Digital Excellence in Chicago A Citywide View of Technology Use
Karen Mossberger, Ph.D., Public Administration, University of Illinois at Chicago Caroline J. Tolbert, Ph.D., Political Science, University of Iowa Commissioned by: City of Chicago Department of Innovation and Technology Supported by: John D. and Catherine T. MacArthur Foundation State of Illinois Department of Commerce and Economic Opportunity July 2009
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TABLEOFCONTENTSExecutiveSummary 4PartI.Introduction:WhyDigitalExcellence? 10PartII.InternetAccessandUse 11
ReadingtheResults 13 InternetUseinAnyPlace 16 HomeInternetUse 17 NeighborhoodVariationforInternetUseAnywhereandatHome 18 BroadbandAccess 20 NeighborhoodVariationforHomeBroadband 22 SummarizingWhatMattersforUseandAccess 22 DescribingtheLess‐Connected 22PartIII.PublicandWirelessAccess 23 InternetUseatLibraries 25 NeighborhoodVariationinLibraryInternetUse 28 CommunityTechnologyCenters 30 WirelessAccess 31PartIV.BarrierstoAccessandPublicPolicy 33 NeighborhoodVariationinInterest 36 NeighborhoodVariationinCostConcerns 38 NeighborhoodVariationinDifficultyUsingtheInternet 40 PolicyImplications 42PartV. OnlineActivitiesthatInfluenceOpportunitiesforResidents 43 EmploymentandTraining 43 NewsandPoliticsOnline 46 DigitalGovernment 47 HealthCare 50 SummaryonInternetActivities 51PartVI.Conclusion:ChallengesandOpportunitiesforDigitalExcellenceinChicago 51 PublicOpinionasanOpportunity 52 ChallengesandOpportunitiesforAddressingDisparities 53References 54AppendixA.LogisticRegressionModels 56AppendixB.MultilevelModels 60AppendixC.Survey 76
TABLESWhatMattersTableA:WhoUsestheInternetinAnyPlaceandWhoHasHomeAccess? 15WhatMattersTableB:WhoHasaBroadbandConnectionComparedtoDial‐Up? 21WhatMattersTableC:WhoUsestheInternetatPublicLibraries? 26WhatMattersTableD:WhatAretheReasonsChicagoResidentsDoNotHaveHomeInternet? 34Table1.InternetUsewithNoHomeAccessby2007FamilyIncome 23Table2.ReasonsforUsingtheInternetattheChicagoPublicLibrary 25Table3.FrequencyofWirelessUseinPublicPlacebyAge 31Table4.FrequencyofWirelessUseinPublicPlacebyRaceandEthnicity 31
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Table5.FrequencyofCellPhoneUsetoConnecttoInternetbyAge 32Table6.FrequencyofCellPhoneUsetoConnecttoInternetbyRaceandEthnicity 32Table7.ReasonsforNoInternetatHome 33Table8.MainReasonforNoInternetatHomebyRaceandEthnicity 34Table9.ActivitiesOnline 43Table10.FrequencyofInternetUseforJobbyEducation 44Table11.WhereShouldaWirelessAvailabilityProjectStart? 52Table12.WouldYouSupportaWirelessAvailabilityProjectforaSmallTaxorFeeIncrease? 52
MAPS
PercentWhoHaveInternetAccessatHome 19PecentWhoUsetheInternetattheLibrary 29PercentWhoDoNotUsetheInternetBecauseNotInterested 37CostastheReasonforNoInternetAccess 39PercentWhoHaveNoInternetAccessBecauseofDifficulty 41PercentWhoUsetheCity’sWebsite 49
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EXECUTIVESUMMARY
InMay2007,theMayor’sAdvisoryCouncilonClosingtheDigitalDividedefinedasetofgoalstoachievedigitalexcellence—universal,meaningfulparticipationintechnology—
throughouttheCityofChicagoandidentifiedfivedriversnecessarytoachievedigitalexcellence:effectivenetworkaccess,affordablehardware,suitablesoftware,digitaleducation,andevolvingmind‐sets.Theadvisorycouncilannouncedthat“Closingthedigitaldividemustbeseenaspart
ofthelargeropportunityforChicagototransforminstitutions,theeconomyandcommunities.Thisisaninclusivevision,seekingtoprovideuniversalmeaningfulparticipation,expandedeconomicprosperity,strengthenedcommunitiesandmoreeffectivegovernmentforall.”1
Buildingonavibrantnetworkofcommunityorganizationsandpublicagenciesthathavebeenaddressingtechnologydisparitiesinthecity,theadvisorycouncilcalledforanewfocusondigitalexcellencethatwouldbeintegraltothecity’sabilitytocompeteeconomicallyinthe
twenty‐firstcentury.Therefore,theCityofChicago–incollaborationwithnumerouspartnersfromtheprivatesector,highereducationandnon‐profitsectors–hassinceidentifiedstrategiestohelpChicagoresidentsandbusinessesachievedigitalexcellenceandrealizethevisionofthe
advisorycommittee.2
InanefforttofullyunderstandanddeterminethebarrierstotechnologyusefortheunderservedinChicago,theCityofChicago,theJohnD.andCatherineT.MacArthurFoundationandtheStateofIllinoisDepartmentofCommerceandEconomicOpportunitycommissionedthis
digitalexcellencestudytoidentifythelevelsoftechnologyuseacrossChicago. ThestudywasdesignedbyresearchersfromtheUniversityofIllinoisatChicagoandtheUniversityofIowa,andisbasedonarandom‐sampletelephonesurveyof3453Chicagoresidentsaged18andolder,
conductedbytheUniversityofIowaHawkeyePollinJuneandJuly2008.TheresultingdatadefinetherelevantgapsintechnologyuseinChicagoandprovideabaselineforevaluatingprogressinthefuture.ThereportwillhelptheCityandthebroadercommunitytostrategically
targetdigitalexcellenceeffortsandchangeconditionsandawarenessintheChicagocommunitiesthateitherdonothaveaccesstotechnology,ordohaveaccessbuthavenotachieveddigitalexcellence.
Althoughstudiesofinternetuseorserviceavailabilityhavebeenconductedinothercities,this
studyisthefirstofitskindinseveralways.Itbreaksnewgroundbyshowinghowneighborhoodsacrossthecitydifferintheiruseofthetechnologyaswellasbarrierstotechnologyuse.Clearly,thisisimportantfordesigningprogramsthatmeetthevariedneedsofcommunitiesacrossChicago,andthe
resultsindicatethesignificanceofneighborhoodcharacteristicsforunderstandingtechnologyopportunities.Theneighborhoodestimatesinthisstudyarebasedonsophisticatedmultilevelmodels,buttheresultsdonotrequireanunderstandingofthestatisticalanalysisbehindthem.Thefindings
presentedherearebasedonatelephonesurveythathasalargesamplethatisrepresentativeofthe
1Mayor’sAdvisoryCouncilonClosingtheDigitalDivide2007,3.2CityofChicago.http://www.cityofchicago.org/digitalexcellence.
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citypopulation.Somecitieshaverecentlyusedmailsurveystotracktechnologyuse.3Thesearepoorinstrumentsforreachingconclusionsaboutthepopulationofacity,becausetheyhavelowresponse
ratesandthosewhotakethetimetofilloutandreturnthesesurveysarelikelytobethosewhoaremostinterestedinthetopic,andnottypicalofcityresidents.ResidentswhoareleastlikelytoreturnmailsurveysareindividualswithlimitedliteracyorEnglishproficiency,whoarealsoamongthose
currentlylaggingbehindintechnologyuse.Whiletherehavebeenafewothercitieswithtelephonesurveysontechnologyusethatprovidereliablerandomsamples,theanalysisofferedhereisuniqueinitsabilitytoidentifywhichdisparitiesaresignificant–thatis,whetherrace,oreducation,or
neighborhoodpovertymatterforinternetuse.Thisrequiresstatisticalanalysisthathasnotbeenusedinothermunicipalstudiesoftechnologyuse.Togetherwiththeneighborhooddata,thisinformationiscriticalfordevelopingappropriatepoliciesandtargetedprograms.
Thefollowingtableshowssomekeyindicators–thepercentageofChicagoresidentswhomeet
criteriafordigitalexcellence.
IndicatorsofDigitalExcellence Chicago(citywide)
Internetaccess
%whousetheinternetinanyplace 75%
%whousetheinternetdaily 60%
%whousetheinternetathome 69%%withbroadbandaccessathome 61%%whouseacellphonetoconnecttotheinternet 26%
%whousewirelessinternetinapublicplace 35%
Hardware/Software
%whohaveusedaCommunityTechnologyCenter 16%
%whohaveusedalibraryforinternetaccess 33%
%withahomecomputer 77%
Skills/Education
%whoknowhowtouseasearchengine 70%
%whoknowhowtouseemail 72%
%whoknowhowtouploadimagesorfiles 61%
%whoknowhowtocreateawebsite 25%
%whousetheInternetforwork 48%Continuedonnextpage
3ScarboroughResearch.NYCComparativeComputer&InternetPenetrationData.Datawerecollectedthroughamail‐basedsurveyconductedbetweenFebruary2006andMarch2007;resultsrepresent211,468nationwiderespondentsand4,407NewYorkCityrespondents.
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Awareness/Mindset
%whohavelookedforjobinformationonline 50%
%whohavelookedforhealthcareinformationonline 64%
%whohavereadonlinenews 67%
%whohaveusedtheCity'swebsite 49%
%whohavetakenanonlineclass/training 31%
%whodonotusetheinternetathomebecausetheyarenotinterested
16%
Thefindingssummarizedbelowincludetheresultsofstatisticalanalysisofinternetuseand
accessinChicago(describingless‐connectedaswellasdisconnectedresidents);useofpublicaccesssitessuchaslibraries,communitytechnologycentersandwirelessnetworks;barrierstoaccess;internetuseforeconomicopportunity,civicengagement,healthcare,ande‐government;andsupportforpublic
andprivateeffortstofosterdigitalexcellence.
AccessandUse
Achievingdigitalexcellencerequiresthatresidentsbeabletousetheinternetregularlyandeffectively,includingafull,media‐richexperience.Wethereforeneedtounderstandwhousestheinternetinanyplace,whousestheinternetathome,andwhohashigh‐speedbroadbandconnections
athome.Residentswhoareonlinedailyaremostlikelytohavehomeconnections,andalsotohavebroadband,whichencouragesfrequentuse,awiderrangeofactivitiesonline,andatleastsomebasiclevelofskill.Broadbandisneededforfullconnectivitytothewebaswellasfrequentuse.
Seventy‐fivepercentofChicagoresidentsusetheinternet,atleastoccasionally.Sixpercentof
thecity’spopulation,however,isonlineattimesbutdoesnotusetheinternetathome,andanothereightpercentrelyonslowdial‐upconnections.Thismeansthatnearly40percentofChicagoresidentsareeitherentirelyofflineorhavelimitedaccess.Topromotefullandmeaningfulparticipationonline,it
isnecessarytobetterunderstandwhocomprisesboththedisconnectedandtheless‐connected.
Chicagoanswhoarestatisticallymorelikelytobeofflineorless‐connectedareolder,Latino,African‐American,low‐incomeandless‐educated.ResidentsofneighborhoodswithahighpercentageofAfrican‐AmericansandLatinosareparticularlydisadvantagedintermsofinternetuse.Thereare
somedifferences,however,indisparitiesforuseanywhereversushomeuseandbroadband.
OlderandLatinoresidentsareleastlikelytousetheinternetanywhere(althoughincome,education,andracearesignificantpredictorsforbeingonlineinanyplaceaswell).Thegapsbasedonracealone,forAfrican‐Americans,arerelativelysmallforinternetuseanywhere,andAfrican‐Americans
arenodifferentfromwhiteswhenweaccountfordifferencesintheneighborhoodswheretheylive.But,disparitiesbetweenAfrican‐Americansandwhitesarelargerforhomeaccess,indicatingthatmany
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low‐incomeAfrican‐Americansareamongtheless‐connectedwholackhomeaccess,butgoonlineelsewhere.Incontrast,Latinos(andolderresidents)lagbehindinuseanywhere,homeaccess,and
broadband.
Incomeisthemostimportantfactorinfluencinghomeaccess,incontrasttointernetuseanywhere.Incomealsoaccountsforthelargestdifferencesbetweenbroadbandanddial‐upinternetusers,althoughthesearemoremodest,andthelargerbarrierisacquiringhomeaccessofanykind.This
suggeststhatgreateraffordabilityofinternetservices(aswellasneededhardwareandsoftware)willhelptoclosesomegapsinhomeaccessandbroadbanduse.
PublicAccessandWirelessUse
MostChicagoresidentsareawareofpublicplacestousetheinternet,andmostperceivethemasfairlyaccessible.Thirty‐threepercentofChicagoresidentshaveusedtheinternetatapubliclibrary,
and16percentsaythattheyhaveusedacommunitytechnologycenter.Halfofthosewhouselibrarieshavesoughthelpinfindinginformationonline,andaround30‐40percentoflibraryinternetpatronshaveproblemswithorlackcomputersorinternetconnectionsathome.
ChicagoresidentswhoaremostlikelytousepublicaccessatlibrariesareyoungerandAfrican‐
American.Low‐incomeresidentsarestatisticallymorelikelytousetheinternetatlibraries,butsoarebetter‐educatedChicagoresidents.Latinosareabout8percentmorelikelythannon‐Hispanicwhitestousetechnologyatthelibrary,controllingforotherfactors,butthiscomparesto14percentforAfrican‐
Americans.Homeinternetusersarealsomorelikelytousetechnologyatlibraries,indicatingthatmanylibrarypatronsgothereforhelporconvenience.Still,publiclibrariesarereachinglow‐incomeandminorityresidents,amongothers.
Examininguseofcommunitytechnologycenters(CTCs)inlow‐incomeneighborhoods,wefind
somesimilaritieswithlibraries–youngerandbetter‐educatedresidentsareamongthemostlikelyvisitors.ParentsaremorelikelytouseaCTC,asareAfrican‐Americanresidents.Respondentsresiding
inthepoorestneighborhoodsarealsomostlikelytousetheinternetatacommunitytechnologycenter.
Useofwirelessnetworksinpublicplacesisfairlycommon–35percentofChicagoresidentshaveusedthemtogoonline.Higherpercentagesofyoungpeopleaged18‐29usewirelesshotspots,andthisisalsotheagegroupwiththehighestpercentageconnectingtotheinternetthroughcell
phones.
BarrierstoAccess
Todevisebetterpublicandprivatepoliciesforaddressingdigitaldisparities,weaskedresidentswhytheydidnothavetheinternetathome.Thethreemostcommonchoicesselectedasthemainreasonfornothavinginternetaccessathomewerelackofinterest,cost,anddifficultyofuse.There
werecleardifferencesbetweendemographicgroupsinthereasonsforbeingofflineorless‐connected.
Thosewhoarenotinterestedareolder;ageaccountsforthelargestinfluenceoninterest.Othersmorelikelytosaytheyarenotinterestedarehigher‐incomeresidentswhodon’thavethe
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internet,andless‐educatedresidents.African‐Americansarelesslikelythanwhitestosaythattheyarenotinterested.
Incomeisthemajorfactorexplainingconcernsaboutcost;residentswhosaytheycan’tafford
theinternetarestatisticallymorelikelytobelow‐income.Latinos,andtoalesserextent,African‐Americansarealsoamongthosemorelikelytocitecostasabarrier,controllingforotherfactors.
Chicagoresidentswhoperceivetheinternetastoodifficultareolder,less‐educatedandLatino.African‐Americansarelesslikelythanwhitestosaythattheinternetisdifficulttouse.
GiventhatAfrican‐Americanswhodonothavetheinternetathomearemorelikelytouseit
elsewhere,thisindicatesthatskillandinterestarenottheproblemforthisgroup,butcostisanissueforlow‐incomeAfrican‐Americans.Latinos,incontrast,perceivebothcostandskillasbarriers,andwerealsomorelikelytocitesomeofthelesscommonreasonsfornotbeingonlineaswell.ForLatinos,who
lagfurtherbehindininternetuse,thereappeartobemultipleobstacles.
ActivitiesOnline
Chicagoresidentswhoareonlineengageinavarietyofactivitiesthatillustratethepotentialoftheinternetforimprovingeconomicopportunity,civicengagement,health,andaccesstogovernmentservices.About48percentofChicagoresidents(63percentofemployedresidents)haveusedthe
internetfortheirjobs.Whileinternetuseatworkincreaseswitheducationandincome,itisnotconfinedtohighly‐skilledjobs.ThirtythreepercentofemployedChicagoresidentswithahighschooleducationusetheinternetatworkeitherdailyorseveraltimesperweek.
Themostcommonactivitiesonlineincludedinthesurveyarefollowingthenews(67percentof
Chicagoresidents)andseekinghealthinformation(64percent).Nearlyalloftheactivitiesweaskedaboutwereinfactquitecommon,onceresidentsareonline;forexample,91percentofChicago
internetusersreadnewsonline.Thisshowshowthoroughlydailytaskshavemigratedtotheinternet,andhowintegralthetechnologyisforaccesstoinformationandservices.
Whileyounger,better‐educatedandhigher‐incomeresidentsaremostlikelytoengageinanyactivityonline,therearesomedemographicandneighborhooddifferencesacrossactivities.African‐
Americansarestatisticallymorelikelythanwhitestolookforjobinformationonline.UsersoftheCityofChicagowebsitearemorediversethane‐governmentusersingeneral;womenandparentsareamongthemostlikelyCityofChicagowebsiteusers,andtherearenodifferencesbyraceandethnicity.
Residentsofhigh‐povertyneighborhoodsareamongthosewhoaremostlikelytousepublictransitwebsites.Womenandparentsareamongthemostcommonusersofhealthinformationonline,andtherearenodifferencesbetweenAfrican‐Americansandwhitesforhealthinformationuse.Young
peopleareamongtheresidentswhoaremostlikelytofollowthepoliticsornewsonlineortoaccesse‐government,althoughtheyaretraditionallyleastlikelytobeinterestedinpoliticsorpublicaffairs.Theinternetpresentsthepossibilityofcounteringcurrentinequalitiesforthedisadvantagedorless
engaged.Onceresidentsareonline,theinternetopensnewpossibilitiesfordemocratizinginformationandaccesstoservices.
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PublicPolicySupport
Manyactivitiesareneededtoaddressthevariousdimensionsofdigitalexcellence,butonepolicyinitiativethathasbeendiscussedinChicagoandothercitiesistheprovisionofwirelessinternet
service.Chicagoresidentsexpressedstrongsupportforextendingwirelessaccessinthecity.Atotalof89percentofsurveyrespondentsfavoredsometypeofwirelesspolicy,andwirelessaccessavailablethroughoutthecitywasthepreferredoption(chosenby50percentofrespondents).Therewasalso
supportforalternativessuchaswirelessaccessinschoolsandlibrariesonly(26percent)orinlow‐incomeneighborhoodsonly(13percent).Whenaskedwhethertheywouldbewillingtopayasmalltaxorfeetoprovidewirelessinternetservice,60percentofrespondentsansweredthattheywould.Public
opinionclearlyfavorswirelessinitiativesasonewayofachievingwidespreaddigitalexcellence.
Conclusion
Thesurveyindicatesthatmanygapsininternetuseandaccesspersist,butthatthereareopportunitiesforprogress.Mostresidentsoflow‐incomecommunitiesarenotsimplyuninterestedingoingonline,suggestingthateffortstoprovideaffordableaccess,training,andtechnicalsupportshould
helptonarrowthesegaps.Chicagoresidentsarealreadyengagedinmanyactivitiesonline,forwork,politicalparticipation,healthcareandgovernmentservices.ThissurveyisintendedtoprovidevaluableinsightsforChicagoresidents,businesses,communityorganizations,educationalinstitutionsand
policymakerswhosharethevisionofpromotingdigitalexcellencethroughoutthecity.AsinternetuseexpandsinChicagothroughtechnologicaladvances,inclusivemarketactionsandthoughtfulpublicpolicy,moreresidentswillenjoythebenefitsofparticipatingfullyinsocietyonline.
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PARTI.INTRODUCTION:WHYDIGITALEXCELLENCE?
“Transformingsocietyandeconomythroughdigitalexcellence”istheambitiousgoalthatwassetinMayof2007bytheMayor’sAdvisoryCouncilonClosingtheDigitalDivide.4Theadvisorycouncil
recognizedthatanetworkedcity,withwidespreadinformationtechnologyuse,enjoysadvantagesforattractinghigh‐wagejobs,growingtheeconomythroughinnovation,enrichingcommunitylifethroughinformationandcivicengagement,andrunninggovernmentmoreeffectivelyandefficiently.Thereis
growingevidenceofthebenefitsofinformationtechnologyuseforindividualsaswellassociety.Internetuseatworkincreaseswages,evenwhenwetakeintoaccountdifferencesinotherfactors,suchaseducationandoccupation.5Thisistrueevenforworkerswhohaveahighschooleducationorless,
andthewageincreaseforinternetuseatworkisslightlygreaterforAfrican‐AmericansandLatinosthanforwhitenon‐Hispanicworkers.Internetusealsopromoteshigherlevelsofpoliticalknowledge,discussionandpoliticalinterest,andmanystudieshavelinkedtheinformationandmobilizationcapacity
oftheinternetwithanincreasedlikelihoodofvoting.6E‐governmentusershaveimprovedinteractionswithgovernmentandenjoymoreconvenientaccesstoservices.7Thosewhoareexcludedfromparticipationonlinesufferfromunequalopportunitiesinboththeeconomicandcivicarenas,andthese
disparitiesinturnpreventcommunitiesfromrealizingtheirpotential.
Inclusioninsocietyonlinemeansmorethansimplyhavingsomewayofaccessingtheinternetonanoccasionalbasis.Publicpolicyoftentracksthepercentageofpeoplewhoreportevergoingonline,andthiscertainlyhassomevalue,asweshowhere.But,occasionaluseisnotsufficientto
achievedigitalexcellence,ortoparticipatefullyintheopportunitiesaffordedbytheinternet.Thisrequiresregularandeffectiveaccessandtheskillstousethetechnology.Nationalsurveyshaveshownthathomeaccessisimportantforfrequentuse.Mobiledevicesareincreasinglyexpandingthewaysin
whichtheinternetcanbeaccessed,buthomeinternetconnectionsremainthemostfrequentwaytogoonlineformostinternetusers.Broadbandorhigh‐speedconnectionsarenecessarytotakefull
advantageofcontentonline,especiallythemulti‐mediaandinteractivedimensionsoftheinternet.Broadbandisalsoassociatedwithmorefrequentinternetuseandawiderrangeofuses,andagreaterlikelihoodthatuserswilldevelopnecessaryskills.8Theskillsrequiredforeffectiveinternetusecanbe
describedinavarietyofways,9butcanbethoughtofastechnicalcompetenceandinformationliteracy.Technicalcompetenceistheabilitytousehardwareandsoftware.Informationliteracyonlineincludestheabilitytounderstandwhatinformationmightbeneededtosolveaproblem,tofinditonline,to
evaluateitsutilityandvalidity,andtoapplyit.Obviously,thisrequiresbasicliteracy(orreadingcomprehension)aswellassomeothereducationalcompetenciessuchascriticalthinking.Theinternetisareading‐intensivemedium,andthisisparticularlytrueforfindinginformationaboutpolitics,
governmentservices,healthcare,andjobs.
4Mayor’sAdvisoryCouncilonClosingtheDigitalDivide.2007.TheCitythatNetWorks:TransformingSocietyandEconomyThroughDigitalExcellence.5Mossberger,TolbertandMcNeal2008,chapter2.6Bimber2003;Krueger2002;TolbertandMcNeal2003;Mossberger,TolbertandMcNeal2008.7West2004;Welch,HinnantandMoon2005;TolbertandMossberger2006.8Mossberger,TolbertandMcNeal2008,chapter6.9See,forexampletheliteraciesidentifiedbyMarkWarschauer(2003)andJanVanDijk(2005).
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TheMayor’sAdvisoryCouncil,whichincludedawiderangeofrepresentativesfromfoundations,businesses,community‐basedorganizations,universitiesandgovernment,definedthegoalofachieving
digitalexcellencethroughoutthecity.Accordingtothe2007reportoftheadvisorycouncil,thedriversofdigitalexcellenceare:
1. Effectivenetworkaccessthatishigh‐speed,reliable,affordableandavailableeverywhere.2. Affordablehardwarewithcapacitytoconnecttotheinternetandtapintothefullrangeofits
visualandotherresources.3. Suitablesoftwarethatmeetstheneedsofindividuals,families,business,andcommunities.4. Digitaleducationthatprovidesthetrainingandtechnicalsupportforuserstobecome
comfortableandproficient.5. Evolvingmind‐setsthatvaluelearning,connectingandcommunicatingthroughtechnology,
andthatrecognizethebusinessandotheropportunitiesofexpandinginternetparticipation.
(ExecutiveSummary,p.2,TheCitythatNetWorks:TransformingSocietyandEconomyThroughDigitalExcellence)
ThiscurrentreportprovidesaninitialviewofinternetuseandattitudesaboutinformationtechnologyinChicagotoassessareasofneedforprograms,policiesandtechnologicalinfrastructure.
Thereportprovidesuniqueinsightintowhereandwhya“digitaldivide”existsinalargemunicipalityandthedatacanbeusedtoinformpublicandprivatesectordecision‐makersandcommunityleaders.Wediscussinternetuseinavarietyofsettings,fromhomeaccesstopublicaccess,andtrendstoward
theuseofwirelessdevices.Beyondaccess,weexamineskillsintermsoftheabilitytousetheinternetfrequentlyandtoengageinavarietyofactivitiesonline.WeexplorethereasonswhysomeChicagoresidentsarenotonline,andwhatmightmotivatethemtousetheinternetinthefuture.
Thisreportisbasedonarandom‐sampletelephonesurveyofCityofChicagoresidents,conductedinJuneandJuly2008.ThestudywasdesignedbyresearchersattheUniversityofIllinoisatChicagoandtheUniversityofIowa,andthesurveywasconductedbytheUniversityofIowaHawkeyePoll.The
surveyyieldedasampleof3453respondentsfromChicago’s77communityareas.ThesurveywasconductedinSpanishandEnglish,andthecooperationratewas27percent,whichistypicalfortelephonesurveys.Thesampleofresidents18yearsandolderwasfairlyrepresentativeofChicago’s
population.Ofsurveyrespondents,45percentwerewhitenon‐Hispanic,31percentwereAfrican‐American,3percentAsian‐American,19percentLatinoand3percentotherormixedrace.Thepercentagesreportedinthisstudyareweightedtocorrectfordifferencesbetweenthesampleandthe
population.
PARTII.INTERNETUSEANDACCESS
InternetuseintheCityofChicagolooksremarkablyliketherestofthenation.Chicagoasawholeiskeepingpacewithnationalaverages,butasadiversecity,italsoreflectsthedisparitiesininternetusethatpersistnationwide.Byaddressingthesegaps,Chicagohasthechancetocreate
modelsfora21stcenturycitythatareforwarding‐lookingbothtechnologicallyandsocially.
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Asofsummer2008,75percentofChicagoansusedtheinternet,incomparisonwithnationalfiguresof73percentduringthesameperiod.10SomecautionshouldbeexercisedincomparingChicago
withnationaldata,particularlyinreachingconclusionsbasedondifferencesofafewpercentagepoints.11So,whiletherearesomesmalldifferencesbetweenChicagoandthenation,thesoundestconclusionisthattheyarequitesimilar.
75%ofChicagoresidentsusetheinternetatleastoccasionally
Broadbandconnectsuserstotheinternetathigherspeedsthandial‐upmodem,andismostcommonlyavailablethrougheitheracablemodemordigitalsubscriberline(DSL).12Beyondspeed,
however,broadbandencouragesfrequencyofusebecauseitpermits“alwayson”connectionsthatdon’toccupyaphoneline.InChicago,61percentofthecity’spopulationhasabroadbandconnectionathome,incomparisonwith55percentoftheU.S.population.Thismayreflecttheinfrastructure
advantagesofamajorcityoverruralareas,althoughthedifferencesmaybepartlyduetosamplingforeitherthenationalorChicagosurvey.
61%havebroadband;60%goonlinedaily
Although75percentofcityresidentshavesomeexperiencewiththeinternet,only60percentusetheinternetonadailybasis.Frequentuseisimportantfordigitalexcellence,becausethosewhoareonlinefrequentlyaremorelikelytohavebothregularaccessandatleastsomebasicinternet
knowledgeandskills.
Frequentuseoccursathomeandwithbroadband;4in10facebarrierstofullaccessinChicago
Overall,25percentofChicagoresidentsarecompletelyoffline,another6percentusethe
internetattimesbutlackhomeaccess,and8percenthavemorelimitedandslowdial‐upconnectionsratherthanhigh‐speedbroadband.Approximately60percentofChicagoresidentshaveadequateaccess,butnearly40percenthavesomewhatlimitedornointernetaccess.Thus,4in10face
technologybarriersofvaryingdegrees.
Boththosewholackhomeaccessandbroadbandarelessfrequentinternetusers,andboththedisconnectedandless‐connectedtendtobeeconomicallydisadvantagedorolderresidents.ThelargestgapsarebetweenSpanish‐speakingandEnglish‐speakingresidents,asonly39percentofthosewho
respondedtothesurveyinSpanishusetheinternet,comparedto79percentofthosewhoansweredin
10May2008trackingsurvey,PewInternetandAmericanLifeProject,pewinternet.org11Random‐samplesurveyshaveamarginoferrorofplusorminusafewpercentagepoints.Whatthismeansisthatsurveysmayreflectthepeculiaritiesofthepeoplewhohappenedtorespond,tosomeextent.TheChicagosurveyreportedherehasamarginoferrorofplusorminus1.7percentagepoints.12TheFederalCommunicationsCommissiondefinesbroadbandasanyconnectionwithtransmissionspeedofatleasttwohundredkilobitspersecondinonedirection.
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English,a40percentdifferencebasedonlanguageuse.Latinosasawholestandoutasamongtheleast‐connectedresidents.
Only39%ofrespondentswhotookthesurveyinSpanishusetheinternet
Thenextsectiononinternetaccessoffersamorecompletepictureofdisparitiesininternetuseinanyplace,homeaccess,andbroadbandconnections.Thesethreeelementsareneededto
understandtheneedsoftheless‐connectedaswellasthedisconnectedinChicago.First,however,weexplainhowtocompareandusedifferenttypesofinformationdiscussedinthereport.
ReadingtheResults
Inmanycasesinthisreportweusesimplepercentages,suchasthoseinthesectionabove.Weexaminethekeypointsinmoredepthusingstatisticalanalysis,butpresentthefindingsinwaysthatdo
notrequirethereadertohaveanystatisticalbackground.Thesemoreanalyticalfindingsarereportedin“WhatMatters”boxesthataresetapart.Simplepercentagescantelluswhatproportionofthepopulationdoessomething‐forexample,whatpercentageofLatinosusestheinternet.But,they
cannotexplainwhetherethnicityitselfhasanyeffectoninternetuse,orwhetheritisreallyeducationandincomethatexplaindifferencesbetweenLatinosandnon‐Hispanicrespondents.Todisentangletheeffectsofoverlappinginfluences,weuseamethodcalledmultivariateregression(orlogisticregression)
toisolatethefactorsthatarestatisticallysignificantpredictorsofinternetuse.Whatthisallowsustosay,forexample,iswhetherLatinoethnicityisasignificantpredictoroftechnologyusewhenwecontrolfortheeffectsofincome,educationandage.Themultivariateregressionmodelsarereportedinthe
appendix.Whennumbersarepresentedoutsideofthedesignated“WhatMatters”boxestheyarebasedonsimplepercentages.
Abriefwordisnecessaryonhowtousetheinformationinthe“WhatMatters”tables.Withinthesehighlightedtables,weincludeprobabilities(basedonmultivariateregressionanalysis)that
representthedifferencethateducationmakes,forexample,takingotherinfluencesintoaccount.Thesecanbereadaspercentages–whatpercentagedifferenceahighschooleducationmakesincomparisonwithacollegedegreewhenwecontrolforotherfactors.But,thekeydifferencebetween
ourprobabilitiesandsimplepercentagesisthattheprobabilitiesshowtheestimatedinfluenceofeducationalone,ifwecontrolforotherinfluences.
Anexamplewillhelptoshowthedifferencethismakes.Ifwelookatthesimplepercentagesforeducation,forexample,weseethat58percentofhighschoolgraduatesusetheinternetand88
percentofcollegegraduatesareinternetusers(a30percentdifference).Whenweusepredictedprobabilities,weseethatcollegegraduatesare15percentmorelikelythanhighschoolgraduatestobeinternetusers(not30percent),takingintoaccounttheeffectsofincome,age,gender,race,ethnicity,
andsoon.Bothtypesofinformationareuseful,buttheytellusdifferentthings.Withsimplepercentages,weknowhowcommonitisforcollegegraduatestobeonlineincomparisonwithhighschoolgraduates.Withpredictedprobabilities,weknowthateducationisresponsibleforonlysomeof
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thisdifference(15percent),because,forexample,collegegraduatesalsohavehigherincomes,whichalsocontributetothe30percentage‐pointgapbetweenthesetwogroups.
Oneofthestrengthsofusingpredictedprobabilitiesistheabilitytocomparetheimpactof
differentfactorssuchaseducation,age,orincome.Welookatthepercentagedifferencemovingoneortwo“standarddeviations”fromthemean.Thisdefinesstatistically(basedonthedistributionofresponsesinthesurvey)what“low”or“high”isforeachexplanatoryfactor,anditsimplyallowsusto
comparetheinfluenceofloweducationversuslowincome,forexample.
Thediscussionafterthe“WhatMatters”tablesincludestheresultsofmultilevelmodels(whichcanbefoundinAppendixB).Theseareanalysesthatincludetheinfluenceofcharacteristicsofthesurroundingneighborhood(thecensustract)inadditiontothefactorsthatappearinthe“What
Matters”tables.Wemergedourindividual‐levelsurveydataontechnologywith2000datafromtheU.S.CensusBureauinformationforChicago’s700censustracts.13Multilevelregressionmodelswereestimatedtakingintoaccountindividual‐levelcharacteristics(arespondent’sage,race,ethnicity,
income,education,genderandparentalstatus),aswellasneighborhoodorcensustractlevelcharacteristics(percentLatinopopulation,percentblackpopulation,percentAsianpopulation,percentresidinginpovertyandpercentofthepopulationwithahighschooldegreeorhigher).Thesemultilevel
models(includingneighborhoodcharacteristics)wereusedtoestimateinternetuseforeachofthe77communityareasandeachofthecensustractsinChicago.Communityareamapsincludedinthisreportarebasedonthesemodels,aswellasthediscussionoftheimpactofcommunitycharacteristics
inthetext.14
Theresultsoftheregressionanalysisarehighlightedinthe“WhatMatters”tables,and
discussedinthetextimmediatelyafterwards.WeconsiderthisthemostreliablewaytounderstanddisparitiesintechnologyuseintheCityofChicago.Thepredictedprobabilitiesinthe“WhatMatters”
tablesprovidethemostaccuratepicture,isolatingtheeffectsofoverlappinginfluences.Thisoffersbetterguidanceforpolicy,toknowthetrueimpactofeducationversusincome,forexample,onthedisparitiesthatareevident.
Topromotedigitalexcellence,policy‐makersandcommunityleadersneedtotargetboththose
whoneverusetheinternetandthosewhohavelimitedexperienceonline.Regularandeffectiveuse13Anewvariablewascreatedthatwasthepercentageofthecommunityarea’spopulationthatresidesinthecensustract(totalpopulation/communityareatotalpopulation).Foreachexplanatory/predictorvariable(let'ssaypercentLatino),thefollowingformulawasapplied:(percentLatino/100)*[multipliedby]theweightedpopulationvariable.Thesecensustractvalueswerethencollapsedandsummedtogetthetotalcommunityareavalue.Thenewcommunityareageographiccharacteristicsweremergedbackintotheindividuallevelsurveydata.14ThesemultilevelmodelsprovideaccuratepointestimatesofthepercentofChicago’spopulationwithhomeinternetaccessatthegeographiclevel.ThecensustractlevelinformationwasalsoaggregatedtothecommunityareatoprovideestimatesoftechnologyuseandaccessforChicago’s77communityareas.Foreachdependentvariable,twomultilevelmodelswereestimated:onecombiningthesurveydatawithneighborhood(censustract)information,andonecombiningthesurveydatawithcommunityareageographicinformation.Thesepointestimatescanbereadaspercentages,buttheytakeintoaccountmultipleindividuallevelandgeographiccharacteristicslistedabove.Weconsiderthesemodelsfullyspecified.
15
requireshomeaccessandhigh‐speedconnections,asthissectiondemonstrates.Wecompareinternetuse,homeaccess,andbroadbandinthefollowinganalysis,toshowwherethegreatestgapsexist.
WhatMattersTableA:WhoUsestheInternetinAnyPlaceandWhoHasHomeAccess?
Thefactorslistedbelowhaveastatisticallysignificantinfluenceoninternetuse.Aplussign(+)indicatesincreasedprobabilityofinternetuse,andaminussign(‐)indicatesdecreasedchancesofinternetuse.InternetUseinAnyPlace InternetUseatHomeAge(‐) Income(+)Latino(‐) Age(‐)Income(+) Education(+)Education(+) Latino(‐)African‐American(‐) African‐American(‐) Parent(+)ReadingPredictedProbabilities:Afemale,whitenon‐HispanicChicagoresidentwithnochildrenandaverageage,income,andeducationhasa90percentprobabilityofusingtheinternetinanyplaceandan81percentchanceof
usingtheinternetathome.Resultsshownbelowindicate,forexample,thatarespondentwhoisLatino,butotherwisethesame,hasonlya72percentchanceofusingtheinternetanywhere.Latinoethnicityalonemakesan
18percentdifference(holdingotherfactorsconstant).Readthenumbersnotinparenthesesaspercentages.
UseInternetinAnyPlace UseInternetatHomeWhitenon‐Hispanic(Baseline) .90(.01) .81(.02)Latino .72(.03) .71(.03)DifferenceLatinovs.White ‐.18 ‐.10Black .84(.02) .71(.02)DifferenceBlackvs.White ‐.06 ‐.10AnnualIncome VeryLow($0,‐2SD) .61(.04) .40(.04)Low($10,000‐$20,000,‐1SD) .79(.02) .62(.03)Mean/Average($40,000‐$50,000) .90(.01) .81(.02)High($75‐$100,000,+1SD) .96(.01) .91(.01)VeryHigh(morethan$150,000,+2SD) .98(.00) .95(.01)DifferenceLowtoHigh +.17 +.29EducationLevel LessthanHS .70(.03) .59(.03)HighSchoolGraduate .79(.02) .68(.03)SomeCollege .91(.01) .81(.01)CollegeGraduate .94(.01) .86(.01)GraduateDegree .96(.01) .90(.01)DifferenceHStoCollege +.15 +.18Ageofrespondent Veryyoung(18yrs,‐2SD) .99(.00) .95(.01)Young(31yrs,‐1SD) .97(.00) .91(.01)Mean/Average(49yrs) .90(.01) .81(.02)Old(,67yrs,+1SD) .70(.02) .63(.02)Veryold(85yrs,+2SD) .37(.03) .40(.03)DifferenceYoungtoOld(27‐67yrs) +.27 +.28
Note:PredictedprobabilitiescalculatedwithClarifySoftwarefromthelogisticregressionmodelsreportedinAppendixTables1and2.Probabilitiesestimatedwithcontrolvariablessetatmeanormodalvalues.Standarderrorsoftheprobabilityestimateinparentheses.
16
InternetUseinAnyPlace
“WhatMatters”TableA,column1,showsthatChicagoreflectsthesamegapsininternetusethatarepresentnationally,butthatracialdisparitiesareconsiderablysmallerthanthosebasedonLatinoethnicity.Respondentswereasked“DoyouusetheInternetinanyplace?”andcouldrespond
“yes”or“no.”Thosewhoarelesslikelytousetheinternetinanyplaceareolder,Latino,African‐American,lower‐income,andless‐educatedresidents.Asinnationalsurveyssince2000,womenarenolesslikelytobeonlinethanmen.15
• Thelargestgapsarebasedonage,consistentwithnationalsurveys.Withallotherfactorsheld
constant,ayoungperson(31yearsofage,whichisminusonestandarddeviationfromthemean)hasa97percentprobabilityofinternetuseingeneral,comparedtoa70percentprobabilityforanolderperson(67yearsofage,whichisplusonestandarddeviationfromthe
mean).Thisisa26percentdifferencebasedonagealone.
• Thenextlargestgapisbasedonethnicity.Holdingconstantallotherfactors,Latinosare18percentlesslikelytousetheinternetthannon‐Hispanicwhites.ThiseffectdoesnotreflectlowerincomesofLatinoscomparedtowhites,butisbasedonethnicityaloneandSpanish
languagebarriers.LatinoswhochosetoconductthesurveyinterviewinSpanishratherthanEnglishhaveparticularlylowratesofinternetuse.16
• Incomeranksnextininfluencinguse.Thepoor(withannualincomesofbetween$10,000and$20,000peryear,whichareminusonestandarddeviationfromthemean)are17percentless
likelytousetheinternetingeneralthanthosewithhigherincomes($75,000‐$100,000peryear,whichisplusonestandarddeviationfromthemean).
• Educationgapsarenearlyasimportantasincome.Holdingallotherfactorsconstant,ahighschoolgraduateis15percentlesslikelytousetheinternetthanacollegegraduate.
• African‐Americansare6percentlesslikelytousetheinternetthanwhites.Thisgapissmaller
thanhasbeenreportedbyearliernationalsurveys,showingeitherimprovementoverthepastfewyearsorgreaterprogressinnarrowingracialgapsinChicagoinparticular.17WhilemanyAfrican‐Americansarestilloffline,raceaccountsforasmallerpartoftheexplanationfortheir
lackofinternetusethandisparitiesinincomeandeducation.
ThoseleastlikelytobeonlineareolderandLatinoresidents
15KatzandRice2002;Mossberger,TolbertandStansbury2003.16NationalstudiesofLatinosshowthatlanguageisasignificantfactorforinternetuseamongLatinos,althougheducationisalsosignificant.SeeFoxandLivingston2007.17The2003CurrentPopulationSurveyconductedbytheU.S.BureauoftheCensusshowslargergapsbasedonrace,forexample,asdiscussedinMossberger,TolbertandMcNeal(2008),chapter5.
17
HomeInternetUse
Respondentswerealsoasked“Doyouusetheinternetathome?”18Column2ofWhatMattersTableAshowstheprobabilityofhavingtheinternetathome.Theresultsparallelthefindingsfor
internetuseingeneral,butwithsomeimportantdifferences.
Incomeismostimportantforhomeuse,andtheeffectsofincomearesubstantial
Thesesimulationsdemonstratethatincomecausesthelargestgapsinhomeinternetaccess,followedbyage,whileageandLatinoethnicitycausethelargestgapsinuseoftheinternetinanyplace.
• Thelargestgapinhomeinternetaccessisbasedonarespondent’sfamilyincome,notage.Thepoor(onestandarddeviationbelowthemean)are29percentlesslikelytohaveaccessthanhigher‐incomeresidents(plusonestandarddeviationabovethemean),allelseequal.Poor
Chicagorespondentshaveonlya62percentprobabilityofhavinghomeaccesscomparedtoa91percentprobabilityofhomeaccessforhigher‐incomeresidents.Low‐incomerespondentsare29percentlesslikelytohavehomeaccess,butonly17percentlesslikelytobeinternet
usersanywhere.Thismakessense,giventheinvestmentinhardwareandsoftwareandthemonthlyinternetbillsassociatedwithhomeinternetuse.
• Disparitiesinhomeaccessbasedonagearesignificant.Theyoungare28percentmorelikelytohavehomeinternetaccessthanolderrespondents;a31yearold(onestandarddeviationbelow
themean)hasa91percentprobabilityofhavinghomeaccesscomparedtotheanolderindividual(67years,onestandarddeviationabovethemean),whohasonlya63percentprobabilityofhavingtheinternetathome.
• Educationalattainmentisthenextlargestpredictorofgapsininternetuseathome.Acollege
graduateis18percentmorelikelytohavetheinternetathomethanaresidentwithonlyahighschooldiploma,allelseequal.
• BothAfrican‐AmericansandLatinosare10percentlesslikelytohavehomeInternetaccessthanwhitenon‐Hispanics.GapsforhomeaccessarewiderforAfrican‐Americansthanforinternet
useanywhere,suggestingblacksaremorelikelytotakeadvantageofinternetaccessoutsidethehome.
• Parentsarealsomorelikelytohavetheinternetathome,althoughtheyarenotmorelikelytobeinternetusers.
18Responseswerecoded1foryesand0forno.
18
NeighborhoodVariationforInternetUseAnywhereandatHome
Tounderstandhowneighborhoodsvary,multilevelmodels(AppendixB)canbeusedtoincludethefactorslistedabove,aswellascensusdataonneighborhoodcharacteristics:poverty;percentageof
highschoolgraduates;andpercentageofAfrican‐Americans,LatinosandAsian‐Americans.Neighborhoodshaveasignificantinfluenceonbothinternetuseanywhereandhomeaccess:
InternetUseinAnyPlace
• ResidentsofneighborhoodswithhighpercentagesofAfrican‐AmericansorLatinosarelesslikelytousetheinternetanywhere.
• Infact,thedifferencesbetweenAfrican‐Americansandwhitesinuseanywherecanbeexplainedbyneighborhoodfactors–byresidenceinhigh‐povertyminorityneighborhoods.Thisisconsistentwithnationalresearch.19
HomeInternetUse
• ResidentslivinginneighborhoodswithahigherpercentageofLatinosarealsolesslikelyto
havehomeaccess.
• ResidentsofcommunitieswithahighproportionofAsian‐Americanshavehigherratesofhomeuse.
Thereisconsiderablevariationacrossthecommunityareasforbothinternetuseanywhereand
alsointernetuseathome.Themultilevelmodelswereusedtoestimateinternetuseanywhereandhomeaccessforthe77CommunityAreasinChicago.Theseareasaretraditionallyusedformanyplanningpurposes.
Themaponthenextpagehighlightsthevariationinhomeinternetuserevealedbythemodels.
Redareashavethelowestratesofhomeinternetuseinthecity,andblueareashavethehighestrates.Communityareascoloredinblueareestimatedtohaveatleast65percentofresidentswhohaveinternetaccessathome.Thisisalittleunderthecity‐wideaverageforhomeaccessof69percent.
Areasinbluethereforeapproachorexceedthecityaverage.Areaswithlowerinternetuse(redoryellow)areprimarilylow‐incomeandAfrican‐AmericanorLatinoneighborhoods.Homeaccessisanimportantsteptowarddigitalexcellencebecauseitencouragesfrequentinternetuse,andmanyof
Chicago’ssouthandwestsidecommunityareasaredisadvantagedinthisrespect.
19Mossberger,TolbertandGilbert2006
19
20
BroadbandAccess
Broadbanduseisimportantforanumberofreasons.Today,manyapplicationsrequirebroadbandspeedsforfullconnectivity.Downloadinggraphicsanddocuments,submittingonlineforms,making
commercialtransactionsandpayments,accessingmanywebsites,orviewingvideosonlinecanbedifficultwithdial‐upaccess.Theslowerspeedsareoftenfrustrating,discouragingfrequentinternetuse.Nationalstudieshaveshownthatthosewhousebroadbandaremorelikelytobefrequentusers,to
engageinalargervarietyofactivitiesonline,andtohavehigherlevelsofinternetskill.20Broadbandisclearlythestandard,asmostChicagoresidents(61percent)havehigh‐speedconnectionsathome.
Thefactorsthatinfluencebroadbandusearesimilartothosethataffectinternetusemoregenerally,exceptthatdifferencesbetweenbroadbandanddial‐upusersarelesspronouncedthan
betweenthosewhodonothavehomeaccessatallandthosewhodo.Homeaccessisthelargerhurdle,butbroadbandcostsfurtherrestrictfullaccessforsome.
Thegapsbasedonincomearethelargest,althougholderresidents,less‐educatedresidents,andLatinosareamongtheleastlikelytohavehigh‐speedinternetratherthandial‐upathome.(Seemodelsonthenextpage.)
• Low‐incomeresidentsare12percentlesslikelytohavehigh‐speedconnectionsratherthandial‐up,butare29percentlesslikelytohavehomeinternetaccessofanykind(seeWhatMattersTableA).Giventhatcomparisonsarebetweendifferenttypesofhomeinternetusers,disparitiesaremoremodest.Butincomeisthemostimportantfactorexplainingdifferencesbetweendial‐upandbroadbandusers.
• Agerankssecondinitsinfluenceonbroadbanduse.Anolderrespondent(age67)is10percentlesslikelythanayoungChicagoresident(age31)tohavebroadbandratherthandial‐upathome.
• Latinosare6percentlesslikelythannon‐Hispanicwhitestohavebroadbandathome,controllingforfactorsotherthanethnicity.
• Educationissignificant;collegegraduatesare5percentmorelikelytohavehigh‐speedconnectionsthanhighschoolgraduates.
• OnenotabledifferencecomparingbroadbandwithinternetuseingeneralisthatAfrican‐Americansarejustaslikelytohavebroadbandasnon‐Hispanicwhites,controllingfordifferencesinincomeandeducation.Nationally,therewasadramaticincreaseinbroadbandadoptionsbyAfrican‐Americansin2006,ashigh‐speedsubscriptionratesfellslightlyinpriceandaslessexpensiveDSLbecamemorewidelyavailable.21Incontrast,broadbandusehasnotgrownsubstantiallyamongthepoor,evenwithpricedeclines.22
Incomemattersmostforbroadbandaccess
WHATMATTERSTABLEB.WhoHasaBroadbandConnectionComparedtoDial‐up?20Horrigan2005;Mossberger,TolbertandMcNeal2008,chapter6.21Horrigan2006.22Horrigan2008.
21
Thefactorslistedbelowarethestatisticallysignificantdifferencesbetweeninternetuserswithbroadbandanddial‐upconnectionsathome.Aplussign(+)indicatesincreasedprobabilityofbroadbanduse,andaminussign(‐)indicatesdecreasedchancesofbroadbanduse.BroadbandUsevs.Dial‐Up Income(+) Age(‐)Education(+)Latino(‐)Asian‐American(+)ReadingPredictedProbabilities:Afemale,whitenon‐HispanicChicagointernetuserwithnochildrenandaverageage,income,andeducationhasa92percentprobabilityofusingbroadband.Resultsshownbelowindicate,for
example,thataninternetuserwhoisLatino,butotherwisethesame,hasonlyan87percentchanceofusingbroadband.Latinoethnicityalonemakesa5percentdifference(holdingotherfactorsconstant).Readthe
numbersnotinparenthesesaspercentages.
BroadbandConnectionversusDialupAccess
Baselinea:Whitenon‐Hispanic .92(.01)Latino .87(.02)DifferenceLatinovs.White ‐.05Black .92(.01)DifferenceBlackvs.White 0Income VeryLow($0,‐2SD) .74(.05)Low($10,000‐$20,000,‐1SD) .83(.02)Mean/Average($40,000‐$50,000) .92(.01)High($75‐$100,000,+1SD) .95(.01)VeryHigh(morethan$150,000,+2SD) .96(.01)DifferenceLowtoHigh +.12EducationLevel LessthanHS .84(.03)HighSchoolGraduate .87(.02)SomeCollege .91(.01)CollegeGraduate .93(.01)GraduateDegree .94(.01)DifferenceHStoCollege +.06Ageofrespondent Veryyoung(18yrs,‐2SD) .96(.01)Young(31yrs,‐1SD) .95(.01)Mean/Average(49yrs) .92(.01)Old(,67yrs,+1SD) .85(.02)Veryold(85yrs,+2SD) .75(.03)DifferenceYoungtoOld(2767yrs) ‐.10
Note: Predicted probabilities calculated with Clarify Software from the logistic regression models reported in
AppendixTable3.Probabilitiesestimatedwithcontrolvariablessetatmeanormodalvalues.Standarderrorsoftheprobabilityestimateinparentheses.
NeighborhoodVariationforHomeBroadband
22
Multilevelmodelsthatintroduceneighborhoodcharacteristicsareconsistentwiththeaboveresults(seeAppendixB),exceptthat:
• Broadbanduseissignificantlymorelikelyforresidentsofneighborhoodswithhighereducationalattainment.
Themodelsestimatingbroadbanduseineachofthecommunityareasarenotmappedhere,butwecanseetherangeacrossChicagobyexaminingthecommunityareaswiththehighestandlowestratesofbroadbandpenetration.LincolnPark(CommunityArea7)ranksatthetopforbroadbanduse,with90percentofthepopulationestimatedtobehigh‐speedinternetusers.LincolnParkisacommunityareawithhighincomeandeducationalattainment.AttheotherendofthespectrumisSouthLawndale(CommunityArea30),whereonly25percentofthepopulationhasbroadbandaccordingtothemultilevelestimates.
SummingUpWhatMattersforUseandAccess
Therearesomecommoninfluencesoninternetuse,homeaccess,andbroadbandaccess,withage,income,education,race,andethnicityappearingassignificantfactorstovaryingdegrees.Latinoandolderresidentsareleastlikelytousetheinternetanywhere,andgapsbasedoneducationandincomeranknext.African‐Americansaresignificantlylesslikelythanwhitestousetheinternet,butraceaccountsforthesmallestdisparityininternetuse.Infact,whenwetakeneighborhoodfactorsintoaccount,African‐Americansarenolesslikelytobeonlinethanwhites(butresidentswholiveincommunitieswithhighpopulationsofAfrican‐AmericansorLatinosaredisadvantagedininternetuse).Thissuggeststhatcommunityenvironmenthassomeindependenteffect,beyondindividualcharacteristics.
Forhomeinternetuse,alloftheindividual‐levelfactorsaresignificant,buttheeffectsofincomearemagnified–accountingfora29percentdifferenceinhomeaccessincomparisonwitha17percentdifferenceforuse.African‐Americansexperiencewidergapsforhomeaccessthanforinternetuse,indicatingthatmanyrelyonuseoutsidethehome.LivinginaneighborhoodwithahighpercentageofLatinosalsodecreasesthelikelihoodofhomeinternetuse.Incomeismostimportantforexplainingthedifferencebetweendial‐upandbroadbandusers,althougholder,less‐educatedandLatinoresidentsarealsosignificantlylesslikelytohavebroadbandratherthandial‐up.
DescribingtheLess‐Connected
Policyattentionisoftenfocusedonlyoninternetuse,butthequalityofaccessisalsoimportantfordigitalexcellence.Thosewhoarenotonlineathomeusetheinternetlessfrequently;83percentofChicagoresidentswithhomeaccessareonlinedailyincontrastwith7percentofthosewhodonothavehomeaccess.Similarly,88percentofthosewhohavebroadbandathomeareonlineeveryday,comparedto54percentofthosewhohavedial‐upinternetaccess.
83%ofChicagoresidentswithhomeaccessareonlinedaily,butonly7%ofthosewithouthomeaccessare
Takingacloserlookatthe6percentofcityresidents(10percentofinternetusers)whousetheinternetbutdonothavehomeaccess,wecanseesomerevealingpatternsinthesimplepercentages
thatroundoutthepicturewegetfromtheprobabilities.Theseindividualsareamongtheless‐connectedinternetusersinthecity,fortheyrelysolelyonwork,school,publicaccess,orthehomesof
23
friendsandrelatives.Asthenumbersshow,theyarelow‐incomeresidents.ThesimplepercentagesinTable1showtheproportionsofinternetusersindifferentincomegroupswhodonothavehome
access.
TABLE1.INTERNETUSEWITHNOHOMEACCESSBY2007TOTALFAMILYINCOME
%ofinternetusers withouthomeaccess
Lessthan$5,000 28%5tounder$10,000 26%10tounder$20,000 17% 20tounder$30,000 10%30tounder$40,000 9%40tounder$50,000 5%50tounder$75,000 5% 75tounder$100,000 3% Over$100,000 2% Totalforinternetusers 10%
Amongthosewhodonothaveinternetaccessathome,therearesomedifferencesbasedonraceandethnicity.AhigherpercentageofAfrican‐AmericansandAsian‐Americanswhodonothavethe
internetathomegoonlineanyway.Only22percentofwhiteswithouthomeaccessareinternetusers,whereas27percentofAfrican‐Americanswithouthomeaccessare,and29percentofAsian‐Americanswithouthomeaccessgoonlineelsewhere.Incontrast,only16percentofLatinoswhodonothave
homeaccessareinternetusers.23ThefindingsforAfrican‐AmericansareconsistentwithastudyinnortheastOhiothatdiscoveredthatpoorAfrican‐Americanneighborhoodshadhigherproportionsofinternetuserswholackedhomeaccessthanpoorwhitecommunities.Thisdemonstratedefforttogo
onlinedespitealackofconvenientaccess,butitalsomeansthatpoorAfrican‐Americanstendtobeless‐connectedinternetusers.24Theseresultsalsosuggestthataffordabilityistheissueforlow‐incomeAfrican‐Americansratherthanalackofawarenessofthebenefitsofbeingonline.InChicago,itisclear
thatAsian‐AmericansarealsomorelikelytogoonlinewithouthomeaccessandthatLatinosareleastlikelytobeinternetusersawayfromhome.
Residentswhohaveinternetaccessathomehaverealadvantagesineaseofuse,andgreaterprospectsfordevelopingtheskillneededforinternetuseonthejob.High‐speedconnectionsfacilitate
frequentinternetuseandthemigrationoftasksonline,sothatbroadbandusersengageinagreaterrangeofactivitiesonlineandgaingreaterfamiliaritywiththeinternet.Convenient,qualityaccessathomeallowsresidentstofollowpoliticsorneighborhoodeventsonline,helpchildrenwithhomework
assignments,learnmoreabouthealthissues,searchforjobs,takeanonlineclass,orstartasmallbusiness.Manyoftheseactivitiesaredifficulttosustainwithonlyintermittentaccess.
23Thesearesimplepercentagesratherthanprobabilitiesbasedonregressionanalysis.24Mossberger,KaplanandGilbert2008
24
Still,publicaccessinlibrariesandcommunitytechnologycenterscontinuestobeanimportantresourceforthosewhowouldotherwisenotbeonlineatall.Inaddition,publicaccesssitescanprovide
trainingortechnicalsupporttoencourageinternetuseanddevelopdigitalskills.Thenextsectionexaminesinternetuseinpublicplaces,particularlylibrariesandcommunitytechnologycenters,aswellaspublicwirelessaccess.
PARTIII.PUBLICANDWIRELESSACCESS
Inadditiontobusiness‐sponsoredhotspots(i.e.cafesandrestaurants),theCityofChicago,its
sisteragencies,andanarrayofnonprofitorganizationsofferpublicinternetaccessinplacesacrossthecity.WirelessInternetZones,freeWi‐FihotspotsprovidedbytheCityasaservicetoresidentsandvisitors,areavailableatpublicplacessuchasMillenniumPark,theChicagoCulturalCenter,andRichard
J.DaleyPlaza.FreeWi‐Fiandcomputerandinternetaccessareavailableatall79ChicagoPublicLibrarybranches.LibrariansandCyberNavigatorsprovidevaluabletechnologyassistanceandtrainingtoresidents.TheCity’sSeniorCenters,YouthCareerDevelopmentCenters,andWorkforceDevelopment
Centersprovidecomputerandinternetaccessandtechnologytraining;and,theDepartmentofBusinessAffairsoffersfreemonthlytechnologytrainingthroughitsbusinesseducationworkshops.TheChicagoHousingAuthorityprovidesresidentswithcomputerandinternetaccess.SomeChicagoPublicSchools
provideparentswithcomputerandinternetaccessandtraining.
Nonprofitorganizationsalsooffertechnologyaccess,trainingandsupportatcommunitytechnologycenters(CTCs)locatedprimarilywithinlow‐incomeneighborhoods.Thereisnosinglelistingofsuchcenters,butCTCNetChicagohasapproximately40memberorganizations.25TheStateofIllinois
providedsupportforapproximately100communitytechnologycentersinChicagothroughtheDigitalDivideInitiativegrantprogramduring2008.Thisprogram,administeredbytheIllinoisDepartmentof
CommerceandEconomicOpportunity,supportstraininginbasiccomputerskills,vocationalskillsrelatedtotechnologyoccupations,literacyskills,computerapplicationsforsmallbusinesses,andassistivetechnologyforindividualswithdisabilities.
GiventheeffortsofCTCsandlibrariesinChicago,weaskedresidentswhetheritwaseasyor
difficulttogetto“placesinyourcommunitywithpublicaccesstotheinternet,likealibraryor
communitytechnologycenter.”Mostrespondentsbelievethatitisrelativelyeasytoreachpublicaccesssites,with76percentsayingthatitiseitherveryeasyorsomewhateasytogetpublicinternetaccesswithintheircommunities.Fourteenpercentfeltthatitwaseithersomewhatorverydifficultto
usepublicaccess,and10percentdidnotknow.Chicagoresidentsseemtofeelpositivelyabouttheavailabilityofpublicaccessintheircommunities.Howmanyhaveusedpublicaccesssites,andwhatarethecharacteristicsofpublicaccessusers?Wediscusslibrariesinmoredetailnext,andthenpresent
findingsoncommunitytechnologycentersandwirelessaccess.
InternetUseatLibraries
25Seehttp://www.connectchicago.net/ChicagoInitiatives.aspx
25
Useofpublicaccesstechnologyatlibrariesismostcommon,as33percentofthecity’sresidents(44percentofChicagointernetusers)havegoneonlineataChicagoPublicLibrarybranch.Because
librarieshavefunctionsotherthantechnologyuse,internetuserstheremaysimplycheckemailorotherinformationwhilevisitingthelibraryforbooksorothermedia.Toestablishwhatproportionoflibraryinternetusersdependeduponpublicaccess,weaskedaboutreasonsforusingtheinternetatthe
library.TheresultsaredisplayedinTable2below.
One‐thirdofChicagoresidentsusetheinternetatapubliclibrary
TABLE2.REASONSFORUSINGTHEINTERNETATTHECHICAGOPUBLICLIBRARY
Percentagesforlibraryinternetusersonly;multipleresponsespossible
Convenience 70%Needhelptofindinformation 46%Computerathomenotworking 39%Nocomputerathome,computerslow 33%Nointernetathome 29%Totakeaclass 25%Totakemychildrenforhomework 25%Needhelptousecomputer 17% Librariesclearlyservethosewhohavelimitedaccessorwhoneedhelp,buttheyhaveabroaderaudienceofcasualusersaswell.Convenienceisthemostimportantreasonforinternetuseatthe
library(at70percent),indicatingthatnotalllibrarypatronslackhomeinternetaccess.Still,between30and40percentciteproblemswithcomputersorconnectivityathomeasareasontoseekoutthelibrary.Obtaininghelpinfindinginformationisthemotivationfornearlyhalfofthosewhousethe
internetatpubliclibraries,althoughtheneedforhelpwithcomputerhardwareislessprevalent(at17percent).Twenty‐fivepercentoflibrarypatronsusethelibraryforformalinstructionthroughcomputerclassesorforchildren’shomework.
Regressionanalysisallowsustobetterunderstandthecharacteristicsoftechnologyusersat
libraries,introducingfactorssuchasparentalstatus,awarenessofpublicaccessintheneighborhood,easeofaccesstoapublicinternetfacilityintheneighborhood,andwhetherornottherespondentusestheinternetathome.TheresultsaredisplayedinWhatMattersTableCbelow.
26
WHATMATTERSTABLEC.WhoUsestheInternetatPublicLibraries?
Thefactorsbelowarestatisticallysignificantinfluencesontechnologyuseatpubliclibraries.Aplussign(+)indicatesincreasedprobabilityoflibraryinternetuse,andaminussign(‐)indicatesdecreasedchancesoflibraryinternetuse.LibraryInternetUse
Age(‐) AwarenessofOtherPublicAccess(+)HomeInternetUse(+) Education(+) African‐American(+) PerceivedEaseofUse(+)Income(‐) Latino(+)ReadingPredictedProbabilities:Afemale,whitenon‐HispanicChicagoresidentwithnochildren,internetaccessathome,whoisawareofpublicaccessintheneighborhoodandperceivesveryeasyaccesstothepublicinternetfacility,andwhohasaverageage,income,andeducationhasa39percentprobabilityofusingtheinternetatthelibrary.Resultsshownbelowindicate,forexample,thataresidentwhoisLatino,butotherwisethesame,hasa47percentchanceofusingtheinternetatthelibrary.Latinoethnicityalonemakesan8percentdifference(holdingotherfactorsconstant).Readnumbersnotinparenthesesaspercentages. UseInternetattheLibrary
Whitenon‐Hispanic(Baseline) .39(.02)Latino .47(.03)DifferenceLatinovs.White +.08Black .53(.03)DifferenceBlackvs.White +.14AnnualIncome VeryLow($0,‐2SD) .52(.04)Low($10,000‐$20,000,‐1SD) .46(.03)Mean/Average($40,000‐$50,000) .39(.02)High($75‐$100,000,+1SD) .33(.02)VeryHigh(morethan$150,000,+2SD) .29(.03)DifferenceLowtoHigh ‐.13EducationLevel LessthanHS .28(.03)HighSchoolGraduate .32(.03)SomeCollege .40(.02)CollegeGraduate .44(.02)GraduateDegree .48(.03)DifferenceHStoCollege +.12Ageofrespondent Veryyoung(18yrs,‐2SD) .66(.03)Young(31yrs,‐1SD) .55(.03)Mean/Average(49yrs) .39(.02)Old(,67yrs,+1SD) .25(.02)Veryold(85yrs,+2SD) .14(.02)DifferenceYoungtoOld(2767yrs) ‐.30Donotuseinternetathome .17(.02)Useinternetathome .39(.02)DifferenceNoUseatHometoUse +.22
CONTINUEDONNEXTPAGE
27
WHATMATTERSTABLEC(CONTINUED)WhoUsestheInternetatPublicLibraries?
UseInternetattheLibrary
Notawareofpublicinternetfacility .26(.03)Awareofpublicinternetfacility .39(.02)DifferenceNotAwaretoAware +.13Verydifficulttoaccesspublicinternetfacility .27(.03)Veryeasytoaccesspublicinternetfacility .39(.02)DifferenceVeryDifficulttoVeryEasy +.12
Note: Predicted probabilities calculated with Clarify Software from the logistic regression models reported inAppendixTables4.Probabilitiesestimatedwithcontrolvariablessetatmeanormodalvalues.Standarderrorsoftheprobabilityestimateinparentheses.
YoungerresidentsandAfrican‐Americansareamongthemostlikelytousepublicaccessatlibraries
The results reinforce conclusions that libraries serve residentswho seek themoutbasedonbothneedandconvenience.Youngerandbetter‐educatedresidentsarebothmorelikelytousetheinternet
atthe library (aswellastousethe internet ingeneral).Atthesametime,African‐Americans,Latinos,and lower‐income residents are also significantly more likely to go online at the library. There aresubstantial gapsbetweenAfrican‐Americansand Latinos in libraryuse,however, again indicating that
Latinoresidentsareamongthemostdisadvantagedintermsofinternetuse.Particularlyhighratesoflibrary internet use among African‐Americans support findings that many go online in some settingdespitelowerratesofhomeaccess.
• Ageisthemost importantfactoraffectingtechnologyuseatChicagopublic libraries. Younger
residents (age31)are30percentmore likely tousethe internetatapublic library thanolderresidents(age67),evenwhenwecontrolforinternetuse.YoungerChicagoresidentsaremoreengagedintechnologyingeneral,andaremoreinclinedtousetheinternetatpubliclibraries.
• Home internet use is the next most significant influence on technology use at the library.
Although it is clear that somepatrons lackhome internet connections, residentswhouse theinternet at home are 22 percent more likely to go online at the library. This supports thefindingsonconvenience,butalsoindicatesthatlibrariesprovideassistanceforthosewhohave
the internet at home – including the 46 percent of library users who need help findinginformationorthe25percentwhotaketechnologyclasses.
• African‐AmericansinChicagoare14percentmorelikelythanwhiteresidentstousetheinternetat apublic library, controlling forother factors. National surveyshave indicated thatAfrican‐
Americans have more positive attitudes toward use of public access,26 but this study showssubstantial differences in actual use (not just attitudes). Given that a higher percentage of
26Mossberger,TolbertandStansbury2003,chapter3.
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internetuserswithouthomeaccessareAfrican‐American,publiclibrariesarefillingpartoftheneedforaccessamongtheseinternetusers.
• Incomeisnearlyasimportantasrace,forlow‐incomeresidentsare13percentmorelikelythan
higher‐income respondents to use the internet at a public library. Again, this indicates thatlibrariesarereachingneedypopulationsinthecity.
• But,educationhastheoppositeeffect. Internetuseat libraries increaseswitheducation,andcollege‐educatedresidentsare12percentmorelikelythanhighschoolgraduatestouselibrary
technologyfacilities.
• Awarenessofcommunitytechnologycenters (CTCs) intheirneighborhoodandperceivedeaseofuseforpublicaccessaccountfor12and13percentincreasesintechnologyuseat libraries,holdingotherfactorsconstant.Libraryinternetusersaremorelikelytobeawareofotherpublic
accessinthecommunity,andtoalsoperceivepublicaccessasconvenienttouse.
• While Latinos alsouse technology at public librariesmore thannon‐Hispanicwhites, they areonlyabouthalfaslikelyasAfrican‐Americanstobelibraryinternetusers.Latinosare8percentmorelikelythannon‐Hispanicwhitestousetheinternetatthelibrary.
NeighborhoodVariationinLibraryInternetUse
The neighborhood results provide further support for the dual nature of library internet use:
convenience for residentswhoare frequent librarypatronsor frequent internetusers,andassistanceforindividualswithlimitedaccessorskills.Therearesomewhatopposingpatternsforneighborhoods:
• ResidentsofneighborhoodswithhigherpercentagesofAfrican‐AmericanandLatino residentsare less likely to use public access at the library than residents in neighborhoodswith higher
percentagesofnon‐Hispanicwhites.
• Residents of neighborhoodswith high poverty rates are less likely to use the internet at thelibrary.
• Yet,Chicagoans living inneighborhoodswitha lowerpercentageofhigh schoolgraduatesare
alsomorelikelytouselibraries.So,somelow‐incomecommunitieshaverelativelymorelibraryuse.
Themaponthenextpageshowslibraryuseacrossthecommunityareas.Thereisonecommunityareawithveryhighuse (ArmourSquare, inblue). Theyellowareasareestimated tohaveat least35
percentofthepopulationusingthe internetattheChicagoPublicLibrary‐somewhathigherthanthecity‐wideaverageof33percent. Some,butnotall low‐incomecommunitieshavehigherusageoftheinternetatthelibrary.Theredareasareestimatedtohavelessthan35percentofresidentswhouse
theinternetatthelibrary,andarearoundthecity‐wideaverageorlower.
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30
Librariesareclearlyfulfillinganimportantneedforsomeoftheless‐connectedinChicago,atthesametimethattheyappeal tothosewhoare frequentlyonline. Towhatextentarecommunitytechnology
centersbeingusedbyChicagoresidents,andwhoismostlikelytousepublicaccessthere?
CommunityTechnologyCenters
Communitytechnologycenters(CTCs)arealsoimportantforpublicaccessinChicago,as16percentofcityresidents(21percentofChicagointernetusers)reporthavingusedacommunitytechnologycenter.Surveyrespondentswereasked,“Asfarasyouknow,isthereaplacewhereyou
cangoinyourneighborhoodwheretheinternetispubliclyavailabletoanyonewhowantstouseit?SuchplacesareoftencalledCommunityTechnologyCenters.”Respondentswerethenaskedafollow‐upquestionaboutwhethertheyhadeverusedtheinternetatsuchaplace.
Becausecommunitytechnologycenterstargetlow‐incomeneighborhoods,wefocusedthe
regressionanalysisonlow‐incomecommunitiesonly(censustractswithpovertyratesabovethemean).Giventhepotentialimportanceoftheneighborhoodcontext,themainmodelforinternetuseatcommunitytechnologycenters(CTCs)isamultilevelmodelthatincludesneighborhoodcharacteristics.
Accordingtothemultilevelmodel,whichincludesbothindividual‐levelandneighborhood
characteristics(AppendixB):
• ParentsaremorelikelytouseCTCsinthislow‐incomesample,andthiscontrastswiththefindingsforlibraries.
• African‐AmericansaremorelikelytovisitaCTCthanwhiteresidents.
• Similartothelibraryfindings,communitytechnologycenterusersarealsoyounger,better‐educated,andthosewhoperceivetheCTCtobeconvenient.
• CTCuseincreasesasneighborhoodpovertyincreases,eveninthissampleofpoor
neighborhoods.
ThissuggeststhatCTCsarecertainlyreachingsomeofthepoorestresidents.Again,African‐Americansarefrequentusersofpublicaccess,butLatinosarenotsignificantlymorelikelythanwhitestouseCTCs
inlow‐incomecommunities.
Lookingatlow‐incomeneighborhoodsonly,CTCuseishigherinthepoorestcommunities;parentsarealsomorelikelytouseCTCs.
Therearesomecommonalitiesinuseofpublicaccessacrossthecity.WhilelibrariesandCTCsseemtobereachingdisadvantagedresidents,usersalsotendtobebetter‐educatedandyounger.
Therearetwoco‐existingpatterns–userswhoaremorelikelytobeinterestedintechnology,butalsothosewhoareless‐connected.Low‐incomeresidentsareservedbybothlibrariesandCTCs.Thisisparticularlyevidentincommunitytechnologycenters,whereuseincreaseswithneighborhoodpoverty.
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WirelessAccess
Wirelesscanprovidemorefrequentaccessforresidentsandbusinessesthroughnetworksthataccommodatemobileusewithlaptopsandavarietyofhandheldwirelessdevices.Wirelessnetworks
canalsoprovideconnectionsforindividualswithouthomeaccess.Currently,thisisavailableinlibrariesandotherpublicplaces,aswellas“hotspots”thataresponsoredbysomebusinesses,suchascoffeehouses.Thereisthepotentialtoprovidebothmobileandhomeaccessthroughwirelessnetworksthat
coverresidentialareasofthecity.
Whatdoeswirelessuselooklikecurrently?Usingsimplepercentages,itisclearthatwirelessuseisfairlycommonnowandpromisestogrowinthefuture.Morethanone‐thirdofcityresidents(35percent)andnearlyhalfofChicagointernetusers(46percent)havegoneonlineusingwirelessaccessin
apublicplace.YoungChicagoresidentsaged18‐29arethemostlikelyusersofwireless,as55percentusepublicwirelessnetworks,and35percentdosoatleastafewtimespermonth.Whetheritisthroughlaptops,cellphones,orothermobiledevices,thereisafairamountofuseofwirelessnetworks
inthecity.Table3showssimplepercentagesforfrequencyofwirelessusebyage.
TABLE3.FREQUENCYOFWIRELESSUSEINPUBLICPLACEBYAGEPercentofcitypopulation
18‐29 30‐59 60‐74 75andoverTotal,allagesDaily/fewtimespermonth 35% 25% 9% 1% 21%Rarely 20% 15% 10% 2% 14%Totalpercentforagegroup 55% 40% 19% 3% 35%
Table4,below,displayspercentagesforwirelessusebyraceandethnicity,demonstratingthat
Asian‐Americansareaheadofothergroups.Nearlyone‐thirdofAfrican‐AmericansandLatinoshaveusedwirelessinChicago,althoughthisislessthanothergroups.
TABLE4.FREQUENCYOFWIRELESSUSEINPUBLICPLACEBYRACEANDETHNICITYPercentofcitypopulation WhiteNon‐Hispanic Black Asian Latino TotalDaily/fewtimesperweek 25% 18% 38% 19% 21%Rarely 15% 12% 16% 12% 14%Totalpercentforgroup 40% 30% 54% 31% 35%
Hand‐heldwirelessdevicessuchasinternet‐enabledcellphonesor“smartphones”alsoprovideawaytoaccesstheinternet,andtodaytheycanaccomplisharangeofusesdespitetheirsmallerscreens.Thisposesthequestionofwhethersomeresidentswhodonothavehomeinternetaccessare
goingonlinethroughcellphonesratherthanthroughpersonalcomputers.Insomenewly‐industrializingcountries,forexample,cellphoneuseisthemostcommonwaytogoonline.CouldthisbetrueintheU.S.,atleastamongsomewhowerenotpreviouslyinternetusers?Table5belowshowssimple
percentagesdescribinginternetaccessthroughcellphonesbyageinChicago.
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TABLE5.FREQUENCYOFCELLPHONEUSETOCONNECTTOINTERNETBYAGEPercentofcitypopulation
18‐29 30‐59 60‐74 75andoverTotal,allagesDaily/fewtimespermonth 26% 14% 4% 1% 13%Rarely 13% 7% 3% 1% 7%Totalpercentforagegroup 39% 21% 7% 2% 20%
One‐fifthofChicagointernetusershaveusedacellphoneforinternetaccess.Thisisclearlymoreimportantforyoungerresidents,with26percentof18‐29year‐oldsreportingthattheygoonlinethiswayatleastafewtimespermonth(doubletheaverageof13percentforthecity).Personal
computersremainthedominantwaytoaccesstheinternetinChicago,buttherelativelyhighratesofuseamongtheyoungindicatepotentialforfuturegrowth.PatternsofcellphoneusebyraceandethnicityareindicatedinsimplepercentagesinTable6below,whichalsoshowsthepercentageof
residentswhousecellphonestoconnecttotheinternetbuthavenohomeinternetaccess.Thisprovidesawaytoassesstheextenttowhichcellphoneuseissubstitutingforhomeinternetuse,especiallyamongpopulationsthathavetraditionallybeenless‐connected.
TABLE6.FREQUENCYOFCELLPHONEUSETOCONNECTTOINTERNETBYRACEANDETHNICITYPercentofcitypopulation WhiteNon‐Hispanic Black Asian Latino TotalDaily/fewtimespermonth 12% 12% 20% 12% 13%Rarely 6% 8% 10% 7% 7%Totalpercentforgroup 18% 20% 30% 19% 20%Percentofpopulationwhousedaily/fewtimespermonthandhavenohomeinternet 1% 3% 0% 2% 2%
CellphoneusetogoonlineishigheramongAsian‐Americans,butthereisclearlynosubstitutionforhomeaccessinthisgroup.African‐AmericansandLatinosappeartousecellphonestoconnectto
theinternetasasubstituteforhomecomputersattimes.However,suchsmalldifferencesmaybeduetosampling.Overall,cellphonesstilldonotreplacehomeaccess,accordingtotheseresults.
Insummary,wirelessnetworkspresentanumberofopportunitiestosupplementhomeuseortoprovidelow‐costinternetconnectionsforcityresidents.Theythereforeencouragemorewidespread
use,morefrequentuse,flexibility,andinnovativeapplicationsinnewsettings.OverathirdofChicagoresidentshaveaccessedtheinternetthroughsometypeofwirelessdevice,andtheconcentrationofsuchuseamongresidentsunder30suggeststhatthistrendislikelytoincreaseinthefuture,especially
withadvancesintechnology.Freeandpublicwirelessaccesscanencouragefrequencyofuse,andmayextendaccessforsome,especiallyifitisavailableinmoreareasofthecity.Willthismakemuchdifference,however?Towhatextentisthereasonthatpeopleareofflineamatterofcost,forexample,
orasimplelackofinterest?Towhatextentareskillsandtechnicalsupportalsoneeded?Weexaminethebarriersthatresidentsthemselvesperceiveforachievingdigitalexcellence.
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PARTIV.BARRIERSTOACCESSANDPUBLICPOLICY
Homeaccessisanimportantresourceforachievingdigitalexcellence.Weaskedthosewhodonotusetheinternetatallaswellasthosewhodonotuseitathometochooseanyandallreasonsfor
notusingtheinternetathome,andthenaskedthemtoselectthemostimportantreasonfornothavinganinternetconnectionathome.Inthisway,wecouldbetterunderstandwhetherrespondentswhosaidthattheycan’taffordtheinternetmightsimplybeuninterestedaswell,andthereforenotvery
motivatedtospendmoneyonacomputeroramonthlyinternetbill.
TABLE7.REASONSFORNOINTERNETATHOMEPercentofrespondentswhodonotusetheinternetathome Mainreason Onereason Don’tneedit/notinterested 30% 48% Costistoohigh 27% 52% Canuseitelsewhere 5% 52%Don’thavetime 5% 24%Toodifficulttouse 9% 43%Iamworriedaboutprivacy 2% 57%Theinternetisdangerous 2% 46%HardtouseinformationinEnglish 1% 19%Physicalimpairment 3% 13%Other 16% ‐‐
Whenrespondentsareallowedtogivemultipleanswers,issuessuchasprivacyanddangeremergeassecondaryreasonsformanyrespondents,eventhoughfewresidentscitethemasthemain
reasonfornothavingtheinternetathome.Difficultyisalsomoreimportantasasecondaryreason–peoplewhodonothavetheinternetathomemaynotchoosethisastheonlyreasonfornotinvestingintheinternet,buttheyarelessconfidentoftheirskills.Only5percentsaythatuseoutsidethehomeis
theirmainreasonfornothavinghomeaccess,butoverhalfoftherespondentscanusetheinternetsomewhereelse.Still,thereislittlestatisticalrelationshipbetweenthereasonsfornotusingtheinternetathomeinTable7below,evenwhenrespondentscouldchoosemultipleanswers.27Inother
words,ouranalysisshowsthatthosewhoarenotinterestedinhavingtheinternetathome,forexample,arenotthesamerespondentswhosaythatcostistheissue.
Table7showsthatinterest,affordability,andskillstandoutasthemostimportantmainreasonsfornothavingahomeconnection,andthattherearesomeinterestingpatternsbyraceand
ethnicitywhenweexaminesimplepercentagesforthemainreasonfornothavingtheinternetathome.Table8,below,showsthesepatternsbyraceandethnicity.
27Thiswasexploredthroughfactoranalysisandthroughcorrelations.
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TABLE8.MAINREASONFORNOINTERNETATHOMEBYRACEANDETHNICITYPercentofrespondentswhodonotusetheinternetathome WhiteNon‐Hispanic Black Asian Latino TotalDon’tneedit/notinterested 42% 29% 42% 19% 31% Costistoohigh 14% 30% 12% 37% 27%Toodifficulttouse 9% 8% 9% 13% 9% Thereisconsiderablevariationbyraceandethnicityinthemainreasonfornothavingtheinternetathome.MorewhiteandAsian‐Americanresidentswhodonotcurrentlyusetheinternetathomearenotinterested,andAfrican‐AmericansandLatinosarebyfarmoreconcernedaboutcost.
Latinosarethegroupmostlikelytosaythatdifficultyusingtheinternetisthemainreasonfornothavingitathome.
Tosortoutdifferencesinreasonsfornothavinghomeaccess,weconductedmultivariateregression.WhatMattersTableDonthenextpagepresentstheresultsforlackofinterest,cost,and
difficultyusingthetechnology.AppendixAalsoshowstheresultsoftheregressionanalysisforseveralotherreasonsthatwerelesscommonasmainreasonsfornotusingtheinternetathome:useelsewhere;lackoftime;andprivacyconcerns.Latinosandhigher‐incomeresidentsarestatistically
morelikelytobeamongthosewhosaytheydonothavetime.Latinosandwomenaremorelikelytohaveprivacyconcerns.Thosewhousetheinternetelsewherearealsomoreeducatedandyoungerthanotherswithouthomeaccess.Becauseofspaceconsiderationswedonotanalyzethesehere,but
readerscanconsulttheresultsintheappendix.
WhatMattersTableD:WhataretheReasonsChicagoResidentsDoNotHaveHomeInternet?
Thefactorslistedbelowarethestatisticallysignificantinfluencesonthefollowingreasonsfornotusingtheinternetathome.Aplussign(+)indicatesincreasedprobabilityforgivingthisreason,andaminussign(‐)indicatesadecreasedchanceofcitingthisreason.NotInterested Age(+)Income(+)Education(‐)African‐American(‐)CostisTooHigh Income(‐)Latino(+)Female(+)Education(‐)DifficulttoUseAge(+)Education(‐)Latino(+)African‐American(‐)**borderlinesignificance
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WhatMattersTableDcontinued:WhataretheReasonsChicagoResidentsDoNotHaveHome
Internet?
ReadingPredictedProbabilities:Afemale,whitenon‐HispanicChicagoresidentwithnochildrenandaverageage,
income,andeducationwhodoesnotusetheinternetathomehasa50percentprobabilityofsayingthatthisisbecausesheisnotinterested.Resultsshownbelowindicate,forexample,thatarespondentwhoisLatino,but
otherwisethesame,hasa48percentchanceofsayingsheisnotinterested.Latinoethnicityalonemakesa2percentdifference,holdingotherfactorsconstant,andisnotsignificant.Readthenumbersnotinparenthesesas
percentages.
NotInterested
CostisToo
High
TooDifficult
toUseWhitenon‐Hispanic(Baseline) .50(.04) .54(.04) .43(.04)Latino .48(.04) .69(.05) .57(.04)DifferenceLatinovs.White ‐.02 +.15 +.14Black .43(.04) .57(.03) .36(.03)DifferenceBlackvs.White ‐.07 +.03 ‐.07Male .55(.04) .39(.04) .37(.04)DifferenceFemalevs.Male ‐.05 +.15 +.06AnnualIncome VeryLow($0,‐2SD) .40(.05) .72(.04) .50(.05)Low($10,000‐$20,000,‐1SD) .47(.04) .59(.03) .45(.04)Mean/Average($40,000‐$50,000)
.50(.04) .54(.04) .43(.04)
High($75‐$100,000,+1SD) .62(.05) .29(.04) .35(.05)VeryHigh(morethan$150,000,+2SD)
.66(.05) .21(.04) .31(.05)
DifferenceLowtoHigh +.15 ‐.30 ‐.10EducationLevel LessthanHS .54(.04) .58(.04) .52(.04)HighSchoolGraduate .52(.04) .56(.04) .47(.04)SomeCollege .46(.04) .52(.04) .37(.04)CollegeGraduate .43(.04) .50(.04) .32(.04)GraduateDegree .40(.05) .47(.05) .28(.04)DifferenceHStoCollege ‐.09 ‐.06 ‐.15Ageofrespondent Veryyoung(18yrs,‐2SD) .24(.05) .50(.06) .15(.04)Young(31yrs,‐1SD) .32(.05) .51(.05) .22(.04)Mean/Average(49yrs) .50(.04) .54(.04) .43(.04)Old(,67yrs,+1SD) .56(.03) .55(.03) .52(.03)Veryold(85yrs,+2SD) .68(.03) .57(.03) .67(.03)DifferenceYoungtoOld(2767yrs)
+.24 +.04 +.30
Note: Predicted probabilities calculated with Clarify Software from the logistic regression models reported inAppendixTable5.Probabilitiesestimatedwithcontrolvariablessetatmeanormodalvalues.Standarderrorsof
theprobabilityestimateinparentheses.
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Respondentswereaskedwhytheydidnothaveinternetaccessathomeandcouldgivemultiplereasons.Themostfrequentlycitedanswersforthemainreasonwere“Idon’tneedit/notinterested,”
“thecostistoohigh,”“It’stoodifficulttouse.”Columns1,2and3ofWhatMattersCshowthepredictedprobabilityofcitingoneoftheaboveresponses,respectively,bydemographicattributesoftherespondents.
Theanalysisshowsthatamongthosewhodonothavetheinternetathome,olderandhigher
incomerespondentsareuninterested,andAfrican‐Americansaresignificantlylesslikelythanotherracialandethnicgroupstosaythattheyhavenointerestintheinternet.
Olderandmoreaffluentrespondentswithouthomeaccesscitelackofinterest
• Olderrespondentsare24percentmorelikelytocitealackofinterestasthereasontheyareofflinecomparedtoyoungrespondents;a31year‐old(onestandarddeviationbelowthemean)hasonlya32percentprobabilityofsayingheorsheisnotinterested,comparedtoanolder
individual(67years,onestandarddeviationabovethemean),whohasa56percentprobabilityofcitingthisreason.
• Higher‐incomeresidentsarealsomorelikelytosaythattheyareuninterested.Residentswithannualfamilyincomesbetween$75,000and$100,000are15percentmorelikelytocitelackof
interestthanrespondentswithincomesbetween$10,000and$20,000.
• Incomparison,educationmakesasmallerdifferencethanageandincome.Residentswithahighschooldiplomaare9percentmorelikelythancollegegraduatestosaytheyarenotinterestedintheinternet.
• African‐Americansare7percentlesslikelythanwhitestocitealackofinterestingoingonline.
NeighborhoodVariationinInterest
Multilevelmodels(seeAppendixB)showthatincomemattersatthecommunitylevelaswellasat
theindividuallevel:
• Residentsofmoreaffluentneighborhoodswithouthomeinternetaccessaremorelikelytosaythattheyarenotinterestedingoingonline.
Themaponthenextpageshowsclearlythispatternforincome.Communityareasinblueareestimatedtohave50percentormoreofresidentswithouthomeaccesswholackinterestinthe
internet.Communityareasinredareestimatedtohavebetween25and35percentofthosewithouthomeaccesswhogivethisreason.Redareastendtobeamonglow‐incomeareasinChicago.
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Low‐incomerespondentsandLatinosareamongthosewhocitecostasthemainfactor,asseenincolumn2.
Residentscitingcostareinfactlow‐income;Latinosareamongthemostlikelytoviewcostasabarrier
• Thelargestfactorinfluencingthosewhosaythatcostistoohighis,notsurprisingly,familyincome.Thepoor(withincomesbetween$10,000‐$20,000onestandarddeviationbelowthemean)are30percentmorelikelytoperceivecostasabarriertohomeaccessthantheaffluent
(incomesbetween$75,000‐$100,000,plusonestandarddeviationabovethemean),allelseequal.PoorChicagoresidentshavea60percentprobabilityofcitingcostbarriers,comparedtohigher‐incomeresidents,whohavelessthana30percentchanceofsayingthis.
• Holdingarespondent’sincome,educationandageconstant,Latinoswere15percentmorelikelytosaycostisaproblemforinternetaccessthannon‐Hispanics.
• African‐Americans,incontrast,wereonly3percentmorelikelythanwhitestosaycostisanissueforhomeaccess,controllingforotherfactors.
• Interestingly,womenwere15percentmorelikelythanmentomentioncostasareasonfornot
havinghomeaccess,allelseequal.
NeighborhoodVariationinCostConcerns
Accordingtothemultilevelmodels(AppendixB),thereisvariationacrossChicagoneighborhoodsincostasabarriertohomeaccess:
• ResidentsofcommunitieswithhighAfrican‐Americanpopulationsaremorelikelytostatethatcostisthemainreasonfornothavingtheinternetathome.
• ResidentsinneighborhoodswithhighproportionsofLatinosarealsomorelikelytocitecost.
• Costconcernsaremorelikelyinneighborhoodswithahigherlevelofhighschoolgraduationas
well.Moreeducatedenvironmentsmayincreaseinterest,absentworriesaboutcost.
Themaponthenextpageshowscommunityareasmarkedinredwhere39percentormoreofthepopulationwithouthomeinternetconnectionscitecostbarriers.Costconcernsarefairlyimportantoverall,buttheredareasclearlycovermanyneighborhoodswithhighproportionsofLatinosand
African‐Americans.
CostismorelikelythemainreasonfornothavinginternetathomeinneighborhoodswithhighpercentagesofAfrican‐AmericansandLatinos
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40
ThelastcolumnofWhatMattersTableDshowsthatless‐educated,olderandLatinorespondentsaremorelikelytosaythattheyhavedifficultywiththeinternet.
Olderandless‐educatedresidentsfindtheinternetdifficult,asdoLatinos
• Olderrespondents(onestandarddeviationabovethemean)were30percentmorelikelytociteskillbarrierscomparedtotheyoung(onestandarddeviationbelowthemean).
• Respondentswithonlyahighschooldegreewere15percentmorelikelytosaytheinternetis
“toodifficulttouse”comparedtothosewithacollegedegree.
• Latinosare14percentmorelikelytocitealackofskillsordifficultygoingonlineasabarriertousethanwhitenon‐Hispanics,againindicatinggreaterdisparitiesforLatinos.
• Incontrast,African‐Americansare7percentlesslikelytociteskillsasabarriertousecomparedtowhiteswhodonothavehomeaccess.Thismayreflectinternetuseoutsidethehomeamong
African‐Americans.
NeighborhoodVariationinDifficultyUsingtheInternet
Whenneighborhoodcharacteristicsareintroducedinmultilevelmodels,therearetwoapparentlycontradictoryfindings:
• Individualsresidinginhigher‐povertycensustractsarelesslikelytocitealackofskillsasareasonfornothavinghomeaccess,controllingforotherfactors.Thismayreflecttheinfluence
ofotherreasons,suchascost.
• Yet,residentsinneighborhoodswithahighpercentageofAfrican‐Americansaremorelikelytomentiondifficultyinuse(althoughattheindividuallevelAfrican‐Americansarenot).
Thismaysuggestsomeskilldeficitsconcentratedintheseareasnotcapturedbytheotherfactorsexaminedhere.Residentsofsuchareasmayhaveexperiencedunequalqualityofeducation.
Themaponthenextpageshowsdiversepatternsaswell.Inthiscase,thecommunityareas
coloredinredareestimatedtohavehigherpercentagesofresidentswithouthomeaccesswhofindinternetusedifficult(between30and45percent).Itisclearthatmanylargely‐African‐Americancommunityareasinthesouthofthecityareonthislist,butothersarealsocoloredinblue,meaning
thattheyhavethelowestratesofresidentswithoutaccesswhohavedifficultyonline(between10and20percent).Themapsarebasedonmultilevelmodelsthatcombineneighborhoodandindividualcharacteristics,andfactorssuchasageorLatinoethnicityofrespondentsarereflectedintheresultsas
well.
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PolicyImplications
Summarizingtheaboveresults,wecanseethattherearesomedistinctdifferencesinreasonsfornothavingtheinternetathome.Thosewhosaytheycan’taffordtheinternetareindeedlower‐
incomeincomparisontootherrespondentswithouthomeaccess,andsomeresidentswhohavedifficultywiththeinternetareinfactless‐educated,controllingforotherfactors.Theseindividualsaredifferentfromthosewhoaresimplyuninterested,andvariedpolicysolutionsareneededtoaddress
dissimilarbarriers.Low‐costinternetconnectionsandhardwarewillhelptobringmorelow‐incomeresidentsonline,buttrainingandsupportareneededforthosewhoareless‐educatedandlessconfidentoftheirskills.
Residentswhoareuninterestedintechnologyaredifferentfromotherswithouthomeaccess
becausetheyareolderandhavehigherincomes.Olderrespondentsaremorelikelytosaythattheyarenotinterestedorthatthetechnologyistoodifficulttouse.Thosewhohavehigherincomes(withinthisgroupwithouthomeaccess)alsolackinterestorsaythattheyhavenotime.28Greaterawarenessof
whatcanbedoneonlineandtheprovisionofsupportcouldchangethemindsofsome.
ThereareclearopportunitiesforexpandinghomeaccessamongAfrican‐Americans,whohavefewernegativeattitudestowardtechnologythanwhites,consistentwithearlierresearchandwithuseoftechnologyoutsidethehome.29Theyarelesslikelytosaythattheyarenotinterested,orthatthe
internetistoodifficulttouse.Theserelativelypositiveattitudestowardtheinternetmightbetranslatedintogreaterhomeaccessifitisaffordable.WhileAfrican‐Americansarenomorelikelythanwhitestocitecostswhenwecontrolforfactorslikeincomeandeducation,thesimplepercentagesshowa
tendencyforAfrican‐Americanstobeamongthelower‐incomeresidentswhoareconcernedwithaffordability.ThemultilevelmodelsalsoshowthatresidentsofpredominantlyAfrican‐Americanand
Latinoneighborhoodsaremorelikelytoseecostasanissue.
PositiveattitudesamongAfrican‐Americanspresentanopportunity;Latinos,however,perceivemanybarriers
Latinosstandoutasperceivingmanybarrierstohomeinternetaccess:costanddifficultywereanalyzedhere,butresultsinAppendixAshowthatLatinosarealsosignificantlymorelikelythannon‐
Hispanicwhitestocitelackoftimeandconcernsaboutprivacy.Latinosarealsoprevalentinthe19percentofrespondentswithouthomeaccesswhomentionlanguagebarriersonline.Affordability,technicalsupportandtrainingareallneededtoaddressdisparitiesforLatinos.Recentimmigrants,in
particular,arelikelytohavealackofexperiencewiththeinternetaswellaslanguagebarriers.
28Theonlymodestcorrelationthatwefoundbetweenanswersinthemultipleresponsesectionwasbetweenlackofinterestandlackoftime.29SeeMossberger,TolbertandStansbury2003.AnationalsurveyshowedthatAfrican‐Americanshadsignificantlymorepositiveattitudestowardpublicaccess,technologytraining,onlineeducation,anduseoftheinternetforeconomicopportunity.
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Anotherwayofthinkingaboutpossiblemotivationstogoonlineistoexaminepatternsofinternetuseamongthosewhoareonline.Aretheresomeactivitiesontheinternetthataremore
frequentlyengagedinbylow‐incomeresidentsorbyminorities,forexample?Couldthistellussomethingaboutpossiblemotivationsforthosewhoarenotonline,butmayhavesimilarinformationneedsorpreferences?
PARTV.ONLINEACTIVITIESTHATINFLUENCEOPPORTUNITIESFORRESIDENTS
OnceChicagoresidentshavebridgedthedigitalaccessdivide,therangeofactivitiesonlineis
almostinfinite.Thissectionfocusesonactivitiesthatpublicpolicymayhavesomeinterestinpromoting,becausetheyhavethepotentialtoenhanceoutcomessuchaseconomicadvancement,civicparticipation,accesstogovernmentservices,orhealthcare.Table8belowshowsthesimple
percentagesofChicagoresidentsandinternetusersengaginginthefollowingactivitiesonline.
TABLE9.ACTIVITIESONLINEHaveeverusedtheinternetto... CityPopulation InternetUsersReadonlinenews 67% 91%Findhealthinformation 64% 86%Findinformationongovernment 57% 76%Getinformationaboutpublictransport 56% 74%Getinformationonpolitics 53% 71%Lookforjobinformation 50% 67%UseCityofChicagowebsite 49% 65%Doworkforyourjob 48% 64%Takeaclassortraining 31% 41%
Internetusersperformmanytasksonline;thewebisreplacingotherwaysoffindinginformationorconductingtransactionsinChicago
Thereisamigrationtotheinternetofactivitiesthatcanbedoneoffline(suchasreadingthe
news,contactinggovernment,orfindinghealthinformation)becauseoftheconvenienceandinformationcapacityonline.Whiletwo‐thirdsormoreofinternetusershaveengagedinmostoftheaboveactivities,cityresidentswhoarenotonlineareexcludedfromthebenefitsoftheinternetforeconomicopportunityandforinformation.Thebalanceofthissectionfocusesonpatternsofuseforthecitypopulation,usingmultilevelmodelsthatincludeneighborhoodcharacteristics.EmploymentandTraining
Internetandcomputerskillsareincreasinglyrequiredforjobsthroughoutthelabormarket,and
thedemandisnotlimitedtothetechnologyindustryortoprofessionaloccupations.Whiletherearecertainlysomelow‐skillpositionsthatdonotrequireinternetuse,occupationsdemandinginternetusepaymore,evenforless‐skilledworkerswhohaveahighschooleducationorless.Onestudyconcluded
44
thatin2003,anaverageworkerwhousedtheinternetonthejobearned$118perweekmoreforinternetuse,controllingforotherfactors,includingeducationandoccupation.Wage‐earnerswitha
highschooleducationorlessgainednearlyasmuchfrominternetuseonthejob‐$111perweek.African‐AmericanandLatinoworkerswithahigh‐schooleducationorlessreceivedaslightlylargerpercentagewageincreasefrominternetusethanwhiteworkers,helpingtonarrowracialandethnic
wagegaps.30Howrelevantisinternetuseforless‐educatedworkersinChicago?Table10belowshowsinternetuseonthejobinChicagobyeducationalattainment,includingsimplepercentagesforfrequencyofuse.ThefiguresareforemployedChicagoresidentsonly,ratherthanallresidents.
TABLE10.FREQUENCYOFINTERNETUSEFORJOBBYEDUCATIONEmployedChicagoresidentsonlyEducationalAttainment %ofEmployedWhoUsetheInternetforWork DailyoraFewTimesPerWeek0‐8Years 9%9‐11Years 13%HighSchoolGraduate 33%Vocational/TechnicalEducation 35%SomeCollege 54%4‐YearCollegeDegree 74%Post‐GraduateStudy 88%TotalEmployed 63%
33percentofemployedChicagoresidentswithahighschooleducationusetheinternetforworkdailyorseveraltimesperweek
Chicagoresidentswhousetheinternetatworkaremostprevalentinhigh‐skilloccupationsheld
byworkerswithabaccalaureateorpost‐graduatedegree.Thereisasteepincreaseininternetuseatworkbyeducationalattainment.Amajorityoftherespondentswithsomepost‐secondaryeducationusetheinternetatleastoccasionallyonthejob.But,itisalsosignificantthat33percentofemployed
respondentswhohaveahighschooleducationusetheinternetforworkonaregularbasis.Thesefiguresshowthatinternetuseisfairlycommoneveninlower‐skilledoccupationsthatdonotrequirecollegedegrees.Internetusethroughoutavarietyofindustriesispredictedtogrownationallyovera
numberofyears,31anddigitalskillswillbeincreasinglyimportantfortheeconomicprospectsofeven
30Mossberger,TolbertandMcNeal2008,41.Controllingforotherfactors,African‐Americanmenearnanaverage18.36%wage“premium”forinternetuseatwork,African‐Americanwomenearn17.31%more,Latinosearn16.99%more,andLatinasearna16.11%increase,whilewhitemenearn14.77%more,andwhitewomengain13.56%.ThebenefitsforinternetuseatworkmeanalargerincreaseforAfrican‐AmericansandLatinosrelativetowhiteworkers,becausetheytendtohavelowerwages.31LitanandRivlin2002(BrookingsInstitution)
45
less‐educatedworkers,andforthecity’sabilitytoattractorcultivateinnovativefirmsseekingtechnology‐skilledemployees.
Howdoesinternetuseatworkvarybyage,andbyraceandethnicity?Multilevelmodels(Appendix
B)wereusedtopredictinternetuseatworkforthosewhoareemployed(ratherthanthewholecitypopulation).
• ChicagoworkerswhoarelesslikelytousetheinternetatworkareLatino,African‐American,low‐income,andless‐educated.
• Residenceinaneighborhoodwithloweducationalattainmentdiscouragesinternetuseatwork.
Internetuseatworktracksdisparitiesininternetusegenerally
Internetuseonthejobcanincreasewages,butinternetuseforjobsearchandtrainingmaycontributetoeconomicadvancementaswell.Nationalresearchshowsthatdespitehavinglowerrates
ofinternetuse,African‐Americansaremorelikelythanothergroupstosearchforjobsonline.32ThisistrueinChicagoaswell.
Multilevelmodelsforthe(AppendixB)demonstratethat:
• Chicagoresidentswhoaremorelikelytosearchforjobsonlineareyounger,African‐American,andresidentswithhigherincomesandhighereducation.
• Latinosarelesslikelytosearchforjobsonlinethanwhitenon‐Hispanics.
• Neighborhoodfactorsarenotsignificant.
Thosewhousetheinternetforjobsearcharegenerallyresidentsmostlikelytousetheinternet–
withtheexceptionofAfrican‐Americans.PriorresearchhasshownthatAfrican‐Americans,inparticular,associateinternetusewitheconomicopportunity.Highuseofonlinejobsearchmaybeperceivedasastrategytocounterdiscriminationinthejobmarket.33
African‐Americansaremorelikelytosearchforjobsonline;Latinosarelesslikely
Distanceeducation,onlinetrainingprovidedbyemployers,andotheronlinecoursesprovidenewpossibilitiesforeconomicadvancementfordisadvantagedworkers.Multilevelmodelsforonline
education(AppendixB)demonstratethat:
• Residentswhoaremorelikelytotakeclassesortrainingovertheinternetareyounger,higher‐incomeandmoreeducatedChicagoresidents.
32PewInternetandAmericanLifeProject,May2008,internettrendsovertimeatpewinternet.org;Mossberger,TolbertandStansbury2003.33Mossberger,TolbertandStansbury2003.
46
• Therearenostatisticaldifferencesbasedonrace,ethnicityorgender.
• Neighborhoodcharacteristicsarenotsignificant.
ClearlytheaspirationtosucceedeconomicallypresentsanopportunitytopromotedigitalexcellenceamongmanyChicagoresidents.Neighborhoodfactorsaresignificantforinternetuseforthe
job,butdonotaffectonlinejobsearchortraining.Researchonlow‐incomecommunitieshasconcludedthatresidentsareoftenisolatedfrombetter‐payingjobsbecausetheylacksufficientinformationaboutsuchopportunitiesintheirinformalinformationnetworks.34Theinternetcanpossiblyextendthose
networks.Internetuseismoreprevalentin,butnotconfinedtojobsrequiringthehighestlevelsofeducation,andjobsrequiringtheinternetofferbettercompensationevenforless‐educatedworkers.Assistancewithjobsearch,onlinetrainingoreducation,anddigitalskillsfortheworkplacehave
particularrelevanceinlow‐incomeandminoritycommunities,andshouldbeanimportantpartofpublicandcommunity‐basedprogramsfordigitalexcellence.
NewsandPoliticsOnline
Surveyrespondentsfrequentlyreportedreadingonlinenews,lookingatpoliticalinformationonline,andusinggovernmentwebsitesforinformationandservices.Followingonlinenewsisthemost
commonactivityincludedinthesurvey,as91percentofChicagointernetusershaveeverreadthenewsonline,and74percentpursuethenewsonlineatleastafewtimesperweek.
Nationalresearchhasdemonstratedthatuseofonlinenewsisrelatedtohigherlevelsofcivicengagement–politicalknowledge,interest,anddiscussion.35Anumberofstudieshavealsoestablished
apositivelinkbetweenuseofonlinenewsandvoting,36andthisrelationshipissignificanteventakingintoaccounttheuseofnewspapersandtelevisionfornews.Whilethosewhofollowthenews(fromanysource)alsotendtohavehigherlevelsofcivicengagementandvoting,theresearchindicatesthat
onlinenewshasincreasedbenefits–perhapsbecauseofitsconvenientavailability,in‐depthcoverage,multi‐mediacapacity,orthevarietyofsourcesthatcanbeaccessed.
Internetusersalsohavemanyoptionsforfindinginformationaboutpoliticsapartfromonline
news.Thesourceshaveproliferatedinrecentyears,withpoliticalblogs,campaignwebsites,interestgroupwebsites,YouTubevideosandFacebookentriesallcontributingtotheflowofpoliticalinformation,especiallyaroundelectiontime.Alittleoverhalfofthecity’sresidentshavegoneonline
forpoliticalinformationaccordingtoourJuneandJuly2008survey.
Multilevelmodels,includingneighborhoodcharacteristics,wereestimatedforuseofpoliticalinformationonlineforthecitypopulation(seeAppendixB).Theyrevealthat:
34TheclassicstudyisGranovetter(1973).35SeeTolbertandMcNeal2003,andchapter3ofMossberger,TolbertandMcNeal(2008)foradescriptionoftheuseoftwo‐stagemodelsinthisresearch,aswellasmoredetailsontheresults.36Bimber2003;TolbertandMcNeal2003,amongothers.
47
• Chicagoresidentsmostlikelytobeinterestedinpoliticsonlineareyounger,whitenon‐Hispanic,higher‐income,better‐educated,andmale.Thisisconsistentwithpublishedresearch.37
• Parents,however,arelesslikelythanthosewithoutchildrentolookforpoliticalinformationon
theinternet.Thismaysuggesttimeconstraintsforeitherpoliticsorinternetuse.
• ResidentsofneighborhoodswithhighpercentagesofLatinosreportmoreinternetuseforpolitics.Thisisanintriguingfindingthatcouldmeritfurtherinvestigation.
• Residentsinneighborhoodswithhigherpercentagesofhighschoolgraduatesaremorelikelytoparticipateinpoliticsonline.Education(attheindividuallevel)isoneofthestrongestpredictors
ofpoliticalparticipationmoregenerally,andfindingsoneducationintheneighborhoodcontextunderscorethispoint.
Politicsonlineengagestheyoung,whoareotherwiselesslikelytobeinvolved
Theinternetischangingpoliticsbecauseofitsattractionfortheyoung.Inotherways,however,itcontinuesmoretraditionaldivisionsinpoliticalparticipation,especiallythosebasedonincomeandeducation.Thefindingsforeducationalattainmentinneighborhoodsreinforcethispattern.Livingina
Latinoneighborhoodisalsoassociatedwithhigheruseoftheinternettofolloworengageinpolitics.ThisrunscountertomoregeneralpatternsoflowerinternetuseinLatinoneighborhoods.Otherwise,disparitiesininternetusebasedonrace,ethnicity,educationandincomethreatentowidengapsin
politicalparticipation.
DigitalGovernment
Governmentwebsitesareanothersourceofinformationonpoliticsandpublicpolicy,buttheyalsocontainvaluableinformationaboutservicesandonlineservicetransactions.E‐governmentusershavegenerallypositiveattitudestowardtheironlineexperiences,includingfeelingsthatgovernmentis
moreresponsive,moreeffective,andefficient.38Governmentonlineincreasestheaccessibilityofgovernmentservices.Paradoxically,low‐incomeresidentsdependmostonmasstransitandotherpublicservices,yetareamongthosewhoareleastlikelytobeonlineandtobenefitfromthe
convenienceandaccessprovidedbye‐government.Nationalstudiesindicatethate‐governmentusersaremorelikelytobeyoung,higher‐income,educatedandmale.39Somenationalsurveyshaveshownthatlocalgovernmentmaybedifferent;higherpercentagesofAfrican‐Americansandwomenuselocal
governmentwebsites.40Itisunclear,however,whetherAfrican‐Americansandwomenaremorelikelytouselocalwebsitescontrollingforfactorssuchasincomeandeducation.
Thesurveycontainedquestionsaboutuseofgovernmentwebsites(foranylevelofgovernment),theCityofChicagowebsite,andpublictransitwebsitesfortheChicagoTransitAuthority
37Krueger2002;Mossberger,TolbertandStansbury2008amongothers.38West2003;Welch,HinnantandMoon2006;TolbertandMossberger2006.39West2005;Mossberger,TolbertandStansbury2003.40LarsenandRainie2002
48
(CTA)orRegionalTransitAuthority(RTA).Multilevelmodelsthatincludeneighborhoodcharacteristicswereestimatedforallthreetypesofgovernmentwebsites(seeAppendixB).Themodelsareforthe
citypopulationasawhole(ratherthaninternetusersonly).
Governmentwebsites(anylevelofgovernment).Useofe‐governmentingeneralbearssomesimilaritiestouseofonlinepoliticalinformationinChicago,andresultsforgenerale‐governmentuseinChicagofitthenationalpatterns,involvingfrequentinternetusers.
• Younger,whitenon‐Hispanic,higher‐incomeandbetter‐educatedresidentsaremorelikelyto
visitgovernmentwebsites.
• ResidentsofneighborhoodswithhigherpercentagesofAsian‐AmericansandAfrican‐Americansarealsomoreattunedtodigitalgovernment.Thismaysuggestsomethingabouttheneighborhoodsthatisnototherwisecapturedintheanalysis.
CityofChicagowebsite.NearlyhalfofChicagoresidents(49percent)haveusedthecity’swebsite–
slightlylessthanthe57percentwhohaveusedanygovernmentwebsite.Resultsforthelocalwebsiteconfirmsomeofthenationalpatternsforlocale‐governmentuseapparentinearlierstudiesthatdidnotusestatisticalanalysis.
• Parentsandfemaleresidentsaremorelikelytousethecity’swebsite.
• Younger,moreaffluent,andmoreeducatedresidentsaresignificantlymorelikelytousethe
city’swebsite.
• Therearenostatisticaldifferencesbyraceorethnicity.
Localgovernmentwebsitesmaybeparticularlyrelevantforthedailyroutinesofresidents,attractingparentsandwomen.Whiletherearenoracialorethnicdifferencesinlocale‐governmentuseoncewecontrolforotherfactors,thiscontrastswiththefindingsforalllevelsofgovernment.In
general,usersofthecity’swebsitearemorediversethangenerale‐governmentusers.
Themaponthenextpageshowscommunityareasinbluewhereuseofthecity’swebsiteisestimatedtobe50percentormore(abovethecity’s49percentaverage).Neighborhoodfactorsaren’t
significantpredictorsofcitywebsiteuse,althoughitisclearthatareaswithhigher‐incomeindividualsaccountforsomeofthehigh‐useareasinblue.Still,somelowerincomeareasareshadedinblueandmanylow‐incomeareasareincludedinthecommunitiescoloredinyellow,wherebetween35and49
percentofresidentsareestimatedtousethecity’swebsite.
UseofChicago’swebsiteismoreinclusivethane‐governmentuseingeneral;womenandparentsaremorelikelytouseit,andtherearenodifferencesby
raceandethnicity
49
50
Masstransituse.PublictransitplaysanimportantroleinChicago.TheChicagoTransitAuthority(CTA)andtheRegionalTransitAuthority(RTA)haveonlinetripplanners,schedules,andonlinetransactions
forfarecards.Seventy‐fourpercentofinternetusers(56percentofcityresidents)haveusedpublictransitwebsitesforinformationortransactions,slightlyhigherthanthepercentageusingthecity’swebsite.
Multilevelmodels(AppendixB)indicatethatChicagotransitwebsiteusersaremostlythosewho
areonlinefrequently,exceptthatneighborhoodpovertyplaysaroleaswell.
• Young,whitenon‐Hispanic,higher‐incomeandbetter‐educatedChicagoansareamongthosewholookuptransitinformationonline.
• Additionally,residentsofneighborhoodswithhighpovertyratesarealsosignificantlymorelikelytousetransitwebsites.Needmattersaswellasinternetuse.
• Controllingforotherfactors(suchasneighborhoodpoverty),African‐AmericanandLatino
neighborhoodsaresomewhatlesslikelytousemasstransitinformationonline.Thisindicatesthatnotallpoorneighborhoodsareequallylikelytohaveresidentswhousetheinternetfortransitinformation.
Residentsofpoorneighborhoodsareamongthemostlikelytousepublictransitwebsites
Thefindingsforthecityandtransitwebsitessuggestthattheneedforgreateraccesstolocalservicesmayalsobeamotivatingfactorforresidentstogoonline,particularlyincommunitieswhere
thereisrelianceonmasstransitandotherpublicservices.Atthesametime,somepoorcommunitiesarelessconnectedtoonlinemasstransitinformation.Forcityservicesmoregenerally,thereismorediversityofuse.
HealthCare
Amongtheonlineactivitiesincludedinthesurvey,lookingforhealthinformationisoneofthe
mostcommon,with64percentofthepopulation(86percentofinternetusers)whohavedonethisatsometime.Healthinformationcanbechallengingtounderstand,assomewebsitesareorientedtopractitioners,andothershavequestionablecredentials.Informationliteracyisparticularlycriticalinthis
area,forinternetusersneedtomakejudgmentsaboutthecredibilityofsourcesandtopayattentiontohowrecentlyinformationhasbeenpostedorupdated.
Multilevelmodelsforuseofonlinehealthinformation(AppendixB)showsomeinterestingpatterns:
• Younger,moreaffluent,andmoreeducatedresidentsaremorelikelytoturntotheinternetfor
healthinformation.
51
• Womenandparentsarealsomorelikelytousetheinternetforthispurpose.Thisfitswithpreviousresearchdemonstratingthatwomenandcaretakersarethemostfrequentusersof
healthinformationontheweb.41
• Latinosaresignificantlylesslikelythannon‐Hispanicwhitestofindhealthinformationonline.
• But,therearenosignificantdifferencesbetweenAfrican‐Americansandwhites,controllingforotherfactorssuchasincome,education,andneighborhood.
• RespondentswholiveinneighborhoodswithahighpercentageofAfrican‐AmericansandLatinosarelesslikelytoresearchhealthonline,indicatingsomespatialpatternstohealth
disparitiesonline.
Womenandparents,inparticular,valueonlinehealthinformation;therearenodifferencesbetweenAfrican‐Americansandwhites
SummaryonInternetActivities
Lookingacrossthesemanyactivities,theimpactofdisparitiesininternetusearevisible,
especiallyforLatinos,low‐income,andless‐educatedresidents.Low‐incomeandminorityneighborhoodsaccountforsomedisparitiesaswell.Buttherearealsoindicationsthatsomeusesoftheinternetmayprovideaparticularmotivationtogoonlineinlow‐incomecommunities.Theinternetcan
beanequalizingforceinprovidingaccesstoinformationandservices.African‐Americansuseonlinejobinformationtoagreaterextentthanwhites.Youngresidentsareamongthemostfrequentusersofpoliticsandnewsonlineaswellase‐government,eventhoughtraditionallytheyaremostapathetic
aboutpoliticsandcivicaffairs.Residentsofpoorcommunitiesusepublictransitwebsitesmore.Locale‐governmentattractsmorewomenandparentsthanothergovernmentsites,andforlocalgovernmenttherearenorealdifferencesbasedonraceorethnicity.African‐Americansarejustaslikelyaswhitesto
lookforhealthinformationontheweb.Embeddedinthesefindingsaresomeindicationsofhowtoengagemoreresidentswhoarenowunconnectedorless‐connected.
VI.CONCLUSION:CHALLENGESANDOPPORTUNITIESFORDIGITALEXCELLENCEINCHICAGO
Although75percentofChicagoresidentshavesomeexperiencewiththeinternet,almost40percentareeitherofflinecompletelyorhavelimitedaccess.Regularandeffectiveuseisbuiltona
foundationofhomeaccessandhigh‐speedconnections,whichfostermorefrequentuse,knowledgeofactivitiesonline,anddigitalskill.Aretheresomesolutionsthatmightbeemployed?Istherepublicsupportforaddressingtheseissues?
PublicOpinionasanOpportunity
Chicagoresidentsfavorpoliciestoclosethesegapsthroughgreateravailabilityofinternet
access.Oneofthequestionsincludedonthesurveyintroducedthetopicofwirelessnetworksand
41Fox2005
52
askedresidentstochooseamongseveraloptions.Respondentsweretold:“There'sbeentalkaboutbuildingawirelessnetworkinneighborhoodsinChicago.Whichofthefollowingshouldbethefocusin
doingthisproject?”Table11comparestheresponsesforthecitypopulation,lower‐incomeneighborhoods,andhigher‐incomeneighborhoods.Lower‐incomeneighborhoodshadmedianincomeslowerthanthecity’smean,andhigher‐incomeneighborhoodswereabovethemean.
TABLE11.WhereShouldaWirelessAvailabilityProjectStart? Choices Respondents CityPopulationLower‐incomeHigher‐income NeighborhoodsNeighborhoodsAlloverthecity 50% 50% 47%Inlow‐incomeneighborhoods 13% 15% 9%Inpublicschools,libraries,publicplaces 26% 24% 29%Shouldn’tworkonthisproject 7% 5% 11%Don’tknow 3% 3% 3% Forthecityasawhole,89percentsupportedsometypeofinitiative.Themostpopularalternativewastoprovidewirelessacrossthecity–supportedbyhalfofChicagoresidents.Approximatelyone‐quarteroftherespondentschosewirelessinpublicplaces,andabouthalfasmany(13percent)favoredprovidingwirelessfirstinlow‐incomeneighborhoods.Therearesmalldifferencesbyneighborhood,withresidentsoflower‐incomeareasslightlymorelikelytochooselow‐incomeareasfirstandslightlylesslikelytosupportwirelessinpublicplacesfirst.Residentsofhigher‐incomeneighborhoodsareabitmorecriticaloftheideaoverall,althoughthedifferencesaremodest–atmost5‐6percentagepointsdifferentfromlow‐incomeareas. Residentswerealsoaskedwhethertheywouldsupportawirelessprojectifitinvolvedasmalltaxorfeeincrease.Asexpected,supportforwirelessdropped,butthereisstillmajoritybackingfortheidea–61percentforthecityasawhole,60percentforlow‐incomeneighborhoods,and56percentforhigher‐incomeneighborhoods.Overall,Chicagoanshaveapositiveviewofwirelessprograms.Thismayindicatemoregeneralsupportfortechnologyinitiativesinthefuture.TABLE12.WouldYouSupportaWirelessAvailabilityProjectforaSmallTaxorFeeIncrease? Respondents CityPopulation Lower‐income Higher‐income Neighborhoods NeighborhoodsYes 61% 60% 56%No 32% 28% 36%Don’tknow 7% 7% 6%Refused/missing ‐‐ 3% 1%
ChallengesandOpportunitiesforAddressingDisparities
Atthesametimethatthisreportprovidesastraightforwardassessmentofexistinginequalities,therearereasonstobeoptimisticaboutthefutureifcurrentchallengesaremet.Digitalgapsarepatternedalongfamiliarlines–age,income,education,raceandethnicity.Latinosstandoutasthe
groupinChicagothatisleast‐connectedtotheinternet,especiallyamongthosewhopredominantly
53
speakSpanish.African‐Americansarestrivingtowardequityonline,asraceaccountsforarelativelysmallgapininternetuseanywhere,butalargergapinhomeaccess.Inturn,African‐Americanshave
higheruseofpublicaccess,andAfrican‐Americanswholackhomeaccesshavemorepositiveattitudestowardtechnologythansimilarly‐situatedwhites.Latinos,whoperceivemorebarrierstotechnologyusethanothergroups,aremuchmorelikelytocitecostratherthanalackofinterestfornotusingthe
internetathome.Thereisanopportunitytoreachdisconnectedorless‐connectedAfrican‐AmericanandLatinoresidentswithaffordableaccessandappropriatetrainingandsupport.Thesignificanceofincome–especiallyforhomeaccessandforbroadbandaswell–demonstratesthatitispoorresidents
whoareoftenexcludedfromthebenefitsoftheinternet.Ourfindingshighlightdifferencesinattitudesandneedsacrosslow‐incomegroups,andcommunity‐basedeffortsarelikelytobemostsuccessfulinaddressingthesevariedneedsforoutreachandassistance.
Ageaccountsforthelargestdisparitiesininternetuse,andisoneofthemostimportantfactors
inhomeaccessandbroadbanduse.Olderresidentsareleastlikelytousepublicaccess,andarealsoleastlikelytoexpressinterestintheinternet.Whiletheagingofcurrentinternetuserswillcontinuetochangethispicturesomewhat,theinitialchallengewithmanyolderresidentswillbeinterestand
awarenessofthepossibilitiesonline.
Publicaccessprovidedbylibrariesandcommunitytechnologycentershasmadeimportantinroads.Amongthosewhousetheseresourcesmostarelow‐income,African‐American,andLatinoresidents.CTCs,inparticular,servethepoorestresidentsofthecity.Wirelessaccessinpublicplaces
accommodatesinternetusersonthemove,andoverone‐thirdofChicagoresidentsgoonlinethisway.But,asfrequentuseismostlikelywithhomeaccessandbroadband,thereisaneedtoencouragehigh‐speedconnectionsathomethroughoutthecity.ExperimentsinseveralChicagoneighborhoodswith
implementingbroadbandaccesssolutionstoreachlocalresidencesandbusinessesareanimportantstepforextendingaccessanddrawinglessonsforthefuture.Criticalelementsintheseeffortsarethe
participationofcommunityorganizationsandtheprovisionoftrainingandsupport,aswellaslow‐costhardwareandsoftware.Publicaccessprovidersintheseneighborhoods,andthroughoutthecity,areimportantpartnersinsupportingthiseffortaswell.
Theinternethasbecomeacriticalresourceforwork,information,civicengagement,accessto
governmentservicesandhealth.Asmoreinformationandservicesmoveonline,thecostsincreaseforresidentswhoareexcludedfromthismedium.Someactivitiesclearlyprovidemotivationtogoonlineamonggroupsthatgenerallylagbehindininternetuse.African‐Americansaremorelikelythanwhites
tosearchforjobsonlineandresidentsofpoorneighborhoodsusetransitwebsitesmore.E‐governmentuseshowsnodifferencesbyraceandethnicity,andAfrican‐Americansarejustaslikelyaswhitestosearchforhealthinformationontheinternet.
WithbetterinformationaboutthestateofinternetuseinChicago,communityorganizations,
residents,nonprofits,businesses,educationalprovidersandpublicinstitutionscanaddressboththechallengesandopportunitiesfordigitalexcellence.
54
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APPENDIXA
Table1:InternetUseinGeneral(LogisticRegression)IndependentVariables
Coef. RobustStd.Err. z P>|z|
Age ‐.077 .004 ‐19.40 .000Latino ‐1.263 .177 ‐7.15 .000Black ‐.525 .134 ‐3.93 .000Asian .296 .464 0.64 .524Income .363 .031 11.74 .000Education .472 .037 12.65 .000Parent .073 .140 0.52 .603Female ‐.144 .112 ‐1.27 .205Constant 2.077 .302 6.89 .000Numberofobs=3259Waldchi2(8)=738.45Prob>chi2=0.0000PseudoR2=0.4042Logpseudolikelihood=‐1097.8317
Table2:InternetUseatHome(LogisticRegression)IndependentVariables
Coef. RobustStd.Err. z P>|z|
Age ‐.051 .003 ‐16.56 .000Latino ‐.546 .154 ‐3.56 .000Black ‐.559 .117 ‐4.77 .000Asian .447 .375 1.19 .233Income .379 .027 13.96 .000Education .371 .034 10.79 .000Parent .228 .119 1.92 .055Female ‐.043 .100 ‐0.42 .672Constant .348 .248 1.40 .161Numberofobs=3259Waldchi2(8)=778.28Prob>chi2=0.0000PseudoR2=0.3283Logpseudolikelihood=‐1355.5936
57
Table3:BroadbandInternetConnectionatHome(LogisticRegression)(1‐Broadband,0‐Dial‐upAccess)
Coef. RobustStd.Err. z P>|z|Age ‐.033 .004 ‐7.58 .000Latino ‐.553 .192 ‐2.87 .004Black .004 .179 0.02 .984Asian 1.337 .766 1.75 .081Income .247 .037 6.63 .000Educate .212 .051 4.13 .000Parent ‐.201 .155 ‐1.30 .193Female ‐.130 .146 ‐0.88 .376Constant 1.522 .365 4.17 .000Numberofobs=2226Waldchi2(8)=186.15Prob>chi2=0.0000PseudoR2=0.1242Logpseudolikelihood=‐693.87635
Table4:InternetUseatthePublicLibrary(LogisticRegression)IndependentVariables Coef. RobustStd.Err. z P>|z|Useinternetathome 1.143 .125 9.14 .000Awarenessofpublicinternetfacilityinneighborhood .187 .055 3.37 .001Easeofaccesstopublicinternetfacilityinneighborhood .608 .141 4.31 .000Age ‐.037 .003 ‐13.15 .000Latino .318 .138 2.30 .021Black .563 .111 5.06 .000Asian .077 .281 0.28 .783Income ‐.108 .025 ‐4.39 .000Education .176 .034 5.17 .000Parent .104 .096 1.08 .281Female ‐.021 .090 ‐0.23 .818Constant ‐1.480 .302 ‐4.91 .000Numberofobs=2815Waldchi2(11)=440.46Prob>chi2=0.0000PseudoR2=0.1424Logpseudolikelihood=‐1582.1751
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Table5:ReasonsforNotUsingInternetatHome(LogisticRegression)
IamNotInterested TheCostIsTooHighIndependentVariables Coef. RobustStd.
Err.P>|z| Coef. RobustStd.
Err.P>|z|
Age .029 .004 .000 .005 .004 .263Latino ‐.079 .225 .725 .647 .225 .004Black ‐.280 .161 .082 .104 .166 .529Asian .784 .746 .293 ‐.879 .815 .281Income .120 .041 .004 ‐.256 .043 .000Female ‐.158 .145 .275 .607 .146 .000Education ‐.115 .045 .012 ‐.084 .047 .073Parent ‐.168 .196 .392 ‐.176 .197 .370Constant ‐1.45 .391 .000 .405 .371 .275 Numberofobs=1011
Waldchi2(8)=90.17Prob>chi2=0.0000PseudoR2=0.0763Logpseudolikelihood=‐645.9321
Numberofobs=1011Waldchi2(8)=103.14Prob>chi2=0.0000PseudoR2=0.0876Logpseudolikelihood=‐637.9946
Table5continued:ReasonsforNotUsingInternetatHome(LogisticRegression)
IcanUseItSomewhereElse IDon'tHaveTimetoUsetheInternetIndependent
Variables Coef. RobustStd.Err.
P>|z| RobustStd.Err.
RobustStd.Err.
P>|z|
Age ‐.032 .004 .000 ‐.005 .005 .273Latino .156 .220 .478 .703 .241 .004Black .184 .165 .264 ‐.402 .201 .046Asian ‐.357 .657 .587 .503 .689 .465Income ‐.025 .041 .541 .076 .044 .082Female .067 .142 .639 ‐.522 .161 .001Education .142 .047 .002 ‐.073 .053 .167Parent ‐.314 .192 .101 ‐.124 .219 .573Constant 1.35 .377 .000 ‐.575 .422 .173 Numberofobs=1011
Waldchi2(8)=74.28Prob>chi2=0.0000PseudoR2=0.0596Logpseudolikelihood=‐658.65633
Numberofobs=1010Waldchi2(8)=54.60Prob>chi2=0.0000PseudoR2=0.0540Logpseudolikelihood=‐514.93941
59
Table5continued:ReasonsforNotUsingInternetatHome(LogisticRegression)
It'sTooDifficulttoUse IamWorriedAboutPrivacyIndependentVariables Coef. RobustStd.
Err.P>|z| Coef. RobustStd.
Err.P>|z|
Age .037 .005 .000 .004 .004 .312Latino .573 .228 .012 1.15 .233 .000Black ‐.273 .169 .107 .034 .163 .835Asian ‐.412 .648 .525 ‐.339 .618 .584Income ‐.087 .041 .033 .019 .040 .628Female .250 .147 .089 .592 .143 .000Education ‐.201 .047 .000 ‐.063 .046 .164Parent .260 .191 .173 .064 .191 .739Constant ‐1.62 .382 .000 ‐.379 .377 .315 Numberofobs=1011
Waldchi2(8)=103.36Prob>chi2=0.0000PseudoR2=0.0930Logpseudolikelihood=‐627.89249
Numberofobs=1010Waldchi2(8)=68.29Prob>chi2=0.0000PseudoR2=0.0515Logpseudolikelihood=‐651.02475
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APPENDIXB.MULTILEVELMODELSTable 1: Probability of Internet Use in Any Place: Multilevel Logistic Regression Estimates, Clustering by Census Tract or Chicago Community Area
Model 1: Census Tract Model 2: Community Area Coef. (S.E.) p>|z| Coef. (S.E.) p>|z| Individual Level Variables Age -0.078 0.000 -0.077 0.000 (0.004) (0.004) Latino -0.964 0.000 -1.041 0.000 (0.201) (0.211) Black -0.099 0.630 -0.281 0.112 (0.205) (0.177) Asian 0.374 0.426 0.335 0.438 (0.470) (0.431) Income 0.359 0.000 0.366 0.000 (0.032) (0.034) Education 0.470 0.000 0.464 0.000 (0.038) (0.042) Parent 0.118 0.425 0.112 0.435 (0.147) (0.143) Female -0.117 0.294 -0.115 0.309 (0.111) (0.113) Geographic Level Variables Pct. Latino -0.010 0.039 -0.006 0.348 (0.005) (0.006) Pct. Black -0.009 0.007 -0.009 0.013 (0.003) (0.004) Pct. Asian -0.001 0.911 -0.011 0.267 (0.010) (0.010) Pct. Below Poverty Line 0.006 0.367 0.023 0.004 (0.007) (0.008) Pct. High School Graduate 0.002 0.762 0.014 0.158 (0.007) (0.010) Constant 2.208 0.002 1.024 0.285 (0.718) (0.959) Observations 3117 3117 Pseudo R-squared 0.4107 0.4102 Log-likelihood -1045.5454 -1046.4632 Wald Chi2 776.7520 821.2099 Prob. > chi2 0.0000 0.0000
Unstandardized logistic regression coefficients with robust standard errors in parentheses. Standard errors adjusted by clustering cases by geographic area (census tract or Chicago community area). Probabilities based on two-tailed significance tests. Variables with a p-value of .10 or lower are considered statistically significant with a 90% confidence interval; a p-value of .05 or lower is considered statistically significant with a 95% confidence interval.
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Table 2: Probability of Home Internet Access: Multilevel Logistic Regression Estimates, Clustering by Census Tract or Chicago Community Area
Model 1: Census Tract Model 2: Community Area Coef. (S.E.) p>|z| Coef. (S.E.) p>|z| Individual Level Variables Age -0.051 0.000 -0.051 0.000 (0.003) (0.003) Latino -0.276 0.117 -0.327 0.067 (0.176) (0.178) Black -0.333 0.082 -0.482 0.001 (0.192) (0.151) Asian 0.410 0.327 0.417 0.155 (0.419) (0.293) Income 0.373 0.000 0.378 0.000 (0.028) (0.026) Education 0.364 0.000 0.365 0.000 (0.036) (0.033) Parent 0.284 0.025 0.273 0.052 (0.126) (0.140) Female -0.011 0.915 -0.011 0.907 (0.099) (0.096) Geographic Level Variables Pct. Latino -0.007 0.031 -0.009 0.003 (0.003) (0.003) Pct. Black -0.003 0.221 -0.005 0.083 (0.003) (0.003) Pct. Asian 0.015 0.099 0.003 0.657 (0.009) (0.007) Pct. Below Poverty Line -0.002 0.708 0.009 0.132 (0.005) (0.006) Constant 0.474 0.112 0.445 0.180 (0.298) (0.332) Observations 3117 3117 Pseudo R-squared 0.3318 0.3308 Log-likelihood -1298.6818 -1300.7910 Wald Chi2 780.9496 827.6910 Prob. > chi2 0.0000 0.0000
Unstandardized logistic regression coefficients with robust standard errors in parentheses. Standard errors adjusted by clustering cases by geographic area (census tract or Chicago community area). Probabilities based on two-tailed significance tests. Variables with a p-value of .10 or lower are considered statistically significant with a 90% confidence interval; a p-value of .05 or lower is considered statistically significant with a 95% confidence interval.
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Table 3: Probability of Internet Use at the Public Library: Multilevel Logistic Regression Estimates, Clustering by Census Tract or Chicago Community Area
Model 1: Census Tract Model 2: Community Area Coef. (S.E.) p>|z| Coef. (S.E.) p>|z| Individual Level Variables Ease of Access (self reported) 0.303 0.000 0.298 0.000 (0.055) (0.050) Age -0.044 0.000 -0.044 0.000 (0.003) (0.003) Latino 0.278 0.064 0.212 0.144 (0.150) (0.146) Black 0.647 0.000 0.677 0.000 (0.163) (0.142) Asian 0.153 0.584 0.179 0.467 (0.280) (0.246) Income -0.053 0.027 -0.055 0.038 (0.024) (0.026) Education 0.259 0.000 0.258 0.000 (0.032) (0.033) Parent 0.136 0.160 0.127 0.167 (0.097) (0.092) Female 0.030 0.730 0.024 0.781 (0.087) (0.088) Geographic Level Variables Pct. Latino -0.017 0.000 -0.016 0.004 (0.004) (0.006) Pct. Black -0.007 0.006 -0.007 0.031 (0.003) (0.003) Pct. Asian 0.002 0.748 0.003 0.621 (0.006) (0.006) Pct. Below Poverty Line -0.011 0.033 -0.020 0.023 (0.005) (0.009) Pct. High School Graduate -0.027 0.000 -0.029 0.001 (0.007) (0.009) Constant 1.899 0.006 2.178 0.007 (0.684) (0.809) Observations 2794 2794 Pseudo R-squared 0.1251 0.1233 Log-likelihood -1596.4057 -1599.5373 Wald Chi2 359.2470 412.7878 Prob. > chi2 0.0000 0.0000
Unstandardized logistic regression coefficients with robust standard errors in parentheses. Standard errors adjusted by clustering cases by geographic area (census tract or Chicago community area). Probabilities based on two-tailed significance tests. Variables with a p-value of .10 or lower are considered statistically significant with a 90% confidence interval; a p-value of .05 or lower is considered statistically significant with a 95% confidence interval.
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Table 4: Probability of Internet Use at a CTC: Multilevel Logistic Regression Estimates, Clustering by Census Tract or Chicago Community Area
Model 1: Census Tract Model 2: Community Area Coef. (S.E.) p>|z| Coef. (S.E.) p>|z| Individual Level Variables Ease of Use (self reported) 0.316 0.009 0.290 0.002 (0.121) (0.095) Age -0.031 0.000 -0.031 0.000 (0.005) (0.005) Latino 0.179 0.598 0.309 0.410 (0.340) (0.376) Black 0.145 0.646 0.513 0.031 (0.316) (0.238) Asian 0.195 0.732 0.659 0.213 (0.569) (0.530) Income 0.012 0.788 0.007 0.874 (0.045) (0.046) Education 0.117 0.065 0.094 0.201 (0.063) (0.074) Parent 0.414 0.049 0.427 0.011 (0.210) (0.167) Female 0.069 0.722 0.045 0.811 (0.194) (0.190) Geographic Level Variables Pct. Latino 0.015 0.265 0.007 0.716 (0.013) (0.018) Pct. Black 0.009 0.394 -0.001 0.961 (0.011) (0.011) Pct. Asian 0.009 0.744 -0.002 0.940 (0.028) (0.028) Pct. Below Poverty Line 0.015 0.074 0.010 0.462 (0.009) (0.014) Pct. High School Graduate 0.008 0.582 0.009 0.659 (0.014) (0.020) Constant -3.981 0.024 -3.086 0.189 (1.765) (2.351) Observations 886 1102 Pseudo R-squared 0.0747 0.0680 Log-likelihood -400.8104 -493.4263 Wald Chi2 64.9925 232.4717 Prob. > chi2 0.0000 0.0000
Unstandardized logistic regression coefficients with robust standard errors in parentheses. Standard errors adjusted by clustering cases by geographic area (census tract or Chicago community area). Probabilities based on two-tailed significance tests. Variables with a p-value of .10 or lower are considered statistically significant with a 90% confidence interval; a p-value of .05 or lower is considered statistically significant with a 95% confidence interval. Subsample of respondents from census tracts with above average poverty levels.
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Table 5: Probability of High Speed (Broadband) versus Dial-up Access: Multilevel Logistic Regression Estimates, Clustering by Census Tract or Chicago Community Area
Model 1: Census Tract Model 2: Community Area Coef. (S.E.) p>|z| Coef. (S.E.) p>|z| Individual Level Variables Age -0.034 0.000 -0.034 0.000 (0.005) (0.005) Latino -0.481 0.021 -0.498 0.021 (0.208) (0.215) Black -0.021 0.936 -0.136 0.627 (0.258) (0.279) Asian 1.371 0.077 1.327 0.088 (0.775) (0.778) Income 0.234 0.000 0.244 0.000 (0.037) (0.036) Education 0.207 0.000 0.202 0.000 (0.051) (0.053) Parent -0.232 0.171 -0.239 0.165 (0.169) (0.172) Female -0.142 0.335 -0.140 0.283 (0.147) (0.130) Geographic Level Variables Pct. Latino 0.002 0.723 0.001 0.932 (0.006) (0.008) Pct. Black 0.003 0.465 0.001 0.894 (0.004) (0.005) Pct. Asian -0.008 0.432 -0.014 0.197 (0.010) (0.011) Pct. Below Poverty Line -0.009 0.356 0.011 0.453 (0.010) (0.015) Pct. High School Graduate 0.007 0.472 0.011 0.001 (0.010) 0.001 Constant 1.236 0.202 0.726 0.594 (0.968) (1.363) Observations 2113 2113 Pseudo R-squared 0.1270 0.1262 Log-likelihood -652.9279 -653.4911 Wald Chi2 190.0820 246.7003 Prob. > chi2 0.0000 0.0000
Unstandardized logistic regression coefficients with robust standard errors in parentheses. Standard errors adjusted by clustering cases by geographic area (census tract or Chicago community area). Probabilities based on two-tailed significance tests. Variables with a p-value of .10 or lower are considered statistically significant with a 90% confidence interval; a p-value of .05 or lower is considered statistically significant with a 95% confidence interval.
65
Table 6: Probability of Citing Cost as a Reason for No Home Internet Access: Multilevel Logistic Regression Estimates, Clustering by Census Tract or Chicago Community Area
Model 1: Census Tract Model 2: Community Area Coef. (S.E.) p>|z| Coef. (S.E.) p>|z| Individual Level Variables Age 0.006 0.143 0.005 0.313 (0.004) (0.005) Latino 0.310 0.212 0.509 0.009 (0.248) (0.196) Black -0.020 0.946 0.052 0.804 (0.299) (0.210) Asian -0.951 0.215 -0.906 0.228 (0.767) (0.752) Income -0.253 0.000 -0.253 0.000 (0.047) (0.046) Education -0.091 0.065 -0.099 0.029 (0.049) (0.046) Parent -0.181 0.387 -0.199 0.309 (0.209) (0.196) Female 0.585 0.000 0.587 0.000 (0.147) (0.126) Geographic Level Variables Pct. Latino 0.020 0.002 0.020 0.007 (0.006) (0.007) Pct. Black 0.008 0.084 0.010 0.013 (0.004) (0.004) Pct. Asian 0.011 0.371 0.018 0.038 (0.013) (0.009) Pct. Below Poverty Line 0.006 0.382 -0.008 0.473 (0.007) (0.011) Pct. High School Graduate 0.019 0.047 0.020 0.114 (0.010) (0.013) Constant -1.807 0.076 -1.737 0.172 (1.018) (1.273) Observations 984 984 Pseudo R-squared 0.0959 0.0924 Log-likelihood -615.4857 -617.8867 Wald Chi2 100.6470 101.4212 Prob. > chi2 0.0000 0.0000
Unstandardized logistic regression coefficients with robust standard errors in parentheses. Standard errors adjusted by clustering cases by geographic area (census tract or Chicago community area). Probabilities based on two-tailed significance tests. Variables with a p-value of .10 or lower are considered statistically significant with a 90% confidence interval; a p-value of .05 or lower is considered statistically significant with a 95% confidence interval.
66
Table 7: Probability of Citing Too Difficulty as a Reason for No Home Internet Access: Multilevel Logistic Regression Estimates, Clustering by Census Tract or Chicago Community Area
Model 1: Census Tract Model 2: Community Area Coef. (S.E.) p>|z| Coef. (S.E.) p>|z| Individual Level Variables Age 0.038 0.000 0.038 0.000 (0.005) (0.005) Latino 0.603 0.022 0.586 0.051 (0.264) (0.300) Black -0.231 0.435 -0.303 0.248 (0.296) (0.262) Asian -0.352 0.557 -0.326 0.610 (0.598) (0.638) Income -0.094 0.027 -0.096 0.021 (0.043) (0.042) Education -0.203 0.000 -0.210 0.000 (0.049) (0.049) Parent 0.256 0.197 0.259 0.210 (0.198) (0.207) Female 0.229 0.144 0.218 0.185 (0.157) (0.165) Geographic Level Variables Pct. Latino 0.003 0.659 0.012 0.107 (0.006) (0.007) Pct. Black 0.005 0.189 0.010 0.036 (0.004) (0.005) Pct. Asian 0.007 0.569 0.010 0.388 (0.013) (0.011) Pct. Below Poverty Line -0.025 0.001 -0.027 0.009 (0.008) (0.010) Pct. High School Graduate -0.007 0.477 0.008 0.538 (0.009) (0.013) Constant -1.034 0.273 -2.408 0.045 (0.943) (1.198) Observations 984 984 Pseudo R-squared 0.1043 0.1039 Log-likelihood -602.4645 -602.7304 Wald Chi2 120.5170 125.6407 Prob. > chi2 0.0000 0.0000
Unstandardized logistic regression coefficients with robust standard errors in parentheses. Standard errors adjusted by clustering cases by geographic area (census tract or Chicago community area). Probabilities based on two-tailed significance tests. Variables with a p-value of .10 or lower are considered statistically significant with a 90% confidence interval; a p-value of .05 or lower is considered statistically significant with a 95% confidence interval.
67
Table 8: Probability of Citing a Lack of Interest as a Reason for No Internet Access: Multilevel Logistic Regression Estimates, Clustering by Census Tract or Chicago Community Area
Model 1: Census Tract Model 2: Community Area Coef. (S.E.) p>|z| Coef. (S.E.) p>|z| Individual Level Variables Age 0.028 0.000 0.029 0.000 (0.005) (0.005) Latino -0.084 0.727 -0.127 0.603 (0.240) (0.243) Black 0.176 0.512 -0.021 0.926 (0.269) (0.229) Asian 0.828 0.288 0.824 0.298 (0.780) (0.791) Income 0.110 0.015 0.119 0.008 (0.045) (0.045) Education -0.123 0.010 -0.130 0.010 (0.048) (0.050) Parent -0.190 0.326 -0.216 0.257 (0.193) (0.191) Female -0.154 0.311 -0.138 0.358 (0.152) (0.150) Geographic Level Variables Pct. Latino 0.003 0.538 0.009 0.189 (0.004) (0.007) Pct. Black -0.003 0.500 0.004 0.453 (0.004) (0.005) Pct. Asian 0.010 0.465 0.020 0.166 (0.014) (0.014) Median Income 0.000 0.099 0.000 0.033 (0.000) (0.000) Constant -1.962 0.000 -2.728 0.000 (0.540) (0.749) Observations 984 984 Pseudo R-squared 0.0812 0.0816 Log-likelihood -625.1008 -624.8473 Wald Chi2 90.5790 86.2566 Prob. > chi2 0.0000 0.0000
Unstandardized logistic regression coefficients with robust standard errors in parentheses. Standard errors adjusted by clustering cases by geographic area (census tract or Chicago community area). Probabilities based on two-tailed significance tests. Variables with a p-value of .10 or lower are considered statistically significant with a 90% confidence interval; a p-value of .05 or lower is considered statistically significant with a 95% confidence interval.
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Table 9: Probability of Internet Use at Work: Multilevel Logistic Regression Estimates, Clustering by Census Tract or Chicago Community Area
Model 1: Census Tract Model 2: Community Area Coef. (S.E.) p>|z| Coef. (S.E.) p>|z| Individual Level Variables Age -0.013 0.008 -0.013 0.002 (0.005) (0.004) Latino -0.833 0.000 -0.754 0.000 (0.190) (0.192) Black -0.495 0.042 -0.521 0.033 (0.243) (0.245) Asian -0.443 0.176 -0.413 0.292 (0.327) (0.392) Income 0.226 0.000 0.225 0.000 (0.036) (0.032) Education 0.499 0.000 0.494 0.000 (0.050) (0.052) Parent -0.070 0.615 -0.047 0.703 (0.139) (0.124) Female -0.035 0.775 -0.049 0.626 (0.124) (0.101) Geographic Level Variables Pct. Latino 0.025 0.000 0.030 0.000 (0.006) (0.007) Pct. Black 0.009 0.014 0.010 0.022 (0.004) (0.004) Pct. Asian 0.001 0.924 -0.003 0.829 (0.008) (0.013) Pct. Below Poverty Line 0.006 0.435 0.013 0.187 (0.007) (0.010) Pct. High School Graduate 0.030 0.001 0.041 0.000 (0.009) (0.011) Constant -5.513 0.000 -6.596 0.000 (0.905) (1.115) Observations 1546 1546 Pseudo R-squared 0.2332 0.2323 Log-likelihood -784.3304 -785.3172 Wald Chi2 328.0555 293.6138 Prob. > chi2 0.0000 0.0000
Unstandardized logistic regression coefficients with robust standard errors in parentheses. Standard errors adjusted by clustering cases by geographic area (census tract or Chicago community area). Probabilities based on two-tailed significance tests. Variables with a p-value of .10 or lower are considered statistically significant with a 90% confidence interval; a p-value of .05 or lower is considered statistically significant with a 95% confidence interval. Subsample of employed respondents (full or part-time) only.
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Table 10: Probability of Internet Use for Health Information: Multilevel Logistic Regression Estimates, Clustering by Census Tract or Chicago Community Area
Model 1: Census Tract Model 2: Community Area Coef. (S.E.) p>|z| Coef. (S.E.) p>|z| Individual Level Variables Age -0.048 0.000 -0.048 0.000 (0.003) (0.003) Latino -0.529 0.001 -0.566 0.000 (0.159) (0.155) Black -0.038 0.828 -0.121 0.471 (0.174) (0.168) Asian 0.203 0.509 0.200 0.444 (0.308) (0.261) Income 0.304 0.000 0.308 0.000 (0.026) (0.031) Education 0.366 0.000 0.362 0.000 (0.032) (0.030) Parent 0.250 0.033 0.244 0.028 (0.117) (0.111) Female 0.283 0.002 0.283 0.004 (0.091) (0.098) Geographic Level Variables Pct. Latino -0.007 0.095 -0.006 0.170 (0.004) (0.004) Pct. Black -0.005 0.059 -0.007 0.011 (0.003) (0.003) Pct. Asian 0.014 0.083 0.006 0.277 (0.008) (0.006) Pct. Below Poverty Line 0.005 0.398 0.018 0.003 (0.006) (0.006) Pct. High School Graduate 0.002 0.719 0.008 0.285 (0.006) (0.008) Constant -0.139 0.830 -0.761 0.324 (0.649) (0.772) Observations 3116 3116 Pseudo R-squared 0.2923 0.2922 Log-likelihood -1438.2169 -1438.3387 Wald Chi2 742.3475 935.8000 Prob. > chi2 0.0000 0.0000
Unstandardized logistic regression coefficients with robust standard errors in parentheses. Standard errors adjusted by clustering cases by geographic area (census tract or Chicago community area). Probabilities based on two-tailed significance tests. Variables with a p-value of .10 or lower are considered statistically significant with a 90% confidence interval; a p-value of .05 or lower is considered statistically significant with a 95% confidence interval.
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Table 11: Probability of Online Job Search: Multilevel Logistic Regression Estimates, Clustering by Census Tract or Chicago Community Area
Model 1: Census Tract Model 2: Community Area Coef. (S.E.) p>|z| Coef. (S.E.) p>|z| Individual Level Variables Age -0.078 0.000 -0.078 0.000 (0.003) (0.003) Latino -0.286 0.078 -0.329 0.045 (0.162) (0.164) Black 0.473 0.003 0.381 0.029 (0.161) (0.174) Asian 0.329 0.203 0.335 0.123 (0.258) (0.217) Income 0.054 0.020 0.057 0.040 (0.023) (0.028) Education 0.314 0.000 0.319 0.000 (0.032) (0.032) Parent 0.015 0.891 0.011 0.937 (0.109) (0.134) Female 0.011 0.905 0.008 0.936 (0.095) (0.096) Geographic Level Variables Pct. Latino -0.006 0.173 -0.008 0.191 (0.004) (0.006) Pct. Black -0.002 0.338 -0.003 0.358 (0.003) (0.003) Pct. Asian 0.001 0.907 -0.004 0.699 (0.009) (0.011) Pct. Below Poverty Line 0.001 0.906 0.001 0.939 (0.005) (0.007) Pct. High School Graduate -0.004 0.587 -0.010 0.336 (0.007) (0.010) Constant 2.313 0.001 2.844 0.005 (0.675) (1.019) Observations 3115 3115 Pseudo R-squared 0.2715 0.2715 Log-likelihood -1572.6548 -1572.6726 Wald Chi2 889.7194 738.0208 Prob. > chi2 0.0000 0.0000
Unstandardized logistic regression coefficients with robust standard errors in parentheses. Standard errors adjusted by clustering cases by geographic area (census tract or Chicago community area). Probabilities based on two-tailed significance tests. Variables with a p-value of .10 or lower are considered statistically significant with a 90% confidence interval; a p-value of .05 or lower is considered statistically significant with a 95% confidence interval.
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Table 12: Probability of Internet Use for Classes or Training: Multilevel Logistic Regression Estimates, Clustering by Census Tract or Chicago Community Area
Model 1: Census Tract Model 2: Community Area Coef. (S.E.) p>|z| Coef. (S.E.) p>|z| Individual Level Variables Age -0.034 0.000 -0.034 0.000 (0.003) (0.003) Latino -0.147 0.324 -0.158 0.249 (0.149) (0.137) Black 0.049 0.739 0.009 0.950 (0.146) (0.149) Asian 0.214 0.384 0.210 0.398 (0.246) (0.249) Income 0.106 0.000 0.106 0.000 (0.023) (0.024) Education 0.361 0.000 0.362 0.000 (0.036) (0.037) Parent 0.060 0.546 0.060 0.592 (0.100) (0.111) Female -0.005 0.957 -0.010 0.909 (0.090) (0.088) Individual Level Variables Pct. Latino -0.001 0.746 -0.003 0.603 (0.004) (0.005) Pct. Black -0.000 0.886 -0.000 0.996 (0.003) (0.003) Pct. Asian 0.003 0.671 0.002 0.768 (0.006) (0.007) Pct. Below Poverty Line 0.003 0.525 0.002 0.797 (0.005) (0.008) Pct. High School Graduate -0.005 0.463 -0.009 0.332 (0.007) (0.009) Constant -1.285 0.054 -0.956 0.264 (0.666) (0.856) Observations 3115 3115 Pseudo R-squared 0.1322 0.1323 Log-likelihood -1662.6053 -1662.4013 Wald Chi2 431.8355 510.0489 Prob. > chi2 0.0000 0.0000
Unstandardized logistic regression coefficients with robust standard errors in parentheses. Standard errors adjusted by clustering cases by geographic area (census tract or Chicago community area). Probabilities based on two-tailed significance tests. Variables with a p-value of .10 or lower are considered statistically significant with a 90% confidence interval; a p-value of .05 or lower is considered statistically significant with a 95% confidence interval.
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Table 13: Probability of Internet Use for Public Transportation Information: Multilevel Logistic Regression Estimates, Clustering by Census Tract or Chicago Community Area
Model 1: Census Tract Model 2: Community Area Coef. (S.E.) p>|z| Coef. (S.E.) p>|z| Individual Level Variables Age -0.053 0.000 -0.052 0.000 (0.003) (0.003) Latino -0.783 0.000 -0.840 0.000 (0.155) (0.137) Black -0.319 0.064 -0.342 0.028 (0.172) (0.156) Asian 0.128 0.687 0.089 0.748 (0.318) (0.278) Income 0.169 0.000 0.176 0.000 (0.023) (0.023) Education 0.263 0.000 0.263 0.000 (0.030) (0.027) Parent 0.119 0.257 0.116 0.260 (0.105) (0.103) Female 0.040 0.633 0.047 0.581 (0.085) (0.085) Geographic Level Variables Pct. Latino -0.005 0.154 -0.004 0.354 (0.004) (0.004) Pct. Black -0.005 0.051 -0.007 0.006 (0.002) (0.003) Pct. Asian -0.003 0.683 -0.008 0.124 (0.007) (0.005) Pct. Below Poverty Line 0.011 0.045 0.026 0.000 (0.005) (0.007) Pct. High School Graduate 0.010 0.071 0.015 0.097 (0.006) (0.009) Constant 0.261 0.645 -0.298 0.691 (0.568) (0.751) Observations 3115 3115 Pseudo R-squared 0.2289 0.2292 Log-likelihood -1652.4466 -1651.7189 Wald Chi2 680.1880 775.0732 Prob. > chi2 0.0000 0.0000
Unstandardized logistic regression coefficients with robust standard errors in parentheses. Standard errors adjusted by clustering cases by geographic area (census tract or Chicago community area). Probabilities based on two-tailed significance tests. Variables with a p-value of .10 or lower are considered statistically significant with a 90% confidence interval; a p-value of .05 or lower is considered statistically significant with a 95% confidence interval.
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Table 13: Probability of Internet Use for Information about Politics: Multilevel Logistic Regression Estimates, Clustering by Census Tract or Chicago Community Area
Model 1: Census Tract Model 2: Community Area Coef. (S.E.) p>|z| Coef. (S.E.) p>|z| Individual Level Variables Age -0.043 0.000 -0.042 0.000 (0.003) (0.003) Latino -0.585 0.000 -0.538 0.000 (0.151) (0.142) Black -0.411 0.011 -0.388 0.018 (0.163) (0.164) Asian 0.067 0.798 0.020 0.934 (0.263) (0.240) Income 0.238 0.000 0.240 0.000 (0.025) (0.029) Education 0.394 0.000 0.381 0.000 (0.033) (0.032) Parent -0.298 0.007 -0.282 0.004 (0.110) (0.098) Female -0.166 0.059 -0.157 0.066 (0.088) (0.085) Geographic Level Variables Pct. Latino 0.007 0.059 0.013 0.021 (0.004) (0.006) Pct. Black 0.003 0.238 0.003 0.397 (0.002) (0.003) Pct. Asian 0.004 0.615 0.007 0.341 (0.007) (0.008) Pct. Below Poverty Line 0.006 0.252 0.024 0.007 (0.006) (0.009) Pct. High School Graduate 0.018 0.004 0.036 0.000 (0.006) (0.010) Constant -2.155 0.000 -3.879 0.000 (0.618) (0.892) Observations 3115 3115 Pseudo R-squared 0.2609 0.2640 Log-likelihood -1592.5272 -1585.8280 Wald Chi2 832.9049 783.1396 Prob. > chi2 0.0000 0.0000
Unstandardized logistic regression coefficients with robust standard errors in parentheses. Standard errors adjusted by clustering cases by geographic area (census tract or Chicago community area). Probabilities based on two-tailed significance tests. Variables with a p-value of .10 or lower are considered statistically significant with a 90% confidence interval; a p-value of .05 or lower is considered statistically significant with a 95% confidence interval.
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Table 14: Probability of Internet Use for Information about Government: Multilevel Logistic Regression Estimates, Clustering by Census Tract or Chicago Community Area
Model 1: Census Tract Model 2: Community Area Coef. (S.E.) p>|z| Coef. (S.E.) p>|z| Individual Level Variables Age -0.038 0.000 -0.037 0.000 (0.003) (0.003) Latino -0.554 0.000 -0.550 0.000 (0.143) (0.142) Black -0.267 0.110 -0.215 0.163 (0.167) (0.154) Asian -0.035 0.896 0.018 0.936 (0.270) (0.219) Income 0.209 0.000 0.211 0.000 (0.023) (0.028) Education 0.367 0.000 0.368 0.000 (0.031) (0.029) Parent 0.150 0.162 0.148 0.199 (0.107) (0.115) Female -0.116 0.183 -0.115 0.122 (0.087) (0.074) Geographic Level Variables Pct. Latino 0.005 0.187 0.005 0.388 (0.004) (0.006) Pct. Black 0.006 0.026 0.003 0.277 (0.003) (0.003) Pct. Asian 0.017 0.017 0.009 0.147 (0.007) (0.006) Pct. Below Poverty Line -0.002 0.681 0.007 0.302 (0.005) (0.007) Pct. High School Graduate 0.009 0.122 0.012 0.147 (0.006) (0.009) Constant -1.418 0.016 -1.751 0.039 (0.586) (0.849) Observations 3116 3116 Pseudo R-squared 0.2250 0.2239 Log-likelihood -1653.2776 -1655.7325 Wald Chi2 704.9155 740.5732 Prob. > chi2 0.0000 0.0000
Unstandardized logistic regression coefficients with robust standard errors in parentheses. Standard errors adjusted by clustering cases by geographic area (census tract or Chicago community area). Probabilities based on two-tailed significance tests. Variables with a p-value of .10 or lower are considered statistically significant with a 90% confidence interval; a p-value of .05 or lower is considered statistically significant with a 95% confidence interval.
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Table 15: Probability of Using the City of Chicago’s Website: Multilevel Logistic Regression Estimates, Clustering by Census Tract or Chicago Community Area
Model 1: Census Tract Model 2: Community Area Coef. (S.E.) p>|z| Coef. (S.E.) p>|z| Individual Level Variables Age -0.027 0.000 -0.027 0.000 (0.002) (0.003) Latino -0.167 0.226 -0.196 0.138 (0.138) (0.132) Black -0.080 0.628 -0.059 0.670 (0.166) (0.138) Asian -0.202 0.426 -0.169 0.500 (0.254) (0.250) Income 0.208 0.000 0.209 0.000 (0.024) (0.025) Education 0.314 0.000 0.321 0.000 (0.030) (0.031) Parent 0.311 0.002 0.305 0.001 (0.102) (0.092) Female 0.184 0.031 0.176 0.035 (0.085) (0.084) Geographic Level Variables Pct. Latino -0.002 0.649 -0.004 0.351 (0.003) (0.005) Pct. Black 0.002 0.513 -0.000 0.968 (0.002) (0.002) Pct. Asian 0.008 0.230 0.003 0.559 (0.006) (0.005) Pct. Below Poverty Line -0.002 0.595 -0.003 0.648 (0.004) (0.006) Pct. High School Graduate -0.008 0.119 -0.016 0.049 (0.005) (0.008) Constant -0.827 0.123 -0.203 0.797 (0.536) (0.788) Observations 3112 3112 Pseudo R-squared 0.1573 0.1578 Log-likelihood -1816.7383 -1815.7021 Wald Chi2 541.2771 634.1826 Prob. > chi2 0.0000 0.0000
Unstandardized logistic regression coefficients with robust standard errors in parentheses. Standard errors adjusted by clustering cases by geographic area (census tract or Chicago community area). Probabilities based on two-tailed significance tests. Variables with a p-value of .10 or lower are considered statistically significant with a 90% confidence interval; a p-value of .05 or lower is considered statistically significant with a 95% confidence interval.
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APPENDIXC
UNIVERSITYOFIOWAHAWKEYEPOLLDEPARTMENTOFPOLITICALSCIENCE
CHICAGOINTERNETSURVEYCONDUCTEDJUNE23‐AUGUST7,2008QUESTIONNAIRE
INTRODUCTION:Hello,Iam_________________,callingfromtheUniversityofIowa.WearestudyingtheroleoftheinternetinChicago.Yourphonenumberwasselectedatrandomtorepresentyourneighborhoodinthisstudy.Iamnotsellinganythingandjustneedafewminutes.ATTEMPTTOIMPROVEYOUNGMALERESPONSERATESYNGMALE:I’dliketoasksomequestionsoftheyoungestmalewhois18yearsorolderandnowathome.[IFRISMALE]Wouldthatbeyou?IFRESP:YESCONTINUEWITH[AGESCREEN]IFRESP:LETMEGETHIMWAITFORNEWPERSON,GOTO[REINTRO]IFRESP:NOMALE,ASK:
Isthereanotherpersonover18Icanspeakwith?CouldIspeakwithyou?IFCURRENTR.GOTO[AGESCREEN]IFWILLGETSOMEONE,WAITFORNEWPERSON,GOTO[REINTRO]IFNO,GOTO[SCHEDULE].REINTRO:Hello,Iam_________________,callingfromtheUniversityofIowa.WearestudyingtheroleoftheinternetinChicago.Yourphonenumberwasselectedatrandomtorepresentyourneighborhoodinthisstudy.Iamnotsellinganythingandjustneedafewminutes.AGESCREEN:SCREENAGEFOR18andOVERQ1AAGE First,Ineedtomakesurewearereachingpeopleofallages18orover.Wouldyoutell
meyourage? ________years 97 97orolder 99 Don’tknow/Refused[VOL.]IFNOT18orOVER GOTOEND[INELIGIBLE]
77
CONSENT: WeinviteyoutoparticipateinastudyabouttechnologyaccessinChicagobeingconductedby
researchersfromtheUniversityofIowa.Yourphonenumberwaschosenatrandomtorepresentyourneighborhood.Ifyouagree,wewouldliketoaskyouaseriesofquestions.Youmayskipanyquestionsthatyouprefernottoanswer.Thiswilltakeabout12minutes.Yourresponsesareconfidentialanditwillnotbepossibletolinkyoutothem.Thissurveyisvoluntary.Yourwillingnesstoanswermyquestionswillindicateyourconsenttouseyouranswersinourresearchproject.Q1BAreyouwillingtoparticipateinthissurvey?
0 NOGOTOEND[ATTEMPTCONVERT]1 YES
Q2INTUSE OK,thanks!First,doyoueverusetheInternetinanyplace(home,work,school,
anywhereelse)?
0 No1 Yes8 Don’tKnow9 Refused
Q3INFO Weareinterestedintheinformationpeoplefeeltheyneedintheirdailyliveswhether
ornotitcomesfromtheinternet.Wouldyousaythatitisveryimportant,important,notveryimportant,ornotimportantatallforyoutogetinformationon:[PROMPTWITHRESPONSEOPTIONSASNEEDED]
Q3A JobsorbetterjobopportunitiesQ3B EducationortrainingformyselfQ3C Mychild'sschoolQ3D HealthcareorhealthissuesQ3E MyneighborhoodQ3F GovernmentorservicesprovidedbygovernmentQ3G Placestolive
RESPONSEOPTIONS
1 Veryimportant2 Important3 Notveryimportant4 Notatallimportant
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8 Don’tKnow9 Refused
IFQ2ISNOTYES(1)GOTOQ6Q4FREQUSE AbouthowoftendoyouusetheInternet?[Readoptions]
1 Severaltimesaday2 Aboutonceaday3 3‐5daysaweek4 1‐2daysaweek5 Everyfewweeks6 Lessoften9 Refused
Q5HOWLONG AbouthowmanyyearshaveyoubeenanInternetuser?[ENTERYEARS]
_______years8 Don’tKnow9 Refused
Q6HCOMPDoyouhaveacomputerathome?
0 NOGOTOQ81 YES8 Don’tKnowGOTOQ89 RefusedGOTOQ8
Q7INETHOM DoyoueverusetheInternetathome?
0 NO1 YESGOTOQ108 Don’tKnow9 Refused
Q8NOACCESS Iamgoingtoreadalistofreasonswhysomepeopledon’tusetheInternetathome.
Foreach,justtellmewhetheritappliestoyoubysayingyesifitdoes,ornoifitdoesnot.
79
Q8A Idon’tneedit,I’mnotinterestedQ8B ThecostistoohighformeQ8C IcanuseitsomewhereelseQ8D Idon’thavetimetousetheInternetQ8E It’stoodifficulttouseQ8F IamworriedaboutprivacyandpersonalinformationonlineQ8G TheInternetisdangerousQ8H It’shardformetousetheinformationinEnglishQ8I IhaveaphysicalimpairmentthatmakesitdifficulttousetheInternet
RESPONSEOPTIONS
0 NO1 YES
8 Don’tKnow9 Refused
Q9MAIN Now,pleasetellmeinacouplewordstheMAINreasonyoudon’tusetheInternetat
home?[DON’TREAD,CODEANSWERTOBESTFIT]
1 Idon’tneedit,I’mnotinterested2 Thecostistoohighforme3 Icanuseitsomewhereelse4 Idon’thavetimetousetheInternet5 It’stoodifficulttouse6 Iamworriedaboutprivacyandpersonalinformationonline7 TheInternetisdangerous8 It’shardformetousetheinformationinEnglish9 IhaveaphysicalimpairmentthatmakesitdifficulttousetheInternet10 Other11 Don’tKnow12 Refused
Q9AINTFUT Isthereanythingthatmightmakeyouinterestedinusingtheinternetinthefuture?If
so,justtellmeinacouplewordswhatitis.Ifnot,justtellmeno.[OPENENDED,RECORDVERBATIM]
IFQ7ISNOT1GOTOQ14Q10HCONTYP DoesthecomputeryouuseatHOMEconnecttotheInternetthroughadial‐up
telephoneline,ordoyouhavesometypeofhighspeedconnection,?
1 Dial‐uptelephone
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2 HighSpeedConnectionGOTOQ128 Don’tKnowGOTOQ129 RefusedGOTOQ12
Q11NOBBND WhatistheMAINreasonyoudonothavehigh‐speed(thatis,fasterthandial‐up)
Internetaccessathome?[DON’TREAD,CODEANSWERTOBESTFIT]
1 Don’tneeditornotinterested2 Costsaretoohighforme3 Canuseitsomewhereelse4 Idon’thavetimetousetheInternet5 Toodifficulttouseordon’tnowhowtouse6 Nocomputerorcomputerinadequate7 Privacyandsecurity8 Notavailableinarea9 Other10 Don’tKnow11 Refused
Q12WHEREINT WherewouldyousaythatyouusetheInternetmostoften?[DON’TREAD,CODEANSWERTO
BESTFIT]
1 Home2 Work3 School4 Alibraryorpublicplace5 Friendorrelative’shouse6 CoffeeShoporInternetCafe7 Other
8 Don’tKnow(Vol.)9 Refused(Vol.)
Q13WHERESEC WherewouldyousaythatyouusetheInternetmostoftenafterthat?[DON’TREAD,
CODEANSWERTOBESTFIT]
1 Home2 Work3 School4 Alibraryorpublicplace5 Friendorrelative’shouse
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6 CoffeeShoporInternetCafe7 Other
8 Don’tKnow(Vol.)9 Refused(Vol.)
Q14CTCAWAR Asfarasyouknow,isthereaplaceyoucangoinyourneighborhoodwherethe
Internetispubliclyavailabletoanyonewhowantstouseit?SuchplacesareoftencalledCommunityTechnologyCenters.
0 NO1 YES
8 Don’tKnow(Vol.)9 Refused(Vol.)
IFQ2ISNOTYES(1)SKIPTOQ16Q15CTCHELP HaveyoueverusedtheInternetorgottenhelpusingtheInternetataCommunity
TechnologyCenter?
0 NO1 YES
8 Don’tKnow(Vol.)9 Refused(Vol.)
Q16PUBLICACC Wouldyousaythatitiseasyordifficulttogettoplacesinyourcommunitywithpublic
accesstotheInternet,likealibraryoracommunitytechnologycenter?Wouldyousaythatitisveryeasy,somewhateasy,somewhatdifficultorverydifficult?
1 Veryeasy2 Somewhateasy3 Somewhatdifficult4 Verydifficult
8 Don’tknow(Vol)9 Refused(Vol)
IFQ2ISNOTYES(1)SKIPTOQ24
82
Q17LIBRARY HaveyouusedtheInternetattheChicagoPublicLibrary?
0 NO1 YES
8 Don’tKnow(Vol.)9 Refused(Vol.)
IFQ15ISNOTYES(1)ANDQ17ISNOTYES(1)GOTOQ19Q18WHYLIB IamgoingtoreadanumberofstatementsaboutwhyyouusetheInternetatthe
libraryoratacommunitytechnologycenter.Pleaserespondyesornotoeachstatement.
Q18A Idon’thaveacomputerathomeormycomputeritslowQ18B Idon’thaveanInternetconnectionathomeQ18C IneededhelptofindinformationQ18D IneededhelptousethecomputerQ18E MycomputerorInternetconnectionsathomearen’tworkingQ18F ItisconvenientQ18G TotakeaclassQ18H Totakemychildrentodotheirhomework
RESPONSEOPTIONS
0 NO1 YES
8 Don’tKnow(Vol.)9 Refused(Vol.)
Q19ACTIVITIES Iamgoingtoreadalistofthingsyoumightdoontheinternet.Pleasetellmehow
frequentlyyoudoeachbysayingifyoudothesethingsdaily,afewtimesperweek,afewtimespermonth,rarely,ornever.[PROMPTWITHOPTIONSASNEEDED]
Q19A GetnewsonlineQ19B DoworkforyourjobQ19C UseasocialnetworkingsitelikeFacebookQ19D SendorreceiveemailQ19E UseacellphonetoconnecttotheInternetQ19F ReadablogQ19G UsewirelessaccesstoconnecttotheInternetinapublicplace
83
RESPONSEOPTIONS
1 Daily2 Afewtimesperweek3 Afewtimespermonth4 Rarely5 Never
8 Don’tKnow(Vol.)9 Refused(Vol.)
Q20ONLINE I’mgoingtoreadanotherlist.ForeachitempleasetellmeifyoueverusetheInternet
todoanyofthefollowingthingsbyjustsayingyesorno.DoyoueverusetheInternetto;[PROMPTASNECESSARY–JUSTTELLMEYESORNO]
Q20A FindhealthinformationQ20B LookforajoborinformationonjobsQ20C TakeaclassortrainingonlineQ20D GetinformationaboutpoliticsQ20E GetinformationabouttrainsorbusesusingtheCTAorRTAwebsiteQ20F Find information on government Q20G UsetheCityofChicagowebsite
RESPONSEOPTIONS
0 NO1 YES
8 Don’tKnow(Vol.)9 Refused(Vol.)
IFQ20GISYES(1)ASKQ21OTHERWISESKIPTOQ23Q21CHICAGO PleasetellmeifyouhaveeverusedtheCityofChicagowebsitetodoanyofthe
following.Justtellmeyesorno.[PROMPTASNECESSARY–JUSTTELLMEYESORNO]
Q21A GetanaddressorphonenumberQ21B ContactofficialsQ21C GettouristorrecreationinformationQ21D Getinformationaboutservices(otherthanrecreationortourism)Q21E Completeatransactiononline,suchaspayingabillorfine,orfilingaformonlineQ21F Lookforgovernmentpoliciesordocuments
84
RESPONSEOPTIONS
0 NO 1 YES 8 Don’tKnow(Vol.)9 Refused(Vol.)
Q22EVALCHI IamgoingtoreadyousomestatementsabouttheCityofChicagowebsite.Pleasetell
mewhetheryoustronglyagree,agree,disagreeorstronglydisagreewitheachstatement.[PROMPTASNECESSARYWITHRESPONSEOPTIONS]
Q22A ThewebsitehadtheinformationIneeded.Q22B Thewebsitewaseasytouseandfindinformation.Q22C Thewebsitewasdifficulttouseandcomplex.
RESPONSEOPTIONS
1 StronglyAgree2 Agree3 Disagree4 StronglyDisagree8 Don’tKnow(Vol.)9 Refused(Vol.)
Q23SKILLS Iamgoingtoreadsomethingspeoplesometimesdoonline.Pleasetellmeifyou
alreadyknowhowtodoeachone,orifyouwouldneedsomeoneelsetohelpyou.
Q23A UseasearchenginetofindinformationonlineQ23B SendandreceiveemailQ23C DownloadandfilloutaformQ23D UploadimagesorfilestoawebsiteoremailQ23E Createawebsite
RESPONSEOPTIONS1Knowhow2Needhelp8Don’tKnow(Vol.)9Refused(Vol.)
Q24
85
POLICY1 There'sbeentalkaboutbuildingawirelessnetworkinneighborhoodsinChicago.Whichofthefollowingshouldbethefocusindoingthisproject?Shoulditbeonmakingwirelessavailable:[RANDOMIZEORDEROFFIRSTTHREEOPTIONS;READINORDER]
1 alloverthecity 2 inlow‐incomeneighborhoods 3 inpublicschools,librariesandotherpublicplaces 4 ordoyouthinktheyshouldnotworkonthisproject?
8 Don’tKnow(Vol.) 9 Refused(Vol.)
Q25POLICY2 Wouldyousupportaprojecttoprovidefreewirelessinternetaccessifitcausedasmall
increaseinfeesortaxes?
0 NO1 YES8 Don’tKnow(Vol.)9 Refused(Vol.)
DemographicInformation–ALLRESPONDENTSNow,justafewlastquestionsforstatisticalpurposesonly.We’realmostdone.Iappreciatethetimeyou’vegivenme.Q26EDUC Whatisthelastgradeorclassthatyoucompletedinschool?
[DONOTREAD;MARKCLOSEST] 1 None,orgrade1‐8 2 Highschoolincomplete(Grades9‐11) 3 Highschoolgraduate(Grade12orGEDcertificate) 4 Technical,trade,orvocationalschoolAFTERhighschool 5 Somecollege,no4‐yeardegree(includingassociatedegree) 6 Collegegraduate(B.S.,B.A.,orother4‐yeardegree) 7 Post‐graduatetrainingorprofessionalschoolingaftercollege (e.g.,towardamaster'sDegreeorPh.D.;lawormedicalschool)
8 Don’tknow(Vol.)9 Refused(Vol.)
Q27RACE Whatisyourrace?Areyouwhite,black,Asian,orsomeother? 1 White
86
2 Black 3 Asian 4 Otherormixedrace
8 Don’tknow(Vol.)9 Refused(Vol.)
Q28HISP Areyou,yourself,ofHispanicoriginordescent,suchasMexican,PuertoRican,Cuban,
orsomeotherSpanishbackground? 0 NO 1 YES
8 Don’tknow(Vol)9 Refused(Vol)
Q29MARITAL Whatisyourmaritalstatus?Areyou…[READ]
1. Married,orwithacommittedpartner 2 Divorced 3 Separated 4 Widowed 5 Neverbeenmarried
8 Don’tknow(Vol.)9 Refused(Vol.)
Q31INCOME Lastyear,thatisin2007,whatwasyourtotalfamilyincomefromallsources,before
taxes?JuststopmewhenIgettotherightcategory.[READ] 1 Lessthan$5,000
1 5tounder$10,0002 10tounder$20,0003 20tounder$30,0004 30tounder$40,0005 40tounder$50,0006 50tounder$75,0007 75tounder$100,0008 100tounder$150,0009 $150,000ormore10 Don’tknow(Vol.)11 Refused(Vol.)
87
IFQ31ISREFUSED(11)ASK:Q31AINCOME2 Justforstatisticalpurposesitwouldbereallyhelpfulifyouwouldtellmeifyourfamily
incomeisabove$20,000.Isit:[readoptions]
1 Above$20,0002 AtorBelow$20,000
8 Don’tKnow9 Refused
Q32CHILD Areyoutheparentorguardianofanychildrenunder18nowlivinginyourhousehold?
0 NO 1 YES 8 Don’tknow 9 Refused
Q33JOB Whatisyouremploymentstatus?Areyou:[READ]
1 Employedfulltime 2 Employedparttime 3 Ahomemakerorstayathomeparent 4 Retired 5 Astudent 6 Unemployed 7 Laidoff 8 Disabled 9 Don’tknow 10 Refused
Q34ZIPCODE Whatisyourzipcode?
_____ EnterZipcode
8 Don’tknow(Vol.)9 Refused(Vol.)
Q35OCCUP Whatisyouroccupation?[OPENENDED,RECORDVERBATIM]Q36
88
CHA AreyoucurrentlyaCHA[ChicagoHousingAuthority]residentorareyouaformerresidentwhowillbereturningtoCHAhousinginthefuture?
0 NO1 YES8 Don’tknow(Vol.)9 Refused(Vol.)
Q37STREETS Whatarethecross‐streetsnearestyourresidence?[OPENENDED,RECORDVERBATIM]Q38SEX [DONOTASK;ENTERRESPONDENT'SAPPARENTSEX]
1 Male2 Female
ENDOFINTERVIEW.THANKRESPONDENTGOTO[COMPLETE][COMPLETE]OK,that’sallIhaveforyourtoday.Thankyouagainforyourtime.Haveaniceday/evening.[END;complete][ATTEMPTCONVERT]Iunderstandwhyyoumightnotwanttotakethetimerightnowtotalkwithus.ButwhatwearedoingisimportanttoChicagoandyouranswerswillhelpthecitybetterunderstandwhatkindoftechnologypeopleneed.Itwillonlytakeabout12minutes.Couldyouhelpusout?
1 YESRETURNTOQ22 NOGOTO[SCHEDULE]
[SCHEDULE]Woulditbepossibletoscheduleanothertimetotalkwithyouorsomeoneelseinyourhousehold?I’dbehappytosetupaspecificdayandtimetocall.
1 YES2 NOOK,thanksforyourtime.[END;Refusal]
Great,thanks.Iamcallingyouat[readphonenumber].Whenwouldyoulikemetocallback?[ENTERDAYANDTIMEFORCALLBACK]CouldyougivemeyourfirstnamesoIknowwhotoaskforwhenIcall?[RECORDFIRSTNAME].Thanks,we’lltalktoyousoon.[END,Callbackscheduled]
89
[PARTIAL]I’msorrythisistakingsolongrightnow,andIknowyouarebusy.CouldIscheduleatimetocallyoubacktofinishthesurvey?Weonlyhaveafewmoreminutestogoandyouranswersareveryimportanttothestudysinceyou’vebeenrandomlyselectedtorepresentyourneighborhood.Woulditbepossibleforustocallyoubackatanothertimeordaytofinishthissurvey?
1 YES2 NOOK,thanksforyourtime.[END;PartialRefusal]
Great,thanks.Iamcallingyouat[readphonenumber].Whenwouldyoulikemetocallback?[ENTERDAYANDTIMEFORCALLBACK]CouldyougivemeyourfirstnamesoIknowwhotoaskforwhenIcall?[RECORDFIRSTNAME].Thanks,talktoyousoon.[END,PartialCallback][INELIGIBLE]OK,we’reonlytalkingtopeople18orovertoday.Thanksforyourtime.[END,ineligible]OTHERCODESTORECORDASNEEDEDOUTOFSAMPLE–BusinessLineDISCONNECT–NumbernotinserviceLANGUAGE–RespondentdoesnotspeakEnglishorSpanish