Firms Diverged Faster than Households: Market-Capitalization Concentration in the S&P 500

The concentration of market capitalisation inside the S&P 500 rose sharply between 2016 and 2025, and it rose far faster than wealth concentration among American households. We establish this by treating index constituents as a population whose income is market capitalisation and applying the measurement apparatus of the income-distribution literature to annual cross-sections. Two construction choices do most of the work and are the paper’s first contribution: year-end membership is reconstructed from a dated historical constituent file rather than projected backwards from the current index, and prices and share counts are reconciled onto a common split basis. The two corrections do not work in the same direction. A roster projected backwards from today’s index produces a rising concentration trend whether or not concentration rose, while the split reconciliation raises the measured 2016 concentration rather than lowering it. A corrections waterfall quantifies each. Across 4443 firm–years, the share of index market capitalisation held by the five largest constituents rose from 13.4% to 30.0%, the Gini coefficient from 0.586 to 0.719, and the effective number of independent positions implied by the Herfindahl–Hirschman index fell from 112 to 43. The top-share trends survive imputation of every missing delisted firm at three plausible sizes, a balanced panel, and every specification of the inference we tried, including a bootstrap that resamples constituent firms rather than years at p<0.0001 throughout. The Gini trend is significant under every imputation at p≤0.0016, although, under the most adverse, its slope falls from 0.0133 to 0.0073 per year. The plug-in Gini understates inequality in fat-tailed samples, and the size of that correction depends on the distribution that is assumed to generate the data: it steepens the trend under a Pareto model, leaves it unchanged under a lognormal, and leaves it insignificant under a Pareto fitted at the Clauset–Shalizi–Newman exponent, so no corrected slope is quoted as the paper’s estimate. Over the same decade the top 1% share of US household net worth moved 0.65 percentage points, so firm concentration moved 24 to 30 times as far depending on the fixed-count basis used. An additive decomposition attributes 32% of the decade’s rise in the Theil index and 46% of the rise in the mean log deviation to the widening gap between digital and non-digital firms; the rest is dispersion within the two groups, so the movement is disproportionately but not wholly digital. Pareto tail indices are reported throughout as a diagnostic of tail shape rather than a headline result because the Hill and Clauset–Shalizi–Newman estimators of the same parameter disagree, a bootstrap goodness-of-fit test rejects the power law at the 5% level in seven of ten years, and the fitted trend does not survive a bootstrap that resamples firms rather than years. The second contribution is that negative finding, which bears on any study estimating tail indices from cross-sections of a few hundred firms.