In low income countries under poor economic conditions and limited employment opportunities individuals face unstable remuneration, underemployment, informal employment, part time jobs, and look for additional employment. Therefore, the fact of being employed does not guarantee the long term job stability and security. This situation can be described with the employment vulnerability concept. Employment vulnerability may cause differences in earnings and different subjective perception of individuals about well-being. The main objective of this research proposal is to investigate characteristics of the employment vulnerability in Kyrgyzstan and its impact on earnings and subjective well-being with special reference to youth and women. According to the NSCKR (2016) data in Kyrgyzstan only 46.0 per cent of wage employees have permanent job, while remaining share has contracts for limited period or work without any contracts. In the context of Kyrgyzstan understanding employment vulnerability and its impact is important for undertaking government policies in labour market. This research will be based on the longitudinal data of the household survey. Because of the endogeneity of employment vulnerability Fixed Effects Instrumental Variable approach will be applied. Moreover, impact of employment vulnerability on earnings may vary depending on the position in earnings distribution. In order to take into consideration these possible effects we will also use Quantile Regression (QR) approach within the Fixed Effects. For this we follow by Koenker (2004) and further modified by Canay (2011) as an extension of the standard QR model to Fixed Effects for longitudinal data. In addition to the Fixed Effect IV method, we will adopt repeated cross sectional analysis. Some parts of potential instrumental variables are time-invariant and can be used in cross-sectional model specification, which is also appropriate for dealing with endogeneity issue. At the same time these repeated cross section models will provide us with a robustness analysis for our main panel data estimations.