With this study, we, the RWI (Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Es-sen) submit the Final Report of the “Study on various aspects of labour market performance using micro data from the European Union Statistics on Income and Living Conditions (EU-SILC)” under the contract VC/2010/0032, which was signed by the last contracting party, and thus entered into force, on 11 February 2010. The study aims at fully exploiting the richness of the EU-SILC micro data to examine labour market transitions. The following research topics are investigated in detail: Task 1: Labour market transitions; Task 2: Taxes/benefits and transitions to employment; Task 3: Part-time/full-time work and temporary contracts; Task 4: How to assess the quality/value of labour market transitions?; Task 5: Pay transitions; Task 6: Issues of data quality and comparability in EU-SILC. In the analysis, all EU Member States as well as Norway are covered and a specific focus will lie on similarities and differences between Member States. The target population consists of individuals aged older than 15 years and younger than 65 which is also investigated at a sufficiently disaggregated level by taking the skill-, gender-, and age-dimension of the research topics into account. Furthermore, we focus on the differences due to household composition. The longitudinal EU-SILC data for the years 2004-2008 form the basis of the empirical analysis. The last topic of this report discusses issues of data quality and comparability in EU-SILC. In Task 6 the experiences gained in the first five tasks are summarized. In the following, we briefly describe how our analysis proceeds for each of the five tasks. In the introduction to each task, we discuss the importance of the topic under investigation, as well as the most important academic literature. The first step of the technical analysis contains the descriptive evidence computed from the EU-SILC data base. For the transitions under investigation, we thus present Markov transition matrices for the dataset as a whole, as well as for different worker groups (e.g. according to age or education), countries, and years. Furthermore, we present cross tables and figures that give some insights into differences between demographic groups and countries. For each of the five tasks, the econometric analysis is conducted in a second step. Here, we use different econometric tools, such as wage regressions, logit models, tobit models, multinomial logit models and ordered logit models, in order to establish the statistical relationship between the variables of interest. The explanatory variables consist of individual and household characteristics, such as age or education, country fixed effects and time dummies. For each task, we summarize the most important results in the concluding section of every chapter. Finally, the study includes a chapter on the data quality of the EU-SILC data and propositions for an improvement of the data set. Furthermore, the data preparation and especially the calculation of earnings are presented.