Pub Date : 2023-12-01DOI: 10.1080/2330443x.2023.2289529
Bernard Grofman, Jonathan Cervas
Years after the election, a substantial portion of the electorate, including a significant majority of Republican voters and numerous Republican officials, continue to believe that the 2020 electio...
选举多年后，相当一部分选民，包括绝大多数共和党选民和众多共和党官员，仍然认为2020年的选举……
{"title":"Statistical Fallacies in Claims about ‘Massive and Widespread Fraud’ in the 2020 Presidential Election: Examining Claims Based on Aggregate Election Results 1,2","authors":"Bernard Grofman, Jonathan Cervas","doi":"10.1080/2330443x.2023.2289529","DOIUrl":"https://doi.org/10.1080/2330443x.2023.2289529","url":null,"abstract":"Years after the election, a substantial portion of the electorate, including a significant majority of Republican voters and numerous Republican officials, continue to believe that the 2020 electio...","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138509832","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}
Pub Date : 2023-11-17DOI: 10.1080/2330443x.2023.2285788
Emerson Elliott, Jonathan Auerbach, C. Citro, Daniel Elchert, Steve Pierson, Marilyn Seastrom, Thomas Snyder, Katherine Wallman, James L. Woodworth
{"title":"Bolstering Education Statistics to Serve the Nation","authors":"Emerson Elliott, Jonathan Auerbach, C. Citro, Daniel Elchert, Steve Pierson, Marilyn Seastrom, Thomas Snyder, Katherine Wallman, James L. Woodworth","doi":"10.1080/2330443x.2023.2285788","DOIUrl":"https://doi.org/10.1080/2330443x.2023.2285788","url":null,"abstract":"","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139264827","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}
Pub Date : 2023-10-06DOI: 10.1080/2330443x.2023.2267617
Angela Zhou, Andrew Koo, Nathan Kallus, Rene Ropac, Richard Peterson, Stephen Koppel, Tiffany Bergin
We conduct an empirical evaluation of the short-term impact of New York’s bail reform on crime. New York State’s Bail Elimination Act went into effect on January 1, 2020, eliminating money bail and pretrial detention for nearly all misdemeanor and nonviolent felony defendants. Our analysis of effects on aggregate crime rates after the reform informs the understanding of bail reform and general deterrence, rather than specific deterrence via re-arrest rates of the detained/released population. We conduct a synthetic control analysis for a comparative case study of the impact of bail reform. We focus on synthetic control analysis of post-intervention changes in crime for assault, theft, burglary, robbery, and drug crimes, constructing a dataset from publicly reported crime data of 27 large municipalities. Due to the short time frame before the onset of COVID-19 and its far-reaching effects, we restrict attention to a short post-intervention time period. Nonetheless, evaluation of short-term impacts may still inform hypotheses of general deterrence of bail reform policy. Our findings, including placebo checks and other robustness checks, show that for assault, theft, and drug crimes, there is no significant impact of bail reform on aggregate crime. For robbery, we find a statistically significant increase; for burglary, the synthetic control is more variable and our analysis is deemed less conclusive. Since our study assesses the short-term impacts, further work studying long-term impacts of bail reform and on specific deterrence remains necessary.
{"title":"Synthetic Control Analysis of the Short-Term Impact of New York State’s Bail Elimination Act on Aggregate Crime","authors":"Angela Zhou, Andrew Koo, Nathan Kallus, Rene Ropac, Richard Peterson, Stephen Koppel, Tiffany Bergin","doi":"10.1080/2330443x.2023.2267617","DOIUrl":"https://doi.org/10.1080/2330443x.2023.2267617","url":null,"abstract":"We conduct an empirical evaluation of the short-term impact of New York’s bail reform on crime. New York State’s Bail Elimination Act went into effect on January 1, 2020, eliminating money bail and pretrial detention for nearly all misdemeanor and nonviolent felony defendants. Our analysis of effects on aggregate crime rates after the reform informs the understanding of bail reform and general deterrence, rather than specific deterrence via re-arrest rates of the detained/released population. We conduct a synthetic control analysis for a comparative case study of the impact of bail reform. We focus on synthetic control analysis of post-intervention changes in crime for assault, theft, burglary, robbery, and drug crimes, constructing a dataset from publicly reported crime data of 27 large municipalities. Due to the short time frame before the onset of COVID-19 and its far-reaching effects, we restrict attention to a short post-intervention time period. Nonetheless, evaluation of short-term impacts may still inform hypotheses of general deterrence of bail reform policy. Our findings, including placebo checks and other robustness checks, show that for assault, theft, and drug crimes, there is no significant impact of bail reform on aggregate crime. For robbery, we find a statistically significant increase; for burglary, the synthetic control is more variable and our analysis is deemed less conclusive. Since our study assesses the short-term impacts, further work studying long-term impacts of bail reform and on specific deterrence remains necessary.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135351849","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}
Pub Date : 2023-09-29DOI: 10.1080/2330443x.2023.2263038
Dennis J. Aigner, Marco del Ángel, Joel Wiles
The measurement of the disparate impact of a particular de facto discriminatory policy on a minority or otherwise legally protected group has been of importance since passage of the Civil Rights Act of 1964. When the data available for the measurement of disparate impact, as embodied in the so-called “disparity ratio,” come from samples, a statistical approach naturally suggests itself. This article reviews both the law and statistics literature with regard to statistical inference applicable to the disparity ratio and related measures of disparate impact. From that review, three primary approaches are evaluated, the difference in so-called “rejection” rates for the protected and non-protected groups, their ratio (the disparity ratio), and the natural logarithm of the disparity ratio. For various reasons, the direct ratio estimator is recommended for use in all but small samples, where the log-ratio approach is to be preferred. The main points are illustrated with two fair housing examples, one being the possible discriminatory effect by race owing to a landlord’s refusal to accept Section 8 housing vouchers in lieu of cash rent, and the other being the effects of occupancy restrictions on families with children. Various methodological issues that arise in the application of these three estimation approaches are addressed in the context of the more complex sample designs that underlie the data utilized.
{"title":"STATISTICAL APPROACHES FOR ASSESSING DISPARATE IMPACT IN FAIR HOUSING CASES","authors":"Dennis J. Aigner, Marco del Ángel, Joel Wiles","doi":"10.1080/2330443x.2023.2263038","DOIUrl":"https://doi.org/10.1080/2330443x.2023.2263038","url":null,"abstract":"The measurement of the disparate impact of a particular de facto discriminatory policy on a minority or otherwise legally protected group has been of importance since passage of the Civil Rights Act of 1964. When the data available for the measurement of disparate impact, as embodied in the so-called “disparity ratio,” come from samples, a statistical approach naturally suggests itself. This article reviews both the law and statistics literature with regard to statistical inference applicable to the disparity ratio and related measures of disparate impact. From that review, three primary approaches are evaluated, the difference in so-called “rejection” rates for the protected and non-protected groups, their ratio (the disparity ratio), and the natural logarithm of the disparity ratio. For various reasons, the direct ratio estimator is recommended for use in all but small samples, where the log-ratio approach is to be preferred. The main points are illustrated with two fair housing examples, one being the possible discriminatory effect by race owing to a landlord’s refusal to accept Section 8 housing vouchers in lieu of cash rent, and the other being the effects of occupancy restrictions on families with children. Various methodological issues that arise in the application of these three estimation approaches are addressed in the context of the more complex sample designs that underlie the data utilized.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135194068","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}
Pub Date : 2023-09-14DOI: 10.1080/2330443x.2023.2221320
Wayne Smith
{"title":"Comment on “What Protects the Autonomy of the Federal Statistical Agencies? An Assessment of the Procedures in Place That Protect the Independence and Objectivity of Official Statistics” by Pierson et al.","authors":"Wayne Smith","doi":"10.1080/2330443x.2023.2221320","DOIUrl":"https://doi.org/10.1080/2330443x.2023.2221320","url":null,"abstract":"","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134971440","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}
Pub Date : 2023-09-14DOI: 10.1080/2330443x.2023.2244026
Michael Cohen
{"title":"Discussion of “What Protects the Autonomy of the Federal Statistical Agencies? An Assessment of the Procedures in Place to Protect the Independence and Objectivity of Official U.S. Statistics” by Citro et al. (2023)","authors":"Michael Cohen","doi":"10.1080/2330443x.2023.2244026","DOIUrl":"https://doi.org/10.1080/2330443x.2023.2244026","url":null,"abstract":"","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134971442","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}
Pub Date : 2023-09-14DOI: 10.1080/2330443x.2023.2221314
Hermann Habermann, Thomas A. Louis, Franklin Reeder
{"title":"Is Autonomy Possible and Is It a Good Thing?","authors":"Hermann Habermann, Thomas A. Louis, Franklin Reeder","doi":"10.1080/2330443x.2023.2221314","DOIUrl":"https://doi.org/10.1080/2330443x.2023.2221314","url":null,"abstract":"","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134971439","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}
Pub Date : 2023-09-14DOI: 10.1080/2330443x.2023.2221324
Claire McKay Bowen
While the threat of biased AI has received considerable attention, another invisible threat to data democracy exists that has not received scientific or media attention. This threat is the lack of autonomy for the 13 principal United States federal statistical agencies. These agencies collect data that informs the United States federal government’s critical decisions, such as allocating resources and providing essential services. The lack of agency-specific statutory autonomy protections leaves the agencies vulnerable to political influence, which could have lasting ramifications without the public’s knowledge. Citro et al. evaluate the professional autonomy of the 13 federal statistical agencies and found that they lacked sufficient autonomy due to the absence of statutory protections (among other things). They provided three recommendations to enhance the strength of the federal statistical agency’s leadership and its autonomy to address each measure of autonomy for all 13 principal federal statistical agencies. Implementing these recommendations is an initial and crucial step toward preventing future erosion of the federal statistical system. Further, statisticians must take an active role in initiating and engaging in open dialogues with various scientific fields to protect and promote the vital work of federal statistical agencies.
{"title":"The Autonomy Gap: Response to Citro et al. and the statistical community","authors":"Claire McKay Bowen","doi":"10.1080/2330443x.2023.2221324","DOIUrl":"https://doi.org/10.1080/2330443x.2023.2221324","url":null,"abstract":"While the threat of biased AI has received considerable attention, another invisible threat to data democracy exists that has not received scientific or media attention. This threat is the lack of autonomy for the 13 principal United States federal statistical agencies. These agencies collect data that informs the United States federal government’s critical decisions, such as allocating resources and providing essential services. The lack of agency-specific statutory autonomy protections leaves the agencies vulnerable to political influence, which could have lasting ramifications without the public’s knowledge. Citro et al. evaluate the professional autonomy of the 13 federal statistical agencies and found that they lacked sufficient autonomy due to the absence of statutory protections (among other things). They provided three recommendations to enhance the strength of the federal statistical agency’s leadership and its autonomy to address each measure of autonomy for all 13 principal federal statistical agencies. Implementing these recommendations is an initial and crucial step toward preventing future erosion of the federal statistical system. Further, statisticians must take an active role in initiating and engaging in open dialogues with various scientific fields to protect and promote the vital work of federal statistical agencies.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134971441","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}
Pub Date : 2023-07-21DOI: 10.1080/2330443x.2023.2239306
Nicholas Scurich, R. John
{"title":"Three-Way ROCs for Forensic Decision Making","authors":"Nicholas Scurich, R. John","doi":"10.1080/2330443x.2023.2239306","DOIUrl":"https://doi.org/10.1080/2330443x.2023.2239306","url":null,"abstract":"","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2023-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43117714","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}
Pub Date : 2023-04-04DOI: 10.1080/2330443x.2023.2199809
A. Barnett, Arnaud Sarfati
Arguably, the single greatest determinant of US public policy is the identity of the president. And if trusted, polls not only provide forecasts about presidential-election outcomes but can act to shape those outcomes. Looking ahead to the 2024 US presidential election and recognizing that polls before the 2020 presidential election were sharply criticized, we consider whether such harsh assessments are warranted. Initially, we explore whether such polls as processed by the sophisticated aggregator FiveThirtyEight successfully forecast actual 2020 state-by-state outcomes. We evaluate FiveThirtyEight’s forecasts using customized statistical methods not used previously, methods that take account of likely correlations among election outcomes in similar states. We find that, taken together, the pollsters and FiveThirtyEight did an excellent job in predicting who would win in individual states, even those “tipping point” states where forecasting is more difficult. However, we also find that FiveThirtyEight underestimated Donald Trump’s vote shares by state to a modest but statistically significant extent. We further consider how the polls performed when the more primitive aggregator Real Clear Politics combined their results, and then how well single statewide polls performed without aggregation. It emerges that both Real Clear Politics and the individual polls fared surprisingly well.
{"title":"The Polls and the US Presidential Election in 2020 ….and 2024","authors":"A. Barnett, Arnaud Sarfati","doi":"10.1080/2330443x.2023.2199809","DOIUrl":"https://doi.org/10.1080/2330443x.2023.2199809","url":null,"abstract":"Arguably, the single greatest determinant of US public policy is the identity of the president. And if trusted, polls not only provide forecasts about presidential-election outcomes but can act to shape those outcomes. Looking ahead to the 2024 US presidential election and recognizing that polls before the 2020 presidential election were sharply criticized, we consider whether such harsh assessments are warranted. Initially, we explore whether such polls as processed by the sophisticated aggregator FiveThirtyEight successfully forecast actual 2020 state-by-state outcomes. We evaluate FiveThirtyEight’s forecasts using customized statistical methods not used previously, methods that take account of likely correlations among election outcomes in similar states. We find that, taken together, the pollsters and FiveThirtyEight did an excellent job in predicting who would win in individual states, even those “tipping point” states where forecasting is more difficult. However, we also find that FiveThirtyEight underestimated Donald Trump’s vote shares by state to a modest but statistically significant extent. We further consider how the polls performed when the more primitive aggregator Real Clear Politics combined their results, and then how well single statewide polls performed without aggregation. It emerges that both Real Clear Politics and the individual polls fared surprisingly well.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2023-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46241583","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：