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

Cowles Foundation Discussion Paper No. 2199R

55 Pages Posted: 22 Apr 2021

See all articles by Minkyung Kim

Minkyung Kim

UNC Chapel Hill Kenan-Flagler Business School

K. Sudhir

Yale School of Management; Yale University-Department of Economics; Yale University - Cowles Foundation

Kosuke Uetake

Yale School of Management

Multiple version iconThere are 2 versions of this paper

Date Written: April 20, 2021

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

JEL Classification: C61, J33, L11, L23, L14, M31, M52, M55

Suggested Citation

Kim, Minkyung and Sudhir, K. and Uetake, Kosuke, A Structural Model of a Multitasking Salesforce: Multidimensional Incentives and Plan Design (April 20, 2021). Cowles Foundation Discussion Paper No. 2199R, Available at SSRN: https://ssrn.com/abstract=3831260 or http://dx.doi.org/10.2139/ssrn.3831260

Minkyung Kim

UNC Chapel Hill Kenan-Flagler Business School ( email )

300 Kenan Drive
Chapel Hill, NC 27599
United States

K. Sudhir (Contact Author)

Yale School of Management ( email )

135 Prospect Street
P.O. Box 208200
New Haven, CT 06520-8200
United States
203-432-3289 (Phone)
203-432-3003 (Fax)

Yale University-Department of Economics ( email )

28 Hillhouse Ave
New Haven, CT 06520-8268
United States

Yale University - Cowles Foundation ( email )

Box 208281
New Haven, CT 06520-8281
United States

Kosuke Uetake

Yale School of Management ( email )

135 Prospect Street
P.O. Box 208200
New Haven, CT 06520-8200
United States

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