Syndicated loan data provided by DealScan is an essential input in banking research to answer urging questions on bank lending, e.g., in the presence of financial or geopolitical shocks or climate change. However, many data options raise the question of how to choose the estimation sample. We employ a standard regression framework analyzing bank lending during the financial crisis of 2007/08 to study how conventional but varying usages of DealScan affect the estimates. The key finding is that the direction of coefficients remains relatively robust. However, statistical significance depends on the data and sampling choice, and we provide guidelines for applied research.

Based on a sufficient statistics approach, we show how the state of technology of European industries relative to the rest of the world can be empirically assessed in a way that is simple in terms of computation, parsimonious in terms of data requirements, but still comprehensive in terms of information. The lack of systematic cross-industry correlation between export specialization and technological advantage suggests that standard measures of revealed comparative advantage only imperfectly capture a country’s technological prowess due to the concurrent influences of factor prices, market size, markups, firm selection and market share reallocation. These findings offer policy insights relevant to the EU’s external competitiveness debate, echoing several recommendations from the Draghi report. Achieving export specialization in key sectors requires more than just technological superiority.

Vietnam, a lower-middle-income economy, faces severe climate risks from heat waves, sea-level rise, and tropical cyclones, which are expected to intensify under ongoing global warming. Using a dynamic general equilibrium model, we analyze economic transition dynamics from 2015 to 2100, incorporating heat-induced labor productivity losses, agricultural land loss, and cyclone-related property damage. We compare a Paris-compatible scenario limiting warming to below 2 °C with a high-emission scenario reaching 4–5 °C. While output and investment impacts remain highly uncertain and statistically indistinguishable across scenarios until 2100, consumption losses are significantly larger under high emissions, mainly driven by heat-related productivity declines, with cyclones contributing most to uncertainty. These findings underscore the importance of considering multiple impact channels beyond output damages in climate-development research.

Predicting IPO first-day returns is inherently challenging due to the wide range of contributing factors, each with distinct statistical properties. We assess the performance of several machine learning (ML) techniques and identify XGBoost as the most statistically effective model for forecasting first-day returns. Using a comprehensive set of 863 pre-IPO variables, our high-performing predictive model accurately estimates both the direction and magnitude of IPO first-day returns. The most influential predictors include underwriter agency measures, price revision, and the free-float fraction. Using a rolling-window predictive approach, the model demonstrates substantial practical value, generating approximately $300 billion in gains from IPOs with positive first-day returns and avoiding more than $22 billion in losses from those with negative returns over the 2000–2016 period.

IFR statistics show that Germany has consistently been a global top 5 robotics market for many years. They also provide distribution by industry. But what it does not show is who exactly is installing these robots and what distinguishes a robot user from a non-user. Data collected from nearly 16,000 plants by the Institute for Employment Research (IAB) of the Federal Employment Agency helps us to learn more about robot users in Germany.