View
0
Download
0
Category
Preview:
Citation preview
1
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.
2
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
3
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.
4
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
5
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,
6
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-
7
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
8
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
--------------------------
9
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
10
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:
11
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
12
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.
13
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.
14
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
15
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
16
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
17
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
18
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
19
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
References
Aguiar, M. and Hurst, E. (2007) „Measuring Trends in Leisure: The Allocation of Time Over
Five Decades‟, Quarterly Journal of Economics, 122(3), 969-1006,
Alesina, A., Glaeser, E.L. and Sacerdote, B. (2006) „Work and Leisure in the United States
and Europe: Why So Different?‟ NBER Macroeconomics Annual, 1-64.
Alvarez-Cuadrado, F. (2007) „Envy, Leisure and Restrictions on Working Hours‟, Canadian
Journal of Economics, 40, 1286-1310.
Atak, J., Bateman, F. and Margo, R.A. (2003) „Productivity in Manufacturing and the Length
of the Working Day: Evidence From the 1880 Census of Manufactures‟, Explorations
in Economic History, 40: 170-194.
Bauman, A., Ma, G.S., Cuevas, F., Omar, Z et al. (2008). „Cross-national Comparisons of
Socioeconomic Differences in the Prevalence of Leisure Time and Occupational
Physical Activity and Active Commuting in Six Asia-Pacific Countries‟, Journal of
Epidemiology and Community Health, 65, 35-43.
Bell, L.A. and Freeman, R.B. (1995) „Why do Americans and Germans Work Different
Hours? In F. Butler, W. Franz, R. Schettkat and D. Soskice (Eds.) Institutional
Frameworks and Labor Market Performance. London: Routledge.
Bell, L.A. and Freeman, R.B. (2001) „The Incentive for Working Hard: Explaining Hours
Worked Differences in the US and Germany‟, Labour Economics, 8, 181-202.
Bishop, J. and Liu, H. (2008). „Liberalization and Rent Seeking in China‟s Labor Market‟,
Public Choice, 135(3-4), 763-793.
Blanchard, O. (2004) „The Economic Future of Europe‟, Journal of Economic Perspectives,
18(4): 3-26.
Bosch, G. and Lehndorff, S. (2001). „Working Time Reduction and Employment Experiences
in Europe and Economic Policy Recommendations‟, Cambridge Journal of
Economics. 25. 209-243.
Bryan, M.L. (2007) „Workers, Workplaces and Working Hours‟, British Journal of Industrial
Relations, 45(4): 735-759.
Burgoon, B. and Baxandall, P. (2004) „Three Worlds of Working Time: The Partisan and
Welfare Politics of Work Hours in Industrialized Countries‟, Politics & Policy, 32(4):
439-473.
Causa, O. (2008) „Explaining Differences in Hours Worked Among OECD Countries: An
Empirical Analysis‟, OECD Economics Department Working Paper, No. 596.
Chan, A. (2003) “A Race to the Bottom: Globalisation and China‟s Labour Standards”, China
Perspectives, 46, 41-49.
24
Chan, A, and Siu, K. (2010) “Analyzing Exploitation”, Critical Asian Studies, 42, 167-190.
Chau, TW., Li, H., Liu, PW. And Zhang, J. (2007). „Testing the Collective Model of
Household Labor Supply: Evidence from China‟, China Economic Review, 18, 389-
402.
Clark, A.E. (2005) „Your Money or Your Life: Changing Job Quality in OECD Countries‟,
British Journal of Industrial Relations, 43(3): 377-400.
Clarke, S. and Pringle, T. (2009) „Can Party-led Trade Unions Represent their Members?‟
Post-Communist Economies, 21(1): 85-101.
Costa, D.L. (1998) „The Unequal Work Day: A Long-Term View‟, American Economic
Review, 88(2): 330-334.
Costa, D.L. (2000) „The Wage and the Length of the Work Day: From the 1890s to 1991‟,
Journal of Labor Economics, 18(1): 156-181.
Darr, W. and Johns, G. (2008). „Work Strain, Health and Absenteeism: A Meta-Analysis‟,
Journal of Occupational Health Psychology, 13(4), 293-318.
Demurger, S., Gurgand, M., Li, S.. and Yue, X. (2009). „Migrants as Second-Class Workers
in Urban China? A Decomposition Analysis‟, Journal of Comparative Economics, 37,
610-628.
Ding, D.Z., Goodall, K. and Warner, M. (2002). „The impact of Economic Reform on the
Role of Trade Unions in Chinese Enterprises‟, International Journal of Human
Resource Management, 13(3): 431-449
DiNardo, J. (1991). „Union Employment Effects: An Empirical Analysis‟. Working Paper No.
90-92-06. Irvine: University of California.
Domenech, J. (2007) „Working Hours in the European Periphery: The Length of the Working
Day in Spain, 1885-1920‟, Explorations in Economic History, 44, 469-486.
Drago, R., Wooden, M. and Black, D. (2009) „Long Work Hours: Volunteers and Conscripts‟,
British Journal of Industrial Relations, 47(3): 571-600.
Earle, J.S. and Pencavel, J. (1990). „Hours of Work and Trade Unionism‟, Journal of Labor
Economics, 8, S150-S174.
Edgren, G. (1995) „Spearheads of Industrialization or Sweatshops in the Sun? A Critical
Appraisal of Labour Conditions in Asian Export Processing Zones‟ In J. Cameron
(Ed.) Poverty and Power: The Role of Institutions and the Market in Development,
Delhi: Oxford University Press.
Fritjers, P., Johnston, D.W. and Meng, X. (2009) „The Mental Health Cost of Long Working
Hours: The Case of Rural Chinese Migrants‟, Unpublished Manuscript, Department
of Economics, University of Queensland.
25
Fortin, B. and LaCroix, G. (1997). „A Test of the Unitary and Collective Models of
Household Labor Supply‟, Economic Journal, 107(443): 933-955.
Golden, L. (2008) „A Brief History of Long Work Time and the Contemporary Sources of
Overwork‟, Journal of Business Ethics, 84, 217-227.
Green, F. (2001) „It‟s Been a Hard Day‟s Night: The Concentration and Intensification of
Work in Late Twentieth Century Britain‟, British Journal of Industrial Relations, 39,
53-80.
Guardian (2010). „Tenth Apparent Suicide at Foxconn I-phone Factory in China‟, The
Guardian Newspaper, 27 May.
Hall, R. (1973). „Wages, Income and Hours of Work in the United States Labor Force‟ in
G.G. Cain and H.W. Watts (eds) Income Maintenance and Labor Supply (Chicago:
Markham).
Hopkins, E. (1982) „Working Hours and Conditions During the Industrial Revolution: A Re-
Appraisal‟, Economic History Review, 35, 52-62.
Houdmont, J., Zhou, J. and Hassard, J. (2011). „Overtime and Psychological Well-being
Among Chinese Office Workers‟, Occupational Medicine, 61, 270-273.
HR Magazine. (2006). „Companies in China Struggle to Keep Staff‟, HR Magazine, October
p.16.
Huberman, M. and Mins, C. (2007) „The Times They are Not Changin‟: Days and Hours of
Work in Old and New Worlds, 1870-2000‟, Explorations in Economic History, 44,
538-567.
Huselid, M. (1995). „The Impact of Human Resource Management Practices on Turnover,
Productivity and Corporate Financial Performance‟, Academy of Management Journal,
38: 635-672.
Jacka, T. (2005). „Finding a Place: Negotiations of Modernization and Globalization Among
Rural Women in Beijing‟, Critical Asian Studies, 37(1): 51-74.
Killingsworth, M. (1983). Labor Supply (Cambridge: Cambridge University Press).
Killingsworth, M. and Heckman, J. (1986). „Female Labor Supply: A Survey‟. In Orley
Ashenfelter and Richard Layard (eds.) Handbook of Labor Economics (Amsterdam:
Elsevier), pp. 102-204.
Kuroda, S. (2010) „Do Japanese Work Shorter Hours than Before? Measuring Trends in
Market Work and Leisure Using 1976-2006 Japanese Time-Use Survey‟, Journal of
the Japanese and International Economies, 24: 481-502.
Lee, C.K. (2007) Against the Law: Labour Protests in China’s Rustbelt and Sunbelt
(Berkeley: University of Berkeley Press)
26
Lee, S., McCann, D. and Messenger, J. (2007). Working Around the World: Trends in
Working Hours, Laws and Policies in a Global Comparative Perspective,
International Labour Office (London: Routledge).
Li, B. (2006) „Floating Population or Urban Citizens? Status, Social Provision and
Circumstances of Rural-urban Migrants in China‟, Social Policy & Administration,
40(2): 174-195.
Li, H. and Zax, J. (2003). „Labour Supply in Urban China‟, Journal of Comparative
Economics, 31, 795-817.
MaCurdy, T.E. (1981). „An Empirical Model of Labor Supply in a Life Cycle Setting‟.
Journal of Political Economy, 89, 1059-1085.
Maurer-Fazio, M., Connelly, R., Chen, L. and Tang, L. (2011). „Childcare, Eldercare and
Labor Force Participation of Married Women in Urban China, 1982-2000‟, Journal of
Human Resources, 46(2), 261-294.
Meng, X. and Bai, N. (2007) „How Much Have the Wages of Unskilled Workers in China
Increased? Data From Seven Factories in Guangdong‟. Unpublished Manuscript,
Department of Economics, Research School of Pacific and Asian Studies, ANU.
Moulton, B. (1990) „An Illustration of a Pitfall in Estimating the Effects of Aggregate
Variables on Micro units‟, Review of Economics and Statistics, 72: 334-338.
Mroz, T. (1987) „The Sensitivity of an Empirical Model of Married Women‟s Hours of Work
to Economic and Statistical Assumptions‟, Econometrica, 55(4): 765-799.
Nakamura, A. and Nakamura, M. (1981). „A Comparison of the Labor Force Behavior of
Married Women in the United States and Canada: With Special Attention to the
Impact of Income Taxes‟, Econometrica, 49, 451-490.
Nakamura, A. and Nakamura, M. (1983). „Part-time and Full-time Work Behavior of Married
Women: A Model with a Doubly-truncated Dependent Variable‟, Canadian Journal
of Economics, 16, 229-257.
Nakamura, A., Nakamura, M. and Cullen, D. (1979). „Job Opportunities, the Offered Wage
and the Labor Supply of Married Women‟, American Economic Review, 69, 787-805.
Nan, L. (2009). „Is There New Hope in Labor Rights Protection for Chinese Migrant
Workers?‟ Asian Pacific Law & Policy Journal, 10, 482-518
OECD (2011). Society at a Glance 2011: OECD Social Indicators (Paris: OECD).
Pencavel, J. (1986). „Labor Supply of Men: A Survey‟. In Orley Ashenfelter and Richard
Layard (eds.) Handbook of Labor Economics (Amsterdam: Elsevier), pp. 3-102.
Perloff, J.M. and Sickles, R.C. (1987). „Union Wage, Hours and Earnings Differentials in the
Construction Industry‟, Journal of Labor Economics, 5, 174-210.
27
Prescott, E. (2004) „Why Do Americans Work So Much More than Europeans?‟ Quarterly
Review, Federal Reserve Bank of Minneapolis, 28(1): 2-13.
Presser, H.B. (1995) „Job, Family and Gender: Determinants of Nonstandard Work Schedules
Among Employed Americans in 1991‟, Demography, 32(4): 577-598.
Pun, N. (2007) „Gendering the Dormitory Labor System: Production, Reproduction and
Migrant Labor in South China‟, Feminist Economics, 13(3): 239-258.
Reynolds, J. (2003) „You Can‟t Always Get the Hours You Want: Mismatches Between
Actual and Preferred Work Hours in the U.S‟, Social Forces, 81(4): 1171-1199.
Roberts, D. (2006). „How Rising Wages are Changing the Game in China‟, BusinessWeek,
March 26, pp. 32-35.
Robinson, C. and Tomes, N. (1985). „More on the Labor Supply of Canadian Women‟,
Canadian Journal of Economics, 18, 156-163.
Rogerson, R. (2006) „Understanding Differences in Hours Worked‟, Review of Economic
Dynamics, 9, 365-409.
Sachs, J. (2004). „Welcome to the Asian Century: By 2050 China and Maybe India Will
Overtake the US Economy in Size‟, Fortune, January 12, accessible at
http://money.cnn.com/magazines/fortune/fortune_archive/2004/01/12/357912/index.h
tm (last accessed, September 9, 2011).
Saffer, H. and Lamiraud, K. (2008) „The Effect of Hours of Work on Social Interaction‟,
National Bureau of Economic Research, Working Paper 13743.
Sahn, DE. and Alderman, H. (1996) „The effect of food subsidies on labor supply in Sri
Lanka‟, Economic Development and Cultural Change, 45(1): 125-145.
Schultz, P. (1980). „Estimating Labor Supply Functions for Married Women‟ in James Smith
(ed.) Female Labor Supply: Theory and Estimation (Princeton: Princeton University
Press).
Schultz, P. and Tansel, A. (1997) „Wage and Labor Supply Effects of Illness in Cote D‟Ivoire
and Ghana: Instrumental Variable Estimates for Days Disabled‟, Journal of
Development Economics, 53(2): 251-286.
Seo, J-W. (2011). „Excessive Overtime, Workers and Productivity: Evidence and
Implications for Better Work‟, Better Work Discussion Paper Series No. 2, Geneva,
ILO.
Sheridan, M., Sunter, D. and Diverty, B. (2001) „The Changing Workweek‟ in G. Wong and
G. Picot (Eds.) Working Time in Comparative Perspective, Volume 1: Patterns,
Trends and Policy Implications of Earnings Inequality and Unemployment, W.E.
Upjohn Institute for Employment Research: Kalamazoo, pp. 13-44.
28
Siu, O., Spector, P., Cooper, C. and Lu, C. (2005). „Work Stress, Self-Efficacy, Chinese
Work Values and Work Well-being in Hong Kong and Beijing‟, International Journal
of Stress Management, 12(3): 274-283.
Smith, C. and Pun, N. (2006) „The Dormitory Labor Regime in China as a Site for Control
and Resistance‟, International Journal of Human Resource Management, 17(8):
1456-1470.
Smyth, R., Qian, X., Nielsen, I and Kaempfer, I. (2012). Working Hours in Supply Chain
Chinese and Thai Factories: Evidence From the Fair Labor Association‟s „Soccer
Project‟‟, British Journal of Industrial Relations (in press).
Spurgeon, A. (2003). Working Time: Its Impact of Safety and Health. International Labour
Office, Occupational Safety and Health Research Institute, Korea Occupational Safety
and Health Agency, Seoul, Korea,
State Statistical Bureau (2008). China City Statistical Yearbook. State Statistical Bureau,
Beijing.
Stock, J.H. and Yogo, M. (2005). „Testing for Weak Instruments in Linear IV Regressions‟.
In D. Andrews and J. Stock (eds.) Identification and Inference for Econometric
Models: A Festschrift in Honor of Thomas Rothenberg (Cambridge: Cambridge
University Press), pp. 80-105.
Taylor, W., Chang, K. and Li, Q. (2003). Industrial Relations in China (Cheltenham: Edward
Elgar).
Vandenbroucke, G. (2009) „Trends in Hours: The U.S. From 1900 to 1950‟, Journal of
Economic Dynamics and Control, 33, 237-249.
Warner, M. (1996). „Economic Reforms, Industrial Relations and Human Resources in the
People‟s Republic of China: An Overview‟, Industrial Relations Journal, 27(3), 195-
210.
Wilkins, R. (2005) „Do Longer Working Hours Lead to More Workplace Injuries? Evidence
From Australian Industry-Level Panel Data‟, Australian Bulletin of Labour, 31(2):
155-171.
Wooden, M., Warren, D. and Drago, R. (2009) „Working Time Mismatch and Subjective
Well-being‟, British Journal of Industrial Relations, 47(1): 147-179.
29
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%.
Recommended