In a data economy, transactions of goods and services generate data, which is stored, traded and depreciates. How are the economics of this economy different from traditional production economies? How do these differences matter for measurement of GDP, firm values, depreciation rates, welfare and externalities? We incorporate active experimentation and data as an intangible asset to devise a tractable recursive representation of the data economy. The model rationalizes why apps are often “free” and why even non-digital economic activity might be greater than GDP suggests. Calibrating the model using a combination of macroeconomic and financial moments suggests that the mis-measurement in U.S. GDP due to missing value of data has been as high as 6% in 2018.

We study a model of over-the-counter trading in which ex ante identical traders invest in a contact technology and participate in bilateral trade. We show that a rich market structure emerges both in equilibrium and in an optimal allocation. There is continuous heterogeneity in market access under weak regularity conditions. If the cost per contact is constant, heterogeneity is governed by a power law and there are middlemen, market participants with unboundedly high contact rates who account for a positive fraction of meetings. Externalities lead to overinvestment in equilibrium, and policies that reduce investment in the contact technology can improve welfare. We relate our findings to important features of real-world trading networks.