Paul Boyle

This paper describes a novel method for re-estimating the migration and commuting data collected in the 1981 and 1991 Censuses for the geographical units used in the 2001 Census in the UK. The estimated flow data are provided as origin-destination flow matrices for wards in England and Wales and pseudo-postcode sectors in Scotland. Altogether, there were about 10,000 zones in 1981, 1991 and 2001, providing huge but sparsely populated matrices of 10,000 by 10,000 cells. Thanks to the boundary changes during inter-censal periods, virtually no work has attempted to compare local level migration and commuting flows in the two decades, 1981-91 and 1991-2001. The re-estimated spatially consistent interaction flows described here provide an opportunity for such comparisons to be made and we use migration change in England and commuting change in Birmingham to demonstrate the value of these new data.

The National Center for Education Statistics reports that over 250,000 high school graduates moved across state lines to enroll in college in 2008. The choices made by these high ability individuals may have long-lasting implications for state economies; not only do they contribute to state and local economies through their tuition and daily living costs while studying, but many of them will be retained in the workforce in the state in which they graduate. This paper argues that inadequate attention has been paid to the spatial processes that underpin such migrations. Specifically, models are required that simultaneously consider the characteristics of both migration origins and destinations and their relative spatial arrangements. Thus, the purpose of this research is to present an alternative, explicitly spatial, approach to modeling the migration of high school graduates to college. Our results provide new insights into the factors that determine such flows and have direct relevance to policy-making in this sphere.

We consider whether widowhood increases mortality risk. Although commonly observed, this "widowhood effect" could be due to selection effects, as married couples share various characteristics related to the risk of death. We therefore consider the widowhood effect by various causes of spousal death; some causes of death are correlated with shared characteristics in couples, while others are not. Using data from the Scottish Longitudinal Study, we compare outcomes for men and women by the causes of death of their spouse, controlling for a range of individual- and household-level characteristics. The widowhood effect in these data is greater than has been found in other recent studies, with adjusted hazard ratios of 1.40 (95% confidence interval = 1.33-1.47) for men and 1.36 (1.30-1.44) for women. The risk is highest shortly after widowhood, but remains raised for at least 10 years. There was little evidence that these hazard ratios differed by any classification of the cause of death of the spouse, but interactions were found for those with pre-existing illness or other risk factors. The hazard ratios for widowhood were lower for persons with preexisting risks. Our analysis of the widowhood effect uses 3 methods of classifying the causes of spousal death in an attempt to control for potential selection effects. Our results are highly consistent and suggest that this is a causal effect, rather than a result of selection.

A significant problem facing geographical researchers who wish to compare migration and commuting flows over time is that the boundaries of the geographical

In this chapter we describe the Samples of Anonymised Records (SARs) and Longitudinal Studies (LSs). The SARs are cross-sectional data like the area and interaction data, but the LSs track people over time. These datasets differ from the United Kingdom’s other census outputs being individual-level ‘microdata’ and population samples. The microdata files are very versatile, allowing multi-way crosstabulations and statistical techniques and enabling application-relevant re-coded variables and study populations to be defined. The SARs files offer UK coverage although a UK-wide study is challenging because data for each country may be in separate files with different access arrangements and variable detail may be country specific. The Office for National Statistics (ONS) Longitudinal Study for England and Wales has underpinned a wide range of research since the 1970s. This well-established source is now complemented by longitudinal data for Scotland and Northern Ireland. Largely driven by the need to ensure respondent confidentiality, the SARs and LSs have some drawbacks for migration-related research. In addition to stringent access arrangements, the geographical area to which individuals are located in the SARs tend to be coarse and although the LS databases record the small area in which the LS member was living at each census, specific ‘place’ information is unlikely to be considered non-disclosive unless for large geographies. However, generic, contextual information about the ‘space’ in which people live is useful even though actual places are not identified. Whilst the SARs and LSs are samples, they are, however, very large samples in comparison with other national surveys and represent first rate resources to complement other sources. In the course of this chapter, along with other references to SARs and LS-based migration research, we review work which utilised these sources to investigate inter-relationships between health, deprivation and migration. The SARs data show that migration is health-selective by age and distance moved and that those persons living in the public housing tenure who are moving into or within deprived areas are most likely to be ill. The role of migration in changing health inequalities between differently deprived areas can be explored using longitudinal data on both origins and destinations. The ONS LS reveals that migrants into and between the least deprived areas have better health than non-migrants, but migrants into and between the most deprived areas have the worst health. The effect of these changes has been to increase the inequality in health between differently deprived areas. A sorting, largely driven by selective migration occurs.