# Simulation of Calibrated Complex Synthetic Population Data with XGBoost
## Appendix A Details on Selected Variables of the EU-SILC Data  
A representative real-world data set from the European Union Statistics of Income and Living Conditions (EU-SILC) survey was selected for this study to demonstrate the application of the developed methods. These data sets primarily serve to measure Europe's risk of poverty and social cohesion, playing an instrumental role in monitoring progress towards the Lisbon 2010 strategy and Europe 2020 objectives outlined by the European Union. The core objective of EU-SILC is the timely and comparable acquisition of cross-sectional and longitudinal data, focusing on income, poverty, social exclusion and living conditions. The survey includes data collected from households and individuals, with around 90% of its output comprising annual variables. The remaining data are either modules collated every three or six years or ad hoc modules designed to address specific policy needs. Participating countries send data on individuals and households to Eurostat in accordance with legally determined deadlines and agreed guidelines and procedures.  
In particular, this study uses the Austrian EU-SILC public use data set from 2013 with 13,513 observations and 62 variables. These cross-sectional data include variables on income, poverty, social exclusion and other living conditions. Information on social exclusion and housing conditions is collected primarily at the household level, while labor, education and health information is sourced from individuals aged 16 years and older. Income variables at the detailed component level are also collected predominantly from individuals. The equivalized household income variable and household sizes are shown in the Appendix A in Table A1. Other relevant variables are shown in the Appendix A in Table A2, where the official names of the variables according to Eurostat are also displayed.  
Design and non-response weights are calculated differently for the primary sampling units (PSU) and the higher-order sampling units (second to fourth order). For PSUs, the Austrian Central Statistical Office calculates the design weights according to the stratified simple random sample with 70 strata. The stratification is based on several criteria, including the province, the number of households in the survey area and the characteristics of the target group. For the latter, households with foreigners and a higher risk of poverty were oversampled. A logistic regression model estimates the response probabilities for the calculation of the non-response weights. The non-response weights for higher-order sampling units are calculated in a similar way as for the primary sampling units. A logistic regression model estimates the response probabilities. Since more information is available from the previous survey, the regression model contains more variables than the PSU model. The model contains 43 predictors ranging from survey attributes, e.g., contact attempt, to characteristics of income and living conditions, e.g., net income. Finally, the weights of the data are calibrated to match the known population characteristics from the Austrian Census.  
For a comprehensive description of the variables used and more information on this data set, the package manual of R package laeken[34; 35] provides a detailed guide.