1. Home
  2. Issues
  3. Volume 20, Issue 2 (2022)
  4. Monitoring COVID-19-Induced Gender Diffe ...

Journal of Data Science


Monitoring COVID-19-Induced Gender Differences in Teleworking Rates Using Mobile Network Data
Volume 20, Issue 2 (2022), pp. 209–227
Pub. online: 20 April 2022      Type: Data Science In Action      Open accessOpen Access
Received
10 February 2022
Accepted
29 March 2022
Published
20 April 2022

Abstract

The COVID-19 pandemic has created a sudden need for a wider uptake of home-based telework as means of sustaining the production. Generally, teleworking arrangements impact directly worker’s efficiency and motivation. The direction of this impact, however, depends on the balance between positive effects of teleworking (e.g. increased flexibility and autonomy) and its downsides (e.g. blurring boundaries between private and work life). Moreover, these effects of teleworking can be amplified in case of vulnerable groups of workers, such as women. The first step in understanding the implications of teleworking on women is to have timely information on the extent of teleworking by age and gender. In the absence of timely official statistics, in this paper we propose a method for nowcasting the teleworking trends by age and gender for 20 Italian regions using mobile network operators (MNO) data. The method is developed and validated using MNO data together with the Italian quarterly Labour Force Survey. Our results confirm that the MNO data have the potential to be used as a tool for monitoring gender and age differences in teleworking patterns. This tool becomes even more important today as it could support the adequate gender mainstreaming in the ‘Next Generation EU’ recovery plan and help to manage related social impacts of COVID-19 through policymaking.

Supplementary material

 Supplementary Material
1. model.R: This is the R code used to run the model, plot the variable importance and the teleworking predictions by region and gender.

References

 
Adams-Prassl A, Boneva T, Golin M, Rauh C (2020). Inequality in the impact of the coronavirus shock: Evidence from real time surveys. Journal of Public Economics, 189: 104245.
 
Al-Zuabi IM, Jafar A, Aljoumaa K (2019). Predicting customer’s gender and age depending on mobile phone data. Journal of Big Data, 6(1): 18.
 
Almaatouq A, Prieto-Castrillo F, Pentland A (2016). Mobile communication signatures of unemployment. In: Social Informatics. SocInfo 2016, volume 10046 of Lecture Notes in Computer Science.
 
Alon T, Doepke M, Olmstead-Rumsey J, Tertilt M (2020). The Impact of COVID-19 on Gender Equality. CRC TR 224 Discussion Paper Series crctr224_2020_163, University of Bonn and University of Mannheim, Germany.
 
Blau FD, Kahn LM (2017). The gender wage gap: Extent, trends, and explanations. Journal of Economic Literature, 55(3): 789–865.
 
Bonacini L, Gallo G, Scicchitano S (2021). Working from home and income inequality: Risks of a ‘new normal’ with COVID-19. Journal of Population Economics, 34(1): 303–360.
 
Casarico A, Lattanzio S (2020). The Heterogeneous Effects of COVID-19 on Labor Market Flows: Evidence from Administrative Data. Centre for Economic Policy Research (CEPR) Press.
 
Caselli FG, Grigoli F, Sandri D, Spilimbergo A (2020). Mobility under the COVID-19 pandemic: Asymmetric effects across gender and age. IMF Working Papers 2020/282, International Monetary Fund.
 
Chung H, Birkett H, Forbes S, Seo H (2021). COVID-19, flexible working, and implications for gender equality in the united kingdom. Gender & Society, 35(2): 218–232.
 
Chung H, van der Horst M (2018). Women’s employment patterns after childbirth and the perceived access to and use of flexitime and teleworking. Human Relations, 71(1): 47–72. PMID: 29276304.
 
Dang H-AH, Viet Nguyen C (2021). Gender inequality during the COVID-19 pandemic: Income, expenditure, savings, and job loss. World Development. 140(C).
 
Dingel JI, Neiman B (2020). How many jobs can be done at home? Journal of Public Economics, 189: 104235.
 
Dunatchik A, Gerson K, Glass J, Jacobs JA, Stritzel H (2021). Gender, parenting, and the rise of remote work during the pandemic: Implications for domestic inequality in the united states. Gender & Society, 35(2): 194–205.
 
Esping-Andersen G, Billari FC (2015). Re-theorizing family demographics. Population and Development Review, 41(1): 1–31.
 
Esping-Andersen G, Boertien D, Bonke J, Gracia P (2013). Couple specialization in multiple equilibria. European Sociological Review, 29(6): 1280–1294.
 
Eurofound (2020). Telework and ICT-Based Mobile Work: Flexible Working in the Digital Age. New Forms of Employment Series. Publications Office of the European Union, Luxembourg.
 
Farré L, Fawaz Y, Gonzalez L, Graves J (2020). How the COVID-19 lockdown affected gender inequality in paid and unpaid work in Spain. IZA Discussion Papers 13434, Institute of Labor Economics (IZA).
 
Fuller S, Hirsh CE (2019). “Family-friendly” jobs and motherhood pay penalties: The impact of flexible work arrangements across the educational spectrum. Work and Occupations, 46(1): 3–44.
 
Fuller S, Qian Y (2021). COVID-19 and the gender gap in employment among parents of young children in canada. Gender & Society, 35(2): 206–217.
 
Greenwell BM, Boehmke BC (2020). Variable importance plots–An introduction to the vip package. The R Journal, 12(1): 343–366.
 
Grubanov-Boskovic S, Tintori G, Biagi F (2020). Gaps in the EU Labour Market Participation Rates: An intersectional assessment of the role of gender and migrant status, EUR 30406 EN. Publications Office of the European Union, Luxembourg (Luxembourg).
 
Hipp L, Bünning M (2021). Parenthood as a driver of increased gender inequality during COVID-19? Exploratory evidence from Germany. European Societies, 23(sup1): S658–S673.
 
Hobson B (2004). The individualised worker, the gender participatory and the gender equity models in Sweden. Social Policy and Society, 3(1): 75–83.
 
Hupkau C, Petrongolo B (2020a). Work, care and gender during the COVID-19 crisis*. Fiscal Studies, 41(3): 623–651.
 
Hupkau C, Petrongolo B (2020b). Work, care and gender during the COVID-19 crisis*. Fiscal Studies, 41(3): 623–651.
 
Iacus SM, Santamaria C, Sermi F, Spyratos S, Tarchi D, Vespe M (2021). Mobility functional areas and COVID-19 spread. Transportation. https://doi.org/10.1007/s11116-021-10234-z.
 
ISTAT (2019). Istruzione, lavoro e spostamenti per studio o lavoro. http://dati-censimentipermanenti.istat.it/OECDStat_Metadata/ShowMetadata.ashx?Dataset=DCSS_ISTR_LAV_PEN_2&ShowOnWeb=true&Lang=en. Accessed: 2010-09-30.
 
Jahani E, Sundsøy P, Bjelland J, Bengtsson L, de Montjoye Y (2017). Improving official statistics in emerging markets using machine learning and mobile phone data. EPJ Data Science, 6.
 
Jo A, Lee S-K, Kim J (2020). Gender gaps in the use of urban space in Seoul: Analyzing spatial patterns of temporary populations using mobile phone data. Sustainability, 12(16).
 
Kristal T, Yaish M (2020). Does the coronavirus pandemic level the gender inequality curve? (It doesn’t). Research in Social Stratification and Mobility, 68: 100520.
 
Milasi S, González-Vázquez I, Fernández-Macías E (2021). Telework before the COVID-19 pandemic: Trends and drivers of differences across the EU. OECD Productivity Working Papers 21, OECD Publishing.
 
OECD (2020). Productivity gains from teleworking in the post COVID-19 era: How can public policies make it happen? OECD Publishing.
 
Petts RJ, Carlson DL, Pepin JR (2021). A gendered pandemic: Childcare, homeschooling, and parents’ employment during COVID-19. Gender, Work & Organization, 28(S2): 515–534.
 
Rubery J, Tavora I (2020). The COVID-19 crisis and gender equality: Risks and opportunities. Social policy in the European Union: state of play, 71–96.
 
Samek Lodovici M, Ferrari E, Paladino E, Pesce F, Frecassetti P, Aram E, et al. (2021). The impact of teleworking and digital work on workers and society. Publication for the committee on Employment and Social Affairs, Policy Department for Economic, Scientific and Quality of Life Policies. European Parliament, Luxembourg.
 
Sundsøy P, Bjelland J, Reme B-A, Jahani E, Wetter E, Bengtsson L (2016). Estimating individual employment status using mobile phone network data.
 
Toole JL, Lin Y-R, Muehlegger E, Shoag D, González MC, Lazer D (2015). Tracking employment shocks using mobile phone data. Journal of The Royal Society Interface, 12(107): 20150185.
 
Vespe M, Iacus SM, Santamaria C, Sermi F, Spyratos S (2021a). On the use of data from multiple mobile network operators in europe to fight COVID-19. Data & Policy, 3: e8.
 
Vespe M, Minora U, Iacus S, Spyratos S, Sermi F, Fontana M, et al. (2021b). Mobility and economic impact of COVID-19 restrictions in Italy using mobile network operator data. (KJ-NA-30630-EN-N (online)).
 
Zien A, Krämer N, Sonnenburg S, Rätsch G (2009). The feature importance ranking measure. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 694–709. Springer.

Related articles PDF XML
Related articles PDF XML

Copyright
2022 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.
Open access article under the CC BY license.

Keywords
coronavirus gender equality homeworking mobility

Metrics
since February 2021
1030

Article info
views

428

PDF
downloads


Share


RSS