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International audience; We compare disagreement in expectations and the frequency of forecast revisions among five categories of agents: households, firms, professional forecasters, policymakers and participants to laboratory experiments. We provide evidence of disagreement among all categories of agents. There is however a strong heterogeneity across categories: while policymakers and professional forecasters exhibit low disagreement, firms and households show strong disagreement. This translates into a heterogeneous frequency of forecast revision across categories of agents, with policymakers revising more frequently their forecasts than firms and professional forecasters. Households last revise less frequently. We are also able to explore the external validity of experimental expectations.

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 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.

International audience; We examine the relation between the probability of future stock price crash and investors’ investment horizons. Using negative skewness as a proxy for firm-specific crash risk, we document a positive association between institutional ownership and stock price crash risk. The relation is, however, driven by short-term institutional investors, while the presence of long-term institutional investors has a negative effect on stock price crash risk. In addition, we find that the presence of short-term institutional investors induces corporate risk-taking behavior. Our results are robust to alternative model specifications, endogeneity concerns, and different measures of crash risk and proxies of investors’ horizons.

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.

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.

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; This article analyses the political significance of the protests that have arisen in Tunisia since the ʻrevolution’ and the establishment of a parliamentary regime. This is what the protests studied have in common: they belong to neglected regions in the country’s hinterland; that they mobilise young local populations; they claim rights over their territories’ soil and subsoil resources exploitation; they occupy a strategic location for a relatively long period of time; and they set up democratic mechanisms for these locations’ self-management, in the form of ‘coordinations’. The description of social logics and the way populations resist, as well as the authoritarian rationality of government action and the inability of elected officials to mediate conflicts, reveal differences between protesters who seek autonomy from state control, while others refer to a rent-centred understanding of the claim. It also shows the emergence of a ‘protest democracy’, itself an expression of a crisis of ‘governmentality’. These two phenomena are symptomatic of a demand for integrating populations and new ways of governing that break with the reeks of past authoritarianism and current representative democracy.

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.

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.

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.

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.

Ten years after the introduction of participatory democracy as one of the democratic foundations of the European Union, this introductory chapter to "Citizen Participation in Democratic Europe" provides some theoretical background on the role citizens play, and should play, within the EU democratic system. As such, it offers a contribution about the place of citizen participation in the wider, ongoing debate about how to develop the EU’s democratic system in a post-pandemic Europe. It sets the scene for the subsequent chapters written, again, not only by academic scholars but also civil society advocates and practitioners from across Europe who experience on a daily basis the realities of EU citizen participation. Ultimately, this edited volume argues how Europe’s long-standing democracy challenge can be addressed through the emergence of a new model of EU citizen participation, and a set of democratic innovations emerging bottom-up across the continent. To do so, it offers a re-evaluation and systematisation of EU level citizen participation and its future development, which remains largely underexplored.

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.

Firms often partially fund new projects for preliminary development rather than fully fund them to fruition. This staged development yields early feedback which can improve an individual project through learning and a portfolio of projects by the termination of weaker ideas. Although prior empirical studies have found a positive relationship between staged development and outcomes, these studies were limited to settings that only allowed for learning, i.e. projects were never terminated. To capture the full effect, this paper exploits a form staged development in the television show industry where a new show’s first episode is filmed and evaluated before commissioning the full show. Using Netflix’s entry into television show production as a source of randomization, a negative treatment effect of piloting on show ratings is measured for shows that are more likely to survive piloting. Mechanism evidence suggests piloting worsens shows by impeding their ability to attract actors and lowering planning of the full season. Hence, staged development can have a downside: the lack of commitment to projects can prevent them from getting the resources and effort necessary for success.

Recent decades have witnessed a growing focus on two distinct income patterns: persistent pay inequity, particularly a gender pay gap, and growing pay inequality. Pay transparency is widely advanced as a remedy for both. Yet we know little about the systemic influence of this policy on the evolution of pay practices within organizations. To address this void, we assemble a novel data set combining detailed performance, demographic and salary data for approximately 100,000 US academics between 1997 and 2017. We then exploit staggered shocks to wage transparency to explore how this change reshapes pay practices. We find evidence that pay transparency causes significant increases in both the equity and equality of pay, and significant and sizeable reductions in the link between pay and individually-measured performance.

How can non-profit organizations improve their governance to increase their social impact? This study examines the effectiveness of a bundle of governance mechanisms (consisting of pro-social incentives and auditing) in the context of a randomized governance program conducted in the Democratic Republic of Congo's healthcare sector. Within the program, a set of health centers were randomly assigned to a governance treatment while others were not. We find that the governance treatment leads to i) higher operating efficiency and ii) improvements in social performance (measured by a reduction in the occurrence of stillbirths and neonatal deaths). Furthermore, we find that funding is not a substitute for governance—health centers that only receive funding increase their scale, but do not show improvements in operating efficiency nor social performance. Overall, our results suggest that corporate governance plays an important role in achieving the non-profits' objectives and increasing the social impact of the funds invested.

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.

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.

This study examines whether and when credit rating agencies (CRAs) take negative rating actions against issuers committing accounting fraud before the fraud is publicly revealed and the economic impact of such rating actions. Our findings show that these fraud firms experience a greater number of negative rating actions during the four quarters prior to the public fraud revelation, including lower ratings, more rating downgrades, and more negative credit watch additions, compared to firms with similar economic fundamentals and stock performance. Our findings also show that such negative rating actions are not limited to fraud firms in financial distress, suggesting that our effect reflects CRA responses to accounting fraud per se. In addition, we find CRAs take more timely actions when frauds are more severe, when they involve accounts more often scrutinized by CRAs during their credit analysis, and when short sellers target firms. Last, we find that CRAs’ negative rating actions against fraud firms are informative to the market and are associated with shorter fraud duration. Overall, we conclude that CRAs possess private information about accounting fraud prior to the public revelation of this fraud and that they incorporate this information into negative ratings actions, accelerating fraud discovery.

A consumption event is memorable if the memory of it affects well-being at times after the material consumption. We develop an axiomatic model of memorable consumption in a dynamic setting. Preferences are represented by the present value of the sum of utilities derived at each date from the current consumption and from recollecting the past. Our model accommodates well-known phenomena in psychology, such as the peak-end rule, duration neglect, and adaptation trends. We provide foundations for a prominent special case of memory that has the Markovian property. The model is illustrated in application to life-cycle consumption-savings decisions and asset pricing.

This study, commissioned by the European Parliament’s Policy Department for Citizens’ Rights and Constitutional Affairs and requested by the European Parliament’s Committee on Civil Liberties, Justice and Home Affairs, assesses the impact of disinformation and strategic political propaganda disseminated through online social media sites. It examines effects on the functioning of the rule of law, democracy and fundamental rights in the EU and its Member States.The study formulates recommendations on how to tackle this threat to human rights, democracy and the rule of law. It specifically addresses the role of social media platform providers in this regard.

Although the level of power held by the marketing department can determine key organizational outcomes, including firm performance, this power often is modest and, in many firms, diminishing. To address this apparent disconnect, the authors propose that the board of directors is a critical but overlooked driver of marketing department power. In particular, directors’ marketing exposure through board service at other firms (i.e., board-interlocked firms) may affect the marketing department’s power in the firms on whose boards they also serve. With a sample of 4,422 firms, spanning 2007–2013, this study reveals that marketing department power in board-interlocked firms significantly and positively drives marketing department power in the focal firms. Consistent with an information sharing view, the magnitude of this effect varies with the focal firm’s network position in the board-interlocked network, such that it strengthens as the focal firm’s network centrality (information amount) and network brokerage (information quality) increase. These robust results suggest that board members and their social networks significantly influence marketing department power; if marketing wants to increase its power, it should get the board “on board.”

Problem Definition: Project managers are responsible for setting and then revising projects' goals. As uncertainty related to project performance is resolved, a project manager is tasked with comparing ongoing costs, and potentially achieved scope, to a baseline plan. We question whether project managers can rationally anticipate and track this revision process. Academic/Practical Relevance: In practice, projects often fail to meet their goals and are subject to changes in scope, cost, and scheduled time. We consider the implications of behavioral tendencies and the revision strategy on decisions made and on overall project performance. Methodology: Our stylized model compares a rational project manager to a behavioral one. Specifically, we offer a framework for modeling mental accounting—which includes loss aversion and reference point updating—and narrow framing. We use the model to explore how project-level decisions are made. Results: We show that mental accounting results in insufficient adjustments of project scope and cost during revisions, and prevents abandoning projects even when doing so is optimal. Ultimately, mental accounting and narrow framing decrease projects' actual profits. Managerial Implications: We offer practical prescriptions for mitigating harmful effects of loss aversion, reference-point updating, and narrow framing. Beyond training, hiring less loss averse project managers, and practicing scenario planning, we show that reviewing a project using a cost milestone instead of a scope milestone helps maintain the reference cost equal to the budgeted cost, thereby inducing overall better decisions.

Problem definition: Contrary to classic applications of matching theory, in most contemporary on-demand service platforms, matches can not be enforced because workers are flexible – they choose their tasks. Such flexibility makes it difficult to manage workers while keeping customers satisfied. We build a framework to compare platform matching policies with less flexible and more flexible workers, and empirically quantify by how much worker flexibility hurts customer satisfaction and customer equity. Academic/Practical relevance: In academic literature, there is no established framework that allows for the comparison of matching policies in on-demand platforms. Further, the link between worker flexibility and customer satisfaction is understudied. Methodology: We propose a tripartite framework for empirical evaluation and comparison of the operational policies with different degrees of worker flexibility. Step 1: Predictive modeling of customer satisfaction based on estimation of individual unobservable characteristics: customer difficulty and worker ability (item-response theory model). Step 2: Evaluation of the effect of matching policy (under a given level of flexibility) on customer satisfaction (bipartite matching). Step 3: Quantification of the associated monetary impact (customer lifetime value model). Results: We apply our framework to the dataset of one of the world's largest on-demand platforms for residential cleanings. We find that customer difficulty and cleaner ability are good predictors of customer satisfaction. Granting full flexibility to workers reduces customer satisfaction by 3% and customer lifetime revenue by 0.2%. We propose a family of matching policies that provide sufficient flexibility to workers, while alleviating 75% of the detrimental effect of worker flexibility on customer satisfaction. Managerial implications: Our results suggest that, in platforms with flexible workforce, the presence of worker and customer heterogeneity reduces customer satisfaction through matching inefficiency. Our empirical framework helps practitioners to decide on the right level of worker flexibility and the means for achieving it.

We use French matched employer-employee data to track skilled individuals entering the labor market during the late 1990s tech bubble. The boom led to a sharp increase in the share of skilled entrants in the tech sector, which offers relative higher wages at the time. When the boom ends, however, the wage premium reverses and these skilled workers end up with a 5.5% wage discount ten years out, relative to similar peers who started in a non-tech sector. Other moments of the wage distribution of the boom, pre-boom, and post-boom cohorts are inconsistent with explanations based on a selection effect or a cycle effect. Instead, we provide suggestive evidence that workers allocated to the booming tech sector accumulate human capital early in their career that rapidly becomes obsolete.

While single-brand reward programs encourage customers to remain loyal to that one brand, coalition programs encourage customers to be “promiscuous” by offering points redeemable across partner stores. Despite the benefits of this “open relationship” with customers, store managers face uncertainty as to how rewards offered by partners influence transactions at their own stores. We use a model of multi-store purchase incidence to show how the value of points shared among partner stores can explain patterns in customer-level purchases across them. The model is used to empirically test hypotheses on how reward spillovers among partners are driven by: (1) differences in policies on reward redemption, (2) the overlap in product categories between stores, and (3) geographic distance between stores within a city. In addition, we leverage variation generated by a natural experiment, i.e., a devaluation of the program's points, to demonstrate how the value of points influences the positioning of partner stores within the coalition and the purchasing patterns across them. We conclude by delineating some managerial implications for the design of a coalition's reward policies, including a simulation showing that customer-centric targeted rewards can be an effective strategy to compensate for the devaluation.

International audience; This paper investigates the loss of the word order ‘Object-Verb’ (OV) in the history of Latin/Romance. It is argued that the immediate cause of this development is an independent change in the grammar of Latin, namely the genesis of A-movement for subjects (which was discussed in detail in Danckaert (2017), ‘The Development of Latin Clause Structure’, OUP). Using Yang’s (2000, 2002) variational acquisition model, I present corpus data which show that despite remaining the minority pattern throughout the lifespan of the Latin language (ca. 200 BC - 600 AD), in Late Latin the order ‘VO’ is in fact cued more robustly than the competing order ‘OV’, an observation which leads to the correct prediction that in the long run the former will oust the latter.

A long tradition in social science research emphasizes the potential for knowledge to flow among firms colocated in dense areas. Scholars have suggested numerous modes for these flows, including the voluntary transfer of private knowledge from one firm to another. Why would the holder of valuable private knowledge willingly transfer it to a potential and closely proximate competitor? In this paper, we argue that geographic concentration has an effect on the expected compliance with norms governing the use of transferred knowledge. The increased expected compliance favors trust and initiates a process of reciprocal exchange. To test our theory, we use a scenario-based field experiment in gourmet cuisine, an industry in which property rights do not effectively protect knowledge and geographic concentration is common. Our results confirm our conjecture by showing that the expectation that a potential colocated firm will abide by norms mediates the relationship between geographic concentration and the willingness to transfer private knowledge.