Modeling and forecasting the effect of COVID-19 in the Ecuadorian labor system

Main Article Content

Jorge Enrique Altamirano Flores
David Gonzalo Vera Alcívar
Luis Bernardo Tonon Ordóñez

Abstract

COVID-19 has caused massive disruption at different levels.  Scholars around the world have produced a significant number of studies to understand, reduce and predict the effects of this pandemic. Prediction models are crucial at this time of uncertainty, and labor indicators are key macroeconomic variables to plan the recovery of the effects of COVID-19. This study aims to model and predict the trend of the Ecuadorian labor system. The statistical analysis model applied for this study was the X-13ARIMA to predict the behavior of four indicators of the Ecuadorian labor system and establish their natural tendency by isolating the COVID-19 variable and determining the quantitative impact of the pandemic. The results show that the labor system was greatly affected by the COVID-19 outbreak; however, a deterioration was already observed in full employment and expanded underemployment. The study concludes that the pandemic altered the seasonality of the labor indicators.

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How to Cite
Altamirano Flores, J. E., Vera Alcívar, D. G. ., & Tonon Ordóñez, L. B. (2022). Modeling and forecasting the effect of COVID-19 in the Ecuadorian labor system. PODIUM, (42), 1–18. https://doi.org/10.31095/podium.2022.42.1
Section
Scientific articles
Author Biographies

Jorge Enrique Altamirano Flores, Universidad Internacional del Ecuador

PhD Development Economics, Newcastle University. Docente-investigador, Universidad Internacional del Ecuador - Ecuador.

David Gonzalo Vera Alcívar, Universidad Internacional del Ecuador

Maestría en Matemáticas Aplicadas, Universidad San Francisco de Quito. Docente-investigador, Universidad Internacional del Ecuador - Ecuador.

Luis Bernardo Tonon Ordóñez, Universidad del Azuay

Maestría en Administración de Empresas, Universidad del Azuay. Docente-investigador, Universidad del Azuay - Ecuador.

References

Aditya Satrio, C. B., Darmawan, W., Nadia, B. U., & Hanafiah, N. (2021). Time series analysis and forecasting of coronavirus disease in Indonesia using ARIMA model and PROPHET. Procedia Computer Science, 179, 524–532. https://doi.org/10.1016/J.PROCS.2021.01.036

Alsharif, M. H., Younes, M. K., & Kim, J. (2019). Time Series ARIMA Model for Prediction of Daily and Monthly Average Global Solar Radiation: The Case Study of Seoul, South Korea. Symmetry 2019, Vol. 11, Page 240, 11(2), 240. https://doi.org/10.3390/SYM11020240

Alshater, M. M., Almansour, A. Y., & Almansour, B. Y. (2021). Performance of ARCH and GARCH Models in Forecasting Cryptocurrency Market Volatility. Industrial Engineering & Management Systems, 20(2), 130–139. https://doi.org/10.7232/iems.2021.20.2.130

Asmelash Gebretensae, Y., & Asmelash, D. (2021). Trend Analysis and Forecasting the Spread of COVID-19 Pandemic in Ethiopia Using Box-Jenkins Modeling Procedure. International Journal of General Medicine, 14, 1485. https://doi.org/https://doi.org/10.2147%2FIJGM.S306250

Benanav, A. (2019). The origins of informality: the ILO at the limit of the concept of unemployment. Journal of Global History, 14(1), 107–125. https://doi.org/https://doi.org/10.1017/S1740022818000372

Benvenuto, D., Giovanetti, M., Vassallo, L., Angeletti, S., & Ciccozzi, M. (2020). Application of the ARIMA model on the COVID-2019 epidemic dataset. Data in Brief, 29, 105340. https://doi.org/10.1016/J.DIB.2020.105340

Bherwani, H., Gautam, S., & Gupta, A. (2021). Qualitative and quantitative analyses of impact of COVID-19 on sustainable development goals (SDGs) in Indian subcontinent with a focus on air quality. International Journal of Environmental Science and Technology 2021 18:4, 18(4), 1019–1028. https://doi.org/10.1007/S13762-020-03122-Z

Bógalo, J., Llada, M., Poncela, P., & Senra, E. (2022). Seasonality in COVID-19 times. Economics Letters, 211, 110206. https://doi.org/10.1016/J.ECONLET.2021.110206

Chung, R. C. P., Ip, W. H., & Chan, S. L. (2009). An ARIMA-intervention analysis model for the financial crisis in China’s manufacturing industry. International Journal of Engineering Business Management, 1(1), 15–18. https://doi.org/10.5772/6785

ENEMDU. (2019). National Survey for Employment, Unemployment and Underemployment. https://www.ecuadorencifras.gob.ec/enemdu-diciembre-2019/

European Union, C. (2017). Release of JDemetra+ version 2.2 as software officially recommended for the seasonal and calendar adjustment of official statistics in the EU. Collaboration in Research and Methodology for Official Statistics. https://bit.ly/3tlLQhc

Fattah, J., Ezzine, L., Aman, Z., Moussami, H. El, & Lachhab, A. (2018). Forecasting of demand using ARIMA model: Https://Doi.Org/10.1177/1847979018808673, 10. https://doi.org/10.1177/1847979018808673

Goel, R. K., Saunoris, J. W., & Goel, S. S. (2021). Supply chain performance and economic growth: The impact of COVID-19 disruptions. Journal of Policy Modeling, 43(2), 298–316. https://doi.org/10.1016/J.JPOLMOD.2021.01.003

Gómez, V., Maravall, A., & Peña, D. (1999). Missing observations in ARIMA models: Skipping approach versus additive outlier approach. Journal of Econometrics, 88(2), 341–363. https://doi.org/10.1016/S0304-4076(98)00036-0

Guleryuz, D. (2021). Forecasting outbreak of COVID-19 in Turkey; Comparison of Box–Jenkins, Brown’s exponential smoothing and long short-term memory models. Process Safety and Environmental Protection, 149, 927–935. https://doi.org/10.1016/J.PSEP.2021.03.032

Hernandez-Matamoros, A., Fujita, H., Hayashi, T., & Perez-Meana, H. (2020). Forecasting of COVID19 per regions using ARIMA models and polynomial functions. Applied Soft Computing, 96, 106610. https://doi.org/10.1016/J.ASOC.2020.106610

Ho, S. L., & Xie, M. (1998). The use of ARIMA models for reliability forecasting and analysis. Computers & Industrial Engineering, 35(1–2), 213–216. https://doi.org/https://doi.org/10.1016/S0360-8352(98)00066-7

Hyndman, R. J., & Khandakar, Y. (2008). Automatic Time Series Forecasting: The forecast Package for R. Journal of Statistical Software, 27(3), 1–22. https://doi.org/10.18637/JSS.V027.I03

Khamis, M., Prinz, D., Newhouse, D., Palacios-Lopez, A., Pape, U., & Weber, M. (2021). The Early Labor Market Impacts of COVID-19 in Developing Countries : Evidence from High-Frequency Phone Surveys. Working Paper;No. 58. World Bank, Washington, DC. © World Bank. https://bit.ly/3QdVFr9

Kim, S., Choi, C.-Y., Shahandashti, M., & Ryu, K. R. (2021). Improving Accuracy in Predicting City-Level Construction Cost Indices by Combining Linear ARIMA and Nonlinear ANNs. Journal of Management in Engineering, 38(2), 04021093. https://doi.org/10.1061/(ASCE)ME.1943-5479.0001008

Lemieux, T., Milligan, K., Schirle, T., & Skuterud, M. (2020). Initial Impacts of the COVID-19 Pandemic on the Canadian Labour Market. Https://Doi.Org/10.3138/Cpp.2020-049, 46(1), S55–S65. https://doi.org/10.3138/CPP.2020-049

Lim, W. M., & Wai-Ming, T. (2022). The economic impact of a global pandemic on the tourism economy: The case of COVID-19 and Macao´s destination-and-gambling-dependent. Current Issues in Tourism, 25(8), 1258–1269. https://doi.org/https://doi.org/10.1080/13683500.2021.1910218

Makridakis, S., & Hibon, M. (1997). ARMA models and the Box-Jenkins methodology. Journal of Forecasting, 16(3), 147–163. https://doi.org/https://doi.org/10.1002/(SICI)1099-131X(199705)16:3%3C147::AID-FOR652%3E3.0.CO;2-X

McBurney, M., Tuaza, L. A., Ayol, C., & Johnson, C. A. (2021). Land and livelihood in the age of COVID-19: Implications for indigenous food producers in Ecuador. Journal of Agrarian Change, 21(3), 620–628. https://doi.org/10.1111/JOAC.12417

Mueller, J. T., McConnell, K., Burow, P. B., Pofahl, K., Merdjanoff, A. A., & Farrell, J. (2021). Impacts of the COVID-19 pandemic on rural America. Proceedings of the National Academy of Sciences, 118(1). https://doi.org/10.1073/PNAS.2019378118

Nadal Rosselló, J., Font Riera, A., & Sansó Rosselló, A. (2004). The economic determinants of seasonal patterns. Annals of Tourism Research, 31(3), 697–711. https://doi.org/https://doi.org/10.1016/j.annals.2004.02.001

Naylor, T. H., Seaks, T. G., & Wichem, D. W. (1972). Box-Jenkins methods: An alternative to econometrics models. International Statistical Review/Revue Internationale de Statisque, 123–137. https://doi.org/https://doi.org/10.2307/1402755

Newbold, P. (1983). ARIMA model building and the time series analysis approach to forecasting. Journal of Forecasting, 2(1), 23–35. https://doi.org/https://doi.org/10.1002/for.3980020104

Parino, F., Zino, L., Porfiri, M., & Rizzo, A. (2021). Modelling and predicting the effect of social distancing and travel restrictions on COVID-19 spreading. Journal of the Royal Society Interface, 18(175). https://doi.org/10.1098/RSIF.2020.0875

Rosén, M., & Stenbeck, M. (2020). Interventions to suppress the coronavirus pandemic will increase unemployment and lead to many premature deaths: Scandinavian Journal of Public Health. https://doi.org/10.1177/1403494820947974

Sagaert, Y. R., Aghezzaf, E. H., Kourentzes, N., & Desmet, B. (2018). Tactical sales forecasting using a very large set of macroeconomic indicators. European Journal of Operational Research, 264(2), 558–569. https://doi.org/10.1016/J.EJOR.2017.06.054

Sakutukwa, T., & Yang, H. S. (2018). The role of uncertainty in forecasting employment by skill and industry. Applied Economics Letters, 25(18), 1288–1291. https://doi.org/https://doi.org/10.1080/13504851.2017.1418069

Sánchez, M., Ochoa M, W. S., Toledo, E., & Ordóñez, J. (2020). The relevance of Index of Sustainable Economic Wellbeing. Case study of Ecuador. Environmental and Sustainability Indicators, 6, 100037. https://doi.org/10.1016/J.INDIC.2020.100037

Sarkodie, S. A., Phebe, ·, Owusu, A., Owusu, P. A., Sarkodie, S. A., & Owusu, P. A. (2021). Global assessment of environment, health and economic impact of the novel coronavirus (COVID-19) Tunisian dinar OECD Organisation for economic co-operation and development RM Malaysian Ringgit NOK Norwegian Krone SARS Severe acute respiratory syndrome-rel. Environment, Development and Sustainability, 23, 5005–5015. https://doi.org/10.1007/s10668-020-00801-2

Siami-Namini, S., & Namin, A. S. (2018). Forecasting Economics and Financial Time Series: ARIMA vs. LSTM. ArXiv Preprint ArXiv:1803.06386. https://doi.org/https://doi.org/10.48550/arXiv.1803.06386

Siami-Namini, S., Tavakoli, N., & Siami Namin, A. (2019). A Comparison of ARIMA and LSTM in Forecasting Time Series. Proceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018, 1394–1401. https://doi.org/10.1109/ICMLA.2018.00227

Sowell, F. (1992). Modeling long-run behavior with the fractional ARIMA model. Journal of Monetary Economics, 29(2), 277–302. https://doi.org/10.1016/0304-3932(92)90016-U

Taneja, K., Ahmad, S., Ahmad, K., & Attri, S. (2016). Time series analysis of aerosol optical depth over New Delhi using Box-Jenkins ARIMA modeling approach. Atmospheric Pollution Research, 7(4), 585–596. https://doi.org/https://doi.org/10.1016/j.apr.2016.02.004

Tseng, F. M., & Tzeng, G. H. (2002). A fuzzy seasonal ARIMA model for forecasting. Fuzzy Sets and Systems, 126(3), 367–376. https://doi.org/10.1016/S0165-0114(01)00047-1

UNECE. (2020). Homepage | UNECE. United Nations. https://unece.org/

Wilke, R. A. (2018). Forecasting Macroeconomic Labour Market Flows: What Can We Learn from Micro-level Analysis? Oxford Bulletin of Economics and Statistics, 80(4), 822–842. https://doi.org/https://doi.org/10.1111/obes.12222

Wolff, J. (2018). Ecuador after Correa: the struggle over the “citizens’’ revolution".” Revista de Ciencia Política, 38(2), 281–302.

World Bank. (2021). Ecuador Overview: Development news, research, data | World Bank. World Bank Publications. https://bit.ly/3xs2SNb