Stochastic forecast of the population of Poland, 2005-2050

Forecasting the population of Poland is very challenging. Firstly, the country has been undergoing rapid demographic changes. In the 1990s, they were influenced by the political, economic, and social consequences of the collapse of the communist regime. Since 2004 they have been shaped by Poland’s entry into the European Union. Secondly, the availability of statistics for Poland on past trends is strongly limited. The resulting high uncertainty of future trends should be dealt with systematically, which is an essential part of the stochastic forecast presented in this paper. The forecast results show that the Polish population will constantly decline during the next decades and Poland will face significant ageing as indicated by a rising old-age dependency-ratio. There is a probability of 50 % that in 2050 the population will number between 27 and 35 millions compared to 38.2 in 2004 and that there will be at least 63 persons aged 65+ per 100 persons aged 19-64.Poland, predictive distributions, stochastic forecast, uncertainty

Analysing a season of death and excess mortality in Scotland’s past

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

Does Migration Make You Happy? A Longitudinal Study of Internal Migration and Subjective Well-Being

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.migration, happiness, subjective well-being, longitudinal data, UK

synthpop: Bespoke Creation of Synthetic Data in R

In many contexts, confidentiality constraints severely restrict access to unique and valuable microdata. Synthetic data which mimic the original observed data and preserve the relationships between variables but do not contain any disclosive records are one possible solution to this problem. The synthpop package for R, introduced in this paper, provides routines to generate synthetic versions of original data sets. We describe the methodology and its consequences for the data characteristics. We illustrate the package features using a survey data example

Facilitating access to administrative records with synthetic data

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