Unlike traditional firm production, gig economy workers provide their own physical capital. As a consequence, the low‐income households for whom gig economy opportunities are most valuable often borrow to participate. In the context of ride share, difference‐in‐difference analysis reveals increased vehicle purchases, borrowing, utilization, and employment around entry, but financially constrained individuals cannot participate. To assess the equilibrium importance of financing, I build and estimate a structural model of the gig economy. Access to finance proves critical for the gig economy's growth: without finance, equilibrium quantities would be 40% lower and prices 90% higher, and only higher‐income households could participate as drivers.

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We study the frictions in dealer-intermediation in residential real estate through the lens of "iBuyers," technology entrants, who purchase and sell residential real estate through online platforms. iBuyers supply liquidity to households by allowing them to avoid a lengthy sale process. They sell houses quickly and earn a 5% spread. Their prices are well explained by a simple hedonic model, consistent with their use of algorithmic pricing. iBuyers choose to intermediate in markets that are liquid, and in which automated valuation models have low pricing error. These facts suggest that iBuyers' speedy offers may come at the cost of information loss concerning house attributes that are difficult to capture in an algorithm, resulting in adverse selection. We calibrate a dynamic structural search model with adverse selection to understand and quantify the economic forces underlying the tradeoffs of dealer intermediation in this market.

We empirically document two adjustment margins that are usually absent from the predominant “bank balance sheet lending” view of financial intermediation. For the shadow bank substitution margin, shadow banks substitute for traditional banks among loans that are easily sold. For the balance sheet retention margin, banks switch between balance sheet lending and selling loans based on their balance sheet strength. Estimates from a structural model show that these margins significantly shape policy responses, dampening the effect of capital requirements on lending whose costs are borne by wealthier borrowers. Secondary-market disruptions such as quantitative easing have significantly larger impacts on lending than capital requirements.

Open banking (OB) empowers bank customers to share their financial transaction data with fintechs and other banks. New cross-country data shows 49 countries adopted OB policies, privacy preferences predict policy adoption, and adoption spurs fintech entry. UK microdata shows that OB enables: (i) consumers to access both financial advice and credit; (ii) SMEs to establish new lending relationships. In a calibrated model, OB universally improves welfare through entry and product improvements when used for advice. When used for credit, OB promotes entry and competition by reducing adverse selection, but higher prices for costlier or privacy-conscious consumers partially offset these benefits.