IDEAS home Printed from https://ideas.repec.org/p/ipt/iptwpa/jrc93302.html
   My bibliography  Save this paper

Future employment in transport: Analysis of labour supply and demand

Author

Abstract

This report presents the results of the analysis carried out by the Joint Research Centre (JRC) in support of DG MOVE for the analysis of employment and skills issues in the EU transport sector, with the purpose of designing the policies targeting an increased competitiveness in the sector, and improving the labour productivity and job quality. The study analyses the development of employment in various transport sectors from different viewpoints, and by means of a variety of analytical approaches. The study addresses both the supply side (i.e. the workforce capacity) and the demand side (i.e., the number of employees required in order to meet the future transport activity). In doing so it aims to identify the gap between the supply and demand side and to provide some indications on the degree of change required in the labour force dynamics in order to close this gap. The analysis mainly focuses on the quantitative discrepancies between capacity and demand, but also addresses relevant qualitative aspects including the demographic composition of the workforce.

Suggested Citation

  • Panayotis Christidis & Elena Navajas Cawood & Martijn Brons & Burkhard Schade & Antonio Soria, 2014. "Future employment in transport: Analysis of labour supply and demand," JRC Research Reports JRC93302, Joint Research Centre.
  • Handle: RePEc:ipt:iptwpa:jrc93302
    as

    Download full text from publisher

    File URL: https://publications.jrc.ec.europa.eu/repository/handle/JRC93302
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Marcel Timmer & Abdul A. Erumban & Reitze Gouma & Bart Los & Umed Temurshoev & Gaaitzen J. de Vries & I–aki Arto & Valeria Andreoni AurŽlien Genty & Frederik Neuwahl & JosŽ M. Rueda?Cantuche & Joseph , 2012. "The World Input-Output Database (WIOD): Contents, Sources and Methods," IIDE Discussion Papers 20120401, Institue for International and Development Economics.
    2. Pfeffermann, Danny, 1991. "Estimation and Seasonal Adjustment of Population Means Using Data from Repeated Surveys: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 9(2), pages 177-177, April.
    3. Pfeffermann, Danny, 1991. "Estimation and Seasonal Adjustment of Population Means Using Data from Repeated Surveys," Journal of Business & Economic Statistics, American Statistical Association, vol. 9(2), pages 163-175, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Urbanek, Anna & Acedański, Jan & Krawczyk, Grzegorz, 2023. "Depopulation or ageing? Decomposing the aggregate effects of projected demographic changes on urban transport systems," Journal of Transport Geography, Elsevier, vol. 111(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jan Kordos, 2012. "Application of rotation methods in sample surveys in Poland," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 13(2), pages 243-260, June.
    2. Danny Pfeffermann, 2022. "Time series modelling of repeated survey data for estimation of finite population parameters," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1757-1777, October.
    3. Jan A. Brakel & Sabine Krieg, 2016. "Small area estimation with state space common factor models for rotating panels," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(3), pages 763-791, June.
    4. Jo Thori Lind, 2005. "Repeated surveys and the Kalman filter," Econometrics Journal, Royal Economic Society, vol. 8(3), pages 418-427, December.
    5. Caterina Schiavoni & Franz Palm & Stephan Smeekes & Jan van den Brakel, 2021. "A dynamic factor model approach to incorporate Big Data in state space models for official statistics," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 324-353, January.
    6. Weigand Roland & Wanger Susanne & Zapf Ines, 2018. "Factor Structural Time Series Models for Official Statistics with an Application to Hours Worked in Germany," Journal of Official Statistics, Sciendo, vol. 34(1), pages 265-301, March.
    7. Krieg, Sabine & van den Brakel, Jan A., 2012. "Estimation of the monthly unemployment rate for six domains through structural time series modelling with cointegrated trends," Computational Statistics & Data Analysis, Elsevier, vol. 56(10), pages 2918-2933.
    8. Jo Thori Lind, 2002. "Small continuous surveys and the Kalman filter," Discussion Papers 333, Statistics Norway, Research Department.
    9. Caio Gonçalves & Luna Hidalgo & Denise Silva & Jan van den Brakel, 2022. "Single‐month unemployment rate estimates for the Brazilian Labour Force Survey using state‐space models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1707-1732, October.
    10. Harm Jan Boonstra & Jan A. Van Den Brakel & Bart Buelens & Sabine Krieg & Marc Smeets, 2008. "Towards small area estimation at Statistics Netherlands," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1), pages 21-49.
    11. Oksana Bollineni‐Balabay & Jan van den Brakel & Franz Palm & Harm Jan Boonstra, 2017. "Multilevel hierarchical Bayesian versus state space approach in time series small area estimation: the Dutch Travel Survey," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(4), pages 1281-1308, October.
    12. Jan van den Brakel & Martijn Souren & Sabine Krieg, 2022. "Estimating monthly labour force figures during the COVID‐19 pandemic in the Netherlands," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1560-1583, October.
    13. Jan van den Brakel & Xichuan (Mark) Zhang & Siu‐Ming Tam, 2020. "Measuring Discontinuities in Time Series Obtained with Repeated Sample Surveys," International Statistical Review, International Statistical Institute, vol. 88(1), pages 155-175, April.
    14. Caterina Schiavoni & Siem Jan Koopman & Franz Palm & Stephan Smeekes & Jan van den Brakel, 2021. "Time-varying state correlations in state space models and their estimation via indirect inference," Tinbergen Institute Discussion Papers 21-020/III, Tinbergen Institute.
    15. Löschel, Andreas & Pothen, Frank & Schymura, Michael, 2015. "Peeling the onion: Analyzing aggregate, national and sectoral energy intensity in the European Union," Energy Economics, Elsevier, vol. 52(S1), pages 63-75.
    16. -, 2016. "The South American input-output table: Key assumptions and methodological considerations," Documentos de Proyectos 40832, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL).
    17. Hermeling, Claudia & Klement, Jan Henrik & Koesler, Simon & Köhler, Jonathan & Klement, Dorothee, 2015. "Sailing into a dilemma," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 34-53.
    18. Lord, Montague, 2015. "Regional Economic Integration in Central Asia and South Asia," MPRA Paper 66436, University Library of Munich, Germany.
    19. Stefan Ederer & Stefan Weingärtner, 2014. "Structural Disparities in Carbon Dioxide Consumption and Trade in the World Economy. WWWforEurope Policy Paper No. 16," WIFO Studies, WIFO, number 47498, March.
    20. Auer, Raphael A. & Mehrotra, Aaron, 2014. "Trade linkages and the globalisation of inflation in Asia and the Pacific," Journal of International Money and Finance, Elsevier, vol. 49(PA), pages 129-151.

    More about this item

    Keywords

    transport; railway; accessibility; welfare;
    All these keywords.

    JEL classification:

    • L90 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - General
    • L99 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Other
    • R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population
    • R40 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - General
    • R49 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Other

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ipt:iptwpa:jrc93302. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Publication Officer (email available below). General contact details of provider: https://edirc.repec.org/data/ipjrces.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.