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":"17 1","pages":"0"},"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":"61 1","pages":"0"},"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":"42 1","pages":"0"},"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":"1 1","pages":""},"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":" ","pages":""},"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}
Pub Date : 2023-03-13DOI: 10.1080/2330443x.2023.2188069
Max D. Morris
{"title":"Comments on: A Re-analysis of Repeatability and Reproducibility in the Ames-USDOE-FBI Study, by Dorfman and Valliant","authors":"Max D. Morris","doi":"10.1080/2330443x.2023.2188069","DOIUrl":"https://doi.org/10.1080/2330443x.2023.2188069","url":null,"abstract":"","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48341026","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 : 2023-03-10DOI: 10.1080/2330443x.2023.2188056
Christopher R. Surfus
{"title":"A Statistical Understanding of Disability in the LGBT Community","authors":"Christopher R. Surfus","doi":"10.1080/2330443x.2023.2188056","DOIUrl":"https://doi.org/10.1080/2330443x.2023.2188056","url":null,"abstract":"","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2023-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45465276","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 : 2023-03-10DOI: 10.1080/2330443x.2023.2188062
C. Citro, Jonathan Auerbach, Katherine Smith Evans, E. Groshen, J. Landefeld, J. Mulrow, Tom Petska, Steve Pierson, N. Potok, C. Rothwell, John Thompson, James L. Woodworth, Edward Wu
The Abstract We assess the professional autonomy of the 13 principal U.S. federal statistical agencies. We define six components or measures of such autonomy and evaluate each of the 13 principal statistical agencies according to each measure. Our assessment yields three main findings: 1. Challenges to the objectivity, credibility, and utility of federal statistics arise largely as a consequence of insufficient autonomy. 2. There is remarkable variation in autonomy protections and a surprising lack of statutory protections for many agencies for many of the proposed measures. 3. Many existing autonomy rules and guidelines are weakened by unclear or unactionable
{"title":"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","authors":"C. Citro, Jonathan Auerbach, Katherine Smith Evans, E. Groshen, J. Landefeld, J. Mulrow, Tom Petska, Steve Pierson, N. Potok, C. Rothwell, John Thompson, James L. Woodworth, Edward Wu","doi":"10.1080/2330443x.2023.2188062","DOIUrl":"https://doi.org/10.1080/2330443x.2023.2188062","url":null,"abstract":"The Abstract We assess the professional autonomy of the 13 principal U.S. federal statistical agencies. We define six components or measures of such autonomy and evaluate each of the 13 principal statistical agencies according to each measure. Our assessment yields three main findings: 1. Challenges to the objectivity, credibility, and utility of federal statistics arise largely as a consequence of insufficient autonomy. 2. There is remarkable variation in autonomy protections and a surprising lack of statutory protections for many agencies for many of the proposed measures. 3. Many existing autonomy rules and guidelines are weakened by unclear or unactionable","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2023-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45997765","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 : 2022-09-28DOI: 10.1080/2330443x.2023.2216748
Kori Khan, A. Carriquiry
Forensic science plays a critical role in the United States criminal justice system. For decades, many feature-based fields of forensic science, such as firearm and toolmark identification, developed outside the scientific community's purview. The results of these studies are widely relied on by judges nationwide. However, this reliance is misplaced. Black-box studies to date suffer from inappropriate sampling methods and high rates of missingness. Current black-box studies ignore both problems in arriving at the error rate estimates presented to courts. We explore the impact of each type of limitation using available data from black-box studies and court materials. We show that black-box studies rely on non-representative samples of examiners. Using a case study of a popular ballistics study, we find evidence that these unrepresentative samples may commit fewer errors than the wider population from which they came. We also find evidence that the missingness in black-box studies is non-ignorable. Using data from a recent latent print study, we show that ignoring this missingness likely results in systematic underestimates of error rates. Finally, we offer concrete steps to overcome these limitations.
{"title":"Shining a Light on Forensic Black-Box Studies","authors":"Kori Khan, A. Carriquiry","doi":"10.1080/2330443x.2023.2216748","DOIUrl":"https://doi.org/10.1080/2330443x.2023.2216748","url":null,"abstract":"Forensic science plays a critical role in the United States criminal justice system. For decades, many feature-based fields of forensic science, such as firearm and toolmark identification, developed outside the scientific community's purview. The results of these studies are widely relied on by judges nationwide. However, this reliance is misplaced. Black-box studies to date suffer from inappropriate sampling methods and high rates of missingness. Current black-box studies ignore both problems in arriving at the error rate estimates presented to courts. We explore the impact of each type of limitation using available data from black-box studies and court materials. We show that black-box studies rely on non-representative samples of examiners. Using a case study of a popular ballistics study, we find evidence that these unrepresentative samples may commit fewer errors than the wider population from which they came. We also find evidence that the missingness in black-box studies is non-ignorable. Using data from a recent latent print study, we show that ignoring this missingness likely results in systematic underestimates of error rates. Finally, we offer concrete steps to overcome these limitations.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":"1 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2022-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44806864","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 : 2022-09-01DOI: 10.1080/2330443X.2022.2120136
W. M. van der Wal
Abstract Right-to-carry (RTC) laws allow the legal carrying of concealed firearms for defense, in certain states in the United States. I used modern causal inference methodology from epidemiology to examine the effect of RTC laws on crime over a period from 1959 up to 2016. I fitted marginal structural models (MSMs), using inverse probability weighting (IPW) to correct for criminological, economic, political and demographic confounders. Results indicate that RTC laws significantly increase violent crime by 7.5% and property crime by 6.1%. RTC laws significantly increase murder and manslaughter, robbery, aggravated assault, burglary, larceny theft and motor vehicle theft rates. Applying this method to this topic for the first time addresses methodological shortcomings in previous studies such as conditioning away the effect, overfit and the inappropriate use of county level measurements. Data and analysis code for this article are available online.
{"title":"Marginal Structural Models to Estimate Causal Effects of Right-to-Carry Laws on Crime","authors":"W. M. van der Wal","doi":"10.1080/2330443X.2022.2120136","DOIUrl":"https://doi.org/10.1080/2330443X.2022.2120136","url":null,"abstract":"Abstract Right-to-carry (RTC) laws allow the legal carrying of concealed firearms for defense, in certain states in the United States. I used modern causal inference methodology from epidemiology to examine the effect of RTC laws on crime over a period from 1959 up to 2016. I fitted marginal structural models (MSMs), using inverse probability weighting (IPW) to correct for criminological, economic, political and demographic confounders. Results indicate that RTC laws significantly increase violent crime by 7.5% and property crime by 6.1%. RTC laws significantly increase murder and manslaughter, robbery, aggravated assault, burglary, larceny theft and motor vehicle theft rates. Applying this method to this topic for the first time addresses methodological shortcomings in previous studies such as conditioning away the effect, overfit and the inappropriate use of county level measurements. Data and analysis code for this article are available online.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":"9 1","pages":"163 - 174"},"PeriodicalIF":1.6,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48333451","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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