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Compared to general elections, research on presidentialprimaries is limited. A t the same time, the body of work on South Carolina's nomination contests pales in comparison to the volume of research on the Iowa caucus and New Hampshire primary. We seek to reconcile these imbalances by building a statistical model that predicts a candidate's vote share in the South Carolina primary and thus helps explain who wins and losses. We fin d that the key predictors of a candidate's vote share include: their volume of statewide endorsements, their share of media attention, their race (Democratic contests only), whether th y are from a neighboring state, their vote share in New Hampshire, and their vote share in Iowa (Republican contests only). In the end, our model explains 78% o f the variation in South Carolina primary outcomes from 1988 to 2016. Because this paper was motivated by the 2016 contests, at the end we discuss the implications of the model for our understanding of the 2016 results.

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