How can we design a market of human-generated content for use in training AI models that both enables technological progress and preserves individual incentives for high-quality content creation? Existing approaches take polar positions: a "free-for-all" model based on fair use and a "strong intellectual property rights" model. We show that both fail: Free-for-all does not compensate creators, and – by modeling as a static Stackelberg game – strong intellectual property rights also underpower creative incentives. We find this especially true for more innovative creators, a phenomenon we term the "originality penalty." Extending this insight to a dynamic model, we find another market failure undermining AI model performance, even for an initially good model: Such a model induces greater reliance by humans on AI-assisted creation, resulting in homogenized content feeding back into training, which degrades the model performance – a "curse of precision." We further propose a market design with a data intermediary internalizing cross-creator externalities and subsidizing innovative contributions, thereby restoring efficiency.

In a data economy, transactions of goods and services generate information, which is stored, traded and depreciates. How are the economics of this economy different from traditional production or innovation economies? How do these differences matter for measurement of GDP, firm values, depreciation rates, welfare and externalities? Despite incorporating active experimentation and data as an intangible asset, we devise a tractable recursive representation. Because the resulting model maps to many observable macro and finance measures, it can be calibrated and estimated like its old-economy DSGE counterpart. The model also delivers insights: It rationalizes why apps are often “free,” why firm size is diverging and why even non-digital economic activity might be greater than GDP suggests.

This paper shows that intermediation in asset markets may emerge exclusively because of rent extraction motives. Among dealers with heterogeneous bargaining skills, those with superior skills become intermediaries and constitute the core of the trading network, while those with inferior skills constitute the periphery. Intermediation is privately profitable because agents with superior bargaining skills can take positions and unwind them in the future at a better price than others could. Intermediation arises endogenously despite being socially worthless and the resources invested in bargaining skills are wasted. Using a dataset on the Indonesian interbank market, we document that prices vary with the centrality of buyers and sellers in a way that is uniquely consistent with our theory.

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.

The decentralized nature of blockchain markets has given rise to a complex and highly heterogeneous market structure, gaining increasing importance as traditional and decentralized (DeFi) finance become more interconnected. This paper introduces the DeFi intermediation chain and provides theoretical and empirical evidence for private information as a key determinant of intermediation rents. We propose a repeated bargaining model that predicts that profit share of Ethereum market participants is positively correlated with their private information, and employ a novel instrumental variable approach to show that a 1 percent increase in the value of intermediaries' private information leads to a 1.4 percent increase in their profit share.

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.

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.

Big data is changing every corner of economics and finance. The largest firms in the US economy are valued chiefly for their data. Yet, these data are largely excluded from macroeconomic and finance research. We review work and relevant tools for measuring economic activity, market power, data markets, and the role of data in financial markets. We also highlight areas where future work is needed.

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.

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.

We study the equilibrium and welfare in a rational model of endogenous cycles generated by the two-way interaction between investors’ choice of lending standards and real outcomes. When lenders optimally choose lax lending standards it leads to low interest rates, high output growth, but also to the deterioration of future credit application quality. When the quality is sufficiently low, lenders endogenously switch to tight standards. This implies high credit spreads and low output, but a gradual improvement in the quality of applications, which eventually triggers a shift back to lax lending standards and the cycle continues. The equilibrium cycle might feature a long boom, a lengthy recovery, or a double-dip recession. It is generically different from the constrained efficient cycle as atomistic lenders ignore their effect on the composition of the pool of borrowers. Carefully chosen macro-prudential or counter-cyclical monetary policy often improves the decentralized equilibrium cycle. JEL codes: D82, E32, E44, G01, G10

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.

The rise of information technology and big data analytics has given rise to "the new economy." But are its economics new? This article constructs a growth model where firms accumulate data, instead of capital. We incorporate three key features of data: 1) Data is a by-product of economic activity; 2) data is information used for prediction, and 3) uncertainty reduction enhances firm profitability. The model can explain why data-intensive goods or services, like apps, are given away for free, why many new entrants are unprofitable and why some of the biggest firms in the economy profit primarily from selling data. While our transition dynamics differ from those of traditional growth models, the long run still features diminishing returns. Just like accumulating capital, accumulating predictive data, by itself, cannot sustain long-run growth.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

We explore a model in which banks strategically hold interconnected and opaque portfolios, despite increasing the likelihood they are subject to financial crises. In our framework, banks choose their degree of exposure to other banks to influence how investors can use their information. In equilibrium banks choose portfolios which are neither fully opaque, nor fully transparent. However, their portfolios are excessively interconnected to obfuscate investor information. Banks can create a degree of opacity that decreases welfare, and makes bank crises more likely. Our model is suggestive about the implications of asset securitization, as well as government bailouts. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

In this paper, we examine the e↵ect of access to individual mobility on labor market outcomes. We exploit exogenous time-series variation in access to individual mobility through credit lotteries that randomly allocate credit designated for motorcycle purchase to participants of a financial product in Brazil. We find that upon access to credit, individuals exhibit higher formal employment rates and earnings. Consistent with a geographically broader job search, individuals move to jobs further away from home and harder to access by public transportation. Investment in individual mobility yields an annual rate of return of 15.6 percent over an individual’s career. JEL Codes: D14, G23, J62, R20, R23.

We propose a rational model of endogenous cycles generated by the two-way interaction between credit market sentiments and real outcomes. Sentiments are high when most lenders optimally choose lax lending standards. This leads to low interest rates and high output growth, but also to the deterioration of future credit application quality. When the quality is sufficiently low, lenders endogenously switch to tight standards, i.e. sentiments become low. This implies high credit spreads and low output, but a gradual improvement in the quality of applications, which eventually triggers a shift back to lax lending standards and the cycle continues. The equilibrium cycle might feature a long boom, a lengthy recovery, or a double-dip recession. It is generically different from the optimal cycle as atomistic lenders ignore their effect on the composition of the pool of borrowers. Carefully chosen macro-prudential or countercyclical monetary policy often improves the decentralized equilibrium cycle. 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. (JEL C55, D83, G12, G14, O33)

We develop a quantitative framework for exploring how individuals trade off the utility benefit of social activity against the internal and external health risks that come with social interactions during a pandemic. We calibrate the model to external targets and then compare its predictions with daily data on social activity, fatalities, and the estimated effective reproduction number R(t) from the COVID-19 pandemic in 2020. While the laissez-faire equilibrium is consistent with much of the decline in social activity in March in the US before any formal stay-at-home orders, optimal policy further imposes immediate and highly persistent social distancing. The expected cost of COVID-19 in the US is substantial, $12,700 in the laissez-faire equilibrium and $8,100 per person under an optimal policy. Optimal policy generates this large welfare gain by shifting the composition of costs from fatalities to persistent social distancing that largely suppresses the outbreak.