Does the presence of high-skilled employees increase total and high-skilled employment in the long run? Evidence from Austria

Abstract

Studies conducted for the US have found a positive effect of human capital endowments on employment growth, with human capital endowments diverging at the same time. In contrast, studies for European countries have found convergence of human capital endowments. This paper tests these relationships for the 99 Austrian districts for the observation period 1971–2011 by estimating how the presence of high-skilled employment affects total, low-skilled and high-skilled employment growth. To this end, OLS, fixed-effects and first-difference regressions are estimated. The results indicate continuous convergence of high-skilled employment which, however, slowed down significantly since the 1990s. In contrast to previous studies, evidence for positive effects of high-skilled on total and low-skilled employment is only weak and varies over time. Furthermore, the results show that total and high-skilled employment in suburban areas grew faster than in other regions, while districts which bordered the Eastern Bloc were disadvantaged. Nevertheless, spatial neighbourhood effects within Austria are only weak.

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Notes

  1. 1.

    Note that the smart city hypothesis with its emphasis on employment and human capital growth differs from Florida’s (2002) work on the creative class.

  2. 2.

    An earlier descriptive study for Western Germany by Bade et al. (2000) finds regional de-concentration processes of human capital between 1976 and 1996.

  3. 3.

    Note that in contrast to Berry and Glaeser (2005), the cited European studies do not restrict their regional sample to urban areas. Instead, they include all types of regions within the respective countries—an approach that is followed in this paper, too. For this reason, the comparability of these studies is limited. For example, if human capital externalities matter more for cities than rural regions, differing conclusions may be just resulting from different sampling strategies.

  4. 4.

    Ranked by GDP per capita in US dollars at market prices and not counting OPEC member states as well as countries with less than one million inhabitants, in 1971 Austria was found at 15th position of all national economies in the world. Countries with a higher GDP per capita in 1971 included France, Germany (in today’s borders) and Great Britain. By 1991, Austria had climbed to ninth position, leaving behind France and Great Britain. By 2011, Austria had also surpassed Germany (data source: United Nations, accessed 15-May-2014).

  5. 5.

    Data source: Eurostat, accessed 23-July-2014.

  6. 6.

    The raw data correspond to the observation years except for 1971, where values of 1973 are used. The data are classified into groups of numbers of employees and the corresponding number of firms, e.g. in district \( i \) at \( t \) there were 382 firms with 1 employed person, 682 firms with 2–4 employed persons, etc.

  7. 7.

    The data for 1971 and 1981 stems from ÖROK (1989), the data for 1991 and 1995 were provided by the Austrian Institute for Economic Research (WIFO). Values for 2001 and 2011 are estimated by the growth rate of the accompanying NUTS3 during the respective periods, with the latter being provided by Statistik Austria.

  8. 8.

    The dissimilarity index as developed by Duncan and Duncan (1955) gives the share of high-skilled employment that has to be reallocated across space so that every region exhibits the same share of high-skilled employment, with \( D_{t} = 0.5\sum\nolimits_{i = 1}^{N} {\left| {{{L_{i,t}^{h} } \mathord{\left/ {\vphantom {{L_{i,t}^{h} } {\sum\nolimits_{i = 1}^{N} {L_{i,t}^{h} - {{\left( {L_{i,t}^{l} + L_{i,t}^{m} } \right)} \mathord{\left/ {\vphantom {{\left( {L_{i,t}^{l} + L_{i,t}^{m} } \right)} {\sum\nolimits_{i = 1}^{N} {\left( {L_{i,t}^{l} + L_{i,t}^{m} } \right)} }}} \right. \kern-0pt} {\sum\nolimits_{i = 1}^{N} {\left( {L_{i,t}^{l} + L_{i,t}^{m} } \right)} }}} }}} \right. \kern-0pt} {\sum\nolimits_{i = 1}^{N} {L_{i,t}^{h} - {{\left( {L_{i,t}^{l} + L_{i,t}^{m} } \right)} \mathord{\left/ {\vphantom {{\left( {L_{i,t}^{l} + L_{i,t}^{m} } \right)} {\sum\nolimits_{i = 1}^{N} {\left( {L_{i,t}^{l} + L_{i,t}^{m} } \right)} }}} \right. \kern-0pt} {\sum\nolimits_{i = 1}^{N} {\left( {L_{i,t}^{l} + L_{i,t}^{m} } \right)} }}} }}} \right|} \), where \( L^{l} \), \( L^{m} \) and \( L^{h} \) symbolise the total numbers of low-skilled, medium-skilled and high-skilled employment, respectively.

  9. 9.

    With Austria being much smaller in size than Germany—hence its regions being relatively less dependent on demand from other Austrian regions—the present study applies productivity instead of market potential.

  10. 10.

    The results are available upon request.

  11. 11.

    Südekum (2008) uses initial employment shares of 27 industries to capture economic structure instead.

  12. 12.

    The index is calculated as \( K_{i,t} = \sum\nolimits_{p = 1}^{k} {\left| {{{Y_{i,p,t} } \mathord{\left/ {\vphantom {{Y_{i,p,t} } {\sum\nolimits_{p = 1}^{k} {Y_{i,p,t} - {{\sum\nolimits_{i = 1}^{n} {Y_{i,p,t} } } \mathord{\left/ {\vphantom {{\sum\nolimits_{i = 1}^{n} {Y_{i,p,t} } } {\sum\nolimits_{i = 1}^{n} {\sum\nolimits_{p = 1}^{k} {Y_{i,p,t} } } }}} \right. \kern-0pt} {\sum\nolimits_{i = 1}^{n} {\sum\nolimits_{p = 1}^{k} {Y_{i,p,t} } } }}} }}} \right. \kern-0pt} {\sum\nolimits_{p = 1}^{k} {Y_{i,p,t} - {{\sum\nolimits_{i = 1}^{n} {Y_{i,p,t} } } \mathord{\left/ {\vphantom {{\sum\nolimits_{i = 1}^{n} {Y_{i,p,t} } } {\sum\nolimits_{i = 1}^{n} {\sum\nolimits_{p = 1}^{k} {Y_{i,p,t} } } }}} \right. \kern-0pt} {\sum\nolimits_{i = 1}^{n} {\sum\nolimits_{p = 1}^{k} {Y_{i,p,t} } } }}} }}} \right|} \), where \( Y_{i,p,t} \) is gross value added of district \( i \) in sector \( p \) at \( t \).

  13. 13.

    Due to the relatively small sample size, type II errors (not rejecting the hypothesis that a coefficient equals zero although it does not) are relatively likely, which is why p-values slightly above 0.1 also get a mention (for a discussion see Verbeek 2008).

  14. 14.

    The following industries are counted as “old”: agriculture, hunting and forestry (NACE codes 01 and 02), manufacture of textiles and textile products (17 and 18), manufacture of leather and leather products (19). The following industries are counted as “new”: manufacture of chemicals, chemical products and man-made fibres (24), manufacture of machinery and equipment n.e.c. (29), manufacture of office machinery and computers (30), manufacture of radio, television and communication equipment and apparatus (32), manufacture of medical, precision and optical instruments, watches and clocks (33), manufacture of transport equipment (34 and 35), post and telecommunications (64), financial intermediation, except insurance and pension funding (65), insurance and pension funding, except compulsory social security (66), computer and related activities (72), research and development (73), other business activities (74).

  15. 15.

    If the Krugman index is replaced by location quotients, the latter are negative and weakly significant or non-significant, which confirms the negative effect of specialisation. The results are available upon request.

  16. 16.

    One outcome of the suburbanisation dynamics and the interconnections between core city and suburban regions is also the practice of the US census bureau to increase the size of metropolitan areas over time (Glaeser 2000).

  17. 17.

    In 2011, Austria ranked 13th within the EU15 and 20th within the EU27 member states when considering the share of inhabitants 15–64 years old who attained ISCED levels 5 or 6 (data source: Eurostat, accessed 21-July-2014).

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Acknowledgments

This work was supported by the City of Vienna WU Jubilee Fund as part of the research project “Wien als Migrationsziel von Humanressourcen”. The authors would also like to thank Assma Hajji for her support as well as Thomas Rusch, Tanja Sinozic and two reviewers for their helpful comments.

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Correspondence to Sascha Sardadvar.

Appendices

Appendix 1: List of districts

  • The official names of the 99 districts considered in this study are, as ordered by their superior federal states:

  • Burgenland: Eisenstadt (Stadt); Rust (Stadt); Eisenstadt-Umgebung; Güssing; Jennersdorf; Mattersburg; Neusiedl am See; Oberpullendorf; Oberwart

  • Carinthia: Klagenfurt (Stadt); Villach(Stadt); Hermagor; Klagenfurt Land; Sankt Veit an der Glan; Spittal an der Drau; Villach Land; Völkermarkt; Wolfsberg; Feldkirchen

  • Lower Austria: Krems an der Donau (Stadt); Sankt Pölten (Stadt); Waidhofen an der Ybbs (Stadt); Wiener Neustadt (Stadt); Amstetten; Baden; Bruck an der Leitha; Gänserndorf; Gmünd; Hollabrunn; Horn; Korneuburg; Krems (Land); Lilienfeld; Melk; Mistelbach; Mödling; Neunkirchen; Sankt Pölten (Land); Scheibbs; Tulln; Waidhofen an der Thaya; Wiener Neustadt(Land); Wien-Umgebung; Zwettl

  • Upper Austria: Linz (Stadt); Steyr (Stadt); Wels (Stadt); Braunau am Inn; Eferding; Freistadt; Gmunden; Grieskirchen; Kirchdorf an der Krems; Linz-Land; Perg; Ried im Innkreis; Rohrbach; Schärding; Steyr-Land; Urfahr-Umgebung; Vöcklabruck; Wels-Land

  • Salzburg: Salzburg (Stadt); Hallein; Salzburg-Umgebung; Sankt Johann im Pongau; Tamsweg; Zell am See

  • Styria: Graz (Stadt); Bruck an der Mur; Deutschlandsberg; Feldbach; Fürstenfeld; Graz-Umgebung; Hartberg; Judenburg; Knittelfeld; Leibnitz; Leoben; Liezen; Mürzzuschlag; Murau; Radkersburg; Voitsberg; Weiz

  • Tyrol: Innsbruck-Stadt; Imst; Innsbruck-Land; Kitzbühel; Kufstein; Landeck; Lienz; Reutte; Schwaz

  • Vorarlberg: Bludenz; Bregenz; Dornbirn; Feldkirch

  • Vienna: Wien

Appendix 2: Accompanying results

Table 6 Cross section analysis with N = 91 for different observation periods
Table 7 Panel analyses with N = 91 for different specifications

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Sardadvar, S., Reiner, C. Does the presence of high-skilled employees increase total and high-skilled employment in the long run? Evidence from Austria. Empirica 44, 59–89 (2017). https://doi.org/10.1007/s10663-015-9311-5

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Keywords

  • Human capital
  • Employment growth
  • Convergence
  • Smart city hypothesis

JEL Classification

  • J24
  • O15
  • R11
  • R12