Does the tendency for “quiet quitting” differ across generations? Evidence from the UK

Introduction: The post-COVID-19 phenomenon of “quiet quitting” could be problematic for UK economic growth because unpaid overtime has been a key contributor to business productivity since the 2008 global financial crisis. Here, we explore the extent to which this phenomenon exists in the UK, and whether the tendency for quiet quitting differs across generations.

Methods: We analyzed data from the UK Quarterly Labor Force Survey (QLFS) between 2007 and 2022 to determine changes in hours worked. Quiet quitting was characterized by notable declines in hours worked between 2019 and 2022, benchmarked against 20072018 trajectories. Analyses were demarcated by four commonly defined generational cohorts (i.e., Generation Z [GenZs; 1997–2004], Generation Y [Millennials; 1981–1996], Generation X [GenXers; 1965–1980], and Baby Boomers [1952–1964]).

Results: Overall, we found that the UK workforce reduced hours by ~28 h per year in the pandemic and post-pandemic periods. Hours lost was most notable in 2022, with hours down by ~36 h. However, in assessing generational differences, quiet quitting was most pronounced in the two younger cohorts. GenZs showed the steepest decline in hours worked, while Millennials worked the least number of hours overall, with no indication of recovery by the end of the study period. Hours declined for GenXers and Baby Boomers, but changes were more moderate, and Baby Boomers showed evidence of a possible rebound to pre-pandemic levels.

Discussion: Given the ~24,568 million UK full-time workers in 2022, our findings equate to over 55 million discretionary hours lost to the labor market per year between 2019 and 2022, 48.1% of which is accounted for by Millennials. Thus, we evidence that quiet quitting has interrupted the recovery of working hours in the UK to pre-pandemic levels, and lost hours are especially attributable to younger cohorts.

JEL: J24 J01.

Introduction

“Quiet quitting isn't just about quitting on a job, it's a step toward quitting on life.” Arianna Huffington

The phenomenon of “quiet quitting” can be understood as employees of younger generations reducing their working efforts and hours worked rather than leaving a job (Masterson, 2022). This is problematic for UK economic growth, given that unpaid overtime has been a key contributor to business productivity since the 2008 global financial crisis (Papagiannaki et al., 2021). Notwithstanding, most advanced economies have experienced poor productivity over this period (Samiri and Millard, 2022), so the curtailment of working hours presents a further threat to productivity. Additionally, UK labor productivity has lagged far behind similarly advanced economies (Mason et al., 2018; Crafts and Mills, 2020), across the pre-pandemic (−2019), pandemic (2020–2021) and post-pandemic (2022–) periods [Office for National Statistics (ONS), 2023a; Milesi-Ferretti, 2021]. In this study we explore the extent to which quiet quitting exists in the UK.

Quit quitting can be understood as adhering to only minimum contractual work requirements. The term became popularized in the post-pandemic period, and has largely been attributed to younger employees of Generation Z (born 1997–2004) and Millennials (born 1981–1996; Fox, 2022). Although it is relatively new conceptually, quiet quitting is characterized by reduced discretionary effort and principled disengagement from extra-role contributions (Patel et al., 2025). Numerous studies have shown employee disengagement (often characterized as “burnout” or lack of engagement) is associated with reductions in behaviors that benefit the broader organization (Cropanzano et al., 2003). One popular measure of these behaviors is discretionary hours worked, with employee hours worked beyond the minimum thought to demonstrate greater engagement and employee motivation to benefit the organization as a whole (Halbesleben and Bowler, 2007). Thus, if the now popularized phenomenon of quiet quitting is to be evidenced, we would expect reduction in discretionary hours worked reflecting this principled disengagement.

Given the recency of the quiet quitting phenomenon, much of the current evidence is anecdotal (Serenko, 2023). An exception is concurrent research in the US led by Lee et al. (2023) who found that US workers reduced their discretionary annual hours worked by 18 h per year between 2019 and 2022; a period capturing the impact and recovery from the COVID−19 pandemic. Though the contexts differ, the present study corresponds well to this US study. Compared with pre-pandemic (−2019), here, we see a significant drop in annual hours worked. We find that compared to the average hours worked in 2019, UK workers worked an average of 28 h less each year in the period between 2020 and 2022 (which was 29 h less than the average annual hours worked in the post-financial crisis period of 2008–2019). This was most pronounced in post-pandemic 2022, where workers worked an average of 36 h (4.5 days) less than they had prior to the pandemic in 2019. Here we provide evidence of quiet quitting among UK workers, especially those in younger generational cohorts.

For our work we draw on the UK Quarterly Labor Force Survey (QLFS; 2007–2022; Office for National Statistics (ONS), 2023b), and investigate the total number of hours worked per week across four generations. With the present study, evidence of quiet quitting is defined by a consistent reduction in hours worked from 2019 (Q4) to the end of the study period (2022 Q4). Analyses are demarcated by generational cohort (i.e., Generation Z [GenZs; born 1997–2004], Generation Y [Millennials; born 1981–1996], Generation X [GenXers; born 1965–1980], and Baby Boomers [born 1952–1964]). We draw on these four commonly accepted categories (Appendix A; Table A1) that have shaped popular views about generations (Dimock, 2019), and for which differences have been proven across a variety of research methods (Lyons and Kuron, 2014). Importantly, these generational categories have shaped discourse around quiet quitting; the voluntary reduction in hours worked by employees following the outbreak of COVID−19 (Lee et al., 2023). This phenomenon was popularized by the social media platform TikTok (Masterson, 2022), with it being largely attributed to GenZs (e.g., Bienasz, 2022; Rieck, 2022; Yang et al., 2020; Newport, 2022).

Overall, our analysis reveals that actual hours worked, from the start of quiet quitting phenomenon declined in three of the four generational cohorts that were analyzed (i.e., GenZs; Millennials; GenXers. The decline in working hours in the pandemic and post-pandemic periods was not accompanied by a decline in wage. Specifically, all generations showed reductions in hours worked from the start of the pandemic, consistent with the perception of younger workers quiet quitting. GenZs showed the steepest reduction in working hours, down 48 h per year in the period post−2019, while Millennials reduced their hours by 38 h per year to work the least number of hours overall, with no indication of recovery by the end of the study period. GenXers and Baby Boomers also had reductions in working hours consistent with quiet quitting, but the changes were more moderate (24 h and 14 h per year respectively). Given the ~24,568 million UK full-time workers across 2022 (Statista, 2023), this equates to an estimated 55,114.2 million discretionary hours lost to the labor market per year between 2020 and 2022, 48.1% of which is accounted for by Millennials. These results can be seen as part of a broader pandemic and post-pandemic trend. Total working hours in the UK reduced following the outbreak of the COVID−19 pandemic (2019 Q4). Since then, there has been an overall decline in hours worked, driven primarily by younger generations (i.e., GenZs and Millennials). Taken together, these results show that quiet quitting is more pronounced among younger generational cohorts in the UK. Only the working hours of Baby Boomers have shown any potential rebound toward pre-pandemic levels (recovering 3 hours worked in the year between 2021 and 2022). In other words, quiet quitting has interrupted the recovery of working hours to pre-pandemic levels. These UK findings are consistent with US data showing a fall in annual hours worked from 2019 to 2022, that is especially pronounced in younger workers (Lee et al., 2023).

Findings by generation were also explored more minutely; first by gender, followed by educational status, and industry. Reduction in hours worked since 2019 were steeper for men than women (−33 h per year in the period for men, compared to −21 h for women). By industry, the strongest evidence of quiet quitting can be found in Finance, Technology, and Professional Services (−46 h per year in the period compared to 2019), with reductions being the lowest in Manufacturing, Agriculture, Energy, and Construction (−7 h). This is intuitive given that it is much harder to quietly lower your hours in sectors where face to face presenteeism is required to do the dominant tasks (e.g., assembly lines in manufacturing, and construction sites in constructions), as compared to sectors where tasks can be done in isolation or off-site (e.g., developer roles in technology firms, or back office support roles in finance). Equally, there were steeper declines in hours worked by those with a degree (−30 h in the period compared to 2019, with a peak of −47 h in 2022) than those without (−21 h per year, with a peak of −23 h in 2022). Again, this was more pronounced among younger generations, which points to quiet quitting. The larger decline in working hours among degree-educated workers, particularly younger men, is consistent with findings from the US (Lee et al., 2023). This decline is likely influenced by two factors. First, the reduction of discretionary (vs. paid) hours worked is stronger among those who can afford to do so. Second, quiet quitting accompanied the switch to remote work due to COVID−19, which was more likely for degree-educated roles. This remote working switch made quiet quitting easier by reducing employer scrutiny over employee hours (Serenko, 2023).

This research contributes to a growing literature on generations and productivity. The role that economic factors play in creating meaningful generational differences is an area of economic, organizational, and popular interest (Levenson, 2010). Economic conditions and formative events are important to shaping generational values, including work-values (Joshi et al., 2010). For example, data collected over a 30-year period in a U.S. population study showed GenXers and Millennials place a higher value on leisure time compared to their Baby Boomer predecessors at the same age (Twenge et al., 2010). The different generational patterns in hours worked post-COVID−19 suggest that the pandemic may have ignited behaviors that align with these values.

Our findings are also consistent with observed declines in work engagement and satisfaction among GenZs and younger Millennials working remotely (Harter, 2022). An alternative explanation of generational value differences might view quiet quitting through a wellbeing lens. Working longer hours than desired can increase unhappiness and depression (Bell and Blanchflower, 2019), especially if hours remain longer than desired for 2 years or more (Angrave and Charlwood, 2015). Given that GenZs and Millennials were shown to place greater value on work-life balance before the COVID−19 pandemic (Sánchez-Hernández et al., 2019), the generational divergence in hours worked might equally be prompted by wellbeing values. Indeed, the relationship between hours worked and productivity is not linear, especially when higher hours reduce wellbeing to the point of stress, illness, or error (Pencavel, 2015).

Our work also contributes to the economic literature on unpaid overtime and hours worked. Unpaid overtime can be seen as an investment in future promotion and career prospects with an organization (Anger, 2008). The number of hours worked by an employee is a way in which they signal their value to the firm (Spence, 1973). As quiet quitting violates this intent, by only working hours that are necessary or contracted, (that is, “doing your job but nothing more”; Jacobs, 2022), reducing hours can be viewed as a proxy for a reduced investment in future career prospects with the organization. At a macro level, “total hours worked” is a valuable productivity measurement because it is more closely related to the quantity of productive services provided by workers than alternative measures, such as head count or wages (Schreyer and Pilat, 2001). Importantly, if workers reduce their unpaid hours, the aggregate hours of an economy will fall unless there is increased labor force participation to make up for this fall (Blundell et al., 2011).

Methods

Data

We draw on the Quarterly Labor Force Survey [QLFS; Office for National Statistics (ONS), 2023b] the largest quarterly prospective cohort study of UK labor market measures used for macroeconomic monitoring, that began in 1992. The sample, measured by the Office for National Statistics (ONS), includes UK nationally representative men and women resident in private households. For each quarter, the sample consists of ~35,000 households in Great Britain (GB) and ~2,500 in Northern Ireland (NI), representing ~0.13% and ~0.30% of these respective UK populations. In the present study, we draw on quarterly data from 2007-Q1 through 2022-Q4.

Generations

Generations were demarcated on the basis of the Pew Research Center classifications (Dimock, 2019). Across the 15 year period (2007–2022), “Generation Y” (Millennials) were 26–41, “Generation X” (GenXers) were 42–57, and “Baby Boomers” were aged 58–70. Upon labor market entry (2015–2022), “Generation Z” (GenZs) were 18–25 (Table A1).

Hours

The continuous measure of actual hours worked (in hours and minutes), accounted for core time worked and overtime throughout the week, across the 15 year period (2007–2022). Hours were restricted to fulltime employees who worked at least 35 h per week, with no upper limit.

Wages

Continuous income was measured as gross annual wages across the 15 year period (2007–2022). To determine real-term wages, values were adjusted by the UK Consumer Price Index. CPI adjusted wages were then restricted to fulltime employees working at least 35 h per week (with no upper limit). Values were reduced by earnings at or above the progressive UK National Minimum Wage rate. Thus, wages refer to gross annual earnings, adjusted to 2022 GBP using CPI. Observations where implied hourly wages fell below the relevant UK National Minimum Wage were excluded as likely reporting errors. To distinguish between changes in hours driven by worker behavior and those driven by changes in earnings potential or compensation structure, we also estimate a supplementary model using log wages as the outcome variable. This allows us to assess whether the observed decline in working hours coincided with a decline in earnings, which would suggest demand-side changes, or whether it occurred independently, consistent with a discretionary reduction in effort (i.e., quiet quitting).

Control variables

We include a number of control variables in our regressions. These were selected a priori and include: age (continuous: 18–70); gender (binary: female/male); ethnicity (binary: White [British; Irish; all other White backgrounds]/non-White [all other backgrounds]); educational status (binary: degree/no degree); marital status (categorical: single/married and partnered/separated and divorced/widowed); parental status (categorical: no children/1 child/2 children/≥3 children); occupational status (categorical: senior managers or executives/professionals/administrators, salespersons or customer service agents/skilled traders or laborers/health or personal service workers/elementary workers); region (North East, North West & Merseyside, Yorkshire & Humberside, East Midlands, West Midlands, Eastern, London, South East, South West, Wales, Scotland, Northern Ireland); industry (categorical: manufacturing, agriculture, energy and construction/retail, transportation and hospitality/financial, technological and professional services/public services/other [a final miscellaneous group that combined more obscure industries that did not correspond to the other categories]).

Methodology

To investigate whether the tendency for quiet quitting varies across generations we estimate:

$\begin{array}{ll}{y}_{\text{iat}}=\delta N{W}_{\text{iat}}+\gamma +X_{\text{iat}}\beta \prime +{\mu }_{\text{at}}+{\epsilon }_{\text{iat}}& (1)\end{array}$

where y_iat is hours of individual i residing in area a in quarter t. NW_iat is an indicator variable capturing the incidence of quiet quitting [that is, it is = 1 for observations in the post−2019 quiet quitting period (2020–2022), and 0 for earlier periods], with δ (delta) representing its marginal effect on hours worked. gamma (γ) is a constant, so represents the model's intercept term. X_iatβ is a vector of individual-level control variables, μ_at is a vector of area-quarter fixed-effect, and ε_iat is a random disturbance term. QLFS weights are applied to ensure that results were nationally representative. Standard errors are clustered at the quarter-region level.

To identify quiet quitting, we estimate Equation 1 separately for each generation, and focus on comparing across these regression on the quarter-year coefficients to reveal changes in hours worked in each quarter from 2007 to 2022. These coefficients represent deviations from the respective generational baseline years: 2007 for Millennials, Generation X, and Baby Boomers; and 2015 for Generation Z, who entered the labor market later. That is, the dummy variable NW_iat in Equation 1 effectively captures the post−2019 quiet quitting period, and a significantly negative δ estimate would indicate that quiet quitting is associated with fewer hours worked, on average. We focus only on full time full year workers in our regressions, and interpret any difference in hours worked among these models group of workers across generation to be owed to quiet quitting after the period 2019 to the end of the study period (2022). Individuals earning below the National Minimum Wage (NMW) based on their reported hours were excluded to mitigate the impact of irregularities in wage data, and to support more consistent comparisons across observations.

The effect of hours and wage rates is identified using within-area-quarter variation, with area-by-quarter fixed effects accounting for both persistent differences between areas and localized time-varying shocks. Results are presented as the regression coefficients and standard errors from Equation 1. The coefficients represent the change from the mean summaries of actual hours worked per week including overtime, which is baseline (Table A2; e.g., a coefficient of −1.0 represents an average weekly reduction of 1 h from baseline). Any minutes reported are calculated from the regression by multiplying the decimal coefficient by 0.6; a derivative of 60 min to the hour (e.g., −0.5 is equivalent to 30 min). The coefficient value is multiplied by 52 to obtain the annual reduction (e.g., a coefficient of −1.5 amounts to an annual reduction of 78 h).

To estimate generational differences in hours worked overtime, we extend Equation 1 to include stratification by generation:

$\begin{array}{ll}\text{yiat(g)=t}\sum {\delta }_{\text{gt}}\text{(g)}\times \text{Tim}{\text{e}}_{\text{t}}\text{+}{\text{X}}_{\text{iat}}\beta \text{(g)+}{\mu }_{\text{at}}\text{+}\gamma \text{(g)+}{\epsilon }_{\text{iat}}& (2)\end{array}$

where y_iat(g) denotes actual hours worked for individual i in area a at time t and belonging to generation g (with g being an indicator of what generation individual i belongs to [e.g., Millennial]). δ_gt(g) captures time-specific deviation in hours worked from the baseline for each generation g. Time_t is a quarter-year fixed effect, and X_iatβ(g) is a vector of individual-level controls with generation-specific coefficients, while μ_at are area-quarter fixed effects,¹ γ(g) is a generation-specific constant term, and ε_iat is a random error term. The resulting δ_gt coefficients are plotted in Figure 1, illustrating how work hours evolved for each generation over time.

Figure 1

Figure 1. This figure depicts an illustration of the coefficients from the regressions described in Equations 1, 2, for actual hours worked, including overtime.

Descriptives

Sample characteristics can be found in Table 1. Before exclusions, there were 4,112,882 observations, pooled from individuals aged 18–70 years. The sample was reduced by 8.5% (n = 355,892) to account for individual reports of gross income that was below the UK National Minimum Wage rate at age 18. The sample was further reduced by 52.8% (n = 1,982,805) to include fulltime employees only.² This left an analytical sample of 1,774,185. Of these, 558,136 had data on actual hours worked, including overtime and 305,965 had data on adjusted gross wages.³

Table 1

Table 1. Sample characteristics.

The mean number of actual hours worked per week, including overtime, was 45.7 (±8.7; range 35–97; Figure B1). Over half of participants worked 35–45 h, and approximately a quarter worked more than 50 h (Table B1). Although the range did not differ, the mean number of hours worked, including overtime was 44.2 ±7.8 for GenZs; 45.6 ±8.5 for Millennials; 46.1 ±8.8 for GenXers; and 45.8 ±8.9 for Baby Boomers. GenZs were the most likely to work under 40 h (42.1%) and least likely to work more than 50 h (14.2%); as compared to Millennials (34.2/19.2%), GenXers (32.1/21.0%), and Baby Boomers (34.7/19.8%) respectively.

Results

Figure 1 shows congruency in the hours worked between Baby Boomers, GenXers, and Millennials immediately after the financial crisis (2008–2011), but there is a substantial divergence for the trends of study interest, indicating quiet quitting (2019–2022). Specifically, there is a notable decline in reported hours worked.

Table 2 presents estimated coefficients and standard errors for year indicators from regression models run separately by generation (as shown in Figure 1). The regressions in Table 2 are generation-specific regressions that reflect within-generation changes in actual working hours relative to each generation's baseline year, as a result of Equation 2. For Millennials, Generation X, and Baby Boomers, the baseline year is 2007, whereas for Generation Z, who enter the labor market later, the baseline year is 2015. As such, the coefficients represent deviations from these respective baseline years and not necessarily changes that result solely from the COVID−19 pandemic. Following the outbreak of the pandemic, GenZs had the steepest immediate decline in hours worked to −0.88 at the lowest point in 2020 (53 min per week below baseline; 45.22 h total for the year). During the pandemic and post-pandemic periods, hours were congruent with pre-pandemic levels for Baby Boomers, but the other generations offered evidence of quiet quitting. Reductions in hours for GenZs and Millennials were significantly different from pre-pandemic (2019) baseline between 2020 and 2022, and this was true for GenXers in 2022, but Baby Boomers did not significantly differ in hours from baseline. Across the 15 year period Millennials consistently worked the least number of hours. They were also the group to provide the strongest evidence of quiet quitting, from −0.11 (0.7 min per week below baseline; 5.72 h in total) in 2019 to −1.08 (1 h 5 min per week below baseline; 56.16 h in total) in 2022. Overall, the hours worked by UK workers reduced by an average of 27.69 h per year, each year over the quiet quitting period (2020–2022) compared to 2019 (Appendix C; Table C1).

Table 2

Table 2. Regression models for actual hours worked, including overtime by generations (2007–2022).

Table B2 reports results from regressions of log wages. These show no substantial decline in wages over the post−2020 period across generations, suggesting that the observed reductions in hours worked are not attributable to falling compensation. This supports the interpretation of these changes as behavioral in nature.

Generations | gender

Appendix D; Table D1 documents estimates for regressions that consider men and women separately. Although mean baseline hours were higher for men (46.41 ± 8.95) than for women (44.40 ± 7.92)⁴ when we consider fulltime full-year workers, the increase from baseline was less for men than it was for women over the 15 year period⁵. Moreover, throughout the pandemic and post-pandemic period, men showed a stronger inclination toward quiet quitting; −0.81 (49 min per week below baseline; 42.12 h in total for the year) at their lowest point in 2022, compared to women at −0.34 (20 min per week below baseline; 17.68 h in total).

Table 3 reflects generational differences in hours worked between men and women. In the pandemic and post-pandemic period, Millennials were the only group to show an equivalent downward trend in hours worked irrespective of gender (between 2019 and 2022). But this decline was moderately more pronounced for men from −0.20 in 2019 to −1.25 in 2022 (12 min to 1 h 15 min per week below baseline; 10.40 h to 65.00 h in total for the year), than it was for women at 0.05 to −0.85 (3 min per week above baseline to 51 min below baseline; 2.60 h above to 44.20 h below in total). Hours worked by GenXers followed a similar trajectory pre-pandemic, but deviated in the pandemic period, with men were more inclined toward quiet quitting than women; −0.08 in 2019 to −0.76 in 2022 (5 min to 46 min per week below baseline; 4.16 h to 39.52 h in total) compared to 0.48 to −0.23 (29 min per week above to 14 min below baseline; 24.96 h above to 11.96 h below in total). Baby Boomer men worked less in the in the pandemic and post-pandemic period, from 0.08 in 2019 to −0.49 in 2021 (5 min per week above and 29 min below baseline respectively; 4.16 h above and 25.48 h below in total). However, Baby Boomer women increased their hours worked from 0.85 in 2019 to 1.28 in 2021 (51 min and 1 h 17 min per week above baseline respectively; 44.20 h and 66.56 h in total). GenZ men showed a greater drop-off in hours worked during the quiet quitting period than GenZ women. Hours worked for men started at 0.75 in 2019 (45 min per week above baseline; 39.00 h above in total) and ended at −0.28 in 2022 (17 min per week below baseline; 14.56 h below in total). Gen Z women started at a substantially lower point at −0.65 in 2019 (39 min per week below baseline; 33.80 h in total) and finished at −1.24 in 2022 (1 h 14 min per week below baseline; 64.80 h in total). However, overall quiet quitting was most prevalent among men, and more pronounced for the two younger generations (i.e., GenZs and Millennials).

Table 3

Table 3. Regression models for actual hours worked, including overtime by generations and gender (2007–2022).

Generations | education

Table D2 documents estimates for regressions that consider educational groups independently. Compared to 2019, those with a degree worked less in 2022, −0.26 to −1.16 (16 min to 1 h 10 min per week below baseline; 13.52 h to 60.32 h in total hours below for the year). This differed to those without a degree at 0.34 to −0.11 (20 min per week above to 7 min below baseline; 17.68 h above to 5.72 h below in total). The difference in trajectories during the quiet quitting period is marked by a significant reduction from baseline hours each year between 2020 and 2022 for degree educated workers, compared to those without a degree for whom hours in the period did not significantly vary.

Table 4 reflects generational differences in hours worked between educational groups. There is stronger evidence of quiet quitting in individuals who have a degree than in those who do not across the generations. Working hours for GenZs with a degree fell from −0.45 in 2019 to −1.84 in 2022 (27 min to 1 h 50 min per week below baseline; 23.40 h to 95.68 h in total for the year). This differed for those without a degree where working hours for GenZs showed moderate changes from 0.52 in 2019 to 0.13 in 2022 (31 min to 8 min per week above baseline; 27.04 h to 6.76 h in total). For the same period, hours fell significantly from baseline for the entire period for Millennials from −0.50 to −1.67 (30 min to 1 h 40 min per week below baseline; 26.00 h−86.84 h in total). Again, this differed for those without a degree where working hours for Millennials had a more moderate decline at 0.21 to −0.40 (13 min per week above to 24 min below baseline; 10.92 h to 20.80 h in total). There were also declines in hours worked for GenZers and Baby Boomers with a degree, but these were more moderate than the younger generations; from 0.09 to −0.68 (5 min per week above to 41 min below baseline; 4.68 h above to 35.36 h below in total), and −0.43 to −0.64, respectively (26 min to 38 min per week below baseline; 22.36 h to 33.28 h in total).

Table 4

Table 4. Regression models for actual hours worked, including overtime by generations and education (2007–2022).

Generations | industry

Table D3 documents estimates for regressions that consider sectors separately. Here the sectors are defined as Manufacturing, Agriculture, Energy, and Construction; Retail, Transportation, and Hospitality; Financial, Technology, and Professional; and Public Services. Hours worked by those in Financial, Technology, and Professional and those in Retail, Transportation, and Hospitality had the steepest reduction in hours between 2019 and 2022; respectively from 0.07 to −1.15 (4 min per week above to 1 h 9 min below baseline; 3.64 h above to 59.80 h below in total for the year), and 0.10 to −0.75 (6 min per week above to 45 min below baseline; 5.20 h above to 39.00 h below in total). In contrast, across the same period, hours worked by those in Manufacturing, Agriculture, Energy, & Construction and those in Public Services had the smallest reduction in hours; respectively from 0.05 to −0.07 (3 min per week above to 4 min below baseline; 2.60 h above to 3.64 h below in total), and 0.19 to −0.36 (11 min per week above to 22 min below baseline; 9.88 h above to 18.72 h below in total.

In looking at generational differences by industry (Tables D4–D8), Millennials offered evidence of quiet quitting across all major industries from 2019–2022. But it was most pronounced in Financial, Technological, and Professional Services (Table D6) at −0.14 to −1.63 (8 min to 1 h 38 min per week below baseline; 7.2 h to 84.76 h in total), which was significantly different from baseline each year between 2020–2022. This evidence was also seen for Retail, Transportation, and Hospitality (Table D5) at 0.02 to −1.34 (1min per week above to 1 h 20 min below baseline; 1.04 h above to 69.68 h below in total), but it was significantly different from baseline between 2021 and 2022. GenZs showed evidence of quiet quitting in Retail, Transportation, and Hospitality (Table D5) at −0.36 in 2019 to −1.10 in 2022 (22 min to 1 h 6 min per week below baseline; 18.72 h above to 57.20 h below in total), which was significantly different from baseline each year between 2020 and 2022. Evidence of quiet quitting was offered also in Public Services (Table D7) across the same period at −0.39 to −1.18 (23 min per week above to 1 h 11 min below baseline; 20.28 h above to 61.36 h below in total), but it was only significantly different from baseline in 2020 and 2022. GenXers indicated quiet quitting in Financial, Technological, and Professional Services only (Table D6), which was significantly different from baseline in each year between 2020 and 2022, but the decline in hours was more moderate than the younger generations; at 0.17 in 2019 to −1.05 in 2022 (10 min per week above to 1 h 3 min below baseline; 8.84 h above to 54.60 h below in total). Baby Boomers were the only generation whose working patterns did not indicate quiet quitting in any industry (Tables D4–8).

Post-hoc analyses

We examined whether quiet quitting patterns hold across prominent subgroups and whether effects are more pronounced among specific types of workers. This analysis considers hours worked, including overtime for each generation, across each industry, by gender (Tables E1–10), and then by educational status (Tables E11–15). We note that the overall conclusions drawn above are robust to these additional analyses. Results confirm that reductions in hours are concentrated among university-educated workers in remote-enabled industries. Further examination using data from the LFS up to 2022 (Figures A1, A2) and 2023–2024 (Figures A3, A4) show that results were not an artifact of the pandemic alone.

Limitations

Even though the results reported here are robust to further analysis, sample attrition was not uniform across generations (Table B1). This disparity reflects differing labor market participation patterns and raises the possibility of selection bias. Specifically, the exclusion of lower-hour workers may bias our estimates downward by omitting those who responded to macroeconomic shocks by reducing their working hours. The focus on full-time, full-year workers is designed to capture changes in discretionary effort rather than structural shifts in employment but may omit those who reduced their hours in response to the pandemic or other macroeconomic factors. While our results are robust among full-time, full-year workers, they may underestimate the extent of reductions in working hours across the workforce by not including those who shifted to part-time or exited the labor force entirely. Furthermore, the 2023–24 Labor Force Survey (LFS) data were affected by low response rates and sampling issues, as highlighted by the Office for National Statistics [(Office for National Statistics (ONS), 2025)], potentially limiting comparability with earlier years but the results remained consistent with the downward trend, supported also in sensitivity analyses.

Conclusions

We explored the extent to which quiet quitting, evidenced by reduction in discretionary hours worked, was prevalent across generational cohorts in the period post-COVID−19 (2020–2022), as compared to the preceding period from 2007. Using data from the UK Quarterly Labor Force Survey, we find that the total number of hours per week showed declines consistent with the quiet quitting phenomenon. Specifically, hours worked including overtime declined in three of the four generational cohorts, and this was more pronounced in younger generations (i.e., GenZs; Millennials). Baby Boomers were the only generation found to be working hours consistent with pre-pandemic levels. In addition, while quiet quitting trends were generally consistent across industries, there was stronger evidence for it in Retail, Transportation, and Hospitality and Financial, Technological, and Professional Services, while the public sector saw more modest reductions in working hours than any other sector. Degree educated workers showed stronger reductions than non-degree educated workers. Given that the opportunities to reduce discretionary hours based on disengagement with work lie mainly with the so called ‘laptop class' who are able to work from home, these educational differences hours worked support the idea of quiet quitting. These results also suggest different generational patterns in hours worked during and post-COVID−19 pandemic may have been driven by value-based behaviors, with younger workers prioritizing leisure, work-life balance, and wellbeing. This decline in hours may also signal reduced investment in future career prospects with the respective organizations.

Our results suggest that quiet quitting in the UK has been even more pronounced than it is in the US, where there have been similar evidence of reductions in working hours in the pandemic and post-pandemic periods (2020–2022) driven by degree-educated younger workers, especially younger men (Lee et al., 2023). Declines in the UK are larger in comparison (scale = 1.5). Given that unpaid overtime has been a key contributor to business productivity in the UK since the 2008 global financial crisis (Papagiannaki et al., 2021), the reductions in hours worked found here can arguably signal a reduction in overall productivity. Taken together, these results suggest that quiet quitting may reference a pronounced reduction in working hours among younger generational cohorts in the UK, interrupting the recovery of working hours to pre-pandemic levels.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

OSH: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. DJ: Conceptualization, Validation, Visualization, Writing – original draft, Writing – review & editing. GL: Conceptualization, Data curation, Funding acquisition, Methodology, Project administration, Resources, Supervision, Validation, Writing – review & editing.

Funding

The author(s) declare that no financial support was received for the research and/or publication of this article.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declare that no Gen AI was used in the creation of this manuscript.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/frbhe.2025.1539771/full#supplementary-material

Footnotes

1. ^Therefore, the generational time coefficients δ (as in Equation 1) capture deviations in hours net of any local area shocks in each period.

2. ^Attrition due to low income and low hours (below 35 h/week) by generational cohort is reported in Appendix Table B1.

3. ^As would be expected, there is a non-linear trend in real gross annual wages across the 15 year period (Figure B2), which is also reflected in the regression coefficients for the log of real gross annual wages (Table B2).

4. ^Working hours are comparably similar between the genders when factoring dispersion.

5. ^Although men have been purported to work longer hours than women, such reports typically consider all workers, including part-time and causal (e.g., Collins et al., 2021). This does not strictly correspond to our sample of exclusively fulltime, full-year workers that shows an equivalence between the genders. It has also been evidenced that recessions impact on the employment drifts of each gender differentially, with a greater impact on men (Hoynes et al., 2012).

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