Elliott Ash
Biographic Data
| ID | 3713735 |
|---|---|
| NAME | Elliott Ash |
| GIVEN NAMES | Elliott |
| FAMILY NAME | Ash |
| SIGNATURE | ASH E |
| AFFILIATIONS | ETH Zurich |
| ORCID | 0000-0002-6817-7529 |
| VERIFIED | Yes |
| TOTAL WORKS | 19 |
| TOTAL CITATIONS | 95 |
| AUTHOR COUNT | 19 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2017 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 7 |
In-Group Bias in the Indian Judiciary
We study judicial in-group bias in Indian criminal courts using newly collected data on over 5 million criminal case records from 2010–2018. After classifying gender and religious identity with a neural network, we exploit quasi-random assignment of cases to judges to determine whether judges favor defendants with similar identities to themselves. In the aggregate, we estimate tight zero effects of in-group bias based on shared gender or religion…
More Laws, More Growth? Evidence from US States
Gender and reactions to speeches in German parliamentary debates
Are nonverbal reactions during parliamentary debate gendered? Do male and female members of parliament (MPs) experience applause or jeering differently? In short, yes, and the gendered nature of a speech matters. Using an original corpus of over 544,000 speeches given in German state parliaments, we first estimate the gendered nature of parliamentary speeches and then examine how reactions to speeches given by male and female MPs differ. Female a…
Conservative News Media and Criminal Justice
Local exposure to conservative news causes judges to impose harsher criminal sentences. Our evidence comes from an instrumental variable analysis, where randomness in television channel positioning across localities induces exogenous variation in exposure to the Fox News Channel. These treatment data on news viewership are taken to outcome data on almost seven million criminal sentencing decisions in the United States for the years 2005–17. Highe…
The Effect of Fox News on Health Behavior during Covid-19
In the early weeks of the 2020 coronavirus (COVID-19) pandemic, the Fox News Channel advanced a skeptical narrative that downplayed the risks posed by the virus. We find that this narrative had significant consequences: in localities with higher Fox News viewership—exogenous due to random variation in channel positioning—people were less likely to adopt behaviors geared toward social distancing (e.g., staying at home) and consumed fewer goods in …
Relatio
Social scientists have become increasingly interested in how narratives—the stories in fiction, politics, and life—shape beliefs, behavior, and government policies. This paper provides an unsupervised method to quantify latent narrative structures in text documents. Our new software package relatio identifies coherent entity groups and maps explicit relations between them in the text. We provide an application to the U.S. Congressional Record to …
What is (and was) a person? Evidence on historical mind perceptions from natural language
An important philosophical tradition identifies persons as those entities that have minds, such that mind perception is a window into person perception. Psychological research has found that human perceptions of mind consist of at least two distinct dimensions: agency (e.g. planning, deciding) and experience (e.g. feeling, hungering). Taking this insight into the semantic space of natural language, we develop a generalizable, scalable computation…
Cross-Domain Topic Classification for Political Texts
We introduce and assess the use of supervised learning in cross-domain topic classification. In this approach, an algorithm learns to classify topics in a labeled source corpus and then extrapolates topics in an unlabeled target corpus from another domain. The ability to use existing training data makes this method significantly more efficient than within-domain supervised learning. It also has three advantages over unsupervised topic models: the…
Measuring Judicial Sentiment
This paper provides a general method for analysing the sentiments expressed in the language of judicial rulings. We apply natural language processing tools to the text of US appellate court opinions to extrapolate judges’ sentiments (positive/good vs. negative/bad) towards a number of target social groups. We explore descriptively how these sentiments vary over time and across types of judges. In addition, we provide a method for using random ass…
Emotion and Reason in Political Language
This paper studies the use of emotion and reason in political discourse. Adopting computational-linguistics techniques to construct a validated text-based scale, we measure emotionality in six million speeches given in U.S. Congress over the years 1858–2014. Intuitively, emotionality spikes during times of war and is highest in speeches about patriotism. In the time series, emotionality was relatively low and stable in earlier years but increased…
Divided government, delegation, and civil service reform
This paper sheds new light on the drivers of civil service reform in US states. We first demonstrate theoretically that divided government is a key trigger of civil service reform, providing nuanced predictions for specific configurations of divided government. We then show empirical evidence for these predictions using data from the second half of the 20th century: states tended to introduce these reforms under divided government, and in particu…
Measuring Discretion and Delegation in Legislative Texts
Bureaucratic discretion and executive delegation are central topics in political economy and political science. The previous empirical literature has measured discretion and delegation by manually coding large bodies of legislation. Drawing from computational linguistics, we provide an automated procedure for measuring discretion and delegation in legal texts to facilitate large-scale empirical analysis. The method uses information in syntactic p…
Automated fact-value distinction in court opinions
This paper studies the problem of automated classification of fact statements and value statements in written judicial decisions. We compare a range of methods and demonstrate that the linguistic features of sentences and paragraphs can be used to successfully classify them along this dimension. The Wordscores method by Laver et al. (Am Polit Sci Rev 97(2):311–331, 2003) performs best in held out data. In an application, we show that the value se…
Text classification of ideological direction in judicial opinions
A research-based ranking of public policy schools
This paper presents rankings of U.S. public policy schools based on their research publication output. In 2016 we collected the names of about 5000 faculty members at 44 such schools. We use bibliographic databases to gather measures of the quality and quantity of these individuals’ academic publications. These measures include the number of articles and books written, the quality of the journals the articles have appeared in, and the number of c…
Fiscal Pressures and Discriminatory Policing
This paper provides evidence of racial variation in traffic enforcement responses to local government budget stress using data from policing agencies in the state of Missouri from 2001 through 2012. Like previous studies, we find that local budget stress is associated with higher citation rates; we also find an increase in traffic-stop arrest rates. However, we find that these effects are concentrated among White (rather than Black or Latino) dri…
Emerging Tools for a »Driverless« Legal System
»Vielmehr bietet [der Kommentar] auf höchstem Niveau eine substantielle Auseinandersetzung mit den Hintergründen, den Zusammenhängen, der Theorie und der Praxis des Grundgesetzes. Besseres lässt sich von einem Verfassungskommentar nicht sagen.“ Herbert Günther Staaatsanzeiger für das Land Hessen 2018 (50), 1494–1495
Sequential decision-making with group identity
Elections and Divisiveness
This article provides a theoretical and empirical analysis of how politicians allocate their time across issues. When voters are uncertain about an incumbent’s preferences, there is a pervasive incentive to “posture” by spending too much time on divisive issues (which are more informative about a politician’s preferences) at the expense of time spent on common-values issues (which provide greater benefit to voters). Higher transparency over the p…
Emotion and Reason in Political Language
This paper studies the use of emotion and reason in political discourse. Adopting computational-linguistics techniques to construct a validated text-based scale, we measure emotionality in six million speeches given in U.S. Congress over the years 1858–2014. Intuitively, emotionality spikes during times of war and is highest in speeches about patriotism. In the time series, emotionality was relatively low and stable in earlier years but increased…
Elections and Divisiveness
This article provides a theoretical and empirical analysis of how politicians allocate their time across issues. When voters are uncertain about an incumbent’s preferences, there is a pervasive incentive to “posture” by spending too much time on divisive issues (which are more informative about a politician’s preferences) at the expense of time spent on common-values issues (which provide greater benefit to voters). Higher transparency over the p…
Gender and reactions to speeches in German parliamentary debates
Are nonverbal reactions during parliamentary debate gendered? Do male and female members of parliament (MPs) experience applause or jeering differently? In short, yes, and the gendered nature of a speech matters. Using an original corpus of over 544,000 speeches given in German state parliaments, we first estimate the gendered nature of parliamentary speeches and then examine how reactions to speeches given by male and female MPs differ. Female a…
The Effect of Fox News on Health Behavior during Covid-19
In the early weeks of the 2020 coronavirus (COVID-19) pandemic, the Fox News Channel advanced a skeptical narrative that downplayed the risks posed by the virus. We find that this narrative had significant consequences: in localities with higher Fox News viewership—exogenous due to random variation in channel positioning—people were less likely to adopt behaviors geared toward social distancing (e.g., staying at home) and consumed fewer goods in …
Measuring Discretion and Delegation in Legislative Texts
Bureaucratic discretion and executive delegation are central topics in political economy and political science. The previous empirical literature has measured discretion and delegation by manually coding large bodies of legislation. Drawing from computational linguistics, we provide an automated procedure for measuring discretion and delegation in legal texts to facilitate large-scale empirical analysis. The method uses information in syntactic p…
Cross-Domain Topic Classification for Political Texts
We introduce and assess the use of supervised learning in cross-domain topic classification. In this approach, an algorithm learns to classify topics in a labeled source corpus and then extrapolates topics in an unlabeled target corpus from another domain. The ability to use existing training data makes this method significantly more efficient than within-domain supervised learning. It also has three advantages over unsupervised topic models: the…
Relatio
Social scientists have become increasingly interested in how narratives—the stories in fiction, politics, and life—shape beliefs, behavior, and government policies. This paper provides an unsupervised method to quantify latent narrative structures in text documents. Our new software package relatio identifies coherent entity groups and maps explicit relations between them in the text. We provide an application to the U.S. Congressional Record to …
More Laws, More Growth? Evidence from US States
Sequential decision-making with group identity
Measuring Judicial Sentiment
This paper provides a general method for analysing the sentiments expressed in the language of judicial rulings. We apply natural language processing tools to the text of US appellate court opinions to extrapolate judges’ sentiments (positive/good vs. negative/bad) towards a number of target social groups. We explore descriptively how these sentiments vary over time and across types of judges. In addition, we provide a method for using random ass…
Divided government, delegation, and civil service reform
This paper sheds new light on the drivers of civil service reform in US states. We first demonstrate theoretically that divided government is a key trigger of civil service reform, providing nuanced predictions for specific configurations of divided government. We then show empirical evidence for these predictions using data from the second half of the 20th century: states tended to introduce these reforms under divided government, and in particu…
Elections and Divisiveness
This article provides a theoretical and empirical analysis of how politicians allocate their time across issues. When voters are uncertain about an incumbent’s preferences, there is a pervasive incentive to “posture” by spending too much time on divisive issues (which are more informative about a politician’s preferences) at the expense of time spent on common-values issues (which provide greater benefit to voters). Higher transparency over the p…
Emerging Tools for a »Driverless« Legal System
»Vielmehr bietet [der Kommentar] auf höchstem Niveau eine substantielle Auseinandersetzung mit den Hintergründen, den Zusammenhängen, der Theorie und der Praxis des Grundgesetzes. Besseres lässt sich von einem Verfassungskommentar nicht sagen.“ Herbert Günther Staaatsanzeiger für das Land Hessen 2018 (50), 1494–1495
Sequential decision-making with group identity
Automated fact-value distinction in court opinions
This paper studies the problem of automated classification of fact statements and value statements in written judicial decisions. We compare a range of methods and demonstrate that the linguistic features of sentences and paragraphs can be used to successfully classify them along this dimension. The Wordscores method by Laver et al. (Am Polit Sci Rev 97(2):311–331, 2003) performs best in held out data. In an application, we show that the value se…
Text classification of ideological direction in judicial opinions
A research-based ranking of public policy schools
This paper presents rankings of U.S. public policy schools based on their research publication output. In 2016 we collected the names of about 5000 faculty members at 44 such schools. We use bibliographic databases to gather measures of the quality and quantity of these individuals’ academic publications. These measures include the number of articles and books written, the quality of the journals the articles have appeared in, and the number of c…
Fiscal Pressures and Discriminatory Policing
This paper provides evidence of racial variation in traffic enforcement responses to local government budget stress using data from policing agencies in the state of Missouri from 2001 through 2012. Like previous studies, we find that local budget stress is associated with higher citation rates; we also find an increase in traffic-stop arrest rates. However, we find that these effects are concentrated among White (rather than Black or Latino) dri…
Measuring Discretion and Delegation in Legislative Texts
Bureaucratic discretion and executive delegation are central topics in political economy and political science. The previous empirical literature has measured discretion and delegation by manually coding large bodies of legislation. Drawing from computational linguistics, we provide an automated procedure for measuring discretion and delegation in legal texts to facilitate large-scale empirical analysis. The method uses information in syntactic p…
Measuring Judicial Sentiment
This paper provides a general method for analysing the sentiments expressed in the language of judicial rulings. We apply natural language processing tools to the text of US appellate court opinions to extrapolate judges’ sentiments (positive/good vs. negative/bad) towards a number of target social groups. We explore descriptively how these sentiments vary over time and across types of judges. In addition, we provide a method for using random ass…
Emotion and Reason in Political Language
This paper studies the use of emotion and reason in political discourse. Adopting computational-linguistics techniques to construct a validated text-based scale, we measure emotionality in six million speeches given in U.S. Congress over the years 1858–2014. Intuitively, emotionality spikes during times of war and is highest in speeches about patriotism. In the time series, emotionality was relatively low and stable in earlier years but increased…
Divided government, delegation, and civil service reform
This paper sheds new light on the drivers of civil service reform in US states. We first demonstrate theoretically that divided government is a key trigger of civil service reform, providing nuanced predictions for specific configurations of divided government. We then show empirical evidence for these predictions using data from the second half of the 20th century: states tended to introduce these reforms under divided government, and in particu…
What is (and was) a person? Evidence on historical mind perceptions from natural language
An important philosophical tradition identifies persons as those entities that have minds, such that mind perception is a window into person perception. Psychological research has found that human perceptions of mind consist of at least two distinct dimensions: agency (e.g. planning, deciding) and experience (e.g. feeling, hungering). Taking this insight into the semantic space of natural language, we develop a generalizable, scalable computation…
Cross-Domain Topic Classification for Political Texts
We introduce and assess the use of supervised learning in cross-domain topic classification. In this approach, an algorithm learns to classify topics in a labeled source corpus and then extrapolates topics in an unlabeled target corpus from another domain. The ability to use existing training data makes this method significantly more efficient than within-domain supervised learning. It also has three advantages over unsupervised topic models: the…
Conservative News Media and Criminal Justice
Local exposure to conservative news causes judges to impose harsher criminal sentences. Our evidence comes from an instrumental variable analysis, where randomness in television channel positioning across localities induces exogenous variation in exposure to the Fox News Channel. These treatment data on news viewership are taken to outcome data on almost seven million criminal sentencing decisions in the United States for the years 2005–17. Highe…
The Effect of Fox News on Health Behavior during Covid-19
In the early weeks of the 2020 coronavirus (COVID-19) pandemic, the Fox News Channel advanced a skeptical narrative that downplayed the risks posed by the virus. We find that this narrative had significant consequences: in localities with higher Fox News viewership—exogenous due to random variation in channel positioning—people were less likely to adopt behaviors geared toward social distancing (e.g., staying at home) and consumed fewer goods in …
Relatio
Social scientists have become increasingly interested in how narratives—the stories in fiction, politics, and life—shape beliefs, behavior, and government policies. This paper provides an unsupervised method to quantify latent narrative structures in text documents. Our new software package relatio identifies coherent entity groups and maps explicit relations between them in the text. We provide an application to the U.S. Congressional Record to …
In-Group Bias in the Indian Judiciary
We study judicial in-group bias in Indian criminal courts using newly collected data on over 5 million criminal case records from 2010–2018. After classifying gender and religious identity with a neural network, we exploit quasi-random assignment of cases to judges to determine whether judges favor defendants with similar identities to themselves. In the aggregate, we estimate tight zero effects of in-group bias based on shared gender or religion…
More Laws, More Growth? Evidence from US States
Gender and reactions to speeches in German parliamentary debates
Are nonverbal reactions during parliamentary debate gendered? Do male and female members of parliament (MPs) experience applause or jeering differently? In short, yes, and the gendered nature of a speech matters. Using an original corpus of over 544,000 speeches given in German state parliaments, we first estimate the gendered nature of parliamentary speeches and then examine how reactions to speeches given by male and female MPs differ. Female a…
Political science (16 works) · Law (12 works) · Computer Science (9 works) · Economics (8 works) · Electoral Systems and Political Participation (6 works) · Law (6 works) · Media Influence and Politics (6 works) · Psychology (6 works) · Sociology (6 works) · Mathematics (5 works)