Pub Date : 2016-12-12DOI: 10.1080/2330443X.2017.1360813
J. Gastwirth
ABSTRACT The Gini index is the most commonly used measure of income inequality. Like any single summary measure of a set of data, it cannot capture all aspects that are of interest to researchers. One of its widely reported flaws is that it is supposed to be overly sensitive to changes in the middle of the distribution. By studying the effect of small transfers between households or an additional increment in income going to one member of the population on the value of the index, this claim is re-examined. It turns out that the difference in the rank order of donor and recipient is usually the most important factor determining the change in the Gini index due to the transfer, which implies that transfers from an upper income household to a low income household receive more weight that transfers involving the middle. Transfers between two middle-income households do affect a higher fraction of the population than other transfers but those transfers do not receive an excessive weight relative to other transfers because the difference in the ranks of donor and recipient is smaller than the corresponding difference in other transfers. Thus, progressive transfers between two households in the middle of the distribution changes the Gini index less than a transfer of the same amount from an upper income household to a lower income household. Similarly, the effect on the Gini index when a household in either tail of the distribution receives an additional increment is larger than when a middle-income household receives it. Contrary to much of the literature, these results indicate that the Gini index is not overly sensitive to changes in the middle of the distribution. Indeed, it is more sensitive to changes in the lower and upper parts of the distribution than in the middle.
{"title":"Is the Gini Index of Inequality Overly Sensitive to Changes in the Middle of the Income Distribution?","authors":"J. Gastwirth","doi":"10.1080/2330443X.2017.1360813","DOIUrl":"https://doi.org/10.1080/2330443X.2017.1360813","url":null,"abstract":"ABSTRACT The Gini index is the most commonly used measure of income inequality. Like any single summary measure of a set of data, it cannot capture all aspects that are of interest to researchers. One of its widely reported flaws is that it is supposed to be overly sensitive to changes in the middle of the distribution. By studying the effect of small transfers between households or an additional increment in income going to one member of the population on the value of the index, this claim is re-examined. It turns out that the difference in the rank order of donor and recipient is usually the most important factor determining the change in the Gini index due to the transfer, which implies that transfers from an upper income household to a low income household receive more weight that transfers involving the middle. Transfers between two middle-income households do affect a higher fraction of the population than other transfers but those transfers do not receive an excessive weight relative to other transfers because the difference in the ranks of donor and recipient is smaller than the corresponding difference in other transfers. Thus, progressive transfers between two households in the middle of the distribution changes the Gini index less than a transfer of the same amount from an upper income household to a lower income household. Similarly, the effect on the Gini index when a household in either tail of the distribution receives an additional increment is larger than when a middle-income household receives it. Contrary to much of the literature, these results indicate that the Gini index is not overly sensitive to changes in the middle of the distribution. Indeed, it is more sensitive to changes in the lower and upper parts of the distribution than in the middle.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":"4 1","pages":"1 - 11"},"PeriodicalIF":1.6,"publicationDate":"2016-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/2330443X.2017.1360813","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60065916","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 : 2016-07-08DOI: 10.1080/2330443X.2016.1277966
Rayleigh Lei, Andrew Gelman, Yair Ghitza
ABSTRACT We present an increasingly stringent set of replications, a multilevel regression and poststratification analysis of polls from the 2008 U.S. presidential election campaign, focusing on a set of plots showing the estimated Republican vote share for whites and for all voters, as a function of income level in each of the states. We start with a nearly exact duplication that uses the posted code and changes only the model-fitting algorithm; we then replicate using already-analyzed data from 2004; and finally we set up preregistered replications using two surveys from 2008 that we had not previously looked at. We have already learned from our preliminary, nonpreregistered replication, which has revealed a potential problem with the earlier published analysis; it appears that our model may not sufficiently account for nonsampling error, and that some of the patterns presented in that earlier article may simply reflect noise. In addition to the substantive interest in validating earlier findings about demographics, geography, and voting, the present project serves as a demonstration of preregistration in a setting where the subject matter is historical (and thus the replication data exist before the preregistration plan is written) and where the analysis is exploratory (and thus a replication cannot be simply deemed successful or unsuccessful based on the statistical significance of some particular comparison). Our replication analysis produced graphs that showed the same general pattern of income and voting as we had found in our earlier published work, but with some differences in particular states that we cannot easily explain and which seem too large to be explained by sampling variation. This process thus demonstrates how replication can raise concerns about an earlier published result.
{"title":"The 2008 Election: A Preregistered Replication Analysis","authors":"Rayleigh Lei, Andrew Gelman, Yair Ghitza","doi":"10.1080/2330443X.2016.1277966","DOIUrl":"https://doi.org/10.1080/2330443X.2016.1277966","url":null,"abstract":"ABSTRACT We present an increasingly stringent set of replications, a multilevel regression and poststratification analysis of polls from the 2008 U.S. presidential election campaign, focusing on a set of plots showing the estimated Republican vote share for whites and for all voters, as a function of income level in each of the states. We start with a nearly exact duplication that uses the posted code and changes only the model-fitting algorithm; we then replicate using already-analyzed data from 2004; and finally we set up preregistered replications using two surveys from 2008 that we had not previously looked at. We have already learned from our preliminary, nonpreregistered replication, which has revealed a potential problem with the earlier published analysis; it appears that our model may not sufficiently account for nonsampling error, and that some of the patterns presented in that earlier article may simply reflect noise. In addition to the substantive interest in validating earlier findings about demographics, geography, and voting, the present project serves as a demonstration of preregistration in a setting where the subject matter is historical (and thus the replication data exist before the preregistration plan is written) and where the analysis is exploratory (and thus a replication cannot be simply deemed successful or unsuccessful based on the statistical significance of some particular comparison). Our replication analysis produced graphs that showed the same general pattern of income and voting as we had found in our earlier published work, but with some differences in particular states that we cannot easily explain and which seem too large to be explained by sampling variation. This process thus demonstrates how replication can raise concerns about an earlier published result.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":"4 1","pages":"1 - 8"},"PeriodicalIF":1.6,"publicationDate":"2016-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/2330443X.2016.1277966","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60065910","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 : 2016-02-22DOI: 10.1080/2330443X.2018.1427012
Ioan Voicu
ABSTRACT This article uses a recent first name list to develop an improvement to an existing Bayesian classifier, namely the Bayesian Improved Surname Geocoding (BISG) method, which combines surname and geography information to impute missing race/ethnicity. The new Bayesian Improved First Name Surname Geocoding (BIFSG) method is validated using a large sample of mortgage applicants who self-report their race/ethnicity. BIFSG outperforms BISG, in terms of accuracy and coverage, for all major racial/ethnic categories. Although the overall magnitude of improvement is somewhat small, the largest improvements occur for non-Hispanic Blacks, a group for which the BISG performance is weakest. When estimating the race/ethnicity effects on mortgage pricing and underwriting decisions with regression models, estimation biases from both BIFSG and BISG are very small, with BIFSG generally having smaller biases, and the maximum a posteriori classifier resulting in smaller biases than through use of estimated probabilities. Robustness checks using voter registration data confirm BIFSG's improved performance vis-a-vis BISG and illustrate BIFSG's applicability to areas other than mortgage lending. Finally, I demonstrate an application of the BIFSG to the imputation of missing race/ethnicity in the Home Mortgage Disclosure Act data, and in the process, offer novel evidence that the incidence of missing race/ethnicity information is correlated with race/ethnicity.
{"title":"Using First Name Information to Improve Race and Ethnicity Classification","authors":"Ioan Voicu","doi":"10.1080/2330443X.2018.1427012","DOIUrl":"https://doi.org/10.1080/2330443X.2018.1427012","url":null,"abstract":"ABSTRACT This article uses a recent first name list to develop an improvement to an existing Bayesian classifier, namely the Bayesian Improved Surname Geocoding (BISG) method, which combines surname and geography information to impute missing race/ethnicity. The new Bayesian Improved First Name Surname Geocoding (BIFSG) method is validated using a large sample of mortgage applicants who self-report their race/ethnicity. BIFSG outperforms BISG, in terms of accuracy and coverage, for all major racial/ethnic categories. Although the overall magnitude of improvement is somewhat small, the largest improvements occur for non-Hispanic Blacks, a group for which the BISG performance is weakest. When estimating the race/ethnicity effects on mortgage pricing and underwriting decisions with regression models, estimation biases from both BIFSG and BISG are very small, with BIFSG generally having smaller biases, and the maximum a posteriori classifier resulting in smaller biases than through use of estimated probabilities. Robustness checks using voter registration data confirm BIFSG's improved performance vis-a-vis BISG and illustrate BIFSG's applicability to areas other than mortgage lending. Finally, I demonstrate an application of the BIFSG to the imputation of missing race/ethnicity in the Home Mortgage Disclosure Act data, and in the process, offer novel evidence that the incidence of missing race/ethnicity information is correlated with race/ethnicity.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":"5 1","pages":"1 - 13"},"PeriodicalIF":1.6,"publicationDate":"2016-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/2330443X.2018.1427012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60065963","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 : 2016-01-01DOI: 10.1080/2330443X.2015.1102667
R. Kaestner
ABSTRACT In an earlier article, Sommers, Long, and Baicker concluded that health care reform in Massachusetts was associated with a significant decrease in mortality. I replicate the findings from this study and present p-values for the parameter estimates reported by Sommers, Long, and Baicker that are based on an alternative and valid approach to inference referred to as randomization inference. I find that estimates of the treatment effects produced by Sommers, Long, and Baicker are not statistically significant when p-values are based on randomization inference methods. Indeed, the p-values of the estimates reported in Sommers, Long, and Baicker derived by the randomization inference method range from 0.22 to 0.78. Therefore, the authors’ conclusion that health reform in Massachusetts was associated with a decline in mortality is not justified. The Sommers, Long, and Baicker analysis is largely uninformative with respect to the true effect of reform on mortality because it does not have adequate statistical power to detect plausible effect sizes.
{"title":"Did Massachusetts Health Care Reform Lower Mortality? No According to Randomization Inference","authors":"R. Kaestner","doi":"10.1080/2330443X.2015.1102667","DOIUrl":"https://doi.org/10.1080/2330443X.2015.1102667","url":null,"abstract":"ABSTRACT In an earlier article, Sommers, Long, and Baicker concluded that health care reform in Massachusetts was associated with a significant decrease in mortality. I replicate the findings from this study and present p-values for the parameter estimates reported by Sommers, Long, and Baicker that are based on an alternative and valid approach to inference referred to as randomization inference. I find that estimates of the treatment effects produced by Sommers, Long, and Baicker are not statistically significant when p-values are based on randomization inference methods. Indeed, the p-values of the estimates reported in Sommers, Long, and Baicker derived by the randomization inference method range from 0.22 to 0.78. Therefore, the authors’ conclusion that health reform in Massachusetts was associated with a decline in mortality is not justified. The Sommers, Long, and Baicker analysis is largely uninformative with respect to the true effect of reform on mortality because it does not have adequate statistical power to detect plausible effect sizes.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":"3 1","pages":"1 - 6"},"PeriodicalIF":1.6,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/2330443X.2015.1102667","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60066184","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 : 2016-01-01DOI: 10.1080/2330443X.2016.1241061
J. Norwood
The federal statistical system in the United States regularly compiles data on the issues that concern the American people, data that play an important role in the lives of our citizens. Although a...
{"title":"Politics and Federal Statistics*","authors":"J. Norwood","doi":"10.1080/2330443X.2016.1241061","DOIUrl":"https://doi.org/10.1080/2330443X.2016.1241061","url":null,"abstract":"The federal statistical system in the United States regularly compiles data on the issues that concern the American people, data that play an important role in the lives of our citizens. Although a...","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":"3 1","pages":"1 - 8"},"PeriodicalIF":1.6,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/2330443X.2016.1241061","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60065863","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 : 2016-01-01DOI: 10.1080/2330443X.2015.1129918
G. Ridgeway
ABSTRACT Particularly with the resurgence of concern over police use of deadly force, there is a pressing need to understand the risk factors that lead to police shootings. This study uses a matched-case–control design to remove confounders of shooting incidents and identify features of officers that increased their risk of shooting. By matching shooting officers to nonshooting officers at the same scene, the analysis isolates the role of the officers’ features from the features of the incident’s environment. The study uses data from the New York City Police Department on 291 officers involved in 106 officer-involved shootings adjudicated between 2004 and 2006. Black officers were 3.3 times and officers rapidly accumulating negative marks in their files were 3.1 times more likely to shoot than other officers. Officers who started their police career later in life were less likely to shoot. The results indicate that officer features related to discharging a firearm are identifiable.
{"title":"Officer Risk Factors Associated with Police Shootings: A Matched Case–Control Study","authors":"G. Ridgeway","doi":"10.1080/2330443X.2015.1129918","DOIUrl":"https://doi.org/10.1080/2330443X.2015.1129918","url":null,"abstract":"ABSTRACT Particularly with the resurgence of concern over police use of deadly force, there is a pressing need to understand the risk factors that lead to police shootings. This study uses a matched-case–control design to remove confounders of shooting incidents and identify features of officers that increased their risk of shooting. By matching shooting officers to nonshooting officers at the same scene, the analysis isolates the role of the officers’ features from the features of the incident’s environment. The study uses data from the New York City Police Department on 291 officers involved in 106 officer-involved shootings adjudicated between 2004 and 2006. Black officers were 3.3 times and officers rapidly accumulating negative marks in their files were 3.1 times more likely to shoot than other officers. Officers who started their police career later in life were less likely to shoot. The results indicate that officer features related to discharging a firearm are identifiable.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":"3 1","pages":"1 - 6"},"PeriodicalIF":1.6,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/2330443X.2015.1129918","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60066233","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 : 2016-01-01DOI: 10.1080/2330443X.2015.1084252
Brian Gill, J. Furgeson, Hanley S. Chiang, Bing-ru Teh, J. Haimson, Natalya Verbitsky Savitz
ABSTRACT A growing literature on within-study comparisons (WSC) examines whether and in what context nonexperimental methods can successfully replicate the results of randomized experiments. WSCs require that the experimental and nonexperimental methods assess the same causal estimand. But experiments that include noncompliance in treatment assignment produce a divergence in the causal estimands measured by standard approaches: the experiment-based estimate of the impact of treatment (the complier average causal effect, CACE) applies only to compliers, while the nonexperimental estimate applies to all subjects receiving treatment, including always-takers. We develop a new replication approach that solves this problem by using nonexperimental methods to produce an estimate that can be compared to the experimental intent-to-treat (ITT) impact estimate rather than the CACE. We demonstrate the applicability of the method in a WSC of the effects of charter schools on student achievement. In our example, some members of the randomized control group crossed over to treatment by enrolling in the charter schools. We show that several nonexperimental methods that incorporate pretreatment measures of the outcome of interest can successfully replicate experimental ITT impact estimates when control-group noncompliance (crossover) occurs—even when treatment effects differ for compliers and always takers.
{"title":"Replicating Experimental Impact Estimates with Nonexperimental Methods in the Context of Control-Group Noncompliance","authors":"Brian Gill, J. Furgeson, Hanley S. Chiang, Bing-ru Teh, J. Haimson, Natalya Verbitsky Savitz","doi":"10.1080/2330443X.2015.1084252","DOIUrl":"https://doi.org/10.1080/2330443X.2015.1084252","url":null,"abstract":"ABSTRACT A growing literature on within-study comparisons (WSC) examines whether and in what context nonexperimental methods can successfully replicate the results of randomized experiments. WSCs require that the experimental and nonexperimental methods assess the same causal estimand. But experiments that include noncompliance in treatment assignment produce a divergence in the causal estimands measured by standard approaches: the experiment-based estimate of the impact of treatment (the complier average causal effect, CACE) applies only to compliers, while the nonexperimental estimate applies to all subjects receiving treatment, including always-takers. We develop a new replication approach that solves this problem by using nonexperimental methods to produce an estimate that can be compared to the experimental intent-to-treat (ITT) impact estimate rather than the CACE. We demonstrate the applicability of the method in a WSC of the effects of charter schools on student achievement. In our example, some members of the randomized control group crossed over to treatment by enrolling in the charter schools. We show that several nonexperimental methods that incorporate pretreatment measures of the outcome of interest can successfully replicate experimental ITT impact estimates when control-group noncompliance (crossover) occurs—even when treatment effects differ for compliers and always takers.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":"214 1","pages":"1 - 11"},"PeriodicalIF":1.6,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79514023","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 : 2016-01-01DOI: 10.1080/2330443X.2016.1241057
E. Groshen
{"title":"Janet Norwood and Federal Statistics","authors":"E. Groshen","doi":"10.1080/2330443X.2016.1241057","DOIUrl":"https://doi.org/10.1080/2330443X.2016.1241057","url":null,"abstract":"","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":"3 1","pages":"1 - 1"},"PeriodicalIF":1.6,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/2330443X.2016.1241057","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60065853","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 : 2016-01-01DOI: 10.1080/2330443X.2015.1129919
N. Sedransk
ABSTRACT The story of the National Institute of Statistical Sciences (NISS) is a story of heroes and obstacles, of wisdom and naiveté; but most of all it is a story of a vision for statistics as fundamental to the understanding of a complex world. This article discusses the formation of the institute and the recollections of many of the leaders who helped form this organization.
{"title":"NISS: From Vision to National Institute","authors":"N. Sedransk","doi":"10.1080/2330443X.2015.1129919","DOIUrl":"https://doi.org/10.1080/2330443X.2015.1129919","url":null,"abstract":"ABSTRACT The story of the National Institute of Statistical Sciences (NISS) is a story of heroes and obstacles, of wisdom and naiveté; but most of all it is a story of a vision for statistics as fundamental to the understanding of a complex world. This article discusses the formation of the institute and the recollections of many of the leaders who helped form this organization.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":"3 1","pages":"1 - 17"},"PeriodicalIF":1.6,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/2330443X.2015.1129919","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60066241","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 : 2016-01-01DOI: 10.1080/2330443X.2016.1182878
Mariesa A. Herrmann, Elias Walsh, Eric Isenberg
ABSTRACT It is common in the implementation of teacher accountability systems to use empirical Bayes shrinkage to adjust teacher value-added estimates by their level of precision. Because value-added estimates based on fewer students and students with “hard-to-predict” achievement will be less precise, the procedure could have differential impacts on the probability that the teachers of fewer students or students with hard-to-predict achievement will be assigned consequences. This article investigates how shrinkage affects the value-added estimates of teachers of hard-to-predict students. We found that teachers of students with low prior achievement and who receive free lunch tend to have less precise value-added estimates. However, in our sample, shrinkage had no statistically significant effect on the relative probability that teachers of hard-to-predict students received consequences.
{"title":"Shrinkage of Value-Added Estimates and Characteristics of Students with Hard-to-Predict Achievement Levels","authors":"Mariesa A. Herrmann, Elias Walsh, Eric Isenberg","doi":"10.1080/2330443X.2016.1182878","DOIUrl":"https://doi.org/10.1080/2330443X.2016.1182878","url":null,"abstract":"ABSTRACT It is common in the implementation of teacher accountability systems to use empirical Bayes shrinkage to adjust teacher value-added estimates by their level of precision. Because value-added estimates based on fewer students and students with “hard-to-predict” achievement will be less precise, the procedure could have differential impacts on the probability that the teachers of fewer students or students with hard-to-predict achievement will be assigned consequences. This article investigates how shrinkage affects the value-added estimates of teachers of hard-to-predict students. We found that teachers of students with low prior achievement and who receive free lunch tend to have less precise value-added estimates. However, in our sample, shrinkage had no statistically significant effect on the relative probability that teachers of hard-to-predict students received consequences.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":"3 1","pages":"1 - 10"},"PeriodicalIF":1.6,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/2330443X.2016.1182878","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60065835","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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