Global Working Hours
What this paper finds — and why it matters
Drawing on about 5,000 labor force and household surveys from 160 countries that cover 97% of the world’s population, this paper builds a new global database of hours worked and shows that hours worked per adult decline only slightly with GDP per capita and are weakly correlated with economic development overall: the unconditional elasticity of hours with respect to GDP is about -0.04 across countries and -0.01 within countries over time, GDP explains roughly 5% of cross-country and under 1% of within-country historical variation in hours, and the implied reduction is 0-20% over the entire development spectrum. The strong age and gender gradients the authors document are, in their cross-country regressions, driven less by development itself than by institutions: hours worked by the young (aged 15-19) and the elderly (aged 60+) fall with development almost entirely because of rising school attendance and public pension coverage, while prime-age (20-59) hours stay roughly flat but undergo what the authors call a “great gender reshuffling,” in which falling male hours per worker are quantitatively offset by rising female labor force participation. Across countries and over time, labor taxes are strongly negatively correlated with prime-age hours worked; controlling for government transfers only partly reduces this link, which the authors read as ruling out income and substitution effects on labor supply as the only driver, while controlling for working-hours regulations and the size of the formal sector reduces the link much more sharply, suggesting to them that regulation—not just the incentive effects of taxes—plays a large role in shortening intensive-margin hours in richer countries. The authors conclude that collective choices and social norms, often encoded in public policy (schooling, pensions, cultural norms about women’s work, and hours regulation), powerfully shape working hours over and above pure economic development. These are correlational cross-country and time-series patterns rather than identified causal effects, and hours are measured as weekly hours in all GDP-producing jobs (including unpaid agricultural work but excluding unpaid home services).
Summary of a forthcoming paper, AI-assisted and human-reviewed. See the linked original for the authoritative claims and full conditions.
Q1. What new data does the paper assemble, and how does it improve on prior global hours databases?
The authors mobilize roughly 5,000 nationally representative household and labor force surveys to build a database of hours worked covering 160 countries and 97% of the world population in cross section, plus time series spanning over 20 years in 86 countries. They combine six groups of sources, principally the ILO’s Microdata Repository (about 1,800 surveys in 150 countries since 1990) and the World Bank’s I2D2 database, which include survey data not publicly disclosed by the countries that created them. This extends the most comprehensive prior effort, Bick, Fuchs-Schündeln, and Lagakos (2018), whose core database covered 49 countries (23% of world population) and whose extended database covered 80 countries (41%); large countries such as China and India (35% of world population) that were absent from that study are now included. The authors state they are publishing and plan to regularly update the underlying database at the country×year×age×gender level so that researchers can reproduce their results.
Q2. How seriously does the seasonality concern affect the estimates?
The authors investigate seasonality directly and conclude that monthly seasonality in hours worked is limited in developing countries—actually larger in richer countries because of summer holidays—which gives them confidence that surveys not fielded over the full year still provide reliable annual hours estimates. This matters because Bick, Fuchs-Schündeln, and Lagakos (2018) had restricted their core sample partly out of concern that surveys run in specific months (e.g., around seasonal agricultural work) could bias hours estimates. Resolving this concern is what lets the authors retain the far larger country coverage.
Q3. How much do hours worked actually vary with economic development?
Hours worked per adult slightly decline with GDP but are only weakly correlated with development overall, with an unconditional elasticity of about -0.04 in the cross section and -0.01 in panel data—implying a reduction in hours of 0-20% over the entire development spectrum. GDP explains around 5% of cross-country variation in hours worked and less than 1% of historical within-country variation. Decomposing the margins, employment rates are essentially uncorrelated with development, while hours per worker are bell-shaped: they rise at low levels of development because of structural change (hours in manufacturing and services are very high in middle-income countries, while agricultural hours are moderate and flat with GDP), then flatten. Globally, 59% of the adult population (aged 15+) is employed, working an average of 42 hours per week, which implies about 25 weekly hours per adult; hours are strongly bell-shaped with age, and women supply 35% of GDP-producing hours versus 65% for men, a gap driven mostly by the extensive employment-rate margin.
Q4. Why do hours worked by the young and the elderly fall with development?
In simple cross-country regressions, the decline in hours worked by the young (15-19) and the elderly (60+) as countries develop is entirely driven by rising school attendance for the young and rising public pension coverage for the elderly, in line with a broad body of prior work. In the time series the two margins diverge: the fall in youth work is particularly pronounced, whereas elderly work is stable rather than falling. The authors read this as consistent with developing countries expanding schooling faster, but rolling out elderly pensions more slowly, than frontier economies did historically.
Q5. What happens to prime-age hours, and what is the “great gender reshuffling”?
Prime-age (20-59) hours worked are flat, if not slightly increasing, with GDP per adult, but this stability masks a large compositional shift the authors term a “great gender reshuffling”: female hours rise with development while male hours decline, and the fall in male hours (driven by reduced hours per worker) is quantitatively offset by increases in female employment rates. The authors interpret this as development tending to equalize hours across genders—shortening the long hours of working men while allowing more women into GDP-generating employment. They emphasize considerable heterogeneity across countries and over time in this pattern.
Q6. What role do religion and political history play in female hours worked?
The authors report that Muslim/Hindu religion depresses female hours worked enormously, while former communist status increases them. Grouping countries into former-communist, Muslim/Hindu-majority, and other categories, they show female hours rise with development on average but with large level differences across these groups, which they treat as evidence that cultural and institutional factors—not development alone—shape the gender allocation of work. These are descriptive cross-country associations, not causal estimates.
Q7. How are labor taxes related to hours worked, and what explains the relationship?
Labor taxes are strongly negatively related to prime-age hours worked, both in international comparisons and within-country time series; once tax variables are controlled for, GDP per capita is only weakly positively correlated with hours, with an elasticity of around 0.1. The authors probe what drives the tax-hours link. Controlling for social spending (cash or quasi-cash transfers) attenuates it, consistent with income effects from transfers playing some role—but the attenuation is only partial, which the authors read as ruling out income and substitution effects on labor supply as the sole driver. Controlling instead for the share of formal workers and working-hours regulations reduces the link much more sharply. They therefore suggest labor taxes depress hours not mainly through income and substitution effects but rather because high labor taxes correlate with the development of a formal sector with regulated working hours.
Q8. Can a standard labor supply model rationalize these findings?
The authors note that a standard labor supply model with a low uncompensated but large compensated labor supply elasticity can rationalize the joint pattern of weak hours-GDP but strong hours-tax correlations. The logic they invoke from the macroeconomics literature is that economic growth raises the wage rate (an uncompensated labor supply effect, which is weak here) while labor taxes fund transfers (a compensated labor supply effect, which is stronger). The partial attenuation of the tax effect when social spending is controlled is consistent with this account, but the sharper attenuation from regulation and formal-sector controls leads the authors to give regulation a large role alongside—rather than instead of—these labor supply channels.
Q9. What is the paper’s overall interpretation?
The authors conclude that collective choices and public policies—schooling and pension systems, cultural norms regarding women, and regulations on hours worked—have first-order effects on the level and allocation of working hours by age and gender, over and above economic development. They argue that while growth may help develop such institutions, many are only partially determined by it, which is why large cross-country variations in hours worked persist at all levels of development. The paper is framed as documenting and interpreting robust correlations across countries and over time, not as identifying causal policy effects.
Q10. What are the main scope conditions and caveats?
Throughout, hours worked follow international conventions: weekly hours in all jobs that contribute to GDP, including unpaid agricultural work but excluding unpaid home services such as cleaning, cooking, and care. Coverage is 97% of world population, with the missing 3% concentrated in parts of the Middle East and North Africa. The central results on taxes, transfers, regulations, religion, and communist history are correlational—drawn from cross-country regressions and within-country time series—and the authors repeatedly use calibrated language (“correlated,” “suggests,” “consistent with”) rather than claiming identified causal effects.
Key concepts
Hours worked (GDP-producing) : Weekly hours in all jobs that contribute to GDP, following international conventions—this includes unpaid agricultural work (which produces goods counted in GDP) but excludes unpaid home services such as cleaning, cooking, and caring for children or the elderly. Great gender reshuffling : The paper’s term for the pattern in which, as countries develop, declining male hours per worker are quantitatively offset by rising female labor force participation, leaving prime-age (20-59) hours worked roughly stable while its gender composition shifts markedly. Unconditional elasticity of hours with respect to GDP : The raw cross-country (about -0.04) or panel (about -0.01) elasticity of hours worked to GDP per adult before conditioning on taxes, transfers, or institutions; its small size is the paper’s headline evidence that development per se explains little hours variation. Uncompensated vs. compensated labor supply elasticity : In the standard labor supply model the authors invoke, growth raises wages (an uncompensated effect, weak in their data) while labor taxes fund transfers (a compensated effect, stronger in their data); a low uncompensated and large compensated elasticity reconciles weak hours-GDP with strong hours-tax correlations. Formal sector / working-hours regulations : Regulated wage employment in which statutory limits on hours bind; the authors emphasize that the expansion of this regulated formal sector with development, rather than the incentive effects of taxes alone, is the channel that most sharply accounts for shorter intensive-margin hours in richer countries.
Key concepts
Hours worked (GDP-producing) : Weekly hours in all jobs that contribute to GDP, following international conventions—this includes unpaid agricultural work (which produces goods counted in GDP) but excludes unpaid home services such as cleaning, cooking, and caring for children or the elderly. Great gender reshuffling : The paper’s term for the pattern in which, as countries develop, declining male hours per worker are quantitatively offset by rising female labor force participation, leaving prime-age (20-59) hours worked roughly stable while its gender composition shifts markedly. Unconditional elasticity of hours with respect to GDP : The raw cross-country (about -0.04) or panel (about -0.01) elasticity of hours worked to GDP per adult before conditioning on taxes, transfers, or institutions; its small size is the paper’s headline evidence that development per se explains little hours variation. Uncompensated vs. compensated labor supply elasticity : In the standard labor supply model the authors invoke, growth raises wages (an uncompensated effect, weak in their data) while labor taxes fund transfers (a compensated effect, stronger in their data); a low uncompensated and large compensated elasticity reconciles weak hours-GDP with strong hours-tax correlations. Formal sector / working-hours regulations : Regulated wage employment in which statutory limits on hours bind; the authors emphasize that the expansion of this regulated formal sector with development, rather than the incentive effects of taxes alone, is the channel that most sharply accounts for shorter intensive-margin hours in richer countries.