Piece Rates, Fixed Wages, and Incentive Effects: Statistical Evidence from Payroll Records

TLDR

The model implies optimal decision rules for a firm’s choice of compensation system as a function of working conditions. We develop and estimate an agency model of worker behavior under piece rates and fixed wages. The model provides upper and lower bounds on the incentive effect that can be estimated via regression methods. Using daily productivity data from a British Columbia tree‑planting firm, we estimate bounds of an 8.8 % and 60.4 % productivity increase, and structural estimation suggests a 22.6 % increase, though part of this is offset by quality reductions.

Abstract

We develop and estimate an agency model of worker behavior under piece rates and fixed wages. The model implies optimal decision rules for the firm's choice of a compensation system as a function of working conditions. Our model also implies an upper and lower bound to the incentive effect (the productivity gain realized by paying workers piece rates rather than fixed wages) that can be estimated using regression methods. Using daily productivity data collected from the payroll records of a British Columbia tree‐planting firm, we estimate these bounds to be an 8.8 and a 60.4 percent increase in productivity. Structural estimation, which accounts for the firm's optimal choice of a compensation system, suggests that incentives caused a 22.6 percent increase in productivity. However, only part of this increase represents valuable output because workers respond to incentives, in part, by reducing quality.