Overtime Policy and Labor Market Outcomes: Evidence from South Korea

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1 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 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 yson8@uic.edu.

2 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 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 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 1

3 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 2

4 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. 3

5 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 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 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 Finally, the policy was extended to all workplaces with 5 or more employees in July 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-ahalf 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 to 1-4

6 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. 5

7 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: max g(h, N, K) whn fn pwmax(0, (h h S))N rk (1) h,n,k where h s is the overtime standard, which is the threshold at which employers are required to pay overtime; 6

8 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: F h = MC h = wn if h h S (2) = wn(1 + p) if h > h S F N = MC N = wh + f if h h (3) = wh + f + pw(h h S ) if h > h S where F h and F N are marginal products of h and N, and MC h and MC N are marginal costs of h and N, respectively. These can be rearranged as: F h F N = MC h = wn MC N wh + f wn(1 + p) = wh + f + pw(h h S ) if h h S (4) if h > h S Figure 1 shows iso-cost curves for the original overtime standard, h S 0, and the reduced overtime standard, h S 1. The marginal cost schedule is kinked because the slope of the curve is MC h MC N = wn wh+f for wn(1+p) hours below h S and MC h = for hours worked beyond h MC N wh+f+pw(h h S ) S. A reduction of the weekly overtime standard (from h S 0 to h S 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 < h S 1 < h S 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 h S 1 < h S 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 7

9 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 h 1 S < h < h 0 S ), 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 8

10 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 9

11 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 10

12 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 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 11

13 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 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 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, hours and 45 or more hours in 2003 and in The table shows that for both male and female workers, the fraction of workers who work 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, 12

14 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 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 Korea was one of the Asian countries most severely affected by 13

15 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: Y it = ωδ it + φx it + ζ i + θ t + ν it (5) where Y it is firm outcomes, including hours, the log of total number of employees, log profit, log labor cost per worker, and log capital; δ it is the zero-one indicator, which is equal to unity if the policy change is applied to firm i in period t; X it is observable time-varying firm characteristics; ζ i is timeinvariant firm attributes; θ t is the time effect common to all firms in period t; and υ it is all other errors. As noted above, the reduced weekly overtime standard was implemented stepwise based on establishment size beginning in Using establishment size, a dummy variable, δ it, 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 14

16 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 preexisting 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 15

17 change affects number of firms going out of business. Thus, I estimate this effect by using 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 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 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: Y it = γδ it + μx it + η i + m t + ε it (6) where δ it is the zero-one indicator, which is equal to unity if the new overtime policy is applied to 16

18 individual i in period t; X it is observable individual time-varying characteristics; η i is time-invariant individual attributes; m t is the time effect common to all individuals in period t; and ε it is all other errors. The outcome variable, Y it, includes hours worked, logarithm of hourly wage, log of monthly wage, and several measures of worker well-being. Using establishment size, a dummy variable, δ it, 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 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. 17

19 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. 18

20 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 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, 19

21 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 nonrandom 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 20

22 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, 21