1,721,075 research outputs found
MNP: R Package for Fitting the Multinomial Probit Model
Full text linkMNP is a publicly available R package that fits the Bayesian multinomial probit model via Markov chain Monte Carlo. The multinomial probit model is often used to analyze the discrete choices made by individuals recorded in survey data. Examples where the multinomial probit model may be useful include the analysis of product choice by consumers in market research and the analysis of candidate or party choice by voters in electoral studies. The MNP software can also fit the model with different choice sets for each individual, and complete or partial individual choice orderings of the available alternatives from the choice set. The estimation is based on the efficient marginal data augmentation algorithm that is developed by Imai and van Dyk (2005).
Supplemental Material, Online_Appendix_729711 - Sensitive Survey Questions with Auxiliary Information
No full text Supplemental Material, Online_Appendix_729711 for Sensitive Survey Questions with
Auxiliary Information by Winston Chou, Kosuke Imai and Bryn Rosenfeld in Sociological
Methods & Research </p
Name Dictionaries for "wru" R Package
No full textWe provide four dictionaries that provide the racial distributions associated with names in the United States. These dictionaries are used by the latest iteration of the "WRU" package (Khanna et al., 2022) to make probabilistic predictions about the race of individuals, given their names and geolocations. The probabilities cover five racial categories: White, Black, Hispanic, Asian, and Other.
We provide two surname dictionaries. The first provides entries P(race | surname) for about 160K names, derived from the 2010 Census surname list, aggregated with the Census Spanish surname list. The second provides analogous probabilities for 1.48MM surnames. This dictionary is created by starting with the Census-based dictionary and supplementing it with race distributions estimated from the voter files of six Southern states -- Alabama, Florida, Georgia, Louisiana, North Carolina, and South Carolina -- that collect race data.
We also provide dictionaries estimating P(race | first name) and P(race | middle name). These dictionaries -- which contain 1.04MM and 1.16MM names respectively -- are sourced exclusively from the voter files of the six Southern states.
References
Kabir Khanna, Brandon Bertelsen, Santiago Olivella, Evan Rosenman and Kosuke Imai (2022). wru: Who are You? Bayesian Prediction of Racial
Category Using Surname, First Name, Middle Name, and Geolocation. R package version 1.0.0. https://CRAN.R-project.org/package=wr
Explaining Support for Combatants during Wartime: A Survey Experiment in Afghanistan (SWP 17)
Full text linkJason Lyall homepage: http://politicalscience.yale.edu/people/jason-lyall Kosuke Imai homepage: http://imai.princeton.edu Graeme Blair homepage: http://graemeblair.co
Replication data for: The Essential Role of Pair-Matching in Cluster-Randomized Experiments, with Application to the Mexican Universal Health Insurance Evaluation: Rejoinder
No full textN/
The Origins of Terrorism: Cross-Country Estimates on Socio-economic Determinants of Terrorism
Full text linkAs a prerequisite of an appropriate anti-terror strategy, it is indispensable to assess the underlying causes of terror. We examine social and economic conditions in the country of origin of terrorist attacks, claiming that low opportunity costs of terror, e.g., approximated by slow growth and poor institutions raise the likelihood of terror and the willingness in the population to support terror. Using a negative binomial regression model, we are able to show that unfortunate socio-economic conditions in a country are likely to reduce the opportunity costs of potential terrorists and increase the number of terrorist attacks originating from a specific country. Interestingly, this effect is particularly relevant after a certain level of development has been reached. We therefore distinguish between several broad country groups, namely the OECD, Europe and Islamic countries.terror attacks, openness, discrete choice analysis, institutions
Estimating Sensitive Behavior: The ICT and High-Incidence Electoral Behavior
Full text linkFunding This work is supported by the Austrian National Election Study, a National Research Network sponsored by the Austrian Science Fund (S10902-G11). Acknowledgments The authors thank the anonymous reviewers for their constructive feedback and Graeme Blair and Kosuke Imai for their helpful support.Peer reviewe
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
The Oregon Health Insurance Experiment: Evidence from the First Year
Full text linkIn 2008, a group of uninsured low-income adults in Oregon was selected by lottery to be given the chance to apply for Medicaid. This lottery provides a unique opportunity to gauge the effects of expanding access to public health insurance on the health care use, financial strain, and health of low-income adults using a randomized controlled design. In the year after random assignment, the treatment group selected by the lottery was about 25 percentage points more likely to have insurance than the control group that was not selected. We find that in this first year, the treatment group had substantively and statistically significantly higher health care utilization (including primary and preventive care as well as hospitalizations), lower out-of-pocket medical expenditures and medical debt (including fewer bills sent to collection), and better self-reported physical and mental health than the control group.
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