2026-05-04T05:13:23Zhttps://dataverse.harvard.edu/oai

doi:10.7910/DVN/0ILLXT2025-04-14T20:44:43ZUVA_Authored_Datasetssocial_science_and_humanities

Replication Data for: Appellate Court Influence over District Courts in the United Statesdoi:10.7910/DVN/0ILLXTHarvard Dataverse2022-02-112Olson, Michael P.; Rivero, Albert H., 2022, "Replication Data for: Appellate Court Influence over District Courts in the United States", https://doi.org/10.7910/DVN/0ILLXT, Harvard Dataverse, V2, UNF:6:1L/gxexNZaCyBT2F5lPoAg== [fileUNF]Replication Data for: Appellate Court Influence over District Courts in the United Statesdoi:10.7910/DVN/0ILLXTOlson, Michael P.Rivero, Albert H.Harvard DataverseRivero, AlbertRivero, Albert2022-02-11Social SciencesJudicial politicsDistrict courtsFederal courtsJudicial hierarchyThe 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.<a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0</a>Olson, Michael P., and Albert H. Rivero. "Appellate Court Influence over District Courts in the United States." Journal of Political Institutions and Political Economy.Olson, Michael P., and Albert H. Rivero. "Appellate Court Influence over District Courts in the United States." Journal of Political Institutions and Political Economy.analysis.Rtype/x-r-syntaxcircuit_wild_ci.xlsxapplication/vnd.openxmlformats-officedocument.spreadsheetml.sheetdistrict.tabtext/tab-separated-valuesdistrict_wild_ci.xlsxapplication/vnd.openxmlformats-officedocument.spreadsheetml.sheetget_panel_means.Rtype/x-r-syntaxjcs_circuit_month.tabtext/tab-separated-valuespanel_means.rdsapplication/octet-streamREADME.txttext/plainstate_wild_ci.xlsxapplication/vnd.openxmlformats-officedocument.spreadsheetml.sheetwild_bootstrap_analyses.doapplication/x-stata-syntax

doi:10.7910/DVN/0NDRHU2025-04-14T20:44:42ZUVA_Authored_Datasetssocial_science_and_humanities

Replication Data for: The Geography of Citizenship Practice: How the Poor Engage the State in Rural and Urban Indiadoi:10.7910/DVN/0NDRHUHarvard Dataverse2020-03-131Kruks-Wisner, Gabrielle; Auerbach, Adam, 2020, "Replication Data for: The Geography of Citizenship Practice: How the Poor Engage the State in Rural and Urban India", https://doi.org/10.7910/DVN/0NDRHU, Harvard Dataverse, V1, UNF:6:HiHNQ93VDS097h0GHZQAfg== [fileUNF]Replication Data for: The Geography of Citizenship Practice: How the Poor Engage the State in Rural and Urban Indiadoi:10.7910/DVN/0NDRHUKruks-Wisner, GabrielleAuerbach, AdamHarvard DataverseKruks-Wisner, GabrielleAuerbach, AdamKruks-Wisner, Gabrielle2020-01-02Social SciencesReplication data and code for "The Geography of Citizenship Practice: How the Poor Engage the State in Rural and Urban India."<a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0</a>Auerbach, Adam and Kruks-Wisner, Gabrielle. "The Geography of Citizenship Practice: How the Poor Engage the State in Rural and Urban India." Perspectives on Politics.Auerbach, Adam and Kruks-Wisner, Gabrielle. "The Geography of Citizenship Practice: How the Poor Engage the State in Rural and Urban India." Perspectives on Politics.aa_gkw_pop_read_me.txttext/plaingeo_citizen_practice_rural_code.doCode for rural analysisapplication/x-stata-syntaxgeo_citizen_practice_rural_data.tabData for rural analysistext/tab-separated-valuesgeo_citizen_practice_urban_code.doCode for urban analysisapplication/x-stata-syntaxgeo_citizen_practice_urban_data.tabData for urban analysistext/tab-separated-values

doi:10.7910/DVN/0YEW8D2026-02-19T23:21:03ZUVA_Authored_Datasets

Replication Data for: Online Propagation of Emotions: A Study of Resharing Dynamics on Social Media Following Celebrity Suicidesdoi:10.7910/DVN/0YEW8DHarvard Dataverse2025-10-291Nouri, Ehsan; Saraf, Nilesh; Goh, Jie Mein; Srabana Dasgupta; Dianne Cyr, 2025, "Replication Data for: Online Propagation of Emotions: A Study of Resharing Dynamics on Social Media Following Celebrity Suicides", https://doi.org/10.7910/DVN/0YEW8D, Harvard Dataverse, V1, UNF:6:xAoyGtrU9xXoBGV4g9JXHA== [fileUNF]Replication Data for: Online Propagation of Emotions: A Study of Resharing Dynamics on Social Media Following Celebrity Suicidesdoi:10.7910/DVN/0YEW8DNouri, EhsanSaraf, NileshGoh, Jie MeinSrabana DasguptaDianne CyrHarvard DataverseNouri, EhsanNouri, Ehsan2025-10-29Computer and Information ScienceMedicine, Health and Life SciencesSocial SciencesThis 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.<a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0</a>cascades_dataset.tabdataset including 91,868 cascade recordstext/tab-separated-valuesdataset_metadata.tabMetadatatext/tab-separated-values

doi:10.7910/DVN/1O8LCD2025-04-14T20:44:42ZUVA_Authored_Datasets

Replication Data for: Slow-Rolling, Fast-Tracking, and the Pace of Bureaucratic Decisions in Rulemakingdoi:10.7910/DVN/1O8LCDHarvard Dataverse2016-09-271Potter, Rachel Augustine, 2016, "Replication Data for: Slow-Rolling, Fast-Tracking, and the Pace of Bureaucratic Decisions in Rulemaking", https://doi.org/10.7910/DVN/1O8LCD, Harvard Dataverse, V1, UNF:6:bQY7l7ELOzDjQ4K3sDOSVA== [fileUNF]Replication Data for: Slow-Rolling, Fast-Tracking, and the Pace of Bureaucratic Decisions in Rulemakingdoi:10.7910/DVN/1O8LCDPotter, Rachel AugustineHarvard DataversePotter, Rachel AugustinePotter, Rachel Augustine2016-09-20Social Sciencesseparation of powers, bureaucracy, regulationData and code to replicate all results in the published paper.<a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0</a>slowroll.replication.do.do file to replicate paper resultsapplication/x-stata-syntaxslowroll.tabunderlying data to accompany "slowroll.replication.do"text/tab-separated-values

doi:10.7910/DVN/1PEUPZ2025-04-14T20:44:42ZUVA_Authored_Datasets

Nosek, Banaji, & Greenwald (2002): Math = Male, Me = Female, therefore Math ^= Medoi:10.7910/DVN/1PEUPZHarvard Dataverse2009-01-211Brian Nosek, 2009, "Nosek, Banaji, & Greenwald (2002): Math = Male, Me = Female, therefore Math ^= Me", https://doi.org/10.7910/DVN/1PEUPZ, Harvard Dataverse, V1Nosek, Banaji, & Greenwald (2002): Math = Male, Me = Female, therefore Math ^= Medoi:10.7910/DVN/1PEUPZBrian NosekBrian Nosek2002Harvard DataverseBrian NosekMurray Research ArchiveBrian Nosek2008-02-172008-02-17implicit social cognitionWe 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.1996-091997-061996-091997-06United StatesNew Haven, CTNew Haven, CTSubject: STANDARD DEPOSIT TERMS 1.0 Type: DATAPASS:TERMS:STANDARD:1.0 Notes: This study was deposited under the of the Data-PASS standard deposit terms. A copy of the usage agreement is included in the file section of this study.;<a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0</a>Nosek, B. A., Banaji, M. R., & Greenwald, A. G. (2002). Math = Male, Me = Female, therefore Math ^= Me. Journal of Personality and Social Psychology, 83(1), 44-59.Nosek, B. A., Banaji, M. R., & Greenwald, A. G. (2002). Math = Male, Me = Female, therefore Math ^= Me. Journal of Personality and Social Psychology, 83(1), 44-59.feelings about the Arts.docSample attitude questionnaire from studies. application/mswordidentificationmath.docSample identity questionnaire from studies.application/mswordNBG2002.analyses.010901.sasSAS analysis script for some analyses on combined datasettext/x-sas-syntax; charset=US-ASCIINBG2002.library.creator.all.sasSAS analysis script for importing data for all three studiestext/x-sas-syntax; charset=US-ASCIINBG2002.Study1.data.zipRaw data for Study 1application/zipNBG2002.Study2.data.zipRaw data for Study 2application/zipNBG2002.Study3.data.zipRaw data for Study 3application/zipnosekmath2002.pdfArticle published in JPSPapplication/pdf

doi:10.7910/DVN/1RRYTT2025-04-14T20:44:43ZUVA_Authored_Datasets

Replication data for: The Cross-Sectional Distribution of Price Stickiness Implied by Aggregate Datadoi:10.7910/DVN/1RRYTTHarvard Dataverse2018-12-131Lee, Jae Won; Carvalho, Carlos; Dam, Niels Arne, 2018, "Replication data for: The Cross-Sectional Distribution of Price Stickiness Implied by Aggregate Data", https://doi.org/10.7910/DVN/1RRYTT, Harvard Dataverse, V1Replication data for: The Cross-Sectional Distribution of Price Stickiness Implied by Aggregate Datadoi:10.7910/DVN/1RRYTTLee, Jae WonCarvalho, CarlosDam, Niels ArneHarvard DataverseLee, Jae WonCarvalho, CarlosLee, Jae Won2018-12-11Social SciencesCarvalho, 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.<a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0</a>Code_and_Data_CDL.tarapplication/x-tar

doi:10.7910/DVN/1YG7BI2025-04-14T20:44:42ZUVA_Authored_Datasets

Replication Data for: Simulating Duration Data for the Cox Modeldoi:10.7910/DVN/1YG7BIHarvard Dataverse2018-04-031Harden, Jeffrey J.; Kropko, Jonathan, 2018, "Replication Data for: Simulating Duration Data for the Cox Model", https://doi.org/10.7910/DVN/1YG7BI, Harvard Dataverse, V1Replication Data for: Simulating Duration Data for the Cox Modeldoi:10.7910/DVN/1YG7BIHarden, Jeffrey J.Kropko, JonathanHarden, Jeffrey J.Harvard DataverseHarden, Jeffrey J.Harden, Jeffrey J.2018-01-31Social SciencesThe 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.<a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0</a>Harden, Jeffrey J. and Jonathan Kropko. "Simulating Duration Data for the Cox Model." Forthcoming, Political Science Research and Methods.Harden, Jeffrey J. and Jonathan Kropko. "Simulating Duration Data for the Cox Model." Forthcoming, Political Science Research and Methods.figure1a.pdfapplication/pdffigure1b.pdfapplication/pdffigure1c.pdfapplication/pdffigure1d.pdfapplication/pdffigure1e.pdfapplication/pdffigure1-figure2.RProduces Figure1 and Figure2 (main text).type/x-r-syntaxfigure2.pdfapplication/pdffigure3a.pdfapplication/pdffigure3b.pdfapplication/pdffigure3-table1-tableA4-figureA3a.RProduces Figure 3 and Table 1 (main text), Table A4 row 1 (appendix), and Figure A3a (appendix).type/x-r-syntaxfigureA1.pdfapplication/pdffigureA1.RProduces Figure A1 (appendix).type/x-r-syntaxfigureA2a.pdfapplication/pdffigureA2b.pdfapplication/pdffigureA2-tableA2.RProduces Figure A2 and Table A2 (appendix).type/x-r-syntaxfigureA3a.pdfapplication/pdffigureA3b.pdfapplication/pdfreadmeRead Me file with replication instructions.text/plain; charset=US-ASCIIrfunctions.RNecessary R functions from the coxed package. Included to preserve these results as the package is updated.type/x-r-syntaxtable1.txttext/plaintableA1.RProduces Table A1 (appendix).type/x-r-syntaxtableA1.txttext/plaintableA2.txttext/plaintableA3.RProduces Table A3 (appendix).type/x-r-syntaxtableA3.txttext/plaintableA4-figureA3b.RProduces Table A4 row 2 (appendix) and Figure A3b (appendix).type/x-r-syntaxtableA4.txttext/plain

doi:10.7910/DVN/246722025-04-14T20:44:43ZUVA_Authored_Datasets

Replication data for: Multiple Imputation for Continuous and Categorical Data: Comparing Joint Multivariate Normal and Conditional Approachesdoi:10.7910/DVN/24672Harvard Dataverse2014-02-073Kropko, Jonathan; Goodrich, Ben; Gelman, Andrew; Hill, Jennifer, 2014, "Replication data for: Multiple Imputation for Continuous and Categorical Data: Comparing Joint Multivariate Normal and Conditional Approaches", https://doi.org/10.7910/DVN/24672, Harvard Dataverse, V3, UNF:5:QuxE8nFhbW2JZT+OW9WzWw== [fileUNF]Replication data for: Multiple Imputation for Continuous and Categorical Data: Comparing Joint Multivariate Normal and Conditional Approachesdoi:10.7910/DVN/24672Kropko, JonathanGoodrich, BenGelman, AndrewHill, JenniferPolitical AnalysisHarvard DataverseIQSS Dataverse NetworkJonathan Kropko2014-02-05Volume 22, Issue 4multiple imputationWe 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.<a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0</a>Kropko, Jonathan, Ben Goodrich, Andrew Gelman, and Jennifer Hill. 2014. "Multiple Imputation for Continuous and Categorical Data: Comparing Joint Multivariate Normal and Conditional Approaches." Political Analysis (Autumn 2014) 22 (4): 497-519. <a href= "http://pan.oxfordjournals.org/content/22/4/497.short" target= "_new">article available here</a>10.1093/pan/mpu007Kropko, Jonathan, Ben Goodrich, Andrew Gelman, and Jennifer Hill. 2014. "Multiple Imputation for Continuous and Categorical Data: Comparing Joint Multivariate Normal and Conditional Approaches." Political Analysis (Autumn 2014) 22 (4): 497-519. <a href= "http://pan.oxfordjournals.org/content/22/4/497.short" target= "_new">article available here</a>base2008_3.tabtext/tab-separated-valuesdescription.txttext/plain; charset=US-ASCIIhelper.Rtext/plain; charset=US-ASCIImi_0.9-88.tar.gzapplication/x-gzipmi_0.9-96.tar.gzThe latest version of the MI software, compatible with changes to the dependent packages in Rapplication/x-gziprealhelper.Rtext/plain; charset=US-ASCIIrealsim1.Rtext/plain; charset=US-ASCIIsim1.Rtext/plain; charset=US-ASCII

doi:10.7910/DVN/24CXA72025-04-14T20:44:42ZUVA_Authored_Datasets

Replication Data for: The Democracy of Dating: How Political Affiliations Shape Relationship Formationdoi:10.7910/DVN/24CXA7Harvard Dataverse2020-06-071Holbein, John, 2020, "Replication Data for: The Democracy of Dating: How Political Affiliations Shape Relationship Formation", https://doi.org/10.7910/DVN/24CXA7, Harvard Dataverse, V1, UNF:6:QLSRr7ICMJgHNKFUZb4rJA== [fileUNF]Replication Data for: The Democracy of Dating: How Political Affiliations Shape Relationship Formationdoi:10.7910/DVN/24CXA7Holbein, JohnHarvard DataverseHolbein, JohnHolbein, John2020-06-03Social SciencesReplication Data for: The Democracy of Dating: How Political Affiliations Shape Relationship Formation<a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0</a>01_Prepare_Recode_DatingLabExperimentData.doapplication/x-stata-syntax02_Analyze_DatingLabExperimentData.doapplication/x-stata-syntaxage.csvtext/csvagelabel.doapplication/x-stata-syntaxbalance_experiment_1.tabtext/tab-separated-valuesbalance_experiment_2.tabtext/tab-separated-valuesbalance_experiment_3.tabtext/tab-separated-valuesbalance_experiment_4.tabtext/tab-separated-valuesbalance_experiment_5.tabtext/tab-separated-valuesbalance_experiments_1_5.tabtext/tab-separated-valuesCCES18_BYU_OUTPUT.tabtext/tab-separated-valuesclean_dating2.tabtext/tab-separated-valuescovariate balance all pooled.doapplication/x-stata-syntaxcovariate_balance_experiments_1_5_pooled.pngimage/pngdata_for_analysis.tabtext/tab-separated-valuesDating Survey 4_April 22, 2019_16.00.tabtext/tab-separated-valuesDating Survey 4_April 22, 2019_16.00.tabtext/tab-separated-valuesdemocracy of dating raw data.csvtext/csvd_profilesdisplayed.tabtext/tab-separated-valuesd_surveydemos.tabtext/tab-separated-valuesd_surveyprofileevals.tabtext/tab-separated-valuesd_user.tabtext/tab-separated-valueseduc.csvtext/csveduclabel.doapplication/x-stata-syntaxexperiment1.tabtext/tab-separated-valuesexperiment2.tabtext/tab-separated-valuesexperiment3.tabtext/tab-separated-valuesexperiment4.tabtext/tab-separated-valuesexperiment5.tabtext/tab-separated-valuesFigure Creation.Rtype/x-r-syntaxfinal dataset.tabtext/tab-separated-valuesheight.csvtext/csvheightlabel.doapplication/x-stata-syntaxhets_1_5_pooled.pngimage/pnghets_studies_1_5_pooled_cleaned.tabtext/tab-separated-valuesHolbein Winter 2019_March 7, 2019_15.13.csvtext/csvhuber malhotra same ideology attract.smclapplication/x-stata-smclideo.csvtext/csvideolabel.doapplication/x-stata-syntaxLine Graph_Overall.Rtype/x-r-syntaxMANIFEST.TXTtext/plainmeta analysis.doapplication/x-stata-syntaxmissing_balance_experiment_1.tabtext/tab-separated-valuesnicholson estimates.smclapplication/x-stata-smclOpenEndedResponses.txttext/plainpared_balance_experiment_1.tabtext/tab-separated-valuespared_balance_experiment_2.tabtext/tab-separated-valuespared_balance_experiment_3.tabtext/tab-separated-valuespared_balance_experiment_4.tabtext/tab-separated-valuespared_balance_experiment_5.tabtext/tab-separated-valuespermutation_dist.pngimage/pngpicture.csvtext/csvPOB_Codebook.pdfapplication/pdfPOB_ReplicationData.tabtext/tab-separated-valuesPOB_ReplicationDoFile.doapplication/x-stata-syntaxprofile.csvtext/csvquery.csvtext/csvReadme.rtfapplication/rtfReadMe.txttext/plainreligion.csvtext/csvreligionlabel.doapplication/x-stata-syntaxscreenname.csvtext/csvStudy1.7zapplication/x-7z-compressedStudy 1 Replication Code.doapplication/x-stata-syntaxStudy 2 Replication Code.doapplication/x-stata-syntaxStudy 3 Replication Code.doapplication/x-stata-syntaxstudy_4.pngimage/pngStudy 4 Replication Code.doapplication/x-stata-syntaxstudy_4.tabtext/tab-separated-valuesstudy_5.pngimage/pngStudy 5 Replication Code.doapplication/x-stata-syntaxstudy_5.tabtext/tab-separated-valuesstudy_comparison_18_35.pngimage/pngstudy_comparison_18_35_w_pooled.tabtext/tab-separated-valuesstudy_comparison_factor_weight_scales_meta_analysis.tabtext/tab-separated-valuesstudy_comparison_factor_weight_scales.pngimage/pngstudy_comparison_factor_weight_scales.tabtext/tab-separated-valuesstudy_comparison_lt_dating.pngimage/pngstudy_comparison_lt_dating_w_pooled.tabtext/tab-separated-valuesstudy_comparison_message.pngimage/pngstudy_comparison_message_pooled.tabtext/tab-separated-valuesstudy_comparison.pngimage/pngstudy_comparison_w_pooled.tabtext/tab-separated-valuessurvey.csvtext/csvTableS03_Study1SubjectDemographics.txttext/plaintreatment_permute.tabtext/tab-separated-valuesuser_anonymized.txttext/plain

doi:10.7910/DVN/274662025-04-14T20:44:42ZUVA_Authored_Datasets

Replication data for: Signaling Alliance Commitments: Hand-Tying and Sunk Costs in Extended Nuclear Deterrencedoi:10.7910/DVN/27466Harvard Dataverse2015-01-272Fuhrmann, Matthew; Sechser, Todd, 2015, "Replication data for: Signaling Alliance Commitments: Hand-Tying and Sunk Costs in Extended Nuclear Deterrence", https://doi.org/10.7910/DVN/27466, Harvard Dataverse, V2Replication data for: Signaling Alliance Commitments: Hand-Tying and Sunk Costs in Extended Nuclear Deterrencedoi:10.7910/DVN/27466Fuhrmann, MatthewSechser, ToddMatthew FuhrmannHarvard DataverseMatthew FuhrmannMatthew Fuhrmann2014-09-292014Social SciencesSignals and signalingAlliance commitmentsHow 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.Version Date: 2014Version Text: 1.0<a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0</a>Fuhrmann, Matthew, and Todd S. Sechser. 2014. “Signaling Alliance Commitments: Hand-Tying and Sunk Costs in Extended Nuclear Deterrence.” <i>American Journal of Political Science</i> 58(4): 919–35.10.1111/ajps.12082Fuhrmann, Matthew, and Todd S. Sechser. 2014. “Signaling Alliance Commitments: Hand-Tying and Sunk Costs in Extended Nuclear Deterrence.” <i>American Journal of Political Science</i> 58(4): 919–35.FuhrmannSechserAJPS.zipapplication/zipb2Zmc2V0OjoxMHxzZXQ6OlVWQV9BdXRob3JlZF9EYXRhc2V0c3xwcmVmaXg6Om9haV9kZGk=