2026-05-04T05:11:50Zhttps://dataverse.harvard.edu/oaidoi:10.7910/DVN/0ILLXT2025-04-14T20:44:43ZUVA_Authored_Datasetssocial_science_and_humanitiesReplication Data for: Appellate Court Influence over District Courts in the United Stateshttps://doi.org/10.7910/DVN/0ILLXTOlson, Michael P.Rivero, Albert H.Harvard DataverseThe vast majority of U.S. federal litigation occurs in U.S. District Courts, which are the first -- and for most, the last -- courts in which a case is heard. While these lower courts' judges are insulated from outside influence by their life tenure, they may have incentives to heed the preferences of those above them in the judicial hierarchy. Using data on politicized district court decisions and the ideological preferences of circuit court judges in a two-way fixed effects design, we show that district court judges are responsive to changes in the ideological composition of the circuit court above them. We show that lower court responsiveness is increasing in the rate of appellate review and reversal that these courts face. We find no evidence, however, that this responsiveness is motivated by workload reduction or progressive ambition.Social SciencesJudicial politicsDistrict courtsFederal courtsJudicial hierarchy2022-02-11Rivero, AlbertDatasetdoi:10.7910/DVN/0NDRHU2025-04-14T20:44:42ZUVA_Authored_Datasetssocial_science_and_humanitiesReplication Data for: The Geography of Citizenship Practice: How the Poor Engage the State in Rural and Urban Indiahttps://doi.org/10.7910/DVN/0NDRHUKruks-Wisner, GabrielleAuerbach, AdamHarvard DataverseReplication data and code for "The Geography of Citizenship Practice: How the Poor Engage the State in Rural and Urban India."Social Sciences2020-03-13Kruks-Wisner, GabrielleDatasetdoi:10.7910/DVN/0YEW8D2026-02-19T23:21:03ZUVA_Authored_DatasetsReplication Data for: Online Propagation of Emotions: A Study of Resharing Dynamics on Social Media Following Celebrity Suicideshttps://doi.org/10.7910/DVN/0YEW8DNouri, EhsanSaraf, NileshGoh, Jie MeinSrabana DasguptaDianne CyrHarvard DataverseThis dataset accompanies the study “Online Propagation of Emotions: A Study of Resharing Dynamics on Social Media Following Celebrity Suicides” published in PLOS ONE. It contains data from 91,868 resharing cascades on X (formerly Twitter) that occurred in response to four celebrity suicide events. The dataset includes variables capturing propagation dynamics (cascade size, lifetime, median inter-retweet delay, and time to the fifth retweet), content attributes (word count, presence of hashtags), original author characteristics, and probabilistic emotion scores (anger, sadness, fear, surprise, disgust, joy, and neutral), derived using a fine-tuned DistilRoBERTa language model.Computer and Information ScienceMedicine, Health and Life SciencesSocial Sciences2025-10-29Nouri, EhsanDatasetdoi:10.7910/DVN/1O8LCD2025-04-14T20:44:42ZUVA_Authored_DatasetsReplication Data for: Slow-Rolling, Fast-Tracking, and the Pace of Bureaucratic Decisions in Rulemakinghttps://doi.org/10.7910/DVN/1O8LCDPotter, Rachel AugustineHarvard DataverseData and code to replicate all results in the published paper.Social Sciencesseparation of powers, bureaucracy, regulation2016-09-27Potter, Rachel AugustineDatasetdoi:10.7910/DVN/1PEUPZ2025-04-14T20:44:42ZUVA_Authored_DatasetsNosek, Banaji, & Greenwald (2002): Math = Male, Me = Female, therefore Math ^= Mehttps://doi.org/10.7910/DVN/1PEUPZBrian NosekHarvard DataverseWe examined the role of group membership (being female or male), implicit identity with social groups (me=male/female), and math-gender stereotypes (math=male) in predicting implicit math attitudes (math=good) and math identity (math=me). In addition, we investigated the relationship between implicit and explicit preferences and SAT performance. College students demonstrated negativity toward math and science relative to arts and language on implicit measures. Women showed greater implicit negativity toward math than men did, even in a sample that had selected a math-intensive college major. In addition, associations of math with male and the self with gender related to implicit math attitudes, but those relationships were directly opposing for men and women. Stronger math=male associations corresponded with more negative math attitudes for women, but more positive math attitudes for men. Finally, both implicit and explicit math attitudes were predictive of SAT performance. These results point to the opportunities and constraints on personal preferences that derive from membership in social groups.implicit social cognition2009-01-21Datasetdoi:10.7910/DVN/1RRYTT2025-04-14T20:44:43ZUVA_Authored_DatasetsReplication data for: The Cross-Sectional Distribution of Price Stickiness Implied by Aggregate Datahttps://doi.org/10.7910/DVN/1RRYTTLee, Jae WonCarvalho, CarlosDam, Niels ArneHarvard DataverseCarvalho, Carlos, Dam, Niels Arne, and Lee, Jae Won, (2020) "The Cross-Sectional Distribution of Price Stickiness Implied by Aggregate Data." Review of Economics and Statistics 102:1, 162-179.Social Sciences2018-12-13Lee, Jae WonDatasetdoi:10.7910/DVN/1YG7BI2025-04-14T20:44:42ZUVA_Authored_DatasetsReplication Data for: Simulating Duration Data for the Cox Modelhttps://doi.org/10.7910/DVN/1YG7BIHarden, Jeffrey J.Kropko, JonathanHarvard DataverseThe Cox proportional hazards model is a popular method for duration analysis that is frequently the subject of simulation studies. However, no standard method exists for simulating durations directly from its data generating process because it does not assume a distributional form for the baseline hazard function. Instead, simulation studies typically rely on parametric survival distributions, which contradicts the primary motivation for employing the Cox model. We propose a method that generates a baseline hazard function at random by fitting a cubic spline to randomly-drawn points. Durations drawn from this function match the Cox model's inherent flexibility and improve the simulation's generalizability. The method can be extended to include time-varying covariates and non-proportional hazards.Social Sciences2018-04-03Harden, Jeffrey J.Datasetdoi:10.7910/DVN/246722025-04-14T20:44:43ZUVA_Authored_DatasetsReplication data for: Multiple Imputation for Continuous and Categorical Data: Comparing Joint Multivariate Normal and Conditional Approacheshttps://doi.org/10.7910/DVN/24672Kropko, JonathanGoodrich, BenGelman, AndrewHill, JenniferHarvard DataverseWe consider the relative performance of two common approaches to multiple imputation (MI): joint multivariate normal (MVN) MI, in which the data are modeled as a sample from a joint MVN distribution; and conditional MI, in which each variable is modeled conditionally on all the others. In order to use the multivariate normal distribution, implementations of joint MVN MI typically assume that categories of discrete variables are probabilistically constructed from continuous values. We use simulations to examine the implications of these assumptions. For each approach, we assess (1) the accuracy of the imputed values, and (2) the accuracy of coefficients and fitted values from a model fit to completed datasets. These simulations consider continuous, binary, ordinal, and unordered-categorical variables. One set of simulations uses multivariate normal data and one set uses data from the 2008 American National Election Study. We implement a less restricti
ve approach than is typical when evaluating methods using simulations in the missing data literature: in each case, missing values are generated by carefully following the conditions necessary for missingness to be ``missing at random'' (MAR). We find that in these situations conditional MI is more accurate than joint MVN MI whenever the data include categorical variables.multiple imputation2014-02-07Datasetdoi:10.7910/DVN/24CXA72025-04-14T20:44:42ZUVA_Authored_DatasetsReplication Data for: The Democracy of Dating: How Political Affiliations Shape Relationship Formationhttps://doi.org/10.7910/DVN/24CXA7Holbein, JohnHarvard DataverseReplication Data for: The Democracy of Dating: How Political Affiliations Shape Relationship FormationSocial Sciences2020-06-07Holbein, JohnDatasetdoi:10.7910/DVN/274662025-04-14T20:44:42ZUVA_Authored_DatasetsReplication data for: Signaling Alliance Commitments: Hand-Tying and Sunk Costs in Extended Nuclear Deterrencehttps://doi.org/10.7910/DVN/27466Fuhrmann, MatthewSechser, ToddHarvard DataverseHow can states signal their alliance commitments? Although scholars have developed sophisticated theoretical models of costly signaling in international relations, we know little about which specific policies leaders can implement to signal their commitments. This article addresses this question with respect to the extended deterrent effects of nuclear weapons. Can nuclear states deter attacks against their friends by simply announcing their defense commitments? Or must they deploy nuclear weapons on a protege's territory before an alliance is seen as credible? Using a new dataset on foreign nuclear deployments from 1950 to 2000, our analysis reveals two main findings. First, formal alliances with nuclear states appear to carry significant deterrence benefits. Second, however, stationing nuclear weapons on a protege's territory does not bolster these effects. The analysis yields new insights about the dynamics of hand-tying and sunk cost signals in international politics.Social SciencesSignals and signalingAlliance commitments2015-01-27Matthew FuhrmannDatasetb2Zmc2V0OjoxMHxzZXQ6OlVWQV9BdXRob3JlZF9EYXRhc2V0c3xwcmVmaXg6Om9haV9kYw==