A Structural Model of a Multitasking Salesforce: Multidimensional Incentives and Plan Design

Minkyung Kim, K. Sudhir () and Kosuke Uetake
Additional contact information
Minkyung Kim: UNC Chapel Hill Kenan-Flagler Business School
K. Sudhir: Cowles Foundation & School of Management, Yale University, https://faculty.som.yale.edu/ksudhir/
Kosuke Uetake: School of Management, Yale University

No 2199R, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: 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.

Keywords: Salesforce compensation; Multitasking; Multidimensional incentives; Job design; Private information; Adverse selection (search for similar items in EconPapers)
JEL-codes: C61 J33 L11 L14 L23 M31 M52 M55 (search for similar items in EconPapers)
Pages: 55 pages
Date: 2019-09, Revised 2021-04
New Economics Papers: this item is included in nep-cmp and nep-hrm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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