Using administrative data sources in Labour Market research [pdf 5 ...

Sylvia Dixon 27
accurate. The registered unemployment data analysed by Gobbi and Rea
contained information on everyone who registered as unemployed in the period
1993 to 1997, representing around 1.2 million individuals and 2.8 million distinct
spells of registration. The NZES data contain information on demographic
characteristics such as age, gender, ethnicity, educational attainment and place of
residence, as well as detailed information on registration history and employment
assistance received. The data analysed by Maré provide complete information on
all assistance that was provided to registered jobseekers over a nine-year period
(1989–97).
One of the most important properties of administrative datasets is that the
individuals or firms that interact with the administrative agency are usually
assigned unique identifiers. Consequently, the records relating to each unique
respondent can be accurately linked across time. This enables the creation of
datasets that include time-related and spell variables, such as the duration of time
that jobseekers spend unemployed, or the number of times a firm has been
investigated by OSH. The longitudinal nature of many administrative datasets also
enables researchers to apply panel data techniques in their modelling of outcomes
or policy effects, which can be useful in dealing with classic estimation problems
such as unmeasured heterogeneity among programme participants. Maré and
Papps use a range of different fixed effect specifications of their basic statistical
model in attempt to estimate the effects of OSH compliance assessments,
education, information and advice on the incidence of accidents, while controlling
for firm and industry heterogeneity.
A important disadvantage of administrative data sources is that the range of
variables collected on each individual or firm is often quite limited, hindering the
capacity of the researcher to explore all relevant hypotheses about the causes of
the relationship or outcome of interest, or control for potentially confounding
factors. For example, the OSH data analysed by Maré and Papps contained very
little information on the range of firm characteristics that may be associated with
differences in the level of hazards faced by firms, or differences in the costs of
preventing accidents. This central weakness in the data source limits the weight
that can be attached to the conclusions of their study. A key limitation of the
unemployment register data analysed in the other two studies is that very little
information is recorded on the activities and circumstances of those who leave the
register. This means that transitions from unemployment into employment cannot
be properly distinguished from less favourable outcomes, such as exit from the
labour market.
One response to the problem of omitted variables in administrative datasets is
to link them to other data sources. While the studies in this issue of the Labour
Market Bulletin do not involve any matching of data from different sources, other
New Zealand researchers are currently engaged in innovative projects that are
linking administrative with survey data to investigate problems that could not