We document and dissect a new stylized fact about firm growth: the shift from labor to intermediate inputs. This shift occurs in input quantities, cost and output shares, and output elasticities. We establish this fact using German firm-level data and replicate it in administrative firm data from 11 additional countries. We also document these patterns in micro-aggregated industry data for 20 European countries (and, with respect to industry cost shares, for the US). We rationalize this novel regularity within a parsimonious model featuring (i) an elasticity of substitution between intermediates and labor that exceeds unity, and (ii) an increasing shadow price of labor relative to intermediates, due to monopsony power over labor or labor adjustment costs. The shift from labor to intermediates accounts for one half to one third of the decline in the labor share in growing firms (the remainder is due to wage markdowns and markups) and rationalizes most of the labor share decline in growing industries.

The substantial fluctuations in oil prices in the wake of the COVID-19 pandemic and the Russian invasion of Ukraine have highlighted the importance of tail events in the global market for crude oil which call for careful risk assessment. In this paper we focus on forecasting tail risks in the oil market by setting up a general empirical framework that allows for flexible predictive distributions of oil prices that can depart from normality. This model, based on Bayesian additive regression trees, remains agnostic on the functional form of the conditional mean relations and assumes that the shocks are driven by a stochastic volatility model. We show that our nonparametric approach improves in terms of tail forecasts upon three competing models: quantile regressions commonly used for studying tail events, the Bayesian VAR with stochastic volatility, and the simple random walk. We illustrate the practical relevance of our new approach by tracking the evolution of predictive densities during three recent economic and geopolitical crisis episodes, by developing consumer and producer distress indices that signal the build-up of upside and downside price risk, and by conducting a risk scenario analysis for 2024.

Promoting green product consumption is one important element in building a sustainable society. Yet green products are usually more costly. In times of high inflation, not only budget constraints but also the fear that prices will continue to rise might dampen green product consumption and, hence, limit the effectiveness of exerted efforts to promote sustainable behaviors. To test this suggestion, we conducted a Germany-wide survey with almost 1,200 respondents, followed by a framed field experiment (N=500) to confirm causality. In the survey, respondents’ stated “green” purchasing behavior is, as to be expected, positively correlated with concerns about climate change. It is also negatively correlated with concerns about future inflation and energy costs, but after controlling for observable characteristics such as income and educational level only the correlation with concerns about future prices remains significant. This result is driven by individuals with below-median environmental attitude. In the framed field experiment, we use the priming method to manipulate the saliency of inflation concerns. Whereas sizably relaxing the budget constraint (i.e., by 50 percent) has no impact on the share of organic products in participants’ baskets, the priming significantly decreases the share of organic products for individuals with below-median environmental attitude, similar to the survey data.

This paper compares macroeconomic effects of Knightian uncertainty and risk using policy shocks for the case of Italy. Drawing on the ambiguity literature, I use changes in the bid-ask spread and mid-price of government bonds as distinct measures for uncertainty and risk. The identification exploits the quasi-pessimistic behavior under ambiguity-aversion and the dealer market structure of government bond markets, where dealers must quote both sides of the market. If uncertainty increases, ambiguity-averse dealers will quasi-pessimistically quote higher ask and lower bid prices – increasing the bid-ask spread. In contrast, a pure change in risk shifts the risk-compensating discount factor which is well approximated by the change in bond mid-prices. I evaluate economic effects of the two measures within an instrumental variable local projection framework. The main findings are threefold. First, the resulting shock time series for uncertainty and risk are uncorrelated with each other at the intraday level, however, upon aggregation to monthly level the measures become correlated. Second, uncertainty is an important driver of economic aggregates. Third, macroeconomic effects of risk and uncertainty are similar, except for the response of prices. While sovereign risk raises inflation, uncertainty suppresses price growth – a result which is in line with increased price rigidity under ambiguity.

This paper contributes to a better understanding of the important role that credit demand plays for credit markets and aggregate macroeconomic developments as both a source and transmitter of economic shocks. I am the first to identify a structural credit demand equation together with credit supply, aggregate supply, demand and monetary policy in a Bayesian structural VAR. The model combines informative priors on structural coefficients and multiple external instruments to achieve identification. In order to improve identification of the credit demand shocks, I construct a new granular instrument from regional mortgage origination.
I find that credit demand is quite elastic with respect to contemporaneous macroeconomic conditions, while credit supply is relatively inelastic. I show that credit supply and demand shocks matter for aggregate fluctuations, albeit at different times: credit demand shocks mostly drove the boom prior to the financial crisis, while credit supply shocks were responsible during and after the crisis itself. In an out-of-sample exercise, I find that the Covid pandemic induced a large expansion of credit demand in 2020Q2, which pushed the US economy towards a sustained recovery and helped to avoid a stagflationary scenario in 2022.