A Multi-Model Assessment of Inequality and Climate Change
Marie Young-Brun, et al.
Nature Climate Change,
October
2024
Abstract
Climate change and inequality are critical and interrelated defining issues for this century. Despite growing empirical evidence on the economic incidence of climate policies and impacts, mainstream model-based assessments are often silent on the interplay between climate change and economic inequality. For example, all the major model comparisons reviewed in IPCC neglect within-country inequalities. Here we fill this gap by presenting a model ensemble of eight large-scale Integrated Assessment Models belonging to different model paradigms and featuring economic heterogeneity. We study the distributional implications of Paris-aligned climate target of 1.5 degree and include different carbon revenue redistribution schemes. Moreover, we account for the economic inequalities resulting from residual and avoided climate impacts. We find that price-based climate policies without compensatory measures increase economic inequality in most countries and across models. However, revenue redistribution through equal per-capita transfers can offset this effect, leading to on average decrease in the Gini index by almost two points. When climate benefits are included, inequality is further reduced, but only in the long term. Around mid-century, the combination of dried-up carbon revenues and yet limited climate benefits leads to higher inequality under the Paris target than in the Reference scenario, indicating the need for further policy measures in the medium term.
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Advances in Using Vector Autoregressions to Estimate Structural Magnitudes
Christiane Baumeister, James D. Hamilton
Abstract
This paper discusses drawing structural conclusions from vector autoregressions. We call attention to a common error in estimating structural elasticities and show how to correctly estimate elasticities even in the case when one knows only the effects of a single structural shock and the covariance matrix of the reduced-form residuals. We describe the traditional approach to identification as a claim to have exact prior information about the structural model and propose Bayesian inference as a way to acknowledge that prior information is imperfect or subject to error. We raise concerns about the way that results are typically reported for VARs that are set-identified using sign and other restrictions.
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A Market-based Indicator of Currency Risk: Evidence from American Depositary Receipts
Stefan Eichler, Ingmar Roevekamp
IWH Discussion Papers,
No. 4,
2016
Abstract
We introduce a novel currency risk measure based on American Depositary Receipts(ADRs). Using a multifactor pricing model, we exploit ADR investors’ exposure to potential devaluation losses to derive an indicator of currency risk. Using weekly data for a sample of 831 ADRs located in 23 emerging markets over the 1994-2014 period, we find that a deterioration in the fiscal and current account balance, as well as higher inflation, increases currency risk. Interaction models reveal that these macroeconomic fundamentals drive currency risk, particularly in countries with managed exchange rates, low levels of foreign exchange reserves and a poor sovereign credit rating.
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Sign Restrictions, Structural Vector Autoregressions, and Useful Prior Information
Christiane Baumeister, James D. Hamilton
Econometrica,
No. 5,
2015
Abstract
This paper makes the following original contributions to the literature. (i) We develop a simpler analytical characterization and numerical algorithm for Bayesian inference in structural vector autoregressions (VARs) that can be used for models that are overidentified, just‐identified, or underidentified. (ii) We analyze the asymptotic properties of Bayesian inference and show that in the underidentified case, the asymptotic posterior distribution of contemporaneous coefficients in an n‐variable VAR is confined to the set of values that orthogonalize the population variance–covariance matrix of ordinary least squares residuals, with the height of the posterior proportional to the height of the prior at any point within that set. For example, in a bivariate VAR for supply and demand identified solely by sign restrictions, if the population correlation between the VAR residuals is positive, then even if one has available an infinite sample of data, any inference about the demand elasticity is coming exclusively from the prior distribution. (iii) We provide analytical characterizations of the informative prior distributions for impulse‐response functions that are implicit in the traditional sign‐restriction approach to VARs, and we note, as a special case of result (ii), that the influence of these priors does not vanish asymptotically. (iv) We illustrate how Bayesian inference with informative priors can be both a strict generalization and an unambiguous improvement over frequentist inference in just‐identified models. (v) We propose that researchers need to explicitly acknowledge and defend the role of prior beliefs in influencing structural conclusions and we illustrate how this could be done using a simple model of the U.S. labor market.
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Shocks at Large Banks and Banking Sector Distress: The Banking Granular Residual
S. Blank, Claudia M. Buch, Katja Neugebauer
Journal of Financial Stability,
No. 4,
2009
Abstract
Size matters in banking. In this paper, we explore whether shocks originating at large banks affect the probability of distress of smaller banks and thus the stability of the banking system. Our analysis proceeds in two steps. In a first step, we follow Gabaix and construct a measure of idiosyncratic shocks at large banks, the so-called Banking Granular Residual. This measure documents the importance of size effects for the German banking system. In a second step, we incorporate this measure of idiosyncratic shocks at large banks into an integrated stress-testing model for the German banking system following De Graeve et al. (2008). We find that positive shocks at large banks reduce the probability of distress of small banks.
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Great Moderation at the Firm Level? Unconditional vs. Conditional Output Volatility
Claudia M. Buch, Jörg Döpke, K. Stahn
B.E. Journal of Economic Analysis and Policy,
No. 1,
2009
Abstract
We test whether there has been a “Great Moderation“ of output volatility at the firm level. The multifactor residual model proposed by Pesaran (2006) is used to isolate the idiosyncratic component of firms' sales growth from macroeconomic developments. This methodology is applied to a balanced panel of about 1,200 German firms covering a 35-year period (1971-2005). Our research has three main findings. First, unconditional firm-level volatility and aggregate output volatility have seen similar downward trends. Second, conditional, idiosyncratic firm-level volatility does not exhibit a downward trend. Third, there is a positive link between growth and volatility at the firm level.
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