We present causal evidence on the rent-sharing elasticity of German manufacturing firms. We develop a new firm-level Bartik instrument for firm rents that combines the firms’ predetermined energy input mix with national energy carrier price changes. Instrumental variable estimation yields a rent-sharing elasticity of approximately 0.20 implying that a 10 percent change in rents leads to a 2 percent change in wages. Rent-sharing induced by energy price variation is asymmetric and driven by energy price increases, such that, on average, workers do not benefit from energy price reductions but are harmed by price increases. Reduced-form evidence shows that a 10 percent increase in firm-level energy prices depresses firm-level wage growth by 0.34 percent.

A resolution of the missing deflation puzzle is proposed. Our resolution stresses the importance of nonlinearities in price- and wage-setting when the economy is exposed to large shocks. We show that a nonlinear macroeconomic model with real rigidities resolves the missing deflation puzzle, while a linearized version of the same underlying nonlinear model fails to do so. In addition, our nonlinear model reproduces the skewness of inflation and other macroeconomic variables observed in post-war U.S. data. All told, our results caution against the common practice of using linearized models to study inflation and output dynamics.

I derive a micro-founded framework showing how rising firm market power on product and labor markets and falling aggregate labor output elasticities provide three competing explanations for falling labor shares. I apply my framework to 20 years of German manufacturing sector micro data containing firm-specific price information to study these three distinct drivers of declining labor shares. I document a severe increase in firms’ labor market power, whereas firms’ product market power stayed comparably low. Changes in firm market power and a falling aggregate labor output elasticity each account for one half of the decline in labor's share.

Non-clearing goods markets are an important driver of capacity utilisation and total factor productivity (TFP). The trade-off between goods prices and household search effort is central to goods market matching and therefore drives TFP over the business cycle. In this paper, I develop a New-Keynesian DSGE model with capital utilisation, worker effort, and expand it with goods market search-and-matching (SaM) to model non-clearing goods markets. I conduct a horse-race between the different capacity utilisation channels using Bayesian estimation and capacity utilisation survey data. Models that include goods market SaM improve the data fit, while the capital utilisation and worker effort channels are rendered less important compared to the literature. It follows that TFP fluctuations increase for demand and goods market mismatch shocks, while they decrease for technology shocks. This pattern increases as goods market frictions increase and as prices become stickier. The paper shows the importance of non-clearing goods markets in explaining the difference between technology and TFP over the business cycle.

The Swiss National Bank abolished the exchange rate floor versus the Euro in January 2015. Using a synthetic matching framework, we analyze the impact of this unexpected (and therefore exogenous) policy change on the stock market. The results reveal a significant level shift (decline) in asset prices following the discontinuation of the minimum exchange rate. As a novel finding in the literature, we document that the exchange‐rate elasticity of Swiss asset prices is around −0.75. Differentiating between sectors of the Swiss economy, we find that the industrial, financial and consumer goods sectors are most strongly affected by the abolition of the minimum exchange rate.

We use the World Input–Output Database (WIOD) combined with regional sectoral employment data to estimate the potential regional employment effects of international trade barriers. We study the case of a no-deal Brexit in which imports to the United Kingdom (UK) from the European Union (EU) would be subject to tariffs and non-tariff trade costs. First, we derive the decline in UK final goods imports from the EU from industry-specific international trade elasticities, tariffs and non-tariff trade costs. Using input–output analysis, we estimate the potential output and employment effects for 56 industries and 43 countries on the national level. The absolute effects would be largest in big EU countries which have close trade relationships with the UK, such as Germany and France. However, there would also be large countries outside the EU which would be heavily affected via global value chains, such as China, for example. The relative effects (in percent of total employment) would be largest in Ireland followed by Belgium. In a second step, we split up the national effects on the NUTS-2 level for EU member states and additionally on the county (NUTS-3) level for Germany. The share of affected workers varies between 0.03% and 3.4% among European NUTS-2 regions and between 0.15% and 0.4% among German counties. A general result is that indirect effects via global value chains, i.e., trade in intermediate inputs, are more important than direct effects via final demand.

This paper examines the effect of credit rating disagreements on merger and acquisition (M&A) decisions. We show that acquirers with split ratings prefer to use stock to finance their acquisitions. More importantly, we find that acquirers with split ratings experience lower announcement returns. Further analysis shows that overpayment by acquirers with split ratings is concentrated in acquirers with entrenched managers. Our results are robust to alternative identification strategies. Overall, our evidence indicates that credit rating disagreements are heavily priced in the M&A market.

Measuring innovation is challenging both for researchers and for national statisticians, and it is increasingly important in light of the ongoing digital revolution. National accounts and many other economic statistics were designed before the emergence of the digital economy and the growing importance of intangible capital. They do not yet fully capture the wide range of innovative activity that is observed in modern economies.
This volume examines how to measure innovation, track its effects on economic activity and prices, and understand how it has changed the structure of production processes, labor markets, and organizational form and operation in business. The contributors explore new approaches to, and data sources for, measurement—such as collecting data for a particular innovation as opposed to a firm, and the use of trademarks for tracking innovation. They also consider the connections between university-based R&D and business startups, and the potential impacts of innovation on income distribution.
The research suggests potential strategies for expanding current measurement frameworks to better capture innovative activity, such as more detailed tracking of global value chains to identify innovation across time and space, and expanding the measurement of the GDP impacts of innovation in fields such as consumer content delivery and cloud computing.

Measuring innovation is challenging both for researchers and for national statisticians, and it is increasingly important in light of the ongoing digital revolution. National accounts and many other economic statistics were designed before the emergence of the digital economy and the growing importance of intangible capital. They do not yet fully capture the wide range of innovative activity that is observed in modern economies. This volume examines how to measure innovation, track its effects on economic activity and prices, and understand how it has changed the structure of production processes, labor markets, and organizational form and operation in business. The contributors explore new approaches to, and data sources for, measurement—such as collecting data for a particular innovation as opposed to a firm, and the use of trademarks for tracking innovation. They also consider the connections between university-based R&D and business startups, and the potential impacts of innovation on income distribution. The research suggests potential strategies for expanding current measurement frameworks to better capture innovative activity, such as more detailed tracking of global value chains to identify innovation across time and space, and expanding the measurement of the GDP impacts of innovation in fields such as consumer content delivery and cloud computing.

Using daily data for 34 emerging markets in the period 1994–2016, we find robust evidence that higher export commodity prices are associated with lower sovereign default risk, as measured by lower EMBI spreads. The economic effect is especially pronounced for heavy commodity exporters. Examining the drivers, we find that, first, commodity dependence is higher for countries that export large volumes of commodities, whereas other portfolio characteristics like volatility or concentration are less important. Second, commodity-sovereign risk dependence increases in times of recessions and expansionary U.S. monetary policy. Third, the importance of raw material prices for sovereign financing can likely be mitigated if a country improves institutions and tax systems, attracts FDI inflows, invests in manufacturing, machinery and infrastructure, builds up reserve assets and opens capital and trade accounts. Fourth, the country’s government indebtedness or amount of received development assistance appear to be only of secondary importance for commodity dependence.