Several models posit a positive cross-sectional correlation between markups and firm size, which characterizes misallocation, factor shares, and gains from trade. Accounting for labor market power in markup estimation, we find instead that larger firms have lower product markups but higher wage markdowns. The negative markup-size correlation turns positive when conditioning on markdowns, suggesting interactions between product and labor market power. Our findings are robust to common criticism (e.g., price bias, non-neutral technology) and hold across 19 European countries. We discuss possible mechanisms and resulting implications, highlighting the importance of studying input and output market power in a unified framework.

Abstract In Germany, employers used to pay union members and non-members in a plant the same union wage in order to prevent workers from joining unions. Using recent administrative data, we investigate which workers in firms covered by collective bargaining agreements still individually benefit from these union agreements, which workers are not covered anymore and what this means for their wages. We show that about 9 per cent of workers in plants with collective agreements do not enjoy individual coverage (and thus the union wage) anymore. Econometric analyses with unconditional quantile regressions and firm-fixed-effects estimations demonstrate that not being individually covered by a collective agreement has serious wage implications for most workers. Low-wage non-union workers and those at low hierarchy levels particularly suffer since employers abstain from extending union wages to them in order to pay lower wages. This jeopardizes unions' goal of protecting all disadvantaged workers.

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.

Syndicated loan data provided by DealScan is an essential input in banking research. This data is rich enough to answer urging questions on bank lending, e.g., in the presence of financial 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.

Two contradictory strands of the rating literature criticize that rating agencies merely follow the market on the one hand, and emphasizing that rating changes affect capital movements on the other hand. Both focus on explaining rating levels rather than the timing of rating announcements. Contrarily, we explicitly differentiate between a decision to assess a country and the actual rating decision. We show that this differentiation significantly improves the estimation of the rating function. The three major rating agencies treat economic fundamentals similarly, while differing in their response to other factors such as strategic considerations. This reconciles the conflicting literature.

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.

Despite their importance, the discussion of spillover effects in empirical research often misses the rigor dedicated to endogeneity concerns. We analyze a broad set of workhorse models of firm interactions and show that spillovers naturally arise in many corporate finance settings. This has important implications for the estimation of treatment effects: i) even with random treatment, spillovers lead to a complicated bias, ii) fixed effects can exacerbate the spillover-induced bias. We propose simple diagnostic tools for empirical researchers and illustrate our guidance in an application.

This paper investigates the impact of international bank flows from G10 lender countries on income inequality in 74 borrower countries over 1999–2013. Specifically, we examine the role of international bank flows contingent upon the Basel 2 capital regulation and the level of financial market development in the borrower countries. First, we find that improvements in the borrower country risk weights due to rating upgrades under the Basel 2 framework significantly increase bank flows, leading to improvements in income inequality. Second, we find that the level of financial market development is also important. We report that a well-functioning financial market helps the poor access credit and thereby reduces inequality. Moreover, we employ threshold estimations to identify the thresholds for each of the financial development measures that borrower countries need to reach before realizing the potential reductions in income inequality from international bank financing.

By adopting an identification strategy of difference‐in‐difference estimation combined with propensity score matching between liberalized and closed countries, this paper provides robust evidence that opening the capital account is associated with an increase in income inequality in developing countries. Specifically, capital account liberalization, in the long run, is associated with a reduction in the income share of the poorest half by 2.66–3.79% points and an increase in that of the richest 10% by 5.19–8.76% points. Moreover, directions and categories of capital account liberalization matter. The relationship is more pronounced when liberalizing inward and equity capital flows.

Recurring financial instabilities have led policymakers to rely on early-warning models to signal financial vulnerabilities. These models rely on ex-post optimization of signaling thresholds on crisis probabilities accounting for preferences between forecast errors, but come with the crucial drawback of unstable thresholds in recursive estimations. We propose two alternatives for threshold setting with similar or better out-of-sample performance: (i) including preferences in the estimation itself and (ii) setting thresholds ex-ante according to preferences only. Given probabilistic model output, it is intuitive that a decision rule is independent of the data or model specification, as thresholds on probabilities represent a willingness to issue a false alarm vis-à-vis missing a crisis. We provide real-world and simulation evidence that this simplification results in stable thresholds, while keeping or improving on out-of-sample performance. Our solution is not restricted to binary-choice models, but directly transferable to the signaling approach and all probabilistic early-warning models.