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Biodiversity loss poses a significant threat to the global economy and affects ecosystem services on which most large companies rely heavily. The severe financial implications of such a reduced species diversity have attracted the attention of companies and stakeholders, with numerous calls to increase corporate transparency. Using textual analysis, this study thus investigates the current state of voluntary biodiversity reporting of 359 European blue-chip companies and assesses the extent to which it aligns with the upcoming disclosure framework of the Task Force on Nature-related Financial Disclosures (TNFD). The descriptive results suggest a substantial gap between current reporting practices and the proposed TNFD framework, with disclosures largely lacking quantification, details and clear targets. In addition, the disclosures appear to be relatively unstandardized. Companies in sectors or regions exposed to higher nature-related risks as well as larger companies are more likely to report on aspects of biodiversity. This study contributes to the emerging literature on nature-related risks and provides detailed insights on the extent of the reporting gap in light of the upcoming standards.
With free delivery of products virtually being a standard in E-commerce, product returns pose a major challenge for online retailers and society. For retailers, product returns involve significant transportation, labor, disposal, and administrative costs. From a societal perspective, product returns contribute to greenhouse gas emissions and packaging disposal and are often a waste of natural resources. Therefore, reducing product returns has become a key challenge. This paper develops and validates a novel smart green nudging approach to tackle the problem of product returns during customers’ online shopping processes. We combine a green nudge with a novel data enrichment strategy and a modern causal machine learning method. We first run a large-scale randomized field experiment in the online shop of a German fashion retailer to test the efficacy of a novel green nudge. Subsequently, we fuse the data from about 50,000 customers with publicly-available aggregate data to create what we call enriched digital footprints and train a causal machine learning system capable of optimizing the administration of the green nudge. We report two main findings: First, our field study shows that the large-scale deployment of a simple, low-cost green nudge can significantly reduce product returns while increasing retailer profits. Second, we show how a causal machine learning system trained on the enriched digital footprint can amplify the effectiveness of the green nudge by “smartly” administering it only to certain types of customers. Overall, this paper demonstrates how combining a low-cost marketing instrument, a privacy-preserving data enrichment strategy, and a causal machine learning method can create a win-win situation from both an environmental and economic perspective by simultaneously reducing product returns and increasing retailers’ profits.
Unconventional green
(2023)
We analyze the effects of the PEPP (Pandemic Emergency Purchase Programme), the temporary quantitative easing implemented by the ECB immediately after the burst of the Covid-19 pandemic. We show that the differences in aim, size and flexibility with respect to the traditional Corporate Sector Purchase Programme (CSPP) were able to significantly involve, in addition to the directly targeted bonds, also the green bond segment. Via a standard difference- in-differences model we estimate that the yield on green bonds declined by more than 20 basis points after the PEPP. In order to take into account also the differences attributable to the eligibility to the programme, we employ a triple difference estimator. Bonds that at the same time were green and eligible benefitted of an additional premium of 39 basis points.
By focusing on the cost conditions at issuance, I find that not only the Covid-19 pandemic effects were different across bonds and firms at different stages, but also that the market composition was significantly affected, collapsing on investment- grade bonds, a segment in which the share of bonds eligible to the ECB corporate programmes strikingly increased from 15% to 40%. At the same time the high-yield segment shrunk to almost disappear at 4%. In addition to a market segmentation along the bond grade and the eligibility to the ECB programmes, another source of risk detected in the pricing mechanism is the weak resilience to pandemic: the premium requested is around 30 basis points and started to be priced only after the early containment actions taken by the national authorities. On the contrary, I do not find evidence supporting an increased risk for corporations headquartered in countries with a reduced fiscal space, nor the existence of a premium in favour of green bonds, which should be the backbone of a possible “green recovery”.
We assess the degree of market fragmentation in the euro-area corporate bond market by disentangling the determinants of the risk premium paid on bonds at origination. By looking at over 2,400 bonds we are able to isolate the country-specific effects which are a suitable indicator of the market fragmentation. We find that, after peaking during the sovereign debt crisis, fragmentation shrank in 2013 and receded to pre-crisis levels only in 2014. However, the low level of estimated market fragmentation is coupled with a still high heterogeneity in actual bond yields, challenging the consistency of the new equilibrium.
We analyze the risk premium on bank bonds at origination with a special focus on the role of implicit and explicit public guarantees and the systemic relevance of the issuing institutions. By looking at the asset swap spread on 5,500 bonds, we find that explicit guarantees and sovereign creditworthiness have a substantial effect on the risk premium. In addition, while large institutions still enjoy lower issuance costs linked to the TBTF framework, we find evidence of enhanced market disciple for systemically important banks which face, since the onset of the financial crisis, an increased premium on bond placements.
Chen and Zadrozny (1998) developed the linear extended Yule-Walker (XYW) method for determining the parameters of a vector autoregressive (VAR) model with available covariances of mixed-frequency observations on the variables of the model. If the parameters are determined uniquely for available population covariances, then, the VAR model is identified. The present paper extends the original XYW method to an extended XYW method for determining all ARMA parameters of a vector autoregressive moving-average (VARMA) model with available covariances of single- or mixed-frequency observations on the variables of the model. The paper proves that under conditions of stationarity, regularity, miniphaseness, controllability, observability, and diagonalizability on the parameters of the model, the parameters are determined uniquely with available population covariances of single- or mixed-frequency observations on the variables of the model, so that the VARMA model is identified with the single- or mixed-frequency covariances.
Linear rational-expectations models (LREMs) are conventionally "forwardly" estimated as follows. Structural coefficients are restricted by economic restrictions in terms of deep parameters. For given deep parameters, structural equations are solved for "rational-expectations solution" (RES) equations that determine endogenous variables. For given vector autoregressive (VAR) equations that determine exogenous variables, RES equations reduce to reduced-form VAR equations for endogenous variables with exogenous variables (VARX). The combined endogenous-VARX and exogenous-VAR equations comprise the reduced-form overall VAR (OVAR) equations of all variables in a LREM. The sequence of specified, solved, and combined equations defines a mapping from deep parameters to OVAR coefficients that is used to forwardly estimate a LREM in terms of deep parameters. Forwardly-estimated deep parameters determine forwardly-estimated RES equations that Lucas (1976) advocated for making policy predictions in his critique of policy predictions made with reduced-form equations.
Sims (1980) called economic identifying restrictions on deep parameters of forwardly-estimated LREMs "incredible", because he considered in-sample fits of forwardly-estimated OVAR equations inadequate and out-of-sample policy predictions of forwardly-estimated RES equations inaccurate. Sims (1980, 1986) instead advocated directly estimating OVAR equations restricted by statistical shrinkage restrictions and directly using the directly-estimated OVAR equations to make policy predictions. However, if assumed or predicted out-of-sample policy variables in directly-made policy predictions differ significantly from in-sample values, then, the out-of-sample policy predictions won't satisfy Lucas's critique.
If directly-estimated OVAR equations are reduced-form equations of underlying RES and LREM-structural equations, then, identification 2 derived in the paper can linearly "inversely" estimate the underlying RES equations from the directly-estimated OVAR equations and the inversely-estimated RES equations can be used to make policy predictions that satisfy Lucas's critique. If Sims considered directly-estimated OVAR equations to fit in-sample data adequately (credibly) and their inversely-estimated RES equations to make accurate (credible) out-of-sample policy predictions, then, he should consider the inversely-estimated RES equations to be credible. Thus, inversely-estimated RES equations by identification 2 can reconcile Lucas's advocacy for making policy predictions with RES equations and Sims's advocacy for directly estimating OVAR equations.
The paper also derives identification 1 of structural coefficients from RES coefficients that contributes mainly by showing that directly estimated reduced-form OVAR equations can have underlying LREM-structural equations.
I propose a dynamic stochastic general equilibrium model in which the leverage of borrowers as well as banks and housing finance play a crucial role in the model dynamics. The model is used to evaluate the relative effectiveness of a policy to inject capital into banks versus a policy to relieve households of mortgage debt. In normal times, when the economy is near the steady state and policy rates are set according to a Taylor-type rule, capital injections to banks are more effective in stimulating the economy in the long-run. However, in the middle of a housing debt crisis, when households are highly leveraged, the short-run output effects of the debt relief are more substantial. When the zero lower bound (ZLB) is additionally considered, the debt relief policy can be much more powerful in boosting the economy both in the short-run and in the longrun. Moreover, the output effects of the debt relief become increasingly larger, the longer the ZLB is binding.
This paper analyzes liquidity in an order driven market. We only investigate the best limits in the limit order book, but also take into account the book behind these inside prices. When subsequent prices are close to the best ones and depth at them is substantial, larger orders can be executed without an extensive price impact and without deterring liquidity. We develop and estimate several econometric models, based on depth and prices in the book, as well as on the slopes of the limit order book. The dynamics of different dimensions of liquidity are analyzed: prices, depth at and beyond the best prices, as well as resiliency, i.e. how fast the different liquidity measures recover after a liquidity shock. Our results show a somewhat less favorable image of liquidity than often found in the literature. After a liquidity shock (in the spread or depth or in the book beyond the best limits), several dimension of liquidity deteriorate at the same time. Not only does the inside spread increase, and depth at the best prices decrease, also the difference between subsequent bid and ask prices may become larger and depth provided at them decreases. The impacts are both econometrically and economically significant. Also, our findings point to an interaction between different measures of liquidity, between liquidity at the best prices and beyond in the book, and between ask and bid side of the market.
Venture capital (VC) investment has long been conceptualized as a local business , in which the VC’s ability to source, syndicate, fund, monitor, and add value to portfolio firms critically depends on their access to knowledge obtained through their ties to the local (i.e., geographically proximate) network. Consistent with the view that local networks matter, existing research confirms that local and geographically distant portfolio firms are sourced, syndicated, funded, and monitored differently. Curiously, emerging research on VC investment practice within the United States finds that distant investments, as measured by “exits” (either initial public offering or merger & acquisition) out-perform local investments. These findings raise important questions about the assumed benefits of local network membership and proximity. To more deeply probe these questions, we contrast the deal structure of cross-border VC investment with domestic VC investment, and contrast the deal structure of cross-border VC investments that include a local
partner with those that do not. Evidence from 139,892 rounds of venture capital financing in the period 1980-2009 suggests that cross-border investment practice, in terms of deal sourcing, syndication, and performance indeed change with proximity, but that monitoring practices do not. Further, we find that the inclusion of a local partner in the investment syndicate yields surprisingly few benefits. This evidence, we argue, raises important questions about VC investment practice as well as the ability of firms to capture and lever the presumed benefits of network membership.
We examine the dynamics of assets under management (AUM) and management fees at the portfolio manager level in the closed-end fund industry. We find that managers capitalize on good past performance and favorable investor perception about future performance, as reflected in fund premiums, through AUM expansions and fee increases. However, the penalties for poor performance or unfavorable investor perception are either insignificant, or substantially mitigated by manager tenure. Long tenure is generally associated with poor performance and high discounts. Our findings suggest substantial managerial power in capturing CEF rents. We also document significant diseconomies of scale at the manager level.
The paper considers optimal monetary stabilization policy in a forward-looking model, when the central bank recognizes that private-sector expectations need not be precisely model-consistent, and wishes to choose a policy that will be as good as possible in the case of any beliefs that are close enough to model-consistency. It is found that commitment continues to be important for optimal policy, that the optimal long-run inflation target is unaffected by the degree of potential distortion of beliefs, and that optimal policy is even more history-dependent than if rational expectations are assumed. JEL Classification: E52, E58, E42
This paper considers the desirability of the observed tendency of central banks to adjust interest rates only gradually in response to changes in economic conditions. It shows, in the context of a simple model of optimizing private-sector behavior, that such inertial behavior on the part of the central bank may indeed be optimal, in the sense of minimizing a loss function that penalizes inflation variations, deviations of output from potential, and interest-rate variability. Sluggish adjustment characterizes an optimal policy commitment, even though no such inertia would be present in the case of a reputationless (Markovian) equilibrium under discretion. Optimal interest-rate feedback rules are also characterized, and shown to involve substantial positive coefficients on lagged interest rates. This provides a theoretical explanation for the numerical results obtained by Rotemberg and Woodford (1998) in their quantitative model of the U.S. economy.
The paper illustrates based on an example the importance of consistency between the empirical measurement and the concept of variables in estimated macroeconomic models. Since standard New Keynesian models do not account for demographic trends and sectoral shifts, the authors proposes adjusting hours worked per capita used to estimate such models accordingly to enhance the consistency between the data and the model. Without this adjustment, low frequency shifts in hours lead to unreasonable trends in the output gap, caused by the close link between hours and the output gap in such models.
The retirement wave of baby boomers, for example, lowers U.S. aggregate hours per capita, which leads to erroneous permanently negative output gap estimates following the Great Recession. After correcting hours for changes in the age composition, the estimated output gap closes gradually instead following the years after the Great Recession.
This paper studies the long-run effects of credit market disruptions on real firm outcomes and how these effects depend on nominal wage rigidities at the firm level. I trace out the long-run investment and growth trajectories of firms which are more adversely affected by a transitory shock to aggregate credit supply. Affected firms exhibit a temporary investment gap for two years following the shock, resulting in a persistent accumulated growth gap. I show that affected firms with a higher degree of wage rigidity exhibit a steeper drop in investment and grow more slowly than affected firms with more flexible wages.
This paper investigates the accuracy and heterogeneity of output growth and inflation forecasts during the current and the four preceding NBER-dated U.S. recessions. We generate forecasts from six different models of the U.S. economy and compare them to professional forecasts from the Federal Reserve’s Greenbook and the Survey of Professional Forecasters (SPF). The model parameters and model forecasts are derived from historical data vintages so as to ensure comparability to historical forecasts by professionals. The mean model forecast comes surprisingly close to the mean SPF and Greenbook forecasts in terms of accuracy even though the models only make use of a small number of data series. Model forecasts compare particularly well to professional forecasts at a horizon of three to four quarters and during recoveries. The extent of forecast heterogeneity is similar for model and professional forecasts but varies substantially over time. Thus, forecast heterogeneity constitutes a potentially important source of economic fluctuations. While the particular reasons for diversity in professional forecasts are not observable, the diversity in model forecasts can be traced to different modeling assumptions, information sets and parameter estimates. JEL Classification: C53, D84, E31, E32, E37 Keywords: Forecasting, Business Cycles, Heterogeneous Beliefs, Forecast Distribution, Model Uncertainty, Bayesian Estimation
This contribution draws on two recent publications in which the macroeconomic model data base (www.macromodelbase.com) is employed for model comparisons. The comparative approach is used to base policy analysis on a systematic evaluation of the different implications that a certain economic policy can have when submitted to different modeling approaches. In this manner, policy recommendations are more robust to modeling uncertainty. By extending the comparative approach to forecasting, the authors investigate the accuracy of different forecasting models and obtain more reliable mean forecasts.
In 2011 wurde der Preis für Wirtschaftswissenschaften der schwedischen Reichsbank im Gedenken an Alfred Nobel an die US-Ökonomen Thomas J. Sargent von der New York University und Chistopher A. Sims von Princeton University verliehen. Gerade deutsche Zeitungskommentare kritisierten die Forscher vielfach für die Verwendung „unrealistischer“ Annahmen wie Nutzenmaximierung und rationale Erwartungen. Diese Kritik verkennt den maßgeblichen Beitrag von Sargent und Sims zur Entwicklung der modernen Makroökonomik. Ihre empirischen Methoden sind heute Standardwerkzeuge der akademischen Forschung und werden auch von Ökonomen in Zentralbanken, Finanzministerien und internationalen Organisationen eingesetzt. Sie haben grundlegende neue Erkenntnisse ermöglicht, zum Beispiel über die Wirkungsweise der Geld- und Fiskalpolitik.
In the aftermath of the global financial crisis, the state of macroeconomic modeling and the use of macroeconomic models in policy analysis has come under heavy criticism. Macroeconomists in academia and policy institutions have been blamed for relying too much on a particular class of macroeconomic models. This paper proposes a comparative approach to macroeconomic policy analysis that is open to competing modeling paradigms. Macroeconomic model comparison projects have helped produce some very influential insights such as the Taylor rule. However, they have been infrequent and costly, because they require the input of many teams of researchers and multiple meetings to obtain a limited set of comparative findings. This paper provides a new approach that enables individual researchers to conduct model comparisons easily, frequently, at low cost and on a large scale. Using this approach a model archive is built that includes many well-known empirically estimated models that may be used for quantitative analysis of monetary and fiscal stabilization policies. A computational platform is created that allows straightforward comparisons of models’ implications. Its application is illustrated by comparing different monetary and fiscal policies across selected models. Researchers can easily include new models in the data base and compare the effects of novel extensions to established benchmarks thereby fostering a comparative instead of insular approach to model development.