Overtime Policy and Labor Market Outcomes: Evidence from...

Overtime Policy and Labor Market Outcomes:

Evidence from South Korea

Yeon Jeong Son

University of Illinois at Chicago

November 2016

Abstract

I examine how changes in overtime policy affect firms and workers by studying the staggered rollout of a

2004-2011 South Korean policy that decreased the overtime threshold from 44 to 40 hours per week.

Based on firm-level longitudinal data, I find that firms respond by reducing labor hours and increasing

capital intensity. Though this adjustment helps firms to avoid some of the labor cost increase, it remains

the case that firm profits fall as a result of the policy. Using longitudinal data on workers, I find

heterogeneous treatment effects according to their prior working hours. For those who previously worked

39 hours or less and those who previously worked 40 to 44 hours, the policy increased hours of work and

had little impact on base hourly wages; for those who previously worked more than 44 hours per week, it

decreased hours of work and increased base hourly wages. In addition to providing direct evidence on a

policy-relevant question, this paper informs the broader question of how firms and workers adjust their

labor demand and supply in the face of an exogenous change in compensation policy.

JEL Codes: J08, J22, J23, J80

University of Illinois at Chicago, Department of Economics M/C 144, 601 S. Morgan St. Chicago, Il. 60607. Email: [email protected].

1

I. Introduction

In recent decades, several countries have changed their overtime regulations and reduced their

standard weekly working hours. For instance, France and Germany adopted policies that reduced standard

weekly working hours from 39 to 35 in 2000 and from 40 to 35 in 1995, respectively; Portugal introduced

a reduction of its weekly overtime standard from 44 to 40 hours in 1996; and Chile reduced its overtime

standard from 48 to 45 hours in 2005. In Asia, Japan adopted policies reducing the standard weekly hours

for overtime pay from 44 to 40 in 1997, and Taiwan made a reduction from 48 hours per week to 84 hours

biweekly in 2001.

Given the trend of changing overtime policies around the world, understanding their impact on

the labor market is critically important for policy makers. Economic theory provides a framework for

understanding the likely mechanisms through which overtime policies impact workers and firms, but it

does not make clear-cut predictions about the policies’ impacts on hours and employment. Under labor

demand theory, an overtime threshold reduction will lead to a substitution effect between hours per

worker and the number of workers. The reduction will also lead to a scale effect and a substitution from

labor services to capital. Ultimately, all three effects will combine to determine the impacts of the

overtime threshold reduction on hours and employment. Similarly, under labor supply theory, the

reduction will lead workers to face the tradeoff between enjoying additional leisure and earning more

income. As a consequence of the overtime threshold reduction, both income and substitution effects will

be experienced by workers, affecting hours worked in opposite directions, and the net effect will be

uncertain.

In this study, I use panel data sets on workers and firms to investigate a national overtime policy

change in South Korea. From 2004 to 2011, Korea gradually adopted a new overtime policy that reduced

the overtime standard from 44 to 40 hours according to establishment size. In 2004, the policy initially

covered establishments with 1,000 or more employees, and by 2011, it had been extended to cover all

establishments with 5 or more employees. The staggered implementation enables me to identify the

policy effects. Herein I consider the theoretical impact of both supply and demand factors and empirically

2

assess the impact of the policy at both the firm and individual levels. To do this, I use two distinct panel

datasets: one follows individuals over a 12-year period, and the other tracks firms over a 7-year period. I

exploit the longitudinal structure of each dataset to estimate models that include fixed effects to account

for unobserved heterogeneity of workers and firms.

Estimating the impact of overtime policy on firms and workers is complicated by the fact that

overtime policies may be adopted endogenously based on current economic conditions. In particular, if

governments adopt overtime policies during times of particularly high labor demand, naïve estimates may

conclude that the policies reduce labor demand, simply because of business cycle. In my context, this sort

of concern is mitigated because the staggered rollout of the policy allows me to include year fixed effects

that absorb any fluctuations in macroeconomic conditions.

In addition to being critical to my identification strategy, the panel structure of the worker dataset

allows me to examine heterogeneous treatment effects according to past working hours. This

heterogeneity is motivated by the theoretical prediction that individuals with different pre-policy

equilibrium hours will experience differing impacts of the overtime standard reduction on their desired

hours. To my knowledge, no other research makes this distinction. Empirically, studying the impact of the

policy without considering this heterogeneity would lead to an inadequate understanding of the policy and,

in some cases, misleading results. In fact, for many outcomes, I observe little effect on average, but this

masks large and opposite-signed policy effects for workers with different pre-policy working hours.

The empirical results for firm-related outcomes indicate that the overtime policy decreases hours

worked, decreases sales profit, increases capital investment, but has no significant impact on employment.

For the worker outcomes, I find that the policy increased actual hours worked of both male and female

workers who previously worked 1 to 39 hours and 40 to 44 hours per week and decreased actual hours

worked for those who previously worked 45 hours or more per week. Furthermore, I find that the hourly

wages for workers who previously worked more than 45 hours per week increased, increasing monthly

earnings slightly. Finally, I find no significant association between the reduction of the overtime standard

and job or life satisfaction for most worker groups. However, the policy reform affects satisfaction with

3

hours for particular worker groups: that is, the overtime standard reduction increases satisfaction with

hours for male workers who previously worked 45 hours or more per week but decreases satisfaction with

hours for female workers who previously worked 40 to 44 hours.

Combined with the theoretical predictions, the results for firm-related outcomes suggest that there

was a negative scale effect and substitution from labor services to capital. In addition, the finding that no

change in employment occurred despite the negative scale effect suggests that a substitution effect from

hours to employees offset the negative scale effect.

The rest of this paper proceeds as follows. Section II overviews the institutional background of

the reduced overtime standard in Korea. Section III discusses previous literature on the association

between overtime policies and labor supply and demand decisions around the world. Section IV explains

the theoretical framework for the study, and Section V describes the two longitudinal data sets analyzed.

Section VI presents the estimation results and discusses potential threats to the internal validity of the

study. Concluding remarks are offered in Section VII.

II. Institutional Background

The Labor Standard Act (LSA) introduced in 1953 was the first law in Korean history to regulate

the overtime standard and require that hours worked over the standard had to be paid a premium. The

LSA was enacted to secure and improve the living standard of workers and to develop the nation’s

various regions equally by standardizing working conditions. When it was first mandated, the overtime

standard was 48 hours per week and was applied to every workplace with five or more employees. After

the country became industrialized and experienced rapid economic development from the 1960s through

1980s, the overtime standard was reduced to 46 hours in 1989 and then to 44 hours in 1998 in order to

meet a global standard and improve workers’ quality of life.

In 2003, the Korean government passed a bill that revised the LSA and established a new

overtime policy. In the aftermath of a serious financial crisis in East Asia in the late 1990s, the main

objective of the new overtime regulation was to raise employment by sharing the work available.

4

Moreover, the policy was intended to achieve better living standards for workers by reducing the negative

effects of long hours of work. The policy imposed a reduction in the legal duration of the workweek,

officially making Saturdays non-working days and lowering the weekly overtime standard from 44 to 40.

After the new overtime policy was enacted in 2003, it was gradually implemented beginning in

2004. The policy’s requirements were enforced in a staggered manner based on establishment size:

initially workplaces with 1,000 employees or more were required to adopt the new overtime policy

starting in July 2004. Thereafter, it was applied to workplaces with 300 employees or more as of July

2005, 100 or more as of July 2006, 50 or more as of July 2007, and 20 or more as of July 2008. Finally,

the policy was extended to all workplaces with 5 or more employees in July 2011.

Historically, an overtime rate has been applied to hours worked beyond the legislated standard

weekly hours for overtime pay. Before the 2003 law was implemented, the overtime rate was time-and-a-

half the employees’ hourly wage with a maximum of 12 overtime hours per week. After the new policy

became fully effective, the overtime premium on the first 4 overtime hours decreased to 25%, with the 50%

rate applying to the remainder up to a maximum of 16 overtime hours per week.

III. Related Literature

In various countries, reduction of the weekly overtime standard has typically been introduced

with the aim of increasing employment, though the effectiveness of the policy in meeting this goal is

politically controversial and theoretically questionable. Other rationales for overtime policies include

creating job security for individual workers and improving worker quality of life and health by reducing

the effects of an excessive workload.

Most past studies of overtime policy have focused on the employment effects. The study findings

are quite conflicted, with employment effects varying from country to country and by the empirical

method and data used. In Germany, an early study by Hunt (1999) found that the reduction in the

overtime standard during the 1980s resulted in a reduction of actual hours worked and an increase in the

hourly wage: specifically, a 1-hour reduction in the overtime standard was associated with a 0.88- to 1-

5

hour decrease in actual hours worked. However, the policy was not found to have a significant

employment effect. In France, Hayden (2006) examined the effects of a 35-hour workweek and

discovered positive effects of the reduction of hours worked on both employment and worker quality of

life. In contrast, however, Estevao and Sa (2008) found that the policy in France did not affect

employment. In another study, Raposo and Ours (2008) investigated the impacts of a reduction in the

weekly overtime standard from 44 to 40 in Portugal and found that employment was not affected but

hours worked decreased and hourly wage increased. In Japan, an early study by Brunello (1989) found

that historical overtime standard reductions had increased overtime and reduced employment. In

examining that country’s weekly overtime standard reduction from 48 to 41 hours from 1988 to 1997,

Kawaguchi et al. (2008) observed that job availability for new school graduates declined in response to an

increase in the hourly wage rate.

Although most studies have focused on the total effects of overtime policy changes, some

research evidence sheds light on impacts on specific groups of workers. A study by Bauer and

Zimmerman (1999) revealed negative employment effects on unskilled workers in Germany. In

investigating the impact of reduced standard hours on working hours in Taiwan, Chen and Wang (2011)

found that the impact was smaller for low-income workers compared to their higher-income counterparts.

Like other countries that have reduced their overtime standard, Korea has been the focus of

studies evaluating the success of its overtime standard reduction in terms of employment effect. As is the

case for other countries, the empirical evidence is mixed in Korea (Nho, 2014; Kim, 2008; Kim and Lee,

2012; Kim and Cho, 2014).

Although most previous studies have mainly focused on the employment effect, it is also

important to examine how firms and workers respond to the policy change in order to fully understand the

policy’s long-term effects on the labor market. Hence, this study expands the outcome analyses beyond

the employment effect to include firm profit, labor cost, and capital. In addition, the study investigates

policy impacts on workers, including actual hours worked, wages, and subjective well-being. Another

important feature of this study is that unobserved firm and worker heterogeneity is accounted for.

6

Although theory predicts heterogeneous treatment effects according to past working hours, most previous

studies have masked conflicting effects by aggregating all workers together. From an empirical

perspective, examining the impact of the policy without considering this heterogeneity has resulted in an

inadequate understanding of the policy and, in some cases, misleading findings. For example, any

positive policy impacts on hours worked for those who used to work less hours than the overtime standard

are diluted when all workers are subjected to aggregate analysis because the former group constituted a

minority of Korean workers. Thus, in this study, I exploit the panel structure of an individual-worker

dataset for Korea to examine the heterogeneous treatment effects according to pre-policy working hours.

In addition, I consider the theoretical impact of both supply and demand factors and assess the policy’s

impact at both the firm and individual levels to provide a thorough empirical investigation of the national

overtime policy change in Korea.

IV. Theoretical Framework

Before turning to the empirical study, this section discusses what theory predicts about the

impacts of the new Korean overtime policy on labor market outcomes. I employ a theoretical framework

used in previous studies (Calmfors and Hoel, 1988; Hunt, 1999; Kawaguchi et al., 2008). In the simplest

demand framework, when firms have to pay for marginal weekly hours at a premium overtime rate, an

employer would never choose to pay a worker overtime; instead, it would simply hire another worker.

While there are many theories as to why firms pay overtime, the most common explanation relies on the

existence of fixed costs for each worker hired. For example, firms may need to train each worker hired,

regardless of the number of hours she works. In this case, firms may prefer to pay a previously trained

worker an overtime premium rather than train a new worker.

This model is derived from the following profit-maximization problem:

maxℎ,𝑁,𝐾

𝑔(ℎ, 𝑁, 𝐾) − 𝑤ℎ𝑁 − 𝑓𝑁 − 𝑝𝑤𝑚𝑎𝑥(0, (ℎ − ℎ𝑆))𝑁 − 𝑟𝐾 (1)

where ℎ𝑠 is the overtime standard, which is the threshold at which employers are required to pay overtime;

7

ℎ is hours per worker; 𝑁 is number of employees; 𝑤 is the average hourly wage per employee; 𝑓 is the

fixed cost of employment; p is an overtime premium; r is interest rate; and K is capital.

The first-order conditions for hours and number of employees are given by:

𝐹ℎ = 𝑀𝐶ℎ = 𝑤𝑁 𝑖𝑓 ℎ ≤ ℎ𝑆 (2)

= 𝑤𝑁(1 + 𝑝) 𝑖𝑓 ℎ > ℎ𝑆

𝐹𝑁 = 𝑀𝐶𝑁 = 𝑤ℎ + 𝑓 𝑖𝑓 ℎ ≤ ℎ (3)

= 𝑤ℎ + 𝑓 + 𝑝𝑤(ℎ − ℎ𝑆) 𝑖𝑓 ℎ > ℎ𝑆

where 𝐹ℎ and 𝐹𝑁 are marginal products of ℎ and 𝑁, and 𝑀𝐶ℎ and 𝑀𝐶𝑁 are marginal costs of ℎ

and 𝑁, respectively.

These can be rearranged as:

𝐹ℎ𝐹𝑁

=𝑀𝐶ℎ𝑀𝐶𝑁

=𝑤𝑁

𝑤ℎ + 𝑓 𝑖𝑓 ℎ ≤ ℎ𝑆 (4)

=𝑤𝑁(1 + 𝑝)

𝑤ℎ + 𝑓 + 𝑝𝑤(ℎ − ℎ𝑆) 𝑖𝑓 ℎ > ℎ𝑆

Figure 1 shows iso-cost curves for the original overtime standard, ℎ𝑆0, and the reduced overtime

standard, ℎ𝑆1. The marginal cost schedule is kinked because the slope of the curve is −

𝑀𝐶ℎ

𝑀𝐶𝑁= −

𝑤𝑁

𝑤ℎ+𝑓 for

hours below ℎ𝑆 and −𝑀𝐶ℎ

𝑀𝐶𝑁= −

𝑤𝑁(1+𝑝)

𝑤ℎ+𝑓+𝑝𝑤(ℎ−ℎ𝑆) for hours worked beyond ℎ𝑆. A reduction of the weekly

overtime standard (from ℎ𝑆0 to ℎ𝑆

1) shifts the isocost curve inward from the solid line to the dashed line,

and the change can either raise or lower 𝑁.

As illustrated in Figure 1, we can conceive of three cases in which the policy’s impacts on hours

and employment would differ. In the first case, for firms whose optimal hours are below the overtime

standard before and after the policy change (firms at ℎ∗ < ℎ𝑆1 < ℎ𝑆

0), reduction of the overtime standard

will have no effect. In the second case, for firms whose optimal hours are above the overtime standard

before and after the policy change (firms at ℎ𝑆1 < ℎ𝑆

0 < ℎ∗) due to a high fixed cost of employment

(𝑓), the exogenous reduction in the overtime standard will raise the labor cost and lead to a scale effect

and a substitution from labor services to capital, tending to reduce both hours and employment. In

addition, because the marginal cost of additional overtime is unaffected by the overtime standard as

8

shown in eq. (4) while the marginal cost of an additional employee is increased by a reduction of the

overtime standard, the firm will substitute hours for workers and will tend to decrease employment.

Consequently, we expect the new overtime policy to have a negative employment effect, but the effect on

hours will depend on whether the scale effect and the substitution from labor to capital dominate the

substitution from workers to hours. In the third case, for firms whose optimal hours are not bound by the

old overtime threshold but are bound by the new threshold (firms at ℎ𝑆1 < ℎ∗ < ℎ𝑆

0), the discontinuities of

the marginal costs of hours and employment at the overtime standard will create an incentive for the firms

to set the actual hours at the threshold. Hence, there will be a substitution from hours to employment.

However, the net effects on employment and hours worked will depend on the relative size of this effect

and the combination of the scale effect and the substitution of capital for labor services.

Labor supply theory predicts that no workers will want to work exactly at the overtime threshold.

This prediction is observable in the kinked budget constraint shown in Figure 2. Workers will never

voluntarily choose to work at the kink in their budget set because this is exactly the point at which wages

discontinuously increase. Empirically, however, I demonstrate below that most workers work hours

exactly at the overtime threshold - the exact opposite of the prediction of the labor supply model. As such,

decisions about hours appear to be poorly captured by the labor supply model and instead reflect the

demand forces discussed above. This indicates that workers are likely choosing among hours and wage

combinations offered by firms, which is consistent with the theoretical model developed by Trejo (1991).

Although the simple labor supply model cannot fully explain workers’ behaviors in terms of

working hours, it does provide important insights. In particular, the labor supply model describes the

number of hours that workers would like to work, even if it does not describe the number of hours

actually worked. Workers’ preferences regarding hours will affect the wages that firms must offer to

induce workers to accept various numbers of hours worked. A static labor supply model suggests that

more overtime requirements will lead to income and substitution effects for workers and that the effects

will differ depending on initial working hours. Although the labor supply model predicts hours poorly for

most workers, these effects remain relevant because changes in desired hours may affect equilibrium

9

wages and could affect actual hours for a subset of workers.

Both the labor demand and labor supply models considered above are partial equilibrium models

that assume a fixed standard wage rate. Following implementation of an overtime standard reduction,

equilibrium standard wages may theoretically increase or decrease. According to the efficient contract

model, firms can lower the base hourly wage until weekly earnings are the same as before the overtime

standard reduction (Trejo, 1991). On the other hand, for workers who prefer working longer hours, their

standard hourly wage may increase following the overtime policy change due to the compensating

differential mechanism discussed above. If standard wages adjust due to the policy change, this

adjustment adds further ambiguity to the theoretical effect of the reduced overtime standard on hours,

employment, and capital use. An increasing standard wage will reduce employment through both scale

and substitution effects, while the reverse is true for a reduction in the standard wage. On the other hand,

whether equilibrium wages increase or decrease, there will be an ambiguous effect on capital since the

scale and substitution effects go in opposite directions.

Given the many forces at play, it is theoretically conceivable that wages, employment, hours, and

capital use could move in a variety of directions. This theoretical ambiguity underscores the importance

of analyzing the effect of the reduced overtime standard empirically.

V. Data

This section describes the data used in this study. Two panel survey datasets are employed, one

from the Korean Labor and Income Panel Study (KLIPS) and one from the Workplace Panel Survey

(WPS), both of which were conducted by the Korea Labor Institute (KLI). The KLIPS data collected from

2001 to 2012 are used to examine the impacts of the overtime policy on labor supply, and the WPS data

collected from 2005 to 2011 are used for analysis of the impacts on labor demand and firm-related

outcomes.

In KLIPS, respondents are asked questions about how many hours they work each week on

average. In addition, for the wage-employed, the numbers of weekly overtime standard and weekly

10

overtime hours are asked about separately. Some respondents answer that their workplaces do not have an

overtime standard. Thus, for those whose workplace has an overtime standard, I sum the weekly overtime

standard and overtime hours and use the sum as the actual hours worked per week; for those whose

workplace does not have an overtime standard, I use the average weekly hours worked.

For the analyses of the policy’s impact on wages, hourly wage is calculated using the monthly

wage, overtime premium, overtime standard, and overtime hours. Given that the overtime premium is 50%

of the worker’s usual hourly wage, overtime hours worked are multiplied by 1.5, and 1 month is

transformed to 4.33 weeks in the calculation.

The advantage of using KLIPS data is that it provides detailed socioeconomic and demographic

information on respondents. The following variables are used in the analysis: gender, age, education,

marital status, home ownership, and job type. A worker’s age is classified with one of three separate

indicator variables: 20 through 35, 36 through 55, and 56 through 65 years. To measure a worker’s

education as a continuous variable, the indicator variable of education is converted to years of education:

i.e., elementary school is converted to 6 years of education, middle school to 9 years, high school to 12

years, 2-year college education to 14 years, 4-year college education to 16 years, master’s degree to 18

years, and doctoral degree to 22 years.

In addition to the actual hours of work and hourly wage, utility is an important dependent variable

for the study of the labor supply response. Although a main goal of the policy was improving worker

well-being, the policy impacts on worker happiness were not underlined in many other studies. As a

proxy for utility, this study uses several variables for worker life and job satisfaction. Use of satisfaction

measures as utility is somewhat controversial in social science research, but it is worthwhile to examine

the impacts of the working hour reduction on specific factors related to life and job satisfaction. The

measures of worker life and job satisfaction used in this study are overall life satisfaction; overall job

satisfaction; and satisfaction with wage, income, working hours, leisure, and self-improvement. In KLIPS,

satisfaction is subjectively assessed by workers on a descending scale of 1 (very satisfied) to 5 (very

dissatisfied); in this study, the outcomes are recoded to an ascending scale (i.e., 1 for very dissatisfied, 2

11

for dissatisfied, 3 for average, 4 for satisfied, and 5 for very satisfied).

Considering the economic crisis that occurred in the late 1990s in Korea and that to a large extent

changed the national economic climate, the analysis in this study is limited to data collected from 2001 to

2012. The data analyzed is also restricted to that for workers aged 20 to 65 years. Because the policy

mainly targeted the full-time wage-employed, the analysis is based on KLIPS data for wage-workers; data

for the self-employed is not addressed. In addition, all data that does not provide usable observations on

hours of work or establishment size is excluded. The sample thus consists of a total of 3,519 individuals

and 33,451 observations.

Table 1 reports descriptive sample statistics for the KLIPS data separately according to hours of

work before the policy’s implementation. As shown in this table, both male and female workers who

work between 40 and 44 hours have higher life satisfaction than their counterparts with longer or shorter

hours of work. Workers in this 40- to 44-working hour category are more educated, are less likely to have

children, and are more likely to own their homes. These differences emphasize the importance of

controlling for these worker characteristics in the regression models.

The WPS data collected biannually from 2005 to 2011 is used to investigate the policy’s impacts

on firms, including hours, employment, profits, labor costs, and capital. WPS collects detailed

information on firms, such as their number of employees, labor cost, wage growth rate, profits, and other

fiscal and human resource information. To examine the effects of the overtime policy on firms, several

variables are used as dependent variables: for the first-stage analyses, hours worked per worker is used as

a dependent variable, and for the reduced-form analyses, several firm-related outcomes are used,

including employment, profits, labor cost per capita, base hourly wage, and capital use. The capital use

outcome is defined as physical assets as of the beginning of the year. WPS asks respondents about the

overtime standard. Hours worked per worker is calculated by summing overtime standard and overtime

hours worked per worker.

The firm characteristics included in the analysis are market competitiveness and demand

volatility. Specifically, the variables of market competitiveness and demand volatility are subjectively

12

assessed by representatives of each firm in responding to the WPS. Market competitiveness is evaluated

in terms of degrees of competitiveness of the firm’s main product in the domestic market and demand

volatility is assessed using the demand forecast for the firm’s main product. All these variables are

reported on or converted to an ascending scale of 1 (very low) to 5 (very high). Regional unemployment

rate is also included as a time-varying firm characteristic in the fixed effect model.

Table 2 reports the descriptive statistics for the WPS data. During the sample period, the average

hours worked are 46.4, and the average weekly standard hours are 40.3.

VI. Results

6.1 Trends of Outcome Variables

Before the regression results are presented, this section describes how various outcomes change

over time during the study period (2001 to 2012). Over the past two decades, there has been a substantial

decrease in hours worked in Korea. Figure 3 and Table 3 depict the trend of workers’ hours worked by the

size of establishment in which they were employed. The average hours of work for all worker groups

were over 52 hours per week in the early 2000s but decreased over time and reached about 47 hours in

2012. Between 2004 and 2009 in particular, during which time the new overtime policy was adopted

stepwise, actual hours of work showed a strong tendency to decline for all worker groups. Table 4

describes the fraction of workers who work 1-39 hours, 40-44 hours and 45 or more hours in 2003 and in

2011. The table shows that for both male and female workers, the fraction of workers who work 40-44

hours increases while the fraction of workers who work 45 or more hours decreases. Figure 3 and Tables

3 and 4 suggest that the overtime standard reduction has a certain type of impact on hours. However, the

fact that hours worked fell among all groups during this period suggests that macroeconomic shocks also

may have occurred during the study period. To take contemporaneous shocks into account, I take

advantage of the staggered rollout of the overtime policy to isolate the policy effects.

Figure 4 shows an increasing trend of overall life satisfaction for all worker groups over time.

When compared with Figure 3, this figure gives a first impression of a meaningful correlation. However,

13

both of these trends could simply reflect other, global forces. While Figures 3 and 4 confirm a decreasing

trend of hours worked and increasing trend of worker life satisfaction over time, the question is whether

and how much the overtime policy has contributed to these changes. To answer this question, I put the

analysis in a regression framework that uses the staggered policy implementation for identification of the

policy’s effects.

Although contemporaneous shocks may have occurred, the new Korean overtime policy seems to

have an impact on weekly hours worked. Figure 5 shows how the distribution of actual weekly hours

worked changes after the overtime standard is reduced from 44 to 40 hours. It shows that under the

previous overtime standard, actual hours worked per week from 2001 to 2012 are distributed relatively

equally between 40 and 75 hours. In contrast, under the new standard, actual weekly hours worked spike

at 40 hours; more than 30 percent of wage-workers reported that they worked exactly 40 hours under the

reduced overtime standard. This concentration at 40 hours is consistent with the prediction of the labor

demand model. As shown in Figure 1, a kink in the firms’ cost function occurs at the overtime standard of

40 hours. This situation induces some firms that would have assigned overtime before the overtime

standard reduction to instead choose hours at the overtime threshold to avoid paying the overtime

premium.

6.2. Impacts on Hours, Employment, and Firm Outcomes

As I have discussed, theory does not provide clear predictions about the effects of overtime

standard reduction on hours worked or employment. Hence, whether the new overtime policy has

succeeded in increasing employment and worker well-being is ultimately an empirical question. This

study uses evidence across firm and worker outcomes to shed light on the effects of the overtime standard

reduction.

In examining the policy impacts on labor market outcomes, the potential endogeneity of the

policy may result in biased estimates. During the period examined in this section (2005 to 2011), a severe

global economic crisis occurred in 2008. Korea was one of the Asian countries most severely affected by

14

the crisis because of its large trade volume and financial integration with the western world. In addition,

the Korean economy suffered from the large oil price increases that occurred during the first half of 2008

(Kim and Rhee, 2009). Because these macroeconomic shocks influenced Korean firms’ production

activities and therefore affected the firm-related outcomes analyzed in this study, those shocks should be

considered in the model. Hence, to address the potential endogeneity problems and macroeconomic

shocks, the estimation strategy in this study is to account both for time-invariant firm attributes that affect

both policy adoption and firm-related outcomes and for time effects that affect all firms in common.

The changes in hours, employment, and other firm-related outcomes in response to the overtime

standard reduction can be examined by estimating the following regression model:

𝑌𝑖𝑡 = 𝜔𝛿𝑖𝑡 + 𝜑𝑋𝑖𝑡 + 𝜁𝑖 + 𝜗𝑡 + 𝜈𝑖𝑡 (5)

where 𝑌𝑖𝑡 is firm outcomes, including hours, the log of total number of employees, log profit, log

labor cost per worker, and log capital; 𝛿𝑖𝑡 is the zero-one indicator, which is equal to unity if the policy

change is applied to firm 𝑖 in period 𝑡; 𝑋𝑖𝑡 is observable time-varying firm characteristics; 𝜁𝑖 is time-

invariant firm attributes; 𝜗𝑡 is the time effect common to all firms in period 𝑡; and 𝜐𝑖𝑡 is all other errors.

As noted above, the reduced weekly overtime standard was implemented stepwise based on establishment

size beginning in 2004. Using establishment size, a dummy variable, 𝛿𝑖𝑡, is created to indicate whether

firms are required to comply with the new overtime policy or not.

Table 5 presents the predicted effects of the overtime policy on hours worked per worker, number

of employees, profit, labor cost per worker, base hourly wage, and capital. Although the estimation

strategy applied controls for firm FE and time FE, results of several OLS and FE specifications are

reported for comparison. Some interesting points can be drawn from the results. In Table 5, Column (1)

presents OLS estimates with firm fixed-effect, whereas Column (2) presents estimates for models that

include both firm- and time fixed effect. Column (1) is shown just for comparison purposes since it could

be biased by general trends in employment during this time period. The predicted effect on hours worked

per worker in Column (2) is smaller in absolute value, which highlights the importance of controlling for

15

time fixed effect. Column (3) includes controls for regional unemployment rate, market competitiveness,

and demand volatility as well as firm- and time fixed effects. The estimates are consistent with those in

Column (2). Additionally, to consider the differential trends among firms, I include firm-specific linear

time trend in the model and the result is shown in Column (4). The estimates are very similar with those

in Column (3). While it is not possible to definitively rule out biases coming from time-varying

unobservables, the stability of the point estimates across Columns (2)-(4) provides evidence against this

possibility. The stability of the estimate when observable controls are included provides reassurance that

the result is not sensitive to controlling for some time-varying firm characteristics. The fact that the

estimates are robust to including a firm-specific linear time trend suggests that there are not differential

trends leading up to the policy binding, suggesting that the estimates are not driven by differences in pre-

existing trends between firms that are affected by the policy earlier vs later.

The preferred model specification is the FE model shown in column (3) of Table 5. To control for

the time-invariant firm characteristics that could affect both policy adoption and firm-related outcomes as

well as to capture the causal effects of the policy, all the equations include firm FE and year FE. Column

(3) shows that hours declines by 1.2 hours due to the overtime policy and indicates that the policy has no

significant effect on employment. Furthermore, the results reveal that the overtime policy change

decreases profit but the effects are not statistically significant while it increases capital per worker by

about 11%. There is also increase in labor cost per capita and decrease in base hourly wage, but these

estimates are not statistically significant. The results showing the decrease in profit and decrease in hours

worked per worker suggest that a scale effect occurred as a result of the overtime standard reduction. In

addition, the increase in capital per worker (defined as physical assets per worker) indicates that firms

become more capital-intensive as a reaction to the overtime policy. It is evident that substitution of capital

for labor services occurs due to the higher cost of labor caused by the overtime policy.

Thus far, this study has discussed how the overtime policy change in Korea affects firm-related

outcomes among firms who remain in the business. However, to examine the effectiveness of the

overtime policy change in increasing employment, it is also meaningful to investigate how the policy

16

change affects number of firms going out of business. Thus, I estimate this effect by using 1998-2014

Korean Census on Establishment, in which firms are classified into nine categories according to

establishment size. (For the descriptive statistics of the data, see Appendix Table A7.) The policy’s

impact on number of firms is estimated using a model that controls for firm-size fixed effect and time

fixed effect, and the result is illustrated in Table 6. Although this finding lacks the statistical significance,

the non-zero coefficient raised the possibility of sample selection. I discuss whether this can plausibly

explain my results in the specification checks section.

6.3. Impacts on Worker Well-being

In this subsection, I discuss the policy impacts on worker outcomes. All the analyses in this

section were conducted using KLIPS data from 2001 to 2012.

Similar to the challenges confronted in examining the policy impacts on firms, the obstacles to

obtaining estimates that can be plausibly interpreted as causal include workers’ unobservable

characteristics, which are associated with both their exposure to the policy and their hours worked.

Because the overtime policy was implemented stepwise by establishment size, unobservable individual

characteristics that may affect both one’s choice of firm size and decision-making about hours worked

will bias estimates of the policy’s impacts on hours worked. For example, a worker who enjoys having

multiple responsibilities may prefer a relatively small company because she will have a greater variety of

tasks in a small organization than in a large firm, and also she may tend to voluntarily work longer hours

in order to complete those tasks. In this example, the policy effect on hours worked may be

underestimated. To address such endogeneity problems, this study employs individual fixed-effect (FE)

models to eliminate unobserved factors that may be related to workers’ choice of firm size and hours of

work.

The impact on worker outcomes can be estimated with the empirical model:

𝑌𝑖𝑡 = 𝛾𝛿𝑖𝑡 + 𝜇𝑋𝑖𝑡 + 𝜂𝑖 + 𝑚𝑡 + 𝜀𝑖𝑡 (6)

where 𝛿𝑖𝑡 is the zero-one indicator, which is equal to unity if the new overtime policy is applied to

17

individual 𝑖 in period 𝑡 ; 𝑋𝑖𝑡 is observable individual time-varying characteristics; 𝜂𝑖 is time-invariant

individual attributes; 𝑚𝑡 is the time effect common to all individuals in period 𝑡; and 𝜀𝑖𝑡 is all other errors.

The outcome variable, 𝑌𝑖𝑡, includes hours worked, logarithm of hourly wage, log of monthly wage, and

several measures of worker well-being. Using establishment size, a dummy variable, 𝛿𝑖𝑡, is created to

indicate whether or not workers are working under the new overtime policy. For instance, the indicator is

0 from years 2001 to 2004 and 1 from years 2005 to 2012 for those who worked in a workplace with

1,000 employees, while it is defined to be 0 from years 2001 to 2006 and 1 from years 2007 to 2012 for

those who worked in a workplace with 100 employees. In creating the dummy variable, I use the

establishment size of the firms where employees worked in 2001 or 2002 before the new overtime policy

was enacted in 2003. This approach allows the variations in the dummy variable to be driven solely by the

implementation schedule and not by individuals’ job change patterns.

Tables 7A and 7B report the FE estimates for actual hours worked, hourly wages, and monthly

wages for male and female workers, respectively. In addition, to reflect the non-monotonic association

between the policy and hours of work, regressions are presented separately for male and female worker

groups classified by hours of work prior to the policy’s implementation: workers who worked (1) 30 to 39

hours per week, (2) 40 to 44 hours per week, and (3) 45 hours per week or more. For the full sample of

male workers, the policy has a significant negative effect on hours worked and positive effect on hourly

wages, while for the full sample of female workers, positive but not statistically significant effects are

found on both hours worked and hourly wages. However, the estimates differ by hours worked prior to

the policy change, which is consistent with theoretical predictions. For both male and female workers,

those who worked less than 44 hours prior to policy implementation experienced an increase in actual

hours worked, and those who worked more than 44 hours experienced a decrease in actual hours worked.

For instance, in Table 7A, the coefficients of the estimates in column (1) show that the overtime policy is

associated with increases of about 6.0 and 4.4 hours of work for male workers who worked between 30

and 39 hours and between 40 and 44 hours, respectively, while it is also associated with a decrease of 1.5

actual hours of work for male workers who worked more than 45 hours.

18

For both male and female workers, the policy effects on base hourly wage for those who worked

44 hours or less are not statistically significant. On the other hand, the hourly wage for those who worked

45 hours or more prior to policy implementation shows a significant increase, with the monthly wage

increasing slightly. In addition to the mechanisms discussed in Section IV, an important institutional

factor could help explain why wages rise. In Korea, most workers are salaried and are paid an hourly rate

only for hours above the overtime threshold. While their salaries correspond to an hourly rate, the fact

that contracts specify a monthly salary could be important in this context. In particular, monthly salaries

may be downwardly rigid so that monthly earnings will not fall in proportion with reductions in hours.1 If

workers are paid a fixed salary for hours under the threshold and an hourly rate for hours above the

threshold, shifting the threshold from 44 to 40 hours will mechanically increase standard hourly wages

when salaries are downwardly rigid.

One of the merits of using KLIPS data is that the survey provides a variety of variables for

workers’ subjective well-being. Exploiting this fact, I examine how the overtime policy affects worker

well-being by using overall life satisfaction; overall job satisfaction; and satisfaction with work time,

leisure, wage, and self-improvement as a proxy for worker well-being. The outcomes are self-reported

levels of satisfaction on a scale from 1 (not satisfied) to 5 (very satisfied). Table 8 presents FE estimates

for the effect of the overtime policy on several life and job satisfaction outcomes. For these analyses,

which employ answers to questions on overall life and job satisfaction as well as satisfaction with several

job-related factors, I create dichotomous variables indicating either “satisfied” for those who answer that

they are satisfied or very satisfied or “not satisfied” for those who answer that they are very dissatisfied,

dissatisfied, or fairly satisfied. All the models include controls for regional average per capita income, age,

years of education, marital status, year FE, and individual FE.

Table 8 presents the estimated effects of the overtime standard reduction on worker satisfaction

outcomes. The effect estimation was carried out separately for male and female workers, for the full

1 In fact, the policy includes an explicit provision discouraging employers from reducing monthly salaries. This provision is not generally enforceable, but it provides additional reason to expect downward monthly

salary rigidity.

19

sample and three worker groups categorized according to their pre-policy hours worked, and for the six

satisfaction measures. With one exception, the overtime standard reduction does not have a significant

impact on workers’ overall life and job satisfaction. In the case of overall job satisfaction, while

coefficients for all male workers show positive signs and those for all female workers show negative signs,

none is significant. In the case of overall life satisfaction, there is no clear tendency for signs of

coefficients except that, as shown in column (3), the overtime standard reduction has a significant positive

impact on overall life satisfaction for male workers who previously worked 40 to 44 hours. As shown in

Table 7A, this group of workers experiences an increase in hours worked of 4.4 hours per week and a

consequent increase in monthly earnings of about 7.7%. The substantial increase in monthly earnings is

the likely reason for the improvement in these workers’ life satisfaction.

Interestingly, however, for male workers who previously worked 45 hours or more, the overtime

standard reduction increases satisfaction with hours, while for female workers who previously worked 40

to 44 hours, it decreases satisfaction with hours. Given that the former experienced a decrease in hours

worked as a consequence of the policy change while the latter experienced an increase in hours worked,

the results suggest a disutility of hours worked.

In column (4), for the male workers who previously worked 45 or more hours, a significant

decrease in satisfaction with leisure and increase in self-improvement are also notable. Because the policy

decreases hours worked for this group of workers, the decrease in satisfaction with leisure may seem

paradoxical. However, the apparently contradictory leisure results might be attributable to workers’

spending some of their increased non-labor time on non-leisure activities such as housework or childcare

while also spending some of their increased free time on self-improvement activities. However, arriving

at a more definitive explanation of these results would require analysis of the workers’ time use.

6.4. Specification Checks

Some potential threats to this study’s internal validity should be acknowledged. This study

assumes that those firms not required to follow the new overtime policy are not impacted by it. However,

20

it is possible that such firms comply with the policy before they are required to do so because they

compete for workers with other firms. For example, a small firm may reduce the overtime standard

voluntarily in order to provide a benefit to its workers similar to that offered by large firms in the same

industry. This type of spillover effect would bias the study toward estimating policy impacts that are

smaller than the actual effects.

Additionally, firms may anticipate that they will be required to comply with the policy in the

future and thus may respond earlier. In this case, labor demand may decline during the period between

policy enactment and implementation. Because the new Korean overtime policy was enacted in August

2003 and began to be implemented in July 2004 by large firms, there was ample time for anticipatory

behavior among small firms. As in the case of the spillover effect, if the anticipation effect is in play, the

treatment effects estimated in the present study would be biased toward zero.

To consider the possibilities of a spillover effect among firms and the anticipation effect, the

hours distributions for workers unaffected by the policy and for those affected by the policy in 2006 are

illustrated in Figure 6. In 2006, only workers working in large firms—those with 1,000 employees or

more—and those working in firms with 300 to 999 employees are affected by the policy. As shown in

Figure 6, workers unaffected by the policy do not show a spike at the new overtime threshold, while those

affected by the policy do show a spike at the threshold of 40 hours per week. The evidence in Figure 6

indicates that workers who are not affected by the policy do not show much response.

Another potential validity threat to this study is attrition bias. Like many other longitudinal

survey data, KLIPS and WPS data show sample attrition over time. If non-response/attrition is non-

random and is related to the overtime policy change, the estimates of this study will be biased. For

example, if the attrition mostly occurs in small firms because they are more likely to go out of business

and tend to be less profitable, then the estimated policy’s impact on firm profit will be biased toward zero.

To examine this situation, the policy’s impacts on sample attrition of KLIPS and WPS data are

respectively illustrated in Tables 9A and 9B. The estimated results in the tables indicate that the policy

does not have a statistically significant effect on sample attrition of KLIPS data but increases the

21

possibility of sample attrition for WPS data by 2.7%. Although this is an interesting finding, it is not

possible to distinguish between non-response and attrition due to firms’ going out of business. However,

as shown in section 6.2., the policy has a non-zero negative effect on number of firms going out of

business, suggesting that the sample attrition for WPS data is more likely due to firms’ going out of

business than firms’ refusing to respond to the survey. If this is the case, it will raise a concern about

differential attrition, and if differential attrition occurs, the study estimates could be slightly biased.

However, any differential attrition would not be large enough to form a serious impediment to this study’s

internal validity.

A final concern is that firms could endogenously change their size to avoid the policy

requirement. Although firms may have little incentive to reduce their size merely to delay policy

compliance for one or two years, firms whose size is near the policy requirement threshold may conclude

that such action is worthwhile. To consider this possibility, a robustness check is provided by restricting

the sample to firms whose size is not near the threshold. The estimated policy impacts on firm-related

outcomes with the restricted sample are presented in Appendix Table 2. These estimates are consistent

with the baseline estimates.

To summarize, although some forces threaten the internal validity of this study, they do not

appear to pose a major concern. Moreover, the validity threats would generally result in underestimation

of the policy’s actual effects in absolute terms.

VIII. Conclusion

Overtime standards have been reduced in several countries in recent decades. The justifications

for the reductions vary, ranging from improving individual worker well-being and family-work balance to

encouraging employment by job sharing. However, most previous studies investigating the effects of

reduced overtime standards have concentrated on employment effects. In particular, few studies have

considered the impacts of the reduction on individual labor supply decisions. Thus, this study examines

the impacts of a reduced overtime standard on a variety of outcomes, including hours worked,

22

employment, wages, worker happiness, and other worker- and firm-related outcomes, by exploiting the

Korean policy change that required reduction of the weekly overtime standard from 44 to 40 hours.

Furthermore, most previous studies have ignored the impacts of overtime policy on hours worked

and have assumed that a decrease in hours worked is an inevitable consequence of the policy. However,

theoretically the impacts of the overtime standard reduction are expected to vary according to hours

previously worked. For example, workers who previously worked between 40 and 44 hours will

experience both a substitution effect and income effect, and thus the total effect on their actual hours

worked is uncertain; on the other hand, because those who previously worked more than 44 hours

experience only an income effect, they are predicted to show a decrease in actual hours of work. Hence,

this study also addresses the heterogeneous effects of the overtime policy on hours worked according to

hours worked before the policy change. In addition, by considering distinct labor market environments for

male and female workers and anticipating different reactions to the overtime policy by those groups of

workers, this study accounts for heterogeneous treatment effects by gender.

This study employs a firm-level longitudinal data set to examine the impacts of the overtime

policy on firms including hours, employment, profit, labor cost, and capital. Moreover, using worker-

level longitudinal data set, the study attempts to identify various impacts of the reduced weekly overtime

standard on workers such as hours worked, base hourly wage, monthly wage, and worker life and job

satisfaction.

The main findings are as follows. First, as predicted by theory, analysis of WPS firm-level

longitudinal data for 2005 to 2011 shows that the overtime policy decreased hours. However, the policy

does not appear to have increased employment in Korea. Furthermore, the empirical results indicate that

the policy decreased firm profit and increased capital use. These results in combination with the

theoretical predictions indicate that the scale effect dominates the substitution effect for employment but

not for capital investment. Firms respond to the policy by increasing capital investment. Second, as was

expected, the individual FE models analyzing KLIPS longitudinal data for 2001 to 2012 show that the

reduction of the weekly overtime standard has had heterogeneous impacts on workers according to their

23

hours worked prior to the policy change. The policy increased hours of work for those who previously

worked 1 to 39 hours and 40 to 44 hours and decreased actual hours of work for those who previously

worked 45 hours or more; in addition, the policy increased base hourly wages for the latter group of

workers. Finally, regarding the policy impacts on life and job satisfaction, most of the estimates showed

no significant effect. However, the analyses show that the overtime policy increased satisfaction with

hours worked and with self-improvement for male workers who previously worked 45 hours or more.

Given that this group of workers constitutes a large portion of the worker class in Korea, these effects

merit further consideration.

The empirical evidence provided by this study has important policy implications. Although no

significant employment effect can be attributed to the overtime policy in Korea, it significantly reduced

hours worked for workers who previously worked 45 hours or more per week and increased hours worked

for those who previously worked less than 40 hours. Given that the unemployment rate at the onset of

policy implementation was relatively low at 3.7% and has remained low thereafter, and considering that

the main objective of the policy was to improve worker quality of life by reducing the high level of

working hours, the significant reduction of working hours and increase in satisfaction with hours worked

for male employees who previously worked more overtime are notable effects. The policy’s

heterogeneous treatment effects on both workers and firms indicate that policy-makers in Korea and other

countries should make a careful determination of which populations or industries should be targeted when

revising their overtime policies.

23

References

Ahn, T. (2015). Reduction of working time: Does it lead to a healthy lifestyle?: Working time and health

behaviors. Health Economics, 25(8), 969-983.

Altonji, J. G., & Paxson, C. H. (1992). Labor supply, hours constraints, and job mobility. The Journal of

Human Resources, 27(2), 256-278.

Ashenfelter, O., & Heckman, J. (1974). The estimation of income and substitution effects in a model of

family labor supply. Econometrica (Pre-1986), 42(1), 73.

Baek, E. G., & Oh, W. (2004). The short-run production effect of the reduction of working hours. Journal

of Policy Modeling, 26(1), 123-144.

Bauer, T., & Zimmermann, K. F. (1999). Overtime work and overtime compensation in

Germany. Scottish Journal of Political Economy, 46(4), 419-436.

Binswanger, M. (2006). Why does income growth fail to make us happier? searching for the treadmills

behind the paradox of happiness. Journal of Socio-Economics, 35(2), 366-381.

Blanchflower, D. G., & Oswald, A. J. (2004). Well-being over time in Britain and the USA. Journal of

Public Economics,88(7), 1359-1386.

Blundell, R., & Macurdy, T. (1999). Chapter 27 labor supply: A review of alternative approaches. (pp.

1559-1695) Elsevier B.V.

Boarini, R., Comola, M., Smith, C., Manchin, R., & De Keulenaer, F. (2012).What makes for a better

life?: The determinants of subjective well-being in OECD countries–Evidence from the Gallup World

Poll (No. 2012/3). OECD Publishing.

Bosch, G., & Lehndorff, S. (2001). Working-time reduction and employment: Experiences in Europe and

economic policy recommendations. Cambridge Journal of Economics, 25(2), 209-243.

Brunello, G. (1989). The employment effects of shorter working hours: An application to Japanese

data. Economica, 56(224), 473-486.

Burgert, D. (2006). The impact of German job protection legislation on job creation in small

establishments. Applied Economics Quarterly (formerly: Konjunkturpolitik), 52(2), 123-139.

Chen, L., & Wang, W. (2011). The impact of the overtime policy reform-evidence from the low-paid

workers in Taiwan. Applied Economics, 43(1), 75-90.

Constant, A. F., & Otterbach, S. (2011). Work hours constraints: Impacts and policy implications. IZA

Policy Paper, (35).

Easterlin, R. A. (2001). Income and happiness: Towards a unified theory. The economic

journal, 111(473), 465-484.

24

Ehrenberg, R. G. (1971). Heterogeneous labor, the internal labor market, and the dynamics of the

employment-hours decision. Journal of Economic Theory, 3(1), 85-104.

EHRENBERG, R. G. (1971). The impact of the overtime premium on employment and hours in U.S.

industry. Economic Inquiry, 9(2), 199-207.

Estevao, M., & Sa, F. (2008). The 35-hour workweek in France: Straightjacket or welfare

improvement?. Economic Policy, 23(55), 418-463.

Fagnani, J., & Letablier, M. (2004). Work and family life balance: The impact of the 35-hour laws in

france. Work, Employment & Society, 18(3), 551-572.

Ferrer‐i‐Carbonell, A., & Frijters, P. (2004). How important is methodology for the estimates of the determinants of happiness?. The Economic Journal, 114(497), 641-659.

Flatau, P., Galea, J., & Petridis, R. (2000). Mental health and wellbeing and unemployment. Australian

Economic Review, 33(2), 161-181.

Frey, B. S., & Stutzer, A. (2002). What can economists learn from happiness research?. Journal of

Economic literature, 40(2), 402-435.

Garhammer, M. (1995). Changes in working hours in Germany: The resulting impact on everyday

life. Time & Society, 4(2), 167-203.

Golden, L., & Wiens-Tuers, B. (2006). To your happiness? Extra hours of labor supply and worker well-

being. The Journal of Socio-Economics, 35(2), 382-397.

Greenhaus, J. H., Collins, K. M., & Shaw, J. D. (2003). The relation between work–family balance and

quality of life. Journal of vocational behavior, 63(3), 510-531.

Hamermesh, D. S., Kawaguchi, D., & Lee, J. (2014). Does labor legislation benefit workers? Well-being

after an hours reduction (No. w20398). National Bureau of Economic Research.

Hart, R. A. (2004). The economics of overtime working. Cambridge University Press.

Hayden, A. (2006). France's 35-hour week: Attack on business? Win-win reform? Or betrayal of

disadvantaged workers? Politics & Society, 34(4), 503-542.

Holly, S., & Mohnen, A. (2012). Impact of Working Hours on Work-Life Balance (No. 465). DIW Berlin,

The German Socio-Economic Panel (SOEP).

Hunt, J. (1999). Has work-sharing worked in Germany? The Quarterly Journal of Economics, 114(1),

117-148.

Johnston, D. W., & Lee, W. (2013). Extra status and extra stress: Are promotions good for us? Industrial

and Labor Relations Review, 66(1), 32-54.

Kahn, S., & Lang, K. (1992). Constraints on the choice of work hours: Agency versus specific-

capital. Journal of Human resources, 661-678.

25

Kawaguchi, D., Lee, J., & Hamermesh, D. S. (2013). A gift of time. Labour Economics, 24, 205-216.

Kawaguchi, D., Naito, H., Yokoyama, I. (2008). Labor Market Responses to Legal Work Hour Reduction:

Evidence from Japan (No. 202). Economic and Social Research Institute (ESRI).

Kim, H., and Lee, J. (2012). The impacts of the 40 hour work week standard on actual working hours,

wages and employment. Korean Journal of Labour Economics, 35(3), 83-100.

Kim, I. J., & Rhee, Y. (2009). Global financial crisis and the Korean economy. Seoul Journal of

Economics, 22(2), 145.

Kim, J., & Cho, M. (2014). The effect of working hours reduction on women’s employment. The Journal

of Women and Economics, 11(1), 109-140.

Kim, Y. (2008). The impact of the statutory working hours reduction on real working hours, employment,

and real wages. Korean Journal of Labor Studies, 14(2), 1-21.

Knabe, A., & Rätzel, S. (2010). Income, happiness, and the disutility of labour. Economics Letters, 107(1),

77-79.

Kuroda, S., & Yamamoto, I. (2012). Impact of overtime regulations on wages and work hours. Journal of

the Japanese and International Economies, 26(2), 249-262.

Lee, J., & Lee, Y. (2016). Can working hour reduction save workers? Labour Economics, 40, 25-36.

Leslie, D. (1987). Motivating wage structures. European Economic Review,31(6), 1267-1283.

Lundberg, S. (1988). Labor supply of husbands and wives: A simultaneous equations approach. The

Review of Economics and Statistics, 224-235.

Messenger, J. C. (2004). Working time and workers' preferences in industrialized countries: finding the

balance. Routledge.

Moffitt, R. (1984). The estimation of a joint wage-hours labor supply model. Journal of Labor Economics,

550-566.

Nho, Y. (2014). The employment effects of 40-hour standard workweek. Korean Journal of Industrial

Relations, 24(2), 109-129.

OECD (2014), Society at a Glance: Asia/Pacific 2014, OECD Publishing.

Park, C. (2014). A study of the non-market effects of five-day workweek. Korean Journal of Labour

Economics, 37(4), 59-88.

Park, J., Kwon, O. J., & Kim, Y. (2012). Long working hours in Korea. Industrial health, 50(5), 458-462.

Park, J., Yi, Y., & Kim, Y. (2010). Weekly work hours and stress complaints of workers in

Korea. American journal of industrial medicine, 53(11), 1135-1141.

Pouwels, B., Siegers, J., & Vlasblom, J. D. (2008). Income, working hours, and happiness. Economics

Letters, 99(1), 72-74.

26

Raposo, P. S., & van Ours, J. C. (2010). How working time reduction affects jobs and wages. Economics

Letters, 106(1), 61-63.

Rätzel, S. (2009). Revisiting the neoclassical theory of labour supply–Disutility of labour, working hours,

and happiness (No. 09005). Otto-von-Guericke University Magdeburg, Faculty of Economics and

Management.

Rudolf, R. (2014). Work shorter, be happier? Longitudinal evidence from the Korean five-day working

policy. Journal of Happiness Studies, 15(5), 1139-1163.

Saffer, H., & Lamiraud, K. (2012;2011;). The effect of hours of work on social interaction. Review of

Economics of the Household, 10(2), 237-258.

Sanchez, R. (2013). Do reductions of standard hours affect employment transitions?: Evidence from

chile. Labour Economics, 20, 24-37.

Sousa-Poza, A., & Sousa-Poza, A. A. (2000). Well-being at work: A cross-national analysis of the levels

and determinants of job satisfaction. Journal of Socio-Economics, 29(6), 517-538.

Tausig, M., & Fenwick, R. (2001). Unbinding time: Alternate work schedules and work-life

balance. Journal of Family and Economic Issues, 22(2), 101-119.

Trejo, S. J. (1991). The effects of overtime pay regulation on worker compensation. The American

Economic Review, 81(4), 719-740.

Tummers, M. P., & Woittiez, I. (1991). A simultaneous wage and labor supply model with hours

restrictions. The Journal of Human Resources, 26(3), 393-423.

Wooden, M., & Warren, D. (2004). Non-standard employment and job satisfaction: Evidence from the

HILDA survey: 1. The Journal of Industrial Relations, 46(3), 275.

Yoo, S. (2005). The number of working hours a salary earner is willing to reduce for leisure

activities. Applied Economics Letters, 12(6), 365-368.

27

Figure 1. Isocost Curves

Figure 2. Budget Constraints for Workers

28

Figure 3. Trend of Hours Worked (2001-2012)

Figure 4. Trends of Overall Life Satisfaction of Workers (2001-2012)

29

Figure 5. Distribution of Hours Worked per Week

Before the policy After the policy

SOURCE: KLIPS, 2001-2012.

Figure 6. Distribution of Hours Worked per Week in 2006

Workers unaffected by the policy Workers affected by the policy

NOTE: Firms with 1000+ employees and firms with 300-999 were affected by the policy in 2005.

SOURCE: KLIPS, 2006.

30

Table 1. Sample statistics of the KLIPS data

Male Female

Average hours worked per week before policy change 30-39

hours

40-44

hours 45+ hours TOTAL

30-39

hours

40-44

hours 45+ hours TOTAL

Observations 764 1,981 17,058 20,671 1,392 2,209 8,042 12,780

Age (years)

18-34 12.0 22.1 27.8 26.5 38.4 42.8 39.1 39.5

35-55 53.5 57.8 57.0 56.8 49.5 44.4 46.7 46.8

56-65 25.8 16.2 11.6 12.5 9.4 10.0 10.6 10.2

Marital status

Never married 12.0 17.2 17.8 17.4 19.5 23.9 22.4 22.1

Married 78.4 75.6 77.5 77.5 71.6 65.9 65.4 66.2

Divorced/separated 7.9 5.6 3.8 4.1 4.7 1.6 4.4 3.9

Widowed 1.7 1.7 0.9 1.0 4.3 8.6 7.8 7.7

Parental status

Having a child 34.8 46.5 53.0 51.5 51.1 46.2 39.7 42.9

Number of child 1.7 1.6 1.7 1.7 1.7 1.7 1.6 1.6

Years of education 11.4 13.2 12.6 12.7 12.3 12.7 11.3 11.7

Disability 5.9 2.2 1.7 2.0 0.0 1.1 0.9 0.8

Regular worker 41.8 74.4 87.6 84.4 58.9 81.4 80.8 77.8

Economic status

Monthly wage (in 1,000 KRW) 2,169 2,719 2,435 2,458 1,197 1,618 1,391 1,414

Base hourly wage (in 1,000 KRW) 15.4 14.9 10.9 11.7 8.6 9.1 6.5 7.4

Non-labor income (Annual, in 1,000 KRW) 8,553 8,449 8,198 8,241 8,172 8,301 8,078 8,145

Own house 63.0 61.3 62.6 62.5 57.9 63.8 61.3 61.6

Overall satisfaction 3.1 3.3 3.3 3.3 3.3 3.3 3.3 3.3

Job satisfaction 2.9 2.7 2.7 2.7 2.7 2.6 2.7 2.7

Several measures of job/life satisfaction

Satisfaction with hours 3.1 3.3 3.1 3.1 3.4 3.4 3.1 3.2

Satisfaction with leisure 2.9 3.1 3.0 3.0 3.0 3.0 3.0 3.0

Satisfaction with wage 2.6 2.8 2.8 2.8 2.8 2.9 2.8 2.8

Satisfaction with income 2.6 2.8 2.8 2.8 2.8 2.8 2.8 2.8

Satisfaction with self-improvement 2.9 3.1 3.1 3.1 3.1 3.2 3.1 3.1

NOTES: The hourly wage, is calculated as: (monthly wage)/{(standard working hours + 1.5 × overtime)*4.33}.

31

Table 2. Sample statistics of the WPS data

Mean

Hours worked per worker 46.4

Standard hours 40.3

Overtime 6.1

Number of employees 1,046

Sales profit (in million KRW) 99,108

Labor cost per worker (in million KRW) 47.8

Base hourly wage (in 1,000 KRW) 8.5

Capital (in million KRW) 421,930

Labor union presence 38.6

Relative wage 3.0

Existence of multiple sites 39.9

Market competitiveness 3.8

Demand volatility 3.2

Observations 7,144

NOTES: The base hourly wage is calculated as: (monthly wage)/{(standard working hours + 1.5 × overtime)*4.33}.

32

Table 3. Average Hours Worked by Firm Size (2001-2012)

Firms with

1,000+

employees

Firms with 300-

999 employees

Firms with 100-

299 employees

Firms with 50-

99 employees

Firms with 20-

49 employees

Firms with 5-19

employees Total

2001 50.3 53.3 53.2 53.1 53.8 51.8 52.0

2002 49.9 52.3 53.6 52.7 52.8 51.4 51.8

2003 49.9 52.0 53.9 53.6 53.8 51.5 52.0

2004 48.8 50.9 52.1 53.2 53.7 52.1 51.4

2005 48.3 49.0 51.6 51.0 52.3 50.2 50.3

2006 49.0 49.0 50.8 50.1 52.0 50.6 50.3

2007 48.4 49.5 50.9 50.4 51.2 49.8 49.8

2008 50.4 49.4 52.1 50.8 51.8 49.8 50.4

2009 46.9 46.4 48.5 48.5 51.1 48.4 48.4

2010 47.8 48.5 49.4 49.1 51.3 48.3 49.0

2011 46.8 47.0 48.1 49.3 50.1 47.9 48.0

2012 47.3 46.5 48.9 46.4 47.6 47.4 47.2


Table 4. Distribution of Hours Worked (2003 and 2011)

2003 2011

Hours worked per week Men

(Percentage)

Women

(Percentage)

Men

(Percentage)

Women

(Percentage)

1 to 39 hours per week 4.0 8.5 4.1 6.6

40 to 44 hours per week 18.1 19.8 27.4 23.7

45 or more hours per week 78.1 71.7 68.5 69.8

Mean 53.3 47.8 48.3 44.8

SOURCE: KLIPS, 2003 and 2011.

33

Table 5. Effects of the Policy on Firm-related Outcomes–OLS and FE Specifications

OLS FE FE FE

(1) (2) (3) (4)

Dependent var.

Hours worked per worker -1.5070*** -1.2290*** -1.2051*** -1.2379***

(0.3960) (0.4258) (0.4238) (0.4225)

Log (number of employees) 0.0072 0.0260 0.0261 0.0257

(0.0150) (0.0228) (0.0228) (0.0228)

Log (profit) 0.0812* -0.0511 -0.0542 -0.0528

(0.0460) (0.0617) (0.0616) (0.0615)

Log (labor cost per worker) 0.0409*** 0.0177 0.0178 0.0173

(0.0132) (0.0165) (0.0165) (0.0164)

Log (base hourly wage) -0.1756*** -0.0864 -0.0887 -0.0888

(0.0538) (0.0599) (0.0598) (0.0597)

Log (capital per worker) 0.2264*** 0.1145** 0.1146** 0.1158**

(0.0408) (0.0503) (0.0503) (0.0501)

Firm FE Yes Yes Yes Yes

Year FE No Yes Yes Yes

Controls No No Yes Yes

Firm×Year No No No Yes

NOTES: Standard errors reported in parentheses are clustered at firm level.

Eq.(3) and (4) include controls for regional unemployment rate, market competitiveness, and demand volatility.

*Statistically significant at the 10 percent level; ** at the 5 percent level; *** at the 1 percent level SOURCE: WPS, 2005, 2007, 2009, and 2011.

34

Table 6. Effects of the Policy on Number of Establishments

Dependent var. Log (Number of establishments)

(1)

Overtime policy -0.0505

(0.0314)

Establishment size FE Yes

Year FE Yes

R2 0.856

Observations 136

NOTES: Standard errors reported in parentheses are clustered at establish size level.

*Statistically significant at the 10 percent level; ** at the 5 percent level; *** at the 1 percent level

SOURCE: Korean Census on Establishment, 1998-2014.

35

Table 7A. Effects of the Policy on Male Workers

Weekly hours worked before the policy Weekly hours worked before the policy

Full sample 1-39 hours 40-44 hours 45+ hours Full sample 1-39 hours 40-44 hours 45+ hours

(1) (2) (3) (4) (5) (6) (7) (8)

Dependent var.

Hours worked -0.6884* 5.8213** 5.0919*** -1.4170*** -0.7696* 6.0209*** 4.4433*** -1.4541***

(0.4084) (2.4029) (1.1448) (0.4492) (0.3997) (2.2684) (1.0706) (0.4415)

N=16,149 N=733 N=1,487 N=13,426 N=15,800 N=666 N=1,470 N=13,170

Log (Base hourly wage) 0.0750*** -0.0268 -0.0108 0.0813*** 0.0703*** -0.0809 -0.0381 0.0839***

(0.0123) (0.0981) (0.0387) (0.0131) (0.0122) (0.0915) (0.0375) (0.0131)

N=16,051 N=726 N=1,478 N=13,350 N=15,708 N=660 N=1,461 N=13,099

Log (Monthly earnings) 0.0618*** 0.1332 0.1238*** 0.0486*** 0.0535*** 0.0927 0.0765** 0.0489***

(0.0110) (0.0813) (0.0412) (0.0112) (0.0103) (0.0708) (0.0382) (0.0107)

N=16,106 N=731 N=1,481 N=13,395 N=15,763 N=665 N=1,464 N=13,144

Worker FE Yes Yes Yes Yes Yes Yes Yes Yes

Year FE Yes Yes Yes Yes Yes Yes Yes Yes

Controls No No No No Yes Yes Yes Yes

NOTES: Standard errors reported in parentheses are clustered at worker level.

The dependent variable, base hourly wage, is calculated as: (monthly wage)/{(standard working hours + 1.5 × overtime)*4.33}. Equations (4)-(6) include controls for regional unemployment rate, age, years of education, marital status, year fixed effect, and individual fixed effect.

The treatment variable is a dummy variable that indicates whether the policy was required in the employee’s workplace. This variable is based on the size of the

workplace.



36

Table 7B. Effects of the Policy on Female Workers



(1) (2) (3) (4) (5) (6) (7) (8)

Dependent var.

Hours worked 0.3335 6.9336*** 2.0459** -0.9178 0.3449 6.7168*** 1.8354* -0.7395

(0.5404) (2.4560) (0.9856) (0.6223) (0.5417) (2.4818) (1.0307) (0.6195)

N=8,208 N=1,073 N=1,438 N=5,343 N=8,069 N=1,036 N=1,423 N=5,263

Log (Base hourly wage) 0.0154 -0.1511 -0.0402 0.0484** 0.0127 -0.1465 -0.0324 0.0444**

(0.0179) (0.0967) (0.0372) (0.0190) (0.0179) (0.1005) (0.0384) (0.0189)

N=8,157 N=1,067 N=1,429 N=5,308 N=8,020 N=1,030 N=1,414 N=5,230

Log (Monthly earnings) 0.0092 -0.0321 -0.0107 0.0175 0.0076 -0.0275 -0.0142 0.0187

(0.0172) (0.0942) (0.0381) (0.0178) (0.0168) (0.0980) (0.0361) (0.0172)

N=8,187 N=1,072 N=1,434 N=5,326 N=8,050 N=1,035 N=1,419 N=5,230



Controls No No No No Yes Yes Yes Yes


The dependent variable, base hourly wage, is calculated as: (monthly wage)/{(standard working hours + 1.5 × overtime)*4.33}. Equations (4)-(6) include controls for regional unemployment rate, age, years of education, marital status, year fixed effect, and individual fixed effect.

The treatment variable is a dummy variable that indicates whether the policy was required in the employee’s workplace. This variable is based on the size of the

workplace.



37

Table 8. Effects of the Policy on Worker Satisfaction

Male workers Female workers



(1) (2) (3) (4) (5) (6) (7) (8)

Dependent var.

Life satisfaction 0.0018 -0.0036 0.0760* -0.0089 -0.0017 -0.0496 -0.0269 0.0138

(0.0121) (0.0608) (0.0387) (0.0134) (0.0155) (0.0405) (0.0354) (0.0196)

Job satisfaction 0.0211* 0.0740 0.0095 0.0167 -0.0273 -0.0447 -0.0696 -0.0113

(0.0120) (0.0619) (0.0450) (0.0131) (0.0183) (0.0519) (0.0458) (0.0224)

Hours satisfaction 0.0228* 0.0538 0.0001 0.0243* -0.0182 -0.0896 -0.0861* 0.0131

(0.0127) (0.0575) (0.0441) (0.0140) (0.0194) (0.0595) (0.0462) (0.0236)

Leisure satisfaction -0.0184 0.0701 -0.0053 -0.0220* 0.0071 -0.0107 -0.0184 0.0229

(0.0114) (0.0710) (0.0425) (0.0124) (0.0145) (0.0328) (0.0391) (0.0179)

Wage satisfaction 0.0103 -0.0131 -0.0283 0.0146 -0.0101 -0.0157 -0.0262 -0.0031

(0.0101) (0.0627) (0.0356) (0.0111) (0.0150) (0.0446) (0.0402) (0.0174)

Self-improvement 0.0248** 0.0855 -0.0060 0.0267** -0.0196 -0.0269 -0.0391 -0.0115

(0.0117) (0.0586) (0.0404) (0.0129) (0.0179) (0.0542) (0.0414) (0.0219)



Controls Yes Yes Yes Yes Yes Yes Yes Yes


The dependent variable is a binary variable indicating very satisfied/satisfied vs. fair/dissatisfied/very dissatisfied.

All equations include controls for regional average of per-capita income, age, years of education, marital status, year fixed effect, and individual fixed effect.



38

Table 9A. Effects of the Policy on Sample Attrition (KLIPS data)

Sample attrition

Overtime policy 0.0080

(0.0081)

Worker FE Yes

Year FE Yes

R2 0.048

Observations 27,799

Number of workers 2,912

NOTES: Standard errors reported in parentheses are clustered at worker for panel A and firm level for panel B.


SOURCE: KLIPS, 2001-2012

Table 9B. Effects of the Policy on Sample Attrition (WPS data)

Sample attrition

Overtime policy 0.0269*

(0.0152)

Worker FE Yes

Year FE Yes

R2 0.242

Observations 7,620

Number of workers 1,905

NOTES: Standard errors reported in parentheses are clustered at worker for panel A and firm level for panel B.


SOURCE: WPS, 2005, 2007, 2009, and 2011.

39

Appendix. Table A1. Effect of the Policy on Overtime Hours

Male Female

Dependent var. Hours worked Hours worked

(1) (4)

Full sample 0.2854 0.0807

(0.2594) (0.2678)

N=13,520 N=7,023

Weekly hours worked before the policy

30-39 hours 0.4050 0.3048

(0.7307) (0.2081)

N=359 N=749

40-44 hours 1.1305*** 0.8243**

(0.3439) (0.3206)

N=1,203 N=1,260

45+ hours 0.2006 -0.2528

(0.3005) (0.3668)

N=11,505 N=4,704


All equations include controls for regional unemployment rate, level of educational attainment, marital status, time

fixed effect, and individual fixed effect.

The treatment variable is a dummy variable that indicates whether the policy was required to be implemented in the

employee’s workplace. This variable is based on the size of the workplace.



40

Appendix. Table A2. Effects of the Policy on Firm-related Outcomes – Restricted Sample

FE

(1)

Dependent var.

Hours worked per worker -1.2764***

(0.4434)

Log (number of employees) 0.0482**

(0.0243)

Log (sales profit) -0.1645**

(0.0702)

Log (labor cost per capita) 0.0057

(0.0178)

Log (base hourly wage) -0.1032

(0.0636)

Log (capital) 0.1732***

(0.0558)

Firm FE Yes

Year FE Yes

Controls Yes

Firm×Year No

NOTES Standard errors reported in parentheses are clustered at firm level.

As a robustness check, I use the restricted sample: the sample excludes firms near the thresholds (i.e., firms with

270-330 employees in 2005, firms with 45-55 employees in 2007, and firms with less than 10 employees).

The model includes controls for regional unemployment rate, market competitiveness, and demand volatility.


42

Appendix. Table A3. Effects of the Policy on Firm-related Outcomes–by Industry

Estimated effects on

hours

Estimated effects on log

(Number of employees)

Average hours

before policy

Share of firms with ℎ >44

before policy

(1) (2) (3) (4)

Industry

Manufacturing – labor intensive -1.4042 0.0430 50.4 0.93

Manufacturing – capital intensive -0.6449 -0.0329 50.4 0.94

Construction -2.2495 0.1639 47.9 0.82

Wholesale/retail -2.3858 0.1247 47.5 0.85

Transportation -2.1403 0.1160 47.5 0.81

Hotel/restaurant/leisure -2.0745 -0.1100 48.0 0.85

Information/technology -2.3033 0.0224 45.8 0.79

Finance -5.1813* 0.1513* 44.5 0.79

Education/health -1.2544 0.0360 46.5 0.81

NOTES: Standard errors reported in parentheses are clustered at firm level.

All the equations include controls for regional unemployment rate, market competitiveness, and demand volatility as well as firm fixed effect and time fixed

effect.


43

Appendix. Table A4. Effects of the Policy on Unemployment

Male Female

(1) (2) (3) (4)

Full sample -0.0195** -0.0200** -0.0132 -0.0193

Overtime Policy and Labor Market Outcomes: Evidence from...

Documents

Transcript of Overtime Policy and Labor Market Outcomes: Evidence from...