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When the COVID-19 crisis struck, banks using internal-rating based (IRB) models quickly recognized the increase in risk and reduced lending more than banks using a standardized approach. This effect is not driven by borrowers’ quality or by banks in countries with credit booms before the pandemic. The higher risk sensitivity of IRB models does not always result in lower credit provision when risk intensifies. Certain features of the IRB models – the use of a downturn Loss Given Default parameter – can increase banks’ resilience and preserve their intermediation capacity also during downturns. Affected borrowers were not able to fully insulate and decreased corporate investments.
Previous studies document a relationship between gambling activity at the aggregate level and investments in securities with lottery-like features. We combine data on individual gambling consumption with portfolio holdings and trading records to examine whether gambling and trading act as substitutes or complements. We find that gamblers are more likely than the average investor to hold lottery stocks, but significantly less likely than active traders who do not gamble. Our results suggest that gambling behavior across domains is less relevant compared to other portfolio characteristics that predict investing in high-risk and high-skew securities, and that gambling on and off the stock market act as substitutes to satisfy the same need, e.g., sensation seeking.
Crowdfunding platforms offer project initiators the opportunity to acquire funds from the Internet crowd and, therefore, have become a valuable alternative to traditional sources of funding. However, some processes on crowdfunding platforms cause undesirable external effects that influence the funding success of projects. In this context, we focus on the phenomenon of project overfunding. Massively overfunded projects have been discussed to overshadow other crowdfunding projects which in turn receive less funding. We propose a funding redistribution mechanism to internalize these overfunding externalities and to improve overall funding results. To evaluate this concept, we develop and deploy an agent-based model (ABM). This ABM is based on a multi-attribute decision-making approach and is suitable to simulate the dynamic funding processes on a crowdfunding platform. Our evaluation provides evidence that possible modifications of the crowdfunding mechanisms bear the chance to optimize funding results and to alleviate existing flaws.
Colocation services offered by stock exchanges enable market participants to achieve execution costs for large orders that are substantially lower and less sensitive to transacting against high-frequency traders. However, these benefits manifest only for orders executed on the colocated brokers' own behalf, whereas customers' order execution costs are substantially higher. Analyses of individual order executions indicate that customer orders originating from colocated brokers are less actively monitored and achieve inferior execution quality. This suggests that brokers do not make effective use of their technology, possibly due to agency frictions or poor algorithm selection and parameter choice by customers.
The leading premium
(2022)
In this paper, we consider conditional measures of lead-lag relationships between aggregate growth and industry-level cash-flow growth in the US. Our results show that firms in leading industries pay an average annualized return 3.6\% higher than that of firms in lagging industries. Using both time series and cross sectional tests, we estimate an annual pure timing premium ranging from 1.2% to 1.7%. This finding can be rationalized in a model in which (a) agents price growth news shocks, and (b) leading industries provide valuable resolution of uncertainty about the growth prospects of lagging industries.
Advances in Machine Learning (ML) led organizations to increasingly implement predictive decision aids intended to improve employees’ decision-making performance. While such systems improve organizational efficiency in many contexts, they might be a double-edged sword when there is the danger of a system discontinuance. Following cognitive theories, the provision of ML-based predictions can adversely affect the development of decision-making skills that come to light when people lose access to the system. The purpose of this study is to put this assertion to the test. Using a novel experiment specifically tailored to deal with organizational obstacles and endogeneity concerns, we show that the initial provision of ML decision aids can latently prevent the development of decision-making skills which later becomes apparent when the system gets discontinued. We also find that the degree to which individuals 'blindly' trust observed predictions determines the ultimate performance drop in the post-discontinuance phase. Our results suggest that making it clear to people that ML decision aids are imperfect can have its benefits especially if there is a reasonable danger of (temporary) system discontinuances.
Search costs for lenders when evaluating potential borrowers are driven by the quality of the underwriting model and by access to data. Both have undergone radical change over the last years, due to the advent of big data and machine learning. For some, this holds the promise of inclusion and better access to finance. Invisible prime applicants perform better under AI than under traditional metrics. Broader data and more refined models help to detect them without triggering prohibitive costs. However, not all applicants profit to the same extent. Historic training data shape algorithms, biases distort results, and data as well as model quality are not always assured. Against this background, an intense debate over algorithmic discrimination has developed. This paper takes a first step towards developing principles of fair lending in the age of AI. It submits that there are fundamental difficulties in fitting algorithmic discrimination into the traditional regime of anti-discrimination laws. Received doctrine with its focus on causation is in many cases ill-equipped to deal with algorithmic decision-making under both, disparate treatment, and disparate impact doctrine. The paper concludes with a suggestion to reorient the discussion and with the attempt to outline contours of fair lending law in the age of AI.
Many nations incentivize retirement saving by letting workers defer taxes on pension contributions, imposing them when retirees withdraw their funds. Using a dynamic life cycle model, we show how ‘Rothification’ – that is, taxing 401(k) contributions rather than payouts – alters saving, investment, consumption, and Social Security claiming patterns. We find that taxing pension contributions instead of withdrawals leads to delayed retirement, somewhat lower lifetime tax payments, and relatively small reductions in consumption. Indeed, the two tax regimes generate quite similar relative inequality metrics: the relative consumption inequality ratio under TEE is only four percent higher than in the EET case. Moreover, results indicate that the Gini measures are also strikingly similar under the EET and the TEE regimes for lifetime consumption, cash on hand, and 401(k) assets, differing by only 1-4 percent. While tax payments are higher early in life under the TEE regime, they are slightly lower in the long run. Moreover, higher EET tax payments are also accompanied by higher volatility. We therefore find few reasons for policymakers to favor either tax approach on egalitarian or revenue-enhancing grounds.
We analyze how market fragmentation affects market quality of SME and other less actively traded stocks. Compared to large stocks, they are less likely to be traded on multiple venues and show, if at all, low levels of fragmentation. Concerning the impact of fragmentation on market quality, we find evidence for a hockey stick effect: Fragmentation has no effect for infrequently traded stocks, a negative effect on liquidity of slightly more active stocks, and increasing benefits for liquidity of large and actively traded stocks. Consequently, being traded on multiple venues is not necessarily harmful for SME stock market quality.
The authors propose a new method to forecast macroeconomic variables that combines two existing approaches to mixed-frequency data in DSGE models. The first existing approach estimates the DSGE model in a quarterly frequency and uses higher frequency auxiliary data only for forecasting. The second method transforms a quarterly state space into a monthly frequency. Their algorithm combines the advantages of these two existing approaches.They compare the new method with the existing methods using simulated data and real-world data. With simulated data, the new method outperforms all other methods, including forecasts from the standard quarterly model. With real world data, incorporating auxiliary variables as in their method substantially decreases forecasting errors for recessions, but casting the model in a monthly frequency delivers better forecasts in normal times.
Correction to: Computational Economics https://doi.org/10.1007/s10614-020-10061-x
The original publication has been updated. In the original publication of this article, under the Introduction heading section, the corrections to the second paragraph’s inline equation were not incorporated. The author’s additional corrections have also been incorporated. The publisher apologizes for the error made during production.
Search costs for lenders when evaluating potential borrowers are driven by the quality of the underwriting model and by access to data. Both have undergone radical change over the last years, due to the advent of big data and machine learning. For some, this holds the promise of inclusion and better access to finance. Invisible prime applicants perform better under AI than under traditional metrics. Broader data and more refined models help to detect them without triggering prohibitive costs. However, not all applicants profit to the same extent. Historic training data shape algorithms, biases distort results, and data as well as model quality are not always assured. Against this background, an intense debate over algorithmic discrimination has developed. This paper takes a first step towards developing principles of fair lending in the age of AI. It submits that there are fundamental difficulties in fitting algorithmic discrimination into the traditional regime of anti-discrimination laws. Received doctrine with its focus on causation is in many cases ill-equipped to deal with algorithmic decision-making under both, disparate treatment, and disparate impact doctrine. The paper concludes with a suggestion to reorient the discussion and with the attempt to outline contours of fair lending law in the age of AI.
We investigate the impact of uneven transparency regulation across countries and industries on the location of economic activity. Using two distinct sources of regulatory variation—the varying extent of financial-reporting requirements and the staggered introduction of electronic business registers in Europe—, we consistently document that direct exposure to transparency regulation is negatively associated with the focal industry’s economic activity in terms of inputs (e.g., employment) and outputs (e.g., production). By contrast, we find that indirect exposure to supplier and customer industries’ transparency regulation is positively associated with the focal industry’s economic activity. Our evidence suggests uneven transparency regulation can reallocate economic activity from regulated toward unregulated countries and industries, distorting the location of economic activity.
To ensure the credibility of market discipline induced by bail-in, neither retail investors nor peer banks should appear prominently among the investor base of banks’ loss absorbing capital. Empirical evidence on bank-level data provided by the German Federal Financial Supervisory Authority raises a few red flags. Our list of policy recommendations encompasses disclosure policy, data sharing among supervisors, information transparency on holdings of bail-inable debt for all stakeholders, threshold values, and a well-defined upper limit for any bail-in activity. This document was provided by the Economic Governance Support Unit at the request of the ECON Committee.
European banks have substantial investments in assets that are
measured without directly observable market prices (mark-to-
model). Financial disclosures of these value estimates lack
standardization and are hard to compare across banks. These
comparability concerns are concentrated in large European
banks that extensively rely on level 3 estimates with the most
unobservable inputs. Although the relevant balance sheet
positions only represent a small fraction of these large banks’
total assets (2.9%), their value equals a significant fraction of core
equity tier 1 (48.9%). Incorrect valuations thus have a potential to
impact financial stability. 85% of these bank assets are under
direct ECB supervision. Prudential regulation requires value
adjustments that are apt to shield capital against valuation risk.
Yet, stringent enforcement is critical for achieving this objective.
This document was provided by the Economic Governance
Support Unit at the request of the ECON Committee.
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.
Short sale bans may improve market quality during crises: new evidence from the 2020 Covid crash
(2022)
In theory, banning short selling stabilizes stock prices but undermines pricing efficiency and has ambiguous impacts on market liquidity. Empirical studies find mixed and conflicting results. This paper leverages cross-country policy variation during the 2020 Covid crisis to assess differential impacts of bans on stock liquidity, prices, and volatility. Results suggest that bans improved liquidity and stabilized prices for illiquid stocks but temporarily diminished liquidity for highly liquid stocks.The findings support theories in which short sale bans may improve liquidity by selectively filtering out informed— potentially predatory—traders. Thus, policies that target the most illiquid stocks may deliver better overall market quality than uniform short sale bans imposed on all stocks.
With open banking, consumers take greater control over their own financial data and share it at their discretion. Using a rich set of loan application data from the largest German FinTech lender in consumer credit, this paper studies what characterizes borrowers who share data and assesses its impact on loan application outcomes. I show that riskier borrowers share data more readily, which subsequently leads to an increase in the probability of loan approval and a reduction in interest rates. The effects hold across all credit risk profiles but are the most pronounced for borrowers with lower credit scores (a higher increase in loan approval rate) and higher credit scores (a larger reduction in interest rate). I also find that standard variables used in credit scoring explain substantially less variation in loan application outcomes when customers share data. Overall, these findings suggest that open banking improves financial inclusion, and also provide policy implications for regulators engaged in the adoption or extension of open banking policies.
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.
Financial literacy affects wealth accumulation, and pension planning plays a key role in this relationship. In a large field experiment, we employ a digital pension aggregation tool to confront a treatment group with a simplified overview of their current pension claims across all pillars of the pension system. We combine survey and administrative bank data to measure the effects on actual saving behavior. Access to the tool decreases pension uncertainty for treated individuals. Average savings increase - especially for the financially less literate. We conclude that simplification of pension information can potentially reduce disparities in pension planning and savings behavior.
This paper utilizes a comprehensive worker-firm panel for the Netherlands to quantifythe impact of ICT capital-skill complementarity on the finance wage premium after the Global Financial Crisis. We apply additive worker and firm fixed-effect models to account for unobserved worker- and firm-heterogeneity and show that firm fixed-effects correct for a downward bias in the estimated finance wage premium. Our results indicate a sizable finance wage premium for both fixed- and full-hourly wages. The complementarity between ICT capital spending and the share of high skill workers at the firm-level reduces the full-wage premium considerably and the fixed-wage premium almost entirely.
India has recorded 142,186 deaths over 36 administrative regions placing India third in the world after the US and Brazil for COVID-19 deaths as of 12 December 2020. Studies indicate that south-west monsoon season plays a role in the dynamics of contagious diseases, which tend to peak post-monsoon season. Recent studies show that vitamin D and its primary source Ultraviolet-B (UVB) radiation may play a protective role in mitigating COVID-19 deaths. However, the combined roles of the monsoon season and UVB radiation in COVID-19 in India remain still unclear. In this observational study, we empirically study the respective roles of monsoon season and UVB radiation, whilst further exploring, whether the monsoon season negatively impacts the protective role of UVB radiation in COVID-19 deaths in India. We use a log-linear Mundlak model to a panel dataset of 36 administrative regions in India from 14 March 2020–19 November 2020 (n = 6751). We use the cumulative COVID-19 deaths as the dependent variable. We isolate the association of monsoon season and UVB radiation as measured by Ultraviolet Index (UVI) from other confounding time-constant and time-varying region-specific factors. After controlling for various confounding factors, we observe that a unit increase in UVI and the monsoon season are separately associated with 1.2 percentage points and 7.5 percentage points decline in growth rates of COVID-19 deaths in the long run. These associations translate into substantial relative changes. For example, a permanent unit increase of UVI is associated with a decrease of growth rates of COVID-19 deaths by 33% (= − 1.2 percentage points) However, the monsoon season, mitigates the protective role of UVI by 77% (0.92 percentage points). Our results indicate a protective role of UVB radiation in mitigating COVID-19 deaths in India. Furthermore, we find evidence that the monsoon season is associated with a significant reduction in the protective role of UVB radiation. Our study outlines the roles of the monsoon season and UVB radiation in COVID-19 in India and supports health-related policy decision making in India.
Shares of open-end real estate funds are typically traded directly between the investor and the fund management company. However, we provide empirical evidence for the growth of secondary market activities, i.e., the trading of shares on stock exchanges. We find high trading levels in situations where the fund management company suspends the issue or redemption of shares. Shares trade at a discount when the fund management company suspends the redemption, whereas shares trade at a premium when the fund management company suspends the issue. We also find evidence that secondary market trading activity is increasing since German regulation introduced a minimum holding period and a mandatory notice period for open-end real estate funds.
Consider two independent random walks. By chance, there will be spells of association between them where the two processes move in the same direction, or in opposite direction. We compute the probabilities of the length of the longest spell of such random association for a given sample size, and discuss measures like mean and mode of the exact distributions. We observe that long spells (relative to small sample sizes) of random association occur frequently, which explains why nonsense correlation between short independent random walks is the rule rather than the exception. The exact figures are compared with approximations. Our finite sample analysis as well as the approximations rely on two older results popularized by Révész (Stat Pap 31:95–101, 1990, Statistical Papers). Moreover, we consider spells of association between correlated random walks. Approximate probabilities are compared with finite sample Monte Carlo results.
This note argues that in a situation of an inelastic natural gas supply a restrictive monetary policy in the euro zone could reduce the energy bill and therefore has additional merits. A more hawkish monetary policy may be able to indirectly use monopsony power on the gas market. The welfare benefits of such a policy are diluted to the extent that some of the supply (approximately 10 percent) comes from within the euro zone, which may give rise to distributional concerns.
We collect data on the size distribution of all U.S. corporate businesses for 100 years. We document that corporate concentration (e.g., asset share or sales share of the top 1%) has increased persistently over the past century. Rising concentration was stronger in manufacturing and mining before the 1970s, and stronger in services, retail, and wholesale after the 1970s. Furthermore, rising concentration in an industry aligns closely with investment intensity in research and development and information technology. Industries with higher increases in concentration also exhibit higher output growth. The long-run trends of rising corporate concentration indicate increasingly stronger economies of scale.
The authors present and compare Newton-based methods from the applied mathematics literature for solving the matrix quadratic that underlies the recursive solution of linear DSGE models. The methods are compared using nearly 100 different models from the Macroeconomic Model Data Base (MMB) and different parameterizations of the monetary policy rule in the medium-scale New Keynesian model of Smets and Wouters (2007) iteratively. They find that Newton-based methods compare favorably in solving DSGE models, providing higher accuracy as measured by the forward error of the solution at a comparable computation burden. The methods, however, suffer from their inability to guarantee convergence to a particular, e.g. unique stable, solution, but their iterative procedures lend themselves to refining solutions either from different methods or parameterizations.
Liquidity derivatives
(2022)
It is well established that investors price market liquidity risk. Yet, there exists no financial claim contingent on liquidity. We propose a contract to hedge uncertainty over future transaction costs, detailing potential buyers and sellers. Introducing liquidity derivatives in Brunnermeier and Pedersen (2009) improves financial stability by mitigating liquidity spirals. We simulate liquidity option prices for a panel of NYSE stocks spanning 2000 to 2020 by fitting a stochastic process to their bid-ask spreads. These contracts reduce the exposure to liquidity factors. Their prices provide a novel illiquidity measure refllecting cross-sectional commonalities. Finally, stock returns significantly spread along simulated prices.
In the communication of the European Central Bank (ECB), the statement that „we act within our mandate“ is often referred to. Also among practitioners of the Eurosystem the term „mandate“ has become popular. In his Working Paper, Helmut Siekmann analyzes the legal foundation of the tasks and objectives of the Eurosysstem and price stability as a legal term. He finds that the primary law of the EU only very sparsely employs the term „mandate“. It is never used in the context of monetary policy and its institutions. Moreover, he comes to the conclusion that inflation targeting as a task, competence, or objective of the Eurosystem is legally highly questionable according to the common standards of interpretation.
Identifying the cause of discrimination is crucial to design effective policies and to understand discrimination dynamics. Building on traditional models, this paper introduces a new explanation for discrimination: discrimination based on motivated reasoning. By systematically acquiring and processing information, individuals form motivated beliefs and consequentially discriminate based on these beliefs. Through a series of experiments, I show the existence of discrimination based on motivated reasoning and demonstrate important differences to statistical discrimination and taste-based discrimination. Finally, I demonstrate how this form of discrimination can be alleviated by limiting individuals’ scope to interpret information.
Spillovers of PE investments
(2022)
In this paper, we investigate a primary potential impact of leveraged buyout (LBOs) transactions: the effects of LBOs on the peers of the LBO target in the same industry. Using a data sample based on US LBO transactions between 1985 and 2016, we investigate the impact of the peer firms in the aftermath of the transaction, relative to non-peer firms. To account for potential endogeneity concerns, we employ a network-based instrumental variable approach. Based on this analysis, we find support for the proposition that LBOs do indeed matter for peer firms’ performance and corporate strategy relative to non-peer firms. Our study supports a learning factor hypothesis: peers gain by learning from the LBO target to improve their operational performance. Conversely, we find no evidence to support the conjecture that peers lose due to the increased competitiveness of the LBO target firm.
Vehicle registrations have been shown to strongly react to tax reforms aimed at reducing CO2 emissions from passengers’ cars, but are the effects equally strong for positive and negative tax changes? The literature on asymmetric reactions to price and tax changes has documented asymmetries for everyday goods but has not yet considered durables. We leverage multiple vehicle registration tax (VRT) reforms in Norway and estimate their impact on within car-model substitutions. We estimate stronger effects for cars receiving tax cuts and rebates than for those affected by tax increases. The corresponding estimated elasticity is − 1.99 for VRT decreases and 0.77 for increases. As consumers may also substitute across car models, our estimates represent a lower bound.
The authors study the effects of forward looking communication in an environment of rising inflation rates on German consumers‘ inflation expectations using a randomized control trial. They show that information about rising inflation increases short- and long-term inflation expectations. This initial increase in expectations can be mitigated using forward looking information about inflation. Among these information treatments, professional forecasters‘ projections seem to reduce inflation expectations by more than policymakers‘ characterization of inflation as a temporary phenomenon.
The reuse of collateral can support the efficient allocation of safe assets in the financial system. Exploiting a novel dataset, we show that banks substantially increase their reuse of sovereign bonds in response to scarcity induced by Eurosystem asset purchases. While repo rates react little to purchase-induced scarcity when reuse is low, they become increasingly sensitive at high levels of reuse. An elevated reuse rate is also associated with more failures to deliver and a higher volatility of repo rates in the cross-section of bonds. Our results highlight the trade-off between shock absorption and shock amplification effects of collateral reuse.
Peer effects can lead to better financial outcomes or help propagate financial mistakes across social networks. Using unique data on peer relationships and portfolio composition, we show considerable overlap in investment portfolios when an investor recommends their brokerage to a peer. We argue that this is strong evidence of peer effects and show that peer effects lead to better portfolio quality. Peers become more likely to invest in funds when their recommenders also invest, improving portfolio diversification compared to the average investor and various placebo counterfactuals. Our evidence suggests that social networks can provide good advice in settings where individuals are personally connected.
Cryptocurrencies provide a unique opportunity to identify how derivatives impact spot markets. They are fully fungible, trade across multiple spot exchanges at different prices, and futures contracts were selectively introduced on bitcoin (BTC) exchange rates against the USD in December 2017. Following the futures introduction, we find a significantly greater increase in cross-exchange price synchronicity for BTC--USD relative to other exchange rate pairs, as demonstrated by an increase in price correlations and a reduction in arbitrage opportunities and volatility. We also find support for an increase in price efficiency, market quality, and liquidity. The evidence suggests that futures contracts allowed investors to circumvent trading frictions associated with short sale constraints, arbitrage risk associated with block confirmation time, and market segmentation. Overall, our analysis supports the view that the introduction of BTC--USD futures was beneficial to the bitcoin spot market by making the underlying prices more informative.
he ECB is independent, but it is also accountable to the European parliament (EP). Yet, how the EP has held the ECB accountable has largely been overlooked. This paper starts addressing this gap by providing descriptive statistics of three accountability modalities. The paper highlights three findings. First, topics of accountability have changed. Climate-related accountability has increased quickly and dramatically since 2017. Second, if the relationship between price stability and climate change remains an object of conflict among MEPs, a majority within the EP has emerged to put pressure for the ECB to take a more active stance against climate change, precisely on behalf of its price stability mandate. Third, MEPs engage with the climate topic in very specific ways. There is a gender divide between the climate and the price stability topics. Women engage more actively with climate-related topics. While the Greens heavily dominate the climate topic, parties from the Right dominate the topic of Price stability. Finally, MEPs adopt a more united strategy and a particularly low confrontational tone in their climate-related interventions.
Veronika Grimm, Lukas Nöh, and Volker Wieland assess the possible development of government interest expenditures as a share of GDP for Germany, France, Italy and Spain. Until 2021, these and other member states could anticipate a further reduction of interest expenditure in the future. This outlook has changed considerably with the recent surge in inflation and government bond rates. Nevertheless, under reasonable assumptions current yield curves still imply that interest expenditure relative to GDP can be stabilized at the current level. The authors also review the implications of a further upward shift in the yield curves of 1 or 2 percentage points. These implications suggest significant medium-term risks for highly indebted member states with interest expenditure approaching or exceeding levels last observed on the eve of the euro area debt crisis. In light of these risks, governments of euro area member states should take substantive action to achieve a sustained decline in debt-to-GDP ratios towards safer levels. They bear the responsibility for making sure that government finances can weather the higher interest rates which are required to achieve price stability in the euro area.
Central banks have faced a succession of crises over the past years as well as a number of structural factors such as a transition to a greener economy, demographic developments, digitalisation and possibly increased onshoring. These suggest that the future inflation environment will be different from the one we know. Thus uncertainty about important macroeconomic variables and, in particular, inflation dynamics will likely remain high.
Global consensus is growing on the contribution that corporations and finance must make towards the net-zero transition in line with the Paris Agreement goals. However, most efforts in legislative instruments as well as shareholder or stakeholder initiatives have ultimately focused on public companies.
This article argues that such a focus falls short of providing a comprehensive approach to the problem of climate change. In doing so, it examines the contribution of private companies to climate change, the relevance of climate risks for them, as well as the phenomenon of brown-spinning (ie, the practice of public companies selling their highly polluting assets to private companies). We show that one cannot afford to ignore private companies in the net-zero transition and climate change adaptation. Yet, private companies lack several disciplining mechanisms that are available to public companies, such as institutional investor engagement, certain corporate governance arrangements, and transparency through regular disclosure obligations. At this stage, only some generic regulatory instruments such as carbon pricing and environmental regulation apply to them.
The article closes with a discussion of the main policy implications. Primarily, we discuss and evaluate the recent push to extend climate-related disclosure requirements to private companies. These disclosures would not only help investors by addressing information asymmetry, but also serve a wide group of stakeholders and thus aim at promoting a transition to a greener economy.
The authors study the impact of dissent in the ECB‘s Governing Council on uncertainty surrounding households‘ inflation expectations. They conduct a randomized controlled trial using the Bundesbank Online Panel Households. Participants are provided with alternative information treatments concerning the vote in the Council, e.g. unanimity and dissent, and are asked to submit probabilistic inflation expectations. The results show that the vote is informative.
Households revise their subjective inflation forecast after receiving information about the vote. Dissenting votes cause a wider individual distribution of future inflation. Hence, dissent increases households‘ uncertainty about inflation. This effect is statistically significant once the authors allow for the interaction between the treatments and individual characteristics of respondents.
The results are robust with respect to alternative measures of forecast uncertainty and hold for different model specifications. The findings suggest that providing information about dissenting votes without additional information about the nature of dissent is detrimental to coordinating household expectations.
This paper uses historical monthly temperature level data for a panel of 114 countries to identify the effects of within year temperature level variability on productivity growth in five different macro regions, i.e., (1) Africa, (2) Asia, (3) Europe, (4) North America and (5) South America. We find two primary results. First, higher intra-annual temperature variability reduces (increases) productivity in Europe and North America (Asia). Second, higher intra-annual temperature variability has no significant effects on productivity in Africa and South America. Additional empirical tests indicate also the following: (1) rising intra-annual temperature variability reduces productivity (even thought less significantly)in both tropical and non-tropical regions, (2) inter-annual temperature variability reduces (increases) productivity in North America (Europe) and (3) winter and summer inter-annual temperature variability generates a drop in productivity in both Europe and North America. Taken together, these findings indicate that temperature variability shocks tend to have stronger adverse economic effects among richer economies. In a production economy featuring long-run productivity and temperature volatility shocks, we quantify these negative impacts and find welfare losses of 2.9% (1%) in Europe (North America).
Energy efficiency represents one of the key planned actions aiming at reducing greenhouse emissions and the consumption of fossil fuel to mitigate the impact of climate change. In this paper, we investigate the relationship between energy efficiency and the borrower’s solvency risk in the Italian market. Specifically, we analyze a residential mortgage portfolio of four financial institutions which includes about 70,000 loans matched with the energy performance certificate of the associated buildings. Our findings show that there is a negative relationship between a building’s energy efficiency and the owner’s probability of default. Findings survive after we account for dwelling, household, mortgage, market control variables, and regional and year fixed effect. Additionally, a ROC analysis shows that there is an improvement in the estimation of the mortgage default probability when the energy efficiency characteristic is included as a risk predictor in the model.
We investigate what statistical properties drive risk-taking in a large set of observational panel data on online poker games (n=4,450,585). Each observation refers to a choice between a safe 'insurance' option and a binary lottery of winning or losing the game. Our setting offers a real-world choice situation with substantial incentives where probability distributions are simple, transparent, and known to the individuals. We find that individuals reveal a strong and robust preference for skewness. The effect of skewness is most pronounced among experienced and losing players but remains highly significant for winning players, in contrast to the variance effect.
The authors focus on the stabilizing role of cash from a society-wide perspective. Starting with conceptual remarks on the importance of money for the economy in general, special attention is paid to the unique characteristics of cash. As these become apparent especially during crisis periods, a comparison of the Great Depression (1929 – 1933) and the Great Recession 2008/09 shows the devastating effects of a severe monetary contraction and how a fully elastic provision of cash can help to avoid such a situation.
The authors find interesting similarities to both crises in two separate case studies, one on the demonetization in India 2016 and the other on cash supply during various crises in Greece since 2008. The paper concludes that supply-driven cash withdrawals from circulation (either by demonetization or by capital controls) destabilize the economy if electronic payment substitutes are not instantly available.
However, as there is no perfect substitute for cash due to its unique properties, from the viewpoint of the society as a whole an efficient payment mix necessarily includes cash: It helps to stabilize the economy not only in times of crises in general, no matter which government is in place. The authors argue that it should be the undisputed task of central banks to ensure that cash remains in circulation in normal times and is provided in a fully elastic way in times of crisis.
Solving High-Dimensional Dynamic Portfolio Choice Models with Hierarchical B-Splines on Sparse Grids
(2021)
Discrete time dynamic programming to solve dynamic portfolio choice models has three immanent issues: firstly, the curse of dimensionality prohibits more than a handful of continuous states. Secondly, in higher dimensions, even regular sparse grid discretizations need too many grid points for sufficiently accurate approximations of the value function. Thirdly, the models usually require continuous control variables, and hence gradient-based optimization with smooth approximations of the value function is necessary to obtain accurate solutions to the optimization problem. For the first time, we enable accurate and fast numerical solutions with gradient-based optimization while still allowing for spatial adaptivity using hierarchical B-splines on sparse grids. When compared to the standard linear bases on sparse grids or finite difference approximations of the gradient, our approach saves an order of magnitude in total computational complexity for a representative dynamic portfolio choice model with varying state space dimensionality, stochastic sample space, and choice variables.
Are sanctions sustainable?
(2022)
This work uses financial markets connected by arbitrage relations to investigate the dynamics of price and liquidity discovery, which refer to the cross-instrument forecasting power for prices and liquidity, respectively. Specifically, we seek to understand the linkage between the cheapest to deliver bond and closest futures pairs by using high-frequency data on European governments obligations and derivatives. We split the 2019-2021 sample into three subperiods to appreciate changes in the liquidity discovery induced by the COVID-19 pandemic. Within a cointegration model, we find that price discovery occurs on the futures market, and document strong empirical support for liquidity spillovers both from the futures to the cash market as well as from the cash to the futures market.
Despite the impressive success of deep neural networks in many application areas, neural network models have so far not been widely adopted in the context of volatility forecasting. In this work, we aim to bridge the conceptual gap between established time series approaches, such as the Heterogeneous Autoregressive (HAR) model (Corsi, 2009), and state-of-the-art deep neural network models. The newly introduced HARNet is based on a hierarchy of dilated convolutional layers, which facilitates an exponential growth of the receptive field of the model in the number of model parameters. HARNets allow for an explicit initialization scheme such that before optimization, a HARNet yields identical predictions as the respective baseline HAR model. Particularly when considering the QLIKE error as a loss function, we find that this approach significantly stabilizes the optimization of HARNets. We evaluate the performance of HARNets with respect to three different stock market indexes. Based on this evaluation, we formulate clear guidelines for the optimization of HARNets and show that HARNets can substantially improve upon the forecasting accuracy of their respective HAR baseline models. In a qualitative analysis of the filter weights learnt by a HARNet, we report clear patterns regarding the predictive power of past information. Among information from the previous week, yesterday and the day before, yesterday's volatility makes by far the most contribution to today's realized volatility forecast. Moroever, within the previous month, the importance of single weeks diminishes almost linearly when moving further into the past.
For the academic audience, this paper presents the outcome of a well-identified, large change in the monetary policy rule from the lens of a standard New Keynesian model and asks whether the model properly captures the effects. For policymakers, it presents a cautionary tale of the dismal effects of ignoring basic macroeconomics. The Turkish monetary policy experiment of the past decade, stemming from a belief of the government that higher interest rates cause higher inflation, provides an unfortunately clean exogenous variance in the policy rule. The mandate to keep rates low, and the frequent policymaker turnover orchestrated by the government to enforce this, led to the Taylor principle not being satisfied and eventually a negative coeffcient on inflation in the policy rule. In such an environment, was the exchange rate still a random walk? Was inflation anchored? Does the “standard model”” suffice to explain the broad contours of macroeconomic outcomes in an emerging economy with large identifying variance in the policy rule? There are no surprises for students of open-economy macroeconomics; the answers are no, no, and yes.
The mobile games business is an ever-increasing sub-sector of the entertainment industry. Due to its high profitability but also high risk and competitive atmosphere, game publishers need to develop strategies that allow them to release new products at a high rate, but without compromising the already short lifespan of the firms' existing games. Successful game publishers must enlarge their user base by continually releasing new and entertaining games, while simultaneously motivating the current user base of existing games to remain active for more extended periods. Since the core-component reuse strategy has proven successful in other software products, this study investigates the advantages and drawbacks of this strategy in mobile games. Drawing on the widely accepted Product Life Cycle concept, the study investigates whether the introduction of a new mobile game built with core-components of an existing mobile game curtails the incumbent's product life cycle. Based on real and granular data on the gaming activity of a popular mobile game, the authors find that by promoting multi-homing (i.e., by smartly interlinking the incumbent and new product with each other so that users start consuming both games in parallel), the core-component reuse strategy can prolong the lifespan of the incumbent game.
Why bank money creation?
(2022)
We provide a rationale for bank money creation in our current monetary system by investigating its merits over a system with banks as intermediaries of loanable funds. The latter system could result when CBDCs are introduced. In the loanable funds system, households limit banks’ leverage ratios when providing deposits to make sure they have enough “skin in the game” to opt for loan monitoring. When there is unobservable heterogeneity among banks with regard to their (opportunity) costs from monitoring, aggregate lending to bank-dependent firms is inefficiently low. A monetary system with bank money creation alleviates this problem, as banks can initiate lending by creating bank deposits without relying on household funding. With a suitable regulatory leverage constraint, the gains from higher lending by banks with a high repayment pledgeability outweigh losses from banks which are less diligent in monitoring. Bank-risk assessments, combined with appropriate risk-sensitive capital requirements, can reduce or even eliminate such losses.
The present paper proposes an overview of the existing literature covering several aspects related to environmental, social, and governance (ESG) factors. Specifically, we consider studies describing and evaluating ESG methodologies and those studying the impact of ESG on credit risk, debt and equity costs, or sovereign bonds. We further expand the topic of ESG research by including the strand of the literature focusing on the impact of climate change on financial stability, thus allowing us to also consider the most recent research on the impact of climate change on portfolio management.
We investigate the link between Big Five personality traits and the marginal propensity to consume (MPC) for users of a German financial account aggregator app. We use 1,700 survey responses and transaction data of 56,000 app users to assess whether Big Five personality traits help explain MPC heterogeneity. We find that extraversion corresponds to an increase in consumption whereas agreeableness and neuroticism correspond to a decrease in consumption. We test this with trust and risk preferences and find that risk indicates more explanatory power in consumption response than the Big Five. Our findings help policy makers target individuals more efficiently.
The Russian war of aggression against Ukraine since 24 February 2022 has intensified the discussion of Europe’s reliance on energy imports from Russia. A ban on Russian imports of oil, natural gas and coal has already been imposed by the United States, while the United Kingdom plans to cease imports of oil and coal from Russia by the end of 2022. The German Federal Government is currently opposing an energy embargo against Russia. However, the Federal Ministry for Economic Affairs and Climate Action is working on a strategy to reduce energy imports from Russia. In this paper, the authors give an overview of the German and European reliance on energy imports from Russia with a focus on gas imports and discuss price effects, alternative suppliers of natural gas, and the potential for saving and replacing natural gas. They also provide an overview of estimates of the consequences on the economic outlook if the conflict intensifies.
This briefing paper describes and evaluates the law and economics of institution(al) protection schemes. Throughout our analysis, we use Europe’s largest such scheme, that of German savings banks, as paradigm. We find strengths and weaknesses: Strong network-internal monitoring and early warning seems to be an important contributor to IPS network success. Similarly, the geographical quasi-cartel encourages banks to build a strong client base, including SME, in all regions. Third, the growth of the IPS member institutions may have benefitted from the strictly unlimited protection offered, in terms of euro amounts per account holder. The counterweighing weaknesses encompass the conditionality of the protection pledge and the underinvestment risk it entails, sometimes referred to as blackmailing the government, as well as the limited diversification potential of the deposit insurance within the network, and the near-incompatibility of the IPS model with the provisions of the BRRD, particularly relating to bail-in and resolution. Consequently, we suggest, as policy guidance, to treat large IPS networks similar to large banking groups, and put them as such under the direct supervision of the ECB within the SSM. Moreover, we suggest strengthening the seriousness of a deposit insurance that offers unlimited protection. Finally, to improve financial stability, we suggest embedding the IPS model into a multi-tier deposit re-insurance scheme, with a national and a European layer. This document was provided by the Economic Governance Support Unit at the request of the ECON Committee.
This paper examines auditor liability rules under imperfect information, costly litigation and risk averse auditors. A negligence rule fails in such a setting, because in equilibrium auditors will deviate with positive probability from any given standard. It is shown that strict liability outperforms negligence with respect to risk allocation, and the probability that a desired level of care is met by the audi tor if competitive liability insurance markets exist. Furthermore, our model explains the existence of insurance contracts containing obligations - a type of contract often observed in liability insurance markets.
Real estate is an important asset, but as a direct investment subject to several difficulties. Shares of public open end funds or of real estate stock corporations represent a possible way for an investor to avoid these problems. The focus of this paper is the analysis of inflation risk of European real estate securities. An overview of the institutional frameworks regarding these companies is given. The returns of real estate securities in France, Germany, Switzerland and the United Kingdom are examined for the period 1980:1-1998:12. Besides the classical Fama/Schwert-approach, shortfall risk measurements have been used. In this context, transaction costs in particular have been taken into account.
Digital wealth and its necessary regulation have gained prominence in recent years. The European Commission has published several documents and policy proposals relating, directly or indirectly, to the data economy. A data economy can be defined as an ecosystem of different types of market players collaborating to ensure that data is accessible and usable in order to extract value from data through, for example, creating a variety of applications with great potential to improve daily life. The value of data can increase from EUR 257 billion (1.85 of EU Gross Domestic Product (GDP)) to EUR 643 billion by 2020 (3.17% of EU GDP), according to the EU Commission. The legal implications of the increasing value of the data economy are clear; hence the need to address the challenges presented by its legal regulation.
The health and genetic data of deceased people are a particularly important asset in the field of biomedical research. However, in practice, using them is compli- cated, as the legal framework that should regulate their use has not been fully developed yet. The General Data Protection Regulation (GDPR) is not applicable to such data and the Member States have not been able to agree on an alternative regulation. Recently, normative models have been proposed in an attempt to face this issue. The most well- known of these is posthumous medical data donation (PMDD). This proposal supports an opt-in donation system of health data for research purposes. In this article, we argue that PMDD is not a useful model for addressing the issue at hand, as it does not consider that some of these data (the genetic data) may be the personal data of the living relatives of the deceased. Furthermore, we find the reasons supporting an opt-in model less convincing than those that vouch for alternative systems. Indeed, we propose a normative framework that is based on the opt-out system for non-personal data combined with the application of the GDPR to the relatives’ personal data.
The quality of life: protecting non-personal interests and non-personal data in the age of big data
(2021)
Under the current legal paradigm, the rights to privacy and data protection provide natural persons with subjective rights to protect their private interests, such as related to human dignity, individual autonomy and personal freedom. In principle, when data processing is based on non-personal or aggregated data or when such data pro- cesses have an impact on societal, rather than individual interests, citizens cannot rely on these rights. Although this legal paradigm has worked well for decades, it is increasingly put under pressure because Big Data processes are typically based indis- criminate rather than targeted data collection, because the high volumes of data are processed on an aggregated rather than a personal level and because the policies and decisions based on the statistical correlations found through algorithmic analytics are mostly addressed at large groups or society as a whole rather than specific individuals. This means that large parts of the data-driven environment are currently left unregu- lated and that individuals are often unable to rely on their fundamental rights when addressing the more systemic effects of Big Data processes. This article will discuss how this tension might be relieved by turning to the notion ‘quality of life’, which has the potential of becoming the new standard for the European Court of Human Rights (ECtHR) when dealing with privacy related cases.
Ownership of databases: personal data protection and intellectual property rights on databases
(2021)
When we think on initiatives on access to and reuse of data, we must consider both the European Intellectual Property Law and the General Data Protection Regulation (GDPR). The first one provides a special intellectual property (IP) right – the sui generis right – for those makers that made a substantial investment when creating the database, whether it contains personal or non-personal data. That substantial investment can be made by just one person, but, in many cases, it is the result of the activities of many people and/or some undertakings processing and aggregating data. In the modern digital economy, data are being dubbed the ‘new oil’ and the sui generis right might be con- sidered a right to control any access to the database, thus having an undeniable relevance. Besides, there are still important inconsistences between IP Law and the GDPR, which must be removed by the European legislator. The genuine and free consent of the data subject for the use of his/her data must remain the first step of the legal analysis.
Commercialization of consumers’ personal data in the digital economy poses serious, both conceptual and practical, challenges to the traditional approach of European Union (EU) Consumer Law. This article argues that mass-spread, automated, algorithmic decision-making casts doubt on the foundational paradigm of EU consumer law: consent and autonomy. Moreover, it poses threats of discrimination and under- mining of consumer privacy. It is argued that the recent legislative reaction by the EU Commission, in the form of the ‘New Deal for Consumers’, was a step in the right direction, but fell short due to its continued reliance on consent, autonomy and failure to adequately protect consumers from indirect discrimination. It is posited that a focus on creating a contracting landscape where the consumer may be properly informed in material respects is required, which in turn necessitates blending the approaches of competition, consumer protection and data protection laws.
Large companies are increasingly on trial. Over the last decade, many of the world’s biggest firms have been embroiled in legal disputes over corruption charges, financial fraud, environmental damage, taxation issues or sanction violations, ending in convictions or settlements of record-breaking fines, well above the billion-dollar mark. For critics of globalization, this turn towards corporate accountability is a welcome sea-change showing that multinational companies are no longer above the law. For legal experts, the trend is noteworthy because of the extraterritorial dimensions of law enforcement, as companies are increasingly held accountable for activities independent of their nationality or the place of the activities. Indeed, the global trend required understanding the evolution of corporate criminal law enforcement in the United States in particular, where authorities have skillfully expanded its effective jurisdiction beyond its territory. This paper traces the evolution of corporate prosecutions in the United States. Analyzing federal prosecution data, it then shows that foreign firms are more likely to pay a fine, which is on average 6,6 times larger.
For private investors it is imperative to a) understand and define their own, individual risk preferences, b) assess their financial and demographic circumstances to determine the individual risk-taking potential, and c) form and maintain a well-diversified risky portfolio. The three chapters of my thesis each match one of these three tasks. \\ \noindent The first chapter of my thesis presents novel experimental evidence to test the existence of a potential projection bias in loss aversion, a significant determinant of investor preferences, thus matching task a). The second chapter is devoted to the determination of private investors' risk-taking potential based on their financial and socio-demographic circumstances, matching task b): In a large portfolio experiment, we examine the ability and heterogeneity of lay and professional advisors in matching investor demographics, such as age and income, with risky asset portfolio shares. The third and final chapter addresses the question on how to reach and maintain an efficient risky portfolio, therefore matching task c): It analyzes a decision support system for private investors that allows its users to simulate any arbitrary set of securities, and by reporting aggregated expected return and risk, to optimize their current portfolio.
This dissertation consists of four self-contained chapters in the overlapping fields of industrial organization and organizational economics on the topics pricing, careers and supervision. Each chapter is the result of an independent research project. The dissertation analyzes empirical research topics by exploring novel observational data sets. It sheds light on open questions in the economic profession by extending fundamental models on pricing in the first two chapters and by challenging conventional explanations and methods on careers and supervision in the last two chapters.
- Chapter 1:
The first chapter is based on joint work with Steffen Eibelshäuser. It models price competition among brick-and-mortar retailers with business hours. Specifically, we propose a dynamic model of intraday price competition featuring spatial differentiation and firm size heterogeneity. The model makes detailed predictions concerning equilibrium-pricing patterns. When spatial differentiation is high and consumers cannot easily switch between retailers, equilibrium prices are stable at oligopoly levels. When differentiation is low, equilibrium prices fluctuate in cycles. The shapes of the cycles depend on the level of differentiation and on retailers’ reaction times. When reaction times decrease, the number of price cycles increases. In a second step, we apply the model to the German retail gasoline market. Gasoline retailers have been using digital price tags for decades and fast-paced price competition with more than ten price changes per day is no exception. Our model has successfully predicted the emergence of an additional intraday subcycle in April 2017. Moreover, we were able to confirm several detailed predictions concerning the shape of equilibrium price paths and individual firm behavior. Finally, we calibrate the model using a generalized method of moments. The model fits the data remarkably well, with coefficients of determination ranging from 60% to 80%. We use the fitted model to evaluate a number of policy counterfactuals. Restricting price increases results in higher prices and decreased welfare, leading us to conclude that regulation of dynamic markets is highly complex and can easily backfire.
- Chapter 2:
The second chapter analyzes the price-matching policies of two gasoline retailers. Customers of these retailers that are able to provide evidence of competitors posting lower prices have the ability to claim price matches. As shown in the first chapter, the Edgeworth Cycle model rationalizes price fluctuations in the German gasoline retail market. To determine policy interactions in cycling markets, this chapter extends the classical Edgeworth Cycle model by price-matching. The model predicts that price-matching retailers post higher prices and initiate price increases. The price-consulted firm anticipates this strategy, posts lower prices, and provokes the implementing firm to restore the price more frequently. Consulted stations also anticipate earlier price restoration reactions from implementing stations and, thus, provoke restorations earlier. This effect dominates in welfare calculations, such that price matching has positive welfare implications.
The second part of the chapter tests the hypotheses with price data on the German gasoline retail market. The estimation exploits a discontinuity in the policy-affected retailers. Therefore, the analysis disentangles the competitive effects of implementing and price-consulted market participants in comparison to retailers that are not affected. As predicted, the posted average and minimum prices of one implementing retailer and its consulted competitors increase. For the other price-matching retailer, I find reduced prices that contradict the model. The last part of the chapter relates the empirics to static models and shows that the dynamic component provides previously undiscovered insights.
- Chapter 3:
The third chapter is based on joint work with Emmanuelle Auriol and Guido Friebel. It represents the subtopic of careers in this dissertation. Specifically, the chapter provides the first comprehensive data collection analysis of women’s careers in all European research institutions in the field of economics. Using a web-scraping algorithm that constantly accesses position information on institutions’ websites, we collect a novel data set on researchers in Europe. These details entail information on researchers’ gender obtained by the first name and a face recognition. Similar to survey data on U.S. institutions, we identify a leaky pipeline, as women are less likely to become professors than men are. The situation is very heterogeneous across Europe. The gap is substantially larger in Western and Southern Europe than in Central and Eastern Europe. Furthermore, we identify institutions with a higher research output and a better research-ranking having a systematically lower share of females in full professor positions as well as entry-level positions for Ph.D. graduates. Austria, Belgium, Italy, Portugal, and Spain are the drivers for this correlation. All these results are in line with the “leaky pipeline” hypothesis, in which, over the different stages of a career, the attrition of women is higher than the one of men. We show that the cohort hypothesis arguing that the lag effect between the time of Ph.D. completion and the time of promotion to a full professorship is unable to explain the current low number of females.
- Chapter 4:
The fourth and last chapter "What does Mystery Shopping do?" is based on joint work with Sidney Block, Guido Friebel, Matthias Heinz, and Nick Zubanov. It addresses an auditing practice with a yearly U.S.-turnover of 19.5 billion USD in 2016 (European Society for Opinion and Market Research, 2017: Global Market Research 2017). The term mystery represents the key aspect of the tool. During an anonymous visit, so-called mystery shoppers perform certain predefined tasks such as purchasing a product, asking questions, registering complaints, or behaving in a certain way. Following their visit, the shoppers provide detailed reports about their experiences to the evaluated firms. The chapter investigates whether the practice is suitable to determine employees’ pay. Contrary to the general understanding that firms are able to observe service quality and, in turn, can proxy for business success with mystery shopping, we do not observe mystery-shopping evaluations to correlate positively with firm performance. A decomposition of the evaluation reports indicates that mystery-shopping scores are biased and the shopper’s identity explains up to 20% of the score’s variance. Thus, the shopper’s identity has the largest impact out of all observable characteristics. With the results that mystery-shopping scores are noisy and biased, we conclude that they are not suitable for performance pay in the context of our study. In addition, we show that if the number of observations is sufficiently large, aggregated scores relate to business success. The required number of shops per evaluation period must be, however, larger by a factor between 3 and 30 per evaluated subject. Hence, cost advantages of mystery shopping diminish such that the cost benefits to customer assessments could vanish completely. The current methodology, however, may still be useful for other employee-related purposes like monitoring, which is in line with the policies of the considered firms.
What are the effects of the GDPR on consumer apps? This article presents an analysis of app behavior before and after the regulatory change in data protection in Europe. Based on long-term data collection, we present differences in app permission use and expressed user concerns and discuss their implications. In May 2018, the General Data Protection Regulation (GDPR) changed the data protection obligations of the information industry with the European Union users substantially. One should expect to find changes in code, program behavior and data collection activities. To investigate this expectation, we analyzed data about Android apps request and use of permissions to access sensitive group of data on smartphones, and collected user reviews. Our data shows an overall reduction of both permissions used and of expressed user concern. However, in some areas apps have increased access or user complaints while in addition, many apps carry with them several unused access privileges.
Households regularly fail to make optimal financial decisions. But what are the underlying reasons for this? Using two conceptually distinct measures of time inconsistency based on bank account transaction data and behavioral measurement experiments, we show that the excessive use of bank account overdrafts is linked to time inconsistency. By contrast, there is no correlation between a survey-based measure of financial literacy and overdraft usage. Our results indicate that consumer education and information may not suffice to overcome mistakes in households’ financial decision-making. Rather, behaviorally motivated interventions targeting specific biases in decision-making should also be considered as effective policy tools.
Enabling cybersecurity and protecting personal data are crucial challenges in the development and provision of digital service chains. Data and information are the key ingredients in the creation process of new digital services and products. While legal and technical problems are frequently discussed in academia, ethical issues of digital service chains and the commercialization of data are seldom investigated. Thus, based on outcomes of the Horizon2020 PANELFIT project, this work discusses current ethical issues related to cybersecurity. Utilizing expert workshops and encounters as well as a scientific literature review, ethical issues are mapped on individual steps of digital service chains. Not surprisingly, the results demonstrate that ethical challenges cannot be resolved in a general way, but need to be discussed individually and with respect to the ethical principles that are violated in the specific step of the service chain. Nevertheless, our results support practitioners by providing and discussing a list of ethical challenges to enable legally compliant as well as ethically acceptable solutions in the future.
Public kindergarten, maternal labor supply, and earnings in the longer run: too little too late?
(2021)
By facilitating early re-entry to the labor market after childbirth, public kindergarten might positively affect maternal human capital and labor market outcomes: Are such effects long-lasting? Can we rely on between-individuals differences in quarter of birth to identify them? I isolate the effects of interest from spurious associations through difference-in-difference, exploiting across-states and over-time variation in public kindergarten eligibility regulations in the United States. The estimates suggest a very limited impact in the first year, and no longer-run impacts. Even in states where it does not affect kindergarten eligibility, quarter of birth is strongly and significantly correlated with maternal outcomes.
This policy letter collects elementary economic statistics and provides a very basic look on Russian public finances (i) to inform the reader’s opinion on a possible planning process behind the war against Ukraine and (ii) to discuss prospects of an energy embargo and its capability to affect the stability of the Russian economy.
Agencies around the world are in the process of developing taxonomies and standards for sustainable (or ESG) investment products. A key assumption in our model is that of non-consequentialist private investors (households) who derive a "warm glow" decisional utility when purchasing an investment product that is labelled as sustainable. We ask when such labelling is socially beneficial even when the socialplanner can impose a minimum standard on investment and production. In a model of financial constraints (Holmström and Tirole 1997), which we close to include consumer surplus, we also determine the optimal labelling threshold and show how its stringency is affected by determinants such as the prevalence of warm-glow investor preferences, the presence of social network effects, or the relevance of financial constraints at the industry level.
This paper examines optimal enviromental policy when external financing is costly for firms. We introduce emission externalities and industry equilibrium in the Holmström and Tirole (1997) model of corporate finance. While a cap-and- trading system optimally governs both firms` abatement activities (internal emission margin) and industry size (external emission margin) when firms have sufficient internal funds, external financing constraints introduce a wedge between these two objectives. When a sector is financially constrained in the aggregate, the optimal cap is strictly above the Pigouvian benchmark and emission allowances should be allocated below market prices. When a sector is not financially constrained in the aggregate, a cap that is below the Pigiouvian benchmark optimally shifts market share to less polluting firms and, moreover, there should be no "grandfathering" of emission allowances. With financial constraints and heterogeneity across firms or sectors, a uniform policy, such as a single cap-and-trade system, is typically not optimal.
We study liquidity provision by competitive high-frequency trading firms (HFTs) in a dynamic trading model with private information. Liquidity providers face adverse selection risk from trading with privately informed investors and from trading with other HFTs that engage in latency arbitrage upon public information. The impact of the two different sources of risk depends on the details of the market design. We determine equilibrium transaction costs in continuous limit order book (CLOB) markets and under frequent batch auctions (FBA). In the absence of informed trading, FBA dominates CLOB just as in Budish et al. (2015). Surprisingly, this result does no longer hold with privately informed investors. We show that FBA allows liquidity providers to charge markups and earn profits – even under risk neutrality and perfect competition. A slight variation of the FBA design removes the inefficiency by allowing traders to submit orders conditional on auction excess demand.
While the COVID-19 pandemic had a large and asymmetric impact on firms, many countries quickly enacted massive business rescue programs which are specifically targeted to smaller firms. Little is known about the effects of such policies on business entry and exit, factor reallocation, and macroeconomic outcomes. This paper builds a general equilibrium model with heterogeneous and financially constrained firms in order to evaluate the short- and long-term consequences of small firm rescue programs in a pandemic recession. We calibrate the stationary equilibrium and the pandemic shock to the U.S. economy, taking into account the factual Paycheck Protection Program (PPP) as a specific grant policy. We find that the policy has only a small impact on aggregate employment because (i) jobs are saved predominately in less productive firms that account for a small share of employment and (ii) the grant induces a reallocation of resources away from larger and less impacted firms. Much of this reallocation happens in the aftermath of the pandemic episode. While a universal grant reduces the firm exit rate substantially, a targeted policy is not only more cost-effective, it also largely prevents the creation of “zombie firms" whose survival is socially inefficient.