\begin{table}[htbp]\centering\small
\caption{H3 --- Concentration of listings across merchants (assortment sample)}\label{tab:h3}
\begin{adjustbox}{max width=\textwidth}
\begin{tabular}{lr}\toprule
Statistic & Value \\\midrule
Stores / listings & 1\,557 / 370\,054 \\
Gini of listings across stores [95\% CI] & 0.849 [0.811; 0.878] \\
Share of listings, top 1\% of stores [95\% CI] & 0.443 [0.328; 0.526] \\
Share of listings, top 10\% of stores [95\% CI] & 0.789 [0.730; 0.836] \\
Median / p90 / p99 / max catalogue size & 35 / 312 / 3\,359 / 24\,104 \\
Rank--size slope (log--log) & -1.647 \\
Discrete power law: $x_{\min}$, $\alpha$, KS, tail $n$ & 165, 1.929, 0.028, 288 \\
Vuong LR (power law vs log-normal on the tail): $z$, $p$ & -1.98, 0.048 (log-normal) \\
Preregistered criterion (Gini $\ge$ 0.70 and top-10\% $\ge$ 0.50) & met \\
\bottomrule\end{tabular}
\end{adjustbox}
\begin{minipage}{0.95\textwidth}\vspace{2pt}\footnotesize\textit{Notes:} Bootstrap CIs from 2\,000 resamples of stores. Power-law fit by maximum likelihood with $x_{\min}$ chosen by KS minimisation (Clauset et al.\ 2009); the log-normal is fitted on the same tail. Inconnue-region stores included (all stores).\end{minipage}
\end{table}