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DEPARTMENT OF ECONOMICS

ISSN 1441-5429

DISCUSSION PAPER 10/12

Work Hours in Chinese Enterprises:

Evidence From Matched Employer-Employee Data

Dr. Vinod Mishra* and Prof. Russell Smyth

†

Abstract The purpose of this paper is to explore the factors that are correlated with hours worked in China. A

distinguishing feature of the study is that we use representative matched employer and employee

data. Hence, in addition to the usual worker characteristics examined in conventional economic

models of labour supply, we also take account of the influence of firm characteristics and policies in

influencing the number of hours worked. The results suggest that in addition to the hourly wage

rate, labour supply characteristics and human capital characteristics of the individual, firm-level

differences are important in explaining variation in weekly hours worked in Chinese firms. In

particular, our results suggest that there is a norm of longer working hours in firms which employ a

high proportion of female workers, that hours worked are less in firms which pay overtime and that

hours worked are less in firms in which labour disputes have disrupted production. The implications

of the results for Chinese firms wishing to improve labour management practices are discussed.

Keywords: China, hours worked, wages, firms

JEL Codes: J22, J30

* Dr. Vinod Mishra, Department of Economics, Monash University VIC - 3800, Australia

E-mail: vinod.mishra@monash.edu Phone: +61 3 99047179 † Prof. Russell Smyth, Department of Economics, Monash University VIC - 3800, Australia

E-mail: russell.smyth@monash.edu Phone: +61 3 99051560

© 2012 Vinod Mishra and Russell Smyth

All rights reserved. No part of this paper may be reproduced in any form, or stored in a retrieval system, without the prior written

permission of the author.

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Introduction

There has been a decline in the length of the working week in western countries. Rogerson

(2006) reports that mean hours worked in OECD countries decreased at a fairly steady rate

from the mid-1950s to the mid-1980s, at which point it levels off and that mean hours worked

in 2003 was just under 83 per cent of its value in 1956. Clark (2005) reports that since the

early 1980s, hours of work have fallen in countries such as France, Germany, Japan and

Spain and remained stable in countries such as Canada and the UK. The only two countries in

which hours of work have increased over that period are Sweden (from a low level) and the

US. Overall, the decline in the length of the working week in western countries has been

described as representing one of the larger increases in the standard of living across the

twentieth century (Costa 2000). Asia is an emerging economic powerhouse. The twenty-first

century has been described in some quarters as the Asian century (Sachs, 2004). However, in

contrast to western countries, in Asia economic growth has not translated into lower number

of hours worked. The International Labour Organisation (ILO) points out that despite rapid

economic growth in Asia, workers in Asian countries are still generally working longer hours

than most of their counterparts in developed countries (Lee et al., 2007).

This is particularly true for China. China has one of the highest rates of economic growth in

the world over the last three decades, but the average working week in China is one of the

highest in the world. The OECD (2011) found that the Chinese ranked fourth in the world in

terms of time spent at work, after Mexico, Japan and South Korea. The main beneficiaries of

a decline in the length of the working week in western countries over the course of the

twentieth century were unskilled blue collar workers (Golden, 2008). However, in China it

has been unskilled blue collar workers and, in particular, rural-urban migrants, who have

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been the engine room that has fuelled China‟s high growth rate. According to a conservative

estimate by UNESCO, over the last two decades, rural-urban migrants have contributed 16

per cent of China‟s GDP growth (Lan, 2009) Rural-urban migrants also tend to work the

longest hours of all Chinese workers, typically over 60 hours per week (Meng & Bai, 2007).

Primarily because of long working hours, and poor working conditions, confronting rural-

urban migrants some have argued that while China is the global factory, it is also responsible

for a race to the bottom in terms in global labour standards (see, eg, Chan, 2003). More

generally, long hours of this sort worked by rural-urban migrants are in violation of both

China‟s Labour Laws and ILO regulations on maximum working hours and, as such,

represent a direct challenge to the ILO‟s „decent working time‟ policy framework developed

by the ILO‟s Conditions of Work and Employment Programme (Lee et al., 2007).

The number of hours worked is important for several reasons. Long hours have been shown

to be positively correlated with workplace injuries (Wilkins, 2005), adverse health outcomes

(Spurgeon, 2003) and reduced social capital (Saffer, 2008). In the Chinese context, Bauman

et al. (2011) found that long working hours was associated with increased health risks, such

as cardiovascular disease. Moreover, using Chinese data, Houdmont et al. (2011) found that

long working hours had an adverse effect on psychological wellbeing of office workers.

Frijters et al (2009) found that long work hours had an adverse effect on the mental wellbeing

of rural-urban migrant workers in China. In 2010 several rural-urban migrants committed

suicide at the Foxconn plant in Longhua, as the result of poor working conditions and long

hours (see eg. Guardian, 2010). Siu et al. (2005) found that long working hours in China was

associated with higher levels of occupational stress and family-work conflict.

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The purpose of this paper is to explore the factors that are correlated with hours worked in

China. Rogerson (2006, p. 369) suggests: “understanding which factors give rise to …..

differences in hours of work …. will presumably help us to better understand what factors are

quantitatively important in shaping hours of work more generally”. In this sense, this paper

not only seeks to examine which factors are correlated with hours of work in China, but also

explore why hours of work remain high, despite three decades of high economic growth.

A distinguishing feature of the study is that we use representative matched employer and

employee data. Hence, in addition to the usual worker characteristics examined in

conventional economic models of labour supply, we also take account of the influence of

firm characteristics and policies in influencing the number of hours worked. By contrast, the

limited existing literature on hours worked in China has primarily used a conventional labour

supply function, in which the employer dimension is ignored (see eg Li & Zax, 2003;

Maurer-Fazio et al., 2011). There is increasing evidence, though, that in a competitive labour

market characterized by high labour turnover and a high level of competition between

employers to attract and retain the best staff, the employer dimension cannot be ignored.

The demise of allocated, lifelong jobs in the push towards a market economy has resulted in

the materialization of a competitive labour market in urban China (Warner, 1996). Increased

competition amongst private sector employers and the freedom to diverge from a state

administered labour system has led to increased job turnover and mobility as firms vie to

attract and retain skilled staff and China‟s skilled workforce aim to maximize their

employment opportunities. The data on which the study below is based was collected in

enterprises in Shanghai in 2007 and pertains to the labour market situation in 2006.

According to a special report published in BusinessWeek in 2006 titled, “How rising wages

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are changing the game in China” in that year alone China‟s labour costs increased 10 per cent

and the turnover rate in the urban labour market exceeded 20 per cent (Roberts, 2006).

Length of tenure, according to a study undertaken by Mercer Human Resource Consulting in

over 100 organizations in China, dropped substantially for 25-35 years olds, from an average

3- 5 years in 2004, to only 1-2 years in 2006 (HR Magazine, 2006). There are clear

implications for profit margins. A report by the American Chamber of Commerce in China,

published in 2006, found that rising labour costs and high labour turnover, significantly

decreased margins in 48 per cent of US manufacturing enterprises in China (Roberts, 2006).

Existing Literature

Several studies have documented trends in the number of hours worked. One set of studies

have documented historical trends in hours worked beginning with the onset of the industrial

revolution (Alvarez-Cuadrado, 2007; Costa, 1998; Hopkins, 1982). The main finding from

these studies is that in the initial transformation from agriculture to industrial jobs the number

of hours worked increased, while a decline in the number of hours worked started to spread

with the advent of the shorter hours movement in the last quarter of the nineteenth century

(Golden, 2008). A second set of studies have focused on trends in recent decades in the high-

income G7 countries, such as Japan (Kuroda, 2010), Canada (Sheridan et al., 2001), the

United States (Aguiar & Hurst, 2007) and United Kingdom (Green, 2001). These studies find

that while the average length of the working week has either declined or remained unchanged,

variation around the average has increased (see eg. Wooden et al., 2009).

An extensive literature exists that uses a conventional model of labour supply to estimate the

labour supply of men and women. Most of these studies are primarily interested in estimating

how hours worked respond to variations in wages (see, eg, Killingsworth 1983 or Pencavel,

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1986 for surveys). One extension of this literature has taken an historical perspective and

examined the determinants of changes in hours worked in the nineteenth and early twentieth

centuries or differences in hours worked between the „old world‟ (Europe) and the „new

world‟ (Australasia and North America) (Atak et al., 2003, Costa, 2000; Domenech, 2007;

Huberman & Mins, 2007; Vandenbroucke, 2009). Another extension of this literature has

taken a comparative approach and examined determinants of differences in hours worked

across OECD countries or specifically between Europe and the United States (Alesina et al.,

2006; Bell & Freeman, 1995, 2001; Blanchard, 2004; Burgoon & Baxandall, 2004; Causa,

2008; Clark, 2005; Prescott, 2004). These studies explain cross-country differences in the

number of hours worked in terms of differences in marginal tax rates (Prescott, 2004),

differences in preferences for leisure (Blanchard, 2004) and differences in earnings inequality

and associated incentives to strive for promotion (Bell & Freeman, 2001).

While the conventional labour supply model often forms the basis for such studies,

disciplines other than economics have also examined various aspects of hours worked

including determinants of non-standard working hours (evenings, overtime, shift work)

(Presser, 1995) and mismatch between actual and preferred hours of work (Reynolds, 2003).

Few studies examine the impact of both firm and worker characteristics on hours worked.

One such study is Bryan (2007) who uses matched employer and employee data to examine

the effect of firm and worker characteristics on number of hours worked in the UK. However,

a limitation of Bryan‟s (2007) study was that he did not have data on wages.

There is little evidence on the determinants of hours worked in China. There is a small

literature on labour supply in Chinese urban labour markets (Li & Zax 2003; Chau et al.,

2007) including studies that focus on the labor supply responses of married females (Maurer-

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Fazio et al., 2011). However, these studies focus exclusively on the supply side and do not

consider the relevance of firm characteristics or policies. One existing study for China that

does consider the employer dimension is Smyth et al. (2012). That study uses matched

employer and employee data from the Fair Labor Association to examine the determinants of

excessive hours worked in Chinese and Thai supply chain factories. The present study differs

from Smyth et al. (2012) in four respects. First, that study focused on the determinants of

excessive hours worked, defined as in excess of 60 hours per week, while we look at the

determinants of hours worked more generally. Second, that study was restricted to fifteen

factories in one industry. Supply chain factories in general have long working hours and

hours in such enterprises are not representative of working hours more generally. We look at

determinants of working hours across a range of industries. Third, conventional economic

models of labour supply suggest that the wage rate per hour is a key determinant of hours

worked. That study did not have data on wages, but we include it in this study. Fourth, that

study did not contain any measure of family or non-labour income, which this study does.

To summarize, there are a number of studies documenting trends in hours worked and

exploring the determinants in hours worked. With few exceptions, most of these focus on the

labor supply decision and do not consider firm characteristics. Smyth et al. (2012) considers

the effects of firm and worker characteristics on hours worked in the Chinese context, but that

study was for one specific industry and they did not have data on wages or non-labour

income. This study examines the factors that are correlated with hours worked in Chinese

enterprises. The dataset has the advantage of having wage data and being able to match

employers and employees and hence consider firm and worker characteristics.

Data

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The sample that we use is from a matched worker-firm data set from Minhang district in

Shanghai collected by the Institute of Population and Labour Economics in the Chinese

Academy of Social Sciences in 2007. The dataset contains information on 784 workers across

78 firms (on average 10.05 workers per firm). The dataset was selected by Probability

Proportion to Size sampling according to a list of all manufacturing firms in Minhang district

whose annual sales were at least 5 million RMB. The representativeness of the sample in

terms of number of employees, sales revenue, profits and average wages are considered in

Table 1. The sample is representative of firms in Minhang District and Shanghai as a whole.

----------------------------

Insert Table 1

--------------------------

Table 2 provides descriptive statistics for the variables used in the study. The average number

of hours worked is 46.19 hours per week and the average hourly wage rate is 10.11 RMB.

Among labour supply and human capital characteristics, 53.83 per cent of respondents were

male, 74.71 per cent were married, 10.65 per cent were members of the Chinese Communist

Party, 56.39 per cent held a non-agricultural hukou (household registration), the average age

was 34.41 years and average years of schooling was 11.35. The firm characteristics are the

proportion of female workers (average is 37 per cent), proportion of migrant workers

(average is 34 per cent), whether there is a trade union presence in the firm (51.28 per cent of

firms have a trade union), whether labour disputes affect production (3.85 per cent of the time)

and whether the firm pays overtime (97.44 per cent). The dataset also contains information

on the industry in which the firm is located and the ownership of the firm.

----------------------------

Insert Tables 2-4

--------------------------

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Table 3 shows the overall distribution in number of hours worked per week, plus the

distribution in number of hours worked according to gender and the hukou status of the

respondent. While the vast majority of respondents (63.9 per cent) worked between 41 and

45 hours per week, 28 per cent of respondents worked in excess of 50 hours. There is little

difference in the distribution of hours worked between genders, although agricultural hukou

holders worked longer hours than non-agricultural holders. Table 4 shows the share of

variation in individual characteristics due to workplace affiliation. The top row of Table 4

shows that 31 per cent of the variation in hours worked can be attributed to workplace

affiliation. Overall, this figure implies that weekly hours worked by an individual are fairly

closely correlated with the workplace to which they belong. This is consistent with the

premise above that firm characteristics matter for hours worked. One would expect, though,

that the share due to workplace effects would decline when worker characteristics are added

because some of these other variables may not be randomly distributed across workplaces

(Bryan, 2007). The second row shows that 34 per cent of the variation in the hourly wage rate

can be attributed to workplace affiliation. Among the labour supply and human capital

characteristics of respondents, the shares attributable to workplace affiliation vary between 6

per cent (certification) and 58 per cent (trade union membership) and are predominantly in

the range 20-40 per cent. These estimates suggest that generally these characteristics are not

randomly distributed and that their incidence is relatively well explained by workplace

affiliation. These simple variance shares do not, however, control for other determinants of

working hours; for which, a multivariate decomposition of hours worked is needed.

Empirical Framework

The empirical framework for the study is the conventional labour supply function, extended

to accommodate firm characteristics and policies. The conventional labour supply function

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posits that the natural log of number of hours worked per week is a function of the natural log

of the hourly wage rate and socio-demographic variables correlated with hours worked. Here,

we distinguish between labour supply characteristics (family income, gender, marital status,

communist party membership, hukou status, health and trade union membership) and human

capital characteristics (education, age, language proficiency, tenure with the firm,

certification, received on-job training and feels under pressure to meet deadlines).

One important characteristic that has not received much attention in traditional analysis of

working time at the individual worker level is workplace policies or norms (Bryan, 2007). A

firm‟s policies on paid overtime and whether the firm has a trade union will formulate a set of

norms around what firms expect. Norms are also related to the types of workers firms employ.

In high income countries, the ideal worker norm exists among highly-educated managers and

professionals, with such individuals expecting themselves and others in similar positions to

work long hours for years or even decades (Drago et al., 2009). In manufacturing and service

sector enterprises in developing countries, though, the ideal worker norm is likely to apply to

less educated female and migrant workers who firms will expect to work long hours (Smyth

et al., 2012). Hence, the proportion of female staff and proportion of migrant workers the

firm employs is likely to have a significant impact on the organisational culture of the firm

and underlying norms governing employer expectations of hours worked.

Bringing this together, we express the natural log of hours worked ln(HW) as a function of: (i)

the natural log of the hourly wage rate ln(W), (ii) labour supply characteristics of workers

(LS), (iii) human capital characteristics of workers (HC); and (iv) firm characteristics (FC).

Taking the natural log of hours worked and wages follows MaCurdy (1981). This can be

expressed as follows where is the error term, reflecting unobserved random factors:

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ln(HW)=f(ln(W), LS, HC, FC, ) (1)

The standard errors are clustered by firm to ensure they are not over-estimated (see Moulton,

1990). The effect of wages on hours worked is ambiguous and depends on the magnitude of

the income and substitution effect. In response to a wage increase, an individual will

substitute work hours for leisure hours. Thus, holding all else constant, the substitution effect

is predicted to exert a positive effect on hours worked in response to a wage increase. An

increase in wages will also generate a positive income effect, which increases the individual‟s

wealth and allows him/her to consume more goods, including leisure. Thus, holding all else

constant, the income effect is predicted to exert a negative effect on hours worked in response

to a wage increase, assuming leisure is a normal good. If the income effect outweighs the

substitution effect, the individual will work less in response to a wage increase (the case of

the backward bending labour supply curve), otherwise the individual will work more.

For a long time the accepted wisdom was that the wage elasticity for females is large and

positive, while the wage elasticity for males is small and negative (Pencavel, 1986;

Killingsworth & Heckman, 1986). The former proposition, however, has been challenged in

a series studies of female labour supply, which have found the wage elasticity to be negative

(see eg. Nakamura et al., 1979; Nakamura & Nakamura, 1981, 1983; Robinson & Tomes,

1985). At low wage levels, typical in urban China, one might expect the wage elasticity to be

positive because uncompensated wage increases are likely to represent large changes in the

relative price of leisure, while the income effect might be expected to be comparatively small

(Li & Zax, 2003). But the existing evidence for urban China is mixed. Using data from the

1995 China Household Income Project (CHIP) on 21,700 individuals living in urban areas

across 11 provinces, Li and Zax (2003) found the wage elasticity to be small and positive. On

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the other hand, using data from 1615 urban households across five cities collected in 2002,

Chau et al. (2007) found the wage elasticity for husbands and wives to be negative. In

Shanghai, income is high relative to the rest of China with Shanghai urban residents receiving

relatively large wage increases compared with the rest of the country in the market reform

period. With relatively high wages and long working hours it is conceivable that the income

effect will dominate the substitution effect, giving backward bending labor supply curve.

Moving to the labour supply characteristics of workers, we expect that individuals with

higher non-wage family income will work less hours. Moreover, we expect that females will

work fewer hours because of traditional familial responsibilities (see references cited in

Maurer-Fazio et al., 2011). Similarly, we expect that workers who are married will be more

likely to want to synchronize home time with their partner and, hence, have less flexibility to

work longer hours. This is consistent with findings in Bryan (2007) and Presser (1995). We

expect that those with a non-agricultural hukou will work less hours than those with an

agricultural hukou. This expectation is consistent with a large literature suggesting that rural-

urban migrants work longer hours than their urban counterparts (Meng & Bai, 2007). We

expect that health status will be positively correlated with hours worked because people in

better health will have greater capacity to work longer hours. It is plausible, though, that the

intensity of work can affect health so people who work longer hours have poorer health. In

their meta-analysis on health and absenteeism, Darr and Johns (2008) concluded that poor

health status was associated with increased absenteeism. This result has been reported in

developing countries. Schultz and Tansel (1997) found that hours worked in Cote d‟Ivoire

and Ghana declined significantly with the number of self-reported days disabled.

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The sign on the coefficient for individuals who are members of the Chinese Communist Party

is ambiguous. There are two possibilities suggesting that Communist Party members will

work less than non-members. One is that increased leisure potentially represents an economic

rent for a privileged group. Another is that the Party can be viewed in the same manner as a

college in western countries and act as a screen for talent, motivation and other personal

characteristics associated with productivity (Bishop & Liu, 2008). Appleton et al., (2009, p.

260) note that since the commencement of market reforms in 1979 “the Communist Party

[has] sought to recruit economically productive members. Education [has] replaced class

background as an explicit criterion”. If Party membership is a proxy for talent, individuals

who are members of the Chinese Communist Party can be expected to work less because they

are more productive and complete tasks faster. Alternatively, Party members might work

more than non-members if ideology exhorts such individuals to exceptionally high work

effort (Li & Zax, 2003). If so, Party members can be expected to work more hours.

The expected sign on trade union membership is not clear-cut. Studies in western contexts

have found that being a member of a trade union will be negatively correlated with hours

worked (see eg. Bryan, 2007). However, in China trade unions have traditionally played a

subordinate role in resolving labour disputes and have typically acted as a mediator between

employer and employee, rather than as a representative of labour as in western countries. In

some cases trade unions have appeared in arbitration hearings, acting on behalf of employers

in China (Clarke & Pringle, 2009). Clarke and Pringle (2009) argued that in cases of serious

labour dispute, trade unions are unlikely to take the side of employees and, in instances,

where they have, trade union leaders have been victimized for so doing.

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Human capital characteristics, in general, control for differences in preferences for work

among workers receiving the same wage (Li & Zax, 2003). In particular, years of schooling

may affect hours of work through its effect on home productivity, while age and the square of

age may capture variations in preferences as well as changes in family responsibilities

impacting on hours worked over the course of the life-cycle (Li & Zax, 2003).

Among the firm characteristics, we expect that firms which employ a higher proportion of

females and/or migrant workers will have norms of longer working hours over and above

individual characteristics (Smyth et al., 2012). The reason for this expectation is that firms

which employ a high proportion of female employees are typically in textile, clothing and

footwear factories. Females are represented in disproportionate numbers in such factories also

because of their comparative advantage in the sort of work that is required and generally

because they are compliant (Pun, 2007). On the basis of interviews in China in the 1990s and

2000s Chan and Siu (2010, p.172) state: “managers explained that young female workers

have nimble hands, are more obedient and easier to manage and are faster and more

meticulous”. And while we expect women as a whole to work less hours because of family

responsibilities, women working in such factories are often young and single with few family

responsibilities and this assists to underpin the norm of long working hours. Firms employ

migrant workers in a variety of blue-collar occupations. The norm of long hours in firms that

employ a high proportion of migrant workers is reinforced by the fact that most migrants live

on-site in factory dormitories. Hence, the distinction between work and leisure is clouded.

The effect of union presence in the firm on hours worked is unclear. Perloff and Sickles

(1987), Earle and Pencavel (1990) and DiNardo (1991) all find that unionisation reduces the

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number of hours worked in studies with US data. As mentioned above, unions in China have

played a different role than in the west. Ding et al. (2002) hypothesised that the 1994 Labour

Law in China, which empowered unions to sign „collective labour contracts‟ (jiti hetong)

with the employer on behalf of employees, has started to change the role of trade unions such

that they are more agitators for employees. However, their study failed to find support for

this hypothesis. These authors concluded unionisation conferred relatively few benefits on

workers, compared to the west. Clarke and Pringle (2009) point out that trade unions in

China are attempting to reform to better represent the interests of workers, but a major barrier

to such reform is the traditional subordination of the workplace trade union to management.

Finally, the expected sign on paid overtime is unclear. If firms pay overtime, this might mean

that employers will work longer hours because they are responding to the monetary incentive

to so do. Alternatively, if firms pay overtime, this may indicate better labour management

practices more generally (Seo, 2011). If so, such firms may be better able to schedule their

workload and reduce excessive overtime, hence reducing hours at work.

Because the survey did not contain data on the hourly wage rate, the only way to obtain a

measure of this variable was to divide reported monthly earnings by the monthly number of

hours worked. Deriving the wage rate in this manner means that any errors in the

measurement of monthly hours worked would be repeated in the derivation of the

respondent‟s wage rate. Hence, estimation of Equation (1) using ordinary least squares (OLS)

results in a spurious, inverse correlation between measurement errors in the wage rate and the

error term (Hall, 1973: Schultz, 1980). The spurious correlation biases the estimate of the

wage correlation downward (Killingsworth, 1983). To overcome the problem of biased and

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inconsistent estimates using OLS, a standard approach is to use instrumental variable

estimation (IV) made popular by Hall (1973). The practical difficulty with IV estimation is

finding an instrument or set of instruments that are significantly correlated with wages, but

also orthogonal to the residuals of the main equation (hours worked).

The existing literature relies mainly on worker characteristics for IVs that are normally

excluded from the hours worked equation, such as higher order terms of age or education and

interaction terms of age and education (Mroz, 1987; Sahn & Alderman, 1996; Fortin &

LaCroix, 1997; Li & Zax, 2003; Chau et al., 2007). We use the square of years of schooling

and age interacted with education, which are common IVs in hours worked equations.

It should be noted that components of non-wage family income are also potentially

endogenous. For instance, transfer payments from the government or from individuals

outside the family may depend on hours worked (Li & Zax, 2003). To address this issue, Li

and Zax (2003) use lagged values of non-wage family income to instrument for current non-

wage family income. However, we do not have this information for our dataset or other

appropriate IVs for non-wage family income. Thus, while recognising the problem, we

follow Sahn and Alderman (1996) and treat non-wage family income as being exogenous.

Results

Table 5 contains the results for the OLS estimates for Equation (1). The first column presents

the results where the natural log of the number of hours worked per week is regressed on the

natural log of the hourly wage rate and the labour supply and human capital characteristics of

the individual. In the second column firm characteristics and policies are added and in the

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third column both firm characteristics and policies and industry and ownership dummy

variables are added. Hours worked are significantly and negatively correlated with wages,

suggesting the existence of a backward bending labour supply curve. Among the human

capital characteristics of the individual, education is positively correlated with hours worked,

while the hours-age profile follows a parabolic shape, with hours worked peaking at 31 - 33

years. Individuals with junior or senior certification are more likely to work longer hours

than those with no certification. Overall, the results suggest that those with higher human

capital are more likely to work longer hours. Among the labour supply characteristics of

workers, males work longer hours than females, while those with a non-agricultural hukou

status and with non-wage family income work fewer hours. Among the variables denoting

firm characteristics and policies, the coefficients on proportion of female employees,

proportion of migrant workers, if labour disputes have affected production and whether

workers are paid overtime are all statistically significant in at least one of columns (2) and (3).

Table 6 contains the results for the IV estimates for Equation (1). The validity of years of

schooling squared and years of schooling interacted with age as instruments are considered at

the bottom of Table 6. The IVs satisfy the first component of IV validity, which is relevance.

An F-test indicates that both instruments lead to a significant improvement in the first stage

regression at 1 per cent and the first stage F-test satisfies the Stock and Yogo (2005) test for

one endogenous regressor and two instruments at the 1 per cent level. The IVs also satisfy the

second component of IV validity, which is that the instruments are exogenous. Since our IV

model is over-identified with the number of exogenous instruments exceeding the number of

endogenous variables, we compute the Sargan chi-square statistic and the Anderson LM

statistic. The results of both tests, reported at the bottom of Table 6, suggest that the

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instruments are exogenous and that the IV estimates are identified. Thus, our instruments

satisfy the relevance and exogeneity conditions and, as such, are valid instruments.

Turning to the interpretation of the IV estimates reported in Table 6, the coefficient on the

hourly wage rate is negative in all specifications, suggesting that the income effect outweighs

the substitution effect and that the labour supply curve is backward bending. The IV

estimates suggest that for a 1 per cent increase in wages, the number of hours worked per

week will decrease between 0.28 per cent and 0.33 per cent. In terms of previous studies for

urban China, this finding is consistent with Chau et al. (2007), but differs from Li and Zax

(2003). It suggests that at relatively high wages and long working hours in Shanghai, the

relative value of extra hours in terms of leisure is valued more than extra income.

Among the labour supply characteristics, gender and hukou status are statistically significant

in at least two of the three specifications. Males work between 6.4 per cent and 8.5 per cent

longer hours than females, consistent with expectations. Those with a non-agricultural hukou

work between 3.2 per cent and 3.8 per cent lesser hours than those with an agricultural hukou,

depending on the exact specification. This result is consistent with the widespread evidence

that rural-urban migrants in China face urban labour market discrimination and work long

hours relative to their urban counterparts. For example, in the CHIP 2002 survey rural-urban

migrants worked on average 69 hours per week compared with a comparable figure of 44

hours per week for urban residents. This is consistent with rural-urban migrants working

much longer hours than their urban counterparts in the survey used in this study (see Table 3).

Longer working hours also leaves less time for leisure. Qualitative studies undertaken by

Jacka (2005) and Li (2006) reported that rural-urban migrants in China allocate little time to

leisure activities. Li (2006) interviewed 26 rural-urban migrants in Tianjin about their leisure

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activities. Twenty interviewees in Li‟s sample indicated that they never went out after work

because they were exhausted and wanted to rest or did not want to spend money on

socialising. That rural migrants, who receive lower hourly earnings, also tend to work longer

may imply a strong income effect in labour supply behaviour, consistent with the finding of a

negative wage elasticity, but may also result from working constraints imposed by employers

on workers with limited negotiating power (Demurger et al., 2009). The other labour supply

characteristics, including non-wage family income, are statistically insignificant.

In terms of human capital characteristics, additional years of schooling is associated with

longer hours of work, while the effects of age on hours worked is non-linear as in the OLS

results. The other human capital characteristic that is statistically significant is certification.

Workers with junior or senior certification worked between 7.6 per cent and 8.1 per cent

longer hours than those with no certification. Other human capital characteristics are

insignificant, with the exception of language proficiency in the second column.

The relationship between hours worked and firm characteristics and policies are explored in

the second and third columns of Table 6. Having a trade union in the firm is insignificant.

This most likely reflects the fact that in balancing between worker, state and employer,

unions in China have little effect on hours worked (Taylor et al., 2003). Certainly, the

evidence is that trade unions have played a very limited role in helping rural-urban migrant

workers reduce hours worked (Nan, 2009). This said, labour activism has increased in urban

China. Lee (2007) divides labour activism in urban China into two kinds: the „rust belt‟

protests (workers in deteriorating state-owned enterprises) and the „sun belt‟ protests (rural-

urban migrants in the private sector). Sunbelt protests are often fuelled by the factory

20

dormitory system (Smith & Pun, 2006). The results suggest that if labour disputes have

affected production, hours worked are 10.6 per cent to 12 per cent less. In firms which paid

overtime, employees worked 14 per cent less. This result is consistent with payment of

overtime being a proxy for better labour management practices more generally, in which

firms were able to better schedule their workload and demands on workers (Seo, 2011).

While the coefficient on the proportion of migrant workers is statistically insignificant, the

proportion of female workers that the firm employs is positively correlated with hours

worked over and above the labour supply and human capital characteristics of the individual.

This result suggests that while women are less likely to work longer hours than men, there are

firm norms prescribing longer working hours in firms which employ a higher proportion of

female workers. For each additional 0.1 increase in the proportion of female workers in a

firm, hours worked increases between 8.4 per cent and 9.7 per cent. It is likely that such

norms reinforce demand for compliant task-oriented labourers in particular types of

manufacturing enterprises, which hire a high proportion of female workers. Edgren (1995)

found that such factories in Asia preferred untrained women, since employee vocational skills

made them less dependent on the firm and less compliant. As Edgren (1995, p. 135) put it:

“Women are preferred as workers for most of the factory jobs because they are hardworking,

easy to control, willing to accept tediousness and monotony and have „nimble fingers‟”.

The industry dummy variables show that hours worked in the textile, clothing and footwear

sector, which employs a high proportion of female workers, is higher than machinery and

other sectors. Chan and Siu (2010) also found that workers in the garment sector in China

worked very long hours, relative to other industries. The main reason for long working hours

in this sector is that most workers are unskilled and are paid by piece rates. Edgren (1995)

21

reported that it is common for norms of long working hours to be reinforced by graphs

displaying daily production achievements attached to their bench and competitions to set new

records of productivity. This sets up an internal tournament in which workers compete.

Workers accept long hours to compensate for low wages. The problem is often compounded

by poor internal production systems with tight delivery schedules (Seo, 2011).

Conclusion

Conventional economic analysis of hours worked at the individual worker level has tended to

ignore the employer dimension. This omission results from the fact that conventional labour

supply estimates model hours worked as a function of the hourly wage rate and employee

preferences for work. There are relatively few studies that have considered the role of

employer policies and characteristics in modelling hours worked and these studies omit the

hourly wage rate, potentially biasing the estimates (Bryan, 2007; Smyth et al., 2012). In this

study we have used a representative matched employer-employee dataset from Shanghai to

analyse the correlates of hours worked at the individual worker level, while taking account of

firm characteristics, in China‟s rapidly evolving urban labour market.

The results suggest that in addition to the hourly wage rate, labour supply characteristics

(gender, hukou status) and human capital characteristics (education, age, certification), firm-

level differences (proportion of female workers in the firm, if labour disputes affected

production, if the firm pays overtime as well as industry type) explain variation in hours

worked. China‟s urban labour market is increasingly becoming competitive as firms vie to

attract and retain the best staff. Our results have implications for firms wanting to improve

management practice, reduce hours and turnover. While the norm of long working hours in

textiles, clothing and footwear and in firms employing a high proportion of female workers

22

represents a challenge to the agenda of organisations such as the ILO, the ILO‟s Conditions

on Work and Employment Programme offers one possible approach for a framework on

working time that balances workers‟ needs with business requirements (Lee et al. 2007).

There is a lot of evidence that high performance work practices have a positive effect on

productivity and turnover (see eg. Huselid, 1995). The ILO‟s Conditions on Work and

Employment Programme emphasises that a healthy work-life balance is not just a matter of

corporate social responsibility, but is also good for business and can be used as an effective

competitiveness strategy (Lee et al., 2007). Specifically changing norms of long working

hours can result in considerable productivity gains over time (Bosch & Lehndorff, 2001).

This generally entails changes in work organisation and methods of production (including the

optimum number of hours, optimal beginning and finishing times, optimal rest breaks and

budgeting methods) (Seo, 2011). These changes should be combined with incentives and

proper targets for line managers to reorganise work methods and replace what Seo (2011)

calls „the long work culture‟ with „smart working‟. As long hours of work are positively

correlated with absenteeism and staff turnover, moving to „smart working‟ can also benefit

firms in terms of reduced absenteeism and lower turnover (Seo, 2011). Our results suggest

specific policies that could be introduced to reduce hours that would represent an investment

in high-performance work systems. These are paid overtime, which is likely to be a proxy for

better management practices more generally, and better labour management practices to

reduce hours lost through labour disputes impacting on production.

23

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Table 1 – Representativeness of Sample

Sample Minhang District Shanghai

Number of Employees

(person)

182.82 202.83 190.38

Sales Revenue (10

thousand RMB)

8896.69 11974.22 12445.22

Profits (10 thousand

RMB)

675.27 800.10 866.94

Average Wage of

Employees

(RMB/month)

2145.55 2383.42 2423.25

Source: The data for Minhang District and Shanghai are from SBS (2008).

30

Table 2: Descriptive Statistics

Variable Mean Std. Dev.

Number of hours worked per week 46.19 7.93

Hourly wage rate (RMB) 10.11 7.30

Labor supply characteristics

Gender (Male = 1) Male= 422 (53.83%)

Marital status (Married = 1) Married = 585 (74.71%)

Family Income (RMB per Month) 2556.71 6643.47

Communist Party membership (Yes = 1) Yes = 83 (10.65%)

Hukou status (Non-agriculture = 1) Non-agriculture = 441

(56.39%)

Health status Ordinary = 168 (21.43%)

Good = 250 (31.89%)

Very good = 366 (46.68%)

Trade union membership (Member = 1) Member = 275 (37.01%)

Human capital characteristics

Age (years) 34.41 10.82

Age Squared 1300.95 811.84

Years of Education 11.35 3.01

Language proficiency (Standard = 1) Standard = 504 (64.37%)

Length of time with firm (years) 5.86 3.35

Certification No title = 610 (78.51%)

Elementary = 109 (14.03%)

Junior/Senior = 58 (7.46%)

Received on job training (Yes = 1) Yes = 341 (43.49%)

Feels under pressure to meet deadlines (Yes = 1) Yes = 273 (34.82%)

Firm characteristics and policies

Proportion of female employees 0.37 0.24

Proportion of migrant workers 0.34 0.34

Firm has trade union (Yes = 1) Yes = 40 (51.28%)

Labor disputes affected production (Yes = 1) Yes = 3 (3.85%)

Will be paid overtime (Yes = 1) Yes = 76 (97.44%)

Other

Firm ownership State Own Firms = 7 (8.97%)

Public Firms = 25 (32.05%)

31

Foreign Firms = 31 (39.74%)

Private Firms = 15 (19.23%)

Industries Textile and Fur = 10

(12.82%)

Chemical, Rubber and

Plastics = 20 (25.64%)

Metals and Minerals = 7

(8.97%)

Machinery = 27 (34.62%)

Others = 14 (17.95%)

32

Table 3: Distribution of Number of Hours Worked per Week

Hours worked

per week

Male Female Agriculture

hukou

Non-

agricultural

hukou

Overall

< 40 hours pw 17 (2.17%) 11 (1.40%) 12 (1.53%) 16 (2.04%) 28 (3.57%)

41-45 hours pw 274 (34.95%) 227 (28.95%) 178 (22.70%) 322 (41.07%) 501 (63.90%)

46-50 hours pw 10 (1.27%) 25 (3.18%) 9 (1.147%) 26 (3.31%) 35 (4.46%)

51-55 hours pw 81 (10.33%) 64 (8.16%) 90 (11.47%) 55 (7.01%) 145 (18.49%)

56-60 hours pw 7 (0.89%) 12 (1.53%) 15 (1.91%) 3 (0.38%) 19 (2.42%)

>60 hours pw 33 (4.20%) 23 (2.93%) 37 (4.71%) 19 (2.42%) 56 (7.14%)

33

Table 4: Share of Variation in Individual Characteristics Due to Workplace Affiliation

Dependent variable

(worker level outcome)

Proportion of variation due to

workplace affiliation

Number of hours worked per week 0.31

Hourly wage rate (RMB) 0.34

Labor supply characteristics

Gender (Male = 1) 0.14

Marital status (Married = 1) 0.22

Communist Party membership (Yes = 1) 0.18

Hukou status (Non-agriculture = 1) 0.29

Health status 0.28

Trade union membership (Member = 1) 0.58

Human capital characteristics

Age (years) 0.37

Years of Education 0.33

Language proficiency (Standard = 1) 0.33

Length of time with firm (years) 0.17

Certification 0.06

Received on job training (Yes = 1) 0.31

Feels under pressure to meet deadlines (Yes = 1) 0.22

34

Table 5: OLS Estimates

ln(Number of hours worked per week) (1) (2) (3)

ln(hourly wage rate (RMB)) -0.147*** -0.153*** -0.143***

(-9.126) (-9.398) (-8.799)

Labor supply characteristics

Gender (Male = 1) 0.0399*** 0.0467*** 0.0491***

(2.837) (3.223) (3.382)

Marital Status (Married = 1) 0.00773 0.0138 0.0160

(0.349) (0.629) (0.731)

ln(Family Income) -0.00281 -0.00327* -0.00317*

(-1.487) (-1.749) (-1.702)

Communist party membership (Yes = 1) -0.0139 -0.0146 -0.0160

(-0.605) (-0.644) (-0.703)

Hukou status (Non-agricultural = 1) -0.0487*** -0.0399** -0.0361**

(-2.981) (-2.410) (-2.177)

Health status (Ordinary = 1)

Good 0.0169 0.0229 0.0173

(0.913) (1.244) (0.937)

Very Good 0.0210 0.0191 0.0192

(1.135) (1.038) (1.049)

Trade union membership (Member = 1) -0.0141 -0.00817 -0.00840

(-0.976) (-0.460) (-0.475)

Human capital characteristics

Age 1.667*** 1.428*** 1.317**

(3.072) (2.641) (2.441)

Age2

-0.241*** -0.208*** -0.192**

(-3.141) (-2.723) (-2.520)

Education 0.0130*** 0.0138*** 0.0135***

(4.271) (4.516) (4.406)

Language proficiency (Standard = 1) -0.0230 -0.0273* -0.0228

(-1.443) (-1.734) (-1.445)

Length of time with firm -0.00274 -0.00274 -0.00269

(-1.407) (-1.422) (-1.376)

Certification (No title = 1)

Elementary -0.0109 -0.0221 -0.0156

(-0.575) (-1.169) (-0.821)

Junior/Senior 0.0558** 0.0493* 0.0534**

(2.041) (1.816) (1.970)

Received on job training (Yes = 1) -0.0133 -0.00520 -0.0102

(-0.982) (-0.386) (-0.755)

Feels under pressure to meet deadlines

(Yes = 1)

0.0117 0.00954 0.00850

(0.848) (0.686) (0.609)

Firm characteristics and policies

Proportion of female employees 0.0688** 0.0556*

(2.320) (1.806)

Proportion of migrant workers 0.0490** 0.0634***

(2.262) (2.812)

35

Firm has Trade union (Yes = 1) 0.00470 0.0107

(0.281) (0.630)

Labor disputes affected production (Yes = 1) -0.107*** -0.0801**

(-3.209) (-2.299)

Will be paid overtime (Yes = 1) -0.0709* -0.0671

(-1.683) (-1.558)

Other

Firm Ownership (State Owned =1)

Public Firms -0.0434*

(-1.658)

Foreign Firms -0.0205

(-0.764)

Private Firms -0.0317

(-1.058)

Industries (Textile and Fur = 1)

Chemical, Rubber and Plastics -0.0420*

(-1.749)

Metals and Minerals -0.102***

(-3.320)

Machinery -0.0762***

(-3.359)

Others -0.0832***

(-3.320)

Constant 1.170 1.621* 1.875**

(1.245) (1.729) (1.998)

Observations 662 662 662

R-squared 0.189 0.222 0.247

Notes: *** denotes statistical significance at 1%; ** denotes statistical significance at 5%; *

denotes statistical significance at 10%.

36

Table 6: IV estimates

ln(Number of hours worked per week) (1) (2) (3)

ln(hourly wage rate (RMB)) -0.281*** -0.330*** -0.329***

(-3.096) (-2.941) (-3.127)

Labor supply characteristics

Gender (Male = 1) 0.0640*** 0.0825*** 0.0850***

(2.954) (3.034) (3.347)

Marital Status (Married = 1) 0.00820 0.0161 0.0185

(0.356) (0.683) (0.788)

ln(Family Income) -0.00247 -0.00303 -0.00282

(-1.251) (-1.511) (-1.406)

Communist party membership (Yes = 1) 0.00266 0.00953 0.0103

(0.102) (0.334) (0.362)

Hukou status (Non-agriculture = 1) -0.0377** -0.0315* -0.0268

(-2.038) (-1.702) (-1.449)

Health status (Ordinary = 1)

Good 0.00823 0.0122 0.00802

(0.411) (0.590) (0.391)

Very Good 0.0152 0.0133 0.0136

(0.778) (0.664) (0.682)

Trade union membership (Member = 1) -0.0156 0.000786 0.00190

(-1.038) (0.0397) (0.0961)

Human capital characteristics

Age 2.620*** 2.669*** 2.590***

(3.091) (2.761) (2.825)

Age2 -0.375*** -0.383*** -0.372***

(-3.139) (-2.808) (-2.874)

Education 0.0217*** 0.0241*** 0.0238***

(3.301) (3.330) (3.588)

Language proficiency (Standard = 1) -0.0251 -0.0303* -0.0248

(-1.511) (-1.787) (-1.464)

Length of time with firm -0.00215 -0.00180 -0.00168

(-1.047) (-0.839) (-0.777)

Certification (No title = 1)

Elementary -0.00518 -0.0154 -0.0138

(-0.258) (-0.744) (-0.679)

Junior/Senior 0.0762** 0.0799** 0.0810**

(2.423) (2.298) (2.462)

Received on job training (Yes = 1) -0.00266 0.00879 0.00721

(-0.169) (0.521) (0.411)

Feels under pressure to meet deadlines

(Yes = 1)

0.0145 0.00624 0.00525

(1.007) (0.415) (0.348)

Firm characteristics and policies

Proportion of female employees 0.0974*** 0.0836**

(2.675) (2.289)

Proportion of migrant workers 0.0278 0.0356

(1.039) (1.236)

37

Firm has trade union (Yes = 1) -0.00743 0.00116

(-0.383) (0.0610)

Labor disputes affected production (Yes = 1) -0.120*** -0.106***

(-3.277) (-2.651)

Will be paid overtime (Yes = 1) -0.145** -0.144**

(-2.245) (-2.282)

Other

Firm Ownership (State Owned =1)

Public Firms -0.0227

(-0.746)

Foreign Firms 0.00268

(0.0850)

Private Firms 0.000143

(0.00389)

Industries (Textiles, clothing and footwear =

1)

Chemical, Rubber and Plastics -0.0381

(-1.474)

Metals and Minerals -0.0574

(-1.379)

Machinery -0.0636**

(-2.510)

Others -0.0591**

(-1.968)

Constant -0.344 -0.256 -0.0781

(-0.245) (-0.166) (-0.0527)

Observations 662 662 662

Regression Diagnostics

F test of excluded instruments (p-val). 0.000 0.000 0.000

Under-identification test (Anderson canon.

corr. LM statistic)

22.40*** 15.84*** 18.22***

First stage F-statistics (Stock and Yogo test) 11.24*** 7.81*** 8.92***

Instrument exogeneity (Sargan test) (P-val.) 0.155 0.259 0.249

Notes: *** denotes statistical significance at 1%; ** denotes statistical significance at 5%; *

denotes statistical significance at 10%.