The measurement of the impact of IT spillovers on productivity is an important emerging area of research. Studies of IT spillovers often adopt a “production function” approach commonly used for measuring R&D spillovers, in which an external pool of IT investment is modeled using weighted measures of the IT investments of other firms, industries, or countries. We show that when using this approach, measurement error in a firm’s own IT inputs can exert a significant upward bias on estimates of social returns to IT investment. This problem is particularly severe for IT spillovers because of the high levels of measurement error in most available IT data. The presence of the bias term can be demonstrated by using instrumental variable techniques to remove the effects of measurement error in a firm’s own IT inputs. Using panel data on IT investment, we show that measurement error corrected estimates of IT spillovers are 40% to 90% lower than uncorrected estimates. This bias term is increasing in the correlation between the IT pool and firms’ own IT investment. Therefore, estimates from models of spillover pools are less sensitive to the issues identified in this paper when the spillover paths minimize the correlation between a firm’s own IT investment and the constructed external IT pool. Implications for researchers, policy makers, and managers are discussed.