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Working Paper No. 154 Tertiary education expansion and task demand: Does a rising tide lift all boats?

Tobias Schultheiss, Curdin Pfister, Uschi Backes-Gellner and Ann-Sophie Gnehm

July 2019 (first version: July 2018)

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The Swiss Leading House on Economics of Education, Firm Behavior and Training Policies is a Research Program of the Swiss State Secretariat for Education, Research, and Innovation (SERI).

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Working Paper No. 154 Tertiary education expansion and task demand: Does a rising tide lift all boats?

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Tobias Schultheiss, Curdin Pfister, Uschi Backes-Gellner and Ann-Sophie Gnehm

A previous version of this Working Paper No. 154 was first published in July 2018 under the title “Rising tide effect or crowding out – Does tertiary education expansion lift the tasks of workers without tertiary degree?”.

Tertiary education expansion and task demand: Does

a rising tide lift all boats?*

BY TOBIAS SCHULTHEISS†, CURDIN PFISTER†, USCHI BACKES-GELLNER† AND ANN-SOPHIE

GNEHM†

July 4, 2019

We examine how the establishment of Universities of Applied Sciences (UAS) in Switzerland affects the jobs of not only UAS graduates but also middle-skilled workers with vocational degrees, for whom such tertiary education expansion may crowd out skilled tasks or elevate the task content. Applying machine-learning methods to job ad data, we analyze task content before and after the educational expansion, finding that in affected regions the tasks of both groups contain more R&D and become more like those of academic graduates. UAS graduates build a bridge between academic graduates and middle-skilled workers, facilitating the integration of middle-skilled workers into R&D tasks.

Over the last two decades, an increasing number of students in industrialized countries

have begun attending college and obtaining a tertiary education degree. In the OECD countries

the average share of individuals holding a tertiary degree in 2016 increased by 50 percent over

the share in 2000 (OECD, 2017a). Moreover, as the expansion of tertiary education is the

official policy goal of many OECD countries (OECD, 2017b), student numbers will continue

to rise.

However, the effect of tertiary education expansion on the jobs of tertiary graduates in

general and of middle-skilled workers (without the new tertiary degrees) in particular, remains

unclear. For this second group, on one hand, some researchers argue that such an expansion

* We would like to thank Simon Janssen, Edward Lazear, Samuel Mühlemann, Laura Rosendahl Huber, Guido Schwerdt, Conny Wunsch

and the seminar participants at the University of Zurich for their helpful comments. This study is partly funded by the Swiss State Secretariat for Education, Research, and Innovation (SERI) through its Leading House on the Economics of Education, Firm Behavior and Training Policies.

† University of Zurich, Switzerland. Corresponding author: Tobias Schultheiss. Address: Plattenstrasse 14, CH-8032 Zurich, Switzerland. E-mail: tobias.schultheiss@business.uzh.ch.

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will increase the overall productivity and innovation in firms (Mansfield and Lee, 1996;

Toivanen and Väänänen, 2016; Valero and van Reenen, 2019), in turn even enabling non-

tertiary workers to advance into more skilled tasks. On the other hand, other researchers argue

that a rising number of graduates from tertiary institutions may push nontertiary workers down

the job ladder (Hansson, 2007), forcing them into less attractive and less well-paid jobs (Dupuy,

2008, 2012; Rosen, 1978; Sattinger, 1975).

In this paper, we investigate whether not only the graduates of newly established tertiary

education institutions gain by advancing into jobs with more demanding tasks (such as R&D)

but also those staying at the next lower skill level, i.e., middle-skilled workers that graduated

from a vocational education and training (VET) program. We answer this question by

examining the tasks that firms specify in job ads as the main tasks of workers. Following the

increasingly common method of analyzing job ads for capturing task requirements (Atalay et

al., 2018; Deming and Kahn, 2018; Hershbein and Kahn, 2018; Sahin et al., 2014), we draw on

data from the Swiss Job Market Monitor (SJMM), a large representative sample of print and

online advertisements for job vacancies (hereafter, “job ads”) (Buchmann et al., 2017).

To solve the endogeneity problems usually associated with education expansions, we

exploit a quasi-random natural experiment: the establishment of Universities of Applied

Sciences (UAS) in Switzerland in the late 1990s. UASs constitute a second tier of tertiary

education institutions, teaching and conducting applied research (different from traditional

academic universities teaching and conducting basic research). UAS students come from the

pool of vocationally trained workers, who possess sound professional skills from completing a

VET program. These programs, which take three to four years, follow a formal curriculum for

the training at the workplace and the education at the vocational school.

The location and timing of the establishment of UASs and their campuses was quasi-

random regarding our research questions because the establishment was subject to a complex

political process that was unrelated to the economic environment of a region, the number of

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tertiary graduates, and other educational and economic characteristics. The process, which

involved extensive negotiations, contained political package deals with resolutions on many

noneducation-related and noneconomic political issues. Therefore, the definitive location and

timing of campus openings did not systematically depend on economic factors, including those

that might also have impacted the educational or labor market outcomes that we are interested

in (for more details on the political process, see Pfister et al., 2016). Finding a common

pretreatment trend in the tasks of job vacancies supports this assumption (for other labor market

outcomes, see Lehnert et al., 2017, and Pfister et al., 2016).

The group of workers on whom we focus in this paper are workers starting their

educational career with apprenticeship training and entering the labor market with a federally

recognized VET degree, i.e., about 70 percent of the Swiss labor force. These workers are the

target group of the newly established UASs. After the establishment of UASs, workers from

this group have two options: they can either (1) study at a UAS (15 percent of all apprenticeship

graduates in 2016) or (2) stay on a vocational path (85 percent) (State Secretariat for Education,

Research and Innovation, 2019). By examining the first subgroup, we assess whether

graduating from a UAS and obtaining the new tertiary degree actually enables workers to

advance into more skilled tasks. The second subgroup allows us to study whether educational

expansion leads to either a crowding out or an upskilling of workers who lack this new tertiary

degree. For clarity, we label this second group simply “vocationally trained workers.”

Our results show first, that after the establishment of UASs firms search UAS graduates

for jobs with task profiles that include R&D as the main task and that job ads as such are similar

but not identical to those of graduates from academic universities (hereafter, “academic

graduates”). Thus, graduating from a UAS opens up a new range of jobs, different from middle-

skilled jobs and closer to those of academic graduates. Second, for vocationally trained workers

who do not acquire a UAS degree, our results provide evidence for upskilling rather than

crowding out: after the establishment of UASs, firms are also seeking vocationally trained

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workers more often for jobs with R&D as the main task. Thus, just as a rising tide lifts all boats,

the establishment of UASs clearly elevates the task content of vocationally trained workers both

with and without the new UAS degree.

To check the external validity of our findings, we rely on two additional data sources,

the Swiss Labor Force Survey (SLFS) and the Swiss Earnings Structure Survey (SESS), and

estimate wage and employment effects after the establishment of UASs as alternative outcomes.

These results confirm the results we found for the job ads: In the SESS data we find that firms

actually employ more vocationally trained workers in R&D and in the SLFS data we find that

these workers receive higher wages, reflecting the performance of more skilled tasks. In

summary, from three independent data sources we find clear evidence for upskilling after the

educational expansion—even of those workers who do not use the newly established UASs as

an educational option.

We argue that these results can be explained by the new UAS graduates acting as bridge

builders between academic graduates, i.e., academically trained R&D workers, and

vocationally trained workers with sound professional skills. The combination of a VET degree

(building the connection to the vocationally trained workers) with a UAS degree (building the

connection to the R&D workers trained in academic universities) enables UAS graduates to

effectively build a bridge between the two groups. Thus, firms benefit from the presence of

UAS graduates who have that unique combination of professional skills and applied research

skills. Before the establishment of UASs, the gap between workers with a university degree and

those with a vocational background was too large and caused nonnegligible communication

costs, preventing organizations from efficiently combining such skills for R&D processes, e.g.,

when building testing devices or prototypes (Backes-Gellner and Pfister, 2020).

This study makes contributions to two strands of literature: First, by examining the

effects of tertiary education expansion on the jobs of middle-skilled workers (with a VET

degree, but without upgrading to a UAS degree), we contribute to the literature examining labor

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supply shocks and the firms’ responses to such shocks (Acemoglu, 1996, 2007; Beaudry and

Green, 2003; Blundell et al., 2018; Carneiro et al., 2018; Stuen et al., 2012). Second, by

introducing a text-based upskilling measure, we contribute methodologically to the growing

economics literature that uses text as data to explore labor market phenomena (Atalay et al.,

2018; Deming and Kahn, 2018; Hershbein and Kahn, 2018; Michaels et al., 2018; Sahin et al.,

2014). By employing machine learning algorithms for text vectorization, pioneered by

computer scientists (Bengio et al., 2006; Collobert and Jason, 2008; Turian et al., 2010), this

new measure allows researchers to overcome problems of both synonyms in job ads (e.g.,

“investigating” and “examining”) and vocabulary changes over time that do not reflect changes

in meaning (e.g., “personnel” and “human resources”).

The paper proceeds as follows. Section I discusses the theoretical background of tertiary

education expansions causing either crowding-out or upskilling for workers who remain at their

original educational level and without a tertiary degree. Section II describes the data and

provides descriptive evidence. Section III presents the empirical methodology for estimating

crowding-out or upskilling effects, and Section IV reports the results. Section V examines

employment and wages as alternative outcome measures. Section VI explores the potential

mechanisms underlying our main findings. Section VII concludes.

I. Crowding Out or Upskilling: Theoretical Considerations and Hypotheses

The effect of a labor supply shock of high-skilled workers (e.g., when new UAS

graduates enter the labor market) on the task demand of firms for different skill-groups of

workers is theoretically ambiguous. According to the assignment models of comparative

advantage (e.g., Sattinger, 1975, Rosen, 1978), while such a shock may lead to a crowding out

of skilled tasks for workers without the new educational degree, according to organizational

models involving teams (e.g., Garicano and Rossi-Hansberg, 2006, or McCann et al., 2015), it

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may have the opposite effect, i.e., “upskilling for all”, meaning that an increasing number of

high-skilled workers elevates the tasks across skill categories.

Crowding out.—In assignment models of comparative advantage (for a comprehensive

survey of the literature, see Sattinger, 1993, and Dupuy, 2008), firms partition tasks among

different types of workers to maximize overall output, with each type of worker performing a

bundle of tasks called a “job”. As the optimal allocation of types of workers to tasks follows

the logic of comparative advantage, firms assign types of workers to tasks for which these types

are relatively more capable (Dupuy, 2012, 2015; Sattinger, 1975; Teulings, 1995, 2005).

Therefore, after a high-skilled labor supply shock, firms seek these workers for high-skilled

tasks (i.e., a positive first-order effect). However, in a static model world, these tasks, rather

than being new, stem partly from a reallocation of tasks originally allocated to differently

educated types of workers (Costrell and Loury, 2004). This reallocation of tasks to high-skilled

workers thus results in a crowding out of those tasks for less-skilled workers (i.e., a negative

second-order effect).

Applied to the establishment of UASs, the graduates of these institutions would have a

comparative advantage in applied R&D tasks, for which firms would therefore seek them (first-

order effect). However, in a static world, firms would reallocate the tasks of vocationally trained

workers and assign them to fewer skilled tasks. As a second-order effect, we thus expect a

crowding out of skilled tasks (such as contributing to R&D processes) for vocationally trained

workers (without UAS degrees).

Upskilling.—Organizational models (for an overview, see Garicano and Rossi-

Hansberg, 2015) assume that the combination of tasks to produce final output requires workers

to coordinate and communicate. Workers encounter frictions when combining tasks (Deming,

2017), with communication and coordination being needed to overcome these frictions

(McCann et al., 2015; Morris and Shin, 2007). Therefore, exploiting complementarities (e.g.,

from different sources of knowledge) and realizing the gains from comparative advantage (e.g.,

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from different skill sets) requires different types of workers to be able to communicate with

each other (Garicano and Wu, 2012; Garicano and Rossi-Hansberg, 2006, 2004; Lazear, 1999).

Some types of workers may be particularly qualified for this communication task, e.g., by being

bilingual (in occupation-specific terminology) or well-versed in different cultures

(socioeconomic or departmental) or fields of work (technical or administrative) (Lazear, 1999).

Educationally diverse teams can benefit from complementarities, but may also create

additional costs if communication difficulties cannot be solved (Kurtulus, 2011). Applying this

reasoning to the situation before and after the establishment of UAS, we expect the following

patterns: Before the establishment of UASs, the gap between workers with an academic

background and those with a vocational background may have caused high communication

costs. Consequently, firms could not fully realize the gains from workers’ combining

complementary professional and research skills. With the establishment of UASs as a new

career path, vocationally trained workers graduating from the UASs have an overlapping

background with both academic graduates and workers remaining at the VET level. This

overlap enables UAS graduates to act as bridge builders and communicators, promoting

teamwork across different educational levels in the R&D process and facilitating the creation

of new R&D tasks among vocationally trained workers (e.g., building testing devices and

prototypes).1 According to such a dynamic explanation, the presence of UAS graduates raises

productivity and innovation in firms, resulting in more skilled tasks even for those workers

staying with their initial apprenticeship training (i.e., a positive second-order effect). Thus, we

expect an increase in R&D tasks for vocationally trained workers even without the new UAS

degree.

While the assignment and organizational literatures derive unambiguous theoretical

predictions for the first-order effect (i.e., firms seeking UAS graduates for high-skilled tasks,

1 The idea of a shared background facilitating teamwork can also be found in the organizational psychology literature (Mathieu et al., 2000;

Mohammed et al., 2010; Mohammed and Dumville, 2001; Stout et al., 1999).

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such as R&D), the predictions for the second-order effect are contradictory. Assignment models

suggest a crowding out of skilled tasks from the task bundles of vocationally trained workers,

whereas organizational models predict the opposite, i.e., the integration of additional skilled

tasks into the task bundles. Given that the effect of UAS graduates on the tasks of vocationally

trained workers remains theoretically unclear, the nature of the effect becomes primarily an

empirical question.

II. Data and Descriptives

A. The Swiss Job Market Monitor

To answer how the establishment of UASs affected the main tasks of workers, we draw

on data from the SJMM (Buchmann et al., 2017). The SJMM is based on a representative

sample of job ads for Switzerland from 1950 through 2016. The SJMM sample is collected on

a yearly basis and stratified over advertising channels (e.g., newspaper or firm website), and

the characteristics of the advertising media within each channel (e.g., the print run of the

newspaper). In contrast to the job vacancy data previously used in the literature (e.g., Deming

and Kahn, 2018; Hershbein and Kahn, 2018), the SJMM not only contains job ads over a long

period of time (i.e., from 1950 through 2016) but also covers the entire range of media relevant

to the job market: newspapers, online job boards, and firm websites.

The use of job ads as a source of information for capturing task demand has several

advantages: Job ads share a common information structure. Rafaeli and Oliver (1998) label this

information structure as a “skeleton” that all job ads share: Every job ad contains information

about the identity of the firm, its human resource needs, its requirements for fulfilling those

needs, and the firm’s contact information. This similar structure makes both the categorization

and the comparison of job vacancies possible. Job ads also mirror the firm’s needs at the local

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level. These properties also allow for constructing control and treatment groups at small

geographic levels such as the municipality level.

The SJMM contains the full text for the sampled job ads. In addition, Gnehm (2018)

provides a classification (based on supervised machine learning) for the function of each word

in the job ad: The classification assigns each word to one of eight categories (called

“textzones”), such as the job and firm descriptions, required hard and soft skills, or the firm’s

reason for the vacancy.2 The textzones reflect the structure of a typical job ad. In our study, we

focus on the job description because it contains the words associated with the tasks performed,

such as tasks (“R&D”), software names (“SQL”), or the mode of organization (“teamwork”).

Furthermore, for the text analysis, we use the textzones for both soft skills (e.g., being

“communicative” or “detail-oriented”) and hard skills (e.g., having “work experience in the

profession” or “English proficiency”).

The SJMM team also manually coded the key characteristics of job vacancies. For each

one, the SJMM includes information on the firm that is advertising, such as its industry

affiliation, the geographic location of the workplace (at the municipality level), the occupation

according to the Swiss classification system (SBN2000), formal educational requirements and

specific demands such as experience, special knowledge or specific training, and the main task.

Therefore, this database is particularly useful for investigating changes in local task demands

by firms.

To construct our sample, we use the information on formal educational requirements

and identify job ads aimed at UAS graduates and vocationally trained workers. We only

examine job ads from the German-speaking part of Switzerland (covering 70 percent of the

population and 72 percent of GDP) because the SJMM sample is limited to the German-

2 The supervised machine-learning algorithm for the textzoning classification is trained only for job ads in German. These job ads represent

95.7 percent of all job ads in our sample, with job ads in other languages constituting 4.3 percent. However, job ads in other languages, usually from internationally operating high-profile employers, contain a much higher percentage of skilled tasks, and leaving them out would bias our results. To preserve the validity of our sample, we use the manually coded characteristics of these job ads and match them to “twin” job ads (i.e., with the same occupation and the same main task) in German. We assume that these job ads have the same text characteristics (i.e., numeric textzone vectors) as their German counterparts.

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speaking part of the country before 2000 and the textzone algorithm is only trained for job ads

in German. Furthermore, we only analyze the establishment of UASs in the German-speaking

part because educational traditions differ between the German-speaking part (where vocational

education has much stronger roots) and the Latin part of Switzerland.3

B. Outcome and Main Explanatory Variables

To capture upskilling effects, we use two different methods to measure upskilling in the

job tasks. First, we use the manually coded category “main task” of the job ads to identify

whether an advertised job specifies R&D as its main task. Second, we apply a similarity

measure to the job description to investigate how similar the job descriptions of workers with

different educational backgrounds are. We provide details on our two measures in the following

paragraphs.

R&D as Main Task.—The SJMM defines the main task as the kind of task that is mostly

performed in the advertised job position and is broken down into 21 distinct categories, ranging

from agricultural tasks, or sales and customer service, to publishing and creative work. This

categorization of the main task both follows and augments a task categorization that the German

Socio-Economic Panel (Stoss and Weidig, 1990) used in their questionnaires. Each job ad has

only one main task.

The SJMM staff manually codes and categorizes the main task in job ads according to

the explicit or implicit task description in each ad. For example, a job ad specifying “your

experience and knowledge is needed for repairing and maintaining equipment” would be put

into the category of “repairing and restoring.” Müller and Buchs (2014) find a good reliability

of the main task encoding, with a Krippendorff’s alpha of 0.76.

3 The Latin part is mainly situated in the (south)western regions of Switzerland, with language, culture, and educational traditions differing

from the German-speaking part (Eugster et al., 2011). We follow the argument by Pfister et al. (2016) of focusing the German-speaking part of the country in which the training of apprentices and the UASs as an educational option are most prevalent.

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[Table 1]

For our empirical analyses, we concentrate on the main task of “analyzing, researching,

and monitoring,” because it contains skilled activities associated with upskilling toward the

academic educational level. Table 1 lists the subset of all activities that the SJMM bundles into

this category of a main task. For the sake of brevity, hereafter we refer to “analyzing,

researching, and monitoring” as “R&D” and the upskilling measure as “R&D as main task”.

Our outcome variable is a binary variable indicating whether a job ad has R&D as its main task

(or not). Figure 1 shows the yearly total number of all vacancies (i.e., across all educational

categories) listing “R&D” as the main task in regions with and without a UAS campus.4

[Figure 1]

Similarity with Job Descriptions of Academic Graduates.—For the second measure, we

use the job description text in job ads and compare how similar or dissimilar these descriptions

are for job ads of different educational groups. We use the graduates from academic universities

as a benchmark, because they provide the job descriptions for typical high-skilled jobs with

R&D as the main task and other skilled activities (e.g., leadership tasks). Moreover, the job

descriptions of academic graduates provide a good text benchmark because they are the most

elaborate, covering a wide range of words relative to the job (e.g., “customer care”), the

technology used (e.g., “Python”), and the activities required (e.g., “coordination”).

When constructing the similarity measure over a long period of time, we are faced with

the problems typically encountered in text analyses such as the challenge of the evolving

4 We set the number of job vacancies in 1995 to a base level of 100 and present changes in absolute relation to the base level of 100. By

this procedure, we only set the intercept to 100, but we do not change movement or slope.

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meanings of words. For example, in certain industries and occupations (e.g., consulting), firms

frequently use management buzzwords that change over time. Consequently, word changes

may only reflect changes in vocabulary rather than actual changes in meaning. A similar

problem arises for jobs in science, technology, engineering and mathematics (STEM). These

jobs are characterized by technological progress which rapidly changes job descriptions and the

words identifying upskilling (Deming and Noray, 2018). For example, ten-year old computer

technologies and the names for them are now outdated (e.g., Objective C). To tackle such

challenges, we construct a dynamic yearly measure that we generate from the data, as opposed

to imposing a static keyword measure. As the method behind our text analysis, we use the

vectorizing algorithm “doc2vec” developed by Google (Le and Mikolov, 2014).

Vectorizing the job descriptions enables us not only to deal with an evolving vocabulary

over time but also with the problem of synonyms. We use the lemmatized5 version of the job

description (and also remove stop words such as “at,” “which,” and “and”) and then apply the

doc2vec algorithm (Le and Mikolov, 2014). Doc2vec is an unsupervised machine-learning

algorithm that learns vector representations for variable-length text pieces, such as job

descriptions. Each job description is characterized by a vector of 200 dimensions that one can

interpret as 200 possible subjects that a job description could cover. The doc2vec algorithm

trains neural networks with the fake task that, given a random word (from all job descriptions),

the neural net has to predict the remaining words of that job description. The algorithm is based

on the distributional hypothesis: words appearing in a similar context share a similar meaning

(e.g., “investigate” and “look into” often appear in the same context and thus share a similar

meaning). Consequently, the algorithm assigns semantically similar words to similar word

vectors and, in a second step, similar job descriptions to similar text vectors.

5 A lemma is the dictionary form of a set of words. For example, “develop” represents the lemma for “develop,” “developed,” “develops,”

and “developing.” Lemmatization involves syntactical and morphological analysis, therefore offering better precision than stemming (i.e., reducing words to their word stem). We used TreeTagger (Schmid, 1995) for the lemmatization of job ad texts.

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Drawing on the text vectors for the job descriptions, we create for each year a

representative job description for academic graduates and then measure how similar the tasks

of other job descriptions are to this benchmark (hereafter we refer to this measure as the

“similarity with job descriptions of academic graduates”).6 We calculate the cosine similarity,

which is commonly used by computer scientists (Gomaa and Fahmy, 2013). Cosine similarity

ranges between -1 and +1, with a higher value indicating a higher similarity. If the job ads for

an educational group become more similar to the benchmark of academic graduates, we

interpret this as upskilling. However, if the job ads become less similar, we take this as a

measure for a crowding-out effect. Figure 2 shows how the similarity between academic (the

benchmark) and all nonacademic job descriptions evolves over time in regions with and without

a newly established UAS.7 For our empirical approach we specifically calculate the similarity

with our benchmark for the educational groups of vocationally trained workers and UAS

graduates. In essence, the similarity measure then captures the distance of job descriptions from

each of these two educational groups to our academic benchmark.

[Figure 2]

Explanatory Variables.— In alignment with previous studies on college openings (Che

and Zhang, 2018; Kamhöfer et al., 2018), we exploit a natural experiment—the establishment

of UASs and their campuses—that provides us with quasi-random regional and temporal

variation in high-skilled labor supply, i.e., UAS graduates. We focus only on UAS campuses in

STEM fields, i.e., those that specialize in chemistry, the life sciences, engineering, and IT,

because previous studies find strong effects of STEM graduates on local economies (Peri et al.,

6 To generate the benchmark, we take the mean of the job description vectors from job ads targeting academic graduates. The benchmark

thus represents the tasks described in the average academic job ad. UAS graduates are not included so that the benchmark remains unaffected by the establishment of UASs.

7 The similarity is based on all job ads in the same year, with the ads of academic graduates being excluded (which would bias results by measuring self-similarity of the academic graduates job ads if included).

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2014; Winters, 2014). Furthermore, these UAS campuses conduct applied R&D and provide

their students with all the necessary R&D skills.

We use the information from Pfister et al. (2017) on when and where each of these

campuses were newly established. The establishment process took the form of a staggered

rollout, with the earliest campuses opening in 1997 and the most recent campus in 2003. This

spatial and temporal variation allows us to determine the exact year in which we expect to

observe a local treatment effect resulting from the influx of UAS graduates.

To define whether a firm publishing a job ad for a vocationally trained worker was

treated by the establishment of the UASs, we use information on the location of the workplace,

i.e., the municipality (“Gemeinde”) in which the establishment with the vacancy is located. We

follow the argument of Pfister et al. (2016), who assume that the effect of the tertiary education

expansion is limited to the firms located close to a UAS. To estimate the effect of UASs on

regional patenting activities, these authors exploit the low mobility of individuals living in

Switzerland and the temporal and regional variation of the staggered rollout of UAS campuses.

In line with Pfister et al. (2016), we assume that all new job positions opened within a radius of

25 kilometers around a UAS campus are affected by the intervention (treatment group).8 Job

positions opened by firms without a newly established UAS nearby constitute the control group.

We conduct a subsample analysis to check whether other unobservable factors in our

treatment group drive our overall results. As the establishment of UAS campuses teaching

STEM affects vocational occupations with a link to STEM fields, we should only find an effect

for this subset of occupations but not for occupations that are not STEM-related (e.g., nursing

or social work). However, if unobservable factors are behind our findings, we would likely also

find effects for all other occupations. To test for such a presence of unobservable factors, we

employ a placebo test for the group of unaffected occupations.

8 In Switzerland almost 90 percent of all employees commute less than 25 kilometers (roughly 15 miles) from home to work (for an extensive

discussion, see Pfister et al. (2016)).

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We use the SBN2000 classification to separate STEM occupations (which have a link

to STEM subjects) from nonSTEM occupations. For the most conservative selection of

occupations with a STEM link, we pick technical and IT occupations at the one-digit level of

the SBN2000 classification (SBN2000: 3) and include production workers employed in the

industry and trade sectors (SBN2000: 2).9

[Table 2]

The SJMM also includes information on economic characteristics that potentially affect

the job vacancies. First, we use industry dummies based on the NOGA classification of the

Swiss Federal Statistical Office (2008). The classification breaks down the economy into 10

industry categories. Second, we use information on the advertising channel. Before 2001, the

SJMM sample contained job ads from newspapers. From 2001, the sample also includes job

vacancies that firms announce on their business websites and, from 2006, also captures those

vacancies that firms post on online job boards. According to Sacchi (2014), the absence of

online channels before 2001 does not lower the representativeness of the sample, because before

2001, the majority of job ads posted online still also appeared in newspapers, and the sampling

process therefore captured these ads. Table 2 provides an overview on the descriptive statistics

of all variables that we use in our main regression sample of job ads targeting vocationally

trained workers.

9 In contrast to the International Standard Classification of Occupations (ISCO), the SBN2000 classification does not separate occupations

by educational levels. Instead, the SBN2000 combines academic and vocational occupations to form the same one-digit class, thereby enabling us to map vocational occupations to their corresponding academic occupations. As the vocational subsample of STEM occupations shares one-digit SBN2000 classes with UAS graduates in STEM fields, we ensure that this subsample has a link with UAS graduates. The remaining subsample consists of the all the other one-digit classes, which have no link to UAS graduates in STEM, and represents the unaffected group that we use for our placebo test.

16

III. Empirical Strategy: First- and Second-Order Effect

To investigate the tasks for which firms plan to hire UAS graduates (the first-order

effect), we examine the job ads targeting UAS graduates. However, as UAS graduates did not

exist before the establishment of UASs and neither does another comparable type of worker,

we cannot directly estimate the effect of attending and graduating from a UAS. We therefore

draw on our two upskilling measures, which utilize the rich information contained in job ads,

and provide descriptive evidence on whether firms actually seek UAS graduates for jobs with

a high-skilled task profile, involving R&D, as the first-order effect.

To estimate the effect of the establishment of UAS on vocationally trained workers

without a UAS degree (the second-order effect), we exploit the temporal and regional variation

of the establishment of UAS campuses and apply the difference-in-differences (DiD) method:10

(1) "#$%&''()*+ = - + /012345264() + 78231) + 90:( + ;<() + =()

Our main regression specification is shown in equation (1), with the dependent variable

constituting one of our two measures for upskilling. We first use our variable “R&D as main

task” and estimate a linear probability model. In a second step, we employ our variable

“similarity with job descriptions of academic graduates” and estimate a linear regression model.

Given that students need a minimum of three years to graduate from a bachelor’s

program at a UAS, we assume a lag of three years for the treatment effect and use a

corresponding time lag in our outcome variables for estimating the effects of the establishment

of UASs. Information on the main task in the SJMM is only available from 1995 onward;

therefore, we use 1995 as our first year of observation. To avoid contamination issues that build

10 Pfister et al. (2016) apply a similar diff-in-diff approach to identify innovation effects after the establishment of Universities of Applied

Sciences in Switzerland. Che and Zhang (2018) exploit a natural experiment in the late 1990s in China and apply difference-in-differences estimations to analyze the effect of higher education expansion on productivity.

17

up over the long run as UAS graduates may relocate from control to the treatment group11, we

end our observation period in 2010. Restricting our sample from 1995 to 2010, in total, we

observe 5 years before (pretreatment) and 11 years after the first UAS graduates entered the

labor market (posttreatment).

Subscript i indicates our level of observation, the individual job ad, and t indicates the

year in which the job ad appears. 0:i is a dummy variable indicating that the job ad (and the

firm announcing the vacancy) belongs to the treatment group, while 8231t comprises year

dummies that capture the common time trend of the treatment and control group.

012345264()represents the interaction of belonging to the treatment group and actually

receiving treatment, constituting our main variable of interest. Its coefficient is the difference-

in-difference estimator of the effect of the establishment of UASs on our two upskilling

measures. The subscripts &4 capture the time-variant feature of the staggered rollout of UAS

campuses, with some campuses opening their doors earlier and some later.

<()represents the set of control variables: First, we control for the advertisement

channel. As the SJMM data started including job ads on business websites in 2001 and from

online job boards in 2006, these job ads may be different from the typical newspaper ads.

Second, the industry structure might have undergone some changes on the local level and

simultaneously affected tasks. Therefore, we also control for industry affiliation according to

the NOGA classification.

The main identifying assumption in equation (1) is that there are no systematic

differences in the underlying trends of the upskilling measures among regions receiving a

campus and those that do not.

11 Swiss workers have a low geographic mobility. Using graduate survey data, Pfister et al. (2017) show that 75 percent of all graduates

still live in the same treatment region five years after graduation. However, with information lacking on the long-run mobility, we limit our sample to 2010 to obtain a conservative selection of our sample.

18

[Table 3]

We test the common trend assumption (before the intervention) for each of our two

upskilling measures in a regression framework, by interacting belonging to the treatment group

with year dummies. Columns (1) and (2) of Table 3 show no difference in pretreatment trends

(variable Year * TG) between the treatment and control groups.12

IV. Main Results: The Effects of the Establishment of UASs

A. First-Order Effect: The Jobs of UAS Graduates

We descriptively analyze whether the establishment of UAS led to the creation of jobs

that target UAS graduates and make use of their newly acquired applied research skills (first-

order effect). We use the two previously introduced upskilling measures: (1) Jobs having R&D

as the main task and (2) similarities of job descriptions with our academic benchmark. Table 4

shows the five most frequent main tasks that firms mention when searching for UAS graduates,

with R&D being the fourth most frequent main task. The top-five main tasks of UAS graduates

represent a task profile typically associated with high-skilled jobs (involving R&D, IT and

people management) and usually accompanied by a position in middle management.

[Table 4]

Using our second measure, the similarity with the job descriptions of academic

graduates, we also find evidence that firms do use the newly acquired tertiary-level skills of

12 In addition to the interaction of TGi and year dummies, we also test whether interacting TGi with a linear yearly trend indicates a violation

of the common trend assumption. However, we find that the interaction term does not differ from zero.

19

UAS graduates. Compared to VET graduates which have job description similarity with

academic graduates of 0.3, UAS graduates are more similar in their job descriptions to academic

graduates (job description similarity: 0.37). Graduating from a UAS thus opens up a new range

of jobs, different from the middle-skilled jobs for vocationally trained workers and close (albeit

not identical) to those of academic graduates. Given these findings on R&D as the main task

and on the similarity with the job descriptions for academic graduates, we argue that workers

do upskill by graduating from a UAS.

B. Second-Order Effect: R&D as the Main Task for Vocationally Trained Workers

DiD Estimates.—Table 5 provides the estimated effects of the establishment of UASs

on our first outcome variable, R&D as the main task, for vocationally trained workers. The

columns (1) and (2) report the results for the full sample of apprenticeship occupations, with a

statistically significant treatment effect of an about 1 percentage point (ppt) increase in the

probability of a job ad listing R&D as the main task. As the SJMM data break down main tasks

into 21 categories, with each category representing only a small percentage of job ads (in our

treatment group, 2 percent of the job ads belong to the R&D category), the effect of a 1 ppt

increase is substantial. This effect remains robust when controlling for the industry and

advertisement channel in column (2).13

[Table 5]

To check whether unobservable factors in our treatment group drive the previous results,

we conduct a placebo test by splitting our sample between STEM occupations and all other

13 The industry in either the control or treatment group may have provided a more favorable ground for the implementation of new

technologies (e.g., during the New Economy phase in the late 1990s and early 2000s). However, we find no evidence for this hypothesis, because when controlling for industry, we find only small changes in the effect size and significance of the treatment effect.

20

occupations (placebo group). We expect a zero effect for the placebo group because this group

has no link to the work of UAS graduates working in STEM fields. However, if unobservable

factors are behind our findings, one would also find effects for the placebo. As shown in

columns (5) and (6), we find a zero effect for the placebo and thus argue that it is unlikely that

other unobservable factors in our treatment group drive upskilling.14

Validation.—One potential concern with our analysis is that the establishment of UASs

led to the downsizing of apprenticeship jobs, with only those with R&D as the main task

surviving and those with less skilled tasks disappearing. In this case, the upskilling effect would

be an artifact of a shrinking number of apprenticeship jobs. Therefore, we additionally examine

whether the absolute number of job vacancies for vocationally trained workers changes. We

find that after the establishment of UASs, the absolute number of jobs for vocationally trained

workers remains unchanged in our treatment group.15 This result shows that the treatment effect

is not a consequence of a mere downsizing of jobs without R&D, but rather the addition of new

jobs with R&D as the main task.16

In summary, we find that—with a new UAS campus nearby—firms more often seek

both vocationally trained workers and UAS graduates for jobs with R&D as the main task. Our

findings thus demonstrate a positive first-order effect on the job opportunities of workers

graduating from the new educational institutions, and a positive second-order effect on the

workers not graduating from these institutions but having an education overlapping with the

new graduates. For educational expansions such as the establishment of UASs in Switzerland

14 The size of the treatment effects for STEM occupations and the placebo groups suggest that only occupations with links to the work of

UAS graduates are actually affected by the intervention and drive our overall results. Our treated subsample of STEM occupations makes up 30 percent of the total sample. In a local average treatment effect (LATE) framework, multiplying this subpopulation share with the treatment effect of the subsample should correspond to the treatment effect in the full sample. Indeed, we find that the effect in the subsample almost fully explains the effect in the full sample. This finding shows that placebo and treated subsample are correctly defined and other unobservable factors are unlikely to drive our main results.

15 We performed DiD estimations with the absolute number of job vacancies (or vocationally trained workers) as the dependent variable and find an insignificant treatment effect by the establishment of UASs.

16 We find no evidence that this addition of R&D jobs for vocationally trained workers comes along with a subtraction of such jobs from academic graduates: we find no indication for a decrease in the relative frequency or absolute number of job ads for academic graduates listing R&D as the main task.

21

(which overlap with other educational levels), the evidence on our first upskilling measure

demonstrates a clear upskilling effect across educational groups rather than a crowding out of

less-educated workers from a static number of R&D jobs.

C. Second-Order Effect: The Similarity of VET Job Descriptions with the Job

Descriptions of Academic Graduates

Table 6 reports the results from DiD estimations for our second upskilling measure: the

similarity between VET and academic job descriptions. Columns (1) and (2) show that the

similarity with the job descriptions for academic graduates sample increases by 0.01, which

corresponds to a relative increase of 5 percent in similarity (from 1995 to 2000, i.e., before the

intervention, the mean similarity between VET and academic job descriptions was 0.25; after

the intervention, the mean similarity increased to 0.30). The job content for vocationally trained

workers in affected regions thus became 5 percent more similar to those of academic graduates,

reflecting an aggregate upskilling effect for those workers without the new tertiary degree.

Columns (3) to (6) demonstrate that the upskilling takes place for the subsample of STEM

occupations, with a pronounced increase in similarity of 0.03 (a relative increase of 12 percent),

while for the placebo, the task content does not become more similar, with an insignificant

effect close to zero.

[Table 6]

This finding demonstrates that upskilling also occurs in a broader sense, i.e., the jobs of

vocationally trained workers not only contain R&D more often as the main task but also the job

descriptions as a whole become increasingly similar to the job descriptions of academic

graduates (the benchmark).

22

Based on the combined results from our first and second upskilling measure, we argue

that the establishment of UASs—like a rising tide that lifts all boats—elevates the job content

of workers with the new UAS degree and of those without the new UAS degree but with an

educational background overlapping with that of the newly available UAS graduates. The new

UAS graduates build a bridge between academic graduates and vocationally trained workers,

thereby allowing for better cooperation and communication at the workplace and boosting total

R&D activity.17

V. External Validity of the Upskilling Results

This section briefly describes the sensitivity tests that we conducted to test the external

validity of our upskilling findings. Relying on two alternative data sources, we estimate

employment and wage effects as alternative upskilling outcomes previously used in the

literature (e.g., Böckerman et al., 2009; Frenette, 2009).

Employment.—The potential concerns with our previous analysis are that firms

announce job vacancies, but these vacancies, particularly those containing skilled tasks, may

not be filled. Furthermore, changes in job vacancies may reflect mostly turnover instead of

actual changes in the workforce composition. Analyzing job vacancies may therefore overstate

the true upskilling effects. We follow the work by Lehnert et al. (2017) on the effects of UASs

on R&D personnel and examine whether firms actually employ more vocationally trained

workers in R&D tasks. Using employer survey data from the Swiss Earnings Structure Survey

(SESS), we find that the share of vocationally trained workers in R&D (measured by total VET

employment in the firm) rose by 0.3 ppts in the manufacturing sector of municipalities affected

by the establishment of UASs (the corresponding results can be found under Table A2 in the

17 Our findings of a general upskilling are consistent with findings of increased innovation activities in regions with newly established

UASs. Pfister et al. (2016) find a substantial increase in patenting activities in regions with a newly established UAS in comparison to regions without a UAS.

23

appendix). These findings demonstrate that firms actually fill R&D job positions more often

with vocationally trained workers.

Wages.—Higher wages reflect that workers perform more skilled and thus better-paid

tasks (Acemoglu and Autor, 2011; Autor, 2013). Upskilling for vocationally trained workers in

affected regions should therefore lead to an increase in wages. Indeed, we find that wages

increased by 2.9 percent for workers in STEM occupations in the treated municipalities after

the establishment of UASs (based on data from the Swiss Labor Force Survey, see Table A4 in

the appendix for details).

VI. Potential Mechanism

What is the mechanism behind the upskilling of vocationally trained workers, even

when they remain at their original educational level and do not obtain the new tertiary degree?

We argue that UAS graduates acting as bridge builders between the vocationally trained

workers and academic graduates explain our second-order effect.

Before the establishment of UASs, academically trained university graduates (with their

academic research skills) and vocationally trained workers (with their sound professional and

occupational skills) were far apart from one another in their competencies, professional

knowledge, socialization and occupational language.18 The average distance in the similarity of

job descriptions between these two groups was 0.25. This gap made it difficult for firms to

cross-fertilize the competencies of vocationally trained workers with those of academic

graduates and fully utilize the professional skills of vocationally trained workers in R&D.

The availability of UAS graduates allows for bridging this gap within firms. Drawing

on organizational models involving teams (Garicano and Rossi-Hansberg, 2006; Lazear, 1999;

18 According to studies in organizational psychology, the dissimilarity of team members decreases task performance (Gevers and A. G.

Peeters, 2009), while a shared understanding of tasks boosts performance (Mathieu et al., 2000; Mohammed et al., 2010; Mohammed and Dumville, 2001; Stout et al., 1999).

24

McCann et al., 2015), we argue that UAS graduates are particularly good at the role of bridge

builders because their educational and professional background overlaps with both vocationally

trained workers and academically trained university graduates. Studying at a UAS (as opposed

to studying at an academic university) requires the students to have graduated from a dual

apprenticeship program before advancing to the tertiary level. Therefore, UAS graduates

possess the same educational and professional foundation as vocationally trained workers

staying at their original educational level. During their studies, UAS graduates receive a tertiary

education on top of their vocational foundation and learn to work academically with a focus on

applied research, thereby acquiring competencies that substantially overlap with those of

academically trained graduates from traditional universities. These substantial overlaps with

both academic graduates and vocationally trained workers enable UAS graduates to facilitate

cooperation and communication between the two groups, increasing the overall efficiency of

educationally diverse R&D teams. Firms react to this increase in efficiency by increasing their

R&D efforts and consequently creating additional R&D jobs, not only for UAS graduates but

also for vocationally trained workers.

[Table 7]

Consistent with the role of UAS graduates as bridge builders, Panel A in Table 7 shows

that firms seek UAS graduates for their social and communication skills. Firms require more of

these skills from UAS graduates than from other educational groups: Job ads for UAS graduates

list more words for soft skills, contain a communication requirement more often and very

frequently specify organizing and management as the main task, compared to either

vocationally trained workers or academic graduates.19

19 To control for an underlying time trend towards higher soft skills requirements, we test for differences between educational groups in a

regression framework and find significantly higher requirements for UAS graduates.

25

With UAS graduates filling the role of bridge builders, we expect them not only to be

in high demand for communication tasks (as just shown) but also to affect the communication

requirements for other educational groups. These groups may now concentrate more on their

core competencies, for which firms may plan to hire them even without strong communication

skills. To examine the effects on communication requirements, we estimate changes in job ads

stating the requirement of “organizing and management” as the main task for vocationally

trained workers and academic graduates. Panel B of Table 7 shows a negative treatment effect

on the probability of job ads requiring organizing and management by 3.6 ppts for vocationally

trained workers and by 12 ppts for academic graduates.20 These results are in line with our

explanation of UAS graduates acting as bridge builders: The presence of such bridge builders

lowers the communication and management requirements for other educational groups.

VII. Conclusion

The last three decades have seen increasing numbers of students attending university

and obtaining tertiary education degrees. While some researchers highlight the potential

productivity- and innovation-enhancing effect of such a tertiary education expansion, others

voice the concern that this trend leaves behind those that do not hold a tertiary degree.

In this paper, we demonstrate that the establishment of UASs—as a large-scale

expansion of the Swiss tertiary education system—not only enabled its graduates to advance

into skilled tasks (first-order effect) but also led to upskilling among the workers who did not

acquire the new educational degree but overlap in their educational background with the new

graduates (second-order effect). We find that firms seek the graduates of these new tertiary

education institutions for tasks typical to the high-skilled jobs of academic graduates, with R&D

being among the top-five most frequently mentioned main tasks. We show that the relative

20 To avoid the issue of yearly small cell sizes in our control group, we use a simple DiD setup for academic graduates.

26

frequency of R&D as the main task also rose for vocationally trained workers (i.e., those

without a tertiary degree) by 1 ppt after the establishment of UASs. Additionally, we find that

the similarity between job descriptions of vocationally trained workers with those of academic

graduates increased by 5 percent. In summary, the UASs—like a rising tide that lifts all boats—

elevate the tasks of both workers with and without the new tertiary education degree.

The large overlap of this new type of tertiary education with the two traditional

educational pathways, vocational training and academic education, likely plays an important

role for this key results. We therefore argue that a tertiary education expansion with large

overlaps between educational pathways, as implemented in the Swiss education system,

benefits workers obtaining the new tertiary degree but is not detrimental for those who remain

at their original educational level and do not hold a tertiary degree.

27

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33

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34

Tables & Figures21

Table 1—Activities in R&D as the Main Task

Main task: Analyzing, Researching and Monitoring

Activities

Researching

Analyzing

Measuring

Recording

Controlling

Monitoring

Inspecting

Testing

Note: List of all subcategorical activities included in the main task category “analyzing, researching and monitoring” (source: Swiss Job Market Monitor).

21 As recommended by the SJMM, for all our calculations we account for the complex survey design behind the data set. The SJMM employs

a stratification of newspaper media depending on region and circulation, website media depending on sectors and size of the firm, and job portal media (exhaustive sampling) depending on job categories. Selected advertising media from these strata serve as primary sampling units (PSU). We take the specifics of the PSUs into account and weight observations according to the probability of choosing the advertisement medium in which the job ad was published and selecting this job ad within the medium (two-stage cluster sampling). We use a Taylor-linearized variance estimation, also taking the complex survey design into account for the calculation of standard errors.

35

Table 2—Summary Statistics

N mean Panel A. Upskilling measures R&D as main task 7,671 0.02 Similarity with job descriptions of academic graduates22

7,353 0.30

Panel B. DiD setup Treatment group 7,671 0.85 Treatment 7,671 0.63 Panel C. Control variables Industry Agriculture & private households 7,671 0.01 Chemical, food, textile, timber, print & stone industry

7,671 0.10

Metal, machine, electrical & watchmaking industry

7,671 0.15

Construction 7,671 0.09 Commerce & transportation 7,671 0.20 Hospitality, entertainment & personal services

7,671 0.08

Financial services 7,671 0.09 Real estate, IT & corporate services 7,671 0.10 Education, research, healthcare, welfare & lobby

7,671 0.13

Public administration and services 7,671 0.04 Channels Press 7,671 0.33 Website 7,671 0.52 Jobportal 7,671 0.16

Note: Authors’ calculations with data from the Swiss Job Market Monitor. The summary statistics are reported for job ads targeting vocationally trained workers from 1995 to 2010.23

22 The textzone algorithm of Gnehm (2018) has a varying accuracy for correctly identifying the eight textzones. The algorithm detects our

textzone of interest—the job description—with a high accuracy of about 90 percent. However, a small number of job ads have too short job descriptions to be effectively captured by the textzone algorithm and have the respective text part wrongly assigned to another textzone (and not to the “job description”). For a conservative approach, we treat the job description in such a case as missing (because we have no word output from the algorithm) which leads to a lower observation count for the similarity with job descriptions of academic graduates.

36

Table 3—Common Trend Assumption

Dependent variable: R&D as Main Task Similarity with Job Descriptions of Academic Graduates

(1) (2)

TGj 1.101*** -0.007*

(0.402) (0.004)

1996* TGj -2.454 -0.022

(2.194) (0.020)

1997* TGj -0.142 -0.002

(0.844) (0.013)

1998* TGj -2.417 0.002

(2.818) (0.012)

1999* TGj 0.409 0.011

(0.959) (0.009)

Constant 1.101*** 0.306***

(0.402) (0.003)

Observations 1,002 945

R-squared 0.001 0.037

Notes: Authors’ calculations with data from the Swiss Job Market Monitor. Standard errors according to the complex survey design are reported in parentheses. Coefficients, standard errors, and sample means of the dep. var. “R&D as main task” are multiplied by 100 to represent percentage point changes; * p<0.10, ** p<0.05, *** p<0.01, respectively. P-values of the joint Wald-test for the interaction between the year dummies and the variable TG equal 0.76 for R&D as main task and 0.28 for the similarity with job descriptions of academic graduates.

37

Table 4—Top 5 Main Tasks of UAS graduates

UAS graduates Relative frequency

1. Organizing & Management 25.30

2. Planning, Engineering & Designing 18.62

3. Programming & IT 16.44

4. Analyzing, Researching & Monitoring 10.36

5. Educating, Teaching & Advising 9.38

Note: Authors’ calculations with data from the Swiss Job Market Monitor. The summary statistics are reported for job ads targeting UAS graduates from 2000 to 2010. Relative frequencies are reported in percentage points.

38

Table 5—The Effects of the Establishment of UASs on the Frequency of R&D as the Main Task

Dependent variable R&D as Main Task

Full sample Subsample: STEM

occupations Placebo: NonSTEM

occupations (1) (2) (3) (4) (5) (6)

Treatmentjt

0.963*** 0.880** 3.807*** 3.120*** -0.0695 -0.159

(0.346) (0.375) (0.915) (0.923) (0.305) (0.313)

TGj

0.226 0.232 -0.120 0.441 0.261 0.233

(0.404) (0.425) (1.008) (1.241) (0.335) (0.334)

Constant

0.606 2.072 5.418 7.312* -0.199 -0.688**

(0.690) (1.555) (3.935) (4.256) (0.253) (0.338)

Years

YES YES YES YES YES YES

Channel

NO YES NO YES NO YES

Industry

NO YES NO YES NO YES

Observations

7,671 7,671 2,177 2,177 5,494 5,494

R-squared 0.005 0.037 0.020 0.087 0.004 0.009

Notes: Authors’ calculations with data from the Swiss Job Market Monitor. Standard errors according to the complex survey design are reported in parentheses. Coefficients, standard errors, and sample means of the dep. var. are multiplied by 100 to represent percentage point changes. * p<0.10, ** p<0.05, *** p<0.01, respectively.

39

Table 6—The Effects of the Establishment of UASs on the Similarity with the Job Description of

Academic Graduates

Dependent variable Similarity with Job Descriptions of Academic Graduates

Full sample Subsample: STEM

occupations Placebo: Non-STEM

occupations (1) (2) (3) (4) (5) (6)

Treatmentjt

0.013*** 0.013*** 0.030*** 0.028*** 0.004 0.005

(0.005) (0.004) (0.008) (0.007) (0.005) (0.005)

TGj

-0.018*** -0.013*** -0.020** -0.017* -0.015*** -0.010**

(0.005) (0.004) (0.010) (0.010) (0.005) (0.004)

Constant

0.273*** 0.253*** 0.266*** 0.246*** 0.292*** 0.297***

(0.008) (0.015) (0.014) (0.016) (0.006) (0.016)

Years

YES YES YES YES YES YES

Channel

NO YES NO YES NO YES

Industry

NO YES NO YES NO YES

Observations

7,353 7,353 2,073 2,073 5,280 5,280

R-squared 0.087 0.109 0.125 0.141 0.086 0.120

Notes: Authors’ calculations with data from the Swiss Job Market Monitor. Standard errors according to the complex survey design are reported in parentheses. * p<0.10, ** p<0.05, *** p<0.01, respectively.

40

Table 7—The Mechanism Behind the Upskilling Effects of the Establishment of UASs

Panel A. Soft skill requirements in job ads

Vocationally trained workers

(N=7,671)

UAS graduates (N=1,975)

Academic graduates (N = 1,342)

Mean

SD Mean SD Mean SD

Number of soft skill keywords

7.436 0.276 9.809 0.288 8.947 0.431

Communication requirements

0.286 0.013 0.477 0.019 0.435 0.026

Organizing & management as the main task

0.055 0.004 0.253 0.016 0.192 0.017

Panel B. DiD estimation of organizing and management requirements for vocationally trained workers and academic graduates

Vocationally trained workers

Dependent Variable:

Organizing & Management

Treatmentjt -3.558* -3.296* (2.051) (1.973)

TGj 4.865*** 4.865*** (1.820) (1.820)

Constant 4.595* 4.008 (2.530) (2.696)

Years YES YES

Channel NO YES

Industry NO YES

Observations 7,671 7,671

R2 0.013 0.016

Academic Graduates

Dependent Variable:

Organizing & Management

TGj 20.357*** (4.895)

post2000 15.083*** (4.901)

post2000* TGj -12.201* (6.288)

Constant 8.182 (6.406)

Channel YES

Observations 1,342

R2 0.025

Notes: Authors’ calculations with data from the Swiss Job Market Monitor. Standard errors according to the complex survey design are reported in parentheses. Coefficients, standard errors, and sample means of the dep. var. organizing and management are multiplied by 100 to represent percentage point changes. * p<0.10, ** p<0.05, *** p<0.01, respectively.

41

Figure 1. Total Number of Job Ads with R&D as the Main Task in Regions with and without a UAS Campus (Notes: All educational groups are included. The initial level in both the regions with and without a UAS campus is set to 100, so that the curve of both groups are shifted to the same initial level. We consider those municipalities within a 25 kilometers radius around a UAS campus as being “a region with a campus”, which corresponds to our later definition of treatment and control groups. The vertical dashed line indicates the first UAS graduates entering the labor market in 2000 as the first campuses opened their doors in 1997 and a bachelor program takes 3 years to complete. The last campus was established in 2003.)

First UAS graduates enter the labor market market

42

Figure 2. The Similarity between Nonacademic and Academic Job Descriptions in Regions with and without a UAS Campus (Notes: Average similarity of all nonacademic job ads with the academic benchmark. The graph includes all educational groups except university graduates who constitute the benchmark. Including the benchmark would bias our results. The initial level in both the regions with and without a UAS campus is set to 100, so that the curve of both groups are shifted to the same initial level. We consider those municipalities within a 25 kilometers radius around a UAS campus as “a region with a campus”, which corresponds to our later definition of treatment and control groups. The vertical dashed line indicates the first UAS graduates entering the labor market in 2000 as the first campuses opened their doors in 1997 and a bachelor program takes 3 years to complete. The last campus was established in 2003.)

First UAS graduates enter the labor market

43

Appendix

To validate our empirical findings based on the SJMM, we estimate our difference-in-

differences model using two alternative outcome variables from different data sources.

Employment.—Our first alternative outcome variable aims at measuring whether firms

more likely assigned vocationally trained workers to R&D tasks after the tertiary education

expansion.24 As such a measure requires firm-level data, we use the largest representative

employer survey in Switzerland (the Swiss Earnings Structure Survey SESS), a repeated

biennial cross section containing rich information on both the employer and the employee

level.25 We use information on the employees’ educational background to limit the sample to

firms employing vocationally trained workers. Information on the employees’ main

occupational activity allows us to identify whether these vocationally trained workers conduct

R&D.26 Calculating the relative share of vocationally trained workers conducting R&D in each

firm gives our first alternative outcome variable.27

To estimate whether firms more likely assign vocationally trained workers to R&D tasks

after the tertiary education expansion, we apply the differences-in-differences method from our

baseline specification and estimate equation (1). The outcome variable Upskillit+3 in equation

(1) shows the share of vocationally trained workers conducting R&D (relative to all

vocationally trained workers in the same firm) in year t+3 and firm i:

(1) "#$%&''()*+ = - + /012345264() + 78231) + 90:( + ;<() + =()

24 We thereby follow the empirical approach by Lehnert et al. (2017), who investigate whether the tertiary education expansion in

Switzerland, the establishment of UASs, led to an increase in the firms’ R&D personnel. Using the Swiss Earnings Structure Survey and calculating shares of R&D personnel (relative to total employment per firm), the authors perform difference-in-differences estimations and find a statistically and economically significant increase. In contrast to Lehnert et al. (2017), we limit our sample to vocationally-trained workers to examine whether firms increase the share of these workers employed in R&D.

25 Survey participation is mandatory; the database, therefore, contains more than 35,000 firms and public administrations, and more than 1.6 million employees.

26 The SESS contains 24 main occupational activities, of which we define—equivalent to the categories in the SJMM database—the categories “researching and developing” and “analyzing, programming, operating” as R&D tasks.

27 To consider potential bias by part-time workers, we follow Lehnert et al. (2017) and use full-time equivalents, i.e., we adjust each employee observation by its individual employment level.

44

The variable TGi indicates whether firm i is part of the treatment group. To define

whether a firm is treated by the tertiary education expansion, we use its geographic location.

The SESS provides us with information on the firm’s location at the level of “mobilité spatiale”

(MS) regions.28 As the number of these MS regions equals 106—and their aggregation level

therefore resides between the level of cantons (26 cantons) and the level of municipalities

(approximately 2,300)—we are not able to define the exact borders of the treated area.

Therefore, we follow Lehnert et al. (2017) and define the intensity, the dosage of the treatment,

using census data that inform about the distribution of the treated population in each MS region.

For each MS region, we add up the number of individuals living in the treated region and

calculate the share relative to the entire population in the in the MS region. This share shows

the dosage of the treatment for the MS region. We classify regions that are fully treated (100

percent) to the treatment group; regions that are not fully treated (99 percent or less) constitute

the control group, implying that we estimate a lower bound of the effect.

Our variable of interest, Treatmentit, is a dummy indicating whether firm i is affected

by the tertiary education expansion in year t and thus shows the effect of the expansion on the

relative share of vocationally trained workers who conduct R&D.29 A positive coefficient is an

indication for upskilling.

Yeart includes year dummies that show the time trend common to the treatment and the

control groups. The vector Xit includes controls for the sampling, thereby following Lehnert et

al. (2017).30 εjt is the error term.

Similar to our baseline specification, we estimate equation (1) using the full sample and

using two subsamples: the first subsample consists of firms that are (most likely) affected by

the tertiary education expansion and the second subsample contains firms that are assumed to

28 These MS regions are designed for regional-political or scientific purposes, constituting micro-regional areas that are characterized by

spatial homogeneity and that represent small labor markets (Schuler et al. 2005). For further information, see https://www.bfs.admin.ch/bfs/de/home/statistiken/raum-umwelt/nomenklaturen/msreg.assetdetail.415729.html

29 As an alternative measure, we specify the number of vocationally-trained workers conducting R&D relative to the total number of employees in each firm. Although smaller, the results remain unchanged.

30 These controls include canton fixed effects, dummies for firm size (three categories) and dummies for the industry sector (2-digit NOGA).

45

be unaffected by the expansion. As the SESS does not include information on the employees’

occupation, we use the firm’s industry sector to define potentially affected employees, i.e., firms

classified as “manufacturing of goods”; the remaining firms constitute the placebo group.

Table A1 shows that firms in the treatment and control groups have parallel trends in

the full sample and the two subsamples for manufacturing and nonmanufacturing firms. We

thus find no indication for a violation of the common trends assumption

Table A2 shows the results of equation (1). The first two columns show the results for

the full sample and indicate a small, but insignificant effect. Columns (3) to (4) report the results

for firms manufacturing goods, showing an effect that equals 0.31 ppts. The effect is statistically

significant at the five percent level and—given that the average share of vocationally trained

workers equals 1.8 percent in the sample of manufacturing firms—economically substantial.

Columns five to six show that the effect for the placebo subsample is nonexistent.

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Table A1—Common Trends in R&D Employment

Dependent Variable: Share of vocationally trained workers in R&D

Full Sample Manufacturing Sample

Nonmanufacturing Sample

(1) (2) (3) (4) (5) (6)

TGj 0.0070*** -0.0013 0.0018 -0.0021 0.0096*** -0.0012 (0.0019) (0.0020) (0.0028) (0.0028) (0.0025) (0.0026)

1996*TGj 0.0029 0.0030 0.0056 0.0056 0.0007 0.0023

(0.0032) (0.0030) (0.0042) (0.0040) (0.0041) (0.0038)

1998*TGj 0.0070** 0.0053* 0.0014 -0.0005 0.0075* 0.0073**

(0.0032) (0.0030) (0.0051) (0.0049) (0.0039) (0.0037)

2000*TGj 0.0019 0.0025 0.0028 0.0030 0.0002 0.0021 (0.0038) (0.0035) (0.0057) (0.0054) (0.0048) (0.0044) Constant 0.0108*** 0.0085*** 0.0138*** 0.0072* 0.0092*** 0.0094*** (0.0013) (0.0023) (0.0020) (0.0038) (0.0017) (0.0029) Controls NO YES NO YES NO YES

Observations 22,055 22,055 5,618 5,618 16,437 16,437 R2 0.0037 0.1304 0.0025 0.0874 0.0041 0.1378 Notes: Authors’ calculations, based on SESS. Robust standard errors are reported in parentheses; * statistically significant at the 0.1 level; ** at the 0.05 level; *** at the 0.01 level, respectively. Control variables at the level of stratification include canton fixed effects, dummies for firm size (first category involves firms with less than 20 employees, second category firms with 20 to 49 employees, third category firms with more than 50 employees) and dummies for the industry sector (two-digit NOGA).

47

Table A2—The Effects of the Establishment of UASs on R&D Employment

Dependent Variable: Share of vocationally trained workers in R&D

Full Sample Manufacturing Sample

Nonmanufacturing Sample

(1) (2) (3) (4) (5) (6)

Treatmentjt 0.0014 0.0006 0.0026* 0.0031** 0.0009 -0.0000 (0.0011) (0.0011) (0.0014) (0.0014) (0.0014) (0.0014) TGj 0.0099*** 0.0026** 0.0047*** -0.0000 0.0116*** 0.0035*** (0.0010) (0.0010) (0.0012) (0.0013) (0.0013) (0.0013) Constant 0.0090*** 0.0086*** 0.0120*** 0.0032* 0.0079*** 0.0110*** (0.0011) (0.0013) (0.0015) (0.0019) (0.0015) (0.0017) Years YES YES YES YES YES YES Controls NO YES NO YES NO YES Observations 151,197 151,197 41,947 41,947 109,250 109,250 R2 0.0056 0.1115 0.0024 0.0737 0.0067 0.1172 Notes: Authors’ calculations, based on SESS. Robust standard errors are reported in parentheses; * statistically significant at the 0.1 level; ** at the 0.05 level; *** at the 0.01 level, respectively. Control variables at the level of stratification include canton fixed effects, dummies for firm size (first category involves firms with less than 20 employees, second category firms with 20 to 49 employees, third category firms with more than 50 employees) and dummies for the industry sector (two-digit NOGA).

Wages.—We create a second outcome variable—i.e., wages—to measure the upskilling

effect of tertiary education expansion and, consequently, to validate our empirical findings

based on the SJMM data. To construct this wage variable, we use the Swiss Labor Force Survey

(SLFS), a representative survey from 1991 through 2008 that comprises 16 000 individuals per

year until 2001, and approximately 35 000 individuals per year since 2002. The survey is

particularly appropriate for our analysis because it contains information on each individual’s

educational background, labor market status, occupation, residence, and wage.

48

Our sample includes employed individuals of age 20 to 6531 who work full-time32, who

have Swiss citizenship33, and whose highest educational degree is an apprenticeship certificate.

To construct our alternative outcome variable, we use the natural logarithm of the yearly wages.

The individuals’ residence allows us to classify them to the treatment or the control groups. In

addition, the information on the individuals’ occupations (SBN2000) allows us to distinguish

among subsamples identical to our baseline procedure, i.e., one subsample comprising STEM

occupations, and a placebo subsample including all remaining occupations.

We again estimate equation (1) using the full sample and the two subsamples, reported

in Table A4.34 Column (1) shows the results for the full sample, yielding a slightly positive, but

insignificant effect. Column (2) displays the results for the STEM worker subsample, (most

likely) affected by the establishment of UASs. The coefficient of the variable Treatmentjt equals

2.9 percent and is statistically significant at the five percent level. The average yearly wage in

the affected subsample equals approximately 70 500 CHF ($70 900). The effect of the

establishment of UASs on wages, i.e., more than $2 000, is thus economically significant, too.

For the placebo subsample, displayed in column (3), the results show no wage effect. The

validity test with the wage outcome variable thus strongly suggests an upskilling effect of the

tertiary education expansion on middle-skilled workers.

31 We thus exclude all individuals who are retired—i.e., older than 65—or in education—i.e., younger than 20 years. 32 By using full-time equivalent wages and thereby considering individuals who work part-time, we find the same results as in the baseline

specification. 33 Individuals without Swiss citizenship are oversampled in the later observation period. However, the results remain unchanged if we

include them. 34 Analogous to Table A1, Table A3 shows the results for the common trends assumption for the outcome variable wages. We find no

indication for a violation of the parallel trends assumption. To control for regional characteristics estimating equation (1)—e.g., heterogeneity in taxation—we include cantonal fixed effects.

49

Table A3—Common Trend in Wages Dependent variable: ln(wages)

Full Sample Subsample: STEM occupations

Placebo: NonSTEM occupations

(1) (2) (3)

TGj 0.0302 0.0067 0.0396

(0.0320) (0.0516) (0.0402)

1992*TGj 0.0214 0.0022 0.0332

(0.0434) (0.0638) (0.0548)

1993* TGj 0.0346 0.0652 0.0219

(0.0443) (0.0620) (0.0583)

1994* TGj 0.0100 0.0227 0.0043

(0.0373) (0.0619) (0.0461)

1995* TGj 0.0142 0.0385 0.0039

(0.0370) (0.0560) (0.0489)

1996* TGj 0.0741* 0.0759 0.0761

(0.0388) (0.0641) (0.0500)

1997* TGj 0.0563 0.0398 0.0701

(0.0393) (0.0572) (0.0493)

1998* TGj 0.0208 0.0041 0.0340

(0.0367) (0.0604) (0.0442)

1999* TGj 0.0312 0.0051 0.0441

(0.0382) (0.0599) (0.0471)

2000* TGj 0.0210 0.0171 0.0238

(0.0404) (0.0568) (0.0509)

Constant 10.8592*** 10.9191*** 10.8339*** (0.0281) (0.0480) (0.0344)

Observations 22,905 6,567 16,338

R2 0.0213 0.0267 0.0209

Notes: Authors’ calculations, based on SLFS. Robust standard errors are reported in parentheses; * statistically significant at the 0.1 level; ** at the 0.05 level; *** at the 0.01 level, respectively.

50

Notes: Authors` calculations, based on SLFS. Robust standard errors are reported in parentheses; * statistically significant at the 0.1 level; ** at the 0.05 level; *** at the 0.01 level, respectively.

Table A4—The Effects of the Establishment of UASs on Wages

Dependent variable: ln(wages)

Full Sample Subsample: STEM occupations

Placebo: NonSTEM occupations

(1) (2) (3)

Treatmentjt 0.0124 0.0292** 0.0032

(0.0077) (0.0114) (0.0098)

TGj 0.0169** -0.0098 0.0283***

(0.0078) (0.0113) (0.0100)

Constant 10.8646*** 10.9254*** 10.8379***

(0.0135) (0.0203) (0.0172)

Years YES YES YES

Controls YES YES YES

Observations 49,209 14,255 34,954

R2 0.0480 0.0659 0.0455