A country’s national income broadly depends on the quantity and quality of workers and capital. But how well these factors are managed within and between firms may be a key determinant of a country’s productivity and its GDP.

The rapid adoption of remote work led to a sharply reduced presence of office workers in urban centers, weakening cities' traditional role as a center for production. Despite the adverse effect of remote work on cities, we highlight that cities' role as a center for consumption remains strong and may have risen with increased time flexibility from workers.

The case study "Electronic Financial-Advisor for Tech Savvy" (EFforTS, or Efforts) examines a Robo-Advisor start-up based in Raleigh, North Carolina, founded by tech-industry entrepreneurs. Efforts developed an algorithm-based online investment platform tailored for technology professionals, gaining attention through successful social media marketing.

We present a classical enhancement to improve the accuracy of the Hybrid variant (Hybrid HHL) of the quantum algorithm for solving linear systems of equations proposed by Harrow, Hassidim, and Lloyd (HHL). We achieve this by using higher precision quantum estimates of the eigenvalues relevant to the linear system, and a new classical step to guide the eigenvalue inversion part of Hybrid HHL.

Residential Property Assessed Clean Energy (PACE) loans allow homeowners to fund investments in green residential projects through property tax payments. We assess the housing market effects of PACE using novel loan-level data from Florida merged to property transaction, tax, and permitting records.

Short sellers are widely known to be informed, which would typically suggest that they demand liquidity. We obtain comprehensive transaction-level data to decompose daily short volume into liquidity-demanding and liquidity-supplying components. Contrary to conventional wisdom, we show that the most informed short sellers are actually liquidity suppliers, not liquidity demanders.

Short sellers are widely known to be informed, which would typically suggest that they demand liquidity. We obtain comprehensive transaction-level data to decompose daily short volume into liquidity-demanding and liquidity-supplying components. Contrary to conventional wisdom, we show that the most informed short sellers are actually liquidity suppliers, not liquidity demanders.

We study commitments to reduce emissions by firms subject to the European Union Emission Trading System (EU ETS), the world's largest cap-and-trade program. Commitments are associated with a drop in the number of carbon allowances surrendered, consistent with firms taking actions to reduce their emissions. However, firms subsequently increase their sales of allowances on the secondary market, transferring the right to pollute to others and potentially leaving aggregate emissions unchanged.

We show that stock returns follow predictable patterns before the publication of anomaly trading signals. Moreover, anomaly trading signals derived from financial data are themselves predictable, making it possible to trade before financial statements are released. A trading strategy based on predicted signals earns an annualized return of 3.65% in the quarter before the signal is released.

We use a search and matching model to study the heterogeneous welfare effects of housing market illiquidity due to mortgage lock-in over the lifecycle. We find that younger home buyers are disproportionately affected by mortgage lock-in, which disrupts their typical pattern of moving to higher-quality neighborhoods.

The paper studies the nowcasting of Euro area Gross Domestic Product (GDP) growth using mixed data sampling machine learning panel data regressions with both standard macro releases and daily news data.

This paper uses two large panel data sets in China to study the effects of a health shock on household income mobility from 1991 to 2016. We compare outcomes of households with a member who receives a health shock with comparable households that do not receive any health shocks.