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We extend the canonical income process with persistent and transitory risk to cyclical shock distributions with left-skewness and excess kurtosis. We estimate our income process by GMM for US household data. We find countercyclical variance and procyclical skewness of persistent shocks. All shock distributions are highly leptokurtic. The tax and transfer system reduces dispersion and left-skewness. We then show that in a standard incomplete-markets life-cycle model, first, higherorder risk has sizable welfare implications, which depend on risk attitudes; second, it matters quantitatively for the welfare costs of cyclical idiosyncratic risk; third, it has non-trivial implications for self-insurance against shocks.
We consider an additively time-separable life-cycle model for the family of power period utility functions u such that u0(c) = c−θ for resistance to inter-temporal substitution of θ > 0. The utility maximization problem over life-time consumption is dynamically inconsistent for almost all specifications of effective discount factors. Pollak (1968) shows that the savings behavior of a sophisticated agent and her naive counterpart is always identical for a logarithmic utility function (i.e., for θ = 1). As an extension of Pollak’s result we show that the sophisticated agent saves a greater (smaller) fraction of her wealth in every period than her naive counterpart whenever θ > 1 (θ < 1) irrespective of the specification of discount factors. We further show that this finding extends to an environment with risky returns and dynamically inconsistent Epstein-Zin-Weil preferences.
Using a structural life-cycle model and data on school visits from Safegraph and school closures from Burbio, we quantify the heterogeneous impact of school closures during the Corona crisis on children affected at different ages and coming from households with different parental characteristics. Our data suggests that secondary schools were closed for in-person learning for longer periods than elementary schools (implying that younger children experienced less school closures than older children), and that private schools experienced shorter closures than public schools, and schools in poorer U.S. counties experienced shorter school closures. We then extend the structural life cycle model of private and public schooling investments studied in Fuchs-Schündeln, Krueger, Ludwig, and Popova (2021) to include the choice of parents whether to send their children to private schools, empirically discipline it with data on parental investments from the PSID, and then feed into the model the school closure measures from our empirical analysis to quantify the long-run consequences of the Covid-19 school closures on the cohorts of children currently in school. Future earnings- and welfare losses are largest for children that started public secondary schools at the onset of the Covid-19 crisis. Comparing children from the topto children from the bottom quartile of the income distribution, welfare losses are ca. 0.8 percentage points larger for the poorer children if school closures were unrelated to income. Accounting for the longer school closures in richer counties reduces this gap by about 1/3. A policy intervention that extends schools by 3 months (6 weeks in the next two summers) generates significant welfare gains for the children and raises future tax revenues approximately sufficient to pay for the cost of this schooling expansion.
Using a structural life-cycle model, we quantify the heterogeneous impact of school closures during the Corona crisis on children affected at different ages and coming from households with different parental characteristics. In the model, public investment through schooling is combined with parental time and resource investments in the production of child human capital at different stages in the children’s development process. We quantitatively characterize the long-term consequences from a Covid-19 induced loss of schooling, and find average losses in the present discounted value of lifetime earnings of the affected children of close to 1%, as well as welfare losses equivalent to about 0.6% of permanent consumption. Due to self-productivity in the human capital production function, skill attainment at a younger stage of the life cycle raises skill attainment at later stages, and thus younger children are hurt more by the school closures than older children. We find that parental reactions reduce the negative impact of the school closures, but do not fully offset it. The negative impact of the crisis on children’s welfare is especially severe for those with parents with low educational attainment and low assets. The school closures themselves are primarily responsible for the negative impact of the Covid-19 shock on the long-run welfare of the children, with the pandemic-induced income shock to parents playing a secondary role.
We characterize the optimal linear tax on capital in an Overlapping Generations model with two period lived households facing uninsurable idiosyncratic labor income risk. The Ramsey government internalizes the general equilibrium effects of private precautionary saving on factor prices and taxes capital unless the weight on future generations in the social welfare function is sufficiently high. For logarithmic utility a complete analytical solution of the Ramsey problem exhibits an optimal aggregate saving rate that is independent of income risk, whereas the optimal time-invariant tax on capital implementing this saving rate is increasing in income risk. The optimal saving rate is constant along the transition and its sign depends on the magnitude of risk and on the Pareto weight of future generations. If the Ramsey tax rate that maximizes steady state utility is positive, then implementing this tax rate permanently induces a Pareto-improving transition even if the initial equilibrium capital stock is below the golden rule.
Market risks account for an integral part of life insurers' risk profiles. This paper explores the market risk sensitivities of insurers in two large life insurance markets, namely the U.S. and Europe. Based on panel regression models and daily market data from 2012 to 2018, we analyze the reaction of insurers' stock returns to changes in interest rates and CDS spreads of sovereign counterparties. We find that the influence of interest rate movements on stock returns is more than 50% larger for U.S. than for European life insurers. Falling interest rates reduce stock returns in particular for less solvent firms, insurers with a high share of life insurance reserves and unit-linked insurers. Moreover, life insurers' sensitivity to interest rate changes is seven times larger than their sensitivity towards CDS spreads. Only European insurers significantly suffer from rising CDS spreads, whereas U.S. insurers are immunized against increasing sovereign default probabilities.
Life insurance convexity
(2021)
Life insurers massively sell savings contracts with surrender options which allow policyholders to withdraw a guaranteed amount before maturity. These options move toward the money when interest rates rise. Using data on German life insurers, we estimate that a 1 percentage point increase in interest rates raises surrender rates by 17 basis points. We quantify the resulting liquidity risk in a calibrated model of surrender decisions and insurance cash flows. Simulations predict that surrender options can force insurers to sell up to 3% of their assets, depressing asset prices by 90 basis points. The effect is amplified by the duration of insurers' investments, and its impact on the term structure of interest rates depends on life insurers' investment strategy.
Tail-correlation matrices are an important tool for aggregating risk measurements across risk categories, asset classes and/or business segments. This paper demonstrates that traditional tail-correlation matrices—which are conventionally assumed to have ones on the diagonal—can lead to substantial biases of the aggregate risk measurement’s sensitivities with respect to risk exposures. Due to these biases, decision-makers receive an odd view of the effects of portfolio changes and may be unable to identify the optimal portfolio from a risk-return perspective. To overcome these issues, we introduce the “sensitivity-implied tail-correlation matrix”. The proposed tail-correlation matrix allows for a simple deterministic risk aggregation approach which reasonably approximates the true aggregate risk measurement according to the complete multivariate risk distribution. Numerical examples demonstrate that our approach is a better basis for portfolio optimization than the Value-at-Risk implied tail-correlation matrix, especially if the calibration portfolio (or current portfolio) deviates from the optimal portfolio.
Historical evidence like the global financial crisis from 2007-09 highlights that sector concentration risk can play an important role for the solvency of insurers. However, current microprudential frameworks like the US RBC framework and Solvency II consider only name concentration risk explicitly in their solvency capital requirements for asset concentration risk and neglect sector concentration risk. We show by means of US insurers’ asset holdings from 2009 to 2018 that substantial sectoral asset concentrations exist in the financial, public and real estate sector, and find indicative evidence for a sectoral search for yield behavior. Based on a theoretical solvency capital allocation scheme, we demonstrate that the current regulatory approaches can lead to inappropriate and biased levels of solvency capital for asset concentration risk, and should be revised. Our findings have also important implications on the ongoing discussion of asset concentration risk in the context of macroprudential insurance regulation.
This paper documents that the bond investments of insurance companies transmit shocks from insurance markets to the real economy. Liquidity windfalls from household insurance purchases increase insurers’ demand for corporate bonds. Exploiting the fact that insurers persistently invest in a small subset of firms for identification, I show that these increases in bond demand raise bond prices and lower firms’ funding costs. In response, firms issue more bonds, especially when their bond underwriters are well connected with investors. Firms use the proceeds to raise investment rather than equity payouts. The results emphasize the significant impact of investor demand on firms’ financing and investment activities.
I measure the effects of workers’ mobility across regions of different productivity through the lens of a search and matching model with heterogeneous workers and firms estimated with administrative data. In an application to Italy, I find that reallocation of workers to the most productive region boosts productivity at the country level but amplifies differentials across regions. Employment rates decline as migrants foster job competition, and inequality between workers doubles in less productive areas since displacement is particularly severe for low-skill workers. Migration does affect mismatch: mobility favors co-location of agents with similar productivity but within-region rank correlation declines in the most productive region. I show that worker-firm complementarities in production account for 33% of the productivity gains. Place-based programs directed to firms, like incentives for hiring unemployed or creating high productivity jobs, raise employment rates and reduce the gaps in productivity across regions. In contrast, subsidies to attract high-skill workers in the South have limited effects.
Nations are imposing unprecedented measures at a large scale to contain the spread of the COVID-19 pandemic. While recent studies show that non-pharmaceutical intervention measures such as lockdowns may have mitigated the spread of COVID-19, those measures also lead to substantial economic and social costs, and might limit exposure to ultraviolet-B radiation (UVB). Emerging observational evidence indicates the protective role of UVB and vitamin D in reducing the severity and mortality of COVID-19 deaths. This observational study empirically outlines the protective roles of lockdown and UVB exposure as measured by the ultraviolet index (UVI). Specifically, we examine whether the severity of lockdown is associated with a reduction in the protective role of UVB exposure. We use a log-linear fixed-effects model on a panel dataset of secondary data of 155 countries from 22 January 2020 until 7 October 2020 (n = 29,327). We use the cumulative number of COVID-19 deaths as the dependent variable and isolate the mitigating influence of lockdown severity on the association between UVI and growth rates of COVID-19 deaths from time-constant country-specific and time-varying country-specific potentially confounding factors. After controlling for time-constant and time-varying factors, we find that a unit increase in UVI and lockdown severity are independently associated with − 0.85 percentage points (p.p) and − 4.7 p.p decline in COVID-19 deaths growth rate, indicating their respective protective roles. The change of UVI over time is typically large (e.g., on average, UVI in New York City increases up to 6 units between January until June), indicating that the protective role of UVI might be substantial. However, the widely utilized and least severe lockdown (governmental recommendation to not leave the house) is associated with the mitigation of the protective role of UVI by 81% (0.76 p.p), which indicates a downside risk associated with its widespread use. We find that lockdown severity and UVI are independently associated with a slowdown in the daily growth rates of cumulative COVID-19 deaths. However, we find evidence that an increase in lockdown severity is associated with significant mitigation in the protective role of UVI in reducing COVID-19 deaths. Our results suggest that lockdowns in conjunction with adequate exposure to UVB radiation might have even reduced the number of COVID-19 deaths more strongly than lockdowns alone. For example, we estimate that there would be 11% fewer deaths on average with sufficient UVB exposure during the period people were recommended not to leave their house. Therefore, our study outlines the importance of considering UVB exposure, especially while implementing lockdowns, and could inspire further clinical studies that may support policy decision-making in countries imposing such measures.
By computing a volatility index (CVX) from cryptocurrency option prices, we analyze this market’s expectation of future volatility. Our method addresses the challenging liquidity environment of this young asset class and allows us to extract stable market implied volatilities. Two alternative methods are considered to compute volatilities from granular intra-day cryptocurrency options data, which spans over the COVID-19 pandemic period. CVX data therefore capture ‘normal’ market dynamics as well as distress and recovery periods. The methods yield two cointegrated index series, where the corresponding error correction model can be used as an indicator for market implied tail-risk. Comparing our CVX to existing volatility benchmarks for traditional asset classes, such as VIX (equity) or GVX (gold), confirms that cryptocurrency volatility dynamics are often disconnected from traditional markets, yet, share common shocks.
Do leaders who build a sense of shared social identity in their teams thereby protect them from the adverse effects of workplace stress? This is a question that the present paper explores by testing the hypothesis that identity leadership contributes to stronger team identification among employees and, through this, is associated with reduced burnout. We tested this model with unique datasets from the Global Identity Leadership Development (GILD) project with participants from all inhabited continents. We compared two datasets from 2016/2017 (n = 5290; 20 countries) and 2020/2021 (n = 7294; 28 countries) and found very similar levels of identity leadership, team identification and burnout across the five years. An inspection of the 2020/2021 data at the onset of and later in the COVID-19 pandemic showed stable identity leadership levels and slightly higher levels of both burnout and team identification. Supporting our hypotheses, we found almost identical indirect effects (2016/2017, b = −0.132; 2020/2021, b = −0.133) across the five-year span in both datasets. Using a subset of n = 111 German participants surveyed over two waves, we found the indirect effect confirmed over time with identity leadership (at T1) predicting team identification and, in turn, burnout, three months later. Finally, we explored whether there could be a “too-much-of-a-good-thing” effect for identity leadership. Speaking against this, we found a u-shaped quadratic effect whereby ratings of identity leadership at the upper end of the distribution were related to even stronger team identification and a stronger indirect effect on reduced burnout.
We study the role mutual funds play in the recovery from fast intraday crashes based on data from the National Stock Exchange of India for a single large stock. During normal times, trading activity and liquidity provision by mutual funds is negligible compared to other traders at around 4% of overall activity. Nevertheless, for the two intraday market-wide crashes in our sample, price recovery took place only after mutual funds moved in. Market stability may require the presence of well-capitalized standby liquidity providers for recovery from fast crashes.
Women are overrepresented in informal employment in the Indian labour market. They also devote more time to unpaid care work than men do. The poor working conditions of women are attributable to this double burden of work. Due to the lack of regulatory measures to protect the interest of informal women workers along with rigid gender norms, women’s participation in paid work is drastically reduced. As far as unpaid care work is concerned, feminist economists have been striving to make such work visible for a long time now. There have been some developments in labour statistics, with time use surveys quantifying women’s paid and unpaid contributions to the economy. This article delves into the examination of unpaid care work in India with the help of the Indian government’s recent Time-use Survey. It attempts to study the connection between paid work and unpaid care work and its implications for the working conditions of women in India. Finally, it evaluates the ‘right to work’ as a possible solution by using the example of employment guarantee schemes in India.
This paper explores entrepreneurs’ initially intended exit strategies and compares them to their final exit paths using an inductive approach that builds on the grounded theory methodology. Our data shows that initially intended and final exit strategies differ among entrepreneurs. Two groups of entrepreneurs emerged from our data. The first group comprises entrepreneurs who financed their firms through equity investors. The second group is made up of entrepreneurs who financed their businesses solely with their own equities. Our data shows that the first group originally intended a financial harvest exit strategy and settled with this harvest exit strategy. The second group initially intended a stewardship exit strategy but did not succeed. We used the theory of planned behavior and the behavioral agency model to analyze our data. By examining our results from these two theoretical perspectives, our study explains how entrepreneurs’ exit intentions lead to their actual exit strategies.
This article uses information from two data sources, Compustat and Nexis Uni, and textual analysis to measure and validate the brand focus and customer focus of 109 U.S. listed retailers. The results from an analysis of their 853 earnings calls in 2010 and 2018 outline that on average, both foci increased over time. Although both foci vary substantially, brand focus varies more widely across retailers than their customer focus. Both foci are independent of each other. Specialty retailers have the highest brand focus, and internet & direct marketing retailers have the highest customer focus. A positive correlation exists between a retailer’s customer focus and its profitability, but not between a retailer’s brand focus and its profitability. The authors use the results to generate a research agenda that can direct future research in further systematically exploring firms’ brand and customer focus.
Small businesses face major challenges to becoming more innovative. These challenges are particularly prevalent in emerging economies where high uncertainties are a barrier to innovation. We know from previous studies that linkages to universities, on the one hand, and public procurement, on the other, support large and innovative firms in their efforts to become more innovative. However, we do not know whether these positive effects also hold true for small businesses. In this paper, we focus on how policy strategies reducing information, market and financial uncertainties shape small businesses’ innovation in China. Based on a sample of 926 small businesses derived from the World Bank Enterprises Survey in China (2012), we find that university-industry linkages enhance innovation, though only when it comes to minor forms of innovation. In line with the resource-based view of the firm, this effect is stronger for small businesses with higher capabilities. Moreover, we show that bidding for or delivering contracts to public sector clients has a positive effect on innovation, and in particular of major forms of innovation. In the bidding selection process, private firms and firms with higher capabilities are selected. Our findings show that both policy strategies have enhanced innovation, though with different effects on the degree of novelty. We attribute this finding to the different degrees of uncertainties they address.
The meme stock phenomenon has yet to be explored. In this note, we provide evidence that these stocks display common stylized facts for the dynamics of price, trading volume, and social media activity. Using a regime-switching cointegration model, we identify the meme stock “mementum” which exhibits a different characterization compared to other stocks with high volumes of activity (persistent and not) on social media. Finally, we show that mementum is significant and positively related to the stock’s returns. Understanding these properties helps investors and market authorities in their decisions.
Vulnerability comes, according to Orio Giarini, with two risks: human-made risks, also called entrepreneurial risks, and natural or pure risks such as accidents and earthquakes. Both types of risk are growing in dimension and are increasingly interrelated. To control the vulnerability, sophisticated insurance products are called for. Here, mutual insurance is relevant, in particular when risks are large, probabilities uncertain or unknown, and events interrelated or correlated. In this paper the following three examples are discussed and the advantages of mutual insurance are shown: unknown probabilities connected with unforeseeable events, correlated risks and macroeconomic or demographic risks.
This study explores the implications of rising markups for optimal Mirrleesian income and profit taxation. Using a stylized model with two individuals, the main forces shaping welfare-optimal policies are analytically characterized. Although a higher profit tax has redistributive benefits, it adversely affects market competition, leading to a greater equilibrium cost-of-living. Rising markups directly contribute to a decline in optimal marginal taxes on labor income. The optimal policy response to higher markups includes increasingly relying on the profit tax to fund redistribution. Declining optimal marginal income taxes assists the redistributive function of the profit tax by contributing to the expansion of the profit tax base. This response alone considerably increases the equilibrium cost-of-living. Nevertheless, a majority of the individuals become better off with the optimal policy. If it is not possible to tax profits optimally, due, for example, to profit shifting, increasing redistribution via income taxes is not optimal; every individual is worse off relative to the scenario with optimal profit taxation.
Nowadays, digitalization has an immense impact on the landscape of jobs. This technological revolution creates new industries and professions, promises greater efficiency and improves the quality of working life. However, emerging technologies such as robotics and artificial intelligence (AI) are reducing human intervention, thus advancing automation and eliminating thousands of jobs and whole occupational images. To prepare employees for the changing demands of work, adequate and timely training of the workforce and real-time support of workers in new positions is necessary. Therefore, it is investigated whether user-oriented technologies, such as augmented reality (AR) and virtual reality (VR) can be applied “on-the-job” for such training and support—also known as intelligence augmentation (IA). To address this problem, this work synthesizes results of a systematic literature review as well as a practically oriented search on augmented reality and virtual reality use cases within the IA context. A total of 150 papers and use cases are analyzed to identify suitable areas of application in which it is possible to enhance employees' capabilities. The results of both, theoretical and practical work, show that VR is primarily used to train employees without prior knowledge, whereas AR is used to expand the scope of competence of individuals in their field of expertise while on the job. Based on these results, a framework is derived which provides practitioners with guidelines as to how AR or VR can support workers at their job so that they can keep up with anticipated skill demands. Furthermore, it shows for which application areas AR or VR can provide workers with sufficient training to learn new job tasks. By that, this research provides practical recommendations in order to accompany the imminent distortions caused by AI and similar technologies and to alleviate associated negative effects on the German labor market.
We investigate how financial literacy shapes older Americans’ demand for financial advice. Using an experimental module fielded in the Health and Retirement Study, we show that financial literacy strongly improves the quality but not the quantity of financial advice sought. In particular, more financially literate people seek financial help from professionals. This effect is more pronounced among older people and those with more wealth and more complex financial positions. Our analysis result implies that financial literacy and financial advisory services are complementary with, rather than substitutes for, each other.
Tail-correlation matrices are an important tool for aggregating risk measurements across risk categories, asset classes and/or business segments. This paper demonstrates that traditional tail-correlation matrices—which are conventionally assumed to have ones on the diagonal—can lead to substantial biases of the aggregate risk measurement’s sensitivities with respect to risk exposures. Due to these biases, decision-makers receive an odd view of the effects of portfolio changes and may be unable to identify the optimal portfolio from a risk-return perspective. To overcome these issues, we introduce the “sensitivity-implied tail-correlation matrix”. The proposed tail-correlation matrix allows for a simple deterministic risk aggregation approach which reasonably approximates the true aggregate risk measurement according to the complete multivariate risk distribution. Numerical examples demonstrate that our approach is a better basis for portfolio optimization than the Value-at-Risk implied tail-correlation matrix, especially if the calibration portfolio (or current portfolio) deviates from the optimal portfolio.
We empirically examine how systemic risk in the banking sector leads to correlated risk in office markets of global financial centers. In so doing, we compute an aggregated measure of systemic risk in financial centers as the cumulated expected capital shortfall of local financial institutions. Our identification strategy is based on a double counterfactual approach by comparing normal with financial distress periods as well as office with retail markets. We find that office market interconnectedness arises from systemic risk during financial turmoil periods. Office market performance in a financial center is affected by returns of systemically linked financial center office markets only during a systemic banking crisis. In contrast, there is no evidence of correlated risk during normal times and among the within-city counterfactual retail sector. The decline in office market returns during a banking crisis is larger in financial centers compared to non-financial centers.
Having a gatekeeper position in a collaborative network offers firms great potential to gain competitive advantages. However, it is not well understood what kind of collaborations are associated with such a position. Conceptually grounded in social network theory, this study draws on the resource-based view and the relational factors view to investigate which types of collaboration characterize firms that are in a gatekeeper position, which ultimately could improve firm performance in subsequent periods. The empirical analysis utilizes a unique longitudinal data set to examine dynamic network formation. We used a data crawling approach to reconstruct collaboration networks among the 500 largest companies in Germany over nine years and matched these networks with performance data. The results indicate that firms in gatekeeper positions often engage in medium-intensity collaborations and less likely weak-intensity collaborations. Strong-intensity collaborations are not related to the likelihood of being a gatekeeper. Our study further reveals that a firm's knowledge base is an important moderator and that this knowledge base can increase the benefits of having a gatekeeper position in terms of firm performance.
The CEO beauty premium: Founder CEO attractiveness and firm valuation in initial coin offerings
(2021)
Research summary
We apply insights from research in social psychology and labor economics to the domain of entrepreneurial finance and investigate how founder chief executive officers' (founder CEOs') facial attractiveness influences firm valuation. Leveraging the novel context of initial coin offerings (ICOs), we document a pronounced founder CEO beauty premium, with a positive relationship between founder CEO attractiveness and firm valuation. We find only very limited evidence of stereotype-based evaluations, through the association of founder CEO attractiveness with latent traits such as competence, intelligence, likeability, or trustworthiness. Rather, attractiveness seems to bear economic value per se, especially in a context in which investors base their decisions on a limited information set. Indeed, attractiveness has a sustainable effect on post-ICO performance.
Managerial summary
ICOs allow ventures to collect funding from investors using blockchain technology. We leverage this novel funding context, in which information on the ventures and their future prospects is scarce, to empirically investigate whether the founder CEOs' physical attractiveness is associated with increased funding (i.e., amount raised) and post-funding performance (i.e., buy-and-hold returns). We find that ventures with more attractive founder CEOs outperform ventures with less attractive CEOs in both dimensions. For ICO investors, this suggests that ICOs of firms with more attractive founder CEOs are more appealing investment targets. Our findings are also interesting for startups seeking external finance in uncertain contexts, such as ICOs. If startups can appoint attractive leaders, they may have better access to growth capital.
This paper analyzes the scope of the private market for pandemic insurance. We develop a framework that explains theoretically how the equilibrium price of pandemic insurance depends on accumulation risk, covariance between pandemic claims and other claims, and covariance between pandemic claims and the stock market performance. Using the natural catastrophe (NatCat) insurance market as a laboratory, we estimate the relationship between the insurance price markup and the tail characteristics of the loss distribution. Then, by using the high-frequency data tracking the economic impact of the COVID-19 pandemic in the United States, we calibrate the loss distribution of a hypothetical insurance contract designed to alleviate the impact of the pandemic on small businesses. The pandemic insurance contract price markup corresponds to the top 20% markup observed in the NatCat insurance market. Then we analyze an intertemporal risk-sharing scheme that can reduce the expected shortfall of the loss distribution by 50%.
The current economic landscape is complex and globalized, and it imposes on individuals the responsibility for their own financial security. This situation has been intensified by the COVID-19 crisis, since short-time work and layoffs significantly limit the availability of financial resources for individuals. Due to the long duration of the lockdown, these challenges will have a long-term impact and affect the financial well-being of many citizens. Moreover, it can be assumed that the consequences of this crisis will once again particularly affect groups of people who have already frequently been identified as having low financial literacy. Financial literacy is therefore an important target for educational measures and interventions. However, it cannot be considered in isolation but must take into account the many potential factors that influence financial literacy alone or in combination. These include personality traits and socio-demographic factors as well as the (in)ability to defer gratification. Against this background, individualized support offers can be made. With this in mind, in the first step of this study, we analyze the complex interaction of personality traits, socio-demographic factors, the (in-)ability to delay gratification, and financial literacy. In the second step, we differentiate the identified effects regarding different groups to identify moderating effects, which, in turn, allow conclusions to be drawn about the need for individualized interventions. The results show that gender and educational background moderate the effects occurring between self-reported financial literacy, financial learning opportunities, delay of gratification, and financial literacy.
The intergroup sensitivity effect in mergers and acquisitions: testing the role of merger motives
(2021)
Research has shown that people are more defensive to criticism when it stems from an outgroup member, compared to an ingroup member (the intergroup sensitivity effect: ISE). We conducted two online vignette experiments to examine the ISE in the context of an organizational merger and the role of merger motives for the ISE. We predicted that the ISE would also emerge in mergers and acquisitions (M&As), but people would respond less negatively to criticism from the outgroup when the motive for the merger is described as achieving synergies rather than growth. In Experiment 1 (N = 452), which did not mention any motives behind the acquisition, a significant ISE emerged. Experiment 2 (N = 587) again showed an ISE regardless of the merger motive. In both experiments, the ISE was mediated by perceptions of the outgroup criticism as less legitimate and constructive. Overall, this research points to the intergroup sensitivity effect as a relevant phenomenon during post-merger integration.
The rapid spread of the Coronavirus (COVID-19) confronts policy makers with the problem of measuring the effectiveness of containment strategies, balancing public health considerations with the economic costs of social distancing measures. We introduce a modified epidemic model that we name the controlled-SIR model, in which the disease reproduction rate evolves dynamically in response to political and societal reactions. An analytic solution is presented. The model reproduces official COVID-19 cases counts of a large number of regions and countries that surpassed the first peak of the outbreak. A single unbiased feedback parameter is extracted from field data and used to formulate an index that measures the efficiency of containment strategies (the CEI index). CEI values for a range of countries are given. For two variants of the controlled-SIR model, detailed estimates of the total medical and socio-economic costs are evaluated over the entire course of the epidemic. Costs comprise medical care cost, the economic cost of social distancing, as well as the economic value of lives saved. Under plausible parameters, strict measures fare better than a hands-off policy. Strategies based on current case numbers lead to substantially higher total costs than strategies based on the overall history of the epidemic.
Sample-based longitudinal discrete choice experiments: preferences for electric vehicles over time
(2021)
Discrete choice experiments have emerged as the state-of-the-art method for measuring preferences, but they are mostly used in cross-sectional studies. In seeking to make them applicable for longitudinal studies, our study addresses two common challenges: working with different respondents and handling altering attributes. We propose a sample-based longitudinal discrete choice experiment in combination with a covariate-extended hierarchical Bayes logit estimator that allows one to test the statistical significance of changes. We showcase this method’s use in studies about preferences for electric vehicles over six years and empirically observe that preferences develop in an unpredictable, non-monotonous way. We also find that inspecting only the absolute differences in preferences between samples may result in misleading inferences. Moreover, surveying a new sample produced similar results as asking the same sample of respondents over time. Finally, we experimentally test how adding or removing an attribute affects preferences for the other attributes.
Venture capital-backed firms, unavoidable value-destroying trade sales, and fair value protections
(2021)
This paper investigates the implications of the fair value protections contemplated by the standard corporate contract (i.e., the standard contract form for which corporate law provides) for the entrepreneur–venture capitalist relationship, focusing, in particular, on unavoidable value-destroying trade sales. First, it demonstrates that the typical entrepreneur–venture capitalist contract does institutionalize the venture capitalist’s liquidity needs, allowing, under some circumstances, for counterintuitive instances of contractually-compliant value destruction. Unavoidable value-destroying trade sales are the most tangible example. Next, it argues that fair value protections can prevent the entrepreneur and venture capitalist from allocating the value that these transactions generate as they would want. Then, it shows that the reality of venture capital-backed firms calls for a process of adaptation of the standard corporate contract that has one major step in the deactivation or re-shaping of fair value protections. Finally, it argues that a standard corporate contract aiming to promote social welfare through venture capital should feature flexible fair value protections.
This dissertation analyses the degrees and trajectories of financialisation in the region of South-Eastern Europe. It modifies and applies an eclectic comparative framework for comparing the degrees of financialisation across time and space on different levels. The thesis finds that from the turn of the century until the Great Financial Crisis of 2008, most South-Eastern European countries have increased their degree of financialisation on the different levels, especially on the levels of household, international financialisation and partly the financial sector. Financialisation of non-financial companies is barely existing. After the financial crisis, financialisation is revealed to stagnate in the region. In a second step, the dissertation conducts three case studies on extreme cases: financial sector financialisation in Bulgaria, international financialisation in Serbia and non-financial company and household financialisation in Croatia. Their trajectories are exposed to be mainly driven by deregulation, changed practices by foreign banks, the privatisation of public goods and the liberation of capital controls. The dissertation serves to geographically enlarge the research of financialisation to a peripheral region of the Global North and to add to the discussion on comparative financialisation approaches.
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.
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.
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.
ISOE-Newsletter Nr. 05/2021
(2021)
Erste Professur für Soziale Ökologie und Transdisziplinarität in Deutschland +++ Rückblick ISOE-Symposium zur Verabschiedung von Thomas Jahn: Krise, Katastrophe, Normalität – die Verantwortung der Wissenschaft für die Zukunftsgestaltung +++ Institutsbericht des ISOE erschienen: Rückblick auf das Forschungsjahr 2021 +++ Deutsch-jordanisches Forschungsprojekt zu Katastrophenschutz bei Starkregen gestartet +++ Großtechnische Umsetzung von Gemüseproduktion mit Wasserwiederverwendung +++ Wie Pendeln nachhaltiger werden kann: Ideenfindungsprozess für Reallabore startet +++ Gutes Klima für die Zukunftsstadt? – Frankfurter Stadtgrün im Klimawandel +++ Wissen für den Wandel: Zehn Jahre Ecornet +++ ISOE-Blog +++ Aus dem ISOE +++ Termine +++ Publikationen
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.
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).
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.