Do Incentive Plans for Exemplary
Employees Lead to Productive or
Counterproductive Outcomes?
Carolyn Deller
Tatiana Sandino
Working Paper 16-087
Do Incentive Plans for Exemplary
Employees Lead to Productive or
Counterproductive Outcomes?
Carolyn Deller
Harvard Business School
Tatiana Sandino
Harvard Business School
Working Paper 16-087
Copyright © 2016 by Carolyn Deller and Tatiana Sandino
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.
Do Incentive Plans for Exemplary Employees
Lead to Productive or Counterproductive Outcomes?
Carolyn Deller & Tatiana Sandino*
Harvard Business School
November 24, 2015
Abstract
Using data from a retail chain, this paper studies the effects of a preferential plan providing
incentives only to exemplary employees. Such plans incorporate elements of tournaments
(through the selection of employees chosen largely on the basis of past performance but
incorporating some managerial discretion) and linear incentives to align employees with
company goals and values. We find that, on average, the implementation of the preferential
incentive plan was associated with improvements in sales. Also, we find that this plan was
associated with greater improvements in sales and gross profits, and reductions in the incidence
of bad audits in stores where employees were initially less likely to be aligned with company
goals. However, the plan was associated with lower sales and gross profits, and higher incidence
of bad audits, absenteeism, and turnover in some situations where employees could have
perceived the plan to be unattainable or unfair. Our study sheds light on the impact of
preferential incentive plans, and the conditions under which these plans are more or less
effective.
* Corresponding author’s contact information:
Morgan Hall 367, Harvard Business School, Boston MA 02163
Phone: 617.495.0625
e-mail: tsandino@hbs.edu
Keywords: Preferential incentive plans; exemplary employees; incomplete contracts; company values; tournaments;
subjectivity; equity theory; perceived unfairness; social identity; retail chains.
Acknowledgements: We thank Ryan Buell, Nicole DeHoratius, Daniel Malter, Ken Merchant, Ananth Raman, Pian
Shu, Charles Wang, and seminar participants at the IMO Conference at Harvard Business School, and the
Consortium for Operational Excellence in Retailing for their helpful feedback. We thank Kyle Thomas for excellent
research assistance compiling the data, and Mayuresh Kumar and Ankita Rawat for their research assistance at the
research site. We also thank the mobile phone retail company where we conducted our study for access to data. All
errors remain our own.
1. Introduction
Many organizations rely on incentive plans to motivate high performance. Most incentive
contracts, however, are incomplete and reward only some of the relevant tasks that employees
should pursue to achieve company goals. Incomplete contracts often trigger counterproductive
behaviors that may hurt company objectives, as the employees may focus exclusively on the
tasks that are rewarded at the expense of other relevant tasks (Baker, Gibbons, and Murphy
[1994], Prendergast [1999], Gibbs, Merchant, Van der Stede and Vargus [2004, 2009]).
One way companies implement incentive contracts while discouraging counterproductive
behaviors associated with incomplete contracts is by introducing subjective assessments of
performance into the incentive package (Baker et al. [1994], Murphy and Oyer [2001], Gibbs et
al. [2004, 2009]; Rajan and Reichelstein [2006]). Incentives typically comprising a subjective
element include salary raises, discretionary bonuses, and promotions (Baker, Jensen and Murphy
[1988], Prendergast [1999], Campbell [2008], Grabner and Moers [2013]). The use of
managerial subjectivity enables circumstances that may have not been foreseen and/or are not
captured in an objective quantitative measure (e.g., gaming of an incentive plan, reduced quality,
adherence to core values) to be incorporated into performance assessments. However, subjective
assessments can give rise to supervisor biases, lead workers or teams to engage in unproductive
activities in order to influence these assessments, and/or demotivate workers or teams who
attribute an adverse subjective judgment to inequities in the firm (Prendergast [1999], Murphy
and Oyer [2001], Ittner, Larcker, Meyer [2003], Gibbs et al. [2004], Bol 2011, Du, Tang and
Young [2012]). Additionally, not all firms can offer opportunities for promotions to their best
employees due to lack of growth, limitations related to the employees’ geographic location, and
1
different skill requirements in higher level positions (Baker et al. [1988], Lazear [2004], Grabner
and Moers [2013]).
This study considers the use of a type of incentive plan, specifically a “preferential incentive
plan,” that incorporates managerial discretion to a limited degree, in order to reap the benefits of
both incentive contracts based on objective measures of performance and incentives based on
subjective assessments, while mitigating some of the difficulties associated with each of them.
Specifically, we define a “preferential incentive plan” as an incentive contract with a
tournament component and an incentive pay component. In the tournament component: (1)
objective performance metrics are applied to identify a limited number of employees or teams
that are considered to have the greatest performance (herein, “top-performing” employees or
teams) within a larger group; (2) managers then determine whether each top-performing
employee or team deserves to receive incentives under the preferential plan, affording managers
limited discretion to verify the top performing employees’/teams’ alignment with the spirit of the
plan and the core values of the firm (top employees or teams deemed deserving of receiving
incentives are herein referred to as a “selected” employees or teams). In the incentive pay
component, the selected employees or teams then become eligible for incentives under the
preferential plan (herein, “bonuses”) that are awarded based on objective performance metrics.
Preferential incentives may take different forms: for example, they might include incentive
plans created specifically for selected, exemplary employees (e.g., in the form of bonuses or
employee stock options), or opportunities for increased earnings from incentive plans already
available to all employees (e.g., increases in bonus rates or commissions). A key distinction
between preferential incentive plans and standard non-linear incentive plans including a
threshold is that preferential incentive plans incorporate manager discretion to periodically
2
determine whether top-performing employees deserve to become eligible or not for incentives,
while non-linear plans do not incorporate any discretion to determine which employees are
eligible or not, automatically leading to incentive payouts any time the threshold is achieved.
While the use of preferential incentives is not as widespread as the use of incentive plans
including all employees (with or without a threshold), we have learned of multiple organizations
that identify and reward “deserving” top-employees with additional incentives (more commonly
through employee stock options or stock ownership plans).
The purpose of this study is two-fold. First, we examine the net effects of a preferential
incentive plan on productive and counterproductive outcomes at a retail chain. Second, we
examine whether implementing preferential incentives is associated with more favorable
outcomes for employees less likely to be aligned with company goals and values (e.g., because
they are located in remote locations in an organization where core values are transmitted through
face-to-face interactions) and less favorable outcomes for employees more likely to perceive the
plan as inequitable. Testing whether motivating employees to perform according to company
goals and values can offset potential inequity costs is relevant to practitioners considering
whether to use these plans to drive high levels of desirable performance. Moreover, it is
important to understand how different factors (such as geographic location, the provision of
resources, and social identity) can affect the tradeoffs that may determine the overall effects of
preferential incentive plans on outcomes.
Our analyses use data from a mobile phone retailer (hereafter MPR) operating in Delhi,
India. This retail chain differentiated itself in the market by providing trustworthy, high quality
service. MPR provided us with a suitable setting to examine our research question since it
introduced a preferential incentive plan among its company-owned stores in February of 2013, in
3
which stores competed against each other to become selected and earn the right to receive
incentives on a daily basis. The company introduced this preferential incentive plan to motivate
store teams in high performing stores that were selling in an “honest and consistent way” to exert
effort towards the achievement of daily targets.
We use two years of data (from February 2012 to January 2014) from 35 stores and 731
store-months (the preferential incentive plan was first introduced in February 2013), to examine
the effects of the plan on productive outcomes (i.e., the stores’ sales and gross profits) and
counterproductive outcomes (i.e., “percentage of bad audits,” defined as the percentage of times
when an audit uncovered any items missing from the store or cash shortages, unauthorized
absenteeism, and employee turnover). We also use data (from February 2012 to January 2014)
from 60 franchise stores (1440 franchise store-months) operating in the same areas as companyowned stores, to control for market conditions. We complemented our empirical analysis with
interviews with the managing director as well as 11 store managers (5 from selected stores and 6
from not-selected stores) to validate the results of our analyses.
A difference-in-differences analysis comparing the performance of company-owned stores
with that of the franchise stores reveals a positive net effect of the preferential incentive plan.
The introduction of the preferential incentive plan resulted in a boost in sales for companyowned stores (regardless of whether the stores were “selected” or not during the sample period)
relative to franchise stores. In line with this finding, our follow up interviews suggested a general
pattern whereby store managers from both selected and not-selected stores were highly
motivated to increase their teams’ efforts following the implementation of the plan.
Focusing on the subsample of company-owned stores (i.e., those stores where the preferential
incentive plan was implemented), we explore the tradeoffs implicit in the plan. We begin by
4
examining whether the preferential incentive plan was more beneficial when the distance
between the store and the head office was greater, since, according to the company’s managing
director, employees in more remote stores were generally less engaged and less exposed to the
company’s values. Van den Steen [2010a] provides theoretical support for this argument,
presenting an analytical model that indicates that there will be greater homogeneity in shared
values amongst the employees that make the decisions that have the greatest impact on the firm’s
success; in our setting, these employees were generally located at headquarters and more likely
to interact with store managers of nearby stores. We find that, following the introduction of the
preferential incentive plan, a negative effect of distance from headquarters on both sales and
gross profit was offset. Additionally, a higher incidence of bad audits and store manager turnover
in stores further from headquarters was more than fully offset after the introduction of the
preferential incentive plan.
The effect that the preferential incentive plan would have on not-selected stores was less
clear than the effect it would have on selected stores, since store teams in not-selected stores
were more likely to perceive the plan as “out of reach” and/or “unfair.” Among not-selected
stores, we expected the preferential incentive plan to be least effective in stores receiving fewer
resources to do their work and “compete” for the right to be selected to become bonus-eligible
and in stores where teams had the potential to feel discriminated against since the store
managers’ social identity (defined by religion and/or state of origin; two of the most salient
demographic variables in India) 1 was underrepresented in the group of store managers of
selected stores. Consistent with expectations, we found some evidence that the implementation
of the preferential incentive plan in a not-selected store receiving fewer resources to operate
1
Gender was not highlighted as a relevant characteristic in this context, since all the store managers (except for one
who was present in the store for less than 3 months) were men.
5
(inventory availability, number of store team members, supervisor ranking, and attractiveness of
the store location) was associated with a decrease in sales and gross profits, and an increase in
absenteeism and store manager turnover relative to not-selected stores with greater resources. We
also found some evidence that the effects of the preferential incentive plan were less favorable
(i.e., led to lower sales and gross profits, as well as an increase in bad audits) in not-selected
stores where either the religion or the state of origin of the store manager was underrepresented
among the group of store managers of selected stores.
Our study contributes to the literature in two ways. First, it is well known that incomplete
contracts can lead employees to behave myopically, focusing exclusively on the aspects of the
job rewarded by their contracts at the expense of other duties that are relevant to the achievement
of company values and goals (Baker et al. [1988], Prendergast [1999], Gibbs et al. [2004]). Prior
studies suggest that, in such settings, managers can motivate employees to pursue broader
organizational goals by favoring employees that managers subjectively assess as being better
aligned with company goals, and that are therefore trusted to do “the right thing” (Van Den Steen
[2010a, 2010b], Abernethy, Dekker and Schulz [2015]). Our study tests this idea through a
mechanism, preferential incentives, that enables managers to favor high performing employees
achieving their goals “the right way,” and explores the extent to which this mechanism can
mitigate dysfunctional behaviors unintendedly caused by incomplete contracts. Preferential
incentive plans incorporate elements of implicit incentives (such as those incentives that emerge
when a company evaluates all aspects of an individuals’ job to determine promotions) and
explicit incentive contracts, which can complement each other in incomplete contract settings
(Gibbons and Murphy [1992]).
6
While preferential incentive plans can contribute to aligning employees’ incentives to the
pursuit of organizational goals, they can also demotivate employees who may perceive the plans
as inequitable or unattainable. Our study contributes to the tournament, social identity, and
equity literatures by highlighting the role that equity concerns may play in the effectiveness of
preferential incentives. Overall, our study shows that the net effect of a preferential incentive
plan can be positive. Yet our results caution practitioners that preferential incentives may lead to
unintended behaviors in settings where inequity and discrimination concerns are more likely to
emerge. The plan might be most effectively implemented in firms where resources can be
distributed evenly and the skills of different social identity groups are comparable.
Our study proceeds as follows. Section 2 presents our hypothesis development. Section 3
describes the company and the preferential incentive plan analyzed in this study. Section 4
presents our research design and main empirical analyses. Our first set of analyses compare the
effect of the preferential incentive plan on sales in company-owned stores relative to our control
group of franchise stores where the plan was not introduced. Our second set of analyses focus
exclusively on company-owned stores, examining different store circumstances affecting the
impact of the preferential incentive plan on productive and counterproductive outcomes. Section
5 concludes.
2. Hypotheses Development
2.1
Effect of Preferential Incentives on Outcomes
The initial process by which potential “selected employees/teams” in a preferential incentive
plan are identified (we use the word “potential” since the final group of “selected
employees/teams” is subject to managerial discretion) resembles the process used to identify
7
“winners” in tournaments (Lazear and Rosen [1981], Casas-Arce and Martínez-Jerez [2009]).
And in the same way in which tournaments should provide incentives to perform to “winners”
and “losers,” preferential incentive plans should motivate both selected and not-selected
employees/teams to work hard and in accordance with the spirit of the plan.
Selected
employees/teams should be motivated not only by the additional incentive payouts they can
obtain, but also by the recognition associated with being “selected” for the additional incentive.
Not-selected employees/teams should be motivated to work hard in a way consistent with
company objectives and values in order to be recognized and become eligible for increased
incentives.
Although preferential incentives resemble tournaments, they are different in two important
respects. First, the rewards associated with these plans are not fixed but, instead, consist of
additional incentives that are “turned on” once a participant is “selected.” This feature makes
preferential incentives potentially more effective at driving improved performance than
tournament incentive plans beyond the point of eligibility, since preferential incentive plans
provide selected employees with incentives to continue improving their performance beyond the
point of selection. 2 Prior tournament literature shows that employees with a strong lead become
complacent overtime in situations where the rewards associated with winning the tournament are
fixed (Casas-Arce and Martínez-Jerez [2009]).
Second, unlike many of the tournament incentive plans empirically studied in the literature
(Ehrenberg and Bognanno [1990], Becker and Huselid [1992], Casas-Arce and Martinez-Jerez
[2009]), the selection criteria used in preferential incentive plans is not entirely objective or
2
Providing “incentives” as a reward could also lead to these plans being less effective than traditional tournaments
with fixed rewards, since under these plans employees need to meet two hurdles to receive a payout.
8
determined by formula. Preferential incentive plans not only require employees to outperform
their peers on objective dimensions, but also require them to follow the spirit of the plan and act
in accordance with company values. This feature makes preferential incentive plans particularly
desirable in circumstances where a subset of important employee actions or job outcomes cannot
be easily observed or measured, and/or where adherence to company values should be
strengthened (for instance, to ensure consistency across a retail chain). At the same time, the
managerial discretion used in preferential incentive plans could lead not-selected employees to
question the legitimacy of the selection process. According to equity theory, individuals will
become demotivated if they perceive that the outcomes (in this case the rewards) they receive for
their job inputs (in this case their ability and effort) are not comparable to the outcomes their
peers receive for their inputs. These inequity perceptions can lead workers to shirk at their jobs
and engage in counterproductive behaviors (e.g., Adams [1963], Capelli and Chauvin [1991],
Fehr and Gachter [2000]).
Our discussion above suggests that preferential incentive plans are likely to provide strong
incentives to employees, but could potentially demotivate some or all “not-selected” employees.
Hence the overall effect of a preferential incentive plan on performance and counterproductive
outcomes is an empirical question. However, since a company implementing a preferential
incentive plan would do so only if it expected positive results, we state our first hypothesis as
follows:
Hypothesis 1 (H1): The introduction of a preferential incentive plan in an organization will
have a positive effect on employee/business unit outcomes.
9
2.2
Factors Affecting the Effectiveness of Preferential Incentives
The potential benefits and costs associated with the introduction of a preferential incentive
plan are likely to vary based on employee/business unit characteristics.
In particular, the
introduction of a preferential incentive plan might be most beneficial where there is an
opportunity to improve the alignment of employees with company goals. These situations may
arise when the employees’ circumstances are such that their personal incentives are less aligned
with those of the company. In the context of our study, interviews revealed that employees
tended to be less aligned with company objectives if they operated in remote locations than if
they operated in close proximity to the head office. This makes sense since “more important
employees” (i.e., employees that make the decisions that have the greatest impact on the firm’s
success) will have greater homogeneity in their beliefs (Van den Steen, [2010a]) and thus are best
placed to exemplify the organization’s objectives and values. Since these employees are located
in the head office there is less opportunity for face-to-face interactions between these employees
and employees in remote locations. We predict more favorable effects from preferential
incentives in circumstances requiring greater alignment. Formally,
Hypothesis 2 (H2): The introduction of a preferential incentive plan will be associated
with greater improvements in employee/business unit outcomes in situations where the
employees/teams are located further from the head office.
Despite its advantages, the introduction of a preferential incentive plan could be
inconsequential or even cause demotivation if employees view the plan’s eligibility targets as
unattainable or inequitable. Arguments from tournament theory suggest that biased contests will
result in lower effort (see for example Prendergast [1999]). Furthermore, equity theory suggests
10
that employees who believe they are underpaid relative to comparable peers (i.e., peers who are
similarly skilled and who exert similar effort to them), will not only decrease effort but might
also engage in counterproductive behaviors (Adams [1965]). Prior research has documented a
range of counterproductive behaviors resulting from inequity perceptions such as absenteeism
and theft (Adams [1965], Greenberg [1990], Capelli and Chauvin [1991], Fehr and Gachter
[2000], Chen and Sandino [2012]). According to equity theory, the introduction of a preferential
incentive plan by itself would not lead to inequity perceptions if employees believed the
compensation they received for the effort and skill they invested in their job was commensurate
with that received by their peers. However, differences in pay resulting from a preferential
incentive plan (particularly those resulting from not being selected for the incentive) could cause
negative feelings among employees if they attribute some of those differences to being placed at
a disadvantage relative to their peers (Larkin, Pierce and Gino [2012]). In fact, Matsumura and
Shin [2006] show that relative incentive plans are less effective when employees consider their
local circumstances disadvantageous. Prior research also suggests that an individual’s response to
a tournament-like incentive plan depends on how fairly s/he perceives her/his social identity
group is treated (Schotter and Weigelt [1992]). 3 Individuals belonging to what they perceive to
be disadvantaged groups (e.g. minority groups, potentially subject to discrimination) are more
likely to “drop out” and supply no effort in a tournament unless equal opportunity policies are
put in place (Schotter and Weigelt [1992]).
We conjecture that not-selected employees/business units will be more likely to perceive the
preferential incentive plan as inequitable if (a) they receive fewer resources from the company to
do their jobs, possibly inhibiting their capacity to effectively “compete” for the right to
3
Social identity is defined as “that part of an 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], p.2).
11
participate in the preferential incentive plan, and/or if (b) they perceive there is some bias against
them, which may arise from their social identities being disproportionately underrepresented
among the selected employees/business units. Accordingly, our third set of hypotheses states:
Hypothesis 3a (H3a): Among the subset of employees/business units not-selected for a
preferential incentive plan, the introduction of the plan will be associated with a
deterioration of (an improvement in) outcomes, the fewer (the greater) the resources received
by the employees/business units to do their jobs.
Hypothesis 3b (H3b): Among the subset of employees/business units not-selected for a
preferential incentive plan, the introduction of the plan will be associated with a
deterioration of outcomes in situations where members of the employees’ (or business unit
managers’) social identity group are disproportionately underrepresented among the selected
employees (or managers of the selected business units).
3. Research Setting
We test our hypotheses using two years of monthly data from a mobile phone retail chain
(MPR) operating in Delhi, India that introduced a preferential incentive plan in its 35 companyowned stores. When the company implemented this preferential incentive plan in February of
2013, it already provided traditional sales commissions to its store managers and cashiers on a
monthly basis, primarily based on the number of items sold. While the traditional plan had
yielded acceptable results, the managing director of the retail chain decided to introduce
additional incentives, in the form of a preferential incentive plan (comprising a daily bonus
scheme for “selected” stores) to motivate store teams (all team members in selected stores would
12
receive an incentive payout) in high performing stores that were selling in an “honest and
consistent way” to exert effort towards the achievement of daily targets. Our sample comprises
731 store-months of data from the 35 company-owned stores where the preferential plan was
introduced. We complemented our quantitative analysis with insights from interviews to the
managing director of the company as well as to 11 managers of company-owned stores (5 from
selected and 6 from not-selected stores), where we asked open ended questions about their
general reactions to being selected (or not-selected) for the purposes of the preferential incentive
plan. 4
Most of our analyses focus on analyzing differences in the effect that the preferential
incentive plan had across different company-owned stores. We also assess the overall sales effect
of the preferential incentive plan in our company-owned stores (our “treatment sample”) vis-àvis a control sample comprised of 1,440 store-months of data from 60 franchised stores where
the preferential incentive plan was not introduced, operating in the same areas as the companyowned stores. Figure 1 provides a timeline and description of the sample. We dropped
observations corresponding to the months of November 2012 and 2013 (when holiday incentives
substituted other forms of incentives, including the preferential incentives in company-owned
stores) and observations corresponding to the months of February 2013 – June 2013 for 21 stores
where the introduction of the preferential incentive plan was delayed while the company was
running a pilot study of the plan. 5 Our final sample comprised data from 583 company-owned
4
The store managers were interviewed in September of 2015 (more than a year after the sample period analyzed
ended). At that time, the preferential incentive plan had recently been discontinued. Prior to the discontinuation, the
plan had been slightly modified: the incentives provided to “selected” stores were applied only to weekends and, we
learned from the interviews that, in at least one instance, the winning stores were recognized in a social event.
Generally speaking, we believe that the changes to the plan relative to the plan during our sample period did not
affect the responses that the store managers provided. When we thought the answers could have been affected by
changes (e.g., due to the social event) we asked for clarification. During our presentation of the results of this study
to the company, the managing director expressed interest in reinstating the preferential incentive plan.
5
All of our results are robust to excluding the matching months for those stores in 2012.
13
store months and 1,320 franchised store months. The company-owned stores sold a full range of
mobile phone products, including handsets; accessories; prepaid and post-paid minutes; and
connections (i.e. activation of mobile phone numbers); while most franchised stores focused on
selling connections, prepaid and post-paid minutes. Franchised stores were mom-and-pop stores
that partnered with the company to take advantage of the company’s scale.
In return for
franchise fees, the company completed the activation process for new connections sold by the
franchisees; collected payments from, and negotiated with, suppliers on behalf of the franchisees;
provided franchisee owners with brand recognition; and kept franchisees informed about the
latest news and promotions offered in the mobile market. Although the products sold by the
franchised stores were typically more limited than those sold by company-owned stores, 6 the
franchised stores were a good candidate for a control sample given that, as the managing director
confirmed, the franchisees’ sales reflected the same market demand, shocks and trends as the
company-owned stores. In other words, the data fitted the common trends assumption, which is
the key assumption to conducting difference-in-differences analyses (Angrist and Pischke
[2009], p. 230).
MPR had traditionally paid monthly bonuses to the store employees of its company-owned
stores, but decided to introduce a preferential incentive plan on top of the monthly bonuses in
February of 2013. This plan would be implemented to further reward and motivate its exemplary
store teams by enabling them to participate in a daily bonus scheme. Store teams typically
consisted of two store employees (a store manager and a cashier) plus one or more promoters
provided by the various brands whose products/services were sold in the store. While the
6
Several franchised stores sold mobile phones in addition to prepaid and postpaid minutes as well as other products
unrelated to the mobile phone industry. The data that we have access to includes only revenue data from
connections, prepaid and post-paid minutes.
14
traditional monthly bonuses were awarded only to the store manager and the cashier, the daily
bonus payments of the preferential plan would be awarded to all members of the store team.
Only the top tercile performing company-owned stores would be selected to participate in the
daily bonus scheme. 7 Selected stores needed to demonstrate superior sales performance relative
to other stores, deliver a consistently high level of sales (occasionally reaching at least 100,000
INR/day, roughly US$1,670/day) for at least two to three consecutive months, and have a good
track record of honest behavior and consistent execution on a daily basis. Decisions regarding
which stores were selected and which were not selected were made regularly (roughly, on a
monthly basis), based on the stores’ prior three months performance. The right to participate in
the daily bonus scheme could be taken away from a selected store at any given point in time if its
performance declined relative to that of other top-tercile performing stores or if the store
employees were caught cheating or engaging in counterproductive behaviors (e.g., selling only
on the few days when they would be most likely to receive the daily bonus).
The preferential incentive plan paid each person in a selected store about 150 INR (US$3)
every time the store reached the minimum daily sales target, plus 50 INR (US$0.83) for every
additional 50,000 INR the store team sold beyond the minimum daily sales target. Once a store
was selected to participate in the daily bonus scheme, the minimum daily sales target was
determined for the store based on local market demand. The payoffs from the daily bonus
scheme were significant for store employees whose fixed salaries were typically lower than 150
INR per day and whose total compensation (including commissions) was about 500 INR
(US$8.5) per day in a non-holiday month. Under the plan, store teams also earned recognition in
7
The managing director explained that they had decided to constrain the preferential plan to one third of the stores
because they wanted to recognize top performers; they had budget constraints; and they did not want to reward
stores that were not selling in a consistent way to avoid having stores gaming the system by recording sales from
multiple days on one day.
15
the form of a congratulatory email sent to everyone in the company each time a store earned an
award.
This was an attractive setting to test the effects of a preferential incentive plan, for various
reasons. First, we were able to obtain one year of monthly data, capturing key measures of the
productive outcomes the plan intended to promote (sales and gross profits) and the
counterproductive outcomes the plan intended to discourage (“bad audits,” absenteeism, and
turnover), before and after the introduction of the plan. Second, the stores’ performance and
details of the plan were clear to all store managers. In particular, the managing director of the
company was in constant communication with the store staff through email, announcements
appearing in the company’s extensive database system, and weekly meetings with store
managers. Furthermore, each of the store managers had continuous access to their store’s
performance as well as all other stores’ performance through the database system. 8 These
communication mechanisms, implemented before our sample period, enabled employees to learn
about the preferential incentive plan once it was introduced. After the incentive was put in place,
the managing director reminded the store managers of the selection criteria used to decide which
stores would be selected to participate in the daily bonus scheme and which would not, during
their recurring weekly meetings. Third, there existed enough variation among store
characteristics that could affect the stores’ reactions to the introduction of the preferential
incentive plan, allowing us to test our second and third hypotheses.
Before examining the reaction that selected and not-selected company-owned stores had to
the introduction of the preferential incentive plan, we examined whether the initial set of selected
stores in fact corresponded to the top-tercile performing stores in the company. As illustrated in
8
The weekly meetings were not mandatory. Store managers operating stores that were far from headquarters did
not frequent these meetings and instead learned about incentive schemes through the database system and via email.
16
Table 1, a comparison of the performance of selected and not-selected stores reveals that the
selected stores, with average monthly sales equal to 1,861,715 INR (~US$ 31,030) and monthly
gross profits of 100,800 INR (~US$ 1,680) over the year prior to the implementation of the plan,
significantly outperformed the not-selected stores, with average monthly sales equal to 441,671
INR (~US$ 7,360) and monthly gross profits of 22,749 INR (~US$ 380) in the year prior to the
introduction of the preferential incentive plan. Furthermore, a ranking of the stores based on their
average monthly sales over the year prior to the implementation of the preferential incentive plan
in February 2013 reveals that all but one of the stores selected within the first six months of the
plan being introduced were ranked top-tercile in terms of sales, and all but two were ranked toptercile in terms of gross profits. One store that was not ranked among the top third in either sales
or profits before February 2013 became selected in July 2013 after a store manager from a highly
successful selected store was moved to that unselected store and tripled its sales within one
month (placing the store among the top tercile performers). The second store that was not ranked
among the top-tercile in terms of gross profit was among the top tercile performers in terms of
sales and was just below the top tercile performers in terms of gross profits. According to the
managing director, differences in sales and gross profit performance were predominantly driven
by the store managers’ execution. She assured us, nevertheless, that she considered the store’s
potential (based on peak sales achieved in the past) in deciding which stores fell and which did
not fall among the top-tercile performers. 9 These results suggest that selection was clearly based
on performance.
Our tests comparing other store characteristics of selected and not-selected stores prior to the
implementation of the plan (see Table 1) reveal that annual store manager turnover was three
9
Most top-tercile performing stores were run by store managers that had proven themselves in the past and that had
either transformed a previously unproductive store into a top-performing store or had been moved to a high traffic
store after proving themselves in a smaller market.
17
times larger in not-selected stores than in selected stores, but do not reveal any significant
differences in other counterproductive outcomes (percentage of bad audits and absenteeism)
between these two sets of stores during the pre-period. 10 While this may call into question the
importance that management placed on additional criteria related to core values of the company
(namely consistency of work and integrity), the managing director explained that the relevance
of these criteria was emphasized after the preferential incentive plan was put in place. For
example, the director recalled instances where she stripped stores of their right to receive daily
bonuses due to behavioral issues and lack of trust.
Further comparisons between selected and not-selected stores prior to the implementation of
the plan reveal that selected stores were generally located closer to the head office, had about 3
more store-team members, were more likely to be located in premium areas, kept less days in
inventory, and were more likely to be assigned higher ranked supervisors. Furthermore, a greater
proportion of the store managers in the not-selected stores were of the Hindu religion (the most
common religion among store managers) and/or called the National Capital Territory of Delhi
their state of origin (the most common state of origin among store managers) compared to store
managers in selected stores. 11
10
It is worth noting that the number of audit observations we could employ in this period was limited (with records
for approximately two audits/store, and in a few cases 1 audit/store, during the pre-period).
11
Other store managers in the organization who were not from the Hindu religion or the National Capital Territory
of Delhi state, were either from the Christian or Muslim religions, or reported to be from the states of Bihar, Uttar
Pradesh, Rajasthan, or Uttarakhand. This information was gathered and provided to us by the Head of Human
Resources of the company.
18
4. Empirical Analyses and Results
4.1 Comparison of Stores Where the Preferential Incentive Plan was Implemented vs.
Stores Where It Was Not
We use data from both the company-owned stores (“treatment” sample where the preferential
incentive plan was implemented) and franchised stores (“control” sample where the plan was not
implemented) to examine the effect of the introduction of the preferential incentive plan on sales
performance. As Figure 2 shows, company-owned stores experienced a steeper increase in sales
than franchised stores after the introduction of the preferential incentive plan. A noteworthy
decline in sales among company-owned stores shown in Figure 2 in the month of December
2013 was explained by the managing director as a special circumstance when one of the
company’s major suppliers of handsets temporarily withheld inventory from the company.
We test the overall effect of the introduction of the preferential incentive plan on sales
performance based on the following model:
Ln (Sales)it = β0 + β1 Company-Owned Storei + β2 Post PIPt * Company-Owned Storei + β3
Premium Locationi + βn (Month-Year Fixed Effects)t + εit
(1)
where Ln (Sales) is the natural logarithm of the store sales for store i at time t (measured in
month-years); Company-Owned Store is a dummy variable, indicating that the store belonged to
the company-owned chain where the preferential incentive plan was implemented; Post PIP
identifies the months after the preferential incentive was first introduced; and Premium Location
identifies stores located in large markets, where high premium products were demanded.
The results of our difference-in-differences analysis in equation (1) are presented in the first
column of Table 2. Our results show that the introduction of the preferential incentive plan was
associated with a 75% increase in store sales in company-owned stores relative to franchised
19
stores. 12 Hence we find support consistent with H1, which predicted that the introduction of the
preferential incentive plan would have a positive effect on company outcomes.
To further explore whether the association between the preferential incentive plan and the
increase in sales in company-owned stores was driven by selected stores rather than not-selected
stores, we rerun our analyses in equation (1), splitting our company-owned store variable into a
dummy identifying company-owned stores that were selected to participate in the daily bonus
scheme sometime during the sample period (Company-Owned Selected Store), and a dummy
identifying company-owned stores that were never selected to participate in the scheme
(Company-Owned Not-Selected Store) and rerun our analysis. Table 2, Column 2 suggests that
the sales increase associated with the introduction of the preferential incentive plan was positive
for both selected and not-selected company-owned stores. Furthermore, our test at the bottom of
Table 2 also shows that the difference between the incremental sales associated with the
introduction of the preferential incentive plan in selected and not-selected stores, relative to the
control group, was positive but insignificant.
Consistent with these findings, follow up interviews revealed that both selected and notselected store teams felt motivated not only by the monetary rewards but also by the managing
director’s recognition and the sense of achievement that the store team could draw from being
selected to receive the bonus. One of the store managers of a selected store explained: “When we
got to know about the plan, the first thing we did was to hold a question/answer session to find
out ways to achieve the target. There is always excitement when we realize that we can achieve a
goal.” One of the store managers of a not-selected store indicated: “We took it positively. We
were now thinking about strategy and were determined to not lose any customer. We understood
our faults and tried to overcome them.” Although some of the store managers from not-selected
12
This is calculated based on the coefficient in Column 1 of Table 2 as (e0.562 – 1).
20
stores expressed disappointment for not being selected, rather than “giving up” they indicated a
desire to double their efforts. Some reached out to store managers in selected stores to learn from
them. The managing director indicated, “I think the daily incentive has a very big impact given
that everything else was where it was. We did not make any changes [during the sample
period]… the only intervention was the introduction of the daily incentives.” 13
Though our analysis in Table 2, Columns 1 and 2, suggests that the effect of introducing the
preferential incentive plan in company-owned stores was positive regardless of whether the
stores were selected or not to participate in the daily bonus scheme, our results may be driven by
store specific characteristics or store trends omitted from the analyses.
To evaluate the
robustness of our results to these potentially correlated omitted variables, we replicate our
analyses controlling for store fixed-effects and store-time trends. Untabulated results show that
our main findings in Table 2 are robust to this alternative specification. Specifically, under this
specification, the coefficient associated with the introduction of the plan is associated with an
increase in sales that is 62% higher (Coef=0.483, t-stat=3.89) in company owned-stores than in
franchised stores. Also, the introduction of the plan was associated with significant increases in
the store sales of selected and not-selected company-owned stores, relative to franchised stores.
In summary, the preferential incentive plan had a positive effect on the sales of both selected and
not-selected company-owned stores (relative to the sales of franchised stores).
4.2 Factors Potentially Affecting the Association between the Preferential Incentive
Plan and Store Outcomes
Hypotheses 2 and 3 highlight factors that may affect the relative effectiveness of preferential
incentive plans across different business units exposed to the plan (in our setting, this is the
13
Our understanding from the managing director’s comment was that the company did not make any “significant”
changes to the incentive plans during our sample period.
21
sample of company-owned stores). Table 3 presents a correlation table that includes all the
factors under consideration. The correlation table suggests that the company experienced an
overall increase in sales and gross profit as well as a decrease in turnover following the
implementation of the preferential incentive plan, and did not experience a significant increase in
the percentage of bad audits or absenteeism. The next two sub-sections examine the effect of the
different factors on the association between the introduction of the preferential incentive plan
and store outcomes.
4.2.1 Factors Capturing the Need to Align Employees with Company Goals
Hypothesis 2 highlights circumstances when the preferential incentive plan could yield
greater benefits at the individual store level. To test this hypothesis, we examine whether the
introduction of the preferential incentive plan provided greater benefits for stores located further
from the head office (i.e., where, according to the managing director of the company, the
potential to improve alignment with company goals was greater). Focusing on the subsample of
company-owned stores, we run the following model:
Outcomeit = β0 + β1 Distance to Head Officei + β2 Post PIPt × Distance to Head Officei
+ β3 Selected Storei + β4 Eligible Month for Selected Storeit
+ βm (Control Variables)it + βn (Month-Year Fixed Effects)t + εit
(2)
where, Outcomeit is measured either as a productive outcome (the natural logarithm of
monthly store sales, Ln(Sales)it, or the natural logarithm of the estimated monthly store gross
profit 14, Ln(Gross Profit)it), or a counterproductive outcome (percentage of bad audits, % Bad
14
During our sample period, MPR received additional commissions from Nokia and Samsung (called back-end sales
commissions) for achieving pre-specified sales targets. For practical purposes, the company estimated the stores’
gross profits by adding to the upfront gross profit of each item sold, back-end sales commissions of 6% for Nokia
handset sales and 9% for Samsung handset sales.
22
Audits 15, number of times the store manager had an unauthorized absence during the month,
Absenteeismit, or a dummy capturing whether the store manager left the company within three
months, Turnoverit).
Our main variable of interest, Distance to Head Office, was measured as the number of miles
between the store and the company headquarters. According to the director, employees in
remote locations were less engaged and less aware of the company’s core values due to limited
face-to-face interactions with the executives of the company. Consistent with this, Table 3
shows a negative correlation between the Distance to Head Office and both Sales and Gross
Profit. In our multivariate analyses, we expected to find lower monthly Sales and Gross Profits,
higher percentages of Bad Audits, and greater Absenteeism and Pr(Turnover), in stores located
further from the head office. We also expected the introduction of the preferential incentive plan
to mitigate these unfavorable effects of distance on outcomes.
The model controls for various other factors defined in the Table 4 that could have affected
changes in outcomes over time including eligibility (we include a store dummy, Selected Storei,
identifying stores that became selected for the preferential incentive plan anytime during the
period and a second indicator, Eligible Month for Selected Storeit, indicating the specific months
when a store was selected to participate in the preferential incentive plan- we expected both of
these indicators to be positively associated with productive outcomes and negatively associated
with counterproductive outcomes), availability of resources (including the size of the store team,
the number of days of inventory that the store had available, the store supervisor’s rank, the look
of the store, and whether the store was located in a premium area or not); the store manager’s
15
We did not have access to the percentage of bad audit data for the entire period. Although the company
systematically audited each store about once every two months throughout the sample period, it did not keep
systematic records of its audits at the beginning of our sample period. This limited the number of observations we
could employ (with records for approximately 2 audits/store, and in a few cases 1 audit/store, during the pre-period).
23
ability (measured by the presence of a manager at the store and the store manager’s tenure 16);
other store characteristics including the store’s age, size and distance from the head office, and
the store’s sales growth prior to the introduction of the preferential incentive plan; the number of
days when the store was opened during the month, and time fixed effects (in month-years).
Columns 1 and 2 of Table 4 show that the effect of “Distance to Head Office” on sales and
gross profit was significantly negative prior to the introduction of the preferential incentive plan.
A store’s sales and gross profits declined 3% with every mile that the store was located further
away from the head office. 17 These negative effects, however, were fully offset once the
preferential incentive plan was in place. Our results also suggest that the stores’ distance to the
head office was associated with a higher incidence of bad audits (the percentage of bad audits
increased an average of 2.6% with every additional mile between the store and the head office)
but this effect too was offset after the preferential incentive plan was in place. We also find that,
during the pre-period store manager turnover was higher (though at an insignificant level) in
more distant stores, but that turnover among store managers of distant stores was significantly
reduced after the preferential incentive plan was introduced.
These results, suggesting that the plan had greater impact in stores that were further from
headquarters, resonated with the managing director who indicated that store teams from remote
stores were highly motivated by, and gained visibility with this, plan. She mentioned that store
managers from distant stores let her know that they appreciated the award since it allowed them
to be recognized and interact with her. While we found evidence that the distance to head office
measure affected the impact that the preferential incentive plan had on sales, gross profit,
16
We used the cashier’s tenure if there was no store manager present in the store in the month.
Notice the distance from headquarters was not necessarily a proxy for store traffic, as all stores were located in
high traffic urban areas in Delhi.
17
24
incidence of bad audits, and store manager turnover, we found no evidence that it had a
significant effect on the effect of the incentive plan on patterns of absenteeism at the stores.
Among the control variables, we found that, as expected, stores that were selected had higher
financial performance than those not selected throughout the sample period, and reduced their
percentage of bad audits after the plan was implemented. With respect to store resources, we
found that stores with larger teams generated greater sales and profits, but also suffered from
greater manager absenteeism (possibly because store managers had greater opportunity to free
ride when they were part of a larger team); stores located in premium areas recorded not only
better financial performance but also lower absenteeism; and stores with more days in inventory
suffered from greater turnover (perhaps due to lower inventory turnover). With respect to the
store managers’ ability, we found that stores with longer tenured managers sold more and
experienced less manager turnover, but also had more bad audits. Similarly, stores with a store
manager (as opposed to no store manager) had more bad audits. It’s possible that experienced
store managers knew the system better and hence had greater opportunities to misbehave. Other
store characteristics were also important predictors of outcome variables. Specifically, store size
and sales days were associated with greater sales; store age was associated with greater sales,
greater gross profits, and lower turnover; and sales days was associated with greater absenteeism.
Interestingly, we found an association between pre-period sales growth and both absenteeism
and turnover.
Overall, our results suggest that the preferential incentive plan contributed to motivating
employees working in remote stores to sell more, to generate greater profits, to produce more
favorable audit results, and to stay with the company.
25
4.2.2 Factors Potentially Affecting Perceptions of Inequities Associated With the
Preferential Incentive Plan
Hypotheses 3a and 3b state that employees in not-selected stores who may consider the right
to participate in the daily bonus scheme to be either unattainable or inequitable, might become
demotivated by it. Hypothesis 3a posits that perceptions of unattainability or inequity may arise
in not-selected stores receiving fewer resources to “compete” than their peers. On the flip side,
not-selected stores receiving greater resources should have reacted relatively more favorably to
the preferential incentive plan since they may have perceived a higher likelihood of becoming
selected and may not have perceived that they were treated unfairly.
According to the managing director of the company, the main resources contributing to a
store’s performance are the store team size, the store’s location (whether or not it is located in a
“premium area”), the inventory available for sale at the store, and the ranking of the store’s
supervisor. 18
In support of the director’s claims, Table 3 shows that most of these factors are
positively correlated with productive outcomes (sales and gross profit performance). Consistent
with the notion that employees from stores receiving fewer resources might perceive they had
been treated unfairly, we find that some of these factors are negatively correlated with
counterproductive outcomes (specifically bad audits). Two exceptions to these patterns were
that “team size” was positively associated with (store manager) absenteeism, and “days of sales
in inventory” was negatively associated with financial performance. The association between
team size and store manager absenteeism may suggest that store managers had greater
opportunities to “free-ride” when their store teams were large. The negative association between
“days of sales in inventory” and financial performance may be explained by the fact that our
18
Notice that we don’t include the “New Store Look” variable in this list. This is because only one of the “notselected” stores had a “new look.”
26
“days of sales in inventory” proxy captured both the stock available but also the sales activity at
a store. Regardless of whether resources were positively or negatively associated with
performance, we expected stores having greater resources to have a more favorable response to
the preferential incentive plan.
We examine the extent to which these resources affected the impact of the preferential
incentive plan on not-selected stores, by testing the following model 19:
Outcomeit = β0 + β1 Team Sizeit + β2 Premium Locationi + β3 Days in Inventoryi
+ β4 Supervisor Rankingit + β5 Pre-Period Proximity to Targeti
+ β6 Post PIPt × Team Sizeit + β7 Post PIPt × Premium Locationi
+ β8 Post PIPt × Days in Inventoryi + β9 Post PIPt × Supervisor Rankingit
+ β10 Post PIPt × Pre-Period Proximity to Targeti
+ βm (Control Variables)it + βn (Month-Year Fixed Effects)t + εit
(3)
The definitions of all the variables in equation (3) are the same as those in equation (2).
Additionally, following the prior tournament literature which has found that contestants that are
far from winning lose motivation (Casas-Arce and Martínez-Jerez [2009]), we controlled for the
frequency with which the not-selected store reached 95% of the minimum sales threshold 20 to be
considered to participate in the daily bonus scheme before the introduction of the plan (PrePeriod Proximity to Target).
Table 5 shows some evidence generally consistent with H3a, which predicted that the
resources received by the stores would significantly affect the impact that the preferential
incentive plan would have on productive and counterproductive outcomes. Column 1 shows that
the preferential incentive plan was associated with greater sales in stores with larger team sizes.
19
Some of these resource variables were correlated with each other, though their correlations were not high enough
to justify the estimation of latent factors (via principal components or factor analysis). Variance inflation factor
analyses revealed no multicollinearity concerns for any of the regressions presented in the tables.
20
Our results are robust to controlling for the frequency with which not-selected stores reached 80% of the
minimum sales threshold.
27
Column 2 shows increasing gross profits following the introduction of the preferential incentive
plan in stores with greater inventory available (measured as the number of days in inventory).
The preferential incentive plan was associated with a 6.9% increase in monthly sales for every
additional team member at the store and a 2.4% increase in monthly gross profit for every
additional day of sales in inventory. Column 4 shows that the introduction of the preferential
incentive plan was associated with a one day reduction in a store manager’s unauthorized
monthly absenteeism for each additional 14 days of sales inventory held at the store. 21 Finally,
column 5 shows that the store managers’ likelihood of leaving the store decreased after the
introduction of the preferential incentive plan in stores located in premium locations, stores
overseen by higher ranked supervisors, and stores holding a greater number of days of sales in
inventory.
Our interviews with store managers revealed that managers from not-selected stores
attributed their not being selected to not having enough stock (yet we found those stores
generally had greater days of sales in inventory than selected stores 22) or being in areas with less
customer walk-ins. The interviews also suggested that store managers from not-selected stores
were less active at reaching out to their supervisors than their counterparts in selected stores. A
store manager of a not-selected store indicated that although his store team reacted positively to
the plan, the members in the store were “complaining about stock issues and saying that
customer walk-in was not much.” One of the store managers of a selected store indicated:
This scheme is given to maximum performing stores and it depends upon them to
qualify for it. Now the person who is positive will take it positively and the one
who is negative will only talk in a negative way. Those with positive attitude only
21
We estimate this by dividing 1 by the coefficient 0.071.
It may be that those stores were concerned about the absolute value of stock they were provided with, rather than
days of sales in inventory.
22
28
care about sales and customers. They have to sell the products they have been
given to sell. The negative attitude people just keep complaining that they are not
getting stock and they have pricing issues. But still the majority of them remain
positive.
Although all the results described above support Hypothesis 3a, we found two results
inconsistent with this hypothesis: (a) we found that store managers of stores with larger team
sizes were more likely to leave (rather than stay with) the store following the introduction of the
preferential incentive plan, and (b) we did not find evidence that resources affected the impact
that the preferential incentive plan had on bad audits.
Our next hypothesis (3b) suggested that perceptions of inequity (discrimination) may have
emerged if the store managers of not-selected stores noticed that their social identity groups (e.g.
groups affiliated to their same religion or state of origin) were underrepresented among the store
managers of selected stores. This could easily have happened since the correlations between the
company’s main social groups and performance were significant (see Table 3), suggesting some
groups were more likely to be selected than others. Hypothesis 3b examines whether instances
where underrepresentation existed affected the impact of the preferential incentive plan on store
outcomes. We test this idea by regressing:
Outcomeit = β0 + β1 Store Manager’s Religion Underrepresented among Selected Stores At Any Timei
+ β2 Store Manager’s State Underrepresented Among Selected Stores At Any Timei
+ β3 Pre-Period Proximity to Targeti
+ β4 Store Manager’s Religion Underrepresented Among Selected Stores this Monthit
+ β5 Store Manager’s State Underrepresented Among Selected Stores this Monthit
+ β6 Post PIPt × Pre-Period Proximity to Targeti
+ βm (Control Variables)it + βn (Month-Year Fixed Effects)t + εit
(4)
29
The definitions of all the variables in equation (4) are the same as those in equation (3), but
we add indicator variables to identify stores where the store managers’ religion and/or state of
origin were underrepresented among store managers of selected stores. These indicator variables
are defined both at a store level to identify stores experiencing the underrepresentation of their
store managers’ social identity group anytime during the post period and at the store-month level
indicating the precise months when this underrepresentation emerged (detailed definitions of the
indicator variables are provided in Table 6). 23
Table 6 provides some support for H3b. Column 1 shows an incrementally negative effect of
the preferential incentive plan on sales, whenever the not-selected store manager’s state of origin
was underrepresented among selected stores. Columns 2 and 3 suggest the preferential incentive
plan is associated with a decrease in gross profit and an increase in bad audits in stores where the
store manager’s religion was underrepresented among the selected stores. Although the
managing director had not been aware that the preferential incentive plan could affect parties that
were underrepresented, she commented “I can definitely see that this [underrepresentation of
religion and state of origin] matters a lot in India. I’ve seen that religion matters. I’ve seen their
perception of equity matters… [for example] if I’m rewarding only Hindus and there is no
Muslim representation in the party, I do think it matters… it is very important but it’s unsaid.”
Notwithstanding the results above, we did not find evidence that the under-representation of any
of the store managers’ social groups affected the extent to which the preferential incentive plan
affected the store managers’ absenteeism or turnover.
In summary, our results provide some evidence that factors potentially affecting perceptions
of inequities, due to differences in the resources each store team had to perform their job and
23
Muslims were under-represented in only one month in the post period; with the exception of that month, Hindus
were under-represented in all months in the post period; Christians were never under-represented in the post period
(in fact, only one store manager was of the Christian religion).
30
differences in the social identity composition of not-selected and selected store managers,
affected the impact that the preferential incentive plan had on the stores’ outcomes.
5. Conclusions
This paper studies a class of incentive contracts, “preferential incentive plans,” consisting of
(a) a tournament component, where a manager assesses whether each of a limited number of topperforming employees or teams (identified through the use of objective performance metrics)
should be selected for additional incentives; and (b) an incentive component, where “selected”
employees/teams become eligible to receive bonuses awarded based on objective performance
metrics. These plans could arise in different settings where exemplary employees are given
opportunities to receive increased earnings from (a) their existing incentive plans (for example,
situations where the bonus rates of exemplary employees are increased, or natural settings like
restaurants or sales jobs where top performing employees receiving tips or commissions are
assigned to the most lucrative shifts or customers) or (b) incentive plans specifically designed to
reward exemplary employees (for example, stock options or, as in our MPR setting, bonuses
applying exclusively to exemplary employees).
These plans could be beneficial in incomplete contract settings, where a company may want
to motivate employees to pursue not only objectively measured goals but also relevant tasks not
explicitly included in their contracts. By incorporating a selection step (subject to managerial
discretion) to determine the employees’ participation in a bonus, the company could encourage
employees to perform in the spirit of the plan and the broader objectives of the company.
However, preferential incentive plans could lead to unintended consequences if they trigger
perceptions of inequities.
31
We examine the effects of the preferential incentive plan in MPR and find that the
introduction of the plan was associated with increases in sales performance relative to a control
group where the preferential incentive plan was not implemented. These increases in sales were
present both in selected and in not-selected stores participating in the preferential incentive plan.
Although our tests are conducted using data from a single company, our analyses reveal some
of the factors that can impact the effectiveness of a preferential incentive plan. Specifically, we
show that such plans can be most beneficial as the need to align employees to overall company
goals increases (in our setting, we find that store teams that were most disconnected from
company values due to their geographic distance from the head office benefitted the most from
the implementation of the plan). We also find that preferential incentive plans could be least
beneficial (or possibly even harmful) in situations where teams could perceive the plan to be
unattainable or unfair. We show that, in MPR, not-selected stores receiving fewer resources to do
their job, or led by store managers whose social identity was underrepresented among managers
of selected stores, decreased their sales and gross profit performance and increased their bad
audits following the introduction of the preferential incentive plan. The consideration of these
factors may allow the reader to assess whether the payoffs from using preferential incentive
plans would be more or less beneficial in other settings beyond MPR.
These findings suggest that preferential incentive plans can motivate employees to exert
effort not only on objectively measured tasks incorporated in their contracts, but also to attend to
relevant tasks not explicitly included in their incentive contracts which could be essential to a
company’s objectives (e.g., trustworthiness, consistency). This finding contributes to prior
research studying the use of promotions or tournaments, and subjective assessments, as
complements to incomplete incentive contracts (e.g., Gibbs et al. [2004], Campbell [2008]).
32
Our insights also contribute to the equity theory and social identity literatures by highlighting
the role that equity concerns may play in the effectiveness of incentive contracts that incorporate
subjective assessments and tournament-like features. Our study provides a plausible explanation
for the avoidance of subjective incentives and tournament plans in firms where the inputs
provided to different workers cannot be easily distributed or where differences in the abilities of
social identity groups represented in an organization may trigger perceptions of inequities in
preferential incentive plans.
The results of this study should be interpreted with caution. The fact that our study was
conducted in a single company suggests that there may have been some critical factors associated
with the firm that enabled the preferential incentive plan to be effective. For example, the
selection decisions granting top stores the right to participate in the bonus plan were made by the
managing director of the firm, who is highly trusted and respected by the store managers. Gibbs
et al. [2004] highlight that subjective incentives cannot be effective in the absence of trust. The
fact that we studied this phenomenon in a single firm prevented us from studying in greater detail
how elements of preferential incentive plans, including the percentage of top performers
rewarded or the extent of managerial discretion applied in the selection process, affect the
effectiveness of such plans. These inquiries could provide promising opportunities for future
research.
33
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36
Figure 1:
Store-Months Included in the Sample From Company-Owned Stores Where the
Preferential Incentive Plan (PIP) Was Introduced in Either February or July of
2013, and Franchised Stores Where PIP Was Not Implemented
37
Figure 2:
Indexed Monthly Sales Trends For Company-Owned Stores (where preferential
incentive plan was introduced in February of 2013) and Franchised Stores (where
preferential incentive plan was not introduced)
Base (1): February 2012
This chart compares the average monthly sales of company-owned stores where the preferential incentive plan was
introduced with the average monthly sales of franchised stores where the plan was not introduced. The vertical line
separates the periods before and after the preferential incentive plan was introduced in company-owned stores. The
months of November 2012 and November 2013 are excluded as they represent the holiday season, where different
incentives apply. A noteworthy decline in sales in the month of December 2013 was the result of one of the company’s
major suppliers of handsets temporarily having a conflict with, and withholding inventory from, the company.
38
Table 1: Summary Statistics by Type of Store (“Selected” or “Not-Selected”) Before the
Introduction of the Preferential Incentive Plan
Pre-period mean
Pre-period mean
Not-selected
Difference in
Selected stores
stores
means
Sales
1,861.715
441,671
1,420,044***
100,800
22,749
78,051***
% Bad Audits
70%
73.9%
-3.9%
Absenteeism
1.11
0.65
0.46
Annual Store Manager Turnover
23%
75%
-52%***
Distance to Head Office
7.39
9.61
-2.22***
Team Size
9.27
5.81
3.46***
Premium Location (Dummy)
1.00
0.40
0.60***
Days in Inventory
20.16
24.34
-4.18***
Supervisor Ranking
3.34
2.11
1.23***
Store Managers with Hindu religion
0.39
0.66
-0.27***
Store Manager from the National
Capital Territory of Delhi State
0.55
0.35
0.20***
Gross Profit
*, **, *** denote significance at a 0.10, a 0.05 and a 0.01 level, respectively. Sales is monthly store sales in Indian
Rupees. Gross Profit is monthly store gross profit in Indian Rupees. Store gross profits were estimated by adding
the upfront gross profit of each item sold, plus back-end sales commissions of 6% for Nokia handset sales and 9%
for Samsung handset sales. % Bad Audits is the percentage of times the result of an audit was “bad” (i.e., revealed at
least one missing item or cash shortage without a reasonable explanation) at the store in the month. Annual Store
Manager Turnover is the number of store managers that left the store in the pre-period year, divided by one (since
there is one store manager position per store), and multiplied by 100%. Absenteeism is the number of unauthorized
days the store manager was absent from the store in the month. Distance to Head Office is distance to the head
office in miles. Team Size is the number of individuals working in the store in the month. Premium Location
(Dummy) is a dummy equal to 1 if the store is located in a large market, where high premium products are
demanded (these locations were identified by the managing director). Days in Inventory is the average days in
inventory at the store calculated by dividing the value of inventory "in stock" at the store by the average cost of
goods sold in one day. 24 Supervisor Ranking is a variable equal to 0 if the store is not overseen by any head office
24
This variable was estimated using off-sample data from October 2014 - May 2015. The inventory values were
obtained on the 15th of each month and the daily costs of goods sold were averaged for every month. Differences
across days of inventory of sales are stable over time, suggesting the use of this off-sample variable is reasonable. In
cases when we did not have data because the store had closed, we assumed that the store had as many days in
inventory as a store at the lowest 25th percentile "days of inventory" in the same area. When asked about our
construction of this measure, the managing director indicated “is very reasonable because things wouldn’t change,
not at all. For example Nehru Place would always be loaded and the smaller stores would always be unloaded.”
39
manager; 1 if the store is overseen by a district-manager-in-training; 2 if the store is overseen by a district manager;
3 if the store is overseen by the managing director; 4 if the store is overseen by both a district manager and the
managing director. Store Managers with Hindu religion is a dummy equal to one if the store manager that month is
of the Hindu religion (the most common religion in our sample) and zero otherwise. Store Manager from the
National Capital Territory of Delhi State is a dummy equal to one if the state of origin of the store manager that
month is the National Capital Territory of Delhi (the most common state in our sample) and zero otherwise.
40
Table 2: Coefficients of OLS Regressions Showing the Effect of the Introduction of
the Preferential Incentive at Company-Owned Stores on Sales
(Sample Comprises Company-Owned and Franchised Stores)
Ln (Sales)
VARIABLES
Prediction
Intercept
Company-Owned Store (Dummy)
(1)
(2)
11.163***
(75.751)
1.130***
(6.018)
11.173***
(76.764)
Company-Owned Selected Store (Dummy)
1.997***
(7.434)
1.130***
(6.679)
Company-Owned Not-Selected Store (Dummy)
Post PIP × Company-Owned Store
Post PIP × Company-Owned Selected Store
+/-
0.562***
(3.925)
1.231***
(5.645)
0.626***
(3.408)
0.438***
(2.925)
0.756***
(4.679)
Yes
Yes
Observations
1, 675
1, 675
R-squared
0.541
0.563
Post PIP × Company-Owned Not-Selected Store
+
+/-
Premium Location (Dummy)
Time Fixed Effects?
Difference in Coefficients:
Coef. [Post x Company-Owned Selected Store] Coef. [Post x Company Owned Not-Selected Store]
+
0.867
p-value
0.115
t-statistics based on robust standard errors adjusted for store clusters in parentheses. *, **, *** denote
significance at a 0.10, a 0.05 and a 0.01 level, respectively (p-values are one-tailed for directional
predictions and two-tailed otherwise). Ln(Sales) is the natural logarithm of monthly store sales in Indian
Rupees. Company-Owned Store (Dummy) is a dummy equal to 1 if the store is a company owned store.
Company-Owned Selected Store (Dummy) is a Dummy equal to 1 if the store is a company owned store that
was a “selected store” for the purposes of the preferential incentive plan at any time during the sample
period. Company-Owned Not-Selected Store (Dummy) is a Dummy equal to 1 if the store is a company
owned store that was never “selected” for the preferential incentive plan throughout the sample period. Post
PIP (Dummy) is a dummy equal to 1 if the observation is in the period after the preferential incentive plan
was first introduced. Premium Location (Dummy) is a dummy equal to 1 if the store is located in a large
market, where high premium products are demanded (these locations were identified by the managing
director).
41
Table 3: Pearson’s Correlation Coefficients for Main Variables
(1)
(1) Ln (Sales)
(2) Ln (Gross Profit)
(3) % Bad Audits
(4) Absenteeism
(5) Turnover
(6) Post PIP (Dummy)
(7) Distance to Head Office
(8) Team Size
(9) Premium Location
(Dummy)
(10) Days in Inventory
(11) Supervisor Ranking
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
1.00
0.82***
1.00
(0.00)
-0.21***
(0.01)
0.04
(0.32)
-0.10**
(0.03)
0.25***
(0.00)
-0.21***
(0.00)
0.74***
(0.00)
0.69***
(0.00)
-0.20***
(0.00)
-0.14*
(0.07)
0.01
(0.84)
-0.14***
(0.00)
0.25***
(0.00)
-0.23***
(0.00)
0.56***
(0.00)
0.56***
(0.00)
-0.20***
(0.00)
-0.04
(0.64)
0.15*
(0.10)
0.09
(0.23)
0.05
(0.51)
-0.13
(0.11)
-0.29***
(0.00)
-0.09
(0.25)
0.08*
(0.07)
-0.03
(0.42)
-0.01
(0.73)
0.12***
(0.00)
0.03
(0.47)
-0.02
(0.71)
-0.11***
(0.01)
0.00
(0.96)
-0.04
(0.36)
-0.07
(0.13)
0.12***
(0.01)
-0.17***
(0.00)
-0.11***
(0.01)
0.04
(0.38)
-0.01
(0.82)
-0.06
(0.16)
-0.07*
(0.09)
0.17***
(0.00)
0.62***
(0.00)
-0.20***
(0.00)
-0.23***
(0.00)
1.00
0.53***
(0.00)
-0.07*
(0.07)
0.09**
(0.03)
0.37***
(0.00)
-0.08*
(0.06)
0.08*
(0.07)
-0.16**
(0.05)
-0.02
(0.78)
-0.21***
(0.01)
0.05
(0.23)
-0.03
(0.46)
0.01
(0.86)
0.01
(0.75)
-0.05
(0.23)
0.15***
(0.00)
-0.15***
(0.00)
0.13***
(0.00)
-0.01
(0.89)
-0.07
(0.12)
0.00
(0.96)
0.00
(0.94)
0.51***
(0.00)
-0.06
(0.16)
0.09**
(0.02)
0.40***
(0.00)
-0.05
(0.22)
0.10**
(0.02)
-0.20***
(0.00)
0.07
(0.11)
-0.10**
(0.02)
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
(12) Store Managers
-0.09*
1.00
Hindu Religion
(0.04)
(13) Store Managers from
0.02
-0.07
the National Capital
(0.71)
(0.11)
Territory of Delhi State
p-values are reported in parenthesis. *, **, *** denote significance at a 0.10, a 0.05 and a 0.01 level, respectively. Ln(Sales) is the natural logarithm of monthly store sales in
Indian Rupees. Ln(Gross Profit) is the natural logarithm of monthly store gross profit in Indian Rupees. % Bad Audits is the percentage of times the result of an audit was
“bad” (i.e., revealed at least one missing item or cash shortage without a reasonable explanation) at the store in the month. Absenteeism is the number of unauthorized days
the store manager was absent from the store in the month. Turnover is a dummy equal to 1 if the store manager left the company within 3 months (at time t, t+1, or t+2).
Post PIP (Dummy) is a dummy equal to 1 if the observation is in the period after the preferential incentive plan was first introduced. Distance to Head Office is distance to
42
the head office in miles. Team Size is number of individuals working in the store in the month. Premium Location (Dummy) is a dummy equal to 1 if the store is located in a
large market, where high premium products are demanded (these locations were identified by the managing director). Days in Inventory is the average days in inventory at
the store calculated by dividing the value of inventory "in stock" at the store by the average cost of goods sold in one day. 25 Supervisor Ranking is a variable equal to 0 if the
store is not overseen by any head office manager; 1 if the store is overseen by a district-manager-in-training; 2 if the store is overseen by a district manager; 3 if the store is
overseen by the managing director; 4 if the store is overseen by both a district manager and the managing director. Store Managers with Hindu religion is a dummy equal to
one if the store manager that month is of the Hindu religion (the most common religion in our sample) and zero otherwise. Store Manager from the National Capital
Territory of Delhi State is a dummy equal to one if the state of origin of the store manager that month is the National Capital Territory of Delhi (the most common state in
our sample) and zero otherwise.
25
This variable was estimated using off-sample data from October 2014 - May 2015. The inventory values were obtained on the 15th of each month and the daily
costs of goods sold were averaged for every month. Differences across days of inventory of sales are stable over time, suggesting the use of this off-sample
variable is reasonable. In cases when we did not have data because the store had closed, we assumed that the store had as many days in inventory as a store at the
lowest 25th percentile "days of inventory" in the same area. When asked about our construction of this measure, the managing director indicated “is very
reasonable because things wouldn’t change, not at all. For example Nehru Place would always be loaded and the smaller stores would always be unloaded.”
43
Table 4: Coefficients of OLS or Logit Regressions Showing the Effect of the Preferential Incentive
on Store Outcomes at Selected Company-Owned Stores
(Sample Comprises Company-Owned Stores)
Pred
Intercept
Productive Outcomes
(1)
(2)
Ln (Sales) Ln (Gross Profit)
Pred
Counterproductive Outcomes
(3)
(4)
(5)
% Bad Audits Absenteeism Pr(Turnover)
9.882***
(14.776)
-0.031***
(-2.638)
0.031***
(3.168)
0.343***
(2.156)
0.133
(1.220)
7.383***
(7.689)
-0.033**
(-2.234)
0.030**
(2.043)
0.339*
(1.438)
-0.268
(-1.335)
Store Manager Present in
Store (Dummy)
-0.056
(-0.592)
-0.015
(-0.123)
29.897**
(2.399)
Team Size
0.108***
(6.289)
0.272*
(1.966)
0.002
(0.193)
-0.014
(-0.185)
0.002*
(1.836)
0.011
(0.020)
0.001***
(3.091)
-0.055
(-0.494)
0.224***
(4.497)
0.046*
(1.971)
0.074***
(3.466)
0.298*
(1.804)
-0.013
(-0.936)
-0.056
(-0.521)
0.000
(0.236)
-1.373
(-1.426)
0.001
(1.428)
-0.261
(-1.636)
0.252***
(3.495)
0.028
(0.932)
Times Fixed Effects?
Yes
Observations
Distance to Head Office
-
Post PIP × Distance Head
Office
Selected Store (Dummy)
+
Eligible Month for Selected
Store (Dummy)
+
Premium Location (Dummy)
Days in Inventory
Supervisor Ranking
Store Manager Tenure
Pre-Period Sales Growth
Store Size
New Store Look (Dummy)
Store Age
Sales Days
R-squared Col 1-4 /
Pseudo R-squared Col 5
+
-4.212**
(-2.286)
-0.042
(-1.161)
0.022
(0.334)
0.465
(0.955)
0.341
(0.556)
-4.233
(-1.422)
0.012
(0.207)
-0.378***
(-2.463)
0.487
(0.392)
-0.469
(-0.389)
2.514
(0.710)
-10.904
(-0.965)
-0.686
(-0.550)
-1.001
(-0.205)
0.364*
(1.821)
91.039
(0.952)
-0.033
(-0.809)
9.768
(0.801)
-7.688
(-1.382)
-2.889
(-1.661)
0.159*
(1.789)
-0.884***
(-2.808)
0.018
(1.074)
-0.330
(-0.925)
0.004
(0.479)
6.272**
(2.342)
0.000
(0.219)
-0.860
(-1.604)
0.179
(1.273)
0.145**
(2.667)
0.015
(0.108)
0.214
(0.288)
0.119**
(2.234)
0.480
(0.759)
-0.026**
(-2.205)
13.982***
(2.735)
-0.001
(-0.540)
-0.817
(-0.423)
-0.401**
(-2.003)
-0.005
(-0.046)
Yes
Yes
Yes
Yes
503
486
152
505
390
0.890
0.712
0.325
0.100
0.177
+
-
127.007
(1.408)
2.556*
(1.379)
-2.825*
(-1.367)
11.348
(0.742)
-17.696*
(-1.553)
44
t-statistics are based on robust standard errors adjusted for store clusters in parentheses. *, **, *** denote significance at a
0.10, a 0.05 and a 0.01 level, respectively (p-values are one-tailed for directional predictions and two-tailed otherwise).
Ln(Sales) is the natural logarithm of monthly store sales in Indian Rupees. Ln(Gross Profit) is the natural logarithm of monthly
store gross profit in Indian Rupees. % Bad Audits is the percentage of times the result of an audit was “bad” (i.e., revealed at
least one missing item or cash shortage without a reasonable explanation) at the store in the month. Absenteeism is the number
of unauthorized days the store manager was absent from the store in the month. Turnover is a dummy equal to 1 if the store
manager left the company within 3 months (at time t, t+1, or t+2). Distance to Head Office is distance to the head office in
miles. Team Size is number of individuals working in the store in the month. Post PIP (Dummy) is a dummy equal to 1 if the
observation is in the period after the preferential incentive plan was first introduced. Selected Store (Dummy) is a store-level
dummy equal to 1 if the store was a “selected store” (i.e., selected to participate in the daily bonus scheme) in one or more
months during the sample period. Eligible Month for Selected Store (Dummy) is a dummy equal to 1 if the store was eligible
for the daily bonus scheme (as a “selected store”) in the current month. Store Manager Present in Store (Dummy) is a dummy
equal to 1 if a store manager was present in the store in the month. Team Size is number of individuals working in the store in
the month. Premium Location (Dummy) is a dummy equal to 1 if the store is located in a large market, where high premium
products are demanded (these locations were identified by the managing director). Days in Inventory is the average days in
inventory at the store calculated by dividing the value of inventory "in stock" at the store by the average cost of goods sold in
one day. 26 Supervisor Ranking is a variable equal to 0 if the store is not overseen by any head office manager; 1 if the store is
overseen by a district-manager-in-training; 2 if the store is overseen by a district manager; 3 if the store is overseen by the
managing director; 4 if the store is overseen by both a district manager and the managing director. Store Manager Tenure is
tenure of the store manager in months since joining the company (or tenure of the cashier if the store manager was not
present). 27 Pre-Period Sales Growth is average monthly store sales growth for the 12-month period prior to when the
preferential incentive was first introduced. Store Size is store size in square feet. New Store Look (Dummy) is a dummy equal to
1 if the store has a "new look" (that is a new format adopted by the company which resulted in larger stores, with a modern and
clean look). Store Age is store age in years. Sales Days is the number of days in the month when the store made sales.
26
This variable was estimated using off-sample data from October 2014 - May 2015. The inventory values were
obtained on the 15th of each month and the daily costs of goods sold were averaged for every month. Differences
across days of inventory of sales are stable over time, suggesting the use of this off-sample variable is reasonable. In
cases when we did not have data because the store had closed, we assumed that the store had as many days in
inventory as a store at the lowest 25th percentile "days of inventory" in the same area. When asked about our
construction of this measure, the managing director indicated “is very reasonable because things wouldn’t change,
not at all. For example Nehru Place would always be loaded and the smaller stores would always be unloaded.”
27
We replaced “Store Manager Tenure” missing values for 106 observations (from our sample of 2,171
observations) corresponding to 12 store managers that were already present at the beginning of our sample period (in
February of 2012), but for which we did not receive their store manager starting date from the company. We
assumed that those 12 store managers had been employed for 37 months as of February of 2012 (approximately the
average tenure=37.7 for the other store managers for whom we had data as of February of 2012).
45
Table 5: Coefficients of OLS or Logit Regressions Showing the Moderating Effect of Resources on the Association Between
the Introduction of the Preferential Incentive Plan (PIP) and Not-Selected Store Outcomes
(Sample Comprises Not-Selected Company-Owned Stores)
Productive Outcomes
(1)
VARIABLES
Prediction
(2)
Ln (Sales)
Ln (Gross Profit)
Intercept
9.609***
(18.338)
Team Size
(3)
Prediction
Counterproductive Outcomes
(4)
(5)
% Bad Audits
Absenteeism
Pr(Turnover)
6.711***
(8.910)
111.362
(1.263)
-5.014**
(-2.558)
-5.836*
(-1.656)
0.071***
(3.193)
0.032
(0.831)
3.740
(0.910)
0.194*
(1.866)
-0.132
(-0.870)
Premium Location (Dummy)
0.362**
(2.596)
0.365*
(2.028)
-31.362
(-1.360)
-0.400
(-0.906)
0.461
(0.645)
Days in Inventory
-0.007
(-0.929)
-0.027**
(-2.198)
-0.676
(-0.450)
0.060**
(2.190)
0.193***
(3.556)
Supervisor Ranking
-0.023
(-0.207)
-0.097
(-0.471)
10.418
(0.611)
0.037
(0.131)
1.623***
(3.225)
Pre-Period Proximity to Target
0.053***
(4.481)
0.073***
(3.484)
1.481
(0.478)
0.018
(0.440)
-1.083***
(-3.853)
Post PIP × Team Size
+
0.067**
(1.997)
0.023
(0.579)
-
-2.087
(-0.421)
0.021
(0.111)
6.667
(2.818)
Post PIP × Premium Location
+
-0.086
(-0.502)
0.030
(0.149)
-
19.807
(0.691)
-0.769
(-0.973)
-9.003**
(-2.017)
Post PIP × Days in Inventory
+
0.012
(1.222)
0.024**
(1.811)
-
-0.154
(-0.113)
-0.071*
(-1.474)
-1.461***
(-6.753)
Post PIP × Supervisor Ranking
+
-0.124
(-0.878)
-0.075
(-0.373)
-
-23.316
(-1.264)
0.027
(0.077)
-9.333***
(-4.182)
Post PIP × Pre-Period Proximity to Target
-0.009
(-0.434)
-0.031
(-1.418)
-0.839
(-0.260)
0.043
(0.692)
-16.563***
(-2.928)
Store Manager Present in Store (Dummy)
-0.166
(-1.586)
-0.076
(-0.569)
18.093**
(2.302)
-
-
46
Store Manager Tenure
0.001
(0.535)
0.000
(0.101)
0.143
(0.866)
0.003
(0.402)
-0.013
(-1.076)
Pre-Period Sales Growth
-0.842
(-1.183)
-1.652
(-1.476)
41.647
(0.657)
5.617*
(1.790)
13.662***
(2.990)
Distance to Head Office
-0.012
(-1.112)
-0.007
(-0.489)
1.716*
(1.812)
-0.027
(-0.823)
-0.123*
(-1.885)
Store Size
0.001**
(2.068)
0.001
(0.886)
0.030
(0.572)
-0.001
(-0.661)
0.005*
(1.709)
Store Age
0.177***
(3.295)
0.196**
(2.368)
-9.772***
(-3.202)
0.047
(0.323)
-0.207
(-0.718)
Sales Days
0.080***
(4.515)
0.078***
(3.950)
-1.999
(-1.105)
0.119*
(1.739)
-0.106
(-0.839)
Time Fixed Effects?
Yes
Yes
Yes
Yes
Yes
Observations
438
424
112
440
333
0.881
0.736
0.508
0.103
0.320
R-squared Col 1-4 / Pseudo R-squared Col 5
t-statistics based on robust standard errors adjusted for store clusters in parentheses. *, **, *** denote significance at a 0.10, a 0.05 and a 0.01 level, respectively
(p-values are one-tailed for directional predictions and two-tailed otherwise). Ln(Sales) is the natural logarithm of monthly store sales in Indian Rupees. Ln(Gross
Profit) is the natural logarithm of monthly store gross profit in Indian Rupees. % Bad Audits is the percentage of times the result of an audit was “bad” (i.e.,
revealed at least one missing item or cash shortage without a reasonable explanation) at the store in the month. Absenteeism is the number of unauthorized days
the store manager was absent from the store in the month. Turnover is a dummy equal to 1 if the store manager left the company within 3 months (at time t, t+1, or
t+2). Team Size is number of individuals working in the store in the month. Premium Location (Dummy) is a dummy equal to 1 if the store is located in a large
market, where high premium products are demanded (these locations were identified by the managing director). Days in Inventory is the average days in inventory
at the store calculated by dividing the value of inventory "in stock" at the store by the average cost of goods sold in one day. 28 Supervisor Ranking is a variable
equal to 0 if the store is not overseen by any head office manager; 1 if the store is overseen by a district-manager-in-training; 2 if the store is overseen by a district
manager; 3 if the store is overseen by the managing director; 4 if the store is overseen by both a district manager and the managing director. Pre-Period Proximity
to Target is the average number of days in the month where sales >= 95,000 for the 12-month period prior to when the preferential incentive was first introduced.
Post PIP (Dummy) is a dummy equal to 1 if the observation is in the period after the preferential incentive plan was first introduced. Store Manager Present in
Store (Dummy) is a dummy equal to 1 if a store manager was present in the store in the month. Team Size is number of individuals working in the store in the
28
This variable was estimated using off-sample data from October 2014 - May 2015. The inventory values were obtained on the 15th of each month and the daily
costs of goods sold were averaged for every month. Differences across days of inventory of sales are stable over time, suggesting the use of this off-sample
variable is reasonable. In cases when we did not have data because the store had closed, we assumed that the store had as many days in inventory as a store at the
lowest 25th percentile "days of inventory" in the same area. When asked about our construction of this measure, the managing director indicated “is very
reasonable because things wouldn’t change, not at all. For example Nehru Place would always be loaded and the smaller stores would always be unloaded.”
47
month. Store Manager Tenure is tenure of the store manager in months since joining the company (or tenure of the cashier if the store manager was not present). 29
Pre-Period Sales Growth is average monthly store sales growth for the 12-month period prior to when the preferential incentive was first introduced. Distance to
Head Office is the distance to the head office in miles. Store Size is store size in square feet. Store Age is store age in years. Sales Days is the number of days in
the month when the store made sales.
29
We replaced “Store Manager Tenure” missing values for 106 observations (from our sample of 2,171 observations) corresponding to 12 store managers that
were already present at the beginning of our sample period (in February of 2012), but for which we did not receive their store manager starting date from the
company. We assumed that those 12 store managers had been employed for 37 months as of February of 2012 (approximately the average tenure=37.7 for the
other store managers for whom we had data as of February of 2012).
48
Table 6: Coefficients of OLS or Logit Regressions Showing the Moderating Effect of Social Identity on the Association
Between the Introduction of the Preferential Incentive Plan and Not-Selected Store Outcomes
(Sample Comprises Not-Selected Company-Owned Stores)
Productive Outcomes
(1)
(2)
VARIABLES
Pred.
Ln (Sales)
Ln (Gross Profit)
9.048***
(19.632)
Store Managers’ Religion Underrepresented
among Selected Stores At Any Time
Counterproductive Outcomes
(3)
(4)
% Bad Audits
Absenteeism
Pr(Turnover)
5.776***
(8.984)
131.989**
(2.232)
-0.376
(-0.187)
-3.701
(-0.995)
0.322
(1.462)
0.771***
(3.464)
-16.462
(-0.734)
-3.312***
(-6.720)
0.156
(0.145)
Store Managers’ State Underrepresented
among Selected Stores At Any Time
-0.171*
(-1.787)
-0.164
(-1.361)
6.293
(0.664)
0.310
(1.450)
-0.535
(-1.113)
Pre-Period Proximity to Target
0.047***
(4.537)
0.065***
(3.692)
4.776**
(2.129)
0.038
(0.897)
-0.974***
(-3.914)
Intercept
Pred.
(5)
Store Managers’ Religion Underrepresented
among Selected Stores At Time t
-
-0.066
(-0.763)
-0.132**
(-2.123)
+
9.230*
(1.372)
-1.290
(-1.267)
0.232
(0.482)
Store Managers’ State Underrepresented
among Selected Stores At Time t
-
-0.163**
(-1.770)
-0.003
(-0.029)
+
-17.484
(-1.268)
0.371
(0.331)
-0.372
(-0.438)
Post PIP × Pre-Period Proximity to Target
-0.007
(-0.532)
-0.033***
(-3.288)
-4.091**
(-2.214)
0.002
(0.047)
1.957
(0.609)
Team Size
0.082***
(4.353)
0.040
(1.513)
1.154
(0.429)
0.170***
(2.782)
-0.064
(-0.515)
Premium Location (Dummy)
0.349***
(2.853)
0.427***
(3.098)
-16.404
(-1.643)
-0.797***
(-3.643)
0.288
(0.388)
Days in Inventory
-0.000
(-0.019)
-0.014
(-1.310)
-0.943
(-1.065)
0.010
(0.580)
0.096**
(2.492)
Supervisor Ranking
-0.071
(-0.766)
-0.150
(-0.990)
-5.169
(-0.997)
0.125
(0.560)
0.934**
(1.970)
Store Manager Present in Store (Dummy)
-0.019
(-0.177)
0.013
(0.097)
22.651**
(2.345)
Store Manager Tenure
0.002
(1.114)
0.001
(0.696)
0.112
(0.583)
-0.001
(-0.084)
-0.013
(-1.259)
49
Pre-Period Sales Growth
-0.426
(-0.613)
-1.178
(-1.257)
47.348
(0.634)
3.679
(1.328)
13.260***
(2.599)
Distance to Head Office
-0.014
(-1.398)
-0.010
(-0.796)
2.044*
(1.976)
-0.020
(-0.671)
-0.081*
(-1.849)
Store Size
0.001***
(3.125)
0.001**
(2.227)
0.012
(0.213)
-0.003**
(-2.303)
0.003
(1.251)
Store Age
0.179***
(3.145)
0.181**
(2.425)
-8.642**
(-2.627)
0.203
(1.696)
-0.099
(-0.524)
Sales Days
0.078***
(5.555)
0.072***
(4.269)
-1.409
(-0.796)
0.111*
(1.788)
-0.053
(-0.458)
438
424
112
440
333
0.882
0.741
0.501
0.125
0.206
Time Fixed Effects?
Observations
R-squared Col 1-4/ Pseudo R-squared Col 5
t-statistics based on robust standard errors adjusted for store clusters in parentheses. *, **, *** denote significance at a 0.10, a 0.05 and a 0.01 level,
respectively (p-values are one-tailed for directional predictions and two-tailed otherwise).
After the preferential incentive plan was introduced, we determined whether the group “X” a store manager of a store “i" belonged to (either Muslim, Christian,
or Hindu when referring to Religion, or Bihar, the National Capital Territory of Delhi, Rajasthan, Uttar Pradesh or Uttarakhand when referring to State) was
underrepresented among the selected stores, based on the following steps:
Step 1: We estimated for every month-year t:
# 𝑠𝑠𝑠𝑠𝑠 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 𝑖𝑖 𝐺𝐺𝐺𝐺𝐺 𝑋𝑡
Expected # store managers of Group X in selected storest= Round �# 𝑠𝑠𝑠𝑠𝑠 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 𝑖𝑖 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑠𝑠𝑠𝑠𝑠𝑠𝑡 ∗ �
��
𝑡𝑡𝑡𝑡𝑡 # 𝑠𝑠𝑠𝑠𝑠 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑡
Step 2: We then estimated for month-year t:
Underrepresentation of Group X among store managers of selected storest =
min [0 , (Expected # store managers of Group X in selected storest - Actual # store managers of Group X in selected storest)]
Step 3: We determined whether the store manager of store i at time t was underrepresented if the group “X” he belonged to was underrepresented on that
month-year t. In other words: Underrepresentation of Group X among store managers of selected storest > 0
Store Manager’s Religion Underrepresented Among Selected Stores At Time t is a dummy equal to 1 if the store manager’s religion was underrepresented in
month-year t among store managers of selected stores. Store Manager’s Religion Underrepresented Among Selected Stores At Any Time is a store-level dummy
equal to 1 if the religion of any of the store managers working at the store was underrepresented among store managers of selected stores in any month during
the post period (in other words; this dummy is equal to 1 throughout the period for any store for which the preceding dummy was equal to 1 at any time).
Store Manager’s State Underrepresented Among Selected Stores At Time t is a dummy equal to 1 if the store managers' state of origin was underrepresented in
month-year t among store managers of selected stores. Store Manager’s State Underrepresented Among Selected Stores At Any Time is a store-level dummy
equal to 1 if the state of origin of any of the store managers working at the store was underrepresented among store managers of selected stores in any month
during the post period (in other words, this dummy is equal to 1 throughout the period for any store for which the preceding dummy was equal to 1 at any
time).
50
Ln(Sales) is the natural logarithm of monthly store sales in Indian Rupees. Ln(Gross Profit) is the natural logarithm of monthly store gross profit in Indian
Rupees. % Bad Audits is the percentage of times the result of an audit was “bad” (i.e., revealed at least one missing item or cash shortage without a reasonable
explanation) at the store in the month. Absenteeism is the number of unauthorized days the store manager was absent from the store in the month. Turnover is a
dummy equal to 1 if the store manager left the company within 3 months (at time t, t+1, or t+2). Pre-Period Proximity to Target is the average number of days
in the month where sales >= 95,000 for the 12-month period prior to when the preferential incentive was first introduced. Post PIP (Dummy) is a dummy equal
to 1 if the observation is in the period after the preferential incentive plan was first introduced. Team Size is number of individuals working in the store in the
month. Premium Location (Dummy) is a dummy equal to 1 if the store is located in a large market, where high premium products are demanded (these
locations were identified by the managing director). Days in Inventory is the average days in inventory at the store calculated by dividing the value of inventory
"in stock" at the store by the average cost of goods sold in one day. 30 Supervisor Ranking is a variable equal to 0 if the store is not overseen by any head office
manager; 1 if the store is overseen by a district-manager-in-training; 2 if the store is overseen by a district manager; 3 if the store is overseen by the managing
director; 4 if the store is overseen by both a district manager and the managing director. Store Manager Present in Store (Dummy) is a dummy equal to 1 if a
store manager was present in the store in the month. Store Manager Tenure is tenure of the store manager in months since joining the company (or tenure of
the cashier if the store manager was not present). 31 Pre-Period Sales Growth is average monthly store sales growth for the 12-month period prior to when the
preferential incentive was first introduced. Distance to Head Office is the distance to the head office in miles. Store Size is store size in square feet. Store Age is
store age in years. Sales Days is the number of days in the month when the store made sales.
30
This variable was estimated using off-sample data from October 2014 - May 2015. The inventory values were obtained on the 15th of each month and the daily
costs of goods sold were averaged for every month. Differences across days of inventory of sales are stable over time, suggesting the use of this off-sample
variable is reasonable. In cases when we did not have data because the store had closed, we assumed that the store had as many days in inventory as a store at the
lowest 25th percentile "days of inventory" in the same area. When asked about our construction of this measure, the managing director indicated “is very
reasonable because things wouldn’t change, not at all. For example Nehru Place would always be loaded and the smaller stores would always be unloaded.”
31
We replaced “Store Manager Tenure” missing values for 106 observations (from our sample of 2,171 observations) corresponding to 12 store managers that
were already present at the beginning of our sample period (in February of 2012), but for which we did not receive their store manager starting date from the
company. We assumed that those 12 store managers had been employed for 37 months as of February of 2012 (approximately the average tenure=37.7 for the
other store managers for whom we had data as of February of 2012).
51