{"title":"Chinese Asset Managers' Monetary Policy Forecasts and Fund Performance","authors":"J. Ammer, Jack Rogers, G. Wang, Yang Yu","doi":"10.1287/mnsc.2022.4330","DOIUrl":"https://doi.org/10.1287/mnsc.2022.4330","url":null,"abstract":"","PeriodicalId":18208,"journal":{"name":"Manag. Sci.","volume":"34 1","pages":"598-616"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75056520","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}
C. Y. Zhang, Jeffrey Hemmeter, Judd B. Kessler, R. Metcalfe, R. Weathers
We study a large-scale (n = 50,000) natural field experiment implemented by the U.S. Social Security Administration aimed at increasing the timely and accurate self-reporting of wages by Supplemental Security Income (SSI) recipients. A letter reminding SSI recipients of their wage reporting responsibilities significantly increased both the likelihood of reporting any earnings and the total earnings reported. However, the specific letter content—providing social information or highlighting the salience of penalties—had no systematic effect. We develop a conservative estimate that the letters generated roughly $5.91 in savings per dollar spent, highlighting the value of such a nudge in this important context. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.4645 .
{"title":"Nudging Timely Wage Reporting: Field Experimental Evidence from the U.S. Supplemental Security Income Program","authors":"C. Y. Zhang, Jeffrey Hemmeter, Judd B. Kessler, R. Metcalfe, R. Weathers","doi":"10.1287/mnsc.2022.4645","DOIUrl":"https://doi.org/10.1287/mnsc.2022.4645","url":null,"abstract":"We study a large-scale (n = 50,000) natural field experiment implemented by the U.S. Social Security Administration aimed at increasing the timely and accurate self-reporting of wages by Supplemental Security Income (SSI) recipients. A letter reminding SSI recipients of their wage reporting responsibilities significantly increased both the likelihood of reporting any earnings and the total earnings reported. However, the specific letter content—providing social information or highlighting the salience of penalties—had no systematic effect. We develop a conservative estimate that the letters generated roughly $5.91 in savings per dollar spent, highlighting the value of such a nudge in this important context. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.4645 .","PeriodicalId":18208,"journal":{"name":"Manag. Sci.","volume":"76 1","pages":"1341-1353"},"PeriodicalIF":0.0,"publicationDate":"2022-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83867825","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}
{"title":"From the Editor - January 2023","authors":"D. Simchi-Levi","doi":"10.1287/mnsc.2022.4644","DOIUrl":"https://doi.org/10.1287/mnsc.2022.4644","url":null,"abstract":"","PeriodicalId":18208,"journal":{"name":"Manag. Sci.","volume":"25 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81838600","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}
The COVID-19 pandemic has killed millions and gravely disrupted the world’s economy. A safe and effective vaccine was developed remarkably swiftly, but as of yet, uptake of the vaccine has been slow. This paper explores one potential explanation of delayed adoption of the vaccine, which is data privacy concerns. We explore two contrasting regulations that vary across U.S. states that have the potential to affect the perceived privacy risk associated with receiving a COVID-19 vaccine. The first regulation—an “identification requirement”—increases privacy concerns by requiring individuals to verify residency with government approved documentation. The second regulation—“anonymity protection”—reduces privacy concerns by allowing individuals to remove personally identifying information from state-operated immunization registry systems. We investigate the effects of these privacy-reducing and privacy-protecting regulations on U.S. state-level COVID-19 vaccination rates. Using a panel data set, we find that identification requirements decrease vaccine demand but that this negative effect is offset when individuals are able to remove information from an immunization registry. Our results remain consistent when controlling for CDC-defined barriers to vaccination, levels of misinformation, vaccine incentives, and states’ phased distribution of vaccine supply. These findings yield significant theoretical and practical contributions for privacy policy and public health. This paper was accepted by David Simchi-Levi, information systems. Supplemental Material: Data and the e-companion are available at https://doi.org/10.1287/mnsc.2022.4580 .
{"title":"Privacy Regulation and Barriers to Public Health","authors":"J. Buckman, Idris Adjerid, Catherine Tucker","doi":"10.2139/ssrn.3983334","DOIUrl":"https://doi.org/10.2139/ssrn.3983334","url":null,"abstract":"The COVID-19 pandemic has killed millions and gravely disrupted the world’s economy. A safe and effective vaccine was developed remarkably swiftly, but as of yet, uptake of the vaccine has been slow. This paper explores one potential explanation of delayed adoption of the vaccine, which is data privacy concerns. We explore two contrasting regulations that vary across U.S. states that have the potential to affect the perceived privacy risk associated with receiving a COVID-19 vaccine. The first regulation—an “identification requirement”—increases privacy concerns by requiring individuals to verify residency with government approved documentation. The second regulation—“anonymity protection”—reduces privacy concerns by allowing individuals to remove personally identifying information from state-operated immunization registry systems. We investigate the effects of these privacy-reducing and privacy-protecting regulations on U.S. state-level COVID-19 vaccination rates. Using a panel data set, we find that identification requirements decrease vaccine demand but that this negative effect is offset when individuals are able to remove information from an immunization registry. Our results remain consistent when controlling for CDC-defined barriers to vaccination, levels of misinformation, vaccine incentives, and states’ phased distribution of vaccine supply. These findings yield significant theoretical and practical contributions for privacy policy and public health. This paper was accepted by David Simchi-Levi, information systems. Supplemental Material: Data and the e-companion are available at https://doi.org/10.1287/mnsc.2022.4580 .","PeriodicalId":18208,"journal":{"name":"Manag. Sci.","volume":"1 1","pages":"342-350"},"PeriodicalIF":0.0,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89661821","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}
Jordan M. Martel, Edward Dickersin Van Wesep, R. V. Wesep
Researchers have often attributed discrete messages such as ratings to a difference in preferences between sender and receiver. By extending a standard model of information transmission, we show that discreteness can also arise when preferences are identical but misinterpretation is possible. Whereas discrete messages are less precise, they are easier to interpret. We provide predictions for the distribution of ratings. If we believe that an observed distribution results from cooperative behavior, the model provides a method for inferring the objectives of the sender and receiver. Ratings inflation and deflation arise as emergent properties of an optimal distribution. This paper was accepted by Gustavo Manso, finance.
{"title":"Ratings and Cooperative Information Transmission","authors":"Jordan M. Martel, Edward Dickersin Van Wesep, R. V. Wesep","doi":"10.1287/mnsc.2022.4297","DOIUrl":"https://doi.org/10.1287/mnsc.2022.4297","url":null,"abstract":"Researchers have often attributed discrete messages such as ratings to a difference in preferences between sender and receiver. By extending a standard model of information transmission, we show that discreteness can also arise when preferences are identical but misinterpretation is possible. Whereas discrete messages are less precise, they are easier to interpret. We provide predictions for the distribution of ratings. If we believe that an observed distribution results from cooperative behavior, the model provides a method for inferring the objectives of the sender and receiver. Ratings inflation and deflation arise as emergent properties of an optimal distribution. This paper was accepted by Gustavo Manso, finance.","PeriodicalId":18208,"journal":{"name":"Manag. Sci.","volume":"7 1","pages":"9175-9197"},"PeriodicalIF":0.0,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83161493","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}
Many digital platforms have accrued enormous power and scale, leveraging cross-side network effects between the sides they connect (e.g., producers and consumers or creators and viewers). Platforms motivate a diverse spectrum of producers, large and small, to participate by sharing platform revenue with them, predominantly under a linear revenue-sharing scheme with the same commission rate regardless of producer power or size. Under pressure from society, lawsuits, and antitrust investigations, major platforms have announced revenue sharing designs that favor smaller businesses. We develop a model of platform economics and show that a small-business oriented (SBO) differential revenue sharing design can increase total welfare and outputs on the platform. Although smaller producers almost always benefit from the shift in revenue sharing design, spillover effects can also make large producers better off under some conditions. More interestingly, we show that platforms are the most likely winner under a differential revenue sharing scheme. Hence, an intervention that ostensibly offers concessions and generous treatment to producers might well be self-serving for platforms and good for the entire ecosystem. This paper was accepted by David Simchi-Levi, information systems–fast track.
{"title":"Fending Off Critics of Platform Power with Differential Revenue Sharing: Doing Well by Doing Good?","authors":"H. Bhargava, Kitty Wang, X. Zhang","doi":"10.1287/mnsc.2022.4545","DOIUrl":"https://doi.org/10.1287/mnsc.2022.4545","url":null,"abstract":"Many digital platforms have accrued enormous power and scale, leveraging cross-side network effects between the sides they connect (e.g., producers and consumers or creators and viewers). Platforms motivate a diverse spectrum of producers, large and small, to participate by sharing platform revenue with them, predominantly under a linear revenue-sharing scheme with the same commission rate regardless of producer power or size. Under pressure from society, lawsuits, and antitrust investigations, major platforms have announced revenue sharing designs that favor smaller businesses. We develop a model of platform economics and show that a small-business oriented (SBO) differential revenue sharing design can increase total welfare and outputs on the platform. Although smaller producers almost always benefit from the shift in revenue sharing design, spillover effects can also make large producers better off under some conditions. More interestingly, we show that platforms are the most likely winner under a differential revenue sharing scheme. Hence, an intervention that ostensibly offers concessions and generous treatment to producers might well be self-serving for platforms and good for the entire ecosystem. This paper was accepted by David Simchi-Levi, information systems–fast track.","PeriodicalId":18208,"journal":{"name":"Manag. Sci.","volume":"71 1","pages":"8249-8260"},"PeriodicalIF":0.0,"publicationDate":"2022-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85197835","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}
Markets are increasingly used as information aggregation mechanisms to predict future events. If policymakers and managers use markets to guide policy and managerial decisions, interested parties may attempt to manipulate the market in order to influence decisions. We study experimentally the willingness of managers to base decisions on market information under the shadow of manipulation. We find that when there are manipulators in the market, managers under-utilize the information revealed in prices. Furthermore, mere suspicion of manipulation erodes trust in the market, leading to the implementation of suboptimal policies—even without actual manipulation. This paper was accepted by Yan Chen, behavioral economics and decision analysis.
{"title":"Manipulation and (Mis)trust in Prediction Markets","authors":"Lawrence Choo, Todd R. Kaplan, Ro’i Zultan","doi":"10.1287/mnsc.2021.4213","DOIUrl":"https://doi.org/10.1287/mnsc.2021.4213","url":null,"abstract":"Markets are increasingly used as information aggregation mechanisms to predict future events. If policymakers and managers use markets to guide policy and managerial decisions, interested parties may attempt to manipulate the market in order to influence decisions. We study experimentally the willingness of managers to base decisions on market information under the shadow of manipulation. We find that when there are manipulators in the market, managers under-utilize the information revealed in prices. Furthermore, mere suspicion of manipulation erodes trust in the market, leading to the implementation of suboptimal policies—even without actual manipulation. This paper was accepted by Yan Chen, behavioral economics and decision analysis.","PeriodicalId":18208,"journal":{"name":"Manag. Sci.","volume":"24 1","pages":"6716-6732"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81070296","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}
Hiring in online labor markets involves considerable uncertainty: which hiring choices are more likely to yield successful outcomes and how do employers adjust their hiring behaviors to make such choices? We argue that employers will initially explore the value of available information. When employers observe successful outcomes, they will keep reinforcing their hiring strategies; but when the outcomes are unsuccessful, employers will adjust their hiring behaviors. To investigate these dynamics, we propose a two-component framework that links hiring choices with task outcomes. The framework’s first component, a Hidden Markov Model, captures how employers transition from unsuccessful to successful hiring decisions. The framework’s second component, a conditional logit model, estimates employer hiring choices. Analysis of 238,364 hiring decisions from a large online labor market shows that, often, employers initially explore cheaper contractors with a lower reputation. When these options result in unsuccessful outcomes, employers learn and adjust their hiring behaviors to rely more on reputable contractors who are not as cheap. Such hiring tends to be successful, guiding employers to reinforce their hiring processes. As a result, the market observes employers transition from cheaper, lower-reputation options with poorer performance to more expensive reputable options with better performance. We attribute part of this behavior to employer confidence and risk attitude, which can change over time. This work is the first to investigate how employers learn to make successful hiring choices in online labor markets. As a result, it provides platform managers with new knowledge and analytics tools to target employer interventions. This paper was accepted by Anandhi Bharadwaj, information systems.
{"title":"Learning to Successfully Hire in Online Labor Markets","authors":"Marios Kokkodis, S. Ransbotham","doi":"10.1287/mnsc.2022.4426","DOIUrl":"https://doi.org/10.1287/mnsc.2022.4426","url":null,"abstract":"Hiring in online labor markets involves considerable uncertainty: which hiring choices are more likely to yield successful outcomes and how do employers adjust their hiring behaviors to make such choices? We argue that employers will initially explore the value of available information. When employers observe successful outcomes, they will keep reinforcing their hiring strategies; but when the outcomes are unsuccessful, employers will adjust their hiring behaviors. To investigate these dynamics, we propose a two-component framework that links hiring choices with task outcomes. The framework’s first component, a Hidden Markov Model, captures how employers transition from unsuccessful to successful hiring decisions. The framework’s second component, a conditional logit model, estimates employer hiring choices. Analysis of 238,364 hiring decisions from a large online labor market shows that, often, employers initially explore cheaper contractors with a lower reputation. When these options result in unsuccessful outcomes, employers learn and adjust their hiring behaviors to rely more on reputable contractors who are not as cheap. Such hiring tends to be successful, guiding employers to reinforce their hiring processes. As a result, the market observes employers transition from cheaper, lower-reputation options with poorer performance to more expensive reputable options with better performance. We attribute part of this behavior to employer confidence and risk attitude, which can change over time. This work is the first to investigate how employers learn to make successful hiring choices in online labor markets. As a result, it provides platform managers with new knowledge and analytics tools to target employer interventions. This paper was accepted by Anandhi Bharadwaj, information systems.","PeriodicalId":18208,"journal":{"name":"Manag. Sci.","volume":"46 1","pages":"1597-1614"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89270143","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}
Although corporate insiders face substantial restrictions on stock sales, securing tax deductions through charitable donations of stock is viewed as an attractive alternative. Recent press coverage and growing empirical evidence confirm that insider donations occur frequently and often precede stock price drops. Securities regulators have also taken note, with a recent push for new rules to require rapid disclosure as with insider trades. This paper develops a model of informed stock trading when disposal of stock by insiders takes the form of tax-favored charitable donations rather than direct trading. We demonstrate that charitable gifts by insiders can reflect nonpublic information about firm value and that they do so in a manner that promotes greater market efficiency. Relative to informed trading, insider donations yield greater market liquidity, more efficient equity prices, and superior investor protection. The results suggest that although insider donations deserve scrutiny, they are not equivalent to insider trades. The results also provide a general framework to examine implications of insider donations for tax policy governing philanthropic behavior. This paper was accepted by Ranjani Krishnan, accounting.
{"title":"Tax-Favored Stock Donations by Corporate Insiders and Consequences for Equity Markets","authors":"Anil Arya, B. Mittendorf, Ramachandran Ramanan","doi":"10.1287/mnsc.2022.4514","DOIUrl":"https://doi.org/10.1287/mnsc.2022.4514","url":null,"abstract":"Although corporate insiders face substantial restrictions on stock sales, securing tax deductions through charitable donations of stock is viewed as an attractive alternative. Recent press coverage and growing empirical evidence confirm that insider donations occur frequently and often precede stock price drops. Securities regulators have also taken note, with a recent push for new rules to require rapid disclosure as with insider trades. This paper develops a model of informed stock trading when disposal of stock by insiders takes the form of tax-favored charitable donations rather than direct trading. We demonstrate that charitable gifts by insiders can reflect nonpublic information about firm value and that they do so in a manner that promotes greater market efficiency. Relative to informed trading, insider donations yield greater market liquidity, more efficient equity prices, and superior investor protection. The results suggest that although insider donations deserve scrutiny, they are not equivalent to insider trades. The results also provide a general framework to examine implications of insider donations for tax policy governing philanthropic behavior. This paper was accepted by Ranjani Krishnan, accounting.","PeriodicalId":18208,"journal":{"name":"Manag. Sci.","volume":"54 1","pages":"8506-8514"},"PeriodicalIF":0.0,"publicationDate":"2022-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76118727","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}
In the two weeks after the U.S. Congress passed a package of anti-sex trafficking bills on March 21, 2018, two of the largest online commercial sex advertising platforms ceased operation. On March 23, Craigslist voluntarily removed their personals section, which had been dominated by advertisements for commercial sex. And on April 6, the Department of Justice seized Backpage.com, the largest online platform for commercial sex advertisements. Our research examines the impact of these shutdowns on a variety of important outcome variables, notably prostitution arrests and violence against women—variables that the prior literature has shown were impacted by the introduction of commercial sex advertising platforms. We employ a generalized difference-in-differences model by exploiting cross-city variation in the preshutdown usage of the two shuttered sites. We find no causal effect of the shutdowns on any of the outcome variables we measure. Further analysis suggests that these null results are likely due to the fluidity of online markets. Our data show that the majority of advertisers and users of Backpage and Craigslist’s personals quickly moved to other (often off-shore) commercial sex advertising portals. Our results highlight the challenges that governments face in reducing online sex trafficking, as the market for commercial sex advertising appears agile enough to quickly disperse to offshore sites after a few popular domestic sites are shut down. Our results have general implications for the governance of other illegal activities online. This paper was accepted by D. J. Wu, information systems.
{"title":"Internet Governance Through Site Shutdowns: The Impact of Shutting Down Two Major Commercial Sex Advertising Sites","authors":"Helen Zeng, Brett Danaher, Michael D. Smith","doi":"10.1287/mnsc.2022.4498","DOIUrl":"https://doi.org/10.1287/mnsc.2022.4498","url":null,"abstract":"In the two weeks after the U.S. Congress passed a package of anti-sex trafficking bills on March 21, 2018, two of the largest online commercial sex advertising platforms ceased operation. On March 23, Craigslist voluntarily removed their personals section, which had been dominated by advertisements for commercial sex. And on April 6, the Department of Justice seized Backpage.com, the largest online platform for commercial sex advertisements. Our research examines the impact of these shutdowns on a variety of important outcome variables, notably prostitution arrests and violence against women—variables that the prior literature has shown were impacted by the introduction of commercial sex advertising platforms. We employ a generalized difference-in-differences model by exploiting cross-city variation in the preshutdown usage of the two shuttered sites. We find no causal effect of the shutdowns on any of the outcome variables we measure. Further analysis suggests that these null results are likely due to the fluidity of online markets. Our data show that the majority of advertisers and users of Backpage and Craigslist’s personals quickly moved to other (often off-shore) commercial sex advertising portals. Our results highlight the challenges that governments face in reducing online sex trafficking, as the market for commercial sex advertising appears agile enough to quickly disperse to offshore sites after a few popular domestic sites are shut down. Our results have general implications for the governance of other illegal activities online. This paper was accepted by D. J. Wu, information systems.","PeriodicalId":18208,"journal":{"name":"Manag. Sci.","volume":"507 1","pages":"8234-8248"},"PeriodicalIF":0.0,"publicationDate":"2022-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76395929","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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