SAFE working paper
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277
Cryptocurrencies have received growing attention from individuals, the media, and regulators. However, little is known about the investors whom these financial instruments attract. Using administrative data, we describe the investment behavior of individuals who invest in cryptocurrencies with structured retail products. We find that cryptocurrency investors are active traders, prone to investment biases, and hold risky portfolios. In line with attention effects and anticipatory utility, we find that the average cryptocurrency investor substantially increases log-in and trading activity after his or her first cryptocurrency purchase. Our results document which investors are more likely to adopt new financial products and help inform regulators about investors' vulnerability to cryptocurrency investments.
293
This paper studies a household’s optimal demand for a reverse mortgage. These contracts allow homeowners to tap their home equity to finance consumption needs. In stylized frameworks, we show that the decision to enter a reverse mortgage is mainly driven by the dierential between the aggregate appreciation of the house price and principal limiting factor on the one hand and the funding costs of a household on the other hand. We also study a rich life-cycle model that can explain the low demand for reverse mortgages as observed in US data. In this model, we analyze the optimal response of a household that is confronted with a health shock or financial disaster. If an agent suers from an unexpected health shock, she reduces the risky portfolio share and is more likely to enter a reverse mortgage. On the other hand, if there is a large drop in the stock market, she keeps the risky portfolio share almost constant by buying additional shares of stock. Besides, the probability to take out a reverse mortgage is hardly aected.
267
We study whether and how time preferences change over the life cycle, exploiting representative long-term panel data. We estimate the age patterns of discount rates from age 25 to 80. In order to identify age effects, we have to disentangle them from cohort and period factors. We address this identification problem by estimating individual fixed effects models, where we substitute period effects with determinants of time preferences that depend on calendar years. We find that discount rates decrease with age and the decline is remarkably linear over the life cycle.
269
This paper presents causal evidence of the effects of boardroom networks on firm value. We exploit exogenous variation in network centrality arising from a ban on interlocking directorates of Italian financial and insurance companies. We leverage this shock to show that firms that become more central in the network as a result of the shock experience positive abnormal returns around the announcement date. We find that information dissemination plays a central role: results are driven by firms that have higher idiosyncratic volatility, low analyst coverage, and more uncertainty surrounding their earnings forecasts. We also find that firms benefit more from boardroom centrality when they are more central in the input-output network, as this reinforces information complementarities, or when they are less central in the cross-ownership network, as well as when they suffer from low profitability and low growth opportunities. Network centrality also results in higher compensation for board directors.
272
We use data from a German online brokerage and a survey to show that retail investors sharply reduce risk-taking in response to nearby firm bankruptcies, which are not pre- dictive of returns. The effects on trading are spatially highly concentrated, immediate and not persistent. They seem to operate through more pessimistic expected returns and increased risk aversion and do not reflect wealth effects or changes in background risks. Investors learn about bankruptcies through immediate coverage in local newspapers. Our findings suggest that non-informative local experiences that make downside risks of stock investment more salient contribute to idiosyncratic short-term fluctuations in trading.
287
Using experimental data from a comprehensive field study, we explore the causal effects of algorithmic discrimination on economic efficiency and social welfare. We harness economic, game-theoretic, and state-of-the-art machine learning concepts allowing us to overcome the central challenge of missing counterfactuals, which generally impedes assessing economic downstream consequences of algorithmic discrimination. This way, we are able to precisely quantify downstream efficiency and welfare ramifications, which provides us a unique opportunity to assess whether the introduction of an AI system is actually desirable. Our results highlight that AI systems’ capabilities in enhancing welfare critically depends on the degree of inherent algorithmic biases. While an unbiased system in our setting outperforms humans and creates substantial welfare gains, the positive impact steadily decreases and ultimately reverses the more biased an AI system becomes. We show that this relation is particularly concerning in selective-labels environments, i.e., settings where outcomes are only observed if decision-makers take a particular action so that the data is selectively labeled, because commonly used technical performance metrics like the precision measure are prone to be deceptive. Finally, our results depict that continued learning, by creating feedback loops, can remedy algorithmic discrimination and associated negative effects over time.
192 n
This paper shows that judicial enforcement has substantial effects on firms’ decisions with regard to their employment policies. To establish causality, I exploit a reorganization of the court districts in Italy involving judicial district mergers as a shock to court productivity. I find that an improvement in enforcement, as measured by a reduction in average trial length, has a large, positive effect on firm employment. These effects are stronger in firms with high leverage, or that belong to industries more dependent on external finance and characterized by higher complementarity between labor and capital, consistent with a financing channel driving the results. Moreover, in presence of stronger enforcement, firms can raise more debt to dampen the impact of negative shocks and, in this way, reduce employment fluctuations.
290
Using a structural life-cycle model, we quantify the long-term 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 both the long-term earnings consequences on children from a Covid-19 induced loss of schooling, as well as the associated welfare losses. 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.
268
Using an original dataset on professional networks of directors sitting on the boards of large US corporations, we examine how personal relationships are used by firms to improve job match quality in the high-skill segment of the labor market. Analyzing explicit social connection data between new hires and recruiters, we are able to test predictions of well established job referral models. We find that referred executive directors have a fifteen percent longer tenure than their non-referred counterparts. Referred executive directors also tend to be similar to their referrers on multiple dimensions, giving support to network homophily hypotheses.
285
We employ a representative sample of 80,972 Italian firms to forecast the drop in profits and the equity shortfall triggered by the COVID-19 lockdown. A 3-month lockdown generates an aggregate yearly drop in profits of about 10% of GDP, and 17% of sample firms, which employ 8.8% of the sample’s employees, become financially distressed. Distress is more frequent for small and medium-sized enterprises, for firms with high pre-COVID-19 leverage, and for firms belonging to the Manufacturing and Wholesale Trading sectors. Listed companies are less likely to enter distress, whereas the correlation between distress rates and family firm ownership is unclear.
(JEL G01, G32, G33)