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The Circular Relationship Between Productivity and Hours Worked: A Long-Term Analysis

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Abstract

We analyze the circular relationship between productivity (or wages) and hours worked. Different channels come into play in this circular relationship: productivity (or wages) impacts hours worked through either an income channel or a substitution channel, while returns to scale of hours worked depend on a fixed-cost channel or a fatigue channel. We estimate the two equations of this circular relationship, using the IV estimation method, on two separate datasets for advanced countries: a long-term (1890–2019) country panel database, and a country-industry panel for a shorter, more recent period (1995–2019). The main results are: (i) the income channel outweighs the substitution channel in the long term: increased productivity or higher wages reduce the number of hours worked; (ii) the fatigue channel outweighs the fixed-cost channel: a reduction in hours worked raises productivity (or hourly wages). According to our results, a productivity revival brought about by the digital revolution and resulting in the same productivity growth as was observed in the US from 1900 to 1975 would reduce hours worked to 25 h per week by the end of this century.

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Notes

  1. See the supplementary web appendix for more information on this database and Bergeaud et al. (2016) for a complete description of the methodology used to construct the database. This database can be freely accessed, see Long-Term Productivity Database.

  2. The average annual working time (H) concerns all workers. The employment rate (ER) corresponds here to the ratio employment divided by total population.

  3. For some variables as hours worked and also employment, information is not always annually available in historical or economic literature before WWII and even more before WWI. In these cases, Bergeaud et al. (2016) have built these missing data by interpolation. This may make the short-run effects less visible in pre-WWII estimates.

  4. We use the Release 2021 of the integrated EUKLEMS & INTANProd database run by the Luiss Lab of European Economics.

  5. The countries in our main estimation sample are: Austria, Belgium, the Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Italy, Japan, Lithuania, the Netherlands, Poland, Romania, Slovakia, Slovenia, Spain, Sweden, the United Kingdom, and the United States.

  6. Four industries of the business sector are excluded from our estimation sample because the productivity measurement is particularly difficult concerning these industries: “Agriculture, forestry and fishing”, “Mining and quarrying”, “Manufacture of coke and refined petroleum products”, and “Real estate activities”.

  7. EU KLEMS provides annual average wage, employment and annual average working time. Based on these data, we can easily calculate the average wage per hour.

  8. These 15 countries are: Australia, Austria, Belgium, Canada, Finland, France, Germany, Italy, Japan, the Netherlands, Spain, Sweden, Switzerland, the United Kingdom, and the United States.

  9. These average effects may be induced by a change in the hours worked by the same workers (the intensive margin) or by a composition effect if workers leave/enter employment because of productivity changes (the extensive margin). Our database doesn’t allow to break down between both effects.

  10. The employment rate on our country level database is calculated on the whole population, not only on the working age population as it should be. Therefore, our estimates may overestimate the impact of the employment rate.

  11. The set of fixed effects are the same in columns (3) and (4) as in the sub-periods, so there are respectively two and four fixed effects per country. This is particularly important for the whole period 1891–2019 as the years of WWs and part of the rebuilding period are excluded from the estimation sample.

  12. Estimation results are available from the authors on request.

  13. The two closest countries are those with the highest correlation in terms of hours worked (without year fixed effects) with the country under consideration. The two closest industries are the ones with the highest correlation within country on average across the whole set of countries.

  14. For instance, fast productivity growth in neighboring countries may induce, through higher imports, higher demand for the goods produced in the country. Note, however, that the closest countries in term of hours worked correlations are not always neighboring countries in our sample.

  15. Estimation results are available from the authors on request.

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Authors and Affiliations

  1. Neoma Business School, Mont-Saint-Aignan, France

    Gilbert Cette

  2. Paris School of Economics and Université Paris 1 Panthéon – Sorbonne, Paris, France

    Simon Drapala

  3. Laboratoire d’Economie de Dijon (LEDI), Université de Bourgogne, 5 Boulevard Gabriel, 21000, Dijon, France

    Jimmy Lopez

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  1. Gilbert Cette
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  2. Simon Drapala
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Correspondence to Jimmy Lopez.

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We thank two anonymous reviewers as well as John Fernald, Robert Gordon, Jacob Madsen and all participants of the session “institutions and productivity” hosted by the Association for Comparative Economic Studies at the ASSA 2023 annual meeting organized by the American Economic Association, for helpful comments and suggestions.

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Cette, G., Drapala, S. & Lopez, J. The Circular Relationship Between Productivity and Hours Worked: A Long-Term Analysis. Comp Econ Stud 65, 650–664 (2023). https://doi.org/10.1057/s41294-023-00224-8

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