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The ‘time-diary method’ is generally more reliable and allows a richer analysis of routines, because it measures not only aggregate times but also sequences and clock-times. Time-diary data is less common, but it is available for some countries from the Multinational Time Use Study. We explore time-use ‘tempograms’ from the MTUS in a forthcoming companion post.
Because these estimates include people who are not employed they are much lower than the estimates of working hours per worker we present elsewhere. The estimates also differ because of differences in the sources: time-use surveys compared to labor force surveys and national accounts data.
OECD (2020) Time Use Database.
If you want to dig deeper you can explore gender differences across all other activities directly from our source, via the OECD Data Portal. And you can read more about within-country inequalities in time use along other dimensions, such as income and education, in this Brookings Paper, where the authors focus on the ‘middle class time squeeze’ in the US. See: Sawhill, I. V., & Guyot, K. (2020). The Middle Class Time Squeeze. Economic Studies at Brookings. Brookings Institution.
The underlying data comes from time-use diaries where respondents are asked to record the sequence of what they do over a specific day, and how much they enjoy each ‘episode’ (i.e. what they do) on a scale from 1 to 7. All episodes reported are then coded and grouped into similar activities. To arrive at the mean enjoyment scores, the authors multiply the duration of each episode where the activity category concerned is the primary activity recorded, by the enjoyment level to arrive at the total enjoyment score for that episode. Then they sum these total enjoyment scores for each category of activity across the day, and finally divide these daily enjoyment total scores for each activity by the amount of time devoted to the activity. In this way, they arrive at an appropriately weighted mean enjoyment level for each activity across all those who engage in it. For more details see Gershuny, J., & Sullivan, O. (2019). What We Really Do All Day: Insights from the Centre for Time Use Research. Penguin UK.
You find a very clear and complete explanation of this in Ramey, V. A., & Francis, N. (2009). A century of work and leisure. American Economic Journal: Macroeconomics, 1(2), 189-224.
Gershuny, J., & Sullivan, O. (2019). What We Really Do All Day: Insights from the Centre for Time Use Research. Penguin UK.
When interpreting this chart it’s important to bear in mind that the relationships used to categorise people are not exhaustive (i.e., survey respondents could also list being with people who didn’t fit any of the listed categories, or for whom a relationship was unclear or unknown – we do not count these instances in the estimates). Additionally, time spent with multiple people can be counted more than once; so attending a party with friends and your spouse, for example, would show up for both “friends” and “partner” in our estimates. The implication is that companion categories cannot be stacked to add up total time spent in the company of others.
The full reference for the source paper is: Dotti Sani, G. M., & Treas, J. (2016). Educational gradients in parents’ child‐care time across countries, 1965-2012. Journal of Marriage and Family, 78(4), 1083-1096.
Dotti Sani, G. M., & Treas, J. (2016). Educational gradients in parents’ child‐care time across countries, 1965-2012. Journal of Marriage and Family, 78(4), 1083-1096.
The estimates from Sandberg and Hofferth correspond to weekly hours among children ages 3-12. The estimates cover both ‘engaged time’ and other activities where parents are not engaged but children say they are present. When looking jointly at both parents, Sandberg and Hofferth find that time spent with either parent remains roughly unchanged, which suggests part of the increase corresponds to an intra-household reallocation of time. In the paper you find more details and discussion, including a detailed analysis of trends for more narrow population groups and possible demographic and behavioral drivers: Sandberg, J. F., & Hofferth, S. L. (2001). Changes in children’s time with parents: United States, 1981-1997. Demography, 38(3), 423-436.
For an overview of the literature see (i) Dotti Sani, G. M., & Treas, J. (2016). Educational gradients in parents’ child‐care time across countries, 1965-2012. Journal of Marriage and Family, 78(4), 1083-1096. (ii) Sayer, L. C., Bianchi, S. M., & Robinson, J. P. (2004). Are parents investing less in children? Trends in mothers’ and fathers’ time with children. American journal of sociology, 110(1), 1-43. (iii) Sandberg, J. F., & Hofferth, S. L. (2001). Changes in children’s time with parents: United States, 1981-1997. Demography, 38(3), 423-436.
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The parenting guide from the National Health Insurance in the UK, for example, says “Parents should make the time to play with a first or only child. And while brothers and sisters are natural playmates, parents can also play an active role in siblings’ games…If you’re pressed for time as a parent, it’s a good idea to find ways to involve your child in what you’re doing – even the housework.” You find a review of the academic literature on the importance of parent-child interactions for children’s development in Dotti Sani, G. M., & Treas, J. (2016). Educational gradients in parents’ child‐care time across countries, 1965-2012. Journal of Marriage and Family, 78(4), 1083-1096.
In this chart we have taken the original data published by Huberman & Minns (2007) and extended coverage using an updated vintage of the Penn World Table (PWT), which is in turn based on the same underlying source that Huberman and Minns used for all data since 1950, the Total Economy Database. You can find more details and links to our sources in the ‘Sources’ tab of the chart.
A key point to keep in mind when interpreting these trends is that they refer to working hours per worker, which is different from working hours per person. The per person measure corresponds to working hours per worker multiplied by the employment rate. Hence, changes in employment patterns — such as the historical rise of female participation in paid employment in these countries — mean that changes in hours per worker do not translate directly into changes in hours per person.
A study by Michael Huberman and Frank Lewis reconstructed estimates of working hours in 1870 and 1900 for 48 countries across six continents using data from worker records kept by individual business establishments. They drew from a collection of records published by the US Department of Labor in 1900, and found substantial variation, but very high working hours for many non-industrialized countries. They found for example that in 1870, Colombia, Uruguay and Brazil had similar average working hours per worker as the US. The full reference of the paper is Huberman, M., & Lewis, F. D. (2007). Bend it like Beckham: Hours and wages across forty-eight countries in 1900 (No. 1229). Queen’s Economics Department Working Paper.
The increase in hours between 1938 and 1950 in the chart for some countries is due in part to the uptick during and just after World War II, but also plausibly due in part to differences in the source data and methodology.
Costa, D. L. (2000). The Wage and the Length of the Work Day: From the 1890s to 1991. Journal of Labor Economics, 18(1).
In our first post in the series, we discuss how increases in labor productivity have driven a rise in incomes and a decrease in working hours.
Coyle, D. and Nakamura, L. I. (2019). Toward a Framework for Time Use, Welfare, and Household Centric Economic Measurement. Federal Reserve Bank of Philadelphia Working Paper No. 19-11.
We chose Cambodia and Switzerland here because they both also have working hours data available, but the difference in average income can be even more extreme. For instance, people in Qatar have an average income that is 117-times higher than that of people in the Central African Republic.
These differences refer to GDP per capita measured in international-$ and account for price differences between countries to enable comparisons. You can read more about this here.
But life can also look similar, as you see in the pictures of the homes, computers, and phones of people on similar income levels in the two countries.
These trends in GDP per capita are measured in constant international-$ and account for inflation to enable comparisons over time and between countries. You can read more about this here.
We explore the differences in working hours between similar, highly productive countries — and also the differences within those countries — in forthcoming posts.
For a discussion of how technology drives productivity growth and a rise in incomes (economic growth), see Romer, P. (1990) Endogenous Technological Change. Journal of Political Economy. For a discussion of the relationship between productivity growth, economic growth, and working hours, see Boppart, T. and P. Krusell (2020) Labor Supply in the Past, Present, and Future: A Balanced-Growth Perspective. Journal of Political Economy.
See Figure 18 on p. 28 of Wang et al (2015) Agricultural Productivity Growth in the United States: Measurement, Trends, and Drivers. USDA Economic Research Report 189.
The transition of employment out of agriculture to other economic sectors as countries become richer is known as ‘structural transformation’. You can read more about this in our post Structural transformation: how did today’s rich countries become ‘deindustrialized’?
Pencavel, J. (2015) The productivity of working hours. The Economic Journal.
Agarwal, R. and Gaule, P. (2020) Invisible Geniuses: Could the Knowledge Frontier Advance Faster? American Economic Review: Insights.
Hours actually worked means hours spent directly on work and excludes things like annual leave, sick leave, public holidays, meal breaks, and commuting time. Unpaid family work in this case generally includes market-oriented work, such as for the family business, but not other unpaid work at home such as childcare, cooking, and cleaning. Since the latter type of unpaid work is typically performed by women, this has large implications for understanding gender differences in labor. We discuss these issues as part of our entry on Women’s Employment.
Only covering resident workers means that any cross-border workers are excluded. Only covering workers above a certain age means that any child laborers are excluded. While the incidence of child labor has been going down over time, especially in high-income countries, there are still an estimated 265 million working children in the world (almost 17% of the worldwide child population).
Employers include businesses, non-profits, some government agencies, and other organizations that pay a wage.
Unlike hours actually worked, paid or contractual hours typically include some time not spent working, such as during sick leave, and fail to include time spent working that wasn’t paid or planned, such as overtime.
Activities also include unpaid household work, school, leisure time, eating, and sleeping.
By organizations such as the United Nations, International Labor Organization (ILO), OECD, and Eurostat.
For further discussion of different sources and their comparability, see the methods guides of the OECD and the Total Economy Database and the work of Bick, Brüggemann, and Fuchs-Schündeln (2019).
Huberman, M. and Minns, C. (2007) The times they are not changin’: Days and hours of work in Old and New Worlds, 1870-2000. Explorations in Economic History.
U.S. Department of Labor (1900) Fifteenth Annual Report of the Commissioner of Labor: Wages in Commercial Countries. 2 vols. Washington, DC.
The original sources are: 1870-1913: Huberman (2004) [in turn relying on the US Department of Labor Fifteenth Annual Report, 1900]; 1929-1938: International Labor Organization (1934-39), except for Canada (Ostry and Zaidi, 1972), U.S. (Jones, 1963; Owen, 1988), and Australia (Butlin, 1977); 1950-2000: University of Groningen and the Conference Board GGDC Total Economy Database (2005).
Bick, A., Brüggemann, B., and Fuchs-Schündeln, N. (2019) Hours Worked in Europe and the United States: New Data, New Answers. The Scandinavian Journal of Economics.
PWT sources its working hours data from The Conference Board’s Total Economy Database (TED). For more details on the underlying sources, see the TED guide and the OECD database.
We gain further confidence in these conclusions when they are echoed by research that focuses only on more standardized, comparable sources for a necessarily smaller set of countries, as in the work by Bick, Fuchs-Schündeln, and Lagakos (2018).
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