Replication Data for: City-State Ideological Incongruence and Municipal Preemptiondoi:10.7910/DVN/KU4OCKHarvard Dataverse2021-05-291Barber, Michael; Dynes, Adam M., 2021, "Replication Data for: City-State Ideological Incongruence and Municipal Preemption", https://doi.org/10.7910/DVN/KU4OCK, Harvard Dataverse, V1, UNF:6:+71XZWfuLqUR795rrgEx+w== [fileUNF]Replication Data for: City-State Ideological Incongruence and Municipal Preemptiondoi:10.7910/DVN/KU4OCKBarber, MichaelDynes, Adam M.Barber, MichaelDynes, Adam M.Harvard DataverseBarber, MichaelDynes, Adam M.Dynes, Adam M.2021-04-16Social SciencesPreemptionRepresentationIntergovernmental politicsState politicsLocal politicsElite surveyA growing concern among municipal officials across the U.S. is that their policymaking capacity is under attack by state legislatures who are increasingly likely to preempt those municipalities. However, determining the extent to which municipalities are preempted is challenging. We overcome this by surveying a large sample of municipal officials from across the U.S. We find that officials from municipalities that are more ideologically distant from their state overall are more likely to report being preempted by their state government. Moreover, this pattern is driven by more liberal municipalities in both Republican and Democratic states reporting higher rates of preemption. Additionally, municipalities under unified state governments are more likely to report preemption, especially those under unified Republican control. These findings have important implications for the quality of representation in our federalist system and indicate that preemption is not just an issue between Republican states and liberal urban cities.United StatesThis dataset underwent an independent verification process that replicated the tables and figures in the primary article. For the supplementary materials, verification was performed solely for the successful execution of code. The verification process was carried out by the Odum Institute for Research in Social Science at the University of North Carolina at Chapel Hill. <br></br> Note: To maintain the confidentiality of the participants, the authors cannot share the original data set. The authors shared the original data with Odum Institute for verification purposes. In Dataverse, they have posted a version of the data set with added noise to protect the confidentiality of the respondents. See the readme for more information. To access a version of the dataset without added noise, please contact the authors. <br></br> The associated article has been awarded Open Materials Badge. Learn more about the Open Practice Badges from the <a href="https://osf.io/tvyxz/wiki/home/" target="_blank">Center for Open Science</a>.<br></br> <img src="https://odum.unc.edu/files/2020/03/OpenMaterials_PR-1.png" alt="Open Materials Badge" height="77" width="80">Butler, Daniel M., and Adam M. Dynes. 2016. “2016 AMOS.” American Municipal Officials Survey. http://www.municipalsurvey.org/wp-content/uploads/2017/12/AMOS2016_details-of-sample.pdf. <br></br> Data Trust. 2017. “National Voter File.” Accessed July, 2017. https://thedatatrust.com; (202) 733-5235. <br></br> Tausanovitch, Chris, and Christopher Warshaw. 2013. "Measuring Constituent Policy Preferences in Congress, State Legislatures, and Cities." Journal of Politics 75 (2): 330-342. https://americanideologyproject.com/ <br></br> US Census Bureau. 2016. “2016 Data Release.” American Community Survey. Accessed May 7, 2020. https://www.census.gov/programs-surveys/acs/news/data-releases.2016.html <br></br> US Census Bureau. 2016. “Annual Estimates of the Resident Population for Incorporated Places and Minor Civil Divisions: April 2, 2020 to July 1, 2015.” Accessed September, 2016. https://www.census.gov/data/tables/time-series/demo/popest/2010s-total-cities-and-towns.htmThe <i>American Journal of Political Science</i> and the Odum Institute for Research in Social Science are not responsible for the accuracy or quality of data uploaded within the <i>AJPS</i> Dataverse, for the use of those data, or for interpretations or conclusions based on their use.This dataset is made available with limited information on how it can be used. You may wish to communicate with the Contact(s) specified before use.Barber, Michael, and Adam Dynes. 2021. “City-State Ideological Incongruence and Municipal Preemption.” <i>American Journal of Political Science</i> 67 (1): 119-36.10.1111/ajps.12655Barber, Michael, and Adam Dynes. 2021. “City-State Ideological Incongruence and Municipal Preemption.” <i>American Journal of Political Science</i> 67 (1): 119-36.Barber_Dynes_AJPS_Replication_All_Cities.tab2810431text/tab-separated-valuesUNF:6:jpObNxqsLwuaWDUaoLpdkA==This data set contains a list of all municipalities in the US and is included to examine the representativeness of the officials who participated in our survey. This list of municipalities comes from the US Census Bureau's population estimates for all Incorporated Places, Consolidated Cities, and Minor Civil Divisions. We then narrowed the list down to sub-county or county-consolidate general-purpose local governments as defined by the US Census Bureau. These include the following geographic categorizations from the Census Bureau: • Incorporated Places: In most states, they are called cities, towns, boroughs, and villages. • Consolidated Cities: These are a “unit of government for which the functions of an Incorporated Place and its county or Minor Civil Divisions have merged.” • Minor Civil Divisions (MCDs) in CT, ME, MA, MI, MN, NH, NJ, NY, PA, RI, VT, and WI. In these states, they are usually called townships or towns. We included Minor Civil Divisions from these states based on the Census Bureau's assessment that “Most of the MCDs in [these] twelve states ... serve as general-purpose local governments that can perform the same governmental functions as incorporated places.” (US Census Bureau 2012.) This resulted in a list of 28,104 municipalities. This data set also includes demographic data about the respondents' municipality that comes from the US Census Bureau's 2016 American Community Survey and from Tausanovitch and Warshaw (2013).Barber_Dynes_AJPS_Replication_public.tab3078661text/tab-separated-valuesUNF:6:WxALHO/h7a3DaE1JruR48g==This data set contains the survey responses of municipal officials who participated in the 2016 American Municipal Officials Survey (Butler and Dynes 2016) and answered questions used in the analysis for this research article. The data set also includes demographic data about the respondents' municipality that comes from the US Census Bureau's 2016 American Community Survey and from Tausanovitch and Warshaw (2013). To maintain the confidentiality of the respondents (as per our IRB approval and survey consent form), we have added noise to all of the independent variables at the respondent, city, and state level. This includes randomly assigning the state of a small percentage of cities. Without adding noise, individual respondents could be identified based on their position (e.g., mayor) and a unique city-level variable, such as population or city ideology. We have also de-identified the state since even with noise added to a city's population, the largest city in several states would be easily identified.UNF:6:MzaKyj0Rs86dmFESOsPoZg==UNF:6:9NMBen3vC7XMP37kijK+lA==UNF:6:DHRihYX+6Bwu+/wOyCOiYQ==UNF:6:jZI80b1rl5C7P+rQ3m3b2Q==UNF:6:TaVXQ4cPVcXJt0rIs37CFA==UNF:6:5+E2u1jdY0EIJmI2uLa2jg==UNF:6:V5m38+++GUyvzx8n0nIF7w==UNF:6:A+O3OrJKsVcrFrvMtrsxPA==UNF:6:wcQEP4i+ocM+H8rK9oaInQ==UNF:6:6LVCmrUvxHul6586KSh40g==UNF:6:5MFvs+vZQMl7G8JX7f/mNQ==26794.01310.0-1.01925122737884520.64716899394989010.26331324712267745.-0.06969392426598417-0.04072723537683487UNF:6:0jjwskvr3RPv2j4b3XrkDw==0.16898472372572910.26895088847166915.-1.0980367660522460.195651799440383913.00.917025804519653328091.0UNF:6:iqiuRgUQNHHb5WnDjziirA==1310.00.26331324712267745-0.0407272353768348726794.0.0.6471689939498901-0.06969392426598417-1.0192512273788452UNF:6:0jjwskvr3RPv2j4b3XrkDw==-0.39073616266250610.332897990942001340.0115785775706171990.00.16423360942393660.020308043009114034.28104.0UNF:6:PFnsH6jrZEQH4ywRjx8f6A==0.02199067920446396-0.620895028114318826794.00.84265154600143430.222606443337541451310.0.0.0401443709491377UNF:6:SiqJU/hZaSilYhWv6oF++g==0.842651546001434326794.00.144857027280886260.173665861837309950.136726357042789462.8493255376815796E-41310.0.UNF:6:N55XHwOuZJfIn1C/N/7Dww==0.0219906792044639626794.01310.00.22260644333754145-0.62089502811431880.04014437094913770.8426515460014343.UNF:6:SiqJU/hZaSilYhWv6oF++g==26794.00.144857027280886260.173665861837309950.8426515460014343.0.136726357042789462.8493255376815796E-41310.0UNF:6:N55XHwOuZJfIn1C/N/7Dww==0.01226.08192.12154262897528056.067265.906370882448175133.0.48.0UNF:6:olL8EI7v2FS7kvsswocDzA==0.027903.0201.08461961.01242.069986.395971136258481.149983872578.UNF:6:Bi+WpJtj7pMZuvCR+vndoQ==7.235673037550280527886.07.12528324127197315.9510917663574221.7710623462585358.0.0218.0UNF:6:DU1XJHUEYWjOXAmX/clyhg==0.0.15.04959395691766374.13371705217851219.075.9100.027885.0UNF:6:LSgHCtdh2ANagCHGC3IGAg==25.86674914828755715.04947651205320524.1219.0.27885.0100.00.0UNF:6:uW+Qsg1XSFmTs4BmjARc6Q==5.32048384718638227503.024.8750.9286000132560734.875601.0.2.2985117400092143UNF:6:Nox/Fe0/7AY3doGy1Fu3ZQ==6.897531218601937236.00.027868.05.2582853065027875100.05.9.UNF:6:+M/PmXLVDtMesWgnGgyLGA==.41.88.0200017495779914.6242.042.09826645610511527862.086.5UNF:6:RQEKeeitMTuJq565NuPNqw==.0.03.5555920868058120.1255493275218.067.51.288834191227035427886.0UNF:6:6HFLrGPtQI90h5PIXFJHbw==5.866465979534103.27886.0218.00.50.0100.014.571112217323384UNF:6:Yn3rCnBIglw7ejJFTG5Mtg==12.57260855076891627886.099.8218.02.2114962876.4756823784613960.0.UNF:6:m8Vszv+qLxxhqbsXaMhyfw==27886.0100.094.887.8326289388835317.7026858859392360.0.218.0UNF:6:F+t9DSmRU4QJ+kl5Lzle/A==0.030786.04398.01.02199.51269.61382928335822199.5.UNF:6:Yq0boGpylLQv+3xckG/D8w==.2.0000324830884940.04.04.07.030786.01.0UNF:6:yvrWJPGljWmLfzYpmBmjWA==13.9952986601559126509.0-0.086612094005865351.04277.0-99.03.0.UNF:6:OcNKSRbgEvCmOIPQLl5ZDA==1.00.0694.2297528137351190.54638472032821197.5.2408.030786.0UNF:6:CqC7c3T8Jq1lwVMzxfz8uQ==7482.91108196029230786.015390.015449.5748067303250.0.30453.01274.0UNF:6:brDTkl80SF0UdbEgQA9bnw==0.00.3248406634295981.1.00.030786.00.119892158773469090.0UNF:6:S5qEsW6BjqruJ7Czf0dlVw==0.00.339069161584515170.030786.00.13252777236406351.0.0.0UNF:6:3f2WHgyXMi0LC8fDBhLxXQ==0.66390567140908140.01.030786.00.47237927457767726.1.00.0UNF:6:BX/lDUZomHc72sdMOiXCqA==2810698.846825563330786.0.62.5067710131592949684.264359964514399.967286952047158970.71962657040.0UNF:6:ZqBovdi0iKM90kd3sVXHrg==14.8489437103271489.5749812126159674.1352748870849611.485652215300783630786.0.0.09.59816181079643UNF:6:wN3Vp3p/Zrucn+8Vv8nmuQ==0.038.904605520026838.5701094150543230786.069.17753472328187.7.22325554860239518.298846340179445UNF:6:j1HMg/pgGdiaqCoTySrKXw==0.171107014050484980.01.07404535669088360.8086162181862535.-0.0079514534175395970.856228790734881130786.0UNF:6:U02mIB1HnDnoB3WwynEAyA==30786.00.00.9533472010567785-0.065884480416774750.08705542742522680.1398376165984020.048821169584989554.UNF:6:YE7wIIPLVVdaHV1pjxGYJg==-0.0284133031964302060.0588969304713027750.038138660123653384.0.023745526917278770.00.60205980094708530786.0UNF:6:UBAGSuR7UnTAbxrfeo0K+A==1.00459704786539070.072777831157669420.153685887604503050.030786.00.11525801327730228.-0.07350513339042664UNF:6:pa3NnStpKH/GITAxzhPoeQ==0.06810175006426887.0.3051781776696443330772.00.06266388607025147-0.016212144866585730.03430790040410562614.0UNF:6:avLWEwfzu7B2dypjvJA+TQ==2.92808051493228170.762620033836364725.88033294677734430772.0.6.3233177986658015.71260142111778314.0UNF:6:wQU2GngxO1xXZD1SkdIzyA==0.107656163394451140.645024379832508.0.030786.00.157853109081996261.0634369328618050.640475017234683UNF:6:Q02HQrrw5EH91JCfzj+SBA==30786.00.158898495784394020.00.90753727662563320.363631847511976970.3536806909472564-0.06934721767902374.UNF:6:Xwk/oYjIBB2OLmspemXNeA==0.02167681977152824430786.0-0.0146341963773568460.0-0.4147689566016197.0.39057924598455430.17627077781582076UNF:6:k4Gm7EAs6V/ioV7MUS1GXg==-0.10205815605741902-1.10151205211877820.73355703055858618505.00.3095866168987419522281.0-0.06931054219603539.UNF:6:rwQPvkFaepVXMZo4DbzSaw==0.00.032060027285129381.00.17616239656895044.0.00.030786.0UNF:6:cLwRUx8lLX3lk3ea1rwmdw==-0.00312085550351766640.32588472465146057707.030079.0.0.9323198672335129-0.9104347180831642-1.0316759330368519E-4UNF:6:Dip8xLplTCb99adRR20kTg==.0.202221498786364430366.01.0763816209324940.48655546239270714-0.06928966939449310.47829446419810234420.0UNF:6:oUG0u0G5Rq81JHfvwq1osg==30303.0483.0-0.00129183108983230150.3220056369674728-1.1015120521187782.-0.0064655071643350670.9323198672335129UNF:6:hIfZwVdAyOBzxa1k4kBxXw==0.166856674896565030.4549395330281433330702.0.84.00.90202661255262780.417354298639351750.07772555301586787UNF:6:wZiDXCWm4nUpAVMtw9NPmw==0.0979733874473721630702.084.00.54506046697185660.16685667489656530.92227444698413220.5826457013606483.UNF:6:wD64mQA5ZKUnu01AFd31Mw==-0.079326134175062180.254772984963313460.5850024614483118-0.0925214073012081-1.008227184414863622281.0.8505.0UNF:6:cxLs0UnNmXQ60gVrQAHDVA==0.285027157483766630303.00.81047207092863660.017011342708464133.-1.00822718441486360.008446570476399694483.0UNF:6:MGlwv2XfSgV/ThjWZhY+/A==30303.01.00822718441486360.17542228258721707.0.18333075146778532483.01.6938281372261277E-40.224804765071168UNF:6:9169/rmwop94tpeM4FU80g==0.175083862008352850.1624791771173477222281.08505.00.206908429793293381.00822718441486369.089112281799316E-4.UNF:6:J4AuM/jbB5y+7+RmPe9i/w==504.00.6638109633800542-0.69338360777687020.20782877521159884-0.0670544779768659-0.0554186217269077.30282.0UNF:6:9WTXwJrG2B9/tfDWI1fz7w==2.0920916864086703E-40.6933836077768702504.00.173809075565947460.147158719410575150.132206879237242.30282.0UNF:6:qsDPyjd23b12KLlEB1u4eA==27489.0.1.00.00.03297.00.34013605442176830.47376332070932076UNF:6:yeCEYMt7uZ1FKLf2JnBjdg==27489.00.33773509403761660.472946336598435163297.00.0.0.01.0UNF:6:p0mSMLBsmxA8wSfqZ+ScwA==1.00.0.3647.00.4808479023114104527139.00.63709053391797511.0UNF:6:sMIOa3h+W9Lky8pdETPL3A==0.2486827075426476.0.03647.01.00.432257450264173727139.00.0UNF:6:pi8L8Kr7M+3Cs3s+gzZAxw==5404.0-2.19048118591308625382.0.33.227066040039066.42227315902718.1086669921283336.69295397645889UNF:6:PnAa8J1tftBqjWyXMUz+fA==30541.00.0.1.0245.00.47818184471027840.00.35388494155397454UNF:6:b3Pags6FdHpmcexFVyzYtw==4726.026060.00.369730193076386940.163392171910973720.01.00.0.UNF:6:HxdnSbi9d9EV2yip+N87lw==.0.00.49527036218026020.43133242382901030.01.030786.00.0UNF:6:2dfvDLYbTScgejJNMazdUg==30786.00.212824010914051451.00.00.4093108760183082.0.00.0UNF:6:qLBwKYEeNesOffe6/+tW9Q==UNF:6:ZMr3+0PFbwA3g9R/04TNQA==1.269261744966430326075.09.0.0.01.4626661412898151.04711.0UNF:6:BSi9KrBlUi4oA1iBAvusJQ==0.00.00.0.0.1428571428571428830786.00.34993278946856781.0UNF:6:+h9BQSGSlk7JDxkVMcEaTA==0.0096800215111588973719.00.01.00.0979229138062466327067.00.0.UNF:6:49eHjZqCe10FgXsuf+yxuQ==0.03719.00.123921333832935020.01.00.01559559021242278227067.0.UNF:6:4JfbNXce3YUOLtnM3efrCA==3719.00.025813390696423594.0.15859956858631650.027067.01.00.0UNF:6:q/A5RBZS7Guo4eKUig7xHg==27067.00.0129066953482118280.0.0.03719.01.00.11288728523222909UNF:6:6jFTs4NwlWCCGhXbeo+XKw==0.01.03719.00.00941113202473780527067.00.09656640219062643.0.0UNF:6:DUJvXAf7VD8DvwBGC4+HOA==0.01.00.0126378058617907740.11172040035237110.03719.027067.0.UNF:6:ck5W5/q6rqTdnVhCzko5Xw==.0.093792228214832353719.01.00.00887335305189569527067.00.00.0UNF:6:4BZxH/V6F5pDvLkO+ASRCw==3719.00.00.07494200687125274.0.01.027067.00.005646679214842731UNF:6:9jAwa+WKJoRYqhGE9d19cw==0.0163978500548413141.027067.03719.02.688894864210811E-4.0.00.0UNF:6:b2owhQLPiGPKrtQC4ziC+g==3719.00.01.027067.00.028394269373407225.8.066684592632396E-40.0UNF:6:FY/y6URnyxG79bfobIPpdg==.27067.00.00.117432780755162613719.00.0139822532938962271.00.0UNF:6:d12vvC0ydx2dEPwGMYrB0Q==0.00.022586716859370786.0.148601799557003733719.027067.01.00.0UNF:6:rY1nTkoYK/uHFCUnPHHGdQ==.0.03719.01.00.00779779510621133627067.00.00.0879719867478991UNF:6:53WUE4gfncidQBaJyiMt3w==27067.03719.00.132831406292013840.01.0.0.00.3394380720520439UNF:6:bUC+bc0dNmcasCChpg2tpg==0.219612811017994570.00.00.01.00.4139910522582781.30786.0UNF:6:HGImRujChZD15MqV+q52Gw==1.01.027391.01.00.03395.01.0.UNF:6:3HzclJ9Xa4hf0dL6EJUXug==CODEBOOK FOR DATA_public.pdfThis document defines and provides the source for each variable in the data sets listed above.application/pdfreadme.txttext/plainreplication_code_analyses_public.doThis Stata do file runs all of the analyses presented in the tables in or mentioned in the text of the research article and the supplementary appendix. This Stata do file does not produce any of the figures. Please note that since noise has been added to the independent variables, the results will not perfectly replicate those presented in the final version of the paper. The added noise will also lead to bias in the estimates and larger standard errors. However, the results are still in the same direction as those in the final version of the paper.application/x-stata-syntaxreplication_code_figures_public.RThis R script creates all of the figures in the research article and the supplementary appendix. Please note that since noise has been added to the independent variables, the results will not perfectly replicate those presented in the final version of the paper. The added noise will also lead to bias in the estimates and larger standard errors. However, the results are still in the same direction as those in the final version of the paper.type/x-r-syntax