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https://ssrn.com/abstract=3202916; We show that banker bonuses cannot be understood exclusively as incentive contracts, but also incorporate a significant risk sharing dimension between bank shareholders and bank employees. This contrasts with the conventional view whereby diversified shareholders fully insure risk averse employees. However, financial frictions imply that shareholder value is concave in a bank's cash reserves---making shareholders effectively risk averse. The optimal contract between shareholders and employees then involves some degree of risk sharing. Using extensive payroll data on 1.26 million bank employee years in the Austrian, German, and Swiss banking sectors, we show that the structure of bonus pay within and across banks is compatible with an economically significant risk sharing motive, but difficult to rationalize based on incentive theories of bonus pay only.

We study the interaction between multiple information designers who try to influence the behavior of a set of agents. When the set of messages available to each designer is finite, such games always admit subgame perfect equilibria. When designers produce public information about independent pieces of information, every equilibrium of the direct game (in which the set of messages coincides with the set of states) is an equilibrium with larger (possibly infinite) message sets. The converse is true for a class of Markovian equilibria only. When designers produce information for their own corporation of agents, pure strategy equilibria exist and are characterized via an auxiliary normal form game. In an infinite-horizon multi-period extension of information design games, a feasible outcome which Pareto dominates a more informative equilibrium of the one-period game is supported by an equilibrium of the multi-period game.

How precise should accounting measurements be, if management has discretion to strategically withhold? We examine this question by nesting an optimal persuasion mechanism, which controls what measurements are conducted, within a voluntary disclosure framework a la Dye (85) and Jung and Kwon (1988). In our setting, information has real effects because the firm uses it to make a continuous operating decision, increasing in the market's belief. Absent frictions other than uncertainty about information endowment, we show that imprecision can reduce strategic withholding but always decreases firm value. We then examine plausible environments under which, by contrast, there is an optimal level of imprecision featuring coarseness at the marginal discloser. We offer additional implications in the contexts of enforcement against strategic withholding and financing with collateralized assets.

We study the incentives of a digital business to collect and protect users’ data. The users’ data the business collects improve the service it provides to consumers, but they may also be accessed, at a cost, by strategic third parties in a way that harms users, imposing endogenous users’ privacy costs. We characterize how the revenue model of the business shapes its optimal data strategy: collection and protection of users’ data. A business with a more data-driven revenue model will collect more users’ data and provide more data protection than a similar business that is more usage driven. Consequently, if users have small direct benefit from data collection, then more usage-driven businesses generate larger consumer surplus than their more data-driven counterparts (the reverse holds if users have large direct benefit from data collection). Relative to the socially desired data strategy, the business may over- or undercollect users’ data and may over- or underprotect it. Restoring efficiency requires a two-pronged regulatory policy, covering both data collection and data protection; one such policy combines a minimal data protection requirement with a tax proportional to the amount of collected data. We finally show that existing regulation in the United States, which focuses only on data protection, may even harm consumer surplus and overall welfare. This paper was accepted by Itai Ashlagi, revenue management and market analytics.

We introduce the XPER (eXplainable PERformance) methodology to measure the specific contribution of the input features to the predictive or economic performance of a model. Our methodology offers several advantages. First, it is both model-agnostic and performance metric-agnostic. Second, XPER is theoretically founded as it is based on Shapley values. Third, the interpretation of the benchmark, which is inherent in any Shapley value decomposition, is meaningful in our context. Fourth, XPER is not plagued by model specification error, as it does not require re-estimating the model. Fifth, it can be implemented either at the model level or at the individual level. In an application based on auto loans, we find that performance can be explained by a surprisingly small number of features, XPER decompositions are rather stable across metrics, yet some feature contributions switch sign across metrics. Our analysis also shows that explaining model forecasts and model performance are two distinct tasks.

Organizational adaptation is a ubiquitous concept in management and organization research. Discussed in many theories, sometimes under different labels, the notion of adaptation is related to organizations being congruent to the environments within which they operate. Research traditions including behavioral theory (see A Behavioral Theory of the Firm), contingency theory as explained in Differentiation and Integration in Complex Organizations, population ecology (see The Population Ecology of Organizations), institutional theory elaborated upon in both Institutionalized Organizations: Formal Structure as Myth and Ceremony and The Iron Cage Revisited: Institutional Isomorphism and Collective Rationality in Organizational Fields, resource dependence (see The External Control of Organizations: A Resource Dependence Perspective), and evolutionary economics (see An Evolutionary Theory of Economic Change) all address organizational adaptation—though they emphasize different mechanisms. Adaptation, regarded as the strategic choice that organizations make, is often contrasted with the notion of selection driven by the determinism imposed by environments (see Organizational Adaptation: Strategic Choice and Environmental Determinism). Adaptive organizations are those able to obtain congruence both within organizations, reflected as congruence in internal functions and strategies, and across organizations, reflected as congruence with the needs of the external environment. The form of adaptation is also particularly relevant. Organizational theory has viewed adaptation as a state, an ability, and as a process. States of adaptation discuss stability and adaptation at points in time. From states of adaptation, adaptive abilities determine the impetus of adaptation and the locus of adaptation within organizations. Finally, the adaptation processes describe the means by which organizations adapt the challenges they face in negotiating new positions within environments. Research has covered each of these areas and this bibliography is organized primarily around forms of adaptation to elaborate on the ways in which it has been viewed by organizational theory and strategy scholars. The variety of research traditions examining organizational adaptation have not always aligned, however. Each research tradition seeks to explain different outcomes, from survival to performance to change. Thus, across theoretical traditions, the adaptation construct has been measured in many different ways—and often not as congruence with the external environment. In fact, this represents one of the challenges that recent research has identified as holding back progression of the adaptation research agenda. As such, this bibliography concludes with a section on issues in adaptation research in order to assist researchers in pursuing research in this area.

The COVID-19 outbreak is not the first nor last series of recent real or potential catastrophes - be they natural disasters, terrorist attacks or pandemics - that have taken by surprise governments, globalised firms and the citizenry. 1Yet, due to its near-unprecedented impact on the highly interconnected but vulnerable systems that define the modern world, this pandemic has been testing our ability to govern risk more than any other crisis before. The last time the world responded to a global emerging disease epidemic of the scale of the current novel coronavirus without having access to vaccines was the 1918-1919 H1N1 influenza pandemic. 2Ironically, the measures mobilised today to counter COVID-19 - the so-called “non-pharmaceutical interventions” 3- are essentially the same as those deployed a century ago, and that despite significant social, technological as well as governance differences between 1918 and today. 4

Stochastic independence has a complex status in probability theory. It is not part of the definition of a probability measure, but it is nonetheless an essential property for the mathematical development of this theory. Bayesian decision theorists such as Savage can be criticized for being silent about stochastic independence. From their current preference axioms, they can derive no more than the definitional properties of a probability measure. In a new framework of twofold uncertainty, we introduce preference axioms that entail not only these definitional properties, but also the stochastic independence of the two sources of uncertainty. This goes some way towards filling a curious lacuna in Bayesian decision theory., Comment: In Proceedings TARK 2017, arXiv:1707.08250

We measure the impact of bank capital requirements on corporate borrowing and expansion. We use French loan-level data and take advantage of the transition from Basel I to Basel II. While under Basel I the capital charge was the same for all firms, under Basel II, it depends in a predictable way on both the bank's model and the firm's risk. We exploit this two-way variation to empirically estimate the sensitivity of bank lending to capital requirement. This rich identification allows us to control for firm-level credit demand shocks and bank-level credit supply shocks. We find very large effects of capital requirements on bank lending: A 1 percentage point decrease in capital requirement leads to an increase in loan size by about 5%. At the firm level, borrowing also responds strongly although a bit less, consistent with some limited between-bank substitutability. Investment and employment also increase strongly. Overall, because the transition to Basel II led to an average reduction by 2 percentage points of capital requirements, we estimate that the new regulation led, in France, to an increase in average loan size by 10%, an increase in aggregate corporate lending by 1.5%, an increase in aggregate investment by 0.5%, and the creation or preservation of 235,000 jobs.

Despite the increasing importance of multiple priors in various domains of economics and the significant theoretical advances concerning them, choice-based incentive-compatible multiple-prior elicitation largely remains an open problem. This paper develops a solution, comprising a preference-based identification of a subject’s probability interval for an event, and two procedures for eliciting it. The method does not rely on specific assumptions about subjects’ ambiguity attitudes or probabilistic sophistication. To demonstrate its feasibility, we implement it in two incentivized experiments to elicit the multiple-prior equivalent of subjects’ cumulative distribution functions over continuous-valued sources of uncertainty. We find a predominance of non-degenerate probability intervals among subjects for all explored sources, with intervals being wider for less familiar sources. Finally, we use our method to undertake the first elicitation of the mixture coefficient in the Hurwicz α-maxmin EU model that fully controls for beliefs.

We show that PE sector specialists outperform generalists at every stage of the investment life cycle. Using granular data for thousands of U.S. hotels over the last two decades, we document that specialists exert a greater positive influence on more margins of hotel operations, earn higher net cash flows over the holding period, and achieve larger capital gains upon exit than do their generalist peers and other, non-PE investors backing ex ante equivalent assets. By contrast, PE generalists’ strongest comparative advantage may be better access to attractively priced acquisition financing. Our results provide novel evidence on the heterogeneity of PE investment strategies and associated performance outcomes.

We study product differentiation in the mutual fund industry. We design a model in which funds with heterogeneous perceived quality can choose their level of product differentiation. In equilibrium, high quality funds choose broad market designs (i.e., low differentiation) appealing to many investors, while low quality funds adopt niche designs (i.e., high differentiation) that investors either love or loath. Using as a measure of fund differentiation the degree of textual uniqueness of investment strategy description in fund prospectuses, we confirm empirically that funds with lower expected performance tend to differentiate more. We use the issuance of Morningstar rating to previously unrated funds as an exogenous shock to perceived quality to identify the economic mechanism. We find that funds receiving a low rating increase their product differentiation. The effect is mainly concentrated on funds run by small management companies, a feature associated with lower performance. This increase in product differentiation makes funds more likely to survive. It also has a market-level impact on the menu of funds available to investors.

This paper compares compensation policies from the perspective of providers and policy makers in an environment with the risk of exogenous service shutdowns. Providers tend to push for vouchers as a means of compensation, whereas customers demand cash refunds or generous vouchers. Regulators, at the same time, insist that customers must be granted the right to be reimbursed in money. To address the debate, this paper develops an analytical model to explore how cash refunds and vouchers can be used for recovery from service failures. First, we show that if the regulator does not require offering cash-back as an option, the optimal voucher policy consists of a service replacement with a zero bonus, whereas a positive bonus is optimal if the cash-back option is mandatory. Second, we find that the voucher-only policy can be more profitable and efficient than the hybrid policy that offers customers a choice between cash-back and voucher. Third, we show that the voucher policy leads to the longest survival time under shutdown, followed by the selling as-is policy and then the hybrid policy, whereas the cash-back policy leads to the shortest survival time. Overall, our results show that the expected profit and survival time are two important dimensions for providers to consider when facing the risk of shutdowns, whereas policy makers want to focus on pre-shutdown efficiency, bankruptcy risk, and post-shutdown customer dissatisfaction.

Newcomers are important for the survival of online communities, but their contributions often receive negative reactions and comments from established members. Online communities realize that such negativity can take a toll on newcomers and harm the creation of user-generated content. We study a novel intervention aimed at reducing hostility toward newcomers: a “newcomer nudge” that informs community members when they are interacting with a newcomer’s post and asks them to be more lenient toward its creator. Taking advantage of granular data from a large deal-sharing community and a natural experiment, we use a difference-in-differences approach and find robust evidence that the newcomer nudge induced members to write 46% more responses per day with 10% fewer negative words during the first two days after a deal was published. Our results show that the nudge-induced change in behavior toward newcomers increased newcomer retention. However, we also observe that before the nudge, newcomers’ second posts received more net votes (upvotes minus downvotes) than their first posts. After the nudge, newcomers’ subsequent posts were less popular than their first posts, which indicates that the nudge interrupted newcomers’ learning curve by suppressing helpful feedback.

In credit markets, screening algorithms discriminate between good-type and bad-type borrowers. This is their raison d’être. However, by doing so, they also often discriminate between individuals sharing a protected attribute (e.g. gender, age, race) and the rest of the population. In this paper, we show how to test (1) whether there exists a statistical significant difference in terms of rejection rates or interest rates, called lack of fairness, between protected and unprotected groups and (2) whether this difference is only due to credit worthiness. When condition (2) is not met, the screening algorithm does not comply with the fair-lending principle and can be qualified as illegal. Our framework provides guidance on how algorithmic fairness can be monitored by lenders, controlled by their regulators, and improved for the benefit of protected groups.

Financial reporting technologies can significantly affect how firms construct and disseminate quantitative and qualitative disclosures. Leveraging the opportunity created by the eXtensible Business Reporting Language (XBRL) mandate in the United States, we use a difference-in-differences approach to examine whether and how a firm’s XBRL adoption affects the readability of its textual disclosures. We find that the initial adopters’ HTML-formatted annual reports become harder to read after the XBRL mandate. Further analysis reveals that this effect is concentrated among adopters with more quantitative disclosures, those with smaller firm size, and those with a higher level of financial complexity. Importantly, we show that managers’ reduced attention to preparing HTML-formatted annual reports, rather than increased disclosures, is likely the explanation for this decrease in textual readability. We also find that the negative effect on textual readability persists at least in the subsequent year. Taken together, our results suggest that although XBRL can standardize numerical disclosures, its initial adoption can divert managerial attention and result in reduced readability of textual disclosures.

Chapter for "Handbook on the Business of Sustainability: The Organization, Implementation, and Practice of Sustainable Growth", edited by Gerry George, Martine R. Haas, Havovi Joshi, Anita McGahan and Paul Tracey; Purpose is a set of common beliefs that guide organizational members’ actions toward long-term achievements, independently of an organization’s immediate profitability. Hence, purpose reveals new motivational mechanisms for employees’ engagement that legitimacy-as-judgment research and prosociality literature address. Drawing on these approaches, this chapter investigates the sources and consequences of organizational purpose. We suggest three avenues for research by emphasizing that purpose is not another organizational goal but differs in its conceptual nature, by addressing aggregation of micro-behavior in terms of incentive structures, and by accounting for the emergence and maintenance of purpose itself.

Work-related social media networks (SMNs) like LinkedIn introduce novel networking opportunities and features that promise to help individuals establish, extend, and maintain social capital (SC). Typically, work-related SMNs offer access to advanced networking features only to premium users in order to encourage basic users to become paying members. Yet, little is known about whether access to these advanced networking features has a causal impact on the accumulation of SC. To close this research gap, we conducted a randomized field experiment and recruited 215 freelancers on a freemium, work-related SMN. Out of these recruited participants, more than 70 received a randomly assigned, free, 12-month premium membership voucher. We observe that individuals do not necessarily accumulate more SC from the ability to access advanced networking features, as the treated freelancers did not automatically change their digitized networking engagement. Those features will only unfold their full utility if the individuals are motivated to proactively engage in networking: Freelancers who have access to advanced networking features increase their SC by 4.609% for each unit increase on the strategic networking behavior scale. We confirm this finding in another study utilizing a second, individual-level panel data covering 52,392 freelancers; in tandem, we investigate the dynamics that active vs. passive features play in SC accumulation. Based on the findings, we introduce the “theory of purposeful feature utilization”: essentially, individuals must not only possess an efficacious “networking weapon"––they also need the intent to “shoot” it.

Political activism positively affects firm innovation. Firms that support more politicians, politicians on Congressional committees with jurisdictional authority over the firms’ industries and politicians who join those committees innovate more. We employ instrumental variables estimation and a natural experiment to show a causal effect of political activism on innovation. The results are consistent with the hypothesis that political activism is valuable because it helps reduce political uncertainty, which, in turn, fosters firm innovation. Also consistent with this hypothesis, we show that politically active firms successfully time future legislation and set their innovation strategies in expectation of future legislative changes.Keywords: political contributions, innovation, investment policy, policy uncertainty

This paper argues that central bankers should raise inflation to signal their credibility to forward guidance policies. As inflation can be stabilized in normal times either because of central banker's credibility (e.g. because of reputation concerns) or because of his aversion to inflation, the private sector is unable to infer the central banker's type from observing stable inflation before a liquidity trap, jeopardizing the efficiency of forward guidance policy. We derive optimal policy in a new-Keynesian model subject to liquidity traps where agents are uncertain about the central banker's type and we show that the credible central banker can signal his type by raising inflation before a trap. We show that this signaling motive can justify level of inflation well above 2% but also that the low inflation volatility during the Great Moderation was insufficient to ensure fully efficient forward guidance when needed.

The standard, Bayesian account of rational belief and decision is often argued to be unable to cope properly with severe uncertainty, of the sort ubiquitous in some areas of policy making. This paper tackles the question of what should replace it as a guide for rational decision making. It defends a recent proposal, which reserves a role for the decision maker’s confidence in beliefs. Beyond being able to cope with severe uncertainty, the account has strong normative credentials on the main fronts typically evoked as relevant for rational belief and decision. It fares particularly well, we argue, in comparison to other prominent non-Bayesian models in the literature.

Real and private-value assets—defined here as the sum of real estate, infrastructure, collectibles, and noncorporate business equity—compose an investment class worth an estimated $84 trillion in the U.S. alone. Furthermore, private values can affect pricing in many other financial markets, such as that for sustainable investments. This paper introduces the research on real assets and private values that can be found in this special issue. It also reviews recent advances and highlights new research directions on a number of topics in the real assets space that we believe to be particularly important and exciting.

Design is increasingly adopted by organization as an innovation capability to renew and transform the firm and therefore acting as dynamic capability. However, few article address how firms build a new dynamic capability and especially in the case of the design capability. Based on a longitudinal case study of the building of design as an innovation capability within an insurance company, we suggest a capability-building framework: a capability is built by acquiring resources, deploying them in activities such as projects, capitalizing on the learning from one project to another, and thus building progressively knowledge that is then shared and diffused among the resources resulting in the renewal of their competences. This framework highlights a reinforcement dimension and distinguishes operations (Designing, spreading Design and managing Design) from building on these operations (building the Design expertise and transforming the organization through Design) in order to contribute to the firm’s resources renewal.

We investigate conditions under which a government facing a large set of small private agents can implement its desired outcome when it has only a limited commitment ability to policy actions. We show that, in static contexts, more commitment ability always improves equilibrium outcomes and, in some widely used macro models, an arbitrarily small commitment ability suffices to implement a unique outcome. This contrasts with repeated settings where reputation forces make necessary a more substantial commit- ment ability to obtain a unique outcome and, paradoxically, more commitment ability may lead to worse outcomes and/or to a wider set of equilibria. We derive implications for models of bailouts, inflation bias, and capital taxation.

International audience; Most of France’s local museums are coping with a contrasting situation: school visits are very successful and receive appreciative comments, whereas autonomous leisure family visits are disappointing and are sometimes damaging to the museum’s reputation. Welcoming families for a leisure museum visit raises two issues: the impossibility of offering a guided visit to every family because of a lack of staff and finances, and the need for innovative interactive pathways that allow families to visit without the benefit of a guide or curator. The authors engaged in an action-research process with the Angoulême paper museum, with the aim of designing an autonomous leisure visit for families with “tweens.” A constructivist framework was elaborated to design an interactive family visit using two interconnected mobile applications and to support the scaffolding role of parents with respect to their child.

This paper studies how audience members categorize and evaluate ambiguous offerings. Depending on whether audience members categorize ambiguous offerings based on prototypes or goals, they activate two distinct cognitive mechanisms and evaluate differently ambiguous offerings. We expect that when audiences engage in goal- versus prototype-based categorization, their evaluation of ambiguous products increases. We theorize that, under goal-based categorization, the perceived utility of unclear attributes increases for audiences, which leads them to evaluate more positively ambiguous product offerings. We test and find support for these direct and mediated relationships through a series of laboratory, online, and field experiments. Overall, this study offers important implications for research on product and market categories, optimal distinctiveness, and market agents’ cognitive ascription of value.

In over-the-counter markets for assets, such as bonds and securitizations, large volumes can be split into smaller pieces and gradually sold to several final investors with the intermediation of multiple dealers. This paper proposes a model to study this process, called asset dissemination. A dealer buys several units of an asset from a customer and then sells some units to the dealer’s customers and to a second dealer, who sells to the second dealer’s customers and to a third dealer, and so on. The extent of dissemination is measured by the number of dealers involved and the total customer demand served. We show that asymmetric information on customer demand hinders both dimensions of dissemination. We also study how the quantity to disseminate and the dealers’ funding costs impact dissemination and the prices and quantities in interdealer transactions. This paper was accepted by Gustavo Manso, finance.

Assortment selection is one of the most important decisions faced by retailers. Most existing papers in the literature assume that customers select at most one item out of the offered assortment. While this is valid in some cases, it contradicts practical observations in many shopping experiences, both in online and brick-and-mortar retail, where customers may buy a basket of products instead of a single item. In this paper we incorporate customers' multi-item purchase behavior into the assortment optimization problem. We consider both uncapacitated and capacitated assortment problems under the so-called Multivariate MNL (MVMNL) model, which is one of the most popular multivariate choice models used in the marketing and empirical literature. We first show that the traditional revenue-ordered assortment may not be optimal. Nonetheless, we show that under some mild conditions, a certain variant of this property holds (in the uncapacitated assortment problem) under the MVMNL model---that is, the optimal assortment consists of revenue-ordered local assortments in each group. Finding the optimal assortment is still computationally expensive as the revenue thresholds for different groups cannot be computed separately. We show that the optimization problem under MVMNL is NP-complete even in the setting where there is no interaction among the product categories. Motivated by this result, we develop FPTAS for several variants of (capacitated and uncapacitated) assortment problems under MVMNL. Our analysis reveals that disregarding customers' multi-item purchase behavior in assortment decisions can indeed have a significant negative impact on a retailer’s profitability, demonstrating its practical importance in retail. In particular, we show that our proposed algorithm can improve a retailer's expected total revenues (compared to some benchmark policies that do not properly take into account the impact of customer's multi-item choice behavior in assortment decisions) by around 5-7% for the uncapacitated problems, and around 10-54% for the capacitated problems, both of which are quite significant.

This paper develops a belief update rule under ambiguity, motivated by the maxim: in the face of new information, retain those conditional beliefs in which you are more confident, and relinquish only those in which you have less confidence. We provide a preference-based axiomatisation, drawing on the account of confidence in beliefs developed in Hill (2013) . The proposed rule constitutes a general framework of which several existing rules for multiple priors (Full Bayesian, Maximum Likelihood) are special cases, but avoids the problems that these rules have with updating on complete ignorance. Moreover, it can handle surprising and null events, such as crises or reasoning in games, recovering traditional approaches, such as conditional probability systems, as special cases.

The avalanche of new technologies and artificial intelligence have accelerated financial innovation, compelling the law to undergo a technological update. This phenomenon extends to the likes of regtech, suptech, regulatory big data, algorithmic supervision, etc. The study aims to demonstrate how these new instruments are, in fact, part of an overarching movement observable in numerous fields; that is, the emergence of a scientific, mathematical, algorithmic law driven by risk and technologies (classified as “Smart Law”). The authors will examine the aforementioned theoretical model and highlight the practical implications for banking and finance law. They conclude on the necessity for a structural collaboration between lawyers and engineers in order to manage the inevitable hybridization of the rule of law and its technological integration.

In recent years, greater attention has been given to developing metrics that measure more than a country’s gross domestic product (GDP). Similarly, greater consideration has been given to more than just the financial performance of commercial enterprises; corporations are now expected to conduct business in ways that are responsible and sustainable, giving attention to a triple bottom line where the planet and people are prioritized along with profits. Taking French government policy and the performance of French multinational corporations as a case in point, this article explores the ways in which emerging indicators and instruments on business and human rights are relevant to the impact of business on well-being. This article examines which reporting frameworks and ranking systems best capture human rights and sustainability risks that could compromise well-being. Specifically, the article analyzes the frameworks and indicators used to measure human rights performance and the impact of rights rankings on business management. It also reviews responses by corporations to rights rankings as indicia of how measurements might be perceived as likely to result in changes in investor and consumer behavior or place brand reputation at risk.

When are the incentives of a business ecosystem’s participants aligned with its growth? How is the technology of value creation affecting this alignment? To answer these questions we formally model how value creation determines value capture in a business ecosystem. We find that alignment is typically imperfect compared to an integrated benchmark, highlighting an “ecosystem penalty” whereby participants’ returns to value creation are lower than that of the ecosystem. The occurrence of this penalty depends on whether the technology is loosely coupled (value additive in inputs), exhibits bottlenecks (value constrained by the weaker input), or is strongly supermodular (value multiplicative in inputs). Contrary to conventional wisdom, we find it can be severe for the participants who are constraining the system the most.

The European response to COVID-19 has revealed an inconvenient truth. Despite having integrated public health concerns across all its policies – be it agriculture, consumer protection, or security –, the Union cannot directly act to save people’s lives. Only member states can do so. Yet when they adopted unilateral measures to counter the spread of the virus, those proved not only ineffective but also disruptive on vital supply chains, by ultimately preventing the flow of essential goods and people across the Union. These fragmented efforts in tackling cross-border health threats have almost immediately prompted political calls for the urgent creation of a European Health Union. Yet this call raises more questions than answers. With the aim to offer a rigorous and timely blueprint to decision-makers and the public at large, this Special Issue of the European Journal of Risk Regulation contextualizes such a new political project within the broader constitutional and institutional framework of EU public health law and policy. By introducing the Special, this paper argues that unless the envisaged Health Union will tackle the root causes of what prevented the Union from effectively responding to COVID-19 – the divergent health capacity across the Union –, it might fall short of its declared objective of strengthening the EU’resilience for cross-border health threats.

Do personalized offerings enabled by big data only bring economic benefits to firms but not to consumers? In this paper, we seek to address this question by examining a particular category of personalized offerings provided by algorithmic product recommendation systems. While the economic value of algorithmic product recommendations to firms is mostly understood, their benefits to consumers are not well quantified in the literature. We conduct a randomized field experiment on an apparel retailer’s website in the US to estimate the benefits of recommendations to consumers. We find that recommendations helped consumers discover lower-priced products on the website, which results in a higher probability of product purchase (lower likelihood of failed search efforts) and a lower price of purchased products. Specifically, an additional recommended product page view due to recommendations leads to a 15 percent increase in purchase probability and a $1.59 decrease in the purchase price. The product recommendations are particularly beneficial to consumers for product categories with a higher relative price dispersion and a higher proportion of niche products. Due to these benefits, consumers substitute 0.14 keyword search page views and 0.63 product category page views for each recommended product page view under recommendations. The recommendations generated a surplus of $56,631 (3.8 percent of the total sales), two-thirds of which the retailer captured as additional revenue, and the remaining one-third the consumers as price savings.

We examine whether impact investing is more effective in fostering business venture success and social impact when directed toward ventures located in vs. outside disadvantaged urban areas (i.e., areas with high crime, unemployment, and poverty). We explore this question in the context of loans made to business ventures located in French “banlieues” vs. “non-banlieues”. We find that loans issued to banlieue ventures, compared to non-banlieue ventures, yield greater improvements in financial performance, as well as greater social impact in terms of the creation of local employment opportunities, quality jobs, and jobs for minorities — all of which contribute to the social inclusion of marginalized communities and the development of sustainable cities.

We study how data abundance affects the informativeness of financial analysts' forecasts at various horizons. Analysts produce forecasts of short-term and long-term earnings and choose how much information to collect about each horizon to minimize their expected forecasting error, net of information acquisition costs. When the cost of obtaining short-term information drops (i.e., more data becomes available), analysts change their information collection strategy in a way that renders their short-term forecasts more informative but that possibly reduces the informativeness of their long-term forecasts. Using a large sample of analysts' forecasts at various horizons and novel measures of their exposure to abundant data (e.g., social media data), we provide empirical support for this prediction, which implies that data abundance can impair the quality of long-term forecasts.

Under International Financial Reporting Standards, managers can use two approaches to increase the estimated fair value of goodwill in order to justify not recognizing impairment: (1) make overly optimistic valuation assumptions, and (2) increase future cash flow forecasts by inflating current cash flows. Because enforcement constrains the use of optimistic valuation assumptions, we hypothesize that enforcement influences the relative use of these two choices. We test this hypothesis by comparing a sample of 1,958 firms from 36 countries that are likely to delay recognizing goodwill impairment (suspect firms) to a sample of control firms. First, we find that firms in high‐enforcement countries use a higher discount rate to test goodwill for impairment than firms in low‐enforcement countries. We also find a more positive association between discount rate and upward cash flow management for suspect firms than for control firms. This result is consistent with suspect firms substituting optimistic valuation assumptions with inflated current cash flows. Second, we find that, relative to control firms, suspect firms exhibit higher upward cash flow management in high‐enforcement countries than in low‐enforcement countries. Third, we show that suspect firms in high‐enforcement countries are more likely to eventually impair goodwill.

Previous research have opposed secrecy and openness as mutually exclusive processes. In this paper, we suggest a more nuanced approach to secrecy in open innovation, drawing from Philosophy and Sociology. We have conducted a two-year research program in the defense industry, collecting data on the practices of concealment in open innovation activities. We found that focal actors utilize distinct cognitive techniques to decontextualize knowledge in the course of open innovation projects in order to safeguard secrecy while preserving mutual learning. Further, focal actors design contrasted relational approaches to secrecy with their open innovation partners. Such approaches are based on the positions of secrecy boundaries, which are internal or external to the relationship. In fact, actors intentionally make use of the reversible nature of secrecy, balancing inclusion and exclusion of knowledge in partnerships, to meet objectives that sometimes go beyond knowledge protection. Finally, we build on our findings to introduce a capability-based framework of secrecy management in openness processes. This framework, which we call Knowledge Discretion, suggests that actors overcome tensions stemming from secrecy and openness playing on the contextual depth and relational breadth of external knowledge sharing.

Massive Online Open Courses (MOOCs) are a booming phenomenon in the digital era, having attracted millions of users around the world to date. At the same time, educational delivery via MOOCs comes with its own distinct difficulties for students and instructors, as the online nature of MOOCs creates every opportunity for digital distraction and procrastination. In this work, we consider that the digital nature of MOOCs and online learning management systems (LMSs) may also offer unique opportunities to counteract procrastination. Building on the temporal motivation theory, this study examines a number of calls-to-action (CTAs) pertaining to the completion and submission of course assignments, with an eye toward combatting student procrastination on MOOCs. We report on the results of a randomized field experiment on a leading MOOC platform in China. By randomly treating MOOC users with different CTAs related to active course assignments, we seek to examine the impacts of alternative informational interventions on students’ duration to and probability of on-time assignment submission. We consider multiple types of CTAs: a simple call-to-action, a deadline reminder, descriptive norm interventions (communicating peer assignment completion rates), and a simple CTA combined with a financial incentive. We find that descriptive norms lead to higher probabilities of assignment completion and a shorter time to completion. In contrast, we find that the deadline reminder has a surprisingly counter-productive effect. Subsequently, exploring heterogeneity in the response to our different interventions – considering factors such as course load, education level, and user tenure on the MOOC platform – we find evidence that the deadline reminder, in particular, can backfire if students’ active course load is low. This result suggests that students with low course loads may perceive the deadline to be distant, which reduces their sense of urgency and leads to complacency. We discuss the implications of our findings for both research and practice.

This paper examines how data-driven personalized decisions can be made while preserving consumer privacy. Our setting is one in which the firm chooses a personalized price based on each new customer's vector of individual features; the true set of individual demand-generating parameters is unknown to the firm and so must be estimated from historical data. We extend this classical framework of personalized pricing by requiring also that the firm's pricing policy preserve consumer privacy, or (formally) that it be differentially private -- an industry standard for privacy preservation. The two settings we consider are theoretically and practically relevant: central and local models of differential privacy, which differ in the strength of the privacy guarantees they provide. For both models, we develop privacy-preserving personalized pricing algorithms and derive the theoretical bounds on their performance as measured by the firm's revenue. Our analyses suggest that, if the firm possesses a sufficient amount of historical data, then it can achieve central differential privacy at a cost of the same order as the "classical" loss in revenue due to estimation error. Comparing the two models, we conclude that local differentially private personalized pricing yields better privacy guarantees but leads to much greater revenue loss by the firm. We confirm our theoretical findings in a series of numerical experiments based on synthetically generated and real-world On-line Auto Lending (CPRM-12-001) data sets. Finally, we also apply our theoretical framework to the setting of personalized assortment optimization.

In this paper, we develop and test a behavioral theory of lost leadership. Using insights from the literature on goals as reference points and prospect theory, we predict that former leaders exert more effort compared to otherwise identical competitors. We test this prediction using two contexts. The first, quasi-laboratory, data comes from an educational business game. The second setting draws on field data from a two-month banking sales contest. We find that provision of effort increases following the loss of leadership. We also explore whether past leaders exert more effort in general or simply shift effort from other, potentially less-salient goals. We find evidence of both mechanisms. Finally, we investigate the temporal effects and find that having been a leader has an attenuating effect on subsequent behavior.

Media dissemination plays an important role in facilitating price discovery. Political pressure that restricts media dissemination can hinder this function and affect investors’ perceptions. This paper studies the magnitude of newspaper censorship in China and its economic consequences using a setting of “tunneling” scandals. We find significant evidence of censorship of tunneling-related negative news at the national and local level. We further show that news that survives censorship reduces information asymmetry and improves pricing efficiency. We find that censorship blocks informative tunneling news and delays incorporation of tunneling reporting into prices.

How do banks remunerate risk managers and what are the implications for risk-taking? Studying 127 German banks during the years 2003 to 2007, we show that risk managers' remuneration is positively aligned with performance-linked pay in front offices (FOs). When bonuses in FOs increase by one Euro, the bonus of a risk manager increases by 13.6 to 33.5 Cents, depending on the risk manager’s seniority. Risk-sharing among employees or labor market competition do not explain this finding. Banks with more aligned incentive pay between risk management and FOs during the years before the crisis of 2008-2009 performed better in the crisis.

We consider a firm that faces the following pricing and leadtime disclosure decision. Either, (1) offer the same price to all arriving customers and disclose no leadtime information, in which case customers know only an expected leadtime, or, (2) vary the price dynamically based on the current inventory status, and disclose leadtime information dynamically. The second option allows the firm to offer a price discount for a long leadtime, and to offer a short leadtime when it has a large inventory. In order to determine which pricing and leadtime disclosure policy attains higher maximum profit, we formulate and solve a Markov decision problem for each policy and compare the two solutions. The comparison suggests that, typically, there is a critical inventory level, above which static pricing and no leadtime disclosure yields higher profit, and, below that level, dynamic pricing and leadtime disclosure yields a higher profit. We also identify certain situations where the static policy leads to profits at least as large as those under the dynamic policy, for any state, and we examine the price differences between the two policies. Finally, the structure of the solution is similar to the situation where the system manager can pause the production process, except that now this extra lever partly mitigates the superiority of the static policy.

This paper examines a development process in which a firm explores alternative approaches for the development of a single product. To do this, the firm employs competing internal teams, but it also needs to involve key suppliers who are responsible for the development of a key component (or subsystem) via a collaboration with one or several internal teams. We explore how internal competition affects the suppliers' collaborative efforts and the firm's profits, and also how the firm should allocate the internal teams to potential suppliers. Our analysis suggests that this setting is significantly different from standard tournament settings, implying that existing research results should not be simply extrapolated to supply chain settings. For example, unlike in traditional tournaments, we find that increasing supplier competition by increasing the number of teams per supplier can actually increase supplier efforts. We develop a tournament model with two suppliers who differ both in their expertise for the specific type of development project and in their development costs (i.e., their cost-effectiveness). We also identify a critical influential factor: the cost savings resulting from the similarity between different development efforts by the same supplier (with different internally competing teams). If the cost savings are not very high, i.e., if the different development efforts are not very similar, the firm should optimally allocate more teams to the supplier with less relevant expertise or lower cost-effectiveness. Similarly, involving a third supplier is beneficial only if the cost savings are low. While companies make significant efforts to identify the supplier with the best expertise, our results show that allocating more teams to the stronger supplier generally undermines the suppliers' incentives to exert high levels of effort. Hence, allocating the most teams to that supplier might not always improve firm profits.

Consumers often anthropomorphize non-human entities. In this research, we investigate a novel antecedent of anthropomorphism: language. Some languages (e.g., English) make a grammatical distinction between humans (he, she) and non-humans (it), whereas other languages (e.g., French) do not (all objects are gender-marked). We propose that such grammatical structures of languages influence the way individuals mentally represent non-human entities, and as a result, their generalized tendencies to anthropomorphize such entities. Across 10 studies, we provide evidence that speakers of languages that do not grammatically distinguish between humans and non-humans (it-less languages) anthropomorphize more than do speakers of languages that do make this distinction (non-it-less languages). We demonstrate the effects across natural languages (French, Turkish, English) and by manipulating grammatical gender. We show that the effects are observable in naturally occurring consumer contexts (e.g., secondary sales data), and that gender-marking in it-less languages influences consumers’ interactions with brands, even though the gender-markings are semantically arbitrary, and that these effects occur nonconsciously. Our findings have implications for the broader debate on the extent to which language influences thought, and also suggest ways in which managers can leverage nonconscious grammatical anthropomorphism to influence consumer perceptions, attitudes, and behavior.

Brands and advertisers often tap into the crowd to generate ideas for new products and ad creatives by hosting ideation contests. Content evaluators then winnow thousands of submitted ideas before a separate stakeholder, such as a manager or client, decides on a small subset to pursue. We demonstrate the information value of data generated by content evaluators in past contests and propose a proof-of-concept machine learning approach to efficiently surface the best submissions in new contests with less human effort. The approach combines ratings by different evaluators based on their correlation with the past stakeholder choices, controlling for submission characteristics and textual content features. Using field data from a crowdsourcing platform, we demonstrate that the approach improves performance by identifying nonlinear transformations and efficiently reweighting evaluator ratings. Implementing the proposed approach can affect the optimal assignment of internal experts to ideation contests. Two evaluators whose votes were a priori equally correlated with sponsor choices may provide substantially different incremental information to improve the model-based idea ranking. We provide additional support for our findings using simulations based on a product design survey.

This paper develops a method to flexibly adapt interpolation grids of value function approximations in the estimation of dynamic models using either NFXP (Rust, Econometrica: Journal of the Econom etric Society, 55, 999– 1 033, 1987) or MPEC (Su & Judd, Econometrica: Journal of the Econometric Society, 80, 2213–2230, 2012). Since MPEC requires the grid structure for the value function approximation to be hard-coded into the constraints, one cannot apply iterative node insertion for grid refinement; for NFXP, grid adaption by (iteratively) inserting new grid nodes will generally lead to discontinuous likelihood functions. Therefore, we show how to continuously adapt the grid by moving the nodes, a technique referred to as r-adaption. We demonstrate how to obtain optimal grids based on the balanced error principle, and implement this approach by including additional constraints to the likelihood maximization problem. The method is applied to two models: (i) the bus engine replacement model (Rust, 1987), modified to feature a continuous mileage state, and (ii) to a dynamic model of content consumption using original data from one of the world’s leading user-generated content networks in the domain of music.

Problem definition: We study customer-centric privacy management in service systems. Academic/practical relevance: We explore the consequences of extended control over personal information by customers in such systems. Methodology: We adopt a stylized queueing model to capture a service environment that features a service provider and customers who are strategic in deciding whether to disclose personal information to the service provider—that is, customers’ privacy or information disclosure strategy. A customer’s service request can be one of two types, which affects service time but is unknown when customers commit to a privacy strategy. The service provider can discriminate among customers based on their disclosed information by offering different priorities. Results: Our analysis reveals that, when given control over their personal data, strategic customers do not always choose to withhold them. We find that control over information gives customers a tool they can use to hedge against the service provider’s will, which might not be aligned with the interests of customers. More importantly, we find that under certain conditions, giving customers full control over information (e.g., by introducing a privacy regulation) may not only distort already efficiently operating service system but might also backfire by leading to inferior system performance (i.e., longer average wait time), and it can hurt customers themselves. We demonstrate how a regulator can correct information disclosure inefficiencies through monetary incentives to customers and show that providing such incentives makes economic sense in some scenarios. Finally, the service provider itself can benefit from customers being in control of their personal information by enticing more customers joining the service. Managerial implications: Our findings yield insights into how customers’ individually rational actions concerning information disclosure (e.g., granted by a privacy regulation) can lead to market inefficiencies in the form of longer wait times for services. We provide actionable prescriptions, for both service providers and regulators, that can guide their choices of a privacy and information management approach based on giving customers the option of controlling their personal information.