Abstract
In this paper, the claim that the sojourn times in the UK labor market follow a continuous-time Markov model is investigated. It means that they are independent random variables and mainly they control how rapidly transits take place. In this case sojourn times in a state before they transit another state are exponentially distributed with an appropriate parameter λ_i. The labor market model presented in this paper is based on Markov process techniques and have been developed in Wolfram Mathematica 9. The model allows us to calculate the long-run proportion of workers transitions, first-passage time and the transition state probabilities. These parameters are then used to detect labour market failures and accordingly propose policies and procedures that Government can use to build a more efficient labour market and increase employability.
Highlights
In this paper the labour market dynamics is viewed as a Markovian process with individuals moving between the three labour states, i.e. employed, unemployed and inactive
In this paper I investigate the claim that the sojourn times in the labor market follow a continuous-time Markov model
It means that sojourn times in states before they transit another state are independent random variables and mainly they control how rapidly transits take place
Summary
In this paper the labour market dynamics is viewed as a Markovian process with individuals moving between the three labour states, i.e. employed, unemployed and inactive. The labor market behavior in a continuous-time Markov chain is defined by a stochastic process Y={Y(d): 0≤d} with finite or countable state space S= {1,2,3}. For a continuous- time homogeneous Markov chain Y(d), the transition probability function for d>0 is:. The sojourn times of a continuous-time Markov model are independent random variables and mainly control how rapidly the transit takes place. They are exponentially distributed with parameter λi, so the probability of transition from a particular state ito another state before a time x is given by. It is important to point out that the probability of transition from state ito state j is exponentially distributed but with parameter qij≥0 Where Pij is the probability that the system which is initially in state i will be in state j at d, and ∆d is the time interval length
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