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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.

We show that the feasibility of a booking in the European entry-exit gas market can be decided in polynomial time on single-cycle networks that are passive, i.e. do not contain controllable elements. The feasibility of a booking can be characterized by solving polynomially many nonlinear potential-based flow models for computing so-called potential-difference maximizing load flow scenarios. We thus analyze the structure of these models and exploit both the cyclic graph structure as well as specific properties of potential-based flows. This enables us to solve the decision variant of the nonlinear potential-difference maximization by reducing it to a system of polynomials of constant dimension that is independent of the cycle's size. This system of fixed dimension can be handled with tools from real algebraic geometry to derive a polynomial-time algorithm. The characterization in terms of potential-difference maximizing load flow scenarios then leads to a polynomial-time algorithm for deciding the feasibility of a booking. Our theoretical results extend the existing knowledge about the complexity of deciding the feasibility of bookings from trees to single-cycle networks., SCOPUS: ar.j, info:eu-repo/semantics/published

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.

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.

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.

International audience; In this paper, we address the temporal knapsack problem (TKP). In this generalization of the classical knapsack problem, selected items enter and leave the knapsack at fixed dates. We solve the TKP with a dynamic program of exponential size, which is solved using a method called Successive Sublimation Dynamic Programming (SSDP). This method starts by relaxing a set of constraints from the initial problem, and iteratively reintroduces them when needed. We show that a direct application of SSDP to the temporal knapsack problem does not lead to an effective method, and that several improvements are needed to compete with the best results from the literature.

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.

Food quality schemes (FQS: organic and geographical indication products) are often supposed to be more sustainable by their political advocates. We explore the social sustainability advantage of FQS through the lens of supply chains’ bargaining power (BP) distribution. We propose an indicator synthesizing different sources underlying BP (competition-based, transactional, institutional) and counting two dimensions (fair BP distribution and adaptation capacity), that we apply to 18 FQS supply chains and corresponding reference. FQS perform better than their reference products on both dimensions. This better performance is due to a combination of sources. Objectius de Desenvolupament Sostenible::12 - Producció i Consum Responsables

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.

International audience; We offer a flexible latent type approach to rank populations according to unequal health opportunities. Building upon the latent-class method, an approch increasingly adopted to estimate health inequalities, our contribution is to let the number of socioeconomic groups considered vary to obtain an opportunity-inequality curve for a population that gives how the between-type inequality varies with the number of types. A population A is said to have less inequality of opportunity than population B if its curve is statistically below that of population B. This version of the latent class approach allows for a robust ranking of 31 European countries regarding inequality of opportunity in health.

International audience; In this article, we tackle the problem of a market maker in charge of a book of options on a single liquid underlying asset. By using an approximation of the portfolio in terms of its vega, we show that the seemingly high-dimensional stochastic optimal control problem of an option market maker is in fact tractable. More precisely, when volatility is modeled using a classical stochastic volatility model—e.g. the Heston model—the problem faced by an option market maker is characterized by a low-dimensional functional equation that can be solved numerically using a Euler scheme along with interpolation techniques, even for large portfolios. In order to illustrate our findings, numerical examples are provided.

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.

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.

International audience; The paper proposes a novel point-wise entropy approach to measure the time-varying losses in the value of information that investors associate with market signals, financial and economic indicators, and news. We cast our approach in a Bayesian framework and assume that market agents update their beliefs to incoming signals based on a prior information set. By exploiting the distribution rather than the time-series properties of information signals, our method is able to construct univariate signal-specific, but also composite proxies of information loss, with the latter being more efficient in reducing misleading effects and interpretation errors. As an empirical illustration, we construct information loss proxies for the US equity market from several mainstream information signals and find that the majority of information loss indicators can influence investors’ attention, which then intermediates the impact of information signals on market outcomes. Finally, we show that, by relying on composites rather than univariate proxies, market agents can diversify and thus reduce their information losses when interpreting signals associated with the same underlying event.

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 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.

This paper analyzes the behavior of cross-country growth rates with respect to resource abundance and dependence. We reject the linear model that is commonly used in growth regressions in favor of a multiple-regime alternative. Using a formal sample-splitting method, we find that countries exhibit different behaviors with respect to natural resources depending on their initial level of development. In high-income countries, natural resources play only a minor role in explaining the differences in national growth rates. On the contrary, in low-income countries, abundance seems to be a blessing but dependence restricts growth.

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.

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.

International audience; The standard forms of bootstrap iteration are very computationally demanding. As a result, there have been several attempts to alleviate the computational burden by use of approximations. In this paper, we extend the fast double bootstrap of Davidson and MacKinnon (2007) to higher orders of iteration, and provide algorithms for their implementation. The new methods make computational demands that increase only linearly with the level of iteration, unlike standard procedures, whose demands increase exponentially. In a series of simulation experiments, we show that the fast triple bootstrap improves on both the standard and fast double bootstraps, in the sense that it suffers from less size distortion under the null with no accompanying loss of power.

International audience; Motivated by the pertinence of Pigou–Dalton (PD) transfers for inequality measurement when only one attribute is involved, we show that inframodular functions are consistent with multidimensional PD transfers and that weakly inframodular functions fit more accurately with the traditional notion of PD transfers. We emphasize, for inequality rankings of allocations of multiple attributes in a population, the similarities of the inframodular order, defined using inframodular functions, with the concave order in the unidimensional framework.

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.

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.

We analyze firm-level analyst forecasts during the COVID crisis. First, we describe expectations dynamics about future corporate earnings. Downward revisions have been sharp, mostly focused on 2020, 2021 and 2022, but much less drastic than the lower bound estimated by Gormsen and Koijen (2020). Analyst forecasts do not exhibit evidence of over-reaction: As of mid-May, forecasts over 2020 earnings have progressively been reduced by 16%. Longer-run forecasts, as well as expected “Long-Term Growth” have reacted much less than short-run forecasts, and feature less disagreement. Second, we ask how much discount rate changes explain market dynamics, in an exercise similar to Shiller (1981). Given forecast revisions and price movements, we estimate an implicit discount rate going from 10% in mid-February, to 13% at the end of March, back down to their initial level in mid-May. We then decompose discount rate changes into three factors: changes in unlevered asset risk premium (0%), increased leverage (+1%) and interest rate reduction (-1%). Overall, analyst forecast revisions explain most of the decrease in equity values between January 2020 and mid May 2020, but they do not explain shorter term stock market movements.

We measure how shareholders value a firm's ethical actions via an experiment. Our findings are threefold. First, the "selfish investor hypothesis'' is strongly rejected. Participants are willing to pay $ .7 more for buying a share in a firm giving one more dollar per share to charities. Symmetrically, a firm that makes profits by exercising a negative externality of $1 on a charity is valued $.9 less than a similar company with no externality. The scaling of non-pecuniary preferences is linear: doubling the size of a social externality doubles its impact on willingness to pay. Second, the data show that whether investors are pivotal or not with regard to the ethical actions of the firm does not affect their willingness to pay. Third, when participants make investment decisions on behalf of a third party (delegation), their generosity level remains similar. Our results appear to be compatible with a utility model where non-pecuniary benefits are conditional on stock holding.

We explore the way validity and propriety cues contribute to legitimacy judgments about a practice and explain whether the subunit of a large firm increases or decreases the implementation of this practice. Empirically, we examine the extent to which 65 subsidiaries of a multinational enterprise implemented three corporate social responsibility practices. Adopting a set-theoretic approach, we find that both validity and propriety cues are extremely relevant to the understanding of subunits’ implementation of practices over time. The endorsement of a practice in a subunit’s environment plays a particularly crucial role in the extent to which it is implemented, relative to authorization by the parent firm. Furthermore, subunits strongly rely on the active evaluation of the practice’s propriety, such that the consonance between the two propriety dimensions (strategic importance and value compatibility) is central to implementation increase, while dissonance between them can favor implementation decrease. By advancing our understanding of legitimacy judgment formation and practice implementation patterns, our study enriches explanations of organizational conformity and decoupling, and contributes to our understanding of how firms respond to multiple institutional demands.

Reproducibility is key for building trust in research, yet it is not widespread in economics. We show how external certification can improve reproducibility in economics research. Such certification can be conducted by a trusted third party or agency, which formally tests whether a given result is indeed generated by the code and data used by a researcher. This additional validation step significantly enriches the peer-review process, without adding an extra burden on journals or unduly lengthening the publication process. We show that external certification can accommodate research based on confidential data. Lastly, we present an actual example of external certification.

We estimate the causal effect of state-mandated business closures on economic and health outcomes in the context of the COVID-19 crisis in the US. We first show that business closures lead to a substantial drop in sales, earnings, and market values for affected firms. We then exploit sectoral variations in exposure to these restrictions across areas within the same state, and show that locking down 10% of the labor force is associated with a significant contraction in employment, but allows to reduce COVID-19 weekly infection and death rates by respectively 0.023 and 0.0015 percentage points. The findings translate into 24,000 saved lives for a cost of $115 billion. Finally, our empirical analysis suggests that the cost per life saved associated to business closures could have been significantly reduced if restrictions had targeted areas with intense workplace interactions

We study the accuracy and usefulness of automated (i.e., machine-generated) valuations for illiquid and heterogeneous real assets. We assemble a database of 1.1 million paintings auctioned between 2008 and 2015. We use a popular machine-learning technique - neural networks - to develop a pricing algorithm based on both non-visual and visual artwork characteristics. Our out-of-sample valuations predict auction prices dramatically better than valuations based on a standard hedonic pricing model. Moreover, they help explaining price levels and sale probabilities even after conditioning on auctioneers' pre-sale estimates. Machine learning is particularly helpful for assets that are associated with high price uncertainty. It can also correct human experts' systematic biases in expectations formation - and identify ex ante situations in which such biases are likely to arise.

Research Summary In this study, we replicate and expand Hawn et al.’s study (Strategic Management Journal, 2018, 39, 949–976) that used Dow Jones Sustainability World Index (DJSI) events to measure variations in firms' Corporate Social Responsibility (CSR)‐activism and examined their effect on a firm's stock price. We use DJSI events to capture variations in firms' CSR visibility, holding CSR activism constant by restricting our analyses to CSR‐equivalent firms. First, we find similar results on stock price (i.e., no impact) and on trading volumes. Second, because professional market participants pay more attention to CSR‐oriented firms and use visible cues such as DJSI events, we study and find that additions to DJSI lead to more analysts following a firm, and that continuations on the DJSI lead to an increase in equity being held by long‐term investors. Managerial Summary We replicate and complement a recent study that showed that the events of a firm's addition, continuation, or deletion in a major sustainability index (DJSI) had little impact on stock market reactions (Hawn et al. Strategic Management Journal, 2018, 39, 949–976). We redefine the comparison set that was previously used and compare across observationally equivalent firms in terms of CSR orientation in order to isolate the visibility effect of DJSI events. We find that these events do not significantly impact stock price and trading volumes. However, we expand the analysis and find that sustainability events attract more attention from financial analysts and lead to an increase in the percentage of shares held by long‐term investors indicative of a trend that professional investors pay more attention to CSR‐visible firms over time.

Despite a heated debate on the perceived increasing complexity of financial regulation, there is no available measure of regulatory complexity other than the mere length of regulatory documents. To fill this gap, we propose to apply simple measures from the computer science literature by treating regulation like an algorithm - a fixed set of rules that determine how an input (e.g., a bank balance sheet) leads to an output (a regulatory decision). We apply our measures to the regulation of a bank in a theoretical model, to an algorithm computing capital requirements based on Basel I, and to actual regulatory texts. Our measures capture dimensions of complexity beyond the mere length of a regulation. In particular, shorter regulations are not necessarily less complex, as they can also use more "high-level" language and concepts. Finally, we propose an experimental protocol to validate measures of regulatory complexity.

We examine how the U.S. Food and Drug Administration Amendments Act (FDAAA) of 2007, which requires additional disclosures regarding clinical trial results, impacts information asymmetry between the disclosing pharmaceutical firm and capital market participants, the general public, academics, and practitioners. We document a reduction in information asymmetry in capital markets. We also document an increase in adverse event and product problem complaint reports filed against the pharmaceutical firms to the FDA and a higher number of drug and medical device recalls for affected firms after the FDAAA enactment. Finally, cross-sectional analyses suggest that the increase in FDA complaint reports and recalls after the FDAAA enactment was more prominent in firms with a higher bid-ask spread decrease. Taken together, our results suggest that the FDAAA has some benefits for both investors and consumers.

Research Summary We explore the performance consequences of the simultaneous pursuit of multiple objectives in organizations. Taking advantage of a unique dataset covering both the objectives pursued and performance outcomes, we test the hypothesis that is the cornerstone of multiple objectives theory: performance on a given metric increases when it is pursued as an objective but decreases with the number of other objectives pursued simultaneously. We find overall support for this hypothesis, which holds for most, but not all, objectives. We further unpack the link between multiplicity of objectives and performance, investigating the moderating effects of organization design choices. This study suggests that multiple objectives impose a cost on organizations, but also provide a benefit of alleviating tradeoffs in achieving higher performance in multiple dimensions. Managerial Summary Most organizations simultaneously follow multiple goals, rather than focus on a single, well‐defined objective. For example, manufacturing firms often concurrently strive to decrease costs, increase revenues, and enhance margins. We study the consequences of such pursuit for firm performance. We show that explicitly setting objectives plays an important role in driving performance improvements. We also show that performance on any given dimension decreases with the number of other, simultaneously, followed goals. This regularity holds across different types of organizations, from simple to complex. Finally, we show that setting goals in multiple dimensions can play a beneficial role in forcing firms to actively manage tradeoffs inherent in their strategic choices. Our findings point to how managers could balance the costs and benefits of multiple objectives.

International audience; This paper sheds light on the role of digital platform labour in the development of today's artificial intelligence, predicated on data-intensive machine learning algorithms. Focus is on the specific ways in which outsourcing of data tasks to myriad 'micro-workers', recruited and managed through specialized platforms, powers virtual assistants, self-driving vehicles and connected objects. Using qualitative data from multiple sources, we show that micro-work performs a variety of functions, between three poles that we label, respectively, 'artificial intelligence preparation', 'artificial intelligence verification' and 'artificial intelligence impersonation'. Because of the wide scope of application of micro-work, it is a structural component of contemporary artificial intelligence production processes - not an ephemeral form of support that may vanish once the technology reaches maturity stage. Through the lens of micro-work, we prefigure the policy implications of a future in which data technologies do not replace human workforce but imply its marginalization and precariousness.

We document that since 1997, the rate of startup formation has precipitously declined for firms operated by U.S. PhD recipients in science and engineering. These are supposedly the source of some of our best new technological and business opportunities. We link this to an increasing burden of knowledge by documenting a long-term earnings decline by founders, especially less experienced founders, greater work complexity in R&D, and more administrative work. The results suggest that established firms are better positioned to cope with the increasing burden of knowledge, in particular through the design of knowledge hierarchies, explaining why new firm entry has declined for high-tech, high-opportunity startups.

In the face of rising income inequality (Acemoglu & Autor, 2011; Atkinson, Piketty, & Saez, 2011; Piketty, 2014; World Economic Forum, 2014), one recent proposal is to provide consumers with information about the income inequality across those involved in the production of each good, at the point of purchase. This has been shown to depress overall inequality (Hill, 2020), though its impact depends crucially on whether people are willing to pay more for goods whose production involves less income inequality. Here we investigate this largely unexplored empirical question through an incentivised, behavioural choice experiment on a representative sample of the English population. We find that a large majority are willing to pay significantly more for goods associated with less inequality. How much more people are willing to pay varies with political leaning and increases with the extent of the inequality reduction, but is positive across the political spectrum and for all studied inequality differences. Moreover, it is typically higher when inequality is reported in more intuitive and informa- tive formats. Our results bode well for the effectiveness of product-level inequality information provision as a tool for moderating income inequality, promising impacts even in markets where all goods involve relatively high inequality levels and potential participation across the political spectrum.

Crossmodal sensory correspondences between shape and taste are well-established (e.g., angular–bitter, rounded–sweet). However, the extent to which these correspondences reliably influence consumer taste judgments is less clear, as are the processes underlying the effects. This research addresses both issues. Across seven experiments, we show that whether shape–taste correspondences influence taste judgments depends on their associative strength in memory, and that a significant shape–taste correspondence spontaneously affects taste judgments only when its associative strength reaches a sufficient threshold. We further demonstrate the effects in a child development context, in which children’s age, as a naturally occurring proxy of associative strength, moderates shape–taste crossmodal effects on taste judgments. We also demonstrate that the generation of shape–taste crossmodal effects is driven by a simple spreading activation model that is moderated by associative strength, is highly automatic, and occurs even when cognitive and visual resources are constrained. The findings suggest that 1) managers must go beyond establishing simple crossmodal correspondences to determine whether sufficient thresholds are met, 2) the shape–taste associations can apply to products marketed to older children, and 3) the effects are likely to occur even in cognitively noisy retail environments.