Stanford University


The Stanford IT Tables: Do IT Capabilities Still Drive Productivity and Innovation in the Digital Age?

In light of the increased prevalence of new information technologies, such as cloud computing and machine learning, traditional IT measures based on physical IT capital have become unreliable, while IT complementary human capital has become the new bottleneck. New IT technologies have thus, somewhat paradoxically, made the measurement of IT capabilities and their impact on firm productivity significantly harder than they already were. We create novel IT measurements based on industry-, and firm-level demands for IT skills and occupations from 2010 until 2022. Strong correlations with “official” productivity measures at the industry level validate our approach and suggest their usefulness at the firm level, where no official and reliable measures currently exist. We demonstrate that our measures are robustly associated with higher productivity at both the industry and firm levels, based on a battery of estimation techniques from the productivity literature. Our preferred firm-level estimation implies that a one percent increase in IT skills is associated with a 0.009 percent increase in total sales, which translates to an average gain of $540,000. Our measures are also positively associated with firm innovation, as measured by the total number of patents, citations, and real value of patents, suggesting that IT human capital drives productivity growth through innovation. Our methodology to define these human capital IT measures is general and simple enough to allow for future and backward-compatible extensions.

Data Dictionary – IT Metrics

Data files
Aggregated data for 2-Digit NAICS industries
Aggregated data for 6-Digit NAICS industries
Aggregated data for states
Aggregated data for counties (FIPS code)
– Aggregated data for public firms (pending Lightcast approval)
– Aggregated data for all firms (pending Lightcast approval)

If you are an individual or organization that utilizes this dataset, please cite this paper.

Stanford University