of a computerized system where data input occurs with Personal Digital Assistance (PDA) devices and data input verification is carried out both automatically with a special software and with controllers in real time. Given the introduction of the new panel component in 2006 and the introduction of the PDA assisted survey in 2007, this paper will focus on the period 2007-2011, which accounts for a total of 20 points in time and 16 panel groups of one year in length.

Labor mobility is measured using panel data and by means of Markov chain or transition probabilities matrixes. The probability of transitioning from state _i_ to state _j_ is defined as Pi,j = Pr(j|i) and the percentage sum of probabilities is ∑Pi i,j = 100. Our data allow for only one transition step over a period of one year and for each quarter of the year. Let _i_ and _j_ be two mutually exclusive sectors with i≠j, _N_ the total finite sample of individuals, ni,j the number of sample individuals moving from sector _i_ to sector _j_ , _W_ the total population weight and wi,j the weight which accounts for both the population weight (number of individuals in the population) and sample weight (inverse of the probability of being selected in the sample) for the group moving from sector _i_ to sector _j_ . With nwi,j representing the population moving from sector _i_ to sector _j_ , then the transition probability matrix for the population can be described as in the table below.

**Table 2 – Transitions formulae**

||**Sector**i|**Sector**j|**Total**|
|---|---|---|---|
|**Sector**i|nwi,i/NW|nwi,j/NW|(nwi,i+ nwi,j)/NW|
|**Sector**j|nwj,i/NW|nwj,j/NW|(nwj,i+ nwj,j)/NW|
|**Total**|(nwi,i+ nwj,i)/NW|(nwi,j+ nwj,j)/NW|NW|

We can also define nwi,jand nwj,i as the “movers” and nwi,i and nwj,j as the “stayers”. Our focus throughout the paper is on the movers and to simplify exposition we will mostly report statistics on the movers with the total of movers equal to 100.<sup>6</sup> By design, transition matrixes require the observation of individuals in both the initial and final period. This forcibly excludes new labor market entrants who are present in the sample in the final period but not in the initial period. We will see that this is an important consideration when we wish to relate labor mobility with labor market statistics such as the participation rate. The technical annex discusses the problems related to the estimation of the transition matrixes and the solutions applied to the Moroccan data.

We consider labor market mobility across labor market statuses, economic sectors or contractual arrangements (sectors for short) arranged as multinomial variables and including employed/unemployed/inactive, agriculture/industry-services sectors, private/public sectors, paid/unpaid work, full-time/part-time work and formal/ informal employment. It is possible to provide in this way a tentative normative classification of sectors from the standpoint of the social planner so that we can classify sectors in “better” and “worse” and identify _upward_ and _downward_ mobility. Such normative classification does

> 6 In the results section, transition probabilities matrixes are estimated in STATA using the xttrans2 command.

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