gov3009-l September 2018

gov3009-l@lists.fas.harvard.edu

1 participants
8 discussions

Applied Statistics Workshop Next Week (Wed. 10/3) - Naoki Egami

by Connor Jerzak

Dear workshop community, We will convene for the Applied Statistics Workshop (Gov 3009) next week on Wednesday (10/3). The speaker is* Naoki Egami* (Princeton), who will be presenting his paper "Causal Diffusion Analysis with Stationarity: How Hate Crimes Diffuse across Space" (paper link here <https://scholar.princeton.edu/negami/publications/identification-causal-dif…>). *Where:* CGIS Knafel Building, Room K354 (see this link <https://map.harvard.edu/?bld=04471&level=9> for directions). *When: *Wednesday, October 3rd at 12 noon - 1:30 pm. *Abstract:* Although social scientists have long been interested in the process through which ideas and behavior diffuse, the identification of causal diffusion effects, also known as peer effects, remains challenging. Many scholars consider the commonly used assumption of no omitted confounders to be untenable due to contextual confounding and homophily bias. To address this long-standing identification problem, I introduce a class of *stationary* causal directed acyclic graphs (DAGs), which represent the time-invariant nonparametric causal structure. I first show that this stationary causal DAG implies a new statistical test that can detect a wide range of biases, including the two types mentioned above. The proposed test allows researchers to empirically assess the contentious assumption of no omitted confounders. In addition, I develop a difference-in-difference style estimator that can directly correct biases under an additional parametric assumption. Leveraging the proposed methods, I study the spatial diffusion of hate crimes in Germany. After correcting large upward bias in existing studies, I find hate crimes diffuse only to areas that have a high proportion of school dropouts. To highlight the general applicability of the proposed approach, I also analyze the network diffusion of human rights norms. The proposed methodology is implemented in a forthcoming open source software package. *All are welcome! Lunch is provided! * Best, Connor Jerzak Applied Statistics Workshop -- Graduate Student Coordinator An anonymous feedback form for the workshop can be found here at this link <https://docs.google.com/forms/d/e/1FAIpQLScp4lPVBtp4Akf6K6ggmfcTUSIUHEJX89-…>. Workshop listserv sign-up at this link <https://lists.fas.harvard.edu/mailman/listinfo/gov3009-l>.

5 years, 7 months

Applied Statistics Workshop TOMORROW (Wed. 9/26) - Shihao Yang

by Connor Jerzak

*FINAL REMINDER --- Applied Statistics Workshop TOMORROW (9/26) at 12 noon* *Lunch provided --- All are welcome * ------------------------------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------------------------------ Dear workshop community, We will convene for the Applied Statistics Workshop (Gov 3009) tomorrow on Wednesday (9/26). The speaker is* Shihao Yang* (Harvard Statistics), who will be presenting his paper "Big data, Google, and infectious disease prediction: A statistical perspective". *Where:* CGIS Knafel Building, Room K354 (see this link <https://map.harvard.edu/?bld=04471&level=9> for directions). *When: *Wednesday, September 26th at 12 noon - 1:30 pm. *Abstract:* Big data generated from the internet have great potential in tracking and predicting massive social activities, in particular infectious diseases, whose accurate real-time prediction could help public health officials make timely decisions to save lives. We introduce a model ARGO (AutoRegression with GOogle search data / AutoRegression with General Online data) that has successfully utilized publicly available Google search data, with/without cloud-based electronic health records, to estimate current and near-future influenza-like illness activity level and/or dengue fever activity level for United States and five other countries around the globe. Our regularized multivariate regression model dynamically selects the most appropriate variables for prediction every week, and significantly outperforms all previous internet-based tracking models, including Google Flu Trends and Google Dengue Trends. We further extend the model to multiple geographical resolution, tracking infectious disease not only at national level but also at regional level, with spatial-temporal information pooling, making it flexible, self-correcting, robust and scalable. *All are welcome! Lunch is provided! * Best, Connor Jerzak Applied Statistics Workshop -- Graduate Student Coordinator An anonymous feedback form for the workshop can be found here at this link <https://docs.google.com/forms/d/e/1FAIpQLScp4lPVBtp4Akf6K6ggmfcTUSIUHEJX89-…>.

5 years, 7 months

Applied Statistics Workshop Next Week (9/26) - Shihao Yang

by Connor Jerzak

Dear workshop community, We will convene for the Applied Statistics Workshop (Gov 3009) next week on Wednesday (9/26). The speaker is* Shihao Yang* (Harvard Statistics) who will be presenting his paper "Big data, Google, and infectious disease prediction: a statistical perspective". *Where:* CGIS Knafel Building, Room K354 (see this link <https://map.harvard.edu/?bld=04471&level=9> for directions). *When: *Wednesday, September 26th at 12 noon - 1:30 pm. *Abstract:* Big data generated from the internet have great potential in tracking and predicting massive social activities, in particular infectious diseases, whose accurate real-time prediction could help public health officials make timely decisions to save lives. We introduce a model ARGO (AutoRegression with GOogle search data / AutoRegression with General Online data) that has successfully utilized publicly available Google search data, with/without cloud-based electronic health records, to estimate current and near-future influenza-like illness activity level and/or dengue fever activity level for United States and five other countries around the globe. Our regularized multivariate regression model dynamically selects the most appropriate variables for prediction every week, and significantly outperforms all previous internet-based tracking models, including Google Flu Trends and Google Dengue Trends. We further extend the model to multiple geographical resolution, tracking infectious disease not only at national level but also at regional level, with spatial-temporal information pooling, making it flexible, self-correcting, robust and scalable. *All are welcome! Lunch is provided! * Best, Connor Jerzak Applied Statistics Workshop -- Graduate Student Coordinator An anonymous feedback form for the workshop can be found here at this link <https://docs.google.com/forms/d/e/1FAIpQLScp4lPVBtp4Akf6K6ggmfcTUSIUHEJX89-…>.

5 years, 7 months

Applied Statistics Workshop TOMORROW (9/19) - Aaron Kaufman

by Connor Jerzak

*FINAL REMINDER --- Applied Statistics Workshop TOMORROW (9/19) at 12 noon* *Lunch provided --- All are welcome * ------------------------------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------------------------------ Dear workshop community, We will convene for the Applied Statistics Workshop (Gov 3009) next week on Wednesday (9/19). The speaker is* Aaron Kaufman * (Harvard PhD candidate; job market candidate) who will be presenting his paper "An Automated Method to Estimate Survey Question Bias" (paper link here <https://projects.iq.harvard.edu/files/applied.stats.workshop-gov3009/files/…>). *Where:* CGIS Knafel Building, Room K354 (see this link <https://map.harvard.edu/?bld=04471&level=9> for directions). *When: *Wednesday, September 19th at 12 noon - 1:30 pm. *Abstract: *Many survey researchers are interested in gauging public support for government policy, but there is strong evidence that a question’s wording affects responses to it. I develop the first automated and scalable method to predict the magnitude and direction of the partisan bias a question’s wording may impose on survey responses, and show using a series of survey experiments that it outperforms public opinion scholars in predicting that bias. Using a novel data set of almost one million survey questions from 1997 to 2017, I then examine trends in partisan survey question biases over time. I find that while questions related to economic issues are relatively unbiased, questions related to Barack Obama become steadily more conservatively biased from 2008 to 2017. Questions related to abortion and immigration are generally conservative, while questions related to healthcare and education are consistently liberal. Substantively, my results suggest that measurements of American public opinion are systematically biased; I discuss the implications of this result for democratic representation. Methodologically, this paper opens up new opportunities for studying ideology from text, and for improving survey methodology and measurement in public opinion. *All are welcome! Lunch is provided! * Best, Connor Jerzak Applied Statistics Workshop -- Graduate Student Coordinator An anonymous feedback form for the workshop can be found here at this link <https://docs.google.com/forms/d/e/1FAIpQLScp4lPVBtp4Akf6K6ggmfcTUSIUHEJX89-…>.

5 years, 7 months

Applied Statistics Workshop Next Week (9/19) - Aaron Kaufman

by Connor Jerzak

Dear workshop community, We will convene for the Applied Statistics Workshop (Gov 3009) next week on Wednesday (9/19). The speaker is* Aaron Kaufman * (Harvard PhD candidate; job market candidate) who will be presenting his paper "An Automated Method to Estimate Survey Question Bias" (paper link here <https://projects.iq.harvard.edu/files/applied.stats.workshop-gov3009/files/…>). *Where:* CGIS Knafel Building, Room K354 (see this link <https://map.harvard.edu/?bld=04471&level=9> for directions). *When: *Wednesday, September 19th at 12 noon - 1:30 pm. *Abstract: *Many survey researchers are interested in gauging public support for government policy, but there is strong evidence that a question’s wording affects responses to it. I develop the first automated and scalable method to predict the magnitude and direction of the partisan bias a question’s wording may impose on survey responses, and show using a series of survey experiments that it outperforms public opinion scholars in predicting that bias. Using a novel data set of almost one million survey questions from 1997 to 2017, I then examine trends in partisan survey question biases over time. I find that while questions related to economic issues are relatively unbiased, questions related to Barack Obama become steadily more conservatively biased from 2008 to 2017. Questions related to abortion and immigration are generally conservative, while questions related to healthcare and education are consistently liberal. Substantively, my results suggest that measurements of American public opinion are systematically biased; I discuss the implications of this result for democratic representation. Methodologically, this paper opens up new opportunities for studying ideology from text, and for improving survey methodology and measurement in public opinion. *All are welcome! Lunch is provided! * Best, Connor Jerzak Applied Statistics Workshop -- Graduate Student Coordinator An anonymous feedback form for the workshop can be found here at this link <https://docs.google.com/forms/d/e/1FAIpQLScp4lPVBtp4Akf6K6ggmfcTUSIUHEJX89-…>.

5 years, 7 months

Applied Statistics Workshop TOMORROW (9/12) - Junming Huang

by Connor Jerzak

*FINAL REMINDER --- Applied Statistics Workshop TOMORROW (9/12) at 12 noon* *Lunch provided --- All are welcome * ------------------------------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------------------------------ Dear workshop community, We will convene for the Applied Statistics Workshop (Gov 3009) tomorrow, Wednesday (9/12). The speaker is* Junming Huang * (Princeton postdoc) who will be presenting his paper "Quantifying Gender Inequality in Scientific Careers" (no paper link). *Where:* CGIS Knafel Building, Room K354 (see this link <https://map.harvard.edu/?bld=04471&level=9> for directions). *When: *Wednesday, September 12th at 12 noon - 1:30 pm. *Abstract: *Gender inequality in academic careers, documented across all disciplines and countries, extends beyond the fraction of women involved in research: compared to their male colleagues, women publish less over the course of their careers and their work acquires fewer citations. Yet, all existing evidence is limited to selected countries or disciplines, restricting our ability assess the roots and implications of the gender disparity. Here we analyzed a large corpus of scientific publications since 1900, identifying the gender and reconstructing the full publishing career of over 1.5 million authors from most scientific disciplines and countries, allowing us to quantify the processes and outcomes for women and men in science. We confirm that men have higher total productivity and total impact than women, a pattern impacting all disciplines and most countries. Surprisingly, we find no systematic difference between the annual productivity of male and female scientists, not only offering a gender-invariant productivity measure, but also suggesting that the observed gender gaps are rooted in gender dependent dropout rates. We find that not only do women leave academia at a higher rate than men, but surprisingly, this gap in dropout rate is greater for the more productive women. We show that when we control for these two gender-specific dropout rates, the career gender gaps in both productivity and impact vanish. Identifying the driving forces of gender gaps can help rephrase the conversation about gender inequality around the sustainability of women’s careers in academia, with important consequences for policy makers and academic institutions. *All are welcome! Lunch is provided! * Best, Connor Jerzak Applied Statistics Workshop -- Graduate Student Coordinator An anonymous feedback form for the workshop can be found here at this link <https://docs.google.com/forms/d/e/1FAIpQLScp4lPVBtp4Akf6K6ggmfcTUSIUHEJX89-…>.

5 years, 7 months

Applied Statistics Workshop Next Week (9/12) - Junming Huang

by Connor Jerzak

Dear workshop community, We will convene for the Applied Statistics Workshop (Gov 3009) next week on Wednesday (9/12). The speaker is* Junming Huang * (Princeton postdoc) who will be presenting his paper "Quantifying Gender Inequality in Scientific Careers" (no paper link). *Where:* CGIS Knafel Building, Room K354 (see this link <https://map.harvard.edu/?bld=04471&level=9> for directions). *When: *Wednesday, September 12th at 12 noon - 1:30 pm. *Abstract: *Gender inequality in academic careers, documented across all disciplines and countries, extends beyond the fraction of women involved in research: compared to their male colleagues, women publish less over the course of their careers and their work acquires fewer citations. Yet, all existing evidence is limited to selected countries or disciplines, restricting our ability assess the roots and implications of the gender disparity. Here we analyzed a large corpus of scientific publications since 1900, identifying the gender and reconstructing the full publishing career of over 1.5 million authors from most scientific disciplines and countries, allowing us to quantify the processes and outcomes for women and men in science. We confirm that men have higher total productivity and total impact than women, a pattern impacting all disciplines and most countries. Surprisingly, we find no systematic difference between the annual productivity of male and female scientists, not only offering a gender-invariant productivity measure, but also suggesting that the observed gender gaps are rooted in gender dependent dropout rates. We find that not only do women leave academia at a higher rate than men, but surprisingly, this gap in dropout rate is greater for the more productive women. We show that when we control for these two gender-specific dropout rates, the career gender gaps in both productivity and impact vanish. Identifying the driving forces of gender gaps can help rephrase the conversation about gender inequality around the sustainability of women’s careers in academia, with important consequences for policy makers and academic institutions. *All are welcome! Lunch is provided! * Best, Connor Jerzak Applied Statistics Workshop -- Graduate Student Coordinator An anonymous feedback form for the workshop can be found here at this link <https://docs.google.com/forms/d/e/1FAIpQLScp4lPVBtp4Akf6K6ggmfcTUSIUHEJX89-…>.

5 years, 7 months

Applied Statistics Workshop This Week (9/5) - Alberto Abadie

by Jerzak, Connor

Dear workshop community, We will convene the first Applied Statistics Workshop (Gov 3009) this week on Wednesday (9/5). The speaker is* Alberto Abadie* (MIT Economics faculty) who will be presenting his paper "Statistical Non-Significance in Empirical Economics" (paper link here <https://economics.mit.edu/files/14851>). *Where:* CGIS Knafel Building, Room K354 (see this link <https://map.harvard.edu/?bld=04471&level=9> for directions). *When: *Wednesday, September 5th at 12 noon - 1:30 pm. *Abstract: *Statistical significance is often interpreted as providing greater information than non-significance. In this article we show, however, that rejection of a point null often carries very little information, while failure to reject may be highly informative. This is particularly true in empirical contexts that are typical and even prevalent in economics, where data sets are large and there are rarely reasons to put substantial prior probability on a point null. Our results challenge the usual practice of conferring point null rejections a higher level of scientific significance than non-rejections. Therefore, we advocate a visible reporting and discussion of non-significant results. *All are welcome! Lunch is provided! * Best, Connor Jerzak Applied Statistics Workshop -- Graduate Student Coordinator

5 years, 7 months

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gov3009-l September 2018