CFDP Revision Date
Journal of Economic Literature (JEL) Code(s)
C61, J33, L11, L23, L14, M31, M52, M55
The paper broadens the focus of empirical research on salesforce management to include multitasking settings with multidimensional incentives, where salespeople have private information about customers. This allows us to ask novel substantive questions around multidimensional incentive design and job design while managing the costs and beneﬁts of private information. To this end, the paper introduces the ﬁrst structural model of a multitasking salesforce in response to multidimensional incentives. The model also accommodates (i) dynamic intertemporal tradeoﬀs in eﬀort choice across the tasks and (ii) salesperson’s private information about customers. We apply our model in a rich empirical setting in microﬁnance and illustrate how to address various identiﬁcation and estimation challenges. We extend two-step estimation methods used for unidimensional compensation plans by embedding a flexible machine learning (random forest) model in the ﬁrst-stage multitasking policy function estimation within an iterative procedure that accounts for salesperson heterogeneity and private information. Estimates reveal two latent segments of salespeople- a “hunter” segment that is more eﬀicient in loan acquisition and a “farmer” segment that is more eﬀicient in loan collection. Counterfactuals reveal heterogeneous eﬀects: hunters’ private information hurts the ﬁrm as they engage in adverse selection; farmers’ private information helps the ﬁrm as they use it to better collect loans. The payoﬀ complementarity induced by multiplicative incentive aggregation softens adverse specialization by hunters relative to additive aggregation, but hurts performance among farmers. Overall, task specialization in job design for hunters (acquisition) and farmers (collection) hurts the ﬁrm as adverse selection harm overwhelms eﬀiciency gain.
Kim, Minkyung; Sudhir, K.; and Uetake, Kosuke, "A Structural Model of a Multitasking Salesforce: Multidimensional Incentives and Plan Design" (2019). Cowles Foundation Discussion Papers. 2614.