Full-text resources of CEJSH and other databases are now available in the new Library of Science.
Visit https://bibliotekanauki.pl

PL EN


2020 | 23 | 87-108

Article title

Gender and skill dimensions of technological transitions at workplaces: the case of The Czech Republic, Poland and Slovakia

Content

Title variants

PL
Zmiany technologiczne w miejscu pracy w kontekście płci i umiejętności. Przypadek Czech, Polski i Słowacji

Languages of publication

EN

Abstracts

EN
The study seeks to investigate the impact of technology on employment by gender and skills of the labour markets in the Czech Republic, Poland and Slovakia, and employs the VAR model to analyse the impact of technology on employment by gender. It also uses a panel cointegrated autoregressive distributed lag model for the impact of technology on employment by skill groups. The study finds that although technology impacts on employment of gender in the three countries, it is however gender neutral. This study also finds that in the long-term, technology impacts negatively on middle skill employment and positively on low skill and high skill employment in a similar pattern before and after the global financial crises. The impact of technology varies across countries in the short term, attributed to cross-country differences in labour market policies and institutions.
PL
W artykule podjęto próbę zbadania wpływu technologii na zatrudnienie według płci i umiejętności na rynkach pracy w Czechach, Polsce i na Słowacji. Do analizy wpływu technologii na zatrudnienie według płci wykorzystano model VAR. Zastosowano ponadto panelowy model autoregresji z opóźnieniami rozłożonymi, by oszacować wpływ technologii na zatrudnienie według kategorii umiejętności. Z badania wynika, że chociaż technologia wpływa na zatrudnienie według płci w każdym z trzech analizowanych krajów, jest płciowo neutralna. W badaniu tym stwierdzono również, że w dłuższej perspektywie technologia wpływa negatywnie na zatrudnienie średnio wykwalifikowanych osób, pozytywnie natomiast na zatrudnienie osób o niskich i wysokich kwalifikacjach, w podobnym zakresie przed globalnym kryzysem finansowym i po nim. W krótkim okresie wpływ technologii jest zróżnicowany w poszczególnych krajach, co przypisuje się odmiennościom polityk oraz instytucji rynku pracy.

Contributors

  • Mendel University, Czech Republic
author
  • Mendel University, Czech Republic

References

  • Acemoglu, D., and Autor, D. H. (2011). Skills, tasks and technologies: implications for employment and earnings. Handbook of Labour Economics, 1043-1171.
  • Acemoglu, D., and Restrepo, P. (2017). Robots and jobs: evidence from US labour markets (NBER Working Paper No. 23285). Cambridge, MA: National Bureau of Economic Research.
  • Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716-723.
  • Arpaia, A., Kiss, A., Palvolgyi, B., and Turrini, S. (2014). Labour mobility and labour market adjustment in the EU. KC-AI-14-539-EN-N (online) (No 396, Development Working Papers from Centro Studi Luca d’Agliano). University of Milano.
  • Atkinson, A. B., Piketty, T., and Saez, E. (2010). Top incomes in the long run of history. Journal of Economic Literature, 49(1), 3-71.
  • Autor, D. H., and Dorn, D. (2013). The growth of low skill service jobs and the polarisation of the U.S. labour market. American Economic Review, 103(5), 1553-1597.
  • Autor, D. H., Kartz, L. F., and Kearnery S. M. (2006). The polarisation of the US labour market. American Economic Review, 96(2), 189-94.
  • Baltagi, B. H., and Griffin, J. M. (1997). Pooled estimators vs. their heterogeneous counterparts in the context of dynamic demand for gasoline. Journal of Econometrics, Elsevier, 77(2), 303-327.
  • Bayoumi, T., and Eichengreen, B. (1993). Shocking aspects of European monetary unification. In F. Torres, F. Giavazzi (Eds.), Adjustment and growth in the European Monetary Union (pp. 193-229). Cambridge: Cambridge University Press.
  • Brynjolfsson, E., and McAfee, A. (2012). Race against the machine. Lexington, MA: Digital Frontier Press.
  • Calvino, F., and Virgillito, M. E. (2018). The innovation-employment nexus: a critical survey of theory and empirics. Journal of Economic Surveys, 32(1), 83-117.
  • Crespi, G., and Tacsir, E. (2011). Effects of innovation on employment in Latin America (Paper presented at The Atlanta Conference on Science and Innovation Policy). Atlanta: Google Scholar.
  • Davis, S. J., and Haltiwanger, J. (2014). Labour market fluidity and economic performance (NBER Working Paper No. 20479). Cambridge, MA: National Bureau of Economic Research.
  • Deane, G. (2013). Technological unemployment: panacea or poison? Institute of Ethics and Emerging Technologies blog, March 5. Retrieved October 10, 2013 from http://ieet.org/index.php/IEET/more/deane20130305
  • Dickey, D. A., and Fuller, W. A. (1979). Distribution of the Estimators for Autoregressive Time Series with a Unit Root. Journal of the American Statistical Association, 74(366), 427-431.
  • Dustmann, C., Glitz, A., and Frattini, T. (2008). The labour market impact of immigration. Oxford Review of Economic Policy, 24(3), 477-494.
  • Eurostat. (2020). Online. Retrieved from https://ec.europa.eu/eurostat/data/database
  • Fox, M. F., Johnson, D. G., and Rosser, S. E. (2006). Women, gender, and technology. Urbana and Chicago: University of Illinois Press.
  • Hainmueller, J., Hiscox, M., and Margalit, Y. (2014). Do concerns about labour market competition shape attitudes towards immigration? New evidence. Journal of International Economics, (97), 193-207.
  • Hannan, E. J., and Quinn, B. G. (1979). The Determination of the Order of an Autoregression. Journal of the Royal Statistical Society. Series B, (41), 190-195.
  • Harrigan, J., Reshef, A., and Toubal, F. (2017). Techies, Productivity and Skill: Firm Level Evidence from France. Retrieved from http://www.cepii.fr/PDF_PUB/wp/2018/wp2018-21.pdf
  • Hausman, J. A. (1978). Specification tests in econometrics. Econometrica, (46), 1251-1271.
  • ILO. (2012). International Standard Classification of Occupations-ISCO-08. Online. Retrieved from https://www.ilo.org/wcmsp5/groups/public/---dgreports/---dcomm/---publ/documents/publication/wcms_172572.pdf
  • Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control, 12(2-3), 231-254.
  • Kao, C. (1999). Spurious regression and residual-based tests for cointegration in panel data. Journal of Econometrics, (90), 1-44.
  • Kitetu, C. W. (2008). Gender, science and technology: perspectives from Africa. Dakar, Senegal: Council for the Development of Social Science Research in Africa.
  • Levin, A., and Chien-Fu, L. (1992). Unit root test in panel data: asymptotic and finite sample properties (Discussion Paper No. 92-93). University of California at San Diego.
  • Marcolin L., Miroudot, S., and Squicciarini, M. (2016). The routine content of occupations: new cross-country measures based on PIAAC (OECD Science, Technology and Industry Working Papers, 2016/02). Paris: OECD.
  • Naticchioni, P., Ragusa, G., and Massari, R. (2014). Unconditional and conditional wage polarisation in Europe. IZA DP, (8465).
  • Nchor, D., and Rozmahel, P. (2020). Job polarisation in Europe: evidence from Central and Eastern European Countries. Danube: Law, Economics and Social Issues Review. Retrieved from http://www.eaco.eu/danube-journal-archive/issue-1-2020/
  • OECD. (2001). Measuring productivity. OECD Publishing.
  • OECD. (2017). OECD employment Outlook 2017. Paris: OECD Publishing.
  • Pesaran, M. H., Shin, Y., and Smith, R. P. (1999). Pooled Mean Group estimation of dynamic heterogeneous panels. Journal of the American Statistical Association, (94), 621-634.
  • Rosser, S. V. (2006). Using the lenses of feminist theories to focus on women and technology. In F. M. Frank, D. G. Johnson, Sue V. Rosser (Eds.). Women, gender, and technology (pp. 13-46). Urbana and Chicago: University of Illinois Press.
  • Rotman, D. (2013). How technology is destroying jobs. Technology Review, July/August, 28-35.
  • Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, (6), 461-464.
  • Spitz-Oener, A. (2006). Technical change, job tasks and rising educational demand: looking outside the wage structure. Journal of Labour Economics, 24(2), 235-270.
  • Tüzemen, D., and Willis, J. (2013). The vanishing middle: job polarisation and workers response to the decline in middle-skill jobs. Economic Review. Federal Reserve Bank of Kansas City, (5).
  • Ugur, M., Churchill, A., and Solomon, E. (2018). Technological innovation and employment in derived labour demand models: a hierarchical meta-regression analysis. Journal of Economic Surveys, (32), 50-82.
  • Vivarelli, M. (2014). Innovation, employment and skills in advanced and developing countries:
  • A survey of economic literature. Journal of Economic Issues, (48), 123-154.
  • Wadhwa, V. (2012). The Future of America’s manufacturing sector. Washington Post, November 18.
  • Wajcman, J. (2004). Technofeminism. Cambridge, UK and Malden, MA: Polity Press.

Document Type

Publication order reference

Identifiers

YADDA identifier

bwmeta1.element.desklight-f2afbe54-c122-4ea3-985f-5d43f5387f94
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.