Visualization Website for the Japan-UK Joint Project “The Future of Unpaid Work:AI’s Potential to Transform Domestic Work in Japan and UK”
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About the data
Japan: The Japanese Survey on Time Use and Leisure Activities (STULA) 2016, the Statistics Bureau of Japan.
The UK: The UK Time Use Survey (UKTUS) 2014-15, Gershuny and Sullivan (2017)
Reference
Gershuny, J., Sullivan, O. (2017). United Kingdom Time Use Survey, 2014-2015. Centre for Time Use Research, IOE, University College London. [data collection]. UK Data Service. SN: 8128, http://doi.org/10.5255/UKDA-SN-8128-1
＊Access to the UKTUS 2014-2015 is obtained through the UK Data Archive https://doi.org/10.5255/UKDA-SN-8128-1. Permission to use the data on STULA 2016 was obtained through the Statistics Bureau of Japan based on the Statistics Act (Act No. 53 of 2007), Article 33 http://www.stat.go.jp/english/data/shakai/index.html.
Terms and Methods
Time Production of Unpaid Work
Average time spent on unpaid work per week by country, sex and age group.
Time Consumption of Unpaid Work
Average time consumed for unpaid work per week by country, sex and age group. It is estimated using the National Time Transfer Accounts (NTTA) method. The details of the method are referred to the NTTA methodology (https://www.countingwomenswork.org/about/methodology).
Net Time Consumption of Unpaid Work
Net time consumption equals time consumption minus time production. When the net time consumption of unpaid work is positive, the per capita consumption of unpaid work exceeds the per capita production in a given country, sex and age group. Thus, the time production of unpaid work is in deficit in the group, and the deficit needs to be filled by the transfer of unpaid work from other groups. The opposite is true when the net time consumption of unpaid work is negative. Those with a surplus of unpaid work production transfer unpaid work time to those who are in a deficit of unpaid work.
Experts’ Prediction of Automation in Time Use of Unpaid Work Activities in 5 years / 10 years
Data is obtained from a Delphi survey inquiring about the probability of automation in five and ten years, as well as the anticipated market prices of the automation services and products related to 17 types of unpaid work activities*. The study involved 65 Japanese and English experts engaged in AI-related research and business. The survey was conducted online from September 2020 to June 2021. For details on the survey methodology, please refer to Lehdonvirta et al. (2023).
*Note: The 17 types of unpaid work activites include 1) cooking, 2) dishing, 3) cleaning, 4) clothes making, 5) laundry, 6) ironing, 7) gardening, 8) pet care, 9) home and car maintenance, 10) grocery shopping, 11) other shopping, 12) use of public and commercial services, and household management, 13) physical care and supervision of infants, 14) teaching children, 15) interact with children, 16) accompanying and transporting children, and 17) caregiving, nursing, and personal care for family members (excluding children). In the analysis and simulation, grocery shopping and other shopping were combined (with the automation probability averaged), presenting 16 types of unpaid work activities.”
Reference
Lehdonvirta V, Shi LP, Hertog E, Nagase N, Ohta Y (2023) The future(s) of unpaid work: How susceptible do experts from different backgrounds think the domestic sphere is to automation? PLoS ONE 18(2): e0281282. https://doi.org/10.1371/journal.pone.0281282
Households’ Preference on the Use of Automation Services/Devices for Unpaid Work
Data was obtained from household surveys conducted in Japan and the UK. The percentage of respondents who answered ‘yes’ to the following questions regarding 17 types of unpaid work activities is displayed by country, sex, and age group:
‘If there were (automation) technology, would you consider using it in your household? Yes = 1, No = 2’
Alternatively, some respondents are asked the following questions; ‘Do you think you would use this technology in your household at ¥¥ yen/ ￡￡pound (the minimum price estimated by experts)? Yes = 1, No = 2’
The survey sample sizes were 24,727 people in Japan and 11,695 people in the UK.
* The reference websites for the Japanese and UK household surveys will be uploaded shortly.
Automation Score of Unpaid Work Activities
The automation probabilities of unpaid work activities were originally obtained from our study. The probabilities are derived by multiplying the automation probabilities of unpaid work activities obtained from Japanese and English experts (by 17 types of unpaid work activities and predictions 5/10 years ahead) with the percentage of individuals who intend to use those unpaid work automation services in households obtained from household surveys in Japan and the UK (broken down by country, sex, and age group). These figures represent the practical automation probabilities of unpaid work activities in households.
As a reference, the study also presents the automation probabilities when applying automation scores for market occupations corresponding to each type of unpaid work activity. For instance, assigning occupations closely related to each unpaid work activity, such as assigning a chef for cooking, a maid or janitor for cleaning, and a launderer, dry cleaner and presser for laundry. Then, the probability of future automation for each occupation is utilized. The occupation automation probabilities can be selected from three sources: Frey and Osborne (F&O) (2017), Nomura Research Institute (NRI) (2015), and The UK Office of National Statistics (ONS) (2019). Please refer to the respective references for details on each set of the automation probabilities of occupation. Additionally, for details on the methodology linking occupation automation probabilities to unpaid work activities for the Japanese and the UK data, please refer to Hertog et al. (2023).
Reference
Frey C and Osborne M (2017) The Future of Employment: How Susceptible Are Jobs to Computerisation? Technological Forecasting and Social Change 114: 254–80.
Hertog, E., S. Fukuda, R. Matsukura, N. Nagase, and V. Lehdonvirta (2023) The future of unpaid work: Estimating the effects of automation on time spent on housework and care work in Japan and the UK, Technological Forecasting and Social Change 191: Article 122443.
Nomura Research Institute (2015) Nihon no rōdō jinkō no 49-pāsento ga jinkō chinō ya robotto-tō de daitai kanō ni (49% of Japan’s working population can be replaced by artificial intelligence and robots). Retrieved from https://www.nri.com/-/media/Corporate/jp/Files/PDF/news/newsrelease/cc/2015/151202_1.pdf.
The UK Office of National Statistics (2019) The Probability of Automation in England: 2011 and 2017. Retrieved from https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/articles/theprobabilityofautomationinengland/2011and2017
Trends in Population Aggregated Net Time Consumption of Unpaid Work: A Case for Non-Automation
The value is obtained by multiplying the per capita net consumption of unpaid work by country, sex, and age (5-year age group) with the corresponding population by country, sex, and age (5-year age group). This represents the total net consumption of weekly unpaid work hours at the population level, expressed in person-hours (people × hours).
Assuming that the per capita net consumption of unpaid work remains constant, this illustrates how the net consumption of unpaid labor hours (the population-aggregated values) would change in the future if only the population structure were to change. The net consumption of unpaid work hours is presented, considering the country’s population size by sex and age.
Similar to per capita net consumption of unpaid work hours, if this value is positive, it indicates that the consumption of unpaid work hours exceeds production (consumption > production), meaning there is a shortage of unpaid work hours. Conversely, if this value is negative, it indicates that the production of unpaid labor hours exceeds consumption (consumption < production), signifying a surplus of unpaid work hours.
Trends in Population Aggregated Net Time Consumption of Unpaid Work: A Case for Automation
The value is calculated by discounting the estimated automation probabilities of unpaid work obtained in this study (for the prediction 10 years ahead) from the aforementioned “Population Aggregated Net Time Consumption of Unpaid Work.” This illustrates the period changes in the net consumption of unpaid work hours by country, sex, and age under the assumption of the potential advancement of unpaid work automation. For the automation probabilities, we present results with our original automation score (for the prediction 10 years ahead) as well as those by applying three sources of occupation automation probabilities from Frey and Osborne (F&O) (2017), Nomura Research Institute (NRI) (2015), and The UK Office of National Statistics (ONS) (2019). Please refer to “Automation Score of Unpaid Work Activities” for further details about these automation probabilities.
The Amount of Unpaid Work Hour Shortage
The value is obtained by summing “Population Aggregated Net Time Consumption of Unpaid Work” over sex and age. This value represents to what extent there will be a shortage of unpaid labor work hours at the population levels in both Japan and the UK in the future. Additionally, the graphs demonstrate to what extent the automation of unpaid work will alleviate the shortage of unpaid work hours in the future.
Potential Impacts of Automation of Unpaid Work on Employment Rate: A Case for the F&O Automation Score
The predicted employment rate in the case where the occupational automation scores from Frey and Osborne (F&O) (2017) are applied to unpaid work, assuming that all the time freed from unpaid work transitions to paid labor. For detailed estimation methodology, please refer to Hertog et al. (2023).
Referece
Frey C and Osborne M (2017) The Future of Employment: How Susceptible Are Jobs to Computerisation? Technological Forecasting and Social Change 114: 254–80.
Hertog, E., S. Fukuda, R. Matsukura, N. Nagase, and V. Lehdonvirta (2023) The future of unpaid work: Estimating the effects of automation on time spent on housework and care work in Japan and the UK, Technological Forecasting and Social Change 191: Article 122443.
Deficit and Surplus of Unpaid Work Hours in Young Ages, Middle Ages and Old Ages
The value is a sum of “Population Aggregated Net Time Consumption of Unpaid Work” for the young age deficit, the middle age surplus and the old age deficit. The figures illustrate the extent of the shortage of unpaid work in the young and old ages and the surplus of unpaid work hours in the middle ages in Japan and the UK, depicting how these amounts change in the future. Additionally, the graphs show how the automation of unpaid work changes the shortage and surplus of unpaid work hours at each life stage.
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