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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.
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.
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.
One of the motivations for establishing a European banking union was the desire to break the ties with between national regulators and domestic financial institutions in order to prevent regulatory capture. However, supervisory authority over the financial sector at the national level can also have valuable public benefits. The aim of this policy letter is to detail these public benefits in order to counter discussions that focus only on conflicts of interest. It is informed by an analysis of how financial institutions interacted with policy-makers in the design of national bank rescue schemes in response to the banking crisis of 2008. Using this information, it discusses the possible benefits of close cooperation between financial institutions and regulators and analyzes these in the wake of a European banking union.
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 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.
The recent decline in euro area inflation has triggered new calls for additional monetary stimulus by the ECB in order to counter the threat of a self‐reinforcing deflation and recession spiral. This note reviews the available evidence on inflation expectations, output gaps and other factors driving current inflation through the lens of the Phillips curve. It also draws a comparison to the Japanese experience with deflation in the late 1990s and the evidence from Japan concerning the outputinflation nexus at low trend inflation. The note concludes from this evidence that the risk of a selfreinforcing deflation remains very small. Thus, the ECB best await the impact of the long‐term refinancing operations decided in June that have the potential to induce substantial monetary accommodation once implemented for the first time in September.
The global financial crisis and the ensuing criticism of macroeconomics have inspired researchers to explore new modeling approaches. There are many new models that deliver improved estimates of the transmission of macroeconomic policies and aim to better integrate the financial sector in business cycle analysis. Policy making institutions need to compare available models of policy transmission and evaluate the impact and interaction of policy instruments in order to design effective policy strategies. This paper reviews the literature on model comparison and presents a new approach for comparative analysis. Its computational implementation enables individual researchers to conduct systematic model comparisons and policy evaluations easily and at low cost. This approach also contributes to improving reproducibility of computational research in macroeconomic modeling. Several applications serve to illustrate the usefulness of model comparison and the new tools in the area of monetary and fiscal policy. They include an analysis of the impact of parameter shifts on the effects of fiscal policy, a comparison of monetary policy transmission across model generations and a cross-country comparison of the impact of changes in central bank rates in the United States and the euro area. Furthermore, the paper includes a large-scale comparison of the dynamics and policy implications of different macro-financial models. The models considered account for financial accelerator effects in investment financing, credit and house price booms and a role for bank capital. A final exercise illustrates how these models can be used to assess the benefits of leaning against credit growth in monetary policy.