Answers to Frequently Asked Questions about Using the Data

1. What is the distinction between "employee of own business" and "employer/self-employed"?

The HILDA Survey mostly adopts standard ABS definitions of labour market variables. The treatment of the self-employed by the ABS, however, is something we are not comfortable with.

To quote from ABS, Labour Statistics: Concepts, Sources and Methods, Aug 2006 (cat.6102.0.55.001), their definition of employee is "a person who works for a public or private employer and receives remuneration in wages, salary, a retainer fee from their employer while working on a commission basis, tips, piece-rates, or payment in kind; or a person who operates his or her own incorporated enterprise with or without hiring employees".

In other words, their definition of employee includes owner managers who operate their own incorporated businesses (they are treated as "employees of their own business"). In contrast, a person who operates their own unincorporated business is treated as an "own account worker" (i.e., self-employed).

We believe that for many research purposes this distinction is misleading and thus in our data release we provide all the information necessary for researchers to construct their own definition of employees and self-employed. If you wish to adopt the ABS definition of "employee" then you should take the variable _esempst and combine the two groups  "employee" (1) and "employee of own business" (2). (Alternatively you can just simply use the variable _es, which is a derived variable that reproduces the ABS definition of employment status.)

Whether you combine "employee of own business" and "employer/self-employed" into one group depends on your research question. If you wish to conform to ABS definitions you would never combine them (you would combine "employee" and "employee of own business"). In Mark Wooden's own research of labour market behaviour, for example, he almost always discard the ABS definition and combine "employee of own business" with the "employer/self-employed" group.

2. Which weight should I use?

You use weights to make inferences from the sample to the population. Which weight you use depends on the question you are answering. The HILDA User Manual provides some guidance on which weight to use in which circumstances. Click here to go to the appropriate section of the manual.

3. Should I weight an unbalanced panel?

Maybe. When you construct an unbalanced panel of responding persons, you take all of the responding persons from each wave and stack them into a long file that has one record per person per wave. The weight that could be used to weight this sample is the cross-sectional responding person weight from each wave. That is, in their wave 1 observation the person would be weighted by their wave 1 cross-sectional responding person weight, their wave 2 observation would be weighted by their wave 2 cross-sectional responding person weight, and so on. Similarly, if you are constructing an unbalanced panel of enumerated persons, then you could use the cross-sectional enumerated person weight.

If you pool, say, 5 waves of data together, the sum of the weights will be around 100 million (that is, 5 times the average population size between 2001 and 2005). Therefore, you may wish to rescale the weights by dividing by the number of waves that you have included in the unbalanced panel.

It will depend on the type of analysis you are doing on this unbalanced panel as to whether weighting the sample in this way makes sense. For example:

• If your analysis is of uncommon events and you are effectively taking a pooled sample, then the weighting strategy suggested above should be fine.
• If your analysis requires at least two observations on the same individual, then you will be dropping those people who are only interviewed once. The cross-sectional weights will, therefore, not be appropriate (nor will the longitudinal weights).

4. What weight should I use if I pool sample across waves?

When you are analysising a uncommon event (for example, divorce) you can pool the sample across waves. As the sample is subject to attrition that is not random, you will need to weight your pooled sample.

If you have pooled responding persons across waves, you should use the cross-sectional responding person weight for the wave from which the case has been contributed.

5. How do I match people across waves?

Use the cross wave identifier xwaveid to match people across waves.

6. How do I match people within households?

People within the same household have the same household identifier _hhrhid (replace the underscore with the appropriate letter for the wave, where 'a' corresponds to wave 1, etc). The household identifier will change from wave to wave. You can only match people over time via their cross wave identifier xwaveid.

7. How do I match couples together?

People who are married or in a defacto relationship can be matched to their partner via either:

• _hhpxid, the partner's cross wave identifier; or
• _hhprtrid, the partner's two digit person number which can be concatenated to the end of the household identifier _hhrhid to create the partner's identifer for that wave.

A partner identifier is only available for partners living in the same household. Same sex couples will have a partner identifier.

Note: Replace the underscore with the appropriate letter for the wave, where 'a' corresponds to wave 1, etc.

8. How do I match children to their parents?

A child can be matched to their mother or father via either:

• _hhfxid and _hhmxid, the father and mother's cross wave identifier; or
• _hhfid and _hhmid, the father and mother's two digit person number which can be concatenated to the end of the household identifier _hhrhid to create the father and mother's identifer for that wave.

Mother and father identifiers are only available for people whose parent(s) live in the same household.

Note: Replace the underscore with the appropriate letter for the wave, where 'a' corresponds to wave 1, etc.