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

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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.

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  • Minkyung Kim & K. Sudhir & Kosuke Uetake, 2019. "A Structural Model of a Multitasking Salesforce: Multidimensional Incentives and Plan Design," Cowles Foundation Discussion Papers 2199R, Cowles Foundation for Research in Economics, Yale University, revised Apr 2021.
  • Handle: RePEc:cwl:cwldpp:2199r
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    More about this item

    Keywords

    Salesforce compensation; Multitasking; Multidimensional incentives; Job design; Private information; Adverse selection;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • J33 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Compensation Packages; Payment Methods
    • L11 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Production, Pricing, and Market Structure; Size Distribution of Firms
    • L23 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Organization of Production
    • L14 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Transactional Relationships; Contracts and Reputation
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing
    • M52 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Personnel Economics - - - Compensation and Compensation Methods and Their Effects
    • M55 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Personnel Economics - - - Labor Contracting Devices

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