Rationalizing entrepreneurs’ forecasts

We analyze, benchmark, and run randomized controlled trials on a panel of 7,463 U.S. entrepreneurs making incentivized sales forecasts. We assess accuracy using a novel administrative dataset obtained in collaboration with a leading US payment processing firm. At baseline, only 13% of entrepreneurs can forecast their firm’s sales in the next three months within 10% of the realized value, with 7.3% of the mean squared error attributable to bias and the remaining 92.7% attributable to noise. Our first intervention rewards entrepreneurs up to $400 for accurate forecasts, our second requires respondents to review historical sales data, and our third provides forecasting training. Increased reward payments significantly reduce bias but have no effect on noise, despite inducing entrepreneurs to spend more time answering. The historical sales data intervention has no effect on bias but significantly reduces noise. Since bias is only a minor part of forecasting errors, reward payments have small effects on mean squared error, while the historical data intervention reduces it by 12.4%. The training intervention has negligible effects on bias, noise, and ultimately mean squared error. Our results suggest that while offering financial incentives make forecasts more realistic, firms may not fully realise the benefits of having easy access to past performance data.