Pub Date : 2017-01-01DOI: 10.1080/2330443X.2017.1399844
Hans Noel
No one needs to be told that 2016 was an unusual election year. For social science, its strangeness has two implications. First, it is a learning opportunity. Whether we think of 2016 as a highleverage case or as off the equilibriumpath, an unusual case gives perspective that we do not usually get to see. This is the potential that Julia Azari and Andrew Gelman have exploited. Second, however, is that unusual cases are, well, unusual. They are often outliers. They differ onmultiple dimensions, and we may not know why they came about. Lessons from them may not generalize. The election of 2016 was unusual or even unprecedented in so many ways. Not only do we want to be cautious about extrapolation, but the way we learn from outliers is different than the way we learn from typical cases. They can function asmuch as counterfactuals as cases, unless, of course, we think they are harbingers of a new normal. It is notable how many of the things Azari and Gelman note we learned from 2016 were things that at least some social scientists had already articulated. And I would argue that many of the othersmay not be as large as they are portrayed here. Despite the outrageousness of the 2016 election in so many ways, its lessons are mostly modest revisions of well-established work or raising still unanswered questions about less-established work. I think Azari and Gelman would agree. Most of their points comewith caveats that predictmy reactions. I think if we amplify the caveats over the initial points, we get a very different thesis. The 2016 election was a strange one, but one that can be explained fairly well by existing social science theory, once we know the parameters.With this inmind, a few reactions to some of the points raised by A&G.
{"title":"What We Learn From Unusual Cases: A Review of Azari and Gelman's “19 Things We Learned From the 2016 Election”","authors":"Hans Noel","doi":"10.1080/2330443X.2017.1399844","DOIUrl":"https://doi.org/10.1080/2330443X.2017.1399844","url":null,"abstract":"No one needs to be told that 2016 was an unusual election year. For social science, its strangeness has two implications. First, it is a learning opportunity. Whether we think of 2016 as a highleverage case or as off the equilibriumpath, an unusual case gives perspective that we do not usually get to see. This is the potential that Julia Azari and Andrew Gelman have exploited. Second, however, is that unusual cases are, well, unusual. They are often outliers. They differ onmultiple dimensions, and we may not know why they came about. Lessons from them may not generalize. The election of 2016 was unusual or even unprecedented in so many ways. Not only do we want to be cautious about extrapolation, but the way we learn from outliers is different than the way we learn from typical cases. They can function asmuch as counterfactuals as cases, unless, of course, we think they are harbingers of a new normal. It is notable how many of the things Azari and Gelman note we learned from 2016 were things that at least some social scientists had already articulated. And I would argue that many of the othersmay not be as large as they are portrayed here. Despite the outrageousness of the 2016 election in so many ways, its lessons are mostly modest revisions of well-established work or raising still unanswered questions about less-established work. I think Azari and Gelman would agree. Most of their points comewith caveats that predictmy reactions. I think if we amplify the caveats over the initial points, we get a very different thesis. The 2016 election was a strange one, but one that can be explained fairly well by existing social science theory, once we know the parameters.With this inmind, a few reactions to some of the points raised by A&G.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/2330443X.2017.1399844","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46105738","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 : 2017-01-01DOI: 10.1080/2330443X.2017.1374897
S. Keller, V. Lancaster, S. Shipp
ABSTRACT Existing data flows at the local level, public and administrative records, geospatial data, social media, and surveys are ubiquitous in our everyday life. The Community Learning Data-Driven Discovery (CLD3) process liberates, integrates, and makes these data available to government leaders and researchers to tell their community's story. These narratives can be used to build an equitable and sustainable social transformation within and across communities to address their most pressing needs. CLD3 is scalable to every city and county across the United States through an existing infrastructure maintained by collaboration between U.S. Public and Land Grant Universities and federal, state, and local governments. The CLD3 process starts with asking local leaders to identify questions they cannot answer and the potential data sources that may provide insights. The data sources are profiled, cleaned, transformed, linked, and translated into a narrative using statistical and geospatial learning along with the communities' collective knowledge. These insights are used to inform policy decisions and to develop, deploy, and evaluate intervention strategies based on scientifically based principles. CLD3 is a continuous, sustainable, and controlled feedback loop.
{"title":"Building Capacity for Data-Driven Governance: Creating a New Foundation for Democracy","authors":"S. Keller, V. Lancaster, S. Shipp","doi":"10.1080/2330443X.2017.1374897","DOIUrl":"https://doi.org/10.1080/2330443X.2017.1374897","url":null,"abstract":"ABSTRACT Existing data flows at the local level, public and administrative records, geospatial data, social media, and surveys are ubiquitous in our everyday life. The Community Learning Data-Driven Discovery (CLD3) process liberates, integrates, and makes these data available to government leaders and researchers to tell their community's story. These narratives can be used to build an equitable and sustainable social transformation within and across communities to address their most pressing needs. CLD3 is scalable to every city and county across the United States through an existing infrastructure maintained by collaboration between U.S. Public and Land Grant Universities and federal, state, and local governments. The CLD3 process starts with asking local leaders to identify questions they cannot answer and the potential data sources that may provide insights. The data sources are profiled, cleaned, transformed, linked, and translated into a narrative using statistical and geospatial learning along with the communities' collective knowledge. These insights are used to inform policy decisions and to develop, deploy, and evaluate intervention strategies based on scientifically based principles. CLD3 is a continuous, sustainable, and controlled feedback loop.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/2330443X.2017.1374897","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48216216","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 : 2017-01-01DOI: 10.1080/2330443X.2017.1356775
A. Gelman, Julia Azari
ABSTRACT We can all agree that the presidential election result was a shocker. According to news reports, even the Trump campaign team was stunned to come up a winner. So now seems like a good time to go over various theories floating around in political science and political reporting and see where they stand, now that this turbulent political year has drawn to a close. In the present article, we go through several things that we as political observers and political scientists have learned from the election, and then discuss implications for the future.
{"title":"19 Things We Learned from the 2016 Election","authors":"A. Gelman, Julia Azari","doi":"10.1080/2330443X.2017.1356775","DOIUrl":"https://doi.org/10.1080/2330443X.2017.1356775","url":null,"abstract":"ABSTRACT We can all agree that the presidential election result was a shocker. According to news reports, even the Trump campaign team was stunned to come up a winner. So now seems like a good time to go over various theories floating around in political science and political reporting and see where they stand, now that this turbulent political year has drawn to a close. In the present article, we go through several things that we as political observers and political scientists have learned from the election, and then discuss implications for the future.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/2330443X.2017.1356775","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42942300","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 : 2017-01-01DOI: 10.1080/2330443X.2017.1399843
S. Masket
Scholars will be analyzing the 2016 presidential election for many years to come, and Julia Azari and Andrew Gelman have done an excellent job laying out many of the important lessons to emerge and...
{"title":"Response to Azari and Gelman","authors":"S. Masket","doi":"10.1080/2330443X.2017.1399843","DOIUrl":"https://doi.org/10.1080/2330443X.2017.1399843","url":null,"abstract":"Scholars will be analyzing the 2016 presidential election for many years to come, and Julia Azari and Andrew Gelman have done an excellent job laying out many of the important lessons to emerge and...","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/2330443X.2017.1399843","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48878864","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 : 2017-01-01DOI: 10.1080/2330443X.2017.1294037
D. Wright
ABSTRACT Value-added models (VAMs) of student test scores are used within education because they are supposed to measure school and teacher effectiveness well. Much research has compared VAM estimates for different models, with different measures (e.g., observation ratings), and in experimental designs. VAMs are considered here from the perspective of graphical models and situations are identified that are problematic for VAMs. If the previous test scores are influenced by variables that also influence the true effectiveness of the school/teacher and there are variables that influence both the previous and current test scores, then the estimates of effectiveness can be poor. Those using VAMs should consider the models that may give rise to their data and evaluate their methods for these models before using the results for high-stakes decisions.
{"title":"Using Graphical Models to Examine Value-Added Models","authors":"D. Wright","doi":"10.1080/2330443X.2017.1294037","DOIUrl":"https://doi.org/10.1080/2330443X.2017.1294037","url":null,"abstract":"ABSTRACT Value-added models (VAMs) of student test scores are used within education because they are supposed to measure school and teacher effectiveness well. Much research has compared VAM estimates for different models, with different measures (e.g., observation ratings), and in experimental designs. VAMs are considered here from the perspective of graphical models and situations are identified that are problematic for VAMs. If the previous test scores are influenced by variables that also influence the true effectiveness of the school/teacher and there are variables that influence both the previous and current test scores, then the estimates of effectiveness can be poor. Those using VAMs should consider the models that may give rise to their data and evaluate their methods for these models before using the results for high-stakes decisions.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/2330443X.2017.1294037","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49178282","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 : 2017-01-01DOI: 10.1080/2330443X.2017.1358125
James E Ciecka, Gary R. Skoog
ABSTRACT We find and estimate probability mass functions for labor force related random variables. Complete life expectancy (by age, gender, and two years of labor force history) is decomposed into expected years of future labor force activity and inactivity as well as into expected years until final separation from the labor force and expected years in retirement. We also calculate expected age at retirement and expected years in retirement for people who actually retire. Two consecutive years of inactivity, especially in middle age, is a key indicator for both men and women when accounting for future labor force participation and retirement. For example, women (men) who are out of the labor force at age 49 and again out of the labor force at age 50, can expect to be in the labor force seven (eight) fewer years in the future than their counterparts who were in the labor force at ages 49 and 50. In addition, they have expected retirement ages 4.5–5.5 years younger than their active counterparts.
{"title":"Expected Labor Force Activity and Retirement Behavior by Age, Gender, and Labor Force History","authors":"James E Ciecka, Gary R. Skoog","doi":"10.1080/2330443X.2017.1358125","DOIUrl":"https://doi.org/10.1080/2330443X.2017.1358125","url":null,"abstract":"ABSTRACT We find and estimate probability mass functions for labor force related random variables. Complete life expectancy (by age, gender, and two years of labor force history) is decomposed into expected years of future labor force activity and inactivity as well as into expected years until final separation from the labor force and expected years in retirement. We also calculate expected age at retirement and expected years in retirement for people who actually retire. Two consecutive years of inactivity, especially in middle age, is a key indicator for both men and women when accounting for future labor force participation and retirement. For example, women (men) who are out of the labor force at age 49 and again out of the labor force at age 50, can expect to be in the labor force seven (eight) fewer years in the future than their counterparts who were in the labor force at ages 49 and 50. In addition, they have expected retirement ages 4.5–5.5 years younger than their active counterparts.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/2330443X.2017.1358125","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46777153","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 : 2016-12-12DOI: 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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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}
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