  Crowdsourcing platforms offer a way to label data by aggregating answers of
multiple unqualified workers. We introduce a \textit{simple} and \textit{budget
efficient} crowdsourcing method named Proxy Crowdsourcing (PCS). PCS collects
answers from two sets of workers: \textit{leaders} (a.k.a proxies) and
\textit{followers}. Each leader completely answers the survey while each
follower answers only a small subset of it. We then weigh every leader
according to the number of followers to which his answer are closest, and
aggregate the answers of the leaders using any standard aggregation method
(e.g., Plurality for categorical labels or Mean for continuous labels). We
compare empirically the performance of PCS to unweighted aggregation, keeping
the total number of questions (the budget) fixed. We show that PCS improves the
accuracy of aggregated answers across several datasets, both with categorical
and continuous labels. Overall, our suggested method improves accuracy while
being simple and easy to implement.
