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However, as the authors discuss, this differential matching rate may reflect different probabilities of benefit and tax credit receipt Respondents who had not received benefits or tax credits would not appear on the DWP records, so it was not expected that there would be a match in the administrative data for all respondents In the MCS, match rates were very high 99 % for birth registration data and 84 % for Hospital Episode Statistics (HES) data There was evidence of differences between those who were matched and those who were not matched to hospital records and the factors associated with successful matching were different in different countries In England, matching was less likely if the mother was not born in the UK, was a lone parent, was Black Caribbean or Black African or if the child was not the first born.

What is therefore needed is an integrated set of software development processes, platforms, and tools that can (1) select a suitable set of middleware and application components, (2) analyze, synthesize, and validate the component con gurations, (3) assemble and deploy groups of related components to their appropriate execution platforms, and (4) dynamically adjust and recon gure the system as operational conditions change, to maintain the required QoS properties of DRE applications This chapter provides the following two contributions to research and development efforts that address the challenges described above: We illustrate how enhancements to standard component middleware can simplify the development of DRE applications by composing QoS provisioning policies statically with applications Our discussion focuses on a QoS-enabled enhancement of standard CCM [26] called the Component-Integrated ACE ORB (CIAO), which is being developed at Washington University in St Louis and the Institute for Software Integrated Systems (ISIS) at Vanderbilt University.

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In Wales, matching was less likely if a language other than English was spoken at home and the child was not the first born In Scotland and Northern Ireland, there were no significant differences between matched and nonmatched cases (Hockley et al, 2008) In the USA, the longitudinal Health and Retirement Survey (HRS) has been linked to administrative data on earnings histories, social security benefit histories and supplemental security income payment histories Due to a combination of refusal to linkage and unsuccessful matching, HRS has linked data for about 75 % of respondents Olson (1999) finds evidence that the characteristics of respondents with linked data are different from those without The success of matching to administrative data will depend in part on the quality of the match variables.

Longitudinal surveys have advantages over cross-sectional surveys as they should be able to provide match variables that are more complete (because there are multiple opportunities to collect the data) and potentially more unique (as there are typically more potential match variables, including longitudinal ones) Commonly used match variables are demographic information (such as sex and date of birth) and name and address In longitudinal surveys these variables are likely to be more accurate and more complete than in a cross-sectional survey as they can be verified on more than one occasion In addition, some of these variables such as surname and postcode can change over time Longitudinal surveys are able to collect updates to these variables at each wave This may be important if repeated linkages or historic linkages are to be attempted This is often necessary as repeated information over time is needed for longitudinal analyses.

Data linkage may be carried out at multiple time points that can be before, during and after the survey has taken place For nonmatched cases, longitudinal surveys are able to go back to respondents specifically in order to try to improve the accuracy of the match variables An example of this is the SIPP in which social security numbers (SSNs) are collected from.

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