I study a model of a firm that buys data from consumers. There are data externalities, whereby data of some consumers reveal information about others. I characterize data externalities that maximize or minimize consumer surplus and the firm’s profit. I use the results to solve an information design problem in which the firm chooses what information to collect from consumers, taking into account the impact of data externalities on the cost of buying data. The firm collects no less information than the efficient amount. In some cases, we can solve the firm’s problem using a two-step concavification method.
{"title":"The Economics of Data Externalities","authors":"Shota Ichihashi","doi":"10.2139/ssrn.3778397","DOIUrl":"https://doi.org/10.2139/ssrn.3778397","url":null,"abstract":"I study a model of a firm that buys data from consumers. There are data externalities, whereby data of some consumers reveal information about others. I characterize data externalities that maximize or minimize consumer surplus and the firm’s profit. I use the results to solve an information design problem in which the firm chooses what information to collect from consumers, taking into account the impact of data externalities on the cost of buying data. The firm collects no less information than the efficient amount. In some cases, we can solve the firm’s problem using a two-step concavification method.","PeriodicalId":119201,"journal":{"name":"Microeconomics: Asymmetric & Private Information eJournal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115916402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We hypothesize a demand-driven information market where information production is tailored by investors’ investment constraints. Using a comprehensive data set of news releases and institutional equity holdings during the 2000–2016 period, we show that more negative (positive) news are produced for stocks overweighed (underweighted) by institutions. A natural experiment based on the 2003 mutual funds scandal confirms the negative relation between institutional investment constraints and news sentiment. The effect is more pronounced when the cost of information production is higher, especially when the distance between the information producer and a firm headquarter is larger. The asymmetry in information production causes stock returns to display negative skewness, increasing the probability for overweighed stocks to experience large negative price movement in the future.
{"title":"The Demand-Driven Information Market","authors":"Shiyang Huang, Dan Li, Bohui Zhang","doi":"10.2139/ssrn.3920444","DOIUrl":"https://doi.org/10.2139/ssrn.3920444","url":null,"abstract":"We hypothesize a demand-driven information market where information production is tailored by investors’ investment constraints. Using a comprehensive data set of news releases and institutional equity holdings during the 2000–2016 period, we show that more negative (positive) news are produced for stocks overweighed (underweighted) by institutions. A natural experiment based on the 2003 mutual funds scandal confirms the negative relation between institutional investment constraints and news sentiment. The effect is more pronounced when the cost of information production is higher, especially when the distance between the information producer and a firm headquarter is larger. The asymmetry in information production causes stock returns to display negative skewness, increasing the probability for overweighed stocks to experience large negative price movement in the future.","PeriodicalId":119201,"journal":{"name":"Microeconomics: Asymmetric & Private Information eJournal","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115021682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A policymaker relies on regulators or bureaucrats to screen agents on her behalf. How can she maintain some control over the design of the screening process? She solves a two-layer mechanism design problem: she restricts the set of allowable allocations, after which a screener picks a menu that maps an agent's costly evidence to this restricted set. In general, the policymaker can set a floor in a way that dominates full delegation no matter how the screener's objectives are misaligned. When this misalignment is only over the relative importance of reducing allocation errors or agent's screening costs, the effectiveness of this restriction hinges sharply on the direction of the screener's bias. If the screener is more concerned with reducing errors, setting this floor is in fact robustly optimal for the policymaker. But if the screener is more concerned with keeping costs down, not only does this particular floor have no effect: any restriction that strictly improves over full delegation is complex and sensitive to the details of the screener's preferences. I consider the implications for regulatory governance.
{"title":"Delegated Screening and Robustness","authors":"Suraj Malladi","doi":"10.2139/ssrn.3740206","DOIUrl":"https://doi.org/10.2139/ssrn.3740206","url":null,"abstract":"A policymaker relies on regulators or bureaucrats to screen agents on her behalf. How can she maintain some control over the design of the screening process? She solves a two-layer mechanism design problem: she restricts the set of allowable allocations, after which a screener picks a menu that maps an agent's costly evidence to this restricted set. In general, the policymaker can set a floor in a way that dominates full delegation no matter how the screener's objectives are misaligned. When this misalignment is only over the relative importance of reducing allocation errors or agent's screening costs, the effectiveness of this restriction hinges sharply on the direction of the screener's bias. If the screener is more concerned with reducing errors, setting this floor is in fact robustly optimal for the policymaker. But if the screener is more concerned with keeping costs down, not only does this particular floor have no effect: any restriction that strictly improves over full delegation is complex and sensitive to the details of the screener's preferences. I consider the implications for regulatory governance.","PeriodicalId":119201,"journal":{"name":"Microeconomics: Asymmetric & Private Information eJournal","volume":"43 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114130526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wei Chen, Shivam Gupta, Milind Dawande, G. Janakiraman
We consider a principal who periodically offers a fixed, binary, and costly non-monetary reward to agents endowed with private information, to incentivize the agents to invest effort over the long run. An agent's output, as a function of his effort, is a priori uncertain and is worth a fixed per-unit value to the principal. The principal's goal is to design an attractive reward policy that specifies how the rewards are to be given to an agent over time, based on that agent's past performance. This problem, which we denote by P, is motivated by practical examples from both academia (a reduced teaching load for achieving a certain research-productivity threshold) and industry ("Supplier of the Year" awards in recognition of excellent past performance). The following "limited-term'' reward policy structure has been quite popular in practice: The principal evaluates each agent periodically; if an agent's performance over a certain (limited) number of periods in the immediate past exceeds a pre-defined threshold, then the principal rewards him for a certain (limited) number of periods in the immediate future. For the deterministic special case of problem P, where there is no uncertainty in any agent's output given his effort, we show that there always exists an optimal policy that is a limited-term policy and also obtain such a policy. When agents' outputs are stochastic, we show that the class of limited-term policies may not contain any optimal policy of problem P but is guaranteed to contain policies that are arbitrarily near-optimal: Given any epsilon>0, we show how to obtain a limited-term policy whose performance is within epsilon of that of an optimal policy. This guarantee depends crucially on the use of sufficiently long histories of the agents' outputs for the determination of the rewards. In situations where access to this historical information is limited, we derive structural insights on the role played by (i) the length of the available history and (ii) the variability in the random variable governing an agent's output, on the performance of this class of policies. Finally, we introduce and analyze the class of "score-based'' reward policies - we show that this class is guaranteed to contain an optimal policy and also obtain such a policy.
{"title":"3 Years, 2 Papers, 1 Course Off: Optimal Non-Monetary Reward Policies","authors":"Wei Chen, Shivam Gupta, Milind Dawande, G. Janakiraman","doi":"10.2139/ssrn.3647569","DOIUrl":"https://doi.org/10.2139/ssrn.3647569","url":null,"abstract":"We consider a principal who periodically offers a fixed, binary, and costly non-monetary reward to agents endowed with private information, to incentivize the agents to invest effort over the long run. An agent's output, as a function of his effort, is a priori uncertain and is worth a fixed per-unit value to the principal. The principal's goal is to design an attractive reward policy that specifies how the rewards are to be given to an agent over time, based on that agent's past performance. This problem, which we denote by P, is motivated by practical examples from both academia (a reduced teaching load for achieving a certain research-productivity threshold) and industry (\"Supplier of the Year\" awards in recognition of excellent past performance). The following \"limited-term'' reward policy structure has been quite popular in practice: The principal evaluates each agent periodically; if an agent's performance over a certain (limited) number of periods in the immediate past exceeds a pre-defined threshold, then the principal rewards him for a certain (limited) number of periods in the immediate future. For the deterministic special case of problem P, where there is no uncertainty in any agent's output given his effort, we show that there always exists an optimal policy that is a limited-term policy and also obtain such a policy. When agents' outputs are stochastic, we show that the class of limited-term policies may not contain any optimal policy of problem P but is guaranteed to contain policies that are arbitrarily near-optimal: Given any epsilon>0, we show how to obtain a limited-term policy whose performance is within epsilon of that of an optimal policy. This guarantee depends crucially on the use of sufficiently long histories of the agents' outputs for the determination of the rewards. In situations where access to this historical information is limited, we derive structural insights on the role played by (i) the length of the available history and (ii) the variability in the random variable governing an agent's output, on the performance of this class of policies. Finally, we introduce and analyze the class of \"score-based'' reward policies - we show that this class is guaranteed to contain an optimal policy and also obtain such a policy.","PeriodicalId":119201,"journal":{"name":"Microeconomics: Asymmetric & Private Information eJournal","volume":"281 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127553565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
How efficient is corporate bankruptcy in the U.S.? Two frictions, asymmetric information and conflicts of interest among creditors, can cause several inefficiencies: excess liquidation, excess continuation, and excess delay. We find that the bankruptcy process is quite inefficient, mainly due to excess delay. Eliminating information asymmetries would increase average total payouts by 4%, and eliminating conflicts of interest would increase them by 18% more. Without these frictions, 14% more cases would be resolved pre-court, and the remaining court cases would be 73% shorter. With less delay, bankruptcy’s indirect costs would be much lower. In contrast, inefficiencies from excess liquidation and excess continuation are quite small.
{"title":"Dissecting Bankruptcy Frictions","authors":"W. Dou, Lucian A. Taylor, Wei Wang, Wenyu Wang","doi":"10.2139/ssrn.3383837","DOIUrl":"https://doi.org/10.2139/ssrn.3383837","url":null,"abstract":"How efficient is corporate bankruptcy in the U.S.? Two frictions, asymmetric information and conflicts of interest among creditors, can cause several inefficiencies: excess liquidation, excess continuation, and excess delay. We find that the bankruptcy process is quite inefficient, mainly due to excess delay. Eliminating information asymmetries would increase average total payouts by 4%, and eliminating conflicts of interest would increase them by 18% more. Without these frictions, 14% more cases would be resolved pre-court, and the remaining court cases would be 73% shorter. With less delay, bankruptcy’s indirect costs would be much lower. In contrast, inefficiencies from excess liquidation and excess continuation are quite small.","PeriodicalId":119201,"journal":{"name":"Microeconomics: Asymmetric & Private Information eJournal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131101220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Imen Zghal, Salah Ben Hamad, H. Eleuch, Haitham Nobanee
Abstract This work addresses the impact of imperfections, such as information asymmetry and market sentiment, on the performance of option pricing models. More precisely, this work compares the option pricing model of Black and Scholes and the same model in the presence of imperfections. This study is based on S&P 500 options that cover the period between 17/03/2000 and 14/06/2013. The achieved results show that, in general, in the presence of imperfections, the model is more effective than the Black and Scholes model. This research appears to be promising for the incorporation of imperfections into the assessment of options.
{"title":"The Effect of Market Sentiment and Information Asymmetry on Option Pricing","authors":"Imen Zghal, Salah Ben Hamad, H. Eleuch, Haitham Nobanee","doi":"10.2139/ssrn.3725455","DOIUrl":"https://doi.org/10.2139/ssrn.3725455","url":null,"abstract":"Abstract This work addresses the impact of imperfections, such as information asymmetry and market sentiment, on the performance of option pricing models. More precisely, this work compares the option pricing model of Black and Scholes and the same model in the presence of imperfections. This study is based on S&P 500 options that cover the period between 17/03/2000 and 14/06/2013. The achieved results show that, in general, in the presence of imperfections, the model is more effective than the Black and Scholes model. This research appears to be promising for the incorporation of imperfections into the assessment of options.","PeriodicalId":119201,"journal":{"name":"Microeconomics: Asymmetric & Private Information eJournal","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124893309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper explores how firms that lack expertise in machine learning (ML) can leverage the so-called AI Flywheel effect. This effect designates a virtuous cycle by which as an ML product is adopted and new user data are fed back to the algorithm, the product improves, enabling further adoptions. However, managing this feedback loop is difficult, especially when the algorithm is contracted out. Indeed, the additional data that the AI Flywheel effect generates may change the provider’s incentives to improve the algorithm over time. We formalize this problem in a simple two-period moral hazard framework that captures the main dynamics among ML, data acquisition, pricing, and contracting. We find that the firm’s decisions crucially depend on how the amount of data on which the machine is trained interacts with the provider’s effort. If this effort has a more (less) significant impact on accuracy for larger volumes of data, the firm underprices (overprices) the product. Interestingly, these distortions sometimes improve social welfare, which accounts for the customer surplus and profits of both the firm and provider. Further, the interaction between incentive issues and the positive externalities of the AI Flywheel effect has important implications for the firm’s data collection strategy. In particular, the firm can boost its profit by increasing the product’s capacity to acquire usage data only up to a certain level. If the product collects too much data per user, the firm’s profit may actually decrease (i.e., more data are not necessarily better). This paper was accepted by Jayashankar Swaminathan, operations management.
{"title":"Contracting, Pricing, and Data Collection Under the AI Flywheel Effect","authors":"Huseyin Gurkan, F. Véricourt","doi":"10.2139/ssrn.3566894","DOIUrl":"https://doi.org/10.2139/ssrn.3566894","url":null,"abstract":"This paper explores how firms that lack expertise in machine learning (ML) can leverage the so-called AI Flywheel effect. This effect designates a virtuous cycle by which as an ML product is adopted and new user data are fed back to the algorithm, the product improves, enabling further adoptions. However, managing this feedback loop is difficult, especially when the algorithm is contracted out. Indeed, the additional data that the AI Flywheel effect generates may change the provider’s incentives to improve the algorithm over time. We formalize this problem in a simple two-period moral hazard framework that captures the main dynamics among ML, data acquisition, pricing, and contracting. We find that the firm’s decisions crucially depend on how the amount of data on which the machine is trained interacts with the provider’s effort. If this effort has a more (less) significant impact on accuracy for larger volumes of data, the firm underprices (overprices) the product. Interestingly, these distortions sometimes improve social welfare, which accounts for the customer surplus and profits of both the firm and provider. Further, the interaction between incentive issues and the positive externalities of the AI Flywheel effect has important implications for the firm’s data collection strategy. In particular, the firm can boost its profit by increasing the product’s capacity to acquire usage data only up to a certain level. If the product collects too much data per user, the firm’s profit may actually decrease (i.e., more data are not necessarily better). This paper was accepted by Jayashankar Swaminathan, operations management.","PeriodicalId":119201,"journal":{"name":"Microeconomics: Asymmetric & Private Information eJournal","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125952516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper provides an econometric framework for analyzing simple contracts where an agent chooses between a fixed-price option and a cost-reimbursement option provided by a principal in each contracting period during possibly multiple periods. First, we propose a consistent procedure for testing the null hypothesis of a corresponding cost function being linear, which is widely assumed for tractability in the literature. Motivated by the rejection of such a null based on our empirical data, next we establish nonparametric identification, without restricting the cost function to be linear, for all model primitives conditioned on the agent exerting nonzero effort. These primitives include agent's cost and disutility functions, distribution of agent efficiency type, and parameters that characterize agent's bargaining power and intertemporal preference. Moreover we propose a consistent procedure to implement the identification results for estimation. In our empirical study, we find strong evidence against linearity of the cost function. The importance of this empirical finding is further evidenced by a welfare analysis, which shows the welfare assessment to be sensitive to the specification of cost function.
{"title":"A Structural Analysis of Simple Contracts","authors":"Yonghong An, Shengjie Hong, Daiqiang Zhang","doi":"10.2139/ssrn.3697409","DOIUrl":"https://doi.org/10.2139/ssrn.3697409","url":null,"abstract":"This paper provides an econometric framework for analyzing simple contracts where an agent chooses between a fixed-price option and a cost-reimbursement option provided by a principal in each contracting period during possibly multiple periods. First, we propose a consistent procedure for testing the null hypothesis of a corresponding cost function being linear, which is widely assumed for tractability in the literature. Motivated by the rejection of such a null based on our empirical data, next we establish nonparametric identification, without restricting the cost function to be linear, for all model primitives conditioned on the agent exerting nonzero effort. These primitives include agent's cost and disutility functions, distribution of agent efficiency type, and parameters that characterize agent's bargaining power and intertemporal preference. Moreover we propose a consistent procedure to implement the identification results for estimation. In our empirical study, we find strong evidence against linearity of the cost function. The importance of this empirical finding is further evidenced by a welfare analysis, which shows the welfare assessment to be sensitive to the specification of cost function.","PeriodicalId":119201,"journal":{"name":"Microeconomics: Asymmetric & Private Information eJournal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129693058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Stefano Nobili, Antonio Scalia, Luana Zaccaria, Alessandra Iannamorelli
Using a comprehensive dataset of Italian SMEs, we find that differences between private and public information on firm creditworthiness affect the decision to issue bonds. Our evidence supports favorable (rather than adverse) selection in corporate bond markets. Specifically, holding public information constant, firms with better private fundamentals are more likely to access bond markets. These effects are weaker for opaque firms and stronger for firms with worse publicly observable risk. Additionally, credit conditions improve for issuers following the bond placement, compared with a matched sample of non-issuers. This is consistent with a model where banks offer more flexibility than markets during financial distress and firms use market lending to signal credit quality to outside stakeholders.
{"title":"Asymmetric Information and Corporate Lending: Evidence from SME Bond Markets","authors":"Stefano Nobili, Antonio Scalia, Luana Zaccaria, Alessandra Iannamorelli","doi":"10.2139/ssrn.3710104","DOIUrl":"https://doi.org/10.2139/ssrn.3710104","url":null,"abstract":"\u0000 Using a comprehensive dataset of Italian SMEs, we find that differences between private and public information on firm creditworthiness affect the decision to issue bonds. Our evidence supports favorable (rather than adverse) selection in corporate bond markets. Specifically, holding public information constant, firms with better private fundamentals are more likely to access bond markets. These effects are weaker for opaque firms and stronger for firms with worse publicly observable risk. Additionally, credit conditions improve for issuers following the bond placement, compared with a matched sample of non-issuers. This is consistent with a model where banks offer more flexibility than markets during financial distress and firms use market lending to signal credit quality to outside stakeholders.","PeriodicalId":119201,"journal":{"name":"Microeconomics: Asymmetric & Private Information eJournal","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116661103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper demonstrates how people deceive themselves into thinking of themselves as altruistic. I present a lab experiment in which subjects need to decide whether to behave altruistically or selfishly in an ambiguous environment. Due to the nature of ambiguity in this environment, those who are pessimistic have a legitimate reason to behave selfishly, even if they are inherently altruistic. For people who are inherently selfish but like to think of themselves as altruistic, this environment can serve as a scapegoat for selfish behavior. That is, by falsely claiming to be pessimistic, individuals can behave selfishly without damaging their self-image of being altruistic. Through two seemingly unrelated experimental tasks, I elicit subjects’ adopted beliefs and true beliefs about the same probability. I find that selfish subjects adopt beliefs that are systematically more pessimistic beliefs than their true beliefs, whereas altruistic subjects adopt beliefs that are in alignment with their true beliefs. The most plausible explanation for why only selfish subjects manipulate their beliefs is that selfish behavior damages their self-image and belief manipulation helps them mitigate that damage; altruistic subjects, by contrast, have no such need for belief manipulation because their behavior does not damage their self-image.
{"title":"Self-Deception: Adopting False Beliefs for a Favorable Self-View","authors":"Zeeshan Samad","doi":"10.2139/ssrn.3673992","DOIUrl":"https://doi.org/10.2139/ssrn.3673992","url":null,"abstract":"This paper demonstrates how people deceive themselves into thinking of themselves as altruistic. I present a lab experiment in which subjects need to decide whether to behave altruistically or selfishly in an ambiguous environment. Due to the nature of ambiguity in this environment, those who are pessimistic have a legitimate reason to behave selfishly, even if they are inherently altruistic. For people who are inherently selfish but like to think of themselves as altruistic, this environment can serve as a scapegoat for selfish behavior. That is, by falsely claiming to be pessimistic, individuals can behave selfishly without damaging their self-image of being altruistic. Through two seemingly unrelated experimental tasks, I elicit subjects’ adopted beliefs and true beliefs about the same probability. I find that selfish subjects adopt beliefs that are systematically more pessimistic beliefs than their true beliefs, whereas altruistic subjects adopt beliefs that are in alignment with their true beliefs. The most plausible explanation for why only selfish subjects manipulate their beliefs is that selfish behavior damages their self-image and belief manipulation helps them mitigate that damage; altruistic subjects, by contrast, have no such need for belief manipulation because their behavior does not damage their self-image.","PeriodicalId":119201,"journal":{"name":"Microeconomics: Asymmetric & Private Information eJournal","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125401986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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