\begin{table}[htbp]\centering\small
\caption{H5 --- Matched-product price dispersion across merchants}\label{tab:h5}
\begin{adjustbox}{max width=\textwidth}
\begin{tabular}{lrr}\toprule
Statistic & primary\_AB & sens\_ABC \\\midrule
Observations & 2\,203 & 2\,512 \\
Match groups & 1\,064 & 1\,189 \\
Stores & 143 & 156 \\
Regions (with $\ge$ 5 matched stores) & 15 (9) & 15 (9) \\
Wald statistic, region effects (df) & 164.2 (14) & 83.2 (14) \\
\quad $p$, wild cluster bootstrap & 0.0975 & 0.4114 \\
Reseller / collective store (SE) & -0.116 (0.026) & -0.101 (0.025) \\
\quad $p$, wild cluster bootstrap & 0.0041 & 0.0170 \\
Certified store (SE) & -0.072 (0.023) & -0.028 (0.018) \\
log catalogue size (SE) & 0.001 (0.008) & 0.009 (0.007) \\
Within-group $R^2$ & 0.166 & 0.139 \\
Within-group gap: median / p90 & 0.047 / 0.288 & 0.051 / 0.333 \\
Share of groups with gap $>$ 5\% & 0.49 & 0.50 \\
Share of groups with identical prices & 0.29 & 0.29 \\
\bottomrule\end{tabular}
\end{adjustbox}
\begin{minipage}{0.95\textwidth}\vspace{2pt}\footnotesize\textit{Notes:} Dependent variable: log posted price at the snapshot. Match-group fixed effects absorbed; region and platform dummies, log catalogue size, reseller/collective and certification indicators. SE clustered by store; $p$-values from a wild cluster bootstrap (Rademacher, 9\,999 draws, restricted residuals). primary\_AB = tiers A (shared SKU) and B (same brand, identical title); sens\_ABC adds fuzzy tier C. Gap = within-group max--min log price.\end{minipage}
\end{table}