Message-ID: <1604118588.3626.1495509502100.JavaMail.confluence@ece-vmapps> Subject: Exported From Confluence MIME-Version: 1.0 Content-Type: multipart/related; boundary="----=_Part_3625_73181696.1495509502099" ------=_Part_3625_73181696.1495509502099 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable Content-Location: file:///C:/exported.html Using Administrative Data to Supplement Statistical Surveys</tit= le> <!--[if gte mso 9]> <xml> <o:OfficeDocumentSettings> <o:TargetScreenSize>1024x640</o:TargetScreenSize> <o:PixelsPerInch>72</o:PixelsPerInch> <o:AllowPNG/> </o:OfficeDocumentSettings> <w:WordDocument> <w:View>Print</w:View> <w:Zoom>90</w:Zoom> <w:DoNotOptimizeForBrowser/> </w:WordDocument> </xml> <![endif]--> <style> <!-- @page Section1 { size: 8.5in 11.0in; margin: 1.0in; mso-header-margin: .5in; mso-footer-margin: .5in; mso-paper-source: 0; } td { page-break-inside: avoid; } tr { page-break-after: avoid; } div.Section1 { page: Section1; } /* Confluence print stylesheet. Common to all themes for print medi= a */ /* Full of !important until we improve batching for print CSS */ #main { padding-bottom: 1em !important; /* The default padding of 6em is too mu= ch for printouts */ } body { font-family: Arial, Helvetica, FreeSans, sans-serif; font-size: 10pt; line-height: 1.2; } body, #full-height-container, #main, #page, #content, .has-personal-sidebar= #content { background: #fff !important; color: #000 !important; border: 0 !important; width: 100% !important; height: auto !important; min-height: auto !important; margin: 0 !important; padding: 0 !important; display: block !important; } a, a:link, a:visited, a:focus, a:hover, a:active { color: #000; } #content h1, #content h2, #content h3, #content h4, #content h5, #content h6 { font-family: Arial, Helvetica, FreeSans, sans-serif; page-break-after: avoid; } pre { font-family: Monaco, "Courier New", monospace; } #header, .aui-header-inner, #navigation, #sidebar, .sidebar, #personal-info-sidebar, .ia-fixed-sidebar, .page-actions, .navmenu, .ajs-menu-bar, .noprint, .inline-control-link, .inline-control-link a, a.show-labels-editor, .global-comment-actions, .comment-actions, .quick-comment-container, #addcomment { display: none !important; } .comment .date::before { content: none !important; /* remove middot for print view */ } h1.pagetitle img { height: auto; width: auto; } .print-only { display: block; } #footer { position: relative !important; /* CONF-17506 Place the footer at end of= the content */ margin: 0; padding: 0; background: none; clear: both; } #poweredby { border-top: none; background: none; } #poweredby li.print-only { display: list-item; font-style: italic; } #poweredby li.noprint { display:none; } /* no width controls in print */ .wiki-content .table-wrap, .wiki-content p, .panel .codeContent, .panel .codeContent pre, .image-wrap { overflow: visible !important; } /* TODO - should this work? */ #children-section, #comments-section .comment, #comments-section .comment .comment-body, #comments-section .comment .comment-content, #comments-section .comment p { page-break-inside: avoid; } #page-children a { text-decoration: none; } /** hide twixies the specificity here is a hack because print styles are getting loaded before the base styles. */ #comments-section.pageSection .section-header, #comments-section.pageSection .section-title, #children-section.pageSection .section-header, #children-section.pageSection .section-title, .children-show-hide { padding-left: 0; margin-left: 0; } .children-show-hide.icon { display: none; } /* personal sidebar */ .has-personal-sidebar #content { margin-right: 0px; } .has-personal-sidebar #content .pageSection { margin-right: 0px; } --> </style> </head> <body> <h1>Using Administrative Data to Supplement Statistical Surveys</h1> <div class=3D"Section1"> <p><strong>8.1       Introduction</st= rong></p> <p>This chapter presents an overview of different models for using administ= rative data to supplement data collected in statistical surveys. It shows h= ow a mixed-source approach can be used to produce statistics at lower cost,= better quality, or both.</p> <p>Many of the issues relating to using and linking statistical and adminis= trative data have already been covered in Chapters 4 and 6, so are not repe= ated here. Instead this chapter focuses on the different models for using d= ata from a mixture of administrative and statistical sources to produce sta= tistical outputs.</p> <p> </p> <p><strong>8.2       Mixed-source Models </st= rong></p> <p><strong><em>1) The Split Population Approach</em></strong></p> <p>In this model the statistical population is split into two or more parts= for data collection purposes. This approach is very similar to that used f= or the maintenance of the Australian statistical business register, as desc= ribed in Chapter 7.3. Data from administrative sources are used for units w= here these data are of sufficient quality, and statistical sources are used= for the remainder of the units.</p> <p>A typical scenario for a business survey is that data for relatively sma= ll businesses with simple structures are taken or derived from tax returns,= whereas surveys are used to collect data from the key units (usually those= that are largest and/or have the most complex structures). For the section= of the population for which tax data are used, the statistical and adminis= trative units are likely to be identical, or very similar, and the impact o= f the difference between statistical concepts and classifications and their= administrative counterparts is likely to be minimal, or at least can be ea= sily modelled.</p> <p>The remainder of the businesses are typically those that have the greate= st individual impact on the quality of the statistics, and therefore are th= e ones for which it is most important to have accurate data. These units ar= e also likely to be the ones with the most complex structures, often requir= ing profiling (as described in Chapter 4.5) in order to define the correct = statistical units for which data are required. These statistical units are = often combinations of administrative units, or parts thereof, and whilst so= me variables such as employment can often simply be summed to give the corr= ect total, others, such as sales and certain other financial variables can = not, as they include a certain amount of intra-unit trade, such that a simp= le summation would result in over-counting.</p> <p>A practical example of the split population approach in business surveys= is the Unified Enterprise Survey conducted by Statistics Canada. This brin= gs together annual business data requirements, combining several previous s= urveys. Administrative data are used instead of data collected through stat= istical questionnaires for over half of the enterprises in the survey that = have a simple structure, resulting in reductions in the statistical respons= e burden of almost 40%.<a href=3D"#_ftn1" title=3D"">[1]</a></p> <p>Where the statistical population is people or households, it may be the = case that surveys are needed for special groups such as students, migrant w= orkers or those with two or more residencies. These are all potential examp= les of units for which administrative data may not be sufficiently up to da= te or accurate, particularly concerning location.</p> <p>As mentioned several times in previous chapters, consideration must also= be given to units not covered by administrative registers, such as illegal= immigrants or businesses operating in the informal economy. Statistical su= rveys are likely to be only of limited use for such groups, so an element o= f estimation may be needed, thus introducing a third source to be used in t= he production of the required statistics. This model is illustrated in Figu= re 8.1 below.</p> <p><strong><em>Figure 8.1 =E2=80=93 The Split Population Model</em></strong= ></p> <p><img class=3D"confluence-embedded-image" width=3D"468" src=3D"fe7f1a19c1= aef50eff6acd3a51235c7a" data-image-src=3D"/stat/platform/download/attachmen= ts/75564350/fig8_1.png?version=3D1&modificationDate=3D1357315825777&= ;api=3Dv2" height=3D"331" /> </p> <p><strong><em>2) The Split Data Approach</em></strong></p> <p>In this approach, a population of statistical units, and a data requirem= ent are identified, for example the population could be all persons living = in a particular country, and the data requirement could be the usual set of= variables required for a population census. Instead of providing all of th= e variables for part of the population, as in the split population model ab= ove, under the split data approach, administrative sources are used to prov= ide some of the variables for all of the population (a third approach is al= so possible where administrative sources provide some of the variables for = some of the population).</p> <p>The split data approach does not, therefore reduce the number of questio= nnaires or interviews required to collect the data, but does reduce the vol= ume of data to be collected in each questionnaire or interview. It is usual= ly most relevant for large and complex data collections where many variable= s are required, hence the example of the population census. Administrative = and survey data need to be integrated for each individual unit in order to = produce the data set used for statistical outputs.</p> <p>The split data approach is often used during the transition to the sort = of register-based statistical system described in the next chapter. Typical= ly, the variables in the statistical data collection are replaced by their = equivalents from administrative sources over a number of survey periods. Ta= ble 8.1 below illustrates this process showing data sources for the Finnish= population and housing census.</p> <p><strong><em>Table 8.1 =E2=80=93 The Split Data Approach in the Finnish P= opulation and Housing Census 1960-2000 </em></strong></p> <div class=3D"table-wrap"> <table class=3D"confluenceTable"> <tbody> <tr> <td class=3D"confluenceTd"><p> </p></td> <td class=3D"confluenceTd"><p align=3D"center"><strong>1960</strong></p></t= d> <td class=3D"confluenceTd"><p align=3D"center"><strong>1970</strong></p></t= d> <td class=3D"confluenceTd"><p align=3D"center"><strong>1980</strong></p></t= d> <td class=3D"confluenceTd"><p align=3D"center"><strong>1990</strong></p></t= d> <td class=3D"confluenceTd"><p align=3D"center"><strong>2000</strong></p></t= d> </tr> <tr> <td class=3D"confluenceTd"><p>Demographic Data</p></td> <td class=3D"confluenceTd"><p align=3D"center">Q</p></td> <td class=3D"confluenceTd"><p align=3D"center">Q/R</p></td> <td class=3D"confluenceTd"><p align=3D"center">R/Q</p></td> <td class=3D"confluenceTd"><p align=3D"center">R</p></td> <td class=3D"confluenceTd"><p align=3D"center">R</p></td> </tr> <tr> <td class=3D"confluenceTd"><p>Economic Data</p></td> <td class=3D"confluenceTd"><p align=3D"center">Q</p></td> <td class=3D"confluenceTd"><p align=3D"center">Q/R</p></td> <td class=3D"confluenceTd"><p align=3D"center">Q/R</p></td> <td class=3D"confluenceTd"><p align=3D"center">R/Q</p></td> <td class=3D"confluenceTd"><p align=3D"center">R/Q</p></td> </tr> <tr> <td class=3D"confluenceTd"><p>Education Data</p></td> <td class=3D"confluenceTd"><p align=3D"center">Q</p></td> <td class=3D"confluenceTd"><p align=3D"center">Q</p></td> <td class=3D"confluenceTd"><p align=3D"center">R</p></td> <td class=3D"confluenceTd"><p align=3D"center">R</p></td> <td class=3D"confluenceTd"><p align=3D"center">R</p></td> </tr> <tr> <td class=3D"confluenceTd"><p>Household and Family Data</p></td> <td class=3D"confluenceTd"><p align=3D"center">Q</p></td> <td class=3D"confluenceTd"><p align=3D"center">Q</p></td> <td class=3D"confluenceTd"><p align=3D"center">R</p></td> <td class=3D"confluenceTd"><p align=3D"center">R</p></td> <td class=3D"confluenceTd"><p align=3D"center">R</p></td> </tr> <tr> <td class=3D"confluenceTd"><p>Dwelling Data</p></td> <td class=3D"confluenceTd"><p align=3D"center">Q</p></td> <td class=3D"confluenceTd"><p align=3D"center">Q</p></td> <td class=3D"confluenceTd"><p align=3D"center">Q</p></td> <td class=3D"confluenceTd"><p align=3D"center">R</p></td> <td class=3D"confluenceTd"><p align=3D"center">R</p></td> </tr> <tr> <td class=3D"confluenceTd"><p>Business Premises Data</p></td> <td class=3D"confluenceTd"><p align=3D"center">Q</p></td> <td class=3D"confluenceTd"><p align=3D"center">Q</p></td> <td class=3D"confluenceTd"><p align=3D"center">R</p></td> <td class=3D"confluenceTd"><p align=3D"center">R</p></td> <td class=3D"confluenceTd"><p align=3D"center">n/c</p></td> </tr> <tr> <td class=3D"confluenceTd"><p>Building Data</p></td> <td class=3D"confluenceTd"><p align=3D"center">Q</p></td> <td class=3D"confluenceTd"><p align=3D"center">Q</p></td> <td class=3D"confluenceTd"><p align=3D"center">Q</p></td> <td class=3D"confluenceTd"><p align=3D"center">R</p></td> <td class=3D"confluenceTd"><p align=3D"center">R</p></td> </tr> <tr> <td class=3D"confluenceTd"><p>Summer Cottage Data</p></td> <td class=3D"confluenceTd"><p align=3D"center">Q</p></td> <td class=3D"confluenceTd"><p align=3D"center">Q/R</p></td> <td class=3D"confluenceTd"><p align=3D"center">Q/R</p></td> <td class=3D"confluenceTd"><p align=3D"center">R</p></td> <td class=3D"confluenceTd"><p align=3D"center">R</p></td> </tr> </tbody> </table> </div> <p>Key:     Q =3D Statistical questionnaire</p> <p>            Q/R = =3D Statistical questionnaire supplemented by administrative register</p> <p>            R/Q = =3D Administrative register supplemented by statistical questionnaire</p> <p>            R =3D= Administrative register</p> <p>            n/c = =3D Not collected</p> <p>Source: This table is a condensed version of Appendix 2 of the paper =E2= =80=9CUse of Registers and Administrative Data Sources for Statistical Purp= oses =E2=80=93 Best Practices of Statistics Finland=E2=80=9D: <a href=3D"ht= tp://unstats.un.org/unsd/EconStatKB/KnowledgebaseArticle10169.aspx" class= =3D"external-link">http://unstats.un.org/unsd/EconStatKB/KnowledgebaseArtic= le10169.aspx</a></p> <p><strong><em>3) Pre-filled Questionnaires</em></strong></p> <p>This approach is really a special case of the split data model in that s= tatistical questionnaires are still used to collect data about statistical = units, but those questionnaires are pre-filled using administrative data as= far as possible, with respondents being asked to merely check and correct = these data where necessary. Figure 8.2 shows an excerpt from a pre-filled q= uestionnaire, this example is from a British statistical survey of enterpri= ses.</p> <p><strong><em>Figure 8.2 =E2=80=93 An Excerpt from a Pre-filled Survey</em= ></strong></p> <p><img class=3D"confluence-embedded-image" width=3D"468" src=3D"d9877aeb05= 539340c42a0eda1a641cc8" data-image-src=3D"/stat/platform/download/attachmen= ts/75564350/fig8_2.png?version=3D1&modificationDate=3D1357315825785&= ;api=3Dv2" height=3D"226" /> </p> <p>Pre-filled questionnaires have three main benefits:</p> <ul> <li>They help reduce the burden on data suppliers by saving them time (assu= ming that checking and correcting is quicker than finding and entering data= )</li> <li>They allow a check of the quality of the administrative data</li> <li>They are very flexible, particularly where the coverage of administrati= ve data is not as complete as would be required for the other models</li> </ul> <p>The main disadvantage is the risk of bias introduced because some respon= dents may simply accept the pre-filled data without checking them, or may c= hoose not to spend time correcting errors.</p> <p><strong><em>4) Using Administrative Data for Non-responders</em></strong= ></p> <p>This approach can be seen as a variant of both the split source and the = split data models. In this case, the statistical survey remains the primary= means of data collection. However, statistical surveys tend to suffer from= varying degrees of non-response, which affects the efficiency of the sampl= ing process, and the quality of the resulting statistics. Non-response typi= cally takes one of two forms, =E2=80=9Cunit non-response=E2=80=9D, in which= no data are supplied for the unit concerned, or =E2=80=9Citem non-response= =E2=80=9D, in which a partial return is provided, but some data items are b= lank.</p> <p>Dealing with non-response can be very costly for a statistical agency, a= s this typically involves repeat contacts by post or telephone to try to co= llect the missing data. This process is usually known as =E2=80=9Cresponse = chasing=E2=80=9D, and tends to be very resource intensive.</p> <p>A cheaper alternative may be to decide that if data not provided by a pa= rticular date, particularly for units that are not vital to the survey resu= lts (e.g. smaller businesses in a business survey), they are instead taken = or derived from administrative sources. This allows any response chasing re= sources to be focused on the units that are considered most important, whic= h should mean that any bias from using administrative data rather than surv= ey data is minimized. This can also help to improve the timeliness of the s= urvey results. As with any quality-related issues, a compromise between cos= t and the different dimensions of quality (see Chapter 5) is inevitable.</p= > <p>Administrative data can also sometimes be used as a basis for imputing m= issing survey data for linked data files<a href=3D"#_ftn2" title=3D"">[2]</= a>.</p> <p><strong><em>5) Using Administrative Data for Estimation</em></strong></p= > <p>When a sample survey is used to collect statistical data, it is often ne= cessary to use estimation techniques, particularly if population totals (ra= ther than proportions) are required. Some basis to estimate the values for = the non-sampled part of the population is therefore needed. Sometimes this = process can use variables from the survey frame used to draw the sample, bu= t in some cases it may be possible to improve accuracy by using data from a= dministrative sources as auxiliary variables in the estimation process<a hr= ef=3D"#_ftn3" title=3D"">[3]</a>. In practice many examples of this approac= h concern using administrative data to improve estimates for small areas<a = href=3D"#_ftn4" title=3D"">[4]</a>.</p> <p> </p> <p><strong>8.3       Further Considerations</= strong></p> <p>In any complex statistical processing system using multiple sources, it = is vital to consider the role of metadata, particularly those metadata rela= ting to the source of a particular data item. This allows for data items to= be treated in different ways throughout the various processes (including u= nforeseen future processes), according to the way in which they were obtain= ed. Information on the data source is also often a powerful quality indicat= or, and can help with decisions on the level of quality of statistical outp= uts.</p> <p>Using a mixture of statistical and administrative data can be seen eithe= r as an end in itself, particularly where the coverage or quality of the ad= ministrative data is not seen as sufficiently high to allow statistical dat= a collection to be stopped altogether. It can also be seen as a step in a g= radual transition towards a register-based statistical system, as demonstra= ted in Table 8.1.</p> <p>Either way, it allows at least some of the benefits of using administrat= ive data to be realised (including cost savings), whilst avoiding some of t= he disadvantages, such as total reliance on an external supplier and loss o= f contact with the general public. It gives the possibility to compare stat= istical and administrative data quality, and allows statisticians to become= familiar with using administrative data, and to develop new techniques to = improve process quality.</p> <p>For these reasons, mixed-source approaches are currently much more commo= n than purely register-based statistical systems, however, over time, confi= dence in administrative data is likely to increase, allowing their use to b= e expanded and further benefits to be realised. As the balance swings furth= er towards administrative data it will eventually become necessary to consi= der whether to switch to the sort of register-based model described in the = next chapter.</p> <div> <br /> <hr /> <div> <p><a href=3D"#_ftnref1" title=3D"">[1]</a> For more information, see the p= aper =E2=80=9CUse of Tax Data in the Unified Enterprise Survey (UES)=E2=80= =9D by Marie Brodeur of Statistics Canada. <a href=3D"http://unstats.un.org= /unsd/economic_stat/Moscow_workshop/Canada%20-%20Use%20of%20tax%20data%20in= %20the%20UES-E.pdf" class=3D"external-link">http://unstats.un.org/unsd/econ= omic_stat/Moscow_workshop/Canada%20-%20Use%20of%20tax%20data%20in%20the%20U= ES-E.pdf</a></p> </div> <div> <p><a href=3D"#_ftnref2" title=3D"">[2]</a> For example see the US Census B= ureau approach in chapter 3 of the publication: Reengineering the Survey of= Income and Program Participation, <a href=3D"http://www.nap.edu/catalog/12= 715.html" class=3D"external-link">http://www.nap.edu/catalog/12715.html</a>= </p> </div> <div> <p><a href=3D"#_ftnref3" title=3D"">[3]</a> For example see: The Use of Adm= inistrative Data Sources for Lithuanian Annual Data of Earnings, <a href=3D= "http://home.lu.lv/~pm90015/workshop2006/papers/Workshop2006_22_Slickute_Se= stokiene.pdf" class=3D"external-link">http://home.lu.lv/~pm90015/workshop20= 06/papers/Workshop2006_22_Slickute_Sestokiene.pdf</a></p> </div> <div> <p><a href=3D"#_ftnref4" title=3D"">[4]</a> For example see: Using Administ= rative Records for Small Area Estimation in the American Community Survey, = <a href=3D"http://www.fcsm.gov/99papers/mcf.html" class=3D"external-link">h= ttp://www.fcsm.gov/99papers/mcf.html</a></p> </div> </div> </div> </body> </html> ------=_Part_3625_73181696.1495509502099 Content-Type: application/octet-stream Content-Transfer-Encoding: base64 Content-Location: file:///C:/d9877aeb05539340c42a0eda1a641cc8 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