For the sectors with the **largest absolute Rotemberg weights** — the share instruments that actually drive the estimate — regresses each sector's share on observable unit characteristics to see how strongly exposure correlates with observables: the key credibility check when identification comes from the shares. Both Goldsmith-Pinkham, Sorkin & Swift (2020) and the Borusyak-Hull-Jaravel JEP practical guide recommend checking balance for the high-|Rotemberg-weight| shares, not the shares with the largest average exposure (which earlier versions used): a high-exposure sector with a near-zero weight contributes almost nothing to the estimate, and misspecification there is largely harmless.
Value
A `data.frame` of slope coefficients and (robust) t-statistics of each covariate in the share regression, one block per tested sector, with the sector's Rotemberg weight in `alpha`.
Examples
sim <- ssb_simulate(n_loc = 80, n_sec = 10, seed = 1)
d <- ssb_design(sim$data, sim$shares, sim$shocks, exogenous = "share")
ssb_share_balance(d, covariates = "w1", top = 3)
#> sector alpha covariate coef t
#> 1 6 0.5849517 w1 0.005229986 0.5201994
#> 2 3 0.1736046 w1 -0.004444499 -0.3769272
#> 3 8 0.1109170 w1 0.004130552 0.3064055