measuring the outputs and outcomes of vocational training

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adds to the knowledge base. However such new insights come at a cost, as every new survey is both a
resource-intensive and a methodologically complex undertaking.
For a cross-country survey, the most challenging stage in the development of a data collection exercise
is to build a consensus among the participating countries on the issues that will be investigated
and then to define and operationalise these in ways that are cross-nationally valid. Even after
agreement is reached, there is scope for various interpretations of the results among the participants.
For instance, in CVTS 2, Italy scored badly in the European context on training activities for the employed
population, with only one company in four carrying out any form of training activity (as opposed
to a 62% average of European companies). However, this could be explained by the peculiarities
of Italian industries, which are traditionally composed of small to medium-sized companies in
sectors such as: clothing, footwear, furnishing, etc. (Bulgarelli, 2005). This was largely explained by a
lower propensity of small companies to provide training to their employees and consequently affected
the results of Italy in the survey (which covered establishments with ten or more employees).
Another issue to be considered is the use of national surveys to measure trends on an internationally
comparable basis. It is largely agreed that changing the definitions and the coverage will always
break a time series. This happened in 2003 with a couple of indicators (on participation in lifelong
learning and early school leaving) calculated from the EU LFS variables. Many countries decided to
change the definition in the national surveys (by reporting separately participation in formal education
and other forms of learning), which has inevitably led to a break in series for these countries. In the
case of participation in lifelong learning, the changes in national surveys have had a broader impact
(Behringer and Pfeifer, 2004). With the implementation of the new definitions (which were designed
to improve the coverage of the survey) an increase in the overall participation has been recorded for
all educational attainment levels (as they are defined in LFS: low, medium, high), but by widening the
gaps between groups, which in previous years had been constant.
Another constraint concerns geographical coverage of a data collection. Most of the indicators used
for monitoring purposes are generated from EU surveys. When trying to incorporate non-European
countries, there is not much information available apart from UOE data. Even if information were
available at national level, it is very difficult to adapt it to the coverage of each individual data collection
(definitions, methodology, timeliness, etc.). Geographical coverage was dealt with by OECD for
analytical purposes for CVTS 2 and IALS data, to increase the coverage of countries. Data on both
participation rates in training and the log of training hours per employee were merged and a crosssubsequently
published. A data generator is an organization that makes use of statistical information which
already exists by putting it into a usable format for comparisons and analysis.