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.
This paper uses newly collected panel data that allow for significant improvements in the measurement and modeling of information technology (IT) productivity to address some longstanding empirical limitations in the IT business value literature. First, we show that using generalized method of moments-based estimators to account for the endogeneity of IT spending produces coefficient estimates that are only about 10% lower than unadjusted estimates, suggesting that the effects of endogeneity on IT productivity estimates may be relatively small. Second, analysis of the expanded panel suggests that (a) IT returns are substantially lower in midsize firms than in Fortune 500 firms; (b) they materialize more slowly in large firms-in midsize firms, unlike in larger firms, the short-run contribution of IT to output is similar to the long-run output contribution; and (c) the measured marginal product of IT spending is higher from 2000 to 2006 than in any previous period, suggesting that firms, and especially large firms, have been continuing to develop new, valuable IT-enabled business process innovations. Furthermore, we show that the productivity of IT investments is higher in manufacturing sectors and that our productivity results are robust to controls for IT labor quality and outsourcing levels.