Competition and Social
Identity in the Workplace:
Evidence from a Chinese
Textile Firm
Takao Kato
Pian Shu
Working Paper
14-011
September 5, 2014
Copyright © 2013, 2014 by Takao Kato and Pian Shu
Working papers are in draft form. This working paper is distributed for purposes of comment and
discussion only. It may not be reproduced without permission of the copyright holder. Copies of working
papers are available from the author.
Competition and Social Identity in the Workplace:
Evidence from a Chinese Textile Firm∗
Takao Kato
Colgate University, CJEB (Columbia),
TCER (Tokyo), CCP (Aarhus) and IZA
Hamilton, NY 13346
tkato@colgate.edu
Pian Shu
Harvard Business School
Boston, MA 02163
pshu@hbs.edu
This Version: August 2014
∗
The data were collected in collaboration with Xiao-Yuan Dong and Derek C. Jones to whom we are
most grateful. We benefitted greatly from comments from Ryan Buell, David Cooper, Andrew King, Karim
Lakhani, Ed Lazear, Cheryl Long, Alan Manning, Kristina McElheran, Matthew Notowidigdo, Hideo Owan,
Yongwook Paik, Lamar Pierce, Tatiana Sandino, Enno Siemsen, Lan Shi, Nachum Sicherman, Bruce Weinberg, Charles C.Y. Wang, and Feng Zhu, as well as seminar participants at the 2010 EALE/SOLE meeting
(London), the 2010 TPLS meeting (UC-Santa Barbara), Aarhus University, Queen’s University, Syracuse
University, Universidad Carlos III, Madrid, University of Lyon, and University of Tokyo. Barrett Hansen
and Zhengyang Jiang provided excellent research assistance. All errors are our own.
1
Abstract
We study the impact of social identity on worker competition by exploiting the
exogenous variations in workers’ origins and the well-documented social divide between
urban resident workers and rural migrant workers in large urban Chinese firms. We
analyze data on weekly output, individual characteristics, and coworker composition for
all weavers in an urban Chinese textile firm between April 2003 and March 2004. The
firm’s relative performance incentive scheme rewards a worker for outperforming her
coworkers. We find that a worker does not act on the monetary incentives to outperform
coworkers who share the same social identity, but does aggressively compete against
coworkers with a different social identity. Our results highlight the important role of
social identity in overcoming self-interest and enhancing intergroup competitions.
1
Introduction
Social identity theory suggests that individuals derive part of their identity from their
perceived membership in a social group and, as a result, behave differently towards ingroup versus out-group members (Tajfel et al., 1971; Tajfel and Turner, 1979). Therefore,
social identity can play a significant role in economic decision making, leading to seemingly
irrational economic behaviors (Akerlof and Kranton, 2000). Despite the importance of social
identity in organizational contexts (Hogg and Terry, 2000), most of the empirical evidence
on the impact of social identity comes from lab experiments, and few studies exist on the
impact of social identity on worker behaviors in real economic contexts.
In this paper we provide novel evidence on the interplay between social identity and
worker competition in a Chinese textile firm. Our focal firm utilizes a relative performance
incentive, in which the performance ranking of each individual worker in a particular work
group partially determines her pay. Using detailed individual-level productivity data, we
investigate whether a worker’s social identity affects her response to relative performance incentives. Do workers try to outperform each other universally, or do they compete differently
2
against coworkers with whom they share a common social identity? Since an individual’s
social identity may originate from multiple sources and depend on an organization’s demographic composition(Chattopadhyay et al., 2004), it can be empirically difficult to measure
social identity. We overcome this challenge by identifying a unique setting where a welldocumented, exogenously-formed, deep social divide separates otherwise similar workers.
Like many typical urban Chinese manufacturing firms, the firm in our study employs both
rural migrant workers and urban resident workers. We argue that the deep-rooted social
and historical institutions in China create powerful group identities in the workplace, where
workers are more likely to identify with other workers of the same rural or urban background
(Afridi et al., 2012). Other than where they come from, the 287 workers in our sample have
almost homogeneous demographic characteristics: all but nine of them are female, they are
all Han Chinese, and all of them have graduated from middle school but not high school.
They work in 18 different departments defined by location and shift.
Our workers are paid based on a combination of absolute and relative performances. By
rewarding a worker according to her performance ranking in the relevant work group, the
relative performance schemes tend to lead to intensified competition, diminished cooperation, and sometimes even sabotage amongst participants (Lazear, 1989; Drago and Garvey,
1998; Harbring and Irlenbusch, 2005, 2008, 2011; Carpenter et al., 2010). The presence
of strong social identity suggests that a worker may compete differently against her ingroup coworkers–defined as those who share the same social identity as the worker–from her
out-group workers. More specifically, there are two potential effects that are not mutually
exclusive: 1) social identity facilitates altruism and mitigates competition among in-group
members; and 2) social identity creates a powerful “us versus them” mentality and intensifies
competition across groups.
To understand the role of social identity in a competitive setting, we study how individual
workers react to their coworkers’ performances and how these responses differ towards ingroup and out-group coworkers. The relative performance schemes provide a worker with
3
incentives to work harder when her coworkers perform better. However, it is crucial for
our empirical estimation to separate competitive responses from other confounding common
determinants of performance correlations such as aggregate market demand. We address this
empirical challenge by exploiting the exogenous fluctuations in the composition of workers
in our firm due to employee turnover and temporary absence, similar to Mas and Moretti
(2009). Since we have a worker’s performance records across time, we can predict her timeinvariant productivity (i.e. ability) based on estimates from a regression model with worker
fixed effects. We then estimate how a worker’s weekly performance changes when the average
ability of her co-workers changes in that week. Such “compositional coworker effect” is not
contaminated by the aforementioned econometric issues since neither the error term nor the
worker’s performance in a given week could influence the average ability of her coworkers,
so long as there is no systematic rule of assigning workers based on their ability. Our field
research confirms that such random assignment is in place, and we also present econometric
evidence of random assignment following the test developed by Guryan et al. (2009).
Our analysis of worker-level data on weekly production during a 12 month period (April
2003-March 2004) reveals that a worker works harder and performs better only when working
with more able out-group coworkers, but not when working with more able in-group coworkers. The results are robust to alternative measures of average coworker ability. A worker in
our setting only acts on the incentives to outperform her coworkers when the source of the
competition is her out-group coworkers, but not when it is her in-group coworkers. Furthermore, we find differential effects for urban and rural workers. The urban resident workers
in our study are particularly sensitive to competitions from their rural migrant coworkers,
whereas the rural migrant workers are less responsive to competitions from urban coworkers. Our findings demonstrate the important role that social identity plays in mitigating or
amplifying the pecuniary incentives created by relative performance schemes.
Our results suggest that managers who design incentive schemes without understanding
the dynamics of social incentives in the workplace may fail to achieve the intended effects
4
on productivities. Bandiera et al. (2005) and Bandiera et al. (2010) provide compelling
evidence that fruit pickers in the UK withhold effort when working alongside their friends
under relative performance schemes and conform to the productivity levels of their friends
under piece rates. They focus on the distinction between friends and others, and show that
the fruit pickers respond to their friends but not to the other coworkers. In contrast, we
focus on the distinction between the two exogenously-formed social groups, and show that
our weavers respond to their out-group coworkers, yet not to their in-group coworkers. We
interpret the contrasting results in light of the fact that the formation of out-group and
in-group coworkers based on friendship differs from the one based on exogenously-formed
social identity. Exogenously-formed social identity may likely accompany an intense sense
of intergroup competition of “us against them”, whereas simple friendship would probably
not amount to the same level of intensity in intergroup competition, if at all. While social
identity may lead to friendship and vice versa, they are clearly different social constructs
(Hogg and Hains, 1998). We believe that our study will complement Bandiera et al. (2005)
and Bandiera et al. (2010) by shedding light on a less explored form of distinction between
in-group and out-group workers and pointing to intensified intergroup competitions as an
alternative manifestation of the interplay between social identity and coworker effects.
2
Related Literature
Social identity is defined as “part of the individual’s self-concept which derives from their
knowledge of their membership of a social group (or groups) together with the value and
emotional significance attached to that membership” (Tajfel, 2010). Economists have become increasingly aware of the importance of social identity and have started to contribute
fresh findings to an already rich body of theory and evidence accumulated by psychologists
(Abrams and Hogg, 1990; Van Knippenberg, 2000; Hewstone et al., 2002). A series of lab
experiments have shown that identity and affiliation with “minimal groups,” which are artificially created in a lab setting, could generate powerful incentives for prosocial and altruistic
5
behaviors towards other members within the same group (Bernhard et al., 2006; Charness
et al., 2007; Chen and Li, 2009) as well as intensified and potentially harmful competitions
across groups (Goette et al., 2012). However, a number of studies cast doubt on the utility
of such lab experiments with minimal groups. For instance, Goette et al. (2006) and Goette
et al. (2012) show that the findings from lab experiments with minimal groups may not
apply when individuals have real social ties. Furthermore, Fershtman and Gneezy (2001)’s
experimental study with real social groups (Ashkenazic Jews and Eastern Jews) points to
the value of studying real social groups from a position rooted in a deep understanding of
the nature of said social groups.
In addition to providing some of the first empirical evidence on the importance of social
identity in a real workplace, our study adds to a large body of literature on relative performance and tournament theory dating back to Lazear and Rosen (1981). A wide variety of
subsequent studies have shown that the effectiveness of relative performance/tournaments
could depend on many factors such as size of contestant pool, structure and size of rewards,
task difficulty, and variance in participants’ ability (Bull et al., 1987; Ehrenberg and Bognanno, 1990; Knoeber and Thurman, 1994; Becker and Huselid, 1992; Eriksson, 1999; Vandegrift and Brown, 2003; Audas et al., 2004; Casas-Arce and MartÃnez-Jerez, 2009; Boudreau
et al., 2012). Our findings show that social identity is an additional variable that needs to be
considered. Goette et al. (2012) provide experimental evidence that intergroup competition
enhances pro-social and altruistic behaviors within a group but also generates antisocial and
harmful behaviors toward out-group agents. However, unlike intergroup competition where
one has the incentive to compete only against out-group members, the incentives in our setting encourage a worker to compete against everyone through relative performance schemes.
As such, our setting presents a unique opportunity to examine three conflicting forces in
the workplace—individual competition among all coworkers, identity-induced altruism, and
identity-induced intergroup competitiveness. Dato and Nieken (2014) obtain evidence from
their tournament experiment in a laboratory that if the gender of the opponent is revealed
6
before the tournament begins, male subjects increase their performance, especially when the
revealed opponent’s gender is female. To the extent to which gender can be interpreted as
social identity, their overall conclusion that men respond to female opponents more strongly
than to male opponents is consistent with our key findings, despite the obvious methodological difference between their lab experimental study and our study.
Our results also complement the peer effects literature by illustrating the important relationship between social identity and the nature of worker interaction. Under piece-rate or
team-based incentives, coworker effects could arise from social pressure, mutual monitoring,
and knowledge sharing (Kandel and Lazear, 1992; Knez and Simester, 2001; Falk and Ichino,
2006; Mas and Moretti, 2009). Studies on coworker effects in relative performance settings
have mixed findings. Guryan et al. (2009) exploit random groupings of professional golfers
and find no evidence for coworker effects in professional golf tournaments. In contrast, Brown
(2011) finds that the presence of a superstar has a negative effect on golfers’ performances.
However, unlike the relative performance incentive in our case, the golf tournaments analyzed
in these studies have over hundreds of participants and the payoff is highly skewed. Chan
et al. (2013) find evidence of negative coworker effects among Chinese cosmetics sales workers who are compensated based on individual performance. Unlike the sales setting where
workers compete for the same pool of customers, the production technology in our firm does
not generate any externality. Chan et al. (2013) show that coworker effects could differ dramatically under different compensation schemes. Our results suggest that social identity is
an additional dimension that could mitigate or intensify the degree of coworker effects. Furthermore, unlike the aforementioned studies which are all in a non-manufacturing context,
we provide one of the first studies on the dynamics of worker interaction in a manufacturing
setting.
Finally, this paper also contributes to the small yet growing literature on workplace diversity and productivity. On the one hand, a number of researchers argue and provide evidence
that heterogeneous work teams consisting of a diverse labor force are more productive than
7
homogeneous teams. For instance, Hamilton et al. (2012)’s econometric case study of a U.S.
apparel firm report that heterogeneous teams consisting of workers with diverse ability levels
are more productive than homogeneous teams. Charness and Villeval (2009), based on new
experimental evidence, draws a similar implication for the advantage of workplace diversity
in the context of mixing senior with junior workers. In addition, Kurtulus (2011) uses subjective evaluations of individual workers as performance measures and provides evidence for
the performance-enhancing effect of workplace diversity in terms of gender, narrowly-defined
tenure within division, education, and wage. On the other hand, however, an empirical
study of a Kenyan flower production plant by Hjort (2014) sheds light on the dark side of
workplace diversity. Unlike previous empirical studies in the literature, Hjort (2014) focuses
on ethnic groups who are hostile to each other. Such workplace diversity is found to reduce
workplace productivity through a misallocation of workplace resources caused by taste-based
discrimination by employees in the short run (or in the absence of complete segregation as
a viable option). Our Chinese weavers also belong to two deeply-divided social groups who
are unfriendly to each other. In this sense, insofar as the source of workplace diversity is
concerned, our study is similar to Hjort (2014)’s Kenyan flower production worker study.
Yet we derive the opposite results, as we find the positive productivity effects of workplace
diversity. The Chinese textile production setting enables us to add a new dimension to the
literature: intergroup competition. Our Chinese textile workplace is proven to be a fertile
ground for such intergroup competition, whereas the Kenyan flower production plant appears
to have very limited room for intergroup competition.
3
Empirical Setting
The Chinese textile firm in our case study, SCT, is based in an industrial city in north-
eastern China.1 Textile manufacturing is one of the city’s most prosperous industries. SCT
was originally a state-owned enterprise (SOE) but it became one of the first large-scale SOEs
1
Our confidentiality agreement with SCT prohibits us from revealing the actual name of the firm.
8
to be privatized in 1998. The ownership and management restructure saved the firm from the
threat of bankruptcy due to outdated facilities, an aging workforce, and a shrinking market.
SCT employed about 3,500 workers as of April 2004.
In collaboration with two other researchers, we collected several types of data from the
firm during a lengthy study period, highlighted by two separate site visits that included
extensive interviews with the Director of Human Resources, the Director of the Weaving Division, a line supervisor and two team leaders at the Weaving Division, and the Director of
Data Management (who was in charge of all internal data). In addition, to help construct an
objective perspective on the firm, we supplemented the site visit with an extensive interview
with a long-term consultant for SCT who has been observing the firm for many years. The
detailed personnel data with which SCT generously provided us included personal characteristics, weekly performance measures, and wage for all of its weavers in the weaving division
over the 53-week span between March 2003 and April 2004. An advantage of this ”insider”
data set is that the individual performance measures are recorded by machines and thus
measured with little error. The data set for our analysis includes 9,966 observations for 287
individual weavers.2
3.1
Performance Metrics
At a first glance, the firm’s operation appears to be fully automated, as cloths are pro-
duced by automated looms rather than weavers. However, a longer and closer observation
of the workplace reveals that the weavers play an essential role in the production process.
Problems such as broken threads occur from time to time. Each weaver’s main task is to
pay close attention to her assigned loom machines and minimize the occurrence of such
operational problems; if problems arise, she must solve them quickly and effectively. For
example, “good weavers” will detect early signs of problems and make timely adjustments
2
We dropped 12 weavers who have worked for only 1 week as well as 115 observations where the weaver
worked for less than 2 days of the week, since we cannot accurately predict abilities for these weavers. We
also dropped one outlier observation, where the defect rate was above 10% (the maximum in the rest of data
is 2.5%) and its daily output was only 5 meters (the mean in the rest is around 500 meters).
9
to the operation process so that problems will not fully materialize and hence no defective
product will result. Should problems actually turn up, “good weavers” will solve them in a
timely and effective manner so that defective output will be minimized. Every product is
inspected and determined whether or not it is defective. Due to the problem-solving nature
of their main task, SCT constantly emphasizes to their weavers the importance of quality
and asks them to work toward “zero defect.”
The firm keeps three performance-related records for each weaver each week: total output
produced, days worked, and defective output produced. From these three variables, we
calculated the following two performance measures for weaver i in week t:
Def ectiveOutputit
∗ 100
T otalOutputit
T otalOutputit − Def ectiveOutputit
=
DaysW orkedit ∗ 10
Def Rateit =
DayOutit
(1)
(2)
These are defect rate (our measure of quality) and average daily non-defective output (our
measure of quantity), respectively. We scaled the variables to make the regression coefficients
more readable. Daily non-defective output is measured in 10 meters and defect rate is out
of 100.
Based on the demand for output, at the beginning of each week SCT develops the upcoming week’s detailed production plan with specific numbers for planned output, days worked,
and defective output assigned to each individual weaver. However, the problem-solving nature of a weaver’s job at SCT implies that discretionary efforts are crucial for quality control,
but not for quantity. The correlation between the planned and actual DayOutit is 0.9562
whereas the correlation between the planned and actual Def Rateit is only 0.0957.
Table 1 reports the summary statistics of the planned and actual performance measures
as well as wage. On average, a weaver produces 545 meters of non-defective output in an
8-hour work day and has a defect rate of 0.24% per week. As expected, the mean and
10
standard deviation of the planned DayOutit are similar to those of the actual DayOutit ,
but the mean and standard deviation of the actual Def Rateit are much smaller than the
planned. Although ”zero defect” is emphasized in the workplace, there does not exist a week
where zero defect rate is actually achieved. The mean of DaysW orkedit is around 6.27 days,
where a day is defined as an 8-hour work period.3 The average weekly wage earned by a
weaver, W ageit , is around 148 in Chinese Renminbi, which is equivalent to around $18 in
2004 US Dollars.
3.2
Composition of Workers
SCT has six weaving rooms and uses a standard three-shift operation, so there are 18
departments defined by combinations of locations and shifts. Each weaver is assigned to
one shift and one weaving room, and does not change her shift or location. We define the
coworkers of a weaver to be the other weavers that work in the same department, i.e. during
the same shift in the same weaving room. There is no team production at the weaving
section of SCT. Each weaver is responsible for her own output while working alongside her
coworkers. The production technology itself generates no externality in productivity. Onthe-job interactions among weavers are also limited because each weaver is required to pay
undivided attention to her machines. However, a weaver may still influence her coworkers’
behaviors through her mere presence in the department.
While a weaver does not change her department, the composition of coworkers changes
from week to week due to employee turnover and temporary absence. Table 2 summarizes
the composition of weavers by department. A department has between 13 and 18 weavers
but not all of them are present every week. An average weaver works for around 35 weeks out
of the 53-week study period. The number of weavers working in a given shift and location
can be as low as one during the holiday season and as high as 16 during the peak season,
and the average is about 10.5 across all the departments and weeks. All but nine weavers
3
Thus, it is possible for a weaver to work more than seven “days” a week.
11
are female. We do not report their education level in the table since there is no variation;
all of them have graduated from junior high school but have not attended high school.
3.3
Relative Performance Scheme
Weavers are long known for their piece rate compensation system (see, for instance,
Biernacki, 1995). SCT’s weavers are no exception. According to SCT’s human resource
manager, each weaver’s own performance (in particular in the area of quality) is an important
determinant of her wage. The human resource manager also indicates that the weaver’s
performance ranking in her department also matters, as the department is the primary
organizational unit at SCT. In other words, relative performance incentive is present among
weavers in the same department.4 To confirm the stated compensation policy including both
piece rate and relative performance scheme, we estimate the following wage equation:
log (wit ) = αi + βyit + λ (relative performance measuresit ) + γ (controls) + it
(3)
where i denotes the worker, and t denotes the week, wit is the average daily wage of worker
i in week t, and yit is the set of own performance measures (Def Rateit , DayOutit , and
their quadratic terms). Additional controls include demand (measured by the planned performance measures and number of workers in the same department), week dummies, and
month dummies interacted with department dummies.
Table 3 reports the OLS results for two sets of relative performance measures. All of the
specifications include worker fixed effects and cluster standard errors by worker. Columns
(1) and (2) use a worker’s numerical rankings in Def Rate and DayOut, respectively, within
her department in week t. Upon achieving the same quantity and quality of output, a worker
receives an approximately 0.2% to 0.3% pay increase if her numerical ranking in Def Rate
within her department improves by one (or her numerical ranking decreases by one). The
4
The use of relative performance for weavers is also documented for Japanese silk reeling firms (Kiyokawa,
1991).
12
table also shows that there is no such significant link between the weaver’s ranking in quantity
(DayOut) and her pay. The results are robust to including various controls.
Columns (3) and (4) consider an alternative way of measuring relative performance—
two indicator variables on whether a worker outperforms the average Def Rate and DayOut
of her coworkers in the same department in week t. Conditional on achieving the same
quantity and quality of output, a worker receives an approximately 1.5% pay increase if
she outperforms her coworkers in the quality of output, but not when she outperforms in
quantity. Again the results are robust to including various controls.5
The last column considers both sets of relative performance measures simultaneously.
Reassuringly, the estimates coefficients on both relative performance measures in quality
change little and remain statistically significant, pointing to the universal presence of relative
performance incentives for all workers in the department. In other words, the weaver has
an incentive to improve her ranking regardless of whether she performs above or below
the average. For instance, consider a below-average performer and suppose the average
performance of her coworkers improves. Since she is already a below-average performer, she
will be even more likely to be a below-average performer as a result of the rise in the average
performance of her coworkers. As such, the rising average performance of her coworkers will
not motivate her to work harder if outperforming the average coworker in Def Rate were the
only significant relative performance measure. However, Column (5) shows that even after
controlling for “Outperform the average coworker in Def Rate,” the estimated coefficient on
ranking within department in Def Rate is still significant. The rising average performance
in quality of her coworkers will still motivate the below-average performer to work harder
5
In regressions not reported here, we show that relative performance incentives only happen within the
same shift and location and that the regression coefficients are not driven by some aggregate shocks in the
whole firm. We include two dummy variables, which indicate whether a worker outperforms the other workers
in her shift or location in Def Rate excluding her coworkers. Both coefficients are small and insignificant,
suggesting that the worker is only rewarded when outperforming her coworkers but not the rest of the firm.
Since the department is the primary organizational unit at SCT, and there is no inter-departmental mobility
among weavers, the weaver knows little about other weavers outside of her department. Thus, it makes sense
to set up a relative performance incentive at the departmental level as opposed to the firm-level.
13
since her wage is sensitive to her ranking.6 Likewise, the rising average performance of the
coworkers will also motivate an above-average performer (even the top performer) to work
harder since her pay is linked to her ranking.
SCT also encourages competition through other forms of incentives. In addition to regular
weekly departmental meetings, each department holds a training session and skill contest
after work from time to time, with the goal of enhancing workers’ skill levels. Through
such departmental activities, each weaver learns about her coworkers and their capabilities.
The company selects “model workers” every year and provides them with nontrivial prizes.
For example, during 2002, just before our data collection began, about 40 model workers
were selected and awarded with a trip to Singapore, Malaysia, and Thailand, staying at
3-star hotels. Though the selection criteria are rather general and subjective, it is quite
conceivable that many above-average performers are motivated to become a model worker by
consistently achieving a high ranking in the department. Furthermore, the ranking system,
skill contest, as well as the selection of model workers, may generate non-pecuniary incentives
for competition associated with status and recognition (Moldovanu et al., 2007; Vidal and
Nossol, 2011).
Table 3 also confirms that quality control is extremely important at SCT. Decreasing
Def Rateit by 0.18, which is approximately one standard deviation across all shifts and
locations, increases wage by nearly 8%. In contrast, increasing the output has insignificant
impact on wage, which is consistent with our field observation that the quantity of output
largely conforms to the production plan.
3.4
Group Identity: Rural-Urban Segregation in China
Table 2 shows that around 67% of weavers are rural migrants and most of the departments
have more rural migrants than urban residents. In the context of China, many rural workers
look for better economic opportunities in non-farming sectors in urban areas, sometimes far
6
In addition, it is plausible that the rising average performance of her coworkers will generate an incentive
for the below-average performer to work harder if it increases her fear of losing her job.
14
from home. The proportion of rural individuals participating in off-farm work has risen from
around 11% to over 43% from 1980 to 2000, with an increasing tendency to migrate to a
different province (de Brauw et al., 2002). Rozelle et al. (1999) estimate that 154 million
individuals from the rural labor force worked off-farm in 1995, out of which 54 million were
long-term migrants. The series of economic reforms and relaxations in regulations in the
1970s and 1980s have led to one of the largest rural-urban migrations in history (Zhang and
Song, 2003).
However, working in an urban city in China does not automatically make someone an
urban resident. Instead, a highly rigid housing registration system (“hukou”) determines
whether someone is “rural” or “urban,” mostly based on the registration of her parents (see
Chan and Zhang, 1999 for a comprehensive discussion of the “hukou” system). Institutional
rigidity, limited quota, and lack of transparency in the process make it extremely difficult for
individuals with a “rural” status to transfer to an “urban” status. The vast majority of rural
migrant workers in urban areas rely on a temporary residence permit, which entitles them
to minimal access to social welfare programs such as health care and subsidized housing.
Survey results show that many urban residents feel hostile towards rural migrant workers,
as they believe that rural migrant workers have taken jobs away from them and made the
city more crowded, dangerous, and dirty (Solinger, 1999; Nielsen et al., 2006). They may also
discriminate against rural migrant workers in the workplace. As such, rural migrant workers
tend to be denied access to high-paying jobs and experience wage discrimination on the job
(Huang, 2001; Lu and Song, 2006). Letters from female rural migrant workers in Shenzhen
(one of the first coastal cities in the South to experience economic reforms and a huge influx
of rural workers), documented in Ngai (2005), suggest that they feel “no matter how many
years they spent there, in the drudgery of labor they would always be recognized as outsiders”
and “disguis[ing] their rural identity could only result in reinforcing their class...differences.”
Using field experiments on Chinese elementary school students, Afridi et al. (2012) show that
social identity derived from one’s urban or rural status significantly impacts the students’
15
responses to economic incentives.
4
Empirical Strategy and Results
There are two ways that coworker effects could be present. First, a weaver may work
harder when working with more able coworkers ( “compositional coworker effects”). Second,
she may work harder if she notices that her coworkers are putting in more effort (“contemporaneous coworker effects”). The identification of contemporaneous coworker effects is difficult
due to the reflection problem, where the direction of causality cannot be identified and the
estimate overstates the true magnitude of worker interaction (Manski, 1993). Furthermore,
they are unlikely to exist at SCT, since each weaver must pay undivided attention to her
machines. In contrast, the department meetings and skill contests clearly allow and encourage each weaver to observe her coworkers’ abilities and compare herself against them. Thus,
we focus on estimating the compositional coworker effects, and our identification comes from
the exogenous changes in the composition of workers at the department level.
4.1
Predicting Permanent Productivity and Verifying Random Assignment to
Departments
To estimate compositional coworker effects, we first need to predict the time-invariant
productivity (ability) of each individual weaver with precision. We do so with a specification
that controls for an extensive list of covariates:
Def Rateit = abilityi + δ (DayOutit , DaysW orkedit ) + ζMit + µCjt + it
(4)
where Cjt is the set of week dummies interacted with the combination of shift and location
dummies and Mit is a set of 287 coworker dummy variables controlling for the presence of
coworkers. For instance, the dummy variable “coworker1” in week t takes a value of 1 if
worker 1 is a coworker to the focal worker i with i 6= 1 in week t. Ideally, we would like to
16
control for all possible compositions of coworkers, i.e. all the combinations of dummies in Mit
(conditional on being realized at the workplace). However, doing so would require estimating
over 5,500 dummies in addition to the existing covariates using less than 10,000 observations
and raise serious concern about the lack of power. We assume that the combination of Cjt
and Mit sufficiently purges the coworker effects. In Section 4.3, we also show that our key
results are robust to relaxing this assumption. We include DayOutit and DaysW orkedit so
that we can measure each weaver’s ability to maintain high quality of the output, holding
the quantity and length of work time constant.7 We exclude planned Def Rateit because we
are not measuring the ability to outperform the plan.
d from Equation (4), there
According to Figure 1 which plots the kernel density of ability
i
is substantial variation in the predicted permanent productivity. If the assignment of coworkers were non-random, then it is possible that unobserved factors other than compositional
changes could drive the estimated coworker effects. Our field research at SCT points to the
absence of any explicit or implicit rules of assigning weavers, and we provide two sets of results that confirm the absence of systematic assignment statistically. First, we test whether
a high-ability weaver is more likely to work with other high-ability weavers at the same time.
To do so, we employ the bias-correction method in Guryan et al. (2009) and run the following
regression:
ai = α + βa−i,jt + γa−i,j + µDj + it
(5)
where ai is the predicted permanent productivity of the focal weaver i, a−i,jt is the average
predicted permanent productivity of weaver i’s coworkers in week t, and a−i,j is the average
predicted permanent productivity of all of weaver i’s possible coworkers, i.e. anyone who has
ever worked in the combination j of shift and location. Since workers do not change shift or
location, their predicted permanent productivities also include potential shift and location
fixed effects. Thus, we control for Dj , which is the set of shift times location dummies.
7
To test for any potential endogeneity in DayOutit , we use the planned output as an instrument for the
actual DayOutit . The results change little and we cannot reject the null that the IV and OLS estimates are
different in a Hausman test.
17
The coefficient β in Equation (5) indicates whether more able weavers are matched with
other more able coworkers at time t. Since one cannot be her own coworker, the pool
that the coworkers at time t are drawn from for a high-ability focal worker has a lower
average permanent productivity than for a low-ability focal worker. This is because the focal
worker is excluded from the otherwise identical pool. Thus, without controlling for a−i,j ,
the estimated β has a downward bias, and the bias is more severe for smaller pools (Guryan
et al., 2009). By construction, ai = N aj − (N − 1)a−i,j where aj is the average ability of
everyone ever present in the combination of shift and location including worker i, and N is
the total number of those workers. Since the shift interacted with location dummies fully
absorb N aj , γ should be close to the mean of −(N − 1) across all shifts and locations, which
is around −15. The coefficient β indicates whether more able weavers are matched with
other more able coworkers in week t. The null hypothesis of random assignment is that β is
zero.
Panel A in Table 4 confirms that the permanent abilities of weavers in a given week are
not correlated. Columns (A1) and (A2) use the bias-correction method and include a−i,j ,
while Columns (A3) and (A4) show the results without controlling for a−i,j . As expected,
the estimated γ is close to −15 and significant at the 1 percent level. More importantly, the
estimated β is close to zero and highly insignificant, and the results are robust to the inclusion
of week fixed effects. Similar to Guryan et al. (2009), we also find that not controlling for
a−i,j introduces a negative bias in the coefficient estimates of β.
Second, we show that the composition of coworkers is not driven by aggregate demand,
using the following first difference model:
4yit0 = β4a−i,jt + θ4njt + γ (controls) + it
(6)
where yit0 is the focal worker’s planned Def Rateit or DayOutit , and njt is the number of
coworkers in week t. As in Equation (5), a−i,jt is the average predicted permanent produc-
18
tivity of worker i’s coworkers in week t. We also control for week dummies.
Panel B in Table 4 shows that there are no correlations between the average permanent
productivity of coworkers and the planned quantity or quality of the focal weaver’s output.
The estimated βs in Equation (6) are highly insignificant. Thus, it is not the case that, for
instance, more able coworkers are present when the weaver is facing challenging production
plans. Taken together, the results in Table 4 support our field observation that there are no
systematic rules of assigning weavers in SCT.
4.2
Estimating Coworker Effects
Having established the random assignment of coworkers, we are now ready to specify our
first-difference models:
4Def Rateit = α0 4a−i,jt + δ (controls) + it
(7)
4Def Rateit = β0 4a−i,gjt + β1 4a−g,jt + δ (controls) + it
(8)
where i denotes a weaver, t denotes a week, j denotes a department, and g denotes a group
(rural or urban); a−i,jt is the average ability of worker i’s coworkers, a−i,gjt is the average
ability of worker i’s in-group coworkers, and a−g,jt is the average ability of worker i’s outgroup coworkers. Controls include first differences in the number of coworkers (Equation
(7)) or the numbers of in-group and out-group coworkers (Equation (8) and (9)), DayOutit ,
days worked, and week dummies. The advantage of using first-differences models is that we
do not need to worry about any unobserved time-invariant determinants of performance in a
department, nor do we need to control for any time-invariant individual characteristics such
as gender or education.
The key coefficient of interest α0 in Equation (7) measures how the focal weaver’s performance changes with the average ability of her coworkers. If the relative performance
incentives are effective overall, we should see a positive and significant estimate for α0 . This
19
is because the monetary and psychological rewards of outperforming one’s coworkers and
improving one’s ranking will generate competition in the department. When the coworkers
are better, a weaver will put in more effort and improve her performance as well.
The parameter β0 in Equation (8) measures how the focal weaver’s performance changes
with the average ability of her in-group coworkers, while β1 measures the effect of a change
in the average ability of her out-group coworkers. Group identity will encourage altruistic
behaviors toward in-group members and enhance intergroup competitions. Thus, a weaver
will only put in more effort to compete against her out-group coworkers but not her in-group
coworkers. We expect only β1 to be significant and positive if the workers compete against
their out-group coworkers but not their in-group coworkers.
Columns (1)-(4) of Table 5 present the OLS estimates of Equation (7) and Equation
(8) with various controls. Since the key independent variables (the ability measures) are
predicted from other regressions, we bootstrap the standard errors with 2000 repetitions and
cluster the standard errors at the individual level. Columns (1) and (2) of Table 5 suggest
that on average, a weaver performs better when working with better coworkers, but the
effect is not statistically significant after controlling for the weekly dummies. The results in
Columns (3) and (4) confirm that workers only compete against their out-group coworkers
but not the in-group coworkers: the estimates of β1 are positive and significant at the 5%
level (at the 10% level controlling for the week fixed effects), and the estimates of β0 are
slightly negative but insignificant. When the average innate ability in quality control of her
out-group coworkers improves by one standard deviation (around 0.2 percentage points) as a
result of exogenous compositional changes, the weaver’s own defect rate would fall by close
to 0.033 percentage points, ceteris paribus. The size of the estimated coworker effect from
out-group coworkers is plausible and economically meaningful; Table 3 implies that a 0.033
percentage-point improvement in Def Rate would increase one’s wage by about 1.2%, holding
the worker’s relative performance constant. In contrast, the estimates of β0 are considerably
smaller and insignificant, suggesting that a worker’s performance does not respond to the
20
compositional changes of her in-group coworkers.
To explore the urban/rural dynamics further, we estimate the following model:
4Def Rateit = γ0 4a−i,gjt + γ1 Rurali ∗ 4a−i,gjt + γ2 4a−g,jt + γ3 Rurali ∗ 4a−g,jt (9)
+δ (controls) + it
(10)
The asymmetric group identity effects at SCT are empirically tested by examining the
sign and size or γ1 and γ3 in Equation (9). We expect identity-induced intergroup competitiveness to be stronger for the urban weavers. The urban weavers on average have much
more experience at the company and higher ability than the rural weavers, reinforcing any
supriority over rural migrants that the urban weavers may already feel as urban residents.
The average tenure at the start of our sample period is over 13 years for the urban workers
and less than 6 years for the rural workers. The average Def Rateit of urban resident workers is 0.23, while that of rural migrant workers is 0.245. The t-tests for both comparisons
are significant at the 1% level.8 Being concerned about their superiority over outsiders, the
urban weavers would respond strongly to any potential threat to their supremacy such as
the rising average ability of their out-group coworkers (rural weavers). The rising average
ability of rural weavers means that what is the norm in the first place, i.e., the urban resident
community outperforming the rural migrant community, has the potential to be overtuned.
In contrast, such a sense of supremacy over outsiders is absent among the rural weavers,
where an inferiority complex may be more likely. The rising average ability of their outgroup coworkers (urban weavers) does not present an existential threat to the rural migrant
worker community. Rather, what is the norm in the first place will now become even more
likely.
It is unclear which group has stronger identity-induced altruism. On the one hand, the
8
In contrast, there is no significant difference in their average planned Def Rate, suggesting that the firm
is not discriminating against the rural weavers.
21
rural weavers live together in the company dormitory free of charge (five or six per room)
and share more social interactions day to day. The urban weavers mostly live close to where
the factory is located and tend to commute from their homes. On the other hand, the urban
weavers have worked together and known each other for much longer. Since we cannot
empirically distinguish altruism from intergroup competitiveness, we expect that in net, the
urban weavers are more likely to be affected by social identity than the rural weavers.
Columns (5) and (6) confirm our expectation. The estimates of γ0 and γ2 suggest that
urban resident weavers are highly responsive to the rising average ability of their rural coworkers, but not to that of their urban coworkers. The estimate of γ3 is negative and statistically
significant, indicating the rural weavers are significantly less responsive to the competition
from their out-group coworkers than the urban weavers. These findings are consistent with
Dato and Nieken (2014), which compares male and female subjects and reports that male
subjects tend to hold a belief that men perform better than women in the tournament.
They find that male subjects increase their performance when their tournament opponents
are revealed to be female but female subjects do not change their performance when their
tournament opponents are revealed to be male. Since gender is also an important aspect of
social identity, both their study and ours consistently show intergroup competition is more
relevant to the group that holds a belief of supremacy over the other group (men in their
case, and urban workers in ours) while less so to the other group (women and rural workers).
Sub-sample Estimates, Falsification Tests, and Robustness Checks
We estimate Equation (8) for two different sub-samples and report the results in Table
6. The sample in Column (1) in Table 6 includes the workers who have worked for SCT for
more than five years (260 weeks) at the beginning of the sample; and the sample in Column
(2) includes those who worked for more than 33 weeks during the 53-week study period. For
both groups of workers, we find similar results to the full-sample estimates, suggesting that
our results are not driven by less experienced workers or temporary workers.
22
Table 7 reports the results from additional falsification tests and robustness checks.
Columns (1) and (2) estimate an equation similar to Equation (8) but uses the focal worker’s
planned Def rate as the dependent variable. Since the production plan is set ahead of time,
it should not be affected by the composition of the in-group and out-group coworkers, and
the results confirm our expectation. Columns (3) and (4) use the actual Def rate as the
dependent variable. However, instead of the average coworker abilities, we use the proportions of “above-median” in-group and out-group coworkers as the key right-hand side
variables, which are more robust to measurement errors in the predicted abilities. A worker
is “above-median” if her predicted ability is above the median of everyone who has worked
in the department. We find qualitatively similar results that an increase in the proportion
of out-group “above-median” coworkers is associated with a decrease in the focal weaver’s
defect rate with marginal statistical significance, while a change in the proportion of in-group
“above-median” coworkers has no effect.
4.3
Implications on Assignment and Hiring
Our results suggest that the positive incentive effect of relative performance will be greater
in the integrated workplace (where different social identities coexist in the same workplace)
than in the segregated workplace (where all workers in the same workplace sharing the same
social identity). More specifically, the management at SCT can maximize competition by
matching high ability rural migrant workers with urban resident workers. It also follows
from our results that a new hire who is a high ability rural worker should be assigned to a
department with many urban workers, whereas the opposite would be true for a relatively
low ability new rural worker.
In our case, the culprit for the productivity advantage of the integrated workplace over
the segregated workplace is powerful intergroup competition for better performance. As
discussed in Section 2, in the absence of such intergroup competition in performance, the
productivity advantage of the integrated workplace may not appear. One example would be
23
the Kenyan flower production plant in Hjort (2014), where the integrated workplace suffer
an efficiency loss resulting from misallocation of resources instead. As such, the integrated
workplace is not always productivity-enhancing, and management ought to pay particular
attention to the nature of production when deliberating on the formation of the integrated
workplace.
5
Conclusion
This paper has combined two important literatures in personnel economics and manage-
ment that have largely developed independently of each other: relative performance incentives and social identity. On the one hand, the relative performance literature shows that the
use of relative performance incentives in organizations promotes competition among coworkers. While such relative performance-induced competition among coworkers has been shown
to lead to higher individual productivity, the literature has also drawn attention to the dark
side of tournament competition: limited cooperation among coworkers and even sabotage.
On the other hand, the social identity literature points to social identity as an important
social construct that can mitigate self-interest and/or promote competitive behaviors. In
this paper we examine what happens when relative performance meets social identity.
We have taken advantage of an unusual opportunity to study a Chinese textile firm in
which every weaver has a relative performance incentive to outperform her coworkers , and
in which there is a natural division of all weavers into two distinct groups: urban resident
and rural migrant workers. Both the rural/urban divide of workers in Chinese factories and
each worker’s strong identification with her natural group (which often results in a powerful
“us versus them” mentality) are well documented. Moreover, the powerful divide between
rural migrant and urban resident workers in Chinese factories were formed historically and
institutionally long before our weavers arrived at the firm. In other words, we are free from
the problem of endogenous group formation to which non-experimental studies are often
subject. We have found that relative performance indeed generates competition, but not
24
among workers who share the same social identity. In short, relative performance incentives
do not apply to workers sharing the same group identity, while it remains fully applicable to
workers with different group identities.
Our results provide a new and important insight for management who might be interested
in introducing relative performance incentives. In addition to standard considerations–prize
size, size of the contestant pool, nature and magnitude of uncertainty, and viability of alternative schemes (such as piece rate)–managers considering introducing relative performance
incentive schemes ought to pay particular attention to the social network and group identity
in the workplace. The introduction of relative performance will certainly create a general
incentive for the worker to outcompete her coworkers. However, in the presence of social
identities in the workplace, relative performance will also bring about rather complex group
dynamics—the worker will ease her competitive spirit against her in-group coworkers while
maintaining or perhaps even strengthening her competitive spirit against her out-group workers.
By taking advantage of the presence of social identities in the workplace, management
can design and implement relative performance schemes that maximize the beneficial effect
of relative performance and minimizes its “dark side.” For instance, the relative performanceinduced competition may be particularly counter-productive when workers have complementary skills. Our study suggests that managers could still take advantage of relative performance if workers with complementary skills belong to the same social groups. Furthermore,
with an existing relative performance incentive in place, management needs to think carefully
about hiring and firing decisions, as changes in the composition of workers could influence
the interplay of social identity and relative performance incentives.
We acknowledge that this paper studies the case of a Chinese textile firm, and hence
our findings and their implications ought to be interpreted as such. Assessing the external
validity of our findings using data from firms in other industries and other countries is left
for future work.
25
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Tables and Figures
0
.5
Density
1
1.5
2
Figure 1: Kernel Density Plot of Predicted Permanent Productivity
−1
−.5
0
.5
Predicted Permanent Productivity
1
kernel = epanechnikov, bandwidth = 0.0698
Notes: This figure plots the kernel density of the workers’ predicted permanent productivity from
estimating Equation (4).
31
Table 1: Summary Statistics of Performance Measures and Wage
Mean Std. Dev. Min. Max.
Actual Def Rateit (percent)
0.24
0.18
0.007 2.50
Planned Def Rateit
7.94
2.91
0
22
Actual DayOutit (in 10 meters) 54.56
18.29
5.91 192.36
Planned DayOutit
51.25
17.53
6.45 227.19
Actual DaysW orkedit
6.27
0.88
2
7.8
Actual W ageit
148.14
30.37
31.7 216.4
N = 9,966
Notes: See text for definitions of Def Rate, DayOut , and DaysW orked. Subscripts i and
t indicate weaver i and week t.
32
Table 2: Composition of Weavers by Shift and Location
No. Weavers
Location Shift Ever Present
1
1
15
1
2
18
1
3
16
2
1
16
2
2
17
2
3
18
3
1
14
3
2
18
3
3
16
4
1
15
4
2
16
4
3
16
5
1
16
5
2
16
5
3
16
6
1
15
6
2
13
6
3
16
Total
287
Prop.
Female
100.00%
100.00%
100.00%
87.50%
100.00%
100.00%
92.86%
94.44%
100.00%
93.33%
100.00%
100.00%
87.50%
100.00%
87.50%
100.00%
100.00%
100.00%
96.86%
Prop.
Rural
26.67%
77.78%
81.25%
31.25%
76.47%
66.67%
42.86%
72.22%
62.50%
60.00%
68.75%
81.25%
75.00%
87.50%
62.50%
66.67%
92.31%
62.50%
66.55%
33
Avg. No.
Weeks Worked
38.27
31.89
32.38
28.31
29.18
36.89
36.36
32.44
37.13
33.60
32.06
40.75
37.88
29.19
43.00
39.13
36.00
38.19
35.06
No. Weavers in a Week
Min Max Mean
3
13
10.75
3
15
10.74
1
15
9.66
2
11
8.42
2
13
9.25
2
15
12.43
1
12
9.47
4
15
10.91
2
14
11.11
2
12
9.43
1
15
9.79
3
15
12.21
1
15
11.30
3
14
8.64
3
16
12.92
3
14
11.02
3
12
8.74
4
16
11.43
1
16
10.46
Table 3: Relative Performance and Wage. Dep = Log Daily Wage
(1)
(2)
-0.002**
(0.001)
-0.000
(0.001)
-0.003***
(0.001)
-0.000
(0.001)
(3)
(4)
(5)
0.015***
(0.004)
0.001
(0.005)
0.017***
(0.004)
0.000
(0.004)
-0.002**
(0.001)
-0.001
(0.001)
0.015***
(0.004)
-0.001
(0.004)
Relative Performance Measures
Rank within department in Def Rate
Rank within department in DayOut
Outperform average coworker
in Def Rate
Outperform average coworker
in DayOut
Own Performance Measures
-0.418***
(0.030)
0.215***
(0.021)
-0.000
(0.001)
-0.000
(0.000)
-0.389***
(0.028)
0.199***
(0.020)
-0.000
(0.001)
-0.000
(0.000)
-0.386***
(0.034)
0.200***
(0.022)
-0.000
(0.001)
-0.000
(0.000)
-0.356***
(0.031)
0.185***
(0.021)
-0.000
(0.001)
-0.000
(0.000)
-0.339***
(0.033)
0.176***
(0.021)
-0.000
(0.001)
-0.000
(0.000)
Additional Controls
Yes
Yes
Yes
Yes
Yes
Month*Department Dummies
No
Yes
No
Yes
Yes
Def Rateit
Def Rate2it
DayOutit
DayOut2it
0.458
0.492
0.459
0.492
0.493
N
9,966
9,966
9,966
9,966
9,966
Notes: See text for definitions of Def Rate and DayOut . All standard errors are clustered at
Adj.R-squared
the individual level. Additional controls include number of workers in the department in week
t, planned Def Rateit , and planned DayOutit , and week dummies. * p < 0.10 ** p < 0.05 ***
p < 0.01.
34
Table 4: Random Assignment Tests
d )
Panel A: Dep. Var. = Focal Worker’s Ability in DefRate ( ability
i
(A1)
(A2)
(A3)
(A4)
Average coworker ability
0.002
-0.002
-2.673*** -2.889***
d
(0.011)
(0.011)
(0.310)
(ability
−i,jt )
Leave-me-out average of the pool -14.933*** -14.928***
d
(ability
(0.131)
(0.132)
−i,j )
Week Dummies
No
Yes
No
Department Dummies
Yes
Yes
Yes
Adj.R-squared
0.998
0.998
0.790
N
9,961
9,961
9,961
Panel B: Dep. Var. = 4FD in Focal Worker’s Production Plan
4Planned Def Rate
4Planned
(B1)
(B2)
(B3)
4Average coworker ability
-0.374
-0.139
-5.813
d
(4ability
−i,jt )
4Number of Coworkers
Week Dummies
Adj.R-squared
N
(0.831)
-0.007
(0.014)
No
-0.000
9,009
(0.873)
-0.036*
(0.020)
Yes
0.040
9,009
(11.752)
0.291**
(0.132)
No
0.001
9,009
(0.323)
Yes
Yes
0.795
9,961
DayOut
(B4)
7.221
(10.714)
0.272
(0.179)
Yes
0.071
9,009
Notes: All standard errors are bootstrapped with 2000 repetitions and clustered at the individual
level. * p < 0.10 ** p < 0.05 *** p < 0.01.
35
Table 5: Group Identity and Effectiveness of Relative Performance Incentives. FullSample Estimates. Dep. Var. = 4Focal Worker’s Def Rate.
(1)
(2)
(3)
(4)
(5)
(6)
4Avg. Coworker Ability
0.402***
0.293
(4a−i,jt )
(0.204)
(0.202)
4Avg. in-group Coworker Ability
-0.005
-0.070
-0.210
-0.218
(4a−i,gjt )
(0.150)
(0.149)
(0.274)
(0.266)
4Avg. Out-group Coworker Ability
0.230**
0.166*
0.713**
0.601*
(4a−g,jt )
(0.111)
(0.101)
(0.334)
(0.314)
Rural * 4Avg. In-group Coworker Ability
0.423
0.314
(Rurali ∗ 4a−i,gjt )
(0.296)
(0.296)
Rural * 4Avg. Out-group Coworker Ability
-0.622*
-0.558*
(Rurali ∗ 4a−g,jt )
(0.343)
(0.336)
Additional Controls
Yes
Yes
Yes
Yes
Yes
Yes
Week Dummies
No
Yes
No
Yes
No
Yes
Adj.R-squared
0.122
0.146
0.114
0.140
0.116
0.141
N
9,009
9,009
8,823
8,823
8,823
8,823
Notes: All standard errors are bootstrapped with 2000 repetitions and clustered at the individual
level. Additional controls include changes in the numbers of coworkers (only in Columns (1) and
(2)), the numbers of in-group and out-group coworkers (Columns (3)-(6)), focal worker’s daily
output, and days worked in a week. * p < 0.10 ** p < 0.05 *** p < 0.01.
Table 6: Group Identity and Effectiveness of Relative Performance Incentives. SubSample Estimates. Dep. Var. = 4Focal Worker’s Def Rate
(1)
Tenure longer
than 5 years
4Avg. In-group Coworker Ability
-0.097
(4a−i,gjt )
(0.181)
4Avg. Out-group Coworker Ability 0.325*
(4a−g,jt )
(0.173)
Additional Controls
Yes
Week Dummies
Yes
Adj.R-squared
0.164
N
4,371
(2)
Worked more
than 33 weeks
-0.109
(0.151)
0.181*
(0.108)
Yes
Yes
0.143
7,340
Notes: All standard errors are bootstrapped with 2000 repetitions and clustered at the individual
level. Additional controls include changes in the numbers of in-group and out-group coworkers,
focal worker’s daily output, and days worked in a week. * p < 0.10 ** p < 0.05 *** p < 0.01.
36
Table 7: Falsification Tests & Robustness Checks
Dep. Var.
4Avg. In-group Coworker Ability
(4a−i,gjt )
4Avg. Out-group Coworker Ability
(4a−g,jt )
4Prop. of In-group “Above-median” Coworkers
4Planned Def Rateit
(1)
(2)
-0.901 -0.870
(0.927) (0.930)
-0.243 -0.057
(0.795) (0.874)
4Prop. of Out-group “Above-median” Coworkers
Additional Controls
Week Dummies
Adj.R-squared
N
Yes
No
0.011
8,823
Yes
Yes
0.047
8,823
4Def Rateit
(3)
(4)
-0.019
(0.027)
-0.049*
(0.030)
Yes
No
0.114
8,823
-0.002
(0.027)
-0.041
(0.028)
Yes
Yes
0.140
8,823
Notes: All standard errors are bootstrapped with 2000 repetitions and clustered at the individual
level. Additional controls include changes in the numbers of in-group and out-group coworkers,
focal worker’s daily output, and days worked in a week. * p < 0.10 ** p < 0.05 *** p < 0.01.
37