I analyze labour market activities. I measure JD: job duration (time between beginning and ending of employment) and PD: professional duration (the time spent in a specific vocation). My sample is episode based (not individual based), thats important for PD: I measure PD as two subsequent job episodes in the same vocation. PD therefore is greater than or equal to JD by definition. Furthermore I have more data points for JD (every single employment gives a data point) than for PD (for every employee two subsequent employments in the same vocation are merged to one data point).

I am interested in the difference between JD and PD and I want to compare this difference for multiple groups.

Testing JD and separately PD isn't desirable. Suppose group A has a short JD and a slightly longer PD. Group B could then have a long JD but a PD that's only slightly longer than B's JD. What test can be performed to check for a difference of the difference between JD and PD?

Does it makes sense to test for a difference in proportions of JD to PD in groups?

Non-sequitor: what does the rho do in R's survdiff-test and how should I choose rho?

My approach still seems unclear. Here's what I do exactly:

I use a database storing people's id (ID), begin data of an occupation (BEGIN), end date of an occupation (END) and type of occupation (TYPE).

  • To gather JD I take BEGIN and END and build different groups using TYPE.
  • To receive PD I do the following: For every ID if 2 subsequent employments (e1, e2) have the same TYPE, I merge them to one episode (with BEGIN(e1) and END(e2)). I repeat this step until no more merges are possible (e.g. if there are 3 subsequent employments (e1, e2, e3) of the same TYPE, I end up with one episode (with BEGIN(e1) and END(e3))

Finally, what I try to find out is, if the difference between JD and PD follows a similar pattern for different types or not.


If you want independent time-to-event models, you could calculate a "delta" ID = PD-JD or an interim duration (unemployment possibly, or time in subsequent jobs of similar function). Time-to-new-vocation is a meaningful outcome on its own. In such models, you could adjust for the JD by having it as a covariate/stratum variable in a Cox proportional hazards model. This would allow you to compare people of different job markets with the same duration of employment. So people in the medical field with 5 years experience versus people in the architecture field with 5 years experience would be compared in their rate of re-employment.

Looking at the fraction of JD to PD is possibly a meaningful analysis, though intuitively I don't believe there's any reason to assume that the shorter the time of employment, the shorter the time until finding another capacity.

I think you should also consider the multiple endpoints of people who float around in several jobs in the same capacity, i.e. people in a desirable market with commitment issues or contractors. It doesn't seem like you've captured that information according to your problem description.

  • $\begingroup$ thank you for improving my question and for your answer. I think my poor wording lead to a misconception: due to the design of the data, I cannot calculate a ID = PD - JD (at least I cannot do that on an individual base), because my observations are "episode based" not "indivdual based". That means, apart from Surv(PD) >= Surv(JD) the two samples don't have much in common... $\endgroup$ – Marcel Feb 12 '13 at 19:18
  • $\begingroup$ You have disparate data sources about length of time in a certain vocation or profession and about length of time in a specific position? There's no way to link them to an individual? If that's the case, then it really makes sense to analyze the data separately. Using one dataset to structurally imply things about another can introduce a lot of bias. You can posit some conclusions about their relationship, but I would need to see individual level PD and JD to be convinced. $\endgroup$ – AdamO Feb 12 '13 at 19:57
  • $\begingroup$ No, it's the same data source. Let's consider an individual that's an architect for one year, then is unemployed and then is an architect again. For JD that's 2 episodes (each 1 year long). For PD that's just 1 episode (length: 3 years). So I could link the episodes to individuals, still I cannot perform ID = PD - JD as the number of episodes is different. $\endgroup$ – Marcel Feb 12 '13 at 21:02
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    $\begingroup$ So you need to explain this in your question. What exactly do you measure? Have you captured the number of episodes and the duration of episodes for each individual and PD is the length from the start of the first to the end of the last? $\endgroup$ – AdamO Feb 12 '13 at 22:40

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