Reduction of working time: Does it lead to a healthy lifestyle?

Taehyun AhnApril 17, 2014

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Working time and health

• Working time has long been recognized as strongly linked with the health and well-being of working people in public/industrial health literature. – Cardiovascular disease, risk of diabetes, stress and

mental health, and work–family conflicts (Caruso, 2006; Nakanishi et al., 2001; Sparks et al., 1997; Virtanen et al., 2012).

• Association between long work hours and workers’ lifestyle habits that affect their health– Smoking, heavy alcohol consumption, and lack of

physical exercise (Maruyama and Morimoto, 1996; Siegrist and Rödel, 2006; Taris et al., 2011 etc)

– Consuming more fatty and sweet food (Oliver and Wardle, 1999)

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Working time and health

• A related strand of research in economics emphasizes the role of unemployment rate, a measure of economic activity, in influencing the health behaviors of individuals.– Some find a positive relationship between economic

activity and alcohol consumption, physical inactivity, and smoking behaviors (Ruhm, 2000; 2005; Ruhm and Black, 2002)

– Some studies find counter cyclicality of drinking (Dee, 2001) or little evidence of the cyclicality of drinking, physical activity, and smoking behaviors (Charles and DeCicca, 2008).

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This paper

• investigates whether working hours have causal effects on workers’ health behaviors.– Despite considerable evidence linking working time,

economic activity, and health-risk behaviors, the causal effects of work hours are still unclear.

– Unobserved third factors such as attitudes toward smoking or drinking can influence both working time and health-risk behaviors.

– Reverse causality—health habits and lifestyle choices influence employment and working time

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This paper

• investigates whether working hours have causal effects on workers’ health behaviors– using within-individual estimators (or individual fixed-

effect models) to eliminate the unobserved factors that yield biased estimates.

– using exogenous variation in policy adoption as an instrument for work hours-- timing of implementing the legislated workweek reduction in South Korea based on establishment size.

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Background• The working hours of South Korea have been among the

highest in the industrialized world. – However, South Korea has experienced the fastest decline in

working hours among the OECD countries over the past decade.

Korea

Chile

Hunga

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Poland

Russia

Czech

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2011

Figure 1: Average actual work hours per worker annually (Source: OECD Online Employment Database)

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Background• It is due to the stepwise introduction of a 40-hour

workweek standard from 2004. – The standard workweek is defined in the Labor

Standard Act (LSA), and was set at 44 hours per week for all workplaces.

– In the wake of the 1997 Asian economic crisis, reducing the statutory workweek was discussed as a way to tackle massive unemployment. However, as the economy recovered at a quick pace, the motivation shifted to reconciling work–family responsibilities and improving the quality of life.

– In August 2003, the bill to revise the LSA passed Congress.

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Adopting the 40-hour workweek system

• Stepwise adoption, based on firm size (to allow employers the time needed to reduce their working hours)

• The overtime premium is 50% of the normal wage rate (25% for the first four hours of overtime during the transition period—

the first three years after adopting the 40-hour workweek) .

Establishment size Timing of adoption

1000 employees or more July 2004

300 employees or more July 2005

100 employees or more July 2006

50 employees or more July 2007

20 employees or more July 2008

5 employees or more July 2011

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Empirical Specification

• I examine three common health-related behaviors—regular physical exercise, smoking, and drinking.

– Yit : health-related behavior of individual i in year t,

– hit : the individual’s actual workweek,

– Xit : the individual’s characteristics and local environment,

– αi : the individual fixed effects.

– εit : the error term with time varying unobservables

it it it i itY h X

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Empirical Specification

• The individual fixed-effect estimates (βFE) compare the weekly hours worked across time and relate these to the changes in health behaviors at the individual level. – While this strategy can eliminate any unobserved time-

invariant factors, some time-varying factors absorbed in the error term can influence both health behaviors and work intensity.

– An attenuation bias that may arise from measurement error in working hours is likely to be more exaggerated in fixed-effects estimates.

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Empirical Specification

• To account for potentially endogenous changes in work hours and mitigate any bias from measurement error, I carry out a within-two-stage least-squares (within-2SLS) analysis. – I exploit the timing of implementing the legislated workweek

reduction in South Korea based on establishment size.– IV: a dummy indicating whether the 40-hour workweek is

mandated in the individuals’ workplacesBased on size of establishment at which the individual

worked in 1999/2000 (before the new law was passed). The variations are driven solely by the differences in the

schedule of law implementation, and not by the differences in the individuals’ job change patterns.

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DataSample

Korean Labor and Income Panel Study (KLIPS)– Nationally representative sample of 5,000 urban

households and their members aged 15 years or older– I use the data from the 2001 and 2005–2010

interviews, when data on health behaviors were collected

– I focus on the paid workers in the initial period, to examine the link between working hours and the lifestyle of the general working population covered by the LSA.

Final sample 4,540

Person-year observations 23,276

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Distribution of workers’ actual workweek

0.0

5.1

.15

.2.2

5.3

10 20 30 40 50 60 70 80 90 100110 10 20 30 40 50 60 70 80 90 100110

Not under 40 hour worweek standard Under 40 hour workweek standard

Fra

ctio

n

Actual weekly hours worked

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Table 1: Average actual hours worked per week

YearNot under 40-hour workweek standard

Under 40-hour workweek standard

All

2001 52.1 - 52.1[100]

2005 51.3 48.0 50.6[79.9] [20.1]

2006 51.6 48.7 50.7[71.1] [28.9]

2007 52.0 48.8 50.8[64.2] [35.8]

2008 51.8 50.4 51.0[45.6] [54.4]

2009 51.8 48.3 49.6[37.5] [62.5]

2010 51.3 48.9 49.8　 [38.1] [61.9] 　All 51.7 49.0 50.7　 [64.4] [35.6] [100]Note: The numbers in square brackets are row percentages.

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Table 2: Summary statistics for variables used in analysis

Variable Mean (%) Std. Dev.Physical inactivity* (against regular exercise) 75.0 43.3Current smoker* 35.7 47.9

Heavy smoker (smokes >= 20 cigarettes per day)* 12.3 32.8 Drinker (any use) 61.9 48.6

Drinks ≥ five times per month* 34.7 47.6Drinks everyday* 2.9 16.8

Multiple health risks (with two or more risk factors, *) 41.1 49.2Femalea 41.0 49.2Age (years)

18–30 17.8 38.331–55 70.9 45.455–65 11.2 31.6

Educationa

Less than high school 22.5 41.8High school graduate 41.1 49.22-Year college 16.3 36.94-year college or above 20.1 40.0

Number of individuals 4,540Number of observations 23,276 　Note: aConstant across observations for a given individual.

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Table 4. Predicted effects of a one-hour increase in workweek (FE-IV)

 Excluding income and

unemployment rate(1)

Including income and unemployment rate

(2) 

OutcomesPredictedeffect

Percent change

Predicted effect

Percent change

 

Regular exercise -0.7238* [-2.8885] -0.6117+ [-2.4411]  (0.2900) (0.3641)  

Current smoker 0.9346** [2.6162] 1.0016** [2.8038]  (0.2301) (0.2985)  

Heavy smoker (smokes ≥ 20 cigarettes per day) 0.5443** [4.4375] 0.7103* [5.7909]  (0.2091) (0.2760)  

Drinker (any use) -6.2034** [-10.0202] -6.2181** [-10.0444](0.8597) (1.0945)  

Drinks ≥ five times per month 0.4698+ [1.3525] 0.4396 [1.2656]  (0.2752) (0.3490)  

Drinks everyday 0.2025+ [6.9622] 0.2182 [7.5019]  (0.1151) (0.1469)  

Multiple health risks 1.0090** [2.4556] 1.1276** [2.7443]  　 (0.2701) 　 (0.3526) 　  Note: Percent changes are calculated by dividing the predicted effect by the mean of the dependent variable. Standard errors shown in parentheses: ** p<0.01, * p<0.05, + p<0

 

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Estimation results

• The magnitudes of the effects of a workweek reduction on health behaviors are quite substantial.

The mandatory workweek reduction (from 44 to 40hrs) – increases the likelihood of regular physical activity by 9.6–11.6%,– decreases the probability of smoking (heavy smoking) by 10.4–11.2%

(17.6–23.2%), – decreases the chances of frequent or daily drinking by 5.1–30% – but increases the probability of drink participation by more than 40%. – lessens the likelihood of multiple health risks by 9.8–11.0%.

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Table 5. The predicted effects for subgroup(FE-IV)

Outcomes

Gender Education Agea

Male FemaleHigh school

or less

More than high school

18–30 31–55 56–65

Regular exercise 0.0705 -2.2286* -1.0937** 0.4334 -0.1304 -0.5125 -1.5287*(0.4311) (0.9624) (0.4160) (0.7467) (1.2792) (0.4197) (0.6874)

Current smoker 1.4229** -0.0644 0.6404* 1.6924* -0.4630 1.2135** 0.7843+(0.4567) (0.1665) (0.2943) (0.6983) (0.9354) (0.3694) (0.4410)

Heavy smoker 1.0503* -0.0196 0.3904 1.2759* -0.1239 0.8800* 0.2577(0.4297) (0.0746) (0.2952) (0.5811) (0.8491) (0.3432) (0.3620)

Drinker (any use) -6.7687** -5.2305** -4.9419** -8.4607** -7.1187 -7.0614** -0.5139(1.3285) (1.9482) (1.0338) (2.5305) (4.6686) (1.3784) (0.5491)

Drinks ≥ five times/month 0.0750 1.1304+ 0.3311 0.5619 0.0251 0.5123 -0.1873(0.4667) (0.6153) (0.3788) (0.6901) (1.2808) (0.4075) (0.4801)

Drinks everyday 0.2596 0.0988 0.2033 0.2521 0.4092 0.1710 -0.0544(0.2198) (0.1041) (0.1782) (0.2258) (0.4591) (0.1815) (0.3031)

Multiple health risks 1.1285* 1.0215+ 0.7884* 1.7421* 0.4002 1.2341** 0.6773　 (0.4560) (0.5626) (0.3591) (0.7762) (1.1669) (0.4144) (0.4769)Note: aAge in year 2001. Standard errors shown in parentheses: ** p<0.01, * p<0.05, + p<0.1

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Summary and Discussion

• The estimates suggest that work hours have causal effects on health-related behaviors.

A reduction in work hours …– induces individuals to exercise regularly,– decreases the likelihood of smoking (more pronounced for heavy

smokers),– substantially increases the probability of drink participation, but

decreases the likelihood of frequent drinking.– The effect on regular exercise is salient among females and older

groups, and on smoking behaviors, more pronounced among males and the middle-aged groups.

• The finding that work time is an important determinant for an individual’s health behaviors provide some evidence on the benefits of reducing the standard workweek, particularly for societies that have long working hours.

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A1: Results for the first-stage model of weekly hours of workVariables (1) (2)Under 40-hour workweek standard -3.0871** -2.4781**(Using size information of the establishment at which the respondent worked prior to initial year 2001)

(0.4064) (0.4139)

Age31–55 -2.2448** -1.8325**

(0.5930) (0.5943)55–65 -11.5229** -10.4571**

(0.9961) (1.0015)Marital statusMarried -10.6456** -10.0871**

(0.7943) (0.7950)Divorced/separated -11.1781** -9.8848**

(1.4166) (1.4215)Widowed -18.5280** -17.4510**

(2.2594) (2.2573)ln (Household income) 0.5806*

(0.2588)Local unemployment rate 2.8138**

(0.3056)Constant 54.9112** 42.0328**

(0.6899) (1.6730)     Number of observations 23,276 23,276Number of individuals 4,540 4,540F (excluded instrument) 57.71 35.85Note: Standard errors shown in parentheses: ** p<0.01, * p<0.05, + p<0.1

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Table 3. Predicted effects of a one-hour increase in workweek (FE model)

 Excluding income and

unemployment rate(1)

Including income and unemployment rate

(2)

OutcomesPredictedeffect

Percent change

Predicted effect

Percent change

Regular exercise -0.1346** [-0.5354] -0.1320** [-0.5274](0.0155) (0.0155)

Current smoker 0.0330** [0.0924] 0.0301** [0.0843](0.0109) (0.0109)

Heavy smoker (smokes ≥ 20 cigarettes per day) 0.0379** [0.3090] 0.0377** [0.3074](0.0110) (0.0110)

Drinker (any use) -0.0724** [-0.1163] -0.0499** [-0.0792](0.0162) (0.0160)

Drinks ≥ five times per month 0.1032** [0.2971] 0.0986** [0.2839](0.0150) (0.0150)

Drinks everyday 0.0119+ [0.4091] 0.0112+ [0.3851](0.0062) (0.0062)

Multiple health risks 0.1147** [0.2791] 0.1119** [0.2723]　 (0.0135) 　 (0.0135) 　Note: Percent changes are calculated by dividing the predicted effect by the mean of the dependent variable. Standard errors shown in parentheses: ** p<0.01, * p<0.05, + p<0.1