PANDEMI COVID-19 DAN TURNOVER KE PEKERJAAN INFORMAL
ANALISIS DATA GOOGLE TRENDS
DOI:
https://doi.org/10.34123/jurnalasks.v14i2.360Keywords:
COVID-19, layoff, new normal, informal, Google TrendsAbstract
Pandemi COVID-19 telah berdampak terhadap keberlangsungan pekerjaan bagi pekerja di Indonesia seperti penurunan pendapatan hingga pemutusan hubungan kerja (PHK). Menurunnya permintaan tenaga kerja memungkinkan pekerja terdampak PHK berpindah ke sektor informal. Dengan menggunakan data pencarian pada Google Trends, studi ini bertujuan untuk mengeksplorasi adanya sinyal turnover ke pekerjaan informal selama pembatasan fisik dan normal baru di Indonesia. Empat kategori kata kunci: PHK, situs lowongan pekerjaan (loker), kurir, driver online, dan berjualan digunakan sebagai analisis terkait PHK dan pencarian kesempatan kerja yang bersifat informal. Hasil analisis tren dengan menggunakan regresi lokal (LOESS) dan metode difference-in-differences (DD) menemukan adanya sinyal turnover selama periode pembatasan fisik dan normal baru. Sinyal turnover tersebut ditunjukkan oleh meningkatknya intensitas pencarian terkait PHK yang diikuti dengan meningkatnya intensitas pencarian terkait kesempatan pekerja atau usaha seperti cara berjualan online dan lowongan kurir.
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References
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Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis (1st ed.). Springer-Verlag New York. https://doi.org/10.1007/978-0-387-98141-3
Borjas, G. J. (2016). Labor Economics (7th ed.). Mc Graw Hill.
BPS. (2020a). Analisis Hasil Survei Dampak COVID-19 terhadap Pelaku Usaha. Badan Pusat Statistik.
BPS. (2020b). Kajian Big Data sebagai Pelengkap Data dan Informasi Statistik Sosial. Badan Pusat Statistik.
Brodeur, A., Clark, A. E., Fleche, S., & Powdthavee, N. (2021). COVID-19, lockdowns and well-being: Evidence from Google Trends. Journal of Public Economics, 193, 104346. https://doi.org/https://doi.org/10.1016/j.jpubeco.2020.104346
Cirillo, A. (2016). RStudio for R Statistical Computing Cookbook. Packt Publishing.
Cleveland, W. S. (1979). Robust Locally Weighted Regression and Smoothing Scatterplots. Journal of the American Statistical Association, 74(368), 829–836.
Cleveland, W. S., & Devlin, S. J. (1988). Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting. Journal of the American Statistical Association, 83(403), 596–610.
Cunningham, S. (2021). Causal Inference: The Mixtape. Yale University Press.
Ding, D., del Pozo Cruz, B., Green, M. A., & Bauman, A. E. (2020). Is the COVID-19 lockdown nudging people to be more active: a big data analysis. British Journal of Sports Medicine, 54(20), 1183 LP – 1184. https://doi.org/10.1136/bjsports-2020-102575
Djalante, R., Lassa, J., Setiamarga, D., Sudjatma, A., Indrawan, M., Haryanto, B., Mahfud, C., Sinapoy, M. S., Djalante, S., Rafliana, I., Gunawan, L. A., Surtiari, G. A. K., & Warsilah, H. (2020). Review and analysis of current responses to COVID-19 in Indonesia: Period of January to March 2020. Progress in Disaster Science, 6, 100091. https://doi.org/https://doi.org/10.1016/j.pdisas.2020.100091
Frey, B. (2020). Platform Labor and In/Formality: Organization among Motorcycle Taxi Drivers in Bandung, Indonesia. Anthropology of Work Review, 41(1), 36–49. https://doi.org/https://doi.org/10.1111/awr.12187
Google. (2022). Menemukan penelusuran terkait. https://support.google.com/trends/answer/4355000
Greyling, T., Rossouw, S., & Adhikari, T. (2021). The good, the bad and the ugly of lockdowns during Covid-19. PLOS ONE, 16(1), e0245546. https://doi.org/10.1371/journal.pone.0245546
Hidayati, D., Prasojo, A. P. S., Kusumaningrum, D., Surtiari, G. A. K., & Aini, Y. N. (2020). Riset dampak PSBB: meski menerima bantuan pemerintah, warga hanya mampu bertahan seminggu. The Conversation. https://theconversation.com/riset-dampak-psbb-meski-menerima-bantuan-pemerintah-warga-hanya-mampu-bertahan-seminggu-140180
Jacoby, W. G. (2000). Loess:: a nonparametric, graphical tool for depicting relationships between variables. Electoral Studies, 19(4), 577–613. https://doi.org/https://doi.org/10.1016/S0261-3794(99)00028-1
Jun, S.-P., Yoo, H. S., & Choi, S. (2018). Ten years of research change using Google Trends: From the perspective of big data utilizations and applications. Technological Forecasting and Social Change, 130, 69–87. https://doi.org/https://doi.org/10.1016/j.techfore.2017.11.009
Kemenkominfo. (2020). Kominfo Next: Lawan COVID-19. Edisi 15 Maret 2020. https://eppid.kominfo.go.id/storage/uploads/3_28_3._KNEXT_-_MAR_-_2020_-_DRAFT_-_6.pdf
Le Pennec, E., & Slowikowski, K. (2019). ggwordcloud: A Word Cloud Geom for “ggplot2.” https://cran.r-project.org/package=ggwordcloud
Mahato, S., Pal, S., & Ghosh, K. G. (2020). Effect of lockdown amid COVID-19 pandemic on air quality of the megacity Delhi, India. Science of The Total Environment, 730, 139086. https://doi.org/https://doi.org/10.1016/j.scitotenv.2020.139086
Mangono, T., Smittenaar, P., Caplan, Y., Huang, V. S., Sutermaster, S., Kemp, H., & Sgaier, S. K. (2021). Information-Seeking Patterns During the COVID-19 Pandemic Across the United States: Longitudinal Analysis of Google Trends Data. J Med Internet Res, 23(5), e22933. https://doi.org/10.2196/22933
Massicotte, P., & Eddelbuettel, D. (2020). gtrendsR: Perform and Display Google Trends Queries. https://cran.r-project.org/package=gtrendsR
Muhtaruddin, & Hartanto, B. D. (2020). Pengaruh Pendemi Covid-19 Terhadap Persepsi Masyarakat Jabodetabek Untuk Melakukan Perjalanan Mudik. Jurnal Penelitian Transportasi Darat, 22(2), 107–114. https://doi.org/10.25104/jptd.v22i2.1657
Naccarato, A., Falorsi, S., Loriga, S., & Pierini, A. (2018). Combining official and Google Trends data to forecast the Italian youth unemployment rate. Technological Forecasting and Social Change, 130, 114–122. https://doi.org/https://doi.org/10.1016/j.techfore.2017.11.022
Nagao, S., Takeda, F., & Tanaka, R. (2019). Nowcasting of the U.S. unemployment rate using Google Trends. Finance Research Letters, 30, 103–109. https://doi.org/https://doi.org/10.1016/j.frl.2019.04.005
Ngadi, Meillianna, R., & Purba, Y. A. (2020). Dampak Pandemi COVID-19 terhadap PHK dan Pendapatan Pekerja di Indonesia. Jurnal Kependudukan Indonesia, Edisi Khus, 43–48.
Pramisti, N. Q. (2020). Mitra GoFood Meningkat 40% Selama Pandemi. Tirto.Id. https://tirto.id/mitra-gofood-meningkat-40-selama-pandemi-fWhj
Prasojo, A. P. S., Aini, Y. N., & Kusumaningrum, D. (2020). Potensi Pola Aliran Mudik pada Masa Pandemi COVID-19. Jurnal Kependudukan Indonesia, Edisi Spes, 21–26.
Prasojo, A. P. S., Kusumaningrum, D., & Dalimunthe, S. A. (2020). Trajektori Pencarian Informasi Warga DKI Jakarta pada Sebelum dan Empat Periode PSBB Menggunakan Google Trends. In Lesson Learned?: Riset Sosial Budaya dalam Pengendalian COVID-19 (pp. 141–174). Yayasan Pustaka Obor Indonesia.
R Core Team. (2020). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. https://www.r-project.org/
Safitri, K. (2020). Belanja Online Meningkat, Sektor Logistik Panen Untung. Kompas.Com. https://money.kompas.com/read/2020/09/28/112458126/belanja-online-meningkat-sektor-logistik-panen-untung
Simionescu, M., Streimikiene, D., & Strielkowski, W. (2020). What Does Google Trends Tell Us about the Impact of Brexit on the Unemployment Rate in the UK? In Sustainability (Vol. 12, Issue 3). https://doi.org/10.3390/su12031011
Statista. (2020). Market share of leading search engines across Indonesia as of August 2020. https://www.statista.com/statistics/954420/indonesia-market-share-of-search-engines/
Sutrisno, B. (2020). ‘Transitional PSBB’: A deciding chapter for Jakarta’s new normal. The Jakarta Post. https://www.thejakartapost.com/news/2020/06/09/transitional-psbb-a-deciding-chapter-for-jakartas-new-normal.html
The Jakarta Post. (2020). Jokowi calls for ‘social distancing’ to stem virus spread. The Jakarta Post. https://www.thejakartapost.com/news/2020/03/15/jokowi-calls-for-social-distancing-to-stem-virus-spread.html
Vyas, M. (2020). Impact of Lockdown on Labour in India. The Indian Journal of Labour Economics, 63(1), 73–77. https://doi.org/10.1007/s41027-020-00259-w
Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis (1st ed.). Springer-Verlag New York. https://doi.org/10.1007/978-0-387-98141-3
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Published
2022-10-17
How to Cite
Prasojo, A. P. S. (2022). PANDEMI COVID-19 DAN TURNOVER KE PEKERJAAN INFORMAL: ANALISIS DATA GOOGLE TRENDS. Jurnal Aplikasi Statistika & Komputasi Statistik, 14(1), 49–62. https://doi.org/10.34123/jurnalasks.v14i2.360
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Statistika Kependudukan
