Značilnosti in izzivi trga dela v izbranih razvitih državah

Kokolj, Domen

| | SLO | ENG | Cookies and privacy

Bigger font | Smaller font

First page > Show document

Show document

Title:	Značilnosti in izzivi trga dela v izbranih razvitih državah
Authors:	ID Kokolj, Domen (Author) ID Boršič, Darja (Mentor) More about this mentor...
Files:	MAG_Kokolj_Domen_2024.pdf (2,44 MB) MD5: FA114DEDA77A0D086742B7429E4BE6A9
Language:	Slovenian
Work type:	Master's thesis/paper
Typology:	2.09 - Master's Thesis
Organization:	EPF - Faculty of Business and Economics
Abstract:	V magistrskem delu smo raziskovali značilnosti in izzive trga dela v izbranih razvitih državah. Države, ki smo jih izbrali so Avstralija, Nemčija, Francija, Velika Britanija, Japonska in ZDA. V uvodnih poglavljih smo predstavili teoretične temelje povpraševanja in ponudbe dela. Nato smo izvedli pregled trga dela v izbranih državah za obdobje od 2004 do 2023. Zanimala nas je predvsem struktura zaposlenosti, in sicer po spolu, starosti in aktivnosti. Prav tako smo se dotaknili brezposelnosti in povprečnega trajanja brezposelnosti v letu 2004 in 2023. V četrtem poglavju smo podrobneje pogledali, kako avtomatizacija dela vpliva na zaposlenost, kako delo od doma vpliva na produktivnost zaposlenih in kaj sploh je umetna inteligenca in njeni vplivi na zaposlenost. Nato smo opravili pregled obstoječih empiričnih študij na področju umetne inteligence in njenega vpliva na zaposlenost. Prav tako smo pregledali študije funkcije zaposlenosti, da smo ugotovili, katere spremenljivke bi bilo smiselno vključiti v naš model. V zadnjem poglavju smo opravili empirično analizo vpliva digitalizacije in umetne inteligence na zaposlenost v izbranih razvitih državah. Odvisna spremenljivka, ki smo jo izbrali, je zaposlenost, neodvisne pa bruto domači proizvod in plače. Kot neodvisni spremenljivki sta bili vključeni tudi digitalizacija, ki je sestavljen indeks iz spremenljivk število fiksnih priklopov internetnih storitev na sto ljudi, izvoza informacijske in komunikacijske tehnologije, število posameznikov, ki uporabljajo internetne storitve kot odstotek celotne populacije, in število naročnin na mobilne storitve na sto ljudi. Indeks digitalizacije smo nato razširili in mu dodali še izdatke za raziskave in razvoj kot odstotek BDP in domača posojila zasebnemu sektorju kot odstotek BDP. Ta razširjen indeks smo označili kot AI – pripravljenost na umetno inteligenco. Ker smo analizirali panelne podatke, smo uporabili model združenih podatkov in model fiksnih učinkov. Da smo ugotovili, kateri izmed omenjenih modelov je primernejši, smo izračunali F-statistko s pomočjo programa EViews. Glede na rezultate F-statistike smo izbrali model fiksnih učinkov. Nato smo model fiksnih učinkov primerjali še z modelom slučajnih učinkov. Da smo ugotovili kateri je primernejši smo uporabili Hausmanov test. Ugotovili smo, da je model fiksnih učinkov primernejši. Na koncu smo še z metodo White cross-section preverjali odpornost modela na heteroskedastičnost in avtokorelacijo. V modelih, ki so ocenjeni z White cross-section metodo imata indeks digitalizacije in pripravljenosti na umetno inteligenco pozitiven predznak, kar pomeni, da pozitivno vplivata na zaposlenost.
Keywords:	digitalizacija, umetna inteligenca, avtomatizacija, delo od doma, ekonometrična analiza, razvite države
Place of publishing:	Maribor
Publisher:	D. Kokolj
Year of publishing:	2024
PID:	20.500.12556/DKUM-90785
UDC:	331.5
COBISS.SI-ID:	211943683
Publication date in DKUM:	17.10.2024
Views:	0
Downloads:	15
Metadata:
Categories:	EPF
:	Copy citation

Average score:	(0 votes)
Your score:	Voting is allowed only for logged in users.
Share:

Hover the mouse pointer over a document title to show the abstract or click on the title to get all document metadata.

Licences

License:	CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International

Link:	http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:	The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:	23.09.2024

Secondary language

Language:	English
Title:	Characteristics and challenges of labour market in selected developed countries
Abstract:	In the master's thesis, we explored the characteristics and challenges of the labor market in selected developed countries. The countries we chose are Australia, Germany, France, the United Kingdom, Japan, and the USA. In the introductory chapters, we presented the theoretical foundations of labor supply and demand. Then, we reviewed the labor markets in the selected countries for the period from 2004 to 2023. We were particularly interested in the structure of employment, specifically by gender, age, and activity. We also touched on unemployment and the average duration of unemployment in 2004 and 2023. In the fourth chapter, we examined in more detail how work automation affects employment, how working from home affects employee productivity, and what artificial intelligence is and its impact on employment. Next, we conducted a review of existing empirical studies on artificial intelligence and its impact on employment. We also reviewed studies on employment functions to determine which variables would be sensible to include in our model. In the final chapter, we performed an empirical analysis of the impact of digitalization and artificial intelligence on employment in selected developed countries. The dependent variable we selected was employment, and the independent variables were gross domestic product and wages. Digitalization, which is a composite index of variables such as the number of fixed internet connections per hundred people, exports of information and communication technology, the number of individuals using internet services as a percentage of the total population, and the number of mobile service subscriptions per hundred people, was also included as an independent variable. We then expanded the digitalization index by adding research and development expenditure as a percentage of GDP and domestic credit to the private sector as a percentage of GDP. We labeled this expanded index as AI – readiness for artificial intelligence. Since we analyzed panel data, we used the pooled data model and the fixed effects model. To determine which of the mentioned models is more appropriate, we calculated the F-statistic using the EViews program. Based on the results of the F-statistic, we selected the fixed effects model. We then compared the fixed effects model with the random effects model. To determine which is more appropriate, we used the Hausman test. We found that the fixed effects model is more appropriate. Finally, we used the White cross-section method to test the model's robustness to heteroskedasticity and autocorrelation. In the models estimated with the White cross-section method, the digitization index and readiness for artificial intelligence have a positive coefficient, meaning they positively influence employment.
Keywords:	digitalization, artificial intelligence, automation, work from home, econometric analysis, developed countries

Comments

Leave comment

You must log in to leave a comment.

Comments (0)

0 - 0 / 0

There are no comments!

Back