We use data on all manufacturer funding and all promotion activity by a major US retailer during a two-year period to compute promotion pass-through and assess its magnitude. Then we estimate a two-tiered probit and lognormal regression model to study drivers of the large variation we observe in pass-through rates. Although our analysis is based on data from a single retailer, it provides a much more complete picture of the magnitude and variability of pass-through than has been available to date. Some key insights from our work are as follows. First, the retailer passes through more than 100% of the total manufacturer funding it receives in aggregate but the median pass-through rate for individual manufacturers is much lower than 100%. Second, some manufacturers are promoted even without funding. This is more likely for private label and high share manufacturers in high lift and high margin categories. Third, a small number of manufacturer and category characteristics explain a significant amount of the variation in pass-through. In particular, passthrough is higher for private label. It increases with the share of the manufacturer in the focal category but also with the sales of that manufacturer in other categories. And categories with high sales, high promotion lift, low concentration, and low margin get more pass-through. We are able to corroborate some recent conclusions in the literature, e.g., that some pass-through rates are much higher than 100% and high share manufacturers get more pass-through. We add several new insights, e.g., on the magnitude of aggregate pass-through, cross pass-through across and within categories, pass-through for private label, and the category drivers of pass-through.