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In the face of Covid‐19, we can look at the past disease outbreaks to find parallels and learn lessons that can be applied today to better respond to the current crisis. We investigated a 20‐year period from 1911 to 1930 which includes the 1918‐19 influenza pandemic known as the “Spanish flu”. We followed the daily death tolls and in particular analysed deaths in excess of what we would expect – we used the same manner as is currently done to track the spread of the Covid‐ 19 pandemic.

The analysis of large administrative data sets can provide researchers with answers to many research and policy questions. Scotland has a wide range of such data available, in many cases with more detail than is available in similar data from other parts of the UK or in other countries. Initiatives to widen the access to these data are in place in Scotland including the Administrative Data Research Centre - Scotland (ADRC-S) and the Longitudinal Study Centre Scotland (LSCS), both led by the University of Edinburgh. Researchers who want to use data from these sources must submit an application justifying their use to panels who will balance the public benefit of their proposed project against its potential for disclosing confidential information. Once the project is approved the researchers will usually have to visit a secure location (safe haven) where the data will be made available to them under supervision. These procedures are necessary because it is widely understood that simply removing identifiers such as names and addresses does not prevent individuals from being identified. These procedures put constraints on researchers who want to use administrative data. It is difficult for them to acquire the experience and skills required to handle these large and often messy data sources. Also, the need to visit a safe haven can restrict users to certain geographic locations. A solution that helps to lessen these limitations is to make synthetic versions of administrative data available to researchers. Synthetic data maintain the analytical properties of the original data but contains no real individuals. They can be made available to researchers to develop exploratory analyses and de-bug code before they visit the secure setting. This means that safe haven visits are mainly used to run the final analyses on the real data. Another popular use of our synthetic data is to create realistic data sets to teach researchers methods for analysing large administrative data sets. The task of producing synthetic data that have the same properties as the original data, i.e. results from analysing them will be close to the original, is a challenging one. To facilitate it we have developed open-source software (synthpop package for R) which we are now using both to make data available to researchers using Scottish Longitudinal Study (SLS) and to create teaching data sets. This presentation will give an overview of synthetic data, highlight some of the difficulties and how we have overcome them. We will illustrate the use of synthpop to create a training data set based on an analysis of young people who are not in education, employment or training (NEETS)

Synthetic data methods were designed to address the conicting demands placed on data holders to unlock the research and policy potential of microdata while at the same time preserving the condentiality of individuals. Recently, these methods have become more widely recognized in the UK and the provision of bespoke synthetic data has been approved to expand the use of one of the UK Longitudinal Studies. The process of producing useful synthetic data involves, however, a substantial investment of research time, as it always requires some customising for the characteristics of an individual data set. At the same time, a substantial part of it can be automated and this is essential when the process has to be conducted rapidly and on a regular basis. This paper describes the application of synthetic data to the UK Longitudinal Studies, details implementation process for the Scottish Longitudinal Study and presents methods used in an R package synthpop that has been developed to facilitate production of non-disclosive entirely synthetic data. A reproducible example using open data is given to illustrate the synthesising procedure and to provide insights into quality of synthetic data generated using dierent automated approaches.

The authors gratefully acknowledge financial support for this research from the Economic and Social Research Council (ESRC) Centre for Population Change (grant number RES-625-28-0001). Life satisfaction and motives for moving home are complex entanglements, reflecting multiple desires and experiences. The aim of this paper is to show that a focused analysis of satisfaction with particular life domains can prove that changing a place of residence is not only a life stressor, but also a positive means leading to enduring improvements in individual satisfaction. Using the British Household Panel Survey we examine overall life satisfaction and satisfaction in various life domains such as housing, job, social life, household income, spouse/partner and health, both prior to and after moving. A temporal pattern of movers’ satisfaction for a number of years before and after the move is derived employing a fixed-effects panel data model. Our results reveal that residential relocation increases housing satisfaction considerably. The positive effect of moving on housing satisfaction is much stronger and endures longer for those with a sustained desire to relocate ahead of movement. Despite some decrease over time, five years after moving survey respondents still had significantly higher housing satisfaction than before their move. Changes in satisfaction with other life domains are much less pronounced and no lasting improvements in satisfaction are observed for them. Publisher PDF

Life satisfaction and motives for migration are both complex entanglements, reflecting multiple desires and experiences. The aim of this paper is to show that a focussed analysis of satisfaction with particular domains of life can lend support to the claim that residential migration is not only a life stressor but also a positive means leading to enduring improvements in individual satisfaction. Using the British Household Panel Survey we examine overall life satisfaction and satisfaction in various life domains such as housing, job, social life, household income, spouse and health, both prior to and after moving. A temporal pattern of migrants’ satisfaction for a number of years before and after the move is derived employing a fixed-effects model. Our results reveal that moving increases housing satisfaction considerably. Despite some decrease over time, five years after migration housing satisfaction is still significantly higher than it was initially. The positive effect of migration on housing satisfaction is much stronger and endures longer for those with a sustained desire to move before migration. Changes in satisfaction with other life domains are much less pronounced and no lasting improvements in satisfaction are observed for them.

Inadequate and inconsistent data are a common and persistent problem in the field of migration. Deficiencies in migration statistics may be tackled using modelling techniques, which has recently been recognised by the European Union (EU) policymakers. The new Regulation on Community statistics on international migration, which obliges countries to supply harmonised statistics, provides for the possibility of using estimation methods to adapt statistics based on national definitions to comply with the required 1-year duration of stay definition. The main objective of this paper is to provide a theoretical probabilistic framework to capture various migration flow statistics that are available. It is a crucial step towards better understanding and then harmonising the data. Different migration measures represent the same continuous data-generating process. They differ depending on how the data happened to be collected and how the statistics happened to be produced. We introduce the key concepts of migration statistics using a simple duration model, namely an exponential distribution. While more complex models can better reflect the reality, they do not fundamentally modify the framework presented. The main focus is put on the time criterion used in the migration definition. It refers to the duration of stay following relocation, which is specified very differently among countries and constitutes the main source of discrepancies in operationalisation of the migration concept in the EU member states. Copyright (C) 2010 John Wiley & Sons, Ltd.

The majority of modelling studies on consequences of internal migration focus almost exclusively on the labour market outcomes and the material well-being of migrants. We investigate whether individuals who migrate within the UK become happier after the move than they were before it and whether the effect is permanent or transient. Using life satisfaction responses from 12 waves of the British Household Panel Survey (BHPS) and employing a fixed-effects model, we derive a temporal pattern of migrants' subjective well-being (SWB) around the time of the migration event. Our findings make an original contribution by revealing for the first time that, on average, migration is preceded by a period when individuals experience a significant decline in happiness. The boost that is received through migration appears to bring people back to their initial level of happiness. As opposed to labour market outcomes of migration, SWB outcomes do not differ significantly between men and women. Perhaps surprisingly, long-distance migrants are at least as happy as short-distance migrants despite the higher social costs that are involved.