Determinants of Unemployment Among Gen Z in South Sulawesi
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Abstract
Indonesia has experienced a demographic bonus since 2015 with the peak period estimated to occur between 2020 and 2035, which is characterized by the population structure being dominated by people of productive age compared to non-productive age. The demographic bonus can be used as an opportunity for the government to improve its regional economy. According to the 2023 Indonesian Youth Statistics, the estimated percentage of youth (population aged 16 - 30 years) in Indonesia is 23.18 percent or almost a quarter of the Indonesian population. Still, the youth open unemployment rate (TPT) is also high, exceeding the national TPT. The 2020 Population Census portrait shows that youth in Indonesia are dominated by Generation Z (Gen Z) with a total population of 74.93 million. Data from the Central Statistics Agency (BPS) shows worrying figures regarding unemployment among Gen Z in Indonesia. As many as 9.9 million Gen Z aged 15-24 years will be recorded as unemployed in 2023, which is equivalent to 22.25% of the total Gen Z population in Indonesia. Gen Z is a population group that dominates the population composition of South Sulawesi and has the potential to become an actor in Indonesia's future development. The analytical method used in this research is binary logistic regression. The results of this study show that the variables gender, marital status, head of household status, education level and type of region have a significant influence on the emergence of unemployment among Gen Z.
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References
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Zainal, Muhammad Fahd. (2018). The Effect of Economic and Demographic Characteristics on Unemployment in South Sulawesi. Jurnal Ilmiah Mahasiswa FEB Universitas Brawijaya, 7(1), 1–13.
ADB. (2023). A Closer Look at Causes of Youth Unemployment in the People’s Republic of China. ADB Briefs No. 247. ADB, Manila. Cited in https://www.adb.org/sites/default/files/publication/890896/adb-brief-247-causes-youth-unemployment-prc.pdf [20 Juni 2024]
Aharon, Tidhar. (2021). Generation Z: Characteristics and Challenges to Entering The World of Work. Cross-Cultural Management Journal, XXIII(1), 107–115.
Alawad, Akram Sh., Fuad Kreishan, and Mohammad Selim (2020). Determinants of Youth Unemployment: Evidence from Jordan. International Journal of Economics and Business Administration, VIII(4), 152–165. http://dx.doi.org/10.35808/ijeba/576
Apriliansyah, Muti Nurjannah Fitri, Sabiq Sofyan, Syifa Rahmawati Hakim and Tiodora Hadumaon Siagian. (2021). Penerapan Regresi Logistik Dalam Menentukan Determinan Pengangguran Usia Muda Terdidik di Provinsi Banten. Jurnal Matematika dan Statistika serta Aplikasinya, 9(2), 59–67. https://doi.org/10.24252/msa.v9i2.21370
Borucka, A. (2020). Logistic regression in modeling and assessment of transport services. Open Eng, 10(1), 26–34. https://doi.org/10.1515/eng-2020-0029
BPS. (2023). Bonus Demografi dan Visi Indonesia Emas 2045. DATAin Edisi 2023.01-2. BPS, Jakarta. Cited in https://bigdata.bps.go.id/documents/datain/2023_01_2_Bonus_Demografi_dan_Visi_Indonesia%20Emas_2045.pdf, [15 Juni 2024]
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Coskun, Sena. (2020). Young, Educated, Unemployed. Universitat Bonn, Bonn. Cited in https://www.wiwi.uni-bonn.de/bgsepapers/boncrc/CRCTR224_2020_134v2.pdf [18 Juni 2024]
DPD-RI. (2024). Bonus Demografi Indonesia: Gen Z Terancam Pengangguran. Senator: Majalah Dewan Perwakilan Daerah Republik Indonesia Edisi 1-15 Juni 2024. DPD-RI, Jakarta. Cited in https://www.dpd.go.id/daftar-majalah-kegiatan/11-edisi-1-15-juni-2024-bonus-demografi-indonesia-gen-z-terancam-pengangguran# [15 Juni 2024]
Haq, R. L. A., & Noorrizki, R. D. (2022). Perilaku Preferensi Menjadi Pengangguran oleh Milenial dan Gen Z dalam Karir Dan Pekerjaan. Flourishing Journal, 2(5), 384-391. http://dx.doi.org/10.17977/um070v2i52022p384-391
Hosmer, David W., Stanley Lemeshow and Rodney X. Sturdivant. (2013). Applied Logistic Regression 3rd Edition. John Wiley & Sons, Inc.
ILO. (2022). Global Employment Trends for Youth 2022. ILO, Geneva. Cited in https://www.ilo.org/publications/major-publications/global-employment-trends-youth-2022-investing-transforming-futures-young [15 Juni 2024]
ILO. (2022a). Global Employment Trends for Youth 2022: Asia and the Pacific. ILO, Geneva. Cited in https://www.ilo.org/resource/brief/global-employment-trends-youth-2022-asia-and-pacific [15 Juni 2024]
Khoitimah, K.M. & P.S. Wulandari. (2014). Model Regresi Logistik Biner Stratifikasi Pada Partisipasi Ekonomi Perempuan Di Provinsi Jawa Timur. J. SAINS DAN SENI POMITS, 3(1), 2337 – 3520. http://dx.doi.org/10.12962/j23373520.v3i1.6096
Pamungkas. (2017). Metode regresi logistik biner pada faktor yang mempengaruhi kesembuhan pasien penderita demam berdarah dengue di RSUD Dr. Iskak Kabupaten Tulungagung. Cited in : http://repository.its.ac.id/42259/1/1314030088-Non_Degree.pdf [20 Juni 2024]
Prieto, Ana Belen Tulcanaza, Paul Alejandro Baez Salazar and Iliana E. Aguilar-Rodriguez. (2023). Determinants of Youth Unemployment in Ecuador in 2019. Economies 2023, 11(2), 59. http://dx.doi.org/10.3390/economies11020059
Ramadhani, A., & Ayu, D. N. (2022). Gambaran makna kerja bagi generasi z di Jakarta. INQUIRY Jurnal Ilmiah Psikologi, 13(1), 41-60. https://doi.org/10.51353/inquiry.v13i01.596
Sinaga, Feisal. (2023). Modal Manusia dan Transisi Pemuda Fresh Graduates ke Sektor Digital di Masa Pandemi COVID-19. Jurnal Ketenagakerjaan, 18(1), 14–3 1. https://doi.org/10.47198/jnaker.v18i1.190
Sipayung, Febrim Leksiando, Andy Fefta Wijaya, Fadillah Putra and Natsumi Aratame. (2022). Analyzing the Characteristics of Highly Educated Unemployment in Indonesia’s Capital City. Jurnal Ekonomi dan Studi Pembangunan, 14(2), 153–170. http://dx.doi.org/10.17977/um002v14i22022p153
Tamirat, Negese. (2023). Determinants of youth unemployment in Southern Ethiopia: Evidence from Duna District, Ethiopia. International Journal of Financial, Accounting, and Management, 4(4), 509–520. https://doi.org/10.35912/ijfam.v4i4.1185
Tampil, Y.A., H. Komalig and Y. Langi. (2017). Analisis Regresi Logistik Untuk Menentukan Faktor-Faktor Yang Mempengaruhi Indeks Prestasi Kumulatif ( IPK ) Mahasiswa FMIPA Universitas Sam Ratulangi Manado. d’CARTESIAN, 6(2), 57 – 62. https://doi.org/10.35799/dc.6.2.2017.17023
Tesyafe, Denano, Sibera Sintayehu, Asfaw Netsanet and Anjulo Eyasu. (2023). Youth unemployment and its determinants in Ethiopia. International Journal of Environmental Quality, 55(2023), 23–32. https://doi.org/10.6092/issn.2281-4485/16710
Umar, Muhammad Agus. (2017). Bonus Demografi Sebagai Peluang dan Tantangan Pengelolaan Sumber Daya Alam di Era Otonomi Daerah. Genta Mulia, VIII (2), 90–99.
Yanindah, A. (2021). An Insight into Youth Unemployment in Indonesia. Proceedings of The 1st International Conference on Data Science and Official Statistics. Politeknik Statistika STIS. Jakarta. https://doi.org/10.34123/icdsos.v2021i1
Zainal, Muhammad Fahd. (2018). The Effect of Economic and Demographic Characteristics on Unemployment in South Sulawesi. Jurnal Ilmiah Mahasiswa FEB Universitas Brawijaya, 7(1), 1–13.
Authors
Ayu, S. J. D. and Diksa, I. G. B. N. (2024) “Determinants of Unemployment Among Gen Z in South Sulawesi”, Jurnal Ketenagakerjaan, 19(3), pp. 368–381. doi: 10.47198/jnaker.v19i3.382.
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