The effect of the foundation of a university on the local economy / Author DI Sebastian Göttfert

JOHANNES KEPLER UNIVERSITY LINZ Altenberger Straße 69 4040 Linz, Austria jku.at Author DI Sebastian Göttfert Submission Department of Economics Thesis Supervisor Christoph Eder, PhD September 2020

THE EFFECT OF THE

FOUNDATION OF A

UNIVERSITY ON THE

LOCAL ECONOMY

Master’s Thesis

to confer the academic degree of

Master of Science

in the Master’s Program

Abstract

Besides being a place of wisdom, universities are also considered an important driver of the local economy. The literature highlights various positive effects of existing universities including increased R&D spending and the foundation of new companies due to spillover effects. This work contributes to the existing literature by estimating the effect of a foundation of a university on the local economy using a two-step approach. First, I identify municipalities similar to the ones where a university was established. Second, I estimate the effect of the university foundation on the local economy via census data using an event study approach. The results provide strong evidence that the foundation of a university has a significant positive effect on the number of people working in a municipality.

Table of contents

1. Introduction ... 7

2. Universities in Austria ... 9

3. Data ... 10

3.1. Universities in Austria ... 10

3.2. Austrian census data ... 13

3.3. University employees ... 14 4. Method ... 16 4.1. Matching ... 16 4.2. Event Study ... 18 5. Results ... 21 5.1. Matching ... 21 5.2. Event Study ... 24 5.3. Plausibility Check ... 28 6. Conclusion ... 30

List of figures

Figure 1: University locations in Austria ...10

Figure 2: Core idea of DiD ...19

Figure 3: People living in the municipality before university foundation ...22

Figure 4: People working in the municipality before university foundation ...23

Figure 5: Graduates living in the municipality before university foundation ...23

Figure 6: People living in the municipality before and after university foundation ...24

Figure 7: Graduates living in the municipality before and after university foundation ...25

List of tables

Table 1: Identified universities ...12

Table 2: Summary statistics of census data ...14

Table 3: Summary statistics of university employees ...15

Table 4: Matched pairs ...22

Table 5: Estimation results from event study ...27

1. Introduction

Since their foundation in the medieval ages universities have constantly played a major role in higher education and research. In the last decades, universities have also started to be recognised as an important factor on driving the local economy besides being a place of wisdom. Obviously, universities act as an employer for a considerable amount of researches and further auxiliary staff which creates a number of jobs. Furthermore, the attracted students have an additional impact on the local economy by the demand of various everyday goods and services which lead to “multiplier effects” (Harris, 1997, p. 608).

Another more indirect effect is based on the universities serving “as an innovative engine of economic development” (Florida et al., 2006, p. 3) by producing knowledge which can be either tacit or explicit (Nonaka & Takeuchi, 2007). Explicit knowledge in this context could be a published paper. This kind of knowledge produced by university researchers is valuable for companies but obviously in the internet age physical distance to the university may not play a role in this example as the paper would be available online from all over the world.

However, in the case of tacit knowledge – personal know-how that is difficult to transfer (Cavusgil et al. 2003) – physical distance is a key factor as it is not written down per definition. Audretsch, Lehmann and Warning (2004) assume that tacit knowledge may be transferred by “personal networks of academic and industrial researchers, participation in conferences and presentations, or fresh candidates” (p. 5) who are up to date with the latest academic knowledge. It is obvious that university spin-offs are usually located in close distance to the university mainly due to organisational reasons. However, the fact that innovative new firms seek to locate near universities conducting research in a similar sector in order to keep knowledge acquisition costs low (Audretsch et al. 2017) supports this hypothesis. Furthermore, even inter-industry knowledge spillovers may occur (Audretsch & Feldman 2004) e.g. in so-called innovation hubs or startup hubs where firms from different sectors meet each other or even work in the same office. Hence, universities are likely to be responsible for indirect job creation in near surrounding.

A literature exists on measuring the effect of an existing university on the local economy in various ways. Audretsch, Lehmann and Warning (2004) find out that high-technology firms tend to locate near universities by analysing firms listed on the Neuer Markt (Germany). Bleaney, Binks, Greenaway, Reed and Whynes (1992) and Harris (1997) both calculate the expenditures of a particular British university including the staff’s salary together with the expenditures of their students. García-Vega and Vicente-Chirivella (2020) show that innovativeness of Spanish firms increases by acquisition of R&D services from universities. Florida, Gates, Knudsen and Stolarick (2006) investigate the effect of universities on R&D spending per capita, brain drain regarding university graduates, and diverse measures for tolerance towards minorities in the US.

However, all of those studies focus on existing universities and the outcome variables on which the effect is estimated are either limited to expenditures or they hardly reflect the actual size of the local economy as a whole. To the author’s knowledge there exists no literature in which the author estimates the effect of the foundation of a university on the local economy using counterfactuals. The goal of this thesis is to fill this gap by comparing Austrian municipalities where a university

has been founded in the last decades with similar municipalities without a university. Austria has experienced a notable amount of university foundations in the last decades due to changes in law which provides ideal conditions for research towards this direction. The comparison is conducted in the form of an event study using census data aggregated on municipality level which shall reflect the size of the local economy. Using data on such an aggregation level may answer the question whether the impact of founding a university has a significant effect on the local economy in total or may only be visible in the narrowly defined outcome variables like they were used in the previous literature.

The remainder of this thesis is structured as follows: chapter 2 delivers background information on the history of universities in Austria explaining why so many of them have been founded in the last decades which allows to conduct a study with recent data. I provide descriptive statistics about the university and census data used as a measure for economic activity in chapter 3. In order to estimate the effect of a university foundation on the local economy I use a research design comprising two steps – matching similar municipalities and estimating the effect within an event study – each of which I explain in chapter 4. Chapter 5 presents the results of the matching between treated and non-treated municipalities as well as the estimated effect on the outcome variables from census data. I give some concluding remarks in the final chapter.

2. Universities in Austria

At the time of writing, Austria has more than 70 institutions of higher education according to the Austrian Federal Ministry of Education, Science and Research (BMBWF, 2020) including the oldest university in the German-speaking area, the Universität Wien which was founded in 1365 (Universität Wien, 2020). However, the majority of those universities was actually founded as late as in the last few decades. Before the year 1800, only six of these institutions existed (Wadsack & Kasparovsky, 2007). In the 19th century, eight new universities were founded as institutes for higher education, mostly in the field of technology and arts. From the end of the 19th century until the early 1960’s no university foundations took place.

Two laws fostered the foundation of new universities after the Second World War:  Fachhochschul-Studiengesetz (FHStG) from 1993

 Universitäts-Akkreditierungsgesetz (UniAkkG) from 1999

According to Allgemeines Hochschulgesetz the goal of Austrian universities is primarily to train scientists of the next generation hence there is an explicit focus on a scientific education. However, following international trends and claims from industries for more practice-oriented education the FHStG enabled the foundation of so-called universities of applied sciences in 1993. In contrast to universities the goal of those new institutions is to ensure a practice-oriented education at a university level according to FHStG and they are not allowed to award doctorates. Furthermore, the majority of the universities of applied sciences are not run by the state but by private organisations. The offered degree programs are usually in the field of business and technology which reflects the demand from the industry. As opposed to long-established universities which are mainly located in capital cities many of the universities of applied sciences are located in non-capital cities within industrial regions.

Before 1999, the state ran all universities except universities of applied sciences. The UniAkkG regulates the accreditation for institutions of higher education as private universities for those institutions that are not already covered by any other Austrian law. The degree programs offered by those private universities are not restricted to be focussing on either scientific or practice-oriented education and can be of any scientific or artistic discipline (Wadsack & Kasparovsky, 2007).

Finally, there is another type of institution which is considered a university today. The former academies of education (Pädagogische Akademien) gained status of universities with the Hochschulgesetz in 2005. However, this law did not lead to numerous foundations like the two mentioned beforehand but defined rules for converting the existing institutions.

3. Data

In order to investigate the potential dynamic treatment effect of university foundations on the local economy, I use data of university foundations in Austria as well as relevant Austrian census data for the last decades. Census data at the municipality level is available for the municipalities in which a university foundation took place (treatment group) and for those where no university exists until today (control group). Furthermore, employee data of the examined universities is used for a cross-check.

3.1. Universities in Austria

At the time of writing, there are 38 universities in Austria of which 16 are private universities and 22 are public universities according to the Austrian Federal Ministry of Education, Science and Research (BMBWF, 2020). The number of university colleges of education amounts to 14 and there are 21 universities of applied sciences some of which are located in more than one municipality (e.g. the FH OÖ Studienbetriebs GmbH possesses university campuses with different academic disciplines in Hagenberg, Linz, Steyr, and Wels). Figure 1 shows the locations of those institutions.

I assume that due to their field of research university colleges of education can be neglected as spillovers from education may play a minor role for the location choice of most new companies. The same applies to universities which have a focus on arts only as well as the special case of Theresianische Militärakademie which is dedicated to military leadership only.

The effect of a subsequent university foundation per municipality may be different from the effect of the first one hence I only consider the first foundation. In addition, by looking at the foundation dates one can observe that it is usually the first university providing the scientific orientation (e.g. business, STEM1_{) for which a notable spillover effect to the economy is assumed. Consequently,} I only investigate the first foundation of a university with academic disciplines of interest per municipality.

Unfortunately, the census data is limited in terms of time. In order to conduct a valuable investigation census data for the municipalities in the treatment group is needed before and after the university foundation to find similar municipality counterparts and to identify a treatment effect (see chapters 4 and 5). For this reason, only those municipalities with university foundations can be included in the treatment group where data is available at least one period before and after the university foundation - all other municipalities with universities have to be excluded (i.e. first university foundation after 2017 or before 1971). Table 1 lists all of the universities which fulfil those criteria and therefore their municipalities are included in the treatment group. Note that the municipality Krems appears two times for completeness but as the two universities have been founded in the same year the municipality Krems is only included once in the treatment group. All other municipalities which do not possess a university until today and for which census data is available are included in the pool of potential control group counterparts.

Municipality Foundation

University

Private Universität für

Gesundheitswissenschaften, Medizinische

Informatik und Technik

Hall in Tirol

2001

Universität für Weiterbildung Krems

Krems an der Donau

1994

Privatuniversität Schloss Seeburg

Seekirchen am Wallersee 2007

University of applied sciences

Fachhochschule Burgenland GmbH

Eisenstadt

1994

Fachhochschule Joanneum GmbH

Bad Gleichenberg

2001

Fachhochschule Joanneum GmbH

Kapfenberg

1995

Fachhochschule Kärnten

Feldkirchen in Kärnten

2002

Fachhochschule Kärnten

Spittal an der Drau

1995

Fachhochschule Kärnten

Villach

1996

Fachhochschule Kufstein Tirol Bildungs GmbH Kufstein

1997

Fachhochschule Salzburg

Kuchl

1995

Fachhochschule Salzburg

Puch bei Hallein

1995

Fachhochschule Sankt Pölten GmbH

Sankt Pölten

1994

Fachhochschule Vorarlberg GmbH

Dornbirn

1994

Fachhochschule Wiener Neustadt GmbH

Tulln an der Donau

2002

Fachhochschule Wiener Neustadt GmbH

Wiener Neustadt

1994

Fachhochschule Wiener Neustadt GmbH

Wieselburg

1999

FH OÖ Studienbetriebs GmbH

Hagenberg im Mühlkreis 1994

FH OÖ Studienbetriebs GmbH

Steyr

1995

FH OÖ Studienbetriebs GmbH

Wels

1994

IMC Fachhochschule Krems GmbH

Krems an der Donau

1994

3.2. Austrian census data

Statistics Austria has a publicly accessible database2 _{which provides various census data on} municipality level between 1951 and 2017 that I use to compare municipalities on. The question is: How to measure economic activity with census data? The number of companies may sound intuitive, but it is tricky to interpret though as a sole proprietorship does not represent the same amount of economic activity as a small and medium-sized company. In order to measure economic activity per municipality I have selected the following three variables of interest out of given census data:

 The number of people living in the municipality  The number of people working in the municipality

 The number of university graduates living in municipality

The first variable - people living in the municipality - is a common proxy for economic activity in the literature (Jedwab & Vollrath, 2015; Acemoglu et al., 2002; Peterson, 2017) especially for periods far back in the past. It is the only variable for which data is publicly available for the periods between 1951 until 2011 in ten-year intervals as well as for 2017 – the other two lack of the periods 1951 and 1961.

The second variable - people working in the municipality - is a more intuitive proxy and does not suffer as much from comparability issues as the variable companies. One could argue that there may exist subtle nuances between the sector in which the people are employed in the sense that a certain number of people working in agriculture would not reflect the same economic activity as the same number of people working in high-tech industry. Although not evenly distributed, there are usually several sectors per municipality for the communities of interest hence those nuances should become neglectable.

The third variable - university graduates living in municipality - is also expected to be positively correlated with economic activity (Glaeser, 2000; Wong & Yip, 1999; Beine et al., 2001).

Finally, the municipality status (one of statutory city, city, market town, or village) is provided for each of the municipalities in the dataset. Admittedly, the difference between the characteristics are subtle sometimes because the a statutory city distinguishes itself from an ordinary city only on administrative level and whether a municipality is a market town or a village has mainly historic reasons. Those subtle differences shall be neglected in the sense that municipality status is only used by the author to distinguish between cities (ordinary or statutory) and non-cities (market towns and villages).

Summary statistics for the three outcome variables are provided in Table 2 for year 1991 - the ultimate year with available census data before any of the observed universities has been founded. Note that due to changes in municipal territory during the observation period some of the Austrian municipalities cannot be used in this dataset and are hence excluded.

N Mean St. Dev. Min Max

People living in the municipality in 1991

Cities

185

13,065.71

27,442.69

526 237,810

Non-Cities 1957

1,784.38

1,403.73

50

18,484

All 2142

2,753.46

8,727.53

50

237,810

People working in the municipality in 1991

Cities

185

7,791.78

19,411.22

198 160,461

Non-Cities 1957

513.78

668.03

2

7,958

All 2142

1,139.56

6,067.04

2

160,461

Graduates living in the municipality in 1991

Cities

185

545.09

1,835.82

10

18,719

Non-Cities 1957

29.94

56.80

0

1,326

All 2142

74.19

558.45

0

18,719

Table 2: Summary statistics of census data

3.3. University employees

Data of universities is publicly available on a website hosted by the Austrian Federal Ministry of Education, Science and Research3_{. Besides data on students (e.g. number of entrants and} graduates per university), there is also data on the number of employees per university in case of universities in the narrow sense and per maintaining organisation for universities of applied sciences. Note that in contrast to the former ones the universities of applied sciences are mostly run by organisations which may have more than one location. For example, the Fachhochschule Kärnten runs four universities of applied sciences4 _{and the number of employees is only available} as a total of all of the four locations together.

3 _{https://unidata.gv.at/Pages/default.aspx}

4 _{The 4}th _{location - Klagenfurt - is not included in Table 1 because there has already been another university foundation}

in Klagenfurt which took place before the relevant observation period and due to the restrictions I discuss in section 3.1 the location is therefore excluded from the dataset. The situation is similar for other maintaining organisations with multiple locations.

The number of employees is given in two measures: headcount and full-time-equivalent; They are available for periods beginning with winter semester 2004. Table 3 summarises data from year 2011 which is the first available data that matches with census data. The ratio between the means of headcount and full-time-equivalent (about 2.4 for all) suggests a high number of part-time employment and marginal-employment which is obviously due to the nature of those institutions (e.g. external lecturers, tutors).

N Mean St. Dev. Min Max

Headcount

Universities of applied sciences

10

889.30

582,78

300

2029

Universities

3

598.00

708.36

79.00

1,405

All

13

822.08

595.55

79.00

2,029

Full-time-equivalent

Universities of applied sciences

10

399.12

285.55

125.50

871.50

Universities

3

156.00

197.89

10.80

381.40

All

13

343.02

281.15

10.80

871.50

4. Method

The aim of this thesis is to compare the outcome variables of a treatment group (i.e. the municipalities where universities have been founded during the observed periods) with those from a control group. The design is similar to other so-called “natural experiments” (Jones & Rice 2009, p. 5) seeking to examine the effect of a policy or a historic event on the local economy. Local in this case is assumed to be the municipality level, hence the unit of observation are municipalities like in Moretti (2019).

In order to estimate the effect of university foundations I use the so-called event study approach which is an extension of the widespread difference-in-differences approach. However, for an event study approach it is indispensable that the common trend assumption holds (Borusyak & Jaravel, 2017; Jones & Rice, 2009). Hence, besides identifying suitable covariates for the regression model one of the more troublesome challenges in this kind of research designs is to find a valuable control group that provides similar values for the pre-treatment variables so that the derived inference of the treatment on the outcome variable is robust. Thus, not all non-treated municipalities might represent a valuable candidate for the control group as the descriptive statistics from the previous chapter suggests (see Table 2). This issue is resolved by restricting the control group to matching counterparts.

Therefore, the research design consists of two steps:

1. Construct a control group with a similar trend before treatment as the one of the treatment group (matching)

2. Estimate the dynamic treatment effect based on the two groups (event study) I discuss each of the two steps in more detail in the following sections.

4.1. Matching

In order to conduct an empirical study, the usage of a suitable control group is absolutely crucial. As mentioned beforehand and discussed in the next section both the treatment group and the control group need to share a similar trend prior to treatment (Jones & Rice, 2009; Wing et al., 2018). With respect to the list of municipalities in which universities have been founded (see Table 1) one can conclude that the choice of the location is not perfectly random. In most cases, it is the capital of a state or the largest non-capital city in a state where a new university is founded. However, even smaller towns like Wieselburg or Kapfenberg provide certain characteristics which qualify them to become a university location. As a consequence, using all non-university-location municipalities as control group would not lead to meaningful results as the economical development of Austrian municipalities is diverse. A common solution to this problem is to restrict the potential pool of control units (i.e. all non-treated municipalities in Austria) to a subset by identifying municipalities which are similar to the ones among treatment group in terms of observed characteristics (O’Neill et al., 2016). By further restricting the approach to select only one single corresponding counterpart for each unit in the treatment group, pairs of treated and non-treated municipalities are formed (Ho et al., 2007; Jones & Rice, 2009).

The question is how to spot matching municipalities. In case the researcher has a sufficiently large dataset of control units it may be feasible to identify counterparts via so-called exact matching. Like the name of the method suggests, two units are considered a match if each of their observed variables are exactly the same. Exact matching may be possible with huge datasets especially in case all of the observed characteristics are discrete variables, but as stated by King and Nielsen (2016) this is rarely the case in real applications. Having to deal with limited data, a common and intuitive approach is to match with the closest control unit which is called nearest-neighbour matching (Jones & Rice, 2009). Now the question remains how to measure distance between units. King and Nielsen (2016) report two dominant distance measures: propensity score and Mahalanobis distance;

Initially proposed by Rosenbaum and Rubin (1983), the propensity score is defined as “the conditional probability of assignment to a treatment given a vector of covariates including the values of all treatment confounders” (Abadie & Imbens, 2016, p. 1). The idea behind this approach is that in order to find the true closest match one hast to include all covariates that affect treatment assignment or the dependent variable in the true model (Ho et al., 2007). However, supposing all necessary data is both identified and available – which is very unlikely in practice – one might still suffer from difficulties when trying to apply matching with high-dimensional data which is known as the “curse-of-dimensionality” (Bergemann et al., 2005, p. 8). To escape from this problem, a scalar value representing the probability of being treated based on the covariates – the propensity score – is calculated for each unit whereby similar vectors of covariates result in similar propensity scores. Those scalar values do not suffer from the curse of dimensionality and can therefore be used for matching.

Unfortunately, in order to use the propensity score matching it is necessary to possess data for all covariates affecting the treatment or the dependent variable which is almost impossible for the research question of this thesis. Furthermore, propensity score matching suffers from practical issues as extensively discussed by King & Nielsen (2016). For example, although similar covariates lead to similar propensity scores it is not excluded that for dissimilar covariates similar propensity scores are calculated too as pointed out by Abadie and Imbens (2016). Hence, matching on the propensity scores may produce a defective control group.

The Mahalanobis distance was introduced by statistician P. C. Mahalanobis (1936) as a measure for the distance between two points in a multidimensional space. Since its introduction it has been applied in a variety of fields (McLachlan, 1999; Aerts & Schmidt, 2008), often together with the k-nearest neighbour method (Yates et al., 2003; Sharif & Burn, 2007) notably in pattern recognition (De Maesschalck et al., 2000). Usually, when calculating the distance between two points in a multidimensional space one uses the so-called Euclidean distance.5 _{Although being quite intuitive} and easily applicable, this measure does neither account for covariances of the dimensions nor for their variances and the researcher must carefully choose the variables’ scale. The Mahalanobis distance on the other hand accounts for those issues by adding the inverse of the covariance

5 _{Straight-line distance between two points in a multidimensional space. The formula can be found in any advanced}

matrix to the formula (Little, 2013). This way, the original variables are transformed in uncorrelated, standardised variables from which the Euclidean distance is calculated (McLachlan, 1999).

𝑀𝑎ℎ𝑎𝑙𝑎𝑛𝑜𝑏𝑖𝑠𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒

= √(𝑋

− 𝑋

)

𝐶𝑜𝑣𝑎𝑟𝑖𝑎𝑛𝑐𝑒𝑀𝑎𝑡𝑟𝑖𝑥

_(𝑋

𝑖

− 𝑋

)

(1)

Although being a powerful technique when used carefully the author refrains from using the propensity score as a distance measure for matching and uses the Mahalanobis distance instead. This way, the likely covariance between the three variables is accounted for and the issue with scales is resolved. Nevertheless, it is still possible to account for covariates by pre-filter the potential control units with exact matching as a refinement like proposed by King & Nielsen (2016). Regardless of the chosen distance measure, matching can be conducted either with or without replacement. In this context, matching with replacement means that each unit of the control group is allowed to be matched multiple times hence two or more units of the treatment group could have the same counterpart (Jones & Rice, 2009). As the pool of control municipalities is quite scarce, matching with replacement is chosen by the author for this study to have a better chance for gaining a comparable control group.

Putting it altogether, I apply the matching in two steps for each unit of the treatment group: 1. Pre-filter the whole pool of potential control units by city/non-city status of the treated unit 2. Apply the nearest-neighbour matching with the Mahalanobis distance as a measure for

similarity on the three observed variables for all periods before university foundation

4.2. Event Study

In the field of economics, a widespread approach for estimating a treatment effect in natural experiments is the so-called Difference-in-Differences method (DiD) (Jones & Rice, 2009; Athey & Imbens, 2006). Using this method two groups are identified whereby one group (the treatment group) is affected by a shock or policy intervention (the treatment) and the other group (the control group) is not. The main identifying assumption of DiD is that without being exposed to the treatment the observed variable of the treatment group would have followed the same trend as the one of the control group. Note that the observed variable of the two groups does not need to be exactly equal before the treatment - only a parallel trend after treatment is assumed. If however there is a deviation from the trend in the treatment group’s outcome variable after the treatment (i.e. the difference between the two groups increases) then this deviation - the difference from the differences - represents the treatment effect. Figure 2 visualises the core idea of DiD.

Originally, the DiD approach only consists of two groups with two periods like in the frequently cited paper from Card and Krueger (1993) but as discussed by Wing, Simon, and Bello-Gomez (2018) the approach can be extended in several ways. One extension is the use of multiple periods which can serve two purposes: First, the additional periods before treatment can be used to strengthen the common trend assumption or detect anticipation of treatment (Borusyak & Jaravel, 2017). Second, the treatment effect is likely to be dependent on exposure to treatment so multiple periods after treatment allow to estimate a dynamic treatment. This is especially helpful in cases where the treatment effect does not occur in the short run but becomes visible in the long run which would not be detected with the basic two-periods approach.

Another extension of the basic DiD approach is to allow the units within the treatment group to receive the treatment at different points in time which can be the case if a policy is not adopted once nation-wide but successively in one state after another like in Bellou and Bhatt (2013). This can be accomplished by switching from calendar time to relative time to treatment using dummy variables as described in Borusyak and Jaravel (2017).

Extending the DiD approach by leads and lags of treatment as well as switching to relative time to treatment as described above has become quite popular in the recent literature and is called event study (Abraham & Sun, 2018). The regression model I use in this thesis to estimate the dynamic effect of university foundations is comparable to most common event study models discussed in the literature (Borusyak & Jaravel, 2017; Abraham & Sun, 2018; Callaway & Sant’Anna, 2018):

𝑌𝑖𝑡= ∑−2𝑘=−∞𝜇𝑘𝑈𝑛𝑖𝑃𝑟𝑒𝑖𝑡𝑘+ ∑∞𝑘=1𝜇𝑘𝑈𝑛𝑖𝑃𝑜𝑠𝑡𝑘𝑖𝑡+ 𝛼𝑖𝑀𝑢𝑛𝑖𝑐𝑖𝑝𝑎𝑙𝑖𝑡𝑦𝑖+ 𝛽𝑡𝑌𝑒𝑎𝑟𝑡+ 𝜆𝑖𝑡𝑌𝑒𝑎𝑟𝑡× 𝑃𝑎𝑖𝑟𝑖+ 𝜖𝑖𝑡

(2)

The regression model is controlling for unit (Municipality) and time (Year) fixed effects including leads (UniPostk_{) and lags (UniPre}k_{) of treatment. Besides those usual components of an event}

study model I add an interaction term of Year and Pair whereby the variable Pair represents the pair formed by matching. Note that the lag for the ultimate period before the university foundation (k = -1) is left out following a suggestion discussed by several authors like Schmidheiny and Siegloch (2019) and Borusyak and Jaravel (2017) in order to normalise the results to the period before treatment.

Finally, two adjustments have to be made in order to increase reliability of the regression results: I have to account for serial correlation because it is likely to be present in the given dataset. Following the suggestion given by Angrist and Pishke (2008), I cluster the standard errors on unit level instead of unit and time.

With respect to the arguably small data sample, the results would be biased using ordinary normal distribution. Instead, I use a t-distribution with degrees of freedom adjustment to calculate the results, following a solution to this issue proposed by several authors (Angrist & Pishke, 2008; MacCaffrey & Bell, 2006; Imbens & Kolesar, 2016). This way, confidence intervals will become wider.

5. Results

5.1. Matching

Most of the municipalities in the treatment group were treated prior to 2001. However, in some of them the university foundation took place after that year hence there is one more period for the matching to account for. Therefore, I apply the matching algorithm two times separately: one time for all municipalities treated prior to 2001 with all available periods until 1991 and a second time for all other municipalities in the treatment group with all available periods until 2001. Table 4 depicts the results of the matching algorithm (i.e. the matched pairs of treated and untreated municipalities). The control unit Bludenz is matched to times by the treated municipalities Villach and Spittal an der Drau. This outcome is in line with the chosen approach because I apply the matching algorithm with replacement. In other words, the matching for each treatment unit is conducted on the whole pool of potential control units. Nevertheless, I have to account for the double-match in the event study by adding Bludenz a second time to the control group so that the number of units in each group remains equal.

Pair Treatment Group Control Group

1

Villach

Bludenz

2

Wels

Kirchdorf an der Krems

3

Sankt Pölten

Leibnitz

4

Dornbirn

Hohenems

5

Steyr

Korneuburg

6

Wiener Neustadt

Attnang-Puchheim

7

Kapfenberg

Voitsberg

8

Krems an der Donau

Sankt Veit an der Glan

9

Spittal an der Drau

Bludenz

10

Kufstein

Vöcklabruck

11

Hall in Tirol

Schwechat

12

Eisenstadt

Weiz

13

Kuchl

Neuhofen an der Krems

15

Wieselburg

Horn

16

Bad Gleichenberg

Heiligenkreuz am Waasen

17

Hagenberg im Mühlkreis

Gunskirchen

18

Seekirchen am Wallersee

Bad Leonfelden

19

Tulln an der Donau

Saalfelden am Steinernen Meer

20

Feldkirchen in Kärnten

Oberwart

Table 4: Matched pairs

Figure 3 to 5 illustrate the average value of the logarithm of the three variables per group along with a 95 percent confidence interval. In order to facilitate a comparison I normalise the values to the period before treatment. At this point, it should be highlighted that due to the limited data set and having university foundations at two different points in time, the first period per variable only includes the three pairs with municipalities treated after 2001 which explains the wider confidence intervals compared to the subsequent periods.

In case of the variables people living and people working in municipality the control group and treatment group show similar trends without remarkable differences at least in the 3 periods prior to the university foundation. The assumption of similarity is strengthened by the fact that both groups’ mean lies within each other’s confidence interval.

In case of the last variable – graduates living in municipality – the two groups share a similar trend as well although there is a deviation in the first period. As for the other two variables the average values lie within each other’s confidence interval except for the first period. Due to the fact that the two groups’ means are pretty close to each other for the three periods directly before the university foundation the sole deviation about 40 years before the treatment may be neglectable.

For all three variables the absence of a significant difference can be further verified by looking at the pre-treatment coefficients of the regression results (see Table 5) where again the minor flaw for the third variable is reinforced. Altogether, the figures proof that the matching algorithm

Figure 5: Graduates living in the municipality before university foundation Figure 4: People working in the municipality before university foundation

succeeded hence the matching’s outcome provides sufficient framework conditions for the subsequent event study.

5.2. Event Study

Figures 6 to 8 represent an extension of the figures in the previous section and visualise the results for the event study’s regression. Table 5 shows the estimation results. Again, I normalize the values for the three variables on the period before the treatment and I plot them together with the 95 percent confidence interval. As all the available periods are used in this setting, the issue of limited data not only concerns the first pre-treatment period containing only three of the 20 pairs, but also the last after-treatment period containing only 17 of the pairs.

There is hardly any treatment effect visible for the variable people living in the municipality, at least no significant one with respect to the ultimate period. In case of the third variable - graduates living in municipality - the two groups are nearly identical after treatment with a tiny gap in the second after-treatment period hence there is no significant effect to be expected either.

However, it is quite contrary in the case of the remaining variable people working in municipality which is depicted in Figure 8. The groups’ mean value diverge notably and is located at the border or even outside each other’s confidence interval which suggests a highly significant treatment effect in all after-treatment periods.

The conclusion drawn from the graphs can again be verified with the regression results given in Table 5 which provides no significant effect for people living in municipality. The tiny gap in the second after-treatment period for graduates living in municipality is confirmed by the regression results. However, this effect is only significant at the ten percent level and disappears in the following period which suggests coincidence. Finally, the graphical results for variable people working in municipality are confirmed by the regression results: The effect of the first period can

Figure 8: People working in the municipality before and after university foundation Figure 7: Graduates living in the municipality before and after university foundation

be interpreted as a difference of seven percent higher growth for the treatment group compared to the control group significant at the five percent level. For both of the following periods the effect continues to grow by about seven and eight percent each which amounts to a difference of 14 and 22 percent for the second and third after-treatment period both significant at the one percent level.

(1) log(People living in municip.) (2) log(People working in municip.) (3) log(Graduates living in municip.) UniPre6 -0.090 (0.076) UniPre5 -0.011 (0.024) UniPre4 -0.019 -0.053 -0.260** (0.017) (0.085) (0.124) UniPre3 -0.004 0.027 -0.022 (0.013) (0.026) (0.062) UniPre2 0.002 -0.008 -0.035 (0.006) (0.015) (0.034) UniPost1 -0.011 0.070** 0.023 (0.016) (0.034) (0.034) UniPost2 0.005 0.142*** 0.094* (0.025) (0.047) (0.051) UniPost3 0.070 0.222*** 0.070 (0.047) (0.070) (0.080)

Time FE Yes Yes Yes

Municipality FE Yes Yes Yes

Year × Pair FE Yes Yes Yes

Observations 320 240 240 R² 0.835 0.767231 0.855946 Adjusted R² 0.602 0.408172 0.633735 F Statistic 4.742380*** (df = 141; 132) (df = 101; 94) 3.067659*** (df = 101; 94) 5.530027*** Note: *p<0.1; **p<0.05; ***p<0.01;

Standard errors are calculated using "Small Sample Correction" and are clustered on

municipality level

5.3. Plausibility Check

The results from the previous section show undoubtedly a significant effect for the variable people working in the municipality. However, by establishing an institution new jobs will be created by nature of the subject. The question remains whether there has been a true spillover creating jobs apart from the university employees. In order to find an answer for this question the average increase for the number of people working in the municipality is again compared between treated and non-treated municipalities before and after university foundation, but this time the former one is adjusted by subtracting the number of people directly employed by the university as shown in the following equation:

𝛿 =

−

(3)

The result of the equation - denoted as δ - represents the difference in average increase between the treatment and the control group. The variable Treati represents the number of people working

in the treated municipality from which the number of people employed by the local university (Adjustmenti) is subtracted. Controli corresponds to the number of people working in the

non-treated municipality. Due to the fact that the number of university employees is only known per maintaining organisation (see section 3.3) the variables have to be aggregated in cases with multiple locations per university. As a consequence, the variable i differs slightly from the pairs found by matching. The reason why the year 2001 is left out is simply because the number of university employees is only available since 2004 for universities of applied sciences and since 2005 for private universities.

Two data limitations have to be taken care of (see section 3.3): First, the number of university employees per maintaining organisation is no precise in some cases because not all of those locations are included in the dataset due to restrictions formulated in section 3.1 (e.g. the location Klagenfurt in case of Fachhochschule Kärnten). Hence, the variable Adjusted is actually too high in some cases. Second, the data for university employees is given as head counts and as full-time-equivalents. Unfortunately, the variable people working in the municipality from census data is only available as head counts but this one does not include marginal employment (i.e. less than 12 to 14 hours per week, depending on the year). Consequently, it is impossible to calculate the adjustment precisely and one has to interpret the result carefully. However, in order to answer the question whether the effect from the event study is only of mechanical nature it shall be sufficient to use the available data.

I conduct the comparison two times whereby one time the number of people working in the municipality is adjusted by the headcount of university employees and the other time it is adjusted by the full-time-equivalents. One can expect that δ is greater than zero at least for the adjustment by full-time-equivalents even though the adjustment is actually too high. If there is a true spillover effect than the adjustment by headcount – which is actually too high – should lead to a difference at least close to zero.

Table 6 shows the results for both calculations. In the case of adjustment by full-time-equivalent the difference δ is by far positive - as expected - and in the case of adjustment by headcount the difference δ is basically zero. Although not being precise, both results together provide strong evidence that the estimated effect from the event study on the variable people working in municipality is not purely mechanical but there is also a true spillover effect.

Adjustment

Full-time-equivalent Headcount

δ

0.037

0.001

6. Conclusion

There is a variety of research about the contribution of universities to the (local) economy using various measures and settings. All of the studies towards this direction find a positive effect of existing universities but the outcome variables hardly reflect the true size of the local economy. This thesis contributes to existing literature by estimating the effect of a university foundation on the local economy using more encompassing measures of economic activity. The fact that many universities have been founded in Austria in the last decades set the ideal framework conditions for research on the question whether the very foundation of a university has a positive effect on the local economy.

In this paper, I conduct an event study on a more general level using census data aggregated on municipality level using a two-step approach. First, I construct a representative control group by matching treated with non-treated municipalities using the Mahalanobis distance on the outcome variables. Second, I estimate the dynamic treatment effect of the university foundation on different outcome variables by using an event study approach. Although the number of people living in the municipality as well as the number of university graduates in the municipality do not show any significant results I find a significant increase in the number of people working in the municipalities in all of the periods after the university foundation compared to similar municipalities without university foundation.

The fact that the number of people living in the municipality shows no effect while the number of people working in the municipality shows a significant increase leaves space for further investigation. Perhaps conducting a similar research design would lead to comparable results concerning the number of people living in the municipality if the number of inhabitants of towns surrounding the treated municipalities were included. Finally, even more direct measures for economic activity like tax revenue may strengthen the results of this thesis as well as of previous literature.

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