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.
273
We report the results of a longitudinal intervention with students across five universities in China designed to reduce online consumer debt. Our research design allocates individuals to either a financial literacy treatment, a self-control training program, or a zero-touch control group. Financial education interventions improve test scores on general financial literacy but only marginally affect future online borrowing. Our self-control treatment features detailed tracking of spending and borrowing activity with a third-party app and introspection about individuals' consumption with a counselor. These sessions reduce future online borrowing, delinquency charges, and borrowing for entertainment reasons - and are driven by the male subjects in the sample. Our results suggest that self-regulation can affect financial behavior in e-commerce platforms.
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.
281
Using a novel experimental design, I test how the exposure to information about a group’s relative performance causally affects the members’ level of identification and thereby their propensity to harm affiliates of comparison groups. I find that both, being informed about a high and poor relative performance of the ingroup similarly fosters identification. Stronger ingroup identification creates increased hostility against the group of comparison. In cases where participants learn about poor relative performance, there appears to be a direct level effect additionally elevating hostile discrimination. My findings shed light on a specific channel through which social media may contribute to intergroup fragmentation and polarization.
291
Market fragmentation and technological advances increasing the speed of trading altered the functioning and stability of global equity limit order markets. Taking market resiliency as an indicator of market quality, we investigate how resilient are trading venues in a high-frequency environment with cross-venue fragmented order flow. Employing a Hawkes process methodology on high-frequency data for FTSE 100 stocks on LSE, a traditional exchange, and on Chi-X, an alternative venue, we find that when liquidity becomes scarce Chi-X is a less resilient venue than LSE with variations existing across stocks and time. In comparison with LSE, Chi-X has more, longer, and severer liquidity shocks. Whereas the vast majority of liquidity droughts on both venues disappear within less than one minute, the recovery is not lasting, as liquidity shocks spiral over the time dimension. Over half of the shocks on both venues are caused by spiralling. Liquidity shocks tend to spiral more on Chi-X than on LSE for large stocks suggesting that the liquidity supply on Chi-X is thinner than on LSE. Finally, a significant amount of liquidity shocks spill over cross-venue providing supporting evidence for the competition for order flow between LSE and Chi-X.
292
This paper studies the link between bank recapitalization and welfare in a dynamic production economy. The model features financial frictions because banks benefit of a cost advantage at monitoring firms and face costly equity issuance. The competitive equilibrium outcome is inefficient because agents do not internalize the effects banks’ capitalization over the allocation of capital, its price and, in turn, firms investments. It follows, individual recapitalizations are sub-optimal and bailout policies may benefit social welfare in the long-run. Bailouts improve capital allocation in states where aggregate banks are poorly capitalized, therefore enhancing their market valuation, fostering investments, and stabilizing the economy recovery path.
271
This paper studies the impact of financial sector size and leverage on business cycles and risk-free rates dynamics. We model a general equilibrium productive economy where financial intermediaries provide costly risk mitigation to households by pooling the idiosyncratic risks of their investment activities. We find that leverage amplifies variations of intermediaries’ relative size, but may also mitigate the business cycle. Moreover, it makes risk-free rates pro-cyclical. Households benefit the most when the financial sector is neither too small, thus avoiding high consumption fluctuations and costly mitigation, nor too big, so that fewer resources are lost after intermediation costs.
295
The paper compares provision of public infrastructure via public-private partnerships (PPPs) with provision under government management. Due to soft budget constraints of government management, PPPs exert more effort and therefore have a cost advantage in building infrastructure. At the same time, hard budget constraints for PPPs introduce a bankruptcy risk and bankruptcy costs. Consequently, if bankruptcy costs are high, PPPs may be less efficient than public management, although this does not result from PPPs’ higher interest costs.
278
This working paper suggests to analyse agencification as a double process of institutional and policy centralisation. To that end, it develops a categorisation of agencies that incorporates these two dimensions. More specifically, it is argued that mixed outcomes where the levels of institutional and policy centralisation diverge can be expected to be the rule rather than the exception, in line with the hybrid nature of EU agencies as inbetweeners. Moreover, the fiduciary setting hits important legal constraints given the limits to delegation in the EU context. Against this backdrop a process whereby institutional centralisation develops incrementally and remains limited, yet is accompanied by a process of substantial policy centralisation, appears as the most promising path for EU agencification. A fiduciary setting, where a strong agency enjoys a high degree of independence and operates in a centralised policy space, by contrast, should be the exception. The comparative study of the process of agencification in the energy and banking sector is insightful in the light of these expectations. The incremental nature of institutional change in energy exemplifies the usual path of agencification, which is conducive to a weak agency operating in a relatively centralised policy space. Agencification in banking, by contrast, has led to a rather unusual outcome where the strong agency model combines with a fragmented policy context.
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.