I study a model of the financial sector in which intermediation among debt-financed banks gives rise to an endogenous core-periphery network. Endogenous intermediation generates excessive systemic risk in the financial network. Financial institutions have incentives to capture intermediation spreads through strategic borrowing and lending decisions. By doing so, they tilt the division of surplus along an intermediation chain in their favor, while at the same time reducing aggregate surplus. The network is inefficient relative to a constrained-efficient benchmark, since banks that make risky investments “overconnect,” exposing themselves to excessive counterparty risk, while banks that mainly provide funding end up with too few connections.

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.

Endogenous cycles emerge through the two-way interaction between lending standards and production fundamentals. Lax lending standards in booms lead to low interest rates and high output but the deterioration of future loan quality. Low borrower quality in turn precipitates tight standards: the economy enters a recession with high credit spreads and low output but a gradual improvement in the quality of loans. This eventually triggers a shift back to a boom with lax lending, and the cycle continues. The capitalization of expert investors determines the strength of capital reallocation in recessions. Furthermore, although the constrained efficient economy is often cyclical, it features both a static and a dynamic externality in credit supply, hence differing from the decentralized equilibrium.

We investigate the heterogeneous boom and bust patterns across countries that emerge as a result of global shocks. Our analysis sheds light on the emergence of core and periphery countries, and the joint determination of the depth of recessions and tightness of credit markets across countries. The model implies that interest rates are similar across core and periphery countries in booms, with larger credit and output growth in periphery countries. However, a common global shock that leads to a credit crunch across the globe gives rise to a sharper spike in interest rates and a deeper recession in periphery countries, while a credit flight to the core alleviates the adverse consequences in these countries. We explore the implication of the model about credit spreads, portfolio rebalancing, investment, non-performing debt and concentration of debt ownership during booms and busts, both in the time series and in the cross-section, and connect them to existing stylized facts. We further demonstrate how the anatomy of the global economy evolves as a result of aggregate demand and supply shocks to financing, such as a global saving glut.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

“Big data” financial technology raises concerns about market inefficiency. A common concern is that the technology might induce traders to extract others’ information, rather than to produce information themselves. We allow agents to choose how much they learn about future asset values or about others’ demands, and we explore how improvements in data processing shape these information choices, trading strategies and market outcomes. Our main insight is that unbiased technological change can explain a market-wide shift in data collection and trading strategies. However, in the long run, as data processing technology becomes increasingly advanced, both types of data continue to be processed. Two competing forces keep the data economy in balance: data resolve investment risk, but future data create risk. The efficiency results that follow from these competing forces upend two pieces of common wisdom: our results offer a new take on what makes prices informative and whether trades typically deemed liquidity-providing actually make markets more resilient.

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.

How should an investor value financial data? The answer is complicated because it depends on the characteristics of all investors. We develop a sufficient statistics approach that uses equilibrium asset return moments to summarize all relevant information about others’ characteristics. Our approach values public or private data, data about one or many assets, and data relevant for dividends or sentiment. While different data types, of course, have different valuations, heterogeneous investors also value the same data very differently. This finding suggests a low price elasticity for data demand. Heterogeneous investors’ data valuations are also affected very differentially by market illiquidity.

Since the finance industry is transforming into a data industry, measuring the quantity of data investors have about various assets is important. Informed by a structural model, we develop such a cross-sectional measure. We show how our measure differs from price informativeness and use it to document a new fact: data about large high-growth firms is becoming increasingly abundant, relative to data about other firms. Our structural model offers an explanation for this data divergence: large high-growth firms’ data became more valuable, as big firms got bigger and growth magnified the effect of these changes in size.