17(17)
Transition from education to
the labour market
Content Page
1. Introduction 2
2. The Swedish system of education statistics 3
2.1 Three different sources 3
2.2 The pros and cons of administrative data and surveys 5
3. Registers 6
4. Student follow-up surveys 8
4.1 Introduction 8
4.2 The transition from upper secondary school to higher education 9
4.3 The entrance to the labour market 11
4.4 Ad hoc surveys 11
5. Panels of pupils for longitudinal studies 12
5.1 Introduction 12
5.2 Cohorts and samples 12
5.3 Co-operation between Statistics Sweden and the researchers 13
5.4 Information collected 13
5.5 Results 14
5.6 .. into the labour market 15
6. Access to data 15
7. The main points again 16
8. References 17
Tables
1. Activity after compulsory school in the autumn after
leaving school 8
2. Plan to start in higher education within three years 9
3. Transition rate to higher education 10
4. Cohorts and samples in the panels of pupils 12
Note. This is a revised version of a paper originally prepared for the 10th CEIES seminar in Thessaloniki, May 11-12, 2000
Transition from education to
the labour market
1. Introduction
In an interview in connection with his retirement a Swedish professor of medicine stated that the personal identification numbers, used in all kinds of official registers, are ”Sweden’s most important contribution to the world”. They make it possible to trace and locate all patients and check, for example, the results for different treatments. Even if this is an facetious overstatement, a statistician using registers and longitudinal data could easily agree.
The statistical system for studying transition from school to work described in this paper stands and falls with the existence of an identification system, which makes it possible to combine data from different registers and to follow large nation-wide representative samples/cohorts through the educational system into adult age and into the labour market.
The Swedish identification system is based on the birth dates: year, month and date give the first six digits. A three-digit code - digits seven to nine - differentiates between all born the same day and a tenth digit (a function of the first nine) checks that the combination is correct. My own personal identification number is 460120-5432, which means that I am born on January 20, 1946. The three-digit code 543 is the number that differentiate me from all other born on that day (if the last digit is uneven, in my case a 3, it shows you are a male, females have an even figure in the ninth position) and 2 is the check digit. Almost all Swedes know their personal identification number by heart.
We will not give a description of the Swedish education system in this paper. The interested reader is referred to Abrahamsson (1999) and/or Ministry of Education and Science (1997).
Statistics Sweden publishes two kind of statistics - statistics based on surveys and statistics based on registers. The register statistics published is based on a system of statistical registers which has been developed by Statistics Sweden on the basis of data from different nation-wide administrative systems. Surveys and registers based on administrative data can be made to interact - surveys can be improved by access to registers and register statistics can be evaluated and improved by surveys. Auxiliary information from registers can reduce sampling errors in surveys and survey data can be used to improve the measurement quality of register statistics.
We will not give a description in this paper of the total register based statistical system in Sweden (see the references e.g. Carling (1996)). We will concentrate on the statistical system for education and training statistics.
2. The Swedish system of education statistics
2.1 Three different sources
Transition from education to the labour market can be measured in at least three different ways, namely through:
* Registers
* Student follow-up surveys (sample surveys)
* Panels of pupils (sample panels)
In Sweden all of these methods are used. We believe they complement each other and all three are needed to get a comprehensive picture of the transition process. Statistics Sweden has for a long time been carrying out student follow-up surveys of young peoples’ transition from education to the labour market. For the past 5 - 10 years Statistics Sweden has also been producing register-based statistics about the relationship between education and labour market. The demand is great for statistics that describes the transition from education to work and the demand has been growing during the nineties due to the high unemployment of young people and especially among young immigrants (Olsson and Arvemo-Notstrand (1997)).
Our statistical system for studying flows through the educational system and into the labour market can be illustrated by the picture on the next page. These flows are not easy to describe today if they ever have been. On the contrary, they are complicated, with persons going to and from different types of education and to and from the labour market.
As a producer of education statistics we want to give basic data for each type of education on:
* Applicants (as a measure of demand for the education)
* Beginners (new entrants)
* Registered (participants)
* Graduates
* Teachers/ Personnel
* Costs
We want background data on the students like gender, age, regional-, social- and educational background and also some results like marks and test results. We really want to know what the pupils learn at school. It is very important to try to measure the outcome from the educational system.
In Sweden this statistical system started with higher education in 1937 and has since been developed for other types of education and the system is still developing. What is rather new is statistics describing the flows. Some of these flows are very well statistically developed like from Upper secondary school to Higher education or from Higher education to the Labour market. Some are less well developed as from Adult education to the Labour market. Ideally, we should follow a cohort from the end of primary school and through the education system and into the labour market.
The strengths of the Swedish system on education statistics are many, but the most important strength is perhaps that it is possible to follow individuals over time and also link their educational data to information about the parents.
This system of education statistics is a gold-mine of information, which we just have started to explore. Statistics Sweden needs co-operation with researchers - sometimes even researchers from abroad visit Sweden to work with our statistical data on education and training - and also with experts in other national agencies to dig out this gold-mine. Statistics Sweden has neither the aim nor the possibilities to do all this work alone.
There are information gaps in the system:
· We are lacking an Occupation register. We are building one, but it will take a couple of years before it is ready. When studying the transition from education to work occupation is much more interesting than industry, which we have in our registers.
· Better categorisation for ‘activity after education’ in the register-based statistics. Now we use four categories: Gainfully employed, gainfully employed and students, students, other. We need a finer categorisation of both gainfully employed and -especially- the other category.
· We need to improve the Adult education part of the statistical system. If we are going to make statistics over life long learning in the future, a better co-ordination of the Swedish Adult education statistics are needed. Among other things we will do a special Adult Education Survey every third year.
· Students leaving the education system without graduating, what happens with them? We need to pay more attention to ”drop-out” students.
2.2 The pros and cons of administrative data and surveys
If we use administrative data instead of executing a sample survey we will reduce costs as well as response burden. But we should not regard administrative data as a cheap alternative to sample surveys. Administrative data and sample surveys should be regarded as two different methodologies where administrative data is the best choice in some situations and sample surveys in other. Thus, the access to administrative data open new possibilities for a National Statistical Institute to build an efficient statistical system. An effective use of administrative data means that a system of statistical registers should be designed and that this register system and the sample surveys should interact. Using register data gives the possibility to identify the same individual in different sources, which enables a deeper analysis, e.g. net count of students.
If we use administrative data we are dependent on the administrative system generating the data. There is a risk that the units and variables defined by the administrative authorities are unsuitable for statistical purposes. But this disadvantage of administrative data can be counteracted by the statistical agency in three ways:
- On the basis of the administrative units, statistically meaningful units should be defined (as we do in the Swedish Business Register where we define economic units of different kinds).
- On the basis of the administrative variables, statistically meaningful variables should be defined.
- By combining many sources we get better possibilities to obtain a statistically relevant data set.
In short we call this transforming administrative registers into statistical registers. The quality in the administrative registers is mostly good, even very good, concerning items/variables of interest to the agency administering the register. Items added to the register solely for statistical purposes and of no interest to the ‘administrator’ are often of less good quality. In these cases we have to put in a lot of effort improving the administrative registers when transforming them to statistical registers. Another disadvantage with register statistics is that it often takes quite a long time to produce the statistics. For example, now in May 2000 we have data from the Employment Register for November 1998.
If we use sample surveys we can define our own variables but we are dependent on the respondents’ capacity and willingness to give answers to our questions. Instead of asking delicate and difficult questions about for instance income and education, it is better to use administrative/statistical registers.
Again we want to stress the fact that register statistics and survey statistics are not methodological rivals - they should instead be regarded as complementary methods. The register statistics give the basic facts about differences between various categories and about changes over time. It is then possible to design a survey which can give a deeper understanding of the causes behind these patterns. With the help of the statistical register the sample can be selected only from the interesting categories in the population.
A great advantage with statistics based on administrative sources is the possibility to report results for many subdivisions. For example, in describing young persons’ transition to the labour market, it is important to report results for different courses and study programmes. In a sample survey of reasonable size it would be difficult to give reliable estimates for many simultaneous subdivisions. This is the reason why administrative sources are very important for regional statistics. The choice is sometimes between register statistics or no statistics at all!
3. Registers
To describe the transition from education to the labour market we use the following registers:
· The Register of Compulsory School Grade 9
· Register of Pupils in Upper Secondary School
· Register of Higher Education
· Register for Municipal Adult Education
From these four registers we get the population of school leavers we want to follow up.
· Total Population Register (RTB after its Swedish abbreviation). The main variables in this register are; Personal Identity Number, Place of Residence, Name, Address, Marital Status, Citizenship, Country of birth, Immigration and Emigration.
· Population Censuses. In our education statistics we mostly use the population censuses to obtain social background for the students through their parents occupations.
· The Education Register. This register contains information on education completed for all persons 16-74 years old living in Sweden.
From these three registers we get background data on the pupils/students.
· The Register of Persons Studying (RPU after its Swedish abbreviation).
· The Employment Register (RAMS after its Swedish abbreviation). This register contains variables like enterprise size and ownership, location and industry of establishment for all gainfully employed in Sweden.
· The Wage Statistics Register (contains -except wages- occupation for about 75% of the gainfully employed and is sometimes used in absence of an Occupation Register)
· The Income Register
· The Central Enterprise and Establishment Register. This is a continually updated data base that describes the current conditions (industry, sector, size, location etc.) among all enterprises and establishments in Sweden. Each enterprise/establishment has a unique identification number.
These registers are used to find out what happens to the students/pupils.
From these registers an integrated data base, called LUCAS (Swedish abbreviation for Longitudinal register for education and labour market statistics), has been created. The objective of preparing integrated data bases is to simplify and cheapen the dissemination of statistical information. LUCAS contains data from most of the registers mentioned above and is specially designed for producing statistics on the flows within the education system and the transition from education to the labour market. The two latest statistical reports are:
· U 81 SM[1] 9901: The transition from education to the labour market 1991 - 1997
· UF 81 SM 0001: Activity after education - industry, sector and income during 1997
As an example on a table from LUCAS you may see below how the labour market after compulsory school has been eradicated:
Table 1. Activity after compulsory school in the autumn after leaving school. Per cent
1990/91 / 1991 / 6 / 10 / 80 / 5 / 100 / 104 000
1991/92 / 1992 / 2 / 6 / 89 / 3 / 100 / 99 000
1992/93 / 1993 / 0 / 2 / 95 / 4 / 100 / 97 000
1993/94 / 1994 / 0 / 2 / 95 / 3 / 100 / 94 000
1994/95 / 1995 / 0 / 2 / 95 / 3 / 100 / 97 000
1995/96 / 1996 / 0 / 2 / 95 / 3 / 100 / 100 000
1996/97 / 1997 / 0 / 2 / 96 / 2 / 100 / 97 000
The most fascinating with the Swedish statistical register system is that it is possible to connect persons and enterprises/establishments. This is possible through the Income Verification Register, which is originally created for applications by the Swedish taxation authorities. This register contains every job being possessed by an employee during a year. The register contains rather few variables, but constitutes the basis for the register system, as every job is attached to the identity of the person and the identity of the enterprise/establishment. As can be imagined this gives enormous statistical opportunities e.g. which types of enterprises recruits students from different educational programmes? Or how is the educational level in different types of enterprises? The Income Verification Register is the base for the Employment Register (RAMS) mentioned above.