The qualifications and occupation projections (QuBe Project) are conducted under the joint management of the Federal Institute for Vocational Education and Training (BIBB) and the Institute for Employment Research (IAB) in conjunction with the Institute of Economic Structures Research (GWS). They provide an overview of prospective development on the German labour market up until the year 2040. The database and the method of modelling used for the projection are updated and revised at regular intervals to take account of constant changes in the prevailing general conditions (Helmrich and Zika 2010; Helmrich, Zika et al. 2012; Zika, Helmrich et al. 2012; Maier, Zika et al. 2014a; Maier, Zika et al. 2014b; Maier, Zika et al. 2016, Maier, Zika et al. 2018; Maier, Zika et al 2020). The sixth wave of the projection, which forecasts developments in the education and training system and on the labour market until the year 2040, is currently available. Results can be sub-categorised in terms of the following characteristics:
Existing labour demand is calculated in accordance with the occupation exercised, requirements level and sector affiliation. Labour supply is updated in differentiated form by occupation in which training has taken place, age, gender and nationality. Designation of an occupational flexibility matrix enables labour supply by occupation in which training has been completed to be converted into potential labour supply by occupation exercised. This is a unique feature, which no other projection has exhibited up until now. At the occupational level, differentiation on both the supply and demand side takes place as far as the occupational groups (three-digit code) of the German Classification of Occupations 2010 (KldB 2010) (Federal Employment Agency 2011).
Since the fourth wave, a specially developed population projection has also been used (Fuchs, Söhnlein et al. 2016). This enables population development to be depicted in a significantly more differentiated way than in the coordinated population projections produced by the Federal Statistical Office. It is then also possible to present the structure of qualifications, occupational mobility and employment behaviour in a way that is differentiated by age, gender and nationality (German, non-German) (see question “How Is the Population Development Projected?”).
The idea behind the concept of the QuBe Project is to update past correlations in the education and training system and on the labour market that are observed in the basic projection. Insofar as dependencies between different variables in the past are identifiable, these dependencies are also taken into account for the future. If no correlations or trends are discernible in certain values, then updates are made on the basis of the status quo. This approach is selected because it permits the most accessible interpretation of the results. These results create visibility in respect of the path on which we are currently embarked and indicate areas in which undesirable developments may need to be corrected. We do not assume that an outcome will actually occur in such a way. Although the individual projections each show development routes that are realistic in their own terms, they are merely based on demonstrated findings from the past. Adaptation reactions between labour supply and demand at the occupational group level (wage increases and changes to occupational flexibilities) are modelled, but only in places where empirical evidence is available. Future patterns may, however, deviate from past behaviour. It is also possible for situations to emerge that were not perceptible in the past. The outbreak of COVID-19, a new type of infectious disease, is one clear example of this in the year 2020. The associated coronavirus crisis has led to breaks in trends and structures in various macroeconomic parameters. In the basic projection, these economic slumps are considered to be an “external shock”. For this reason, the assumption is that the coronavirus pandemic will not lead to any fundamental changes in economic structure and momentum (Maier et al. 2020). Outcomes are therefore always of the nature of an if-then statement. They do not represent a prognosis in the sense of an unchangeable depiction of the future.
The methodology of the QuBe Project undergoes continuous further development and is published in academic research journals (Maier, Mönnig et al. 2015; Maier, Neuber-Pohl et al. 2017). This has enabled several unique features to be developed.
Evaluation of labour supply and demand at the occupational level
From first publication (Helmrich and Zika 2010), the BIBB-IAB qualifications and occupation projections set themselves apart from previous studies (e.g. BLK 2002; Dostal 2002; Bonin, Schneider et al. 2007), by contrasting labour supply and demand at the occupational level. These earlier investigations had largely been restricted to qualification level, individual sectors or abstract task areas. An absence of classifications and of characteristics facilitating manageable alignment between supply and demand meant that projections at an occupational level were not feasible. The QuBe Project facilitates this via vehicles such as a consideration of the occupational flexibility of the labour supply.
A specially developed occupational flexibility matrix (Maier und Helmrich 2012) states the extent to which persons who have completed training in a certain occupation remain within this occupation during their working life and how likely they are to switch to other task areas. Linking occupational flexibility with the occupations in which the work force has trained shows the potential labour supply that may be available for an occupation. Interactions between the demand and supply sides are modelled as part of this process to make occupational flexibility modifiable on the basis of wage adjustments and structural changes within the population.
- Assessment of work volume potential
As well as evaluating labour supply and demand at the level of persons, the QuBe Project is also able to carry out an assessment at the level of work volume in hours. The number of hours offered by the employed persons as per the microcensus is taken into consideration for this purpose (see question “How Is the Potential Volume of Work of the Labour Supply Taken Into Account?”).
- Use of a separate QuBe population projection
A separate QuBe population projection has been used since the fourth wave of the projection (Fuchs, Söhnlein et al. 2016; Gorodetski, Mönnig et al. 2016; Maier, Wolter et al. 2016; Maier, Zika et al. 2016). This facilitates the analysis of aspects such as the impacts of changes in migration movements on the macro economy and on the education and training system (see question “How Is the Population Development Projected?”). The QuBe population projection thus permits the qualification structure, occupational flexibility and employment behaviour to be presented in a way that is differentiated by age, gender and nationality (German, non-German).
- Modelling of adjusted search durations
From the sixth wave onwards, the QuBe Project has issued adjusted search durations in the event of successful recruitment in order to provide a summary of the skilled worker situation in terms of occupational and time comparison. These express the number of days needed by a company to fill a job vacancy. The longer the mean adjusted search duration in an occupation, the more difficult recruitment will prove to be. A detailed presentation of this indicator is contained in Maier, Steeg et al. (2020).
The international comparability of projection results mainly depends on two essential factors: These are the data sources and the classifications selected. The approach of the QuBe Project has its basis in a detailed evaluation of the microcensus. The microcensus represents the German version of the Labour Force Survey. For this reason, international studies, which make use of the Labour Force Survey (such as CEDEFOP’s “forecasting skill demand and supply” project) arrive at developments that are similar to the QuBe Project (Maier 2011). Use of the International Standard Classification of Education (ISCED) means that a comparison at the level of qualifications is possible in principle. The German Classification of Economic Activities (WZ08) is also compatible with the NACR Rev. 2 system, which is deployed internationally. This facilitates comparability of labour demand by economic sectors. Comparability by occupations is somewhat restricted with regard to observations that are based on time series analyses. It is, however, feasible. The switch over from the 1988 and 1992 national occupational classifications to the new German Classification of Occupations 2010 (KldB 2010) also caused an interruption to the time series of the 2008 International Standard Classification of Occupations (ISCO 2008). However, the KldB 2010, the structure of which serves to classify occupational areas (one-digit code), main occupational groups (two-digit code) or occupational groups (three-digit code) from the fifth wave onwards, is capable of being be recoded into ISCO 2008. Nevertheless, differences also occur in the population projection used. Whereas the QuBe Project team draws up its own population projection, European studies make use of preliminary calculations by Eurostat. These also differ from the population projection produced by the Federal Statistical Office.
The essential structural information of the projections has its origins in the microcensus, since this represents the sole official statistic on the population and labour market in Germany. It provides details of such aspects as population structure, the economic and social position of the population, employment activity, job search as well as representative data on the specialism of the highest professional or vocational qualification obtained for the entire working age population in Germany across all educational areas. Other sources used, however, include educational data from the official statistics (institutes of higher education, vocational schools) and the National Accounts of the Federal Statistical Office. Wage information by occupations is taken from the Employee History Data of employees subject to mandatory social insurance contributions. With effect from the fifth wave, wage information was expanded to incorporate Federal Employment Agency (BA) figures on the number of employees subject to mandatory social insurance contributions and on the number of employees solely working in jobs in which only a small number of hours is worked each month and which are subject to flat-rate deductions (annual average).
No explicit assumptions were made in respect of economic growth and productivity development. Because of the multitude of relevant parameters for the recording of macroeconomic complexity, the approach adopted instead was to use a model-based system, the so-called QINFORGE Model, to create a forecast.
The QINFORGE Model differentiates 63 economic sectors on the basis of the German Classification of Economic Activities 2008.
QINFORGE is an econometric prognosis and simulation model for the Federal Republic of Germany which is deeply disaggregated by production areas and groups of goods. It was developed by the Institute of Economic Structures Research (GWS) (Meyer, Lutz et al. 2007; Schnur und Zika 2009; Maier, Zika et al. 2014b). Its particular effectiveness is based on integration into an international composite model. The model has its foundation in “bottom-up” and “full integration” construction principles. “Bottom-up” means that the individual sectors of the economy are modelled in an extremely detailed way and that macroeconomic variables are formed via aggregation in the model context. “Full integration” denotes a complex and simultaneous modelling of inter-industrial supply linkage, of the origin and distribution of incomes, of the distribution activity of the state and of use of incomes by private households.
From the fifth wave onwards, demand for education and teaching staff at general and vocational schools and at institutes of higher education is identified on the basis of the populations status at the respective training centres. Determination of the requirement for nursing staff also takes place via the population structure by age and gender (Stöver, Szlachetka et al. 2015; Bünemann, Sonnenburg et al. 2016). In addition to this, household educational behaviour (differentiated according to Germans and non-Germans) is used to forecast future household figures, which are adduced in line with the estimate of housing needs.
The object of projection is labour supply (labour demand and unemployed persons) together with occupation in which training has taken place. This is projected for both the new and existing labour supply. On the supply side, the projection is conducted in a differentiated way by 144 occupational groups in accordance with the German Classification of Occupations 2010 (KldB 2010) and pursuant to four qualification levels set out in the International Standard Classification of Education (ISCED). From the fifth wave, persons in education are allocated in line with their highest professional or vocational qualification at one of the four qualification levels rather than being stated separately, as was the case in the preceding waves (Maier, Zika et al. 2016).
The labour supply is based firstly on population development (see question “How Is the Population Development Projected?”) and secondly on the educational and employment decisions of the population. The BIBB model (cf. Bonin, Schneider et al. 2007; Kalinowski and Quinke 2010; Kalinowski et al. 2020) sub-divides the population into three groups:
The first group is represented by children who have not yet reached school age and pupils in general schooling. The second group comprises pupils at vocational schools (including at schools within the healthcare system), trainees in company-based vocational education and training and students at institutes of higher education. All other persons, who are referred to in abbreviated form as “persons not in training”, including those performing military and civilian service, form the third group. The second group is of particular relevance with regard to projection of the new labour supply emerging from the education and training system by occupation in which training has taken place. For this reason, it is modelled in great detail. The projection is based on two essential elements: These are the population of the base year of 2017, which is categorised by age, gender, German and non-German nationality, qualification level and occupational groups of the German Classification of Occupations 2010 (KldB 2010), and secondly the module of the vocational education and training system including institutes of higher education. The latter maps the populations of the individual training centres and the transitions between the individual educational and training establishments and the labour market. School attendance rates and transition rates of students from one year of study to the next in 2018 have been kept constant during the projection period. The projected populations of students and trainees at the different training centres indicates a future expectation that the new supply of labour from the higher education sector (universities of applied sciences and academic universities) will be slightly higher than that emerging from dual training. The future qualification structure of the labour supply will, however, also be increasingly determined by persons leaving working life. The latter predominantly display training at the intermediate qualification level. This means that the new labour supply produced by the education and training system exhibits a higher qualification structure than those departing employment. The population by levels of qualification projected via new and existing supply will now take part in working life to a varying extent depending on age, gender, nationality and qualification level.
In some cases, upper secondary schooling has shifted back from eight to nine years. This has been accorded due consideration in respect of the projection of future general school leavers with a higher education entrance qualification. Because of the return to the nine-year upper secondary school model in the federal states of Lower Saxony (from the school leaving year 2020), Bavaria (2025), Schleswig-Holstein and North Rhine-Westphalia (both 2026), the expectation is that incomplete upper secondary cohorts will occur in these years.
This partial absence of upper secondary school leaving certificate cohorts has also been taken into account in the projection of persons entering higher education study. The fall in higher education entrants will, however, be distributed across four further years rather than merely occurring in the school leaving years. The reason for this is delayed commencement of study. Only around 46% of those in possession of a higher education entrance qualification actually begin their studies in the same year (Federal Statistical Office 2017: TAB-13).
Whereas the first three projection waves used the 12th Coordinated Population Projection produced by the Federal Statistical Office, a specially prepared QuBe population projection has served as the basis for the forecast since the fourth wave (Fuchs, Söhnlein et al. 2016; Gorodetski, Mönnig et al. 2016).
The QuBe population projection is based on the population projection of the integrated labour supply and population model developed by the Institute for Employment Research (IAB model). The specific characteristics of this model are that it differentiates between German and non-Germans and that it estimates and updates individual components (birth figures, survival probabilities, influxes and outfluxes and naturalisations) using analytical methods based on time series. Since the fifth wave, the population projection has also facilitated more differentiated results. These enable qualification structure, occupational mobility and employment behaviour to be mapped in terms of nationality (German, non-German) as well as by age and gender. The modelling of the individual components, which are also used for the QuBe population projection, are briefly outlined below. A detailed description for the IAB model is available in Fuchs, Söhnlein et al. (2016).
Birth figures – the age-specific birth figures (15 to 49 years) are determined separately by German and foreign women using a principal component analysis. For German women, this produces a slight rise in the collated birth figure (TFR = total fertility rate) from 1.43 in 2015 to 1.57 in the year 2040. The reason for this is that, as has also occurred in the recent past, the birth rate in the 30 to 35 year age group is increasing, and this is the group in which the rate is strongest anyway. The rate is also rising significantly in the upper age groups (between 36 and 49). Among foreign women, the TFR rises slightly from the current (high) level of 1.96 children in 2015 to 2.05 children in 2040.
Survival probabilities – survival probabilities by individual age (here from 0 to 90 years and older) are also estimated separately for men and women by using a principal component analysis. This produces a life expectancy of 86.7 years for women in 2040 (men: 82.3 years).
Migration – in contrast to the IAB model, migration in the QuBe population projection is determined via the TINFORGE model rather than being estimated using principal components (Mönnig, Wolter 2020). A decision is taken for every country of origin of the migrants as to whether emigration from their homeland is motivated by the local demographic, socio-economic or political situation (Gorodetski, Mönnig et al. 2016). A fourth reason accounting for emigration as a result of acute crisis situations such as war or expulsion was added to the latest version of the immigration model. Such circumstances are frequently accompanied by a large leap in emigration. This approach has the following consequences for the modelling:
- Demographics – migration from the countries of origin to Germany is solely driven by demographic development in these countries of origin. This means that the larger the proportion of younger population classes in the states of origin, the stronger the tendency towards mobilisation will be in these countries.
- Socio-economics – migration from the country of origin takes place on the basis of the local socio-economic situation. This is, for example, clearly visible in respect of countries in southern Europe in the wake of the financial and economic crisis. The assumption here is that these influxes will once again approximate the average in the long term.
- Political – emigration takes place because of the insecure political and societal situation in the country of origin. This may, for example, be estimated via the Fragile States Index. Politically motivated reduction of barriers to trade such as free trade agreements may, however, also increase mobility between Germany and partner states. In such cases, the trend previously observable towards inclination to emigrate to Germany is continued.
- Crises – emigration takes place as a result of acute crises such as war or expulsion and mostly occurs in a volatile way, as for example could be observed in the wake of the “Arab Spring”. This gives rise to the question (often of a political nature) as to how long the crisis will last and when emigration levels can be returned to the pre-crisis level. A time-delayed restitution of rates occurs if countries are declared to be safe states of origin. On the basis of the current political situation, however, the assumption is also made that the influx from Syria will decline.
Emigration – the IAB model is used to calculate age and gender-specific departure rates (0 to 90 years and older) separately by German and foreigners via a principal components analysis using the departures from the migration statistics and the Population Projection of the Federal Statistical Office and is updated for the future.
The population by levels of qualification projected via new and existing supply will take part in working life to a varying extent depending on age, gender, nationality (German non-German) and qualification level. Employment rates, which represent the proportion of the labour supply amongst the German or non-German population of the same age, gender and qualification level, were previously updated via trend estimates. In reality, however, the decision to participate in the labour market is the result of a number of different economically or labour market-related influencing factors. Kriechel, Vogler-Ludwig (2013) or CEDEFOP (2010) show approaches in which the development of employment rates is contingent on various economic and/or labour market-related indicators. The QuBe long-term projection pursues this aim of showing the economic influence on development of participation in employment from the sixth wave onwards, cf. Kalinowski et al. (2020).
Labour supply potential is the sum of the labour supply and the so-called hidden reserves. Hidden reserves are the part of the population which does not voluntarily take part in working life but which would consider becoming economically active under certain circumstances. Hidden reserves have been endogenously determined since the sixth wave of the QuBe projection, using a procedure via which employment rates are defined depending on economic developments rather than being exogenously updated via a time trend. Examples of cause variables include scarcity on the labour market, real wages, GDP per capita or structural change. Readiness to participate in working life is thus explicable and fluctuates in line with the development of the cause variables.
The potential volume of work is a hypothetical notion that shows just how large the labour supply actually is, measured in hours. Figures from the Microcensus, a one-percent random sample of the population resident in Germany, are used in order to calculate this construct. Respondents state the maximum number of hours they would wish to work per week, insofar as this lies above hours of work actually regularly performed (Zika, Helmrich et al. 2012).
The purpose of using the International Standard Classification of Education (ISCED) is to permit comparability with international projection models. The nine stages of the ISCED 2011 (UNESCO Institute for Statistics 2012) were used as an initial basis to sub-divide the qualifications in the projection into four levels containing qualifications, which are the most widespread in terms of numbers and education and training programmes. The four levels comprise: persons without a full vocational qualification (ISCED 010 – 344); persons with a vocational qualification in the upper secondary and post-secondary, non-tertiary sector (ISCED 351 – 444, 454); persons in possession of upgrading training (e.g. master craftsman, technician, certified senior commercial clerk), a bachelor's degree or a degree from a university of applied sciences (ISCED 453, 554 – 655); persons with an academic qualification without a bachelor's degree or a degree from a university of applied sciences including a master's degree, a degree following a “Diplom” programme and a doctorate (ISCED 746 – 844).
The UNESCO General Conference adopted the new International Standard Classification of Education (ISCED) in November 2011, replacing the ISCED 1997, which had been applicable hitherto. With effect from the fifth wave, the ISCED 2011 replaces the ISCED 1997 for the classification of qualification levels in the QuBe Project. The updating of the ISCED led in particular to restructurings in the fields of early childhood and higher education. In the ISCED 2011, Level 5 of the ISCED 1997 was sub-divided into “Short-cycle tertiary education” (Level 5), “Bachelor's or equivalent” (Level 6) and “Master's or equivalent” (Level 7). Level 8 of the 2011 ISCED corresponds to Level 6 of the ISCED 1997 (doctoral or equivalent).
|ISCED LEVEL 1
|ISCED LEVEL 1
|ISCED LEVEL 2
|ISCED LEVEL 2
|ISCED LEVEL 3*
|ISCED LEVEL 3
|ISCED LEVEL 4*
|ISCED LEVEL 4
|ISCED LEVEL 5
|ISCED LEVEL 5
|ISCED LEVEL 6
|ISCED LEVEL 5
|ISCED LEVEL 7
|ISCED LEVEL 5
|ISCED LEVEL 8
|ISCED LEVEL 6
* Content of the category was slightly changed.
Source: (UNESCO Institute for Statistics 2012: p. 63)
Stronger differentiation of the tertiary sector in the ISCED 2011 also made it possible to delineate the German tertiary qualifications in more precise terms. The new classification permits a distinction to be drawn between degrees obtained from universities of applied sciences and from universities. Whereas a university of applied sciences degree is categorised as being equivalent to a bachelor's qualification (ISCED 645), a university “Diplom” is viewed as being at the same level as a master's (ISCED 746). Autorengruppe Bildungsberichterstattung [Vocational Education and Training Reporting Authors’ Group] (2016), pp. XII – XIII provides an exact alignment of German educational programmes and institutions to the levels of the ISCED 2011 and ISCED 1997. A further change affects the orientation of the programmes. Whereas the ISCED 1997 differentiates between general, prevocational and vocational measures, the ISCED 2011 classifies prevocational programmes as being of a general educational nature because they do not represent qualifications, which are of relevance on the labour market.
The ISCED 2011 comprises nine qualification levels. In the projections, these have been conflated to form four levels so that the education and training qualifications that are most relevant to the labour market (vocational qualification, upgrading training, bachelor's degree, more advanced higher education qualifications) can be presented in a differentiated way.
Persons in possession of a higher education entrance qualification (ISCED 344) enjoy much better opportunities on the labour market than persons without a vocational qualification who are unable to demonstrate a higher education entrance qualification (Braun, Bremser et al. 2012). Nevertheless, the number of skilled workers with a vocational qualification is a point of interest for our evaluation. For this reason, we make a separation of general and vocational qualifications between the first and second qualification levels. The first level thus contains both general educational qualifications which provide direct access to the tertiary sector and are therefore equivalent to a (general) higher education entrance qualification (ISCED 344) and also qualifications which do not facilitate this (ISCED 254 and lower). This means that we make no distinction between persons with and without a general higher education entrance qualification who are unable to demonstrate a vocational or higher education qualification. They are counted together as persons without a formal vocational qualification. By way of contrast, the second qualification level “with a vocational qualification” only incorporates vocational qualifications in the upper secondary sector (ISCED 351-354).
Use of the ISCED 2011 instead of the ISCED 1997 produces a different classification of the four qualification levels applied in the QuBe Project from the fifth wave onwards. Whereas the first two levels of “no full vocational qualification” and “with a vocational qualification” continue to be aligned to the same qualifications as when the ISCED 1997 was used, structural changes occurred in the two upper levels. Up until the fourth wave of the projection (Maier, Zika et al. 2016), persons with a bachelor's degree were aligned to the fourth level “with an academic qualification” together with those who had obtained a master's degree or doctorate. On the other hand, skilled workers who had completed upgrading training (master craftsman qualification, technician etc.) were classified individually to the third level. From the fifth wave onwards, bachelor's degrees and degrees from a university of applied sciences are aligned to the third level together with upgrading training qualifications, and master's degrees, doctorates and “Diplom” degrees from a university are separately classified at the fourth level.
The reason for this changed alignment of bachelor's degrees is their classification in the German Qualifications Framework (DQR). The purpose of the DQR is to align German qualifications to the levels of the European Qualifications Framework and thus render them comparable internationally. The levels of the DQR are classified by competence levels, to which qualifications are aligned in accordance with the competencies required to obtain such qualifications. Bachelor's and equivalent qualifications and the vocational qualifications of master craftsman and certified senior clerk are jointly aligned to Level 6. This means that these are equivalent qualifications rather than qualifications of the same type. They enable the performance of tasks of a comparable requirements level (DQR 2018).
Up until the fourth wave of the projection, persons in training forming part of the labour supply were aligned to the category “in education”. However, from the fifth wave onwards, they are categorised in accordance with their highest professional or vocational qualification rather than being listed separately.
The way in which occupations are defined within the German Classification of Occupations 2010 (KldB 2010) encompasses the two dimensions of occupational specialisation and requirements level. Occupational specialisation represents the primary horizontal dimension, which is based on a four-level classification structure. Classification takes place in accordance with similarity of professional competencies required in the occupation and on the basis of tasks exercised. A cluster analysis was used to group occupations in which similar knowledge and skills are needed (Federal Employment Agency 2011). This has removed a significant point of weakness in the previous occupational classification, in which similar tasks were in some cases localised in completely different system positions. For the same reason, flexibilities on the labour market were covered up and made difficult to identify.
At the fifth level, occupations are further differentiated vertically on the basis of the second dimension, the requirements level. The requirements level may be interpreted as a key indicator of the complexity of the task exercised. This enables a differentiation to be made between unskilled/semi-skilled tasks, skilled tasks, complex tasks and highly complex tasks. The requirements level is always typical of a certain occupation and is also independent of a person’s formal qualification. Although the formal qualifications required for the exercising of the occupation are taken into account for the purpose of categorisation, informal training and/or occupational experience are also of significance for this alignment (Federal Employment Agency 2011). In the KldB 2010, this vertical dimension is reflected via the 5th position (occupational type) of the classification indicator allocated.
This reveals that the value of an activity cannot be directly transferred to formal qualifications levels. Persons with a vocational qualification may perform unskilled tasks, and those with an academic qualification also carry out highly complex tasks and skilled tasks alongside complex tasks (Maier, Zika et al. 2016).
So far, this has not formed an explicit thematic focus in the projections, but remains part of other studies. Nevertheless, account is taken of the fact that the requirements level within an occupation changes. For further information, see the results of the “BIBB/BAuA Employment Survey” at https://www.bibb.de/en/2815.php.
The occupation in which training has taken place reflects the abilities and skills learned in the education and training system. These skills enable certain tasks to be performed. Recording the occupation in which training took place and the occupation exercised allows us to show which skills (or occupations) learned lead to which tasks (occupations exercised). This also permits representation of the opportunities and risks that arise when training takes place in a certain occupation.
The microcensus, which is used to calculate the occupation in which training has taken place, records only the highest professional or vocational qualification. Prior certificates and qualifications are thus not included in the calculations. Nevertheless, longitudinal studies reveal that further training programmes undertaken are often in an occupation which is similar to the occupation in which the initial qualification was acquired (Jacob 2004).
The occupational flexibility matrix expresses the likelihood that completion of training in a certain occupation will lead to the exercising of a different occupation. However, this flexibility matrix does not reveal the reasons for switching from the occupation in which training has taken place to a different occupation. These reasons may be multifarious. A switch may for example, be occasioned by improved career opportunities or by an impending or actual loss of a job in the occupation in which training has taken place (e.g. Hall 2010). Because the occupational groups used in the projection already conflate occupations with similar tasks, statistical artefacts of occupational change in the form of changed occupational titles at a small-scale level (e.g. a switch from painter without further specified task to painter in the finishing trades) are usually avoided.
Consideration needs to be accorded to the fact that occupation in which training has taken place and occupation exercised as recorded in the microcensus are based on information supplied by the respondents themselves. The designation of one's own occupation exercised may thus be influenced by the job title and by the task characteristics of the job performed. If a mathematician works as a bank clerk, the skills of a bank clerk are ultimately required rather than those of a mathematician. It is perfectly possible that STEM skills (science, technology, engineering and mathematics) are frequently in strong demand in other areas. However, if different job titles with relevant training occupations and specialisms are applicable in such sectors, recruiting a person who has trained in a different occupation to exercise such a role is an expression of flexibility or substitution potential rather than a necessity.
The flexibility matrix depicts empirically measurable exchange processes between occupation in which training has taken place and occupation exercised. It describes these processes exclusively. Reasons for a switch are not stated.
Changes of occupation are initially neutral for the present analyses. There are indeed changes of occupation, which bring a benefit for the labour demand, as well as switches which are forced and which tend to be associated with negative consequences. Research into these causes is not, however, a primary objective of the projections.
Projections are not factual goals and are not inevitable. They are a magnifying glass for the trends, which currently appear to be most likely. If a projection with a firm basis in academic research that has been conscientiously drawn up does not come true, this does not make it a bad projection. It may have provided points of guidance and/or provoked changes in behaviour. In addition, if it has brought about such behavioural switches, then it will have both fulfilled its purpose and will, of course, also have altered its own projection foundations. For this reason, validation of projection outcomes is usually scarcely possible. The role of all projections is to update past developments for the future via the present. They state what can be expected to take place if the future develops in the same way as the past over a longer period of time, if we take into account what we believe that we already know about the future. Projections are nothing more than a description of what would occur if there were no changes to current trends.
This takes place via a series of assumptions regarding aspects such as economic growth, population development and employment rates. If a parameter alters, then the result will also inevitably change.
The possible generation of “self-fulfilling prophecies”, such as in the form of so-called pork cycles, is viewed as being a particular risk in the case of labour market projections. Engineering and teaching professions are examples of such cyclical labour markets in Germany. Delayed action on the part of skilled worker providers is one cause of such developments.
It is only possible to prevent excessive reactions to known projection results if projections are repeated at regular intervals. This is the only way of taking the impacts of a different starting position into account and of taking counter action if necessary.
In reality, it is not possible for the labour demand (people in employment) to exceed labour supply (labour demand + unemployed persons). In our projections, the total labour supply is therefore always higher than the total labour demand. However, it is possible that negative balances arise in particular occupations. This is firstly due to the fact that the labour force can statistically only be assigned to the labour supply of a single occupation, but due to occupational mobilities there are employment opportunities in several occupations. Furthermore, the possibility of negative labour balances is explicitly allowed in the model in order to show bottlenecks at the occupational level. These arise if the trends and correlations observed in the past persist (baseline projection). In practice, however, there will always be adjustment processes so that such mismatches balance out. For example, a decline in demand can be realised through company restructuring, while changes of occupation increase labour supply.
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