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Ample literature builds on the notion that real estate values boost corporate secured borrowing (“collateral channel”). A comprehensive contract-level database allows us to observe the value, location, and end-use of firms’ real estate holdings in the US and all debts raised against those assets over the 2000–2017 period. Firms raise new debt following an increase in the value of their real estate but use unsecured rather than secured borrowing. We rationalize these findings with a model where firms’ choices between secured and unsecured debt reflect the systematic risk exposures of the assets on their balance sheets. While secured debt may be seen as a safer claim than unsecured debt contractually, we demonstrate that it can be riskier from an economic perspective. Our analysis adds new insight into how firms set their debt structure.

We show that the 2016 Brexit Referendum had multifaceted consequences for corporate America, shaping employment, investment, divestitures, R&D, and savings. The unexpected vote outcome led U.S. firms to cut jobs and investment within U.S. borders. Using establishment-level data, we document that these effects were modulated by the reversibility of capital and labor. American-based job destruction was particularly pronounced in industries with less skilled and more unionized workers. U.K.-exposed firms with less redeployable capital and high input-offshoring dependence cut investment the most. Data on the near universe of U.S. establishments also point to measurable, negative effects on establishment turnover (openings and closings). Our results demonstrate how foreign-born political uncertainty is transmitted across international borders, shaping domestic capital formation and labor allocation.

We show how uncertainty shapes the asset allocation, composition, productivity, and value of capital-intensive firms. We do so using detailed, near-universal data on shipping firms’ new orders, secondary-market transactions, and demolition of ships. Firms curtail both the acquisition and disposal of vessels in response to heightened uncertainty. The mechanism operates primarily through cuts in new ship orders and demolition of older ships — decisions that are costlier to reverse vis-a-vis deals in the used ship market. These dynamics are more pronounced when secondary ship markets are illiquid, as firms face stronger incentives to delay their decisions. The rise of Somali pirate attacks in 2009–2011 is used as a shock to well-defined shipping sectors to corroborate our findings. Critically, uncertainty prompts firms to concentrate their fleets into narrower, less productive portfolios, leading to value losses. Our work is novel in showing that uncertainty hampers "creative destruction," slowing both the adoption of innovation embodied in new capital and the disposal of old capital.

We show how uncertainty shapes the asset allocation, composition, productivity, and value of capital-intensive firms. We do so using detailed, near-universal data on shipping firms’ new orders, secondary-market transactions, and demolition of ships. Firms curtail both the acquisition and disposal of vessels in response to heightened uncertainty. The mechanism operates primarily through cuts in new ship orders and demolition of older ships — decisions that are costlier to reverse vis-a-vis deals in the used ship market. These dynamics are more pronounced when secondary ship markets are illiquid, as firms face stronger incentives to delay their decisions. The rise of Somali pirate attacks in 2009–2011 is used as a shock to well-defined shipping sectors to corroborate our findings. Critically, uncertainty prompts firms to concentrate their fleets into narrower, less productive portfolios, leading to value losses. Our work is novel in showing that uncertainty hampers "creative destruction," slowing both the adoption of innovation embodied in new capital and the disposal of old capital. 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 on job-vacancy postings reveal several dimensions of the impact of COVID-19 on the U.S. job market. Firms have cut back on postings for high-skill jobs more than for low-skill jobs, with small firms nearly halting their new hiring altogether. New-hiring cuts and downskilling are most pronounced in local labor markets lacking depth (where employment is concentrated within a few firms), in low-income areas, and in areas with greater income inequality. Cuts are deeper in industries where workers are more unionized and in the non-tradable sector. Access to finance modulates corporate hiring, with credit-constrained firms curtailing their job postings the most. Our study shows how the early-2020 global pandemic is shaping the dynamics of hiring, identifying the firms, jobs, places, industries, and labor markets most affected by it. Our results point to important challenges to the scale and speed of a recovery.

We show that the 2016 Brexit Referendum led American corporations to cut jobs and investment within US borders. Using establishment-level data, we document that these effects were modulated by the degree of reversibility of capital and labor. American job losses were particularly pronounced in industries with less skilled and more unionized workers. UK-exposed firms with less redeployable capital and high input-offshoring dependence cut investment the most. Data on the near-universe of US establishments also point to measurable, negative effects on establishment turnover (openings and closings). Our results demonstrate how foreign-born political uncertainty is transmitted across international borders, shaping domestic capital formation and labor allocation.

Big data on job vacancy postings reveal multiple dimensions of the impact of Covid-19 on corporate hiring. Firms disproportionately cut hiring for high-skill jobs (within-firm downskilling). Financially-constrained firms scaled back high-skill hiring most, as did firms with workforces more adaptable to "working-from-home." Applying machine learning to job-ad texts, we show that firms have skewed hiring towards operationally-core positions. New positions take longer to fill, displaying greater flexibility regarding schedules, tasks, and requirements. Financing constraints amplify pandemic-induced changes to the nature of positions firms seek to fill, with constrained firms' new hires witnessing larger adjustments to job roles and employment arrangements.

Innovation is difficult to finance as mere disclosure reduces its value. We study intellectual property licenses mandatorily disclosed by US public firms. Firms may temporarily redact information from these filings, offering a counterfactual to full-disclosure. Firms with valuable IP in competitive markets redact more often. Despite increased information asymmetry, investors react positively, inferring redaction signals IP worth hiding. Redactors experience better reactions when concealing tacit knowledge and worse reactions when IP is less protected (natural experiment) or redaction is less credible (repeated redaction, high competition). Our findings show credible non-disclosure partially resolves information frictions for innovative firms in public markets.

We study a novel database of intellectual property (IP) licensing agreements sourced from filings made by publicly-listed corporations, a large fraction of which firms (initially) disclose with redacted terms. In contrast to the benchmark that IP quality alone determines the pricing of IP, we argue that bargaining power plays a critical role and explains several patterns in observed royalty rates. Licensors with differentiated technology and high market power charge higher royalty rates, while larger-than-rival licensees pay lower royalty rates. Licensors command premium royalty rates for contractual features such as exclusivity. Finally, we employ this framework and setting to understand pricing implications of non-disclosure: licensors redact when they transact at lower royalty rates, consistent with preserving bargaining power for future negotiations. Our findings offer a new explanation for innovator secrecy and have several practical takeaways for transfer pricing and patent litigation.