Distributional National Accounts (DINA) with Household Survey Data: Methodology and Results for European Countries

The paper builds Distributional National Accounts (DINA) using household survey data. We present a transparent and reproducible methodology to construct DINA whenever administrative tax data are not available for research and apply it to various European countries. By doing so, we build synthetic microdata files which cover the entire distribution, include all income components individually aligned to national accounts, and preserve the detailed socioeconomic information available in the surveys. The methodology uses harmonized and publicly available data sources (SILC, HFCS) and provides highly comparable results. We discuss the methodological steps and their impact on the income distribution. In particular, we highlight the effects of imputations and the adjustment of the variables to national accounts totals. Furthermore, we compare different income concepts of both the DINA and EG-DNA approach of the OECD in a consistent way. Our results confirm that constructing DINA is crucial to get a better picture of the income distribution. Our methodology is well suited to build synthetic microdata files which can be used for policy evaluation like social impact analysis and microsimulation.Series: INEQ Working Paper Serie

SimCrime: A Spatial Microsimulation Model for the Analysing of Crime in Leeds.

This Working Paper is a part of PhD thesis 'Modelling Crime: A Spatial Microsimulation Approach' which aims to investigate the potential of spatial microsimulation for modelling crime. This Working Paper presents SimCrime, a static spatial microsimulation model for crime in Leeds. It is designed to estimate the likelihood of being a victim of crime and crime rates at the small area level in Leeds and to answer what-if questions about the effects of changes in the demographic and socio-economic characteristics of the future population. The model is based on individual microdata. Specifically, SimCrime combines individual microdata from the British Crime Survey (BCS) for which location data is only at the scale of large areas, with census statistics for smaller areas to create synthetic microdata estimates for output areas ?(OAs) in Leeds using a simulated annealing method. The new microdata dataset includes all the attributes from the original datasets. This allows variables such as crime victimisation from the BCS to be directly estimated for OAs

Building a Static Farm Level Spatial Microsimulation Model: Statistically Matching the Irish National Farm Survey to the Irish Census of Agriculture

This paper looks at the statistical matching technique used to match the Irish Census of Agriculture to the Irish National Farm Survey (NFS) to produce a farm level static spatial microsimulation model of Irish agriculture. The match produces a spatially disaggregated population microdata set of farm households for all of Ireland. Using statistical matching techniques, economists can now create more attribute rich datasets by matching across the common variables in two or more datasets. Static spatial microsimulation then uses these synthetic datasets to analyse the relationships among regions and localities and to project the spatial implications of economic development and policy changes in rural areas. The Irish agriculture microsimulation model uses one of many combinational optimatisation techniques - simulated annealing - to match the Census of Agriculture and the NFS. The static model uses this matched NFS and Census information to produce small area (District Electric Divisions (DED)) population microdata estimates for a particular year. Using the matched NFS/Census microdata, this paper will then analysis the regional farm income distribution for Ireland.

Assessing Poverty and Inequality at a Detailed Regional Level: New Advances in Spatial Microsimulation

poverty, inequality, measurement, Australia

Synthetic Establishment Microdata Around the World

In contrast to the many public-use microdata samples available for individual and household data from many statistical agencies around the world, there are virtually no establishment or firm microdata available. In large part, this difficulty in providing access to business micro data is due to the skewed and sparse distributions that characterize business data. Synthetic data are simulated data generated from statistical models. We organized sessions at the 2015 World Statistical Congress and the 2015 Joint Statistical Meetings, highlighting work on synthetic \emph{establishment} microdata. This overview situates those papers, published in this issue, within the broader literature

Methodological Issues in Spatial Microsimulation Modelling for Small Area Estimation

In this paper, some vital methodological issues of spatial microsimulation modelling for small area estimation have been addressed, with a particular emphasis given to the reweighting techniques. Most of the review articles in small area estimation have highlighted methodologies based on various statistical models and theories. However, spatial microsimulation modelling is emerging as a very useful alternative means of small area estimation. Our findings demonstrate that spatial microsimulation models are robust and have advantages over other type of models used for small area estimation. The technique uses different methodologies typically based on geographic models and various economic theories. In contrast to statistical model-based approaches, the spatial microsimulation model-based approaches can operate through reweighting techniques such as GREGWT and combinatorial optimization. A comparison between reweighting techniques reveals that they are using quite different iterative algorithms and that their properties also vary. The study also points out a new method for spatial microsimulation modellingBayesian prediction approach; combinatorial optimisation; GREGWT; microdata; small area estimation; spatial microsimulation

Proceedings from the Synthetic LBD International Seminar

On May 9, 2017, we hosted a seminar to discuss the conditions necessary to im- plement the SynLBD approach with interested parties, with the goal of providing a straightforward toolkit to implement the same procedure on other data. The proceed- ings summarize the discussions during the workshop