an improvement.

India stood at 689,094 in 2013, before the two paths separate. By 2023 Modi’s current India had risen to 1.2m, while Rahul’s India — the blend of comparison countries that tracked India before 2014 — reached 1.1m.

That leaves Modi’s current India 12% higher than Rahul’s India — better than where comparable economies ended up. But Rahul’s India missed India’s own pre-2014 path by 15.8%, so this difference should not be read as an effect.

Both paths, 1984–2023

Weights fitted on 1984-2013 only; the counterfactual is projected across 2014-2023.

Modi India · actualRahul India · syntheticModi ahead

Divergence

Modi India minus Rahul India, in departures.

What Rahul’s India is made of

Donor weights, solved on pre-2014 data. 29 countries in the pool; those not listed carry zero weight.

  • South Africa
    57.6%
  • Indonesia
    19.9%
  • China
    17.4%
  • Ethiopia
    5.1%

Method & reliability

Measure
air_departures
Source type
Cross-country proxy
Estimation window
1984-2013
Projection window
2014-2023
Donor pool
29 countries
Pre-2014 fit (RMSPE)
15.8% of pre-period mean
Placebo rank
14/30
Standardised p-value
0.467
BH-adjusted q across tier 1
—

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.

Pre-2014 fit is 15.8% of the pre-period mean. The method could not reproduce India’s own path before 2014, so the post-2014 difference is not interpretable as an effect.

airport count -> registered carrier departures

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.

  1. 1Describe India before 2014

    India's operational airports is summarised over 1984-2013 — the value at five evenly spaced years starting 1984, plus three background controls averaged across the whole window: GDP per capita, urbanisation and life expectancy.

  2. 2Find the blend that matches it

    Each of the 29 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.

  3. 3Check the match is good enough

    The blend misses India's actual pre-2014 path by 15.8% on average. That is too loose to interpret — the post-2014 difference is not reported as an effect.

  4. 4Project it forward and compare

    The weights are frozen and applied to 2014-2023, years the optimiser never saw. That projection is Rahul's India. The difference against what actually happened is the gap shown above.

  5. 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 14/30 against those placebos, giving p = 0.467 — well within what chance produces, so this gap is not distinguishable from noise.

Why South Africa?

South Africa carries 58% of the weight here — not because it resembles India economically or politically, but because its operational airports moved like India’s did between 1984 and 2013. On 24 of the other 27 indicators on this site it carries no weight at all; where it does appear, the largest is global innovation index rank at 82%. 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 29 comparison countries carry any weight at all. The rest are assigned zero by the optimiser.

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.