Incentive Design for Heterogeneous Gig Delivery Workers: A Stackelberg Framework with Windfall Decomposition

The rapid expansion of the gig economy has made the efficient management of independent contractors a critical operational challenge. Gig delivery platforms frequently spend heavily on zone-level financial bonuses to stabilize workforce supply. However, a substantial share of this spending inadvertently accrues to workers who would have participated without additional compensation-an inefficiency we term a 'windfall' transfer. To address this, we model the incentive-allocation problem as a rigorous Stackelberg game. The platform acts as the leader, setting zone-specific bonuses under a budget constraint, while heterogeneous workers respond through a logit congestion game that produces endogenous spatial competition. Our analytical results establish two critical boundaries: a no-spend threshold, representing a workforce composition beyond which all bonus spending becomes wasteful, and a zone-concentration condition, defining the demand asymmetry beyond which optimal spending concentrates in a single zone. Comprehensive computational experiments on synthetic instances, parameterized using published elasticity estimates and empirical labor survey data, yield two primary findings. First, the windfall share is governed almost entirely by workforce composition rather than the allocation policy, demonstrating that spatial targeting cannot effectively screen out infra-marginal workers. Second, the Stackelberg optimizer's advantage stems from selectively withholding spending in zones where windfall costs exceed activation benefits, improving overall profit by approximately 40% over the no-bonus baseline. Ultimately, these results suggest that the binding constraint on incentive effectiveness is the inherent share of already-committed workers in the labor pool, rather than the absolute size or spatial allocation of the incentive budget.