[table]capposition=top

††thanks: Acknowledgements: This research has primarily been funded by the IIM
Bangalore. We thank Lise Vesterlund, Sera Linardi, Maria Recalde, Anya Samek,
Lata Gangadharan and seminar participants at University of Pittsburgh and ESA
Global Meetings for their helpful suggestions and comments. We thank Partha
Chatterjee and Sayar Mitra for helping us with conducting the experiment. We
gratefully acknowledge the research assistance from Chacko Babu and Arpit
Sachan. Banerjee can be reached at<EMAIL_ADDRESS>and Mustafi at
<EMAIL_ADDRESS>
11affiliationtext: Indian Institute of Management Bangalore, India
22affiliationtext: University of Pittsburgh, USA

# Using social recognition to address the gender difference in volunteering
for low-promotability tasks

Ritwik Banerjee  Priyoma Mustafi

Research shows that women volunteer significantly more for tasks that people
prefer others to complete. Such tasks carry little monetary incentives because
of their very nature. We use a modified version of the volunteer’s dilemma
game to examine if non-monetary interventions, particularly, social
recognition can be used to change the gender norms associated with such tasks.
We design three treatments, where a) a volunteer receives positive social
recognition, b) a non-volunteer receives negative social recognition, and c) a
volunteer receives positive, but a non-volunteer receives negative social
recognition. Our results indicate that competition for social recognition
increases the overall likelihood that someone in a group has volunteered.
Positive social recognition closes the gender gap observed in the baseline
treatment, so does the combination of positive and negative social
recognition. Our results, consistent with the prior literature on gender
differences in competition, suggest that public recognition of volunteering
can change the default gender norms in organizations and increase efficiency
at the same time.

Keywords: Gender, Social Recognition, Volunteering, Low Promotability Tasks
JEL codes: J16, J71, M12, D91.

##  1 Introduction

The labor market outcomes remains systematically different between men and
women, despite half a century’s research-based policies (Altonji and Blank,
1999; Marianne, 2011). While some scholars have focused on the role of
discrimination and differences in productivity, others have taken preference
or belief -based approaches as key mechanisms driving the stubborn gender gap
in such outcomes. These mechanisms can result in equilibria where the nature
of tasks men and women end up performing are different. Such a separating
equilibrium may be perpetuated for several reasons: managers may assign women
fewer challenging tasks (De Pater et al., 2010) or women themselves may choose
challenging tasks less often (Niederle and Yestrumskas, 2008).

Relatedly, in an influential recent study, Babcock et al. (2017) use field
data from a large public university to show that women are significantly more
likely to volunteer in senate committees than men. Their findings are
consistent with those of Porter (2007) who show that female faculty spend 15
percent more time on committees than their male counterparts. Such service-
oriented tasks, such as volunteering in committees, are called low-
promotability tasks because they matter less either for performance evaluation
or for raising outside options. Babcock et al. (2017) (BRVW, henceforth)
complement these findings with laboratory experiments. These experiments mimic
the incentive structure of low-promotability tasks where the person
volunteering is put at a relative disadvantage, but others enjoy the benefits
of the completed task. The paper shows that women indeed volunteer more than
men in such tasks.111Examples of low-promotability tasks are organizing
recreational events at offices or serving in committees in academic settings.
They trace the origin of this gender gap to differences in beliefs about
volunteering and not preferences.

There are many potential negative consequences of the gender-gap in
volunteering for low-promotability tasks. In the long run, this gap may result
in systematic differences in outcomes across gender, such as a under-
representation of women in leadership positions, a widening wage gap between
men and women, and a differential selection out from competitive environments.
Given these findings, a central question facing organizations is: how should
the incentive structures be designed such that the gender-gap in volunteering
for low-promotability tasks can be minimized?

The standard approach in economics to change behavior is to alter the economic
incentive or, more specifically, monetary incentive. Notice that monetary
incentives cannot be changed for low-promotability tasks since that will
change the very definition of the task, i.e., the task will not remain a low-
promotability one anymore222This is not to suggest that low-promotability
tasks should not be rewarded by organizations, but to highlight that certain
tasks will always rank lower in the hierarchy relative to others, in an
organizational setting.. Moreover, there is little evidence that there is a
difference in the perceived value of monetary incentives across gender. If so,
economic incentives can alter the level of volunteering for both genders
without addressing the gender gap. In this paper, we investigate if
competition for non-monetary incentives, such as positive and negative social
recognition, can help close the gender gap in volunteering for low-
promotability tasks.

We adapt the design of BRVW and conduct a between-subject laboratory
experiment to answer the above question. In the baseline treatment,
participants are randomly divided into groups of three and play ten rounds of
an investment game - a modified version of the volunteer’s dilemma game. In
each round, they decide, within a specific timeout, whether to invest (i.e.
volunteer) or not by clicking on a button. If no one invests, then the group-
members get the default payoff. If someone invests within the timeout, she
earns a little more than the default payoff, but others earn more than her.
Thus, the incentive structure in the game mimics real life situations where
everyone wants others to perform a particular task. A design feature of the
baseline treatment that distinguishes our study from BRVW is the following:
participants begin the game by first choosing gender salient, fictitious
names. The fictitious names of the investors are shown on the screens of the
group members. In the positive social recognition treatment, investors’ real
name is flashed on the screens of the group members alongside a congratulatory
message and a smiling emoji (☺). Besides, investors are publicly applauded
through a pre-recorded audio. Since a name typically signals the gender of the
person, socially recognizing a participant reveals the gender of the investor.
This may have unintended spillover effects on investing from one round to the
other. This is precisely why the investors in the baseline treatment are
identified with a fictitious, gender salient name. The difference between the
baseline and the social recognition treatments determines the uncontaminated
effect of social recognition.

In the negative social recognition treatment, the real names of non-investors
are displayed on the screens of the group members with a frowning emoji (☹).
In the positive and negative social recognition treatment (☺☹), the real name
of the investor is displayed on the screens of the group members with a
congratulatory message and a smiley; in contrast, those of non-investors are
displayed alongside a frownie. A comparison of the investment decision in the
treatment conditions with the baseline helps identify the effect of social
recognition on volunteering, which in turn allows us to comment on the gender
gap in volunteering across different treatment conditions. Note that positive
(negative) social recognition is a non-monetary incentive, which increases the
net benefit (cost) of volunteering (not volunteering). To that extent, it is
plausible that the social recognition interventions increase the overall
chances of investment in a group, as any other standard incentive. Our
experimental design allows us to identify that.

What are our main findings? In the baseline treatment, women invest
significantly more than men, and the difference is significant at the 5%
level. Our baseline treatment replicates the gender-gap in volunteering for
low-promotability tasks. Strikingly, the gender-gap is not significantly
different from zero in the positive social recognition treatment and the
treatment with both positive and negative social recognition. However, in the
negative social recognition treatment, the gender gap is persistent. A period-
wise representation of the investment decisions confirms that the gender-gap
is persistently high over time in the baseline and the negative social
recognition treatments, but not so in the other two treatments. While two of
our interventions successfully eliminate the gender gap in volunteering, a key
question remains. Are the interventions efficient, as measured by the
proportion of groups where investment occurs? Interestingly, the answer is yes
- the proportion of groups where someone invests is significantly higher in
the social recognition treatments relative to the baseline. Thus, not only
does the ☺ and ☺☹ treatments eliminate the gender gap, they do so in a manner
that increases overall efficiency.

Our paper makes several key contributions to the literature. First, to the
best of our knowledge, ours is one of the first papers to successfully test a
policy intervention that can close the gap in low-promotability tasks’
performance. In a related paper, Bacine and Eckel (2020) show that it is
possible to break the gender norm by introducing asymmetric public costs for
volunteering. The asymmetric costs serve as the focal point around which
volunteering is coordinated, and through that, close the gender gap. In
contrast, this paper exploits gender differences in competition for non-
monetary incentives.

Second, we contribute to the literature on social recognition by showing that
it is a useful policy tool through which entrenched gender norms can be
altered. Field-based experiments have demonstrated that social recognition (or
more generally, social incentive) improves performance (Ashraf et al., 2014a),
increases voter turnout (Gerber et al., 2008), increases blood donation
(Lacetera et al., 2009) and increases volunteering activities and employees’
motivation (Gallus, 2017; Gallus and Frey, 2016). Social comparison, on the
contrary, reduces performance (Ashraf et al., 2014b).333For a review of the
literature on social incentive, see Ashraf and Bandiera (2018). On the lab
front, studies have shown that socially recognizing contributors increases the
contribution levels in public good games (Andreoni and Petrie, 2004; Rege and
Telle, 2004), social approval for energy conservation decreases energy
consumption (Schultz et al., 2007) and social visibility and self image
concerns increase volunteering (Jones and Linardi, 2014; Exley, 2018). While
the literature primarily focuses on positive social recognition, Samek and
Sheremeta (2014) have shown that recognizing the non-contributors have the
highest effect on contributions in public good games. We contribute to this
literature by showing that in social dilemma environments, social recognition
can play a significant role in helping society move towards more efficient
outcome.

Third, some recent studies have shown that the effect of social recognition
may differ across gender. Jones and Linardi (2014) demonstrate that women are
more likely to exhibit the ’wallflower effect’ - a tendency to respond under
visibility in a manner which reduces scrutiny. Some find women have a greater
sense of shame than men (Ludwig et al., 2017), a lower self-perception (Exley
and Kessler, 2019) and self-stereotype is one mechanism through which such
gender differences perpetuate (Gallus and Heikensten, 2020). Our research
contributes to this literature by showing that women respond differently to
social recognition than men. In fact, the root of this difference can be
traced back to the literature on gender differences in competition (Niederle
and Vesterlund, 2007), with the important difference being, men, in our case,
compete more than women for a non-monetary reward - positive social
recognition. In other words, we exploit gender difference in one aspect of
behavior to close the gender-gap in another. The precise mechanisms behind the
gender difference in responses to social recognition are important open
question for future research.

The rest of the paper is organized as follows: Section 2 lays out the
experimental design and procedure, Section 3 presents the results, while
Section 4 offers the concluding remark.

##  2 Experimental Design and Procedure

We conduct a laboratory experiment in India to answer the research questions
discussed above. The structure of the game captures the essence of the
incentives faced by a small group that is looking for a volunteer for a task
that everyone prefers undertaken, but none wants to undertake it. In other
words, if no one volunteers the dominant strategy of a player is to volunteer.
The baseline treatment is designed to identify the baseline gender gap in
volunteering for such low-promotability tasks in our sample. The subsequent
three treatments are designed to examine whether non-monetary incentives such
as social recognition can help close this gender gap.

### Baseline Treatment

In the baseline treatment, individuals are randomly and anonymously assigned
to groups of three, in each of the ten rounds of play.444The group assignment
is done in a way such that the exact three members are not repeated twice in a
row. A group has T seconds within which a member can make the investment (or
volunteer), where T is drawn randomly from a uniform distribution with support
from 45 and 90. If no one invests in a particular group within the timeout,
each member earns 100 experimental currency units or Mohars.555Mohars or gold
coins were a medium of exchange in ancient India. The moment one group member
invests, the round ends. The investor receives a payment of $300M$, while the
other two group members each receive $900M$. In case multiple participants
invest simultaneously, the investor is determined randomly. When no one
invests within the timeout, the group members are informed about it and are
given $100M$. The ordinal ranking of the payoffs are the same as in BRVW.
Assuming, rational agents, the equilibria of the game comprises of a pure
strategy Nash equilibrium where one player invests and others do not, a mixed
strategy Nash equilibrium where the likelihood of investment is 0.232, a mixed
strategy Nash equilibrium where one person does not invest and the other two
invest with probability 0.40. Overall, the probability that someone in a group
invests is 1, 0.54 or 0.64, depending on which equilibrium is played.

There are two key differences in our design of the baseline treatment with
respect to BRVW. First, in their design, a group had 2 minutes to invest, but
in our setup, the timeout happens randomly between 45 and 90 seconds.666The
difference was motivated by the results from the pilot data, which suggested
that investment decisions were bunched with little or no variation when the
duration of the round was fixed. Also, given the bunching at the end, the
absence of mouse-click meant participants knew others in the room had not
invested-our design avoids such potential peer effect. Notice, with the
uncertainty related to timeout, risk preference becomes a crucial explanatory
variable, which we control for in our results. Second, participants were
described in neutral terms such as Participant A in BRVW and gender salience
of the participants came from the fact that participants in the lab were only
from one gender or mixed gender. In order to make sure that the gender
information revealed is constant across treatments, we use fictitious,
gendered names for participants in the baseline treatment. Before starting the
experiment, participants are first asked about their gender (and age).
Subsequently, they choose a name for themselves from a list of gender
revealing names, commonplace in India, which they retain through the course of
the session. The chosen names are accepted only if they are consistent with
the reported gender. Once a name is matched to a participant, it is removed
from the choice set and others cannot pick it anymore.777In a separate
incentivized survey with 91 respondents, we elicit the perceptions about the
gender associated with the names we use. The empirical likelihood of correct
perception turned out to be 97.9%. Experiments where participants choose
nicknames have been conducted earlier as well, sometimes with an aim to make
the gender identity salient (Duersch et al., 2009; Wiborg et al., 2020).
Examples of names we used are Gautam and Rohan for males and Ananya and Sonia
for females.888Given these were common Indian names, what if there actually
were session participants with these names? Would they feel socially
recognized even in the baseline treatment? If they did, then the observed
gender-gap in the baseline might be interpreted as a lower bound of the true
gap. If a group member invests in a round, then his/her fictitious name with
the following message is shown on all three participants’ screens:
“$<fictitiousname>$ of your group invested in Round $<x>$”. Besides this
message, their own investment decision and the corresponding earnings in the
round are displayed.

This aspect of the design was necessitated by the following: the social
recognition treatments described below reveal the real names of those who
invest. However, names reveal not only the identity but also the gender.
Consequently, the investment decisions in subsequent rounds may be colored by
not only the effects of social recognition but also the gender of the past
investors.999Although one round is randomly picked for paying subjects, there
are some recent evidence of behavioral spillovers because of information
revealed in one round to behavior in another (Banerjee et al., 2018;
Leibbrandt et al., 2018). An alternative baseline treatment could have been
one where the investors revealed their true names but were not socially
recognized. However, it is hard to imagine that revealing the true name of the
investor will not generate some form of social recognition. For these reasons,
we chose a baseline treatment with fictitious names such that the gender of
the investor is revealed, and the treatment is strategically identical to
BRVW, but the investor is not socially recognized. Overall, this helps
identify the pure effects of social recognition when compared with the other
treatments.

### Treatment ☺

In the positive social recognition treatment, participants first respond to
the survey, type their real names and then start playing the game. At the end
of every round, the name of the investor is displayed on the screens of each
group member along with a congratulatory message and a smiling emoji - ☺.
Along with this, an audio clip of applause is played, and the participants are
encouraged to join the applause. Figure A3 presents the screen where the
investor is positively recognized.

### Treatment ☹

In the negative social recognition treatment, participants type their real
names before they start playing the game. At the end of every round, the names
of the two individuals who chose not to invest is displayed on the screens of
each group member along with a frowning emoji - ☹. Note, information about the
investor is not communicated to the group members in this treatment. Figure A4
presents the screen where the non-investor receives negative recognition.

### Treatment ☺☹

In many practical situations, organizations may find it hard to implement a
policy where non-volunteers are ’called out’. It may be much easier for
organizations to publicize the names of both volunteers and non-volunteers. To
test the effectiveness of such a policy, we conduct a final treatment where we
display the names of the investor and the non-investors. At the end of each
round, the investor’s name is displayed with a congratulatory message and a ☺,
while the names of the two non-investors are displayed along with a ☹. Figure
A5 presents the screen where the investor is positively recognized and the
non-investors are negatively recognized.

Thus, the four treatments we implement differ in the way the investor and/or
the two non-investors are recognized within each group, after each round.
Notably, the interventions discussed above change only the non-monetary payoff
associated with volunteering, while leaving the strategic aspects of the game
unchanged.

### Experimental Procedure

The experiment was coded up in z-Tree (Fischbacher, 2007) and was conducted at
a large private university in India. Participants were recruited from
undergraduate classes and randomized into sessions. None of the participants
had prior experience in economic experiments. Twenty-three sessions were
conducted with participants between 9 and 24 in each. A total of 285 students
participated (75 in Baseline, 75 in Treatment ☺, 60 in Treatment ☹ and 75 in
Treatment ☺☹). Sessions were roughly gender-balanced with the share of women
in a session ranging between 33 and 60 percent. The timer in each round was
implemented only through an audio clip that counted seconds, which in turn
helped mask the sound of mouse clicks. On screen timer was not used. At the
end of the ten rounds, participants answered a short survey, which included
questions on gender, age, religion, caste, average monthly family income and
year in college. The participants earned an average of Rs. 170 ($\approx$10
PPP-USD) for a session which lasted about one and a half hours.

##  3 Results

Table A1 reports the descriptive statistics of the outcomes along with the
behavioral and demographic control variables and compares them across
treatments. The behavioral control variables, namely, altruism, risk, non-
conformity, and agreeableness are balanced across treatments. A t-test with
respect to the baseline reveals only Altruism is significantly different in
Treatment ☹ from the baseline at 10% level. The demographic control variables
are generally well balanced, too, with only Age in Treatment ☺ significantly
different from the baseline, at 10% level and Years in College in Treatment ☺
and ☹ are significantly different from the baseline at 5% level. In the
regression analysis, we control for these variables.

Figure 1: Comparing Individual and Group Investment across Treatments

(a) Mean $TotalInvestment$ across treatment

The average Investment is 2.39 in the Baseline, while it is 2.73 ($t$-test,
$p$-value=0.27) in Treatment ☺, in Treatment ☹ it is 2.88 ($t$-test,
$p$-value=0.20) and 2.93 ($t$-test, $p$-value=0.13) in Treatment ☺☹.

(b) Mean GroupInvestment across treatment

The average GroupInvestment is 71.6% in the Baseline while it is 83% in
Treatment ☺ (Fisher exact test, $p$-value$<$0.01), 86% (Fisher exact test,
$p$-value$<$0.01) and 87% for Treatment ☹ (Fisher exact test, $p$$<$0.01).

We start the analysis by comparing the average investment per participant
across the treatments. Figure 1(b)(a) reports the average number of times an
individual invested in the ten rounds. The social recognition treatments
increase investment, but the difference is not statistically significant when
compared to the baseline. Figure 1(b)(b) plots the average number of times an
investment takes place in a group, across the treatments. The group investment
increases from 71.6% in the baseline to 82% in the ☺ treatment (Fisher’s exact
test, p-value$<$0.01), 86% in the ☹ treatment (Fisher’s exact test,
p-value$<$0.01) and 88% in the ☺☹ treatment (Fisher’s exact test, p$<$0.01).
These results hold in a regression framework which controls for demographic
and behavioral variables and clusters standard errors at the session level, as
reported in Table 1. The ☺ treatment dummy is positive and significant at 5%
level, while ☹ and ☺☹ treatment dummies are significant at 1% level. Overall,
the group investments are consistently higher in the social recognition
treatments relative to the baseline. This indicates that socially recognizing
voluntary contributions in organizational settings can play the role of an
effective non-monetary incentive. The period-wise probability of individual
investment and group investment are plotted in Figure A1(a) and A1(b),
respectively. Interestingly, the proportion of groups in the baseline
treatment where investment occurs is significantly greater than either 0.54 or
0.64 and lower than 1 - the equilibrium predictions ($t$-test, $p$-values
$<$0.01, $<$0.01, $<$0.01, respectively). Thus, these is some suggestive
evidence that participants in our sample are either behavioral or hold
different views about which of the equilibria will be played by the other
participants. We now have our first set of results:

######  Result 1.

Social recognition does not significantly increase the rate at which an
individual volunteers.

######  Result 2.

Social recognition significantly increases the likelihood that someone in the
group volunteers.

Table 1: Treatment Differences in the Likelihood of Individual Investment and
Group Investment

| Investment | GroupInvestment
---|---|---
| (1) | (2) | (3) | (4) | (5)
Treatment ☺ | 0.0365 | 0.0330 | 0.0291 | 0.0822** | 0.0706**
| (0.0334) | (0.0339) | (0.0347) | (0.0407) | (0.0342)
Treatment ☹ | 0.0520 | 0.0521 | 0.0543 | 0.118*** | 0.108***
| (0.0418) | (0.0411) | (0.0412) | (0.0314) | (0.0213)
Treatment ☺☹ | 0.0568 | 0.0590 | 0.0547 | 0.135*** | 0.125***
| (0.0380) | (0.0369) | (0.0377) | (0.0293) | (0.0220)
Round | -0.00355 | -0.00355 | -0.00351 |  | -0.0106**
| (0.00258) | (0.00260) | (0.00260) |  | (0.00419)
Female Session Share |  |  | 0.0410 |  | 0.345***
|  |  | (0.131) |  | (0.118)
Observations | 2,850 | 2,850 | 2,850 | 950 | 950
Behavioral Controls | No | Yes | Yes | - | -
Demographic Controls | No | No | Yes | - | -
Cluster | Individual | Individual | Individual | Session | Session

  * †

The table presents marginal effects. Standard errors are reported in
parentheses, clustered as individual level for col (1)-(3) and session level
for col (4)-(5). Treatment dummies = 0 for baseline. The dependent variable
for Col(1)-(3) is Investment (1-invest, 0-don’t invest). Behavioral controls
include controls for preferences for risk , altruism, non-conformity and
agreeableness. Demographic controls include controls for age, religion, caste,
years in college and family income categories. With group as the unit of
observation, the dependent variable for col (4)-(5) is GroupInvestment
(1-atleast one investor in group, 0-no investor in group). *** p$<$0.01, **
p$<$0.05, * p$<$0.1

We now come to the main result in our paper. Figure 2 plots the probability of
investment by gender across treatments. In the following analysis, we
statistically compare the count variable, $TotalInvestment$, across treatment,
and gender. In the baseline treatment, on average, males invest 1.9 times,
while females invest 2.8 times. The gender gap in the number of times
participants invest is 0.9 and is statistically significant (t-test,
p-value=0.038). The median $TotalInvestment$ for males and females are 2 and
2.5, respectively (Mann Whitney test, $p$-value=0.045). Table 2 regresses the
dummy variable Investment on Female using a probit model. The most stringent
version of the model includes behavioral and demographic controls, session
dummies and clusters standard error at the individual level. The behavioral
controls, constructed in the same way as in BVWR, are: risk, altruism, non-
conformity and agreeableness. In addition, we control for the share of females
in a session to account for the behavioral responses owing to the variation in
the number of women in a session. The demographic controls are: age, religion,
caste, years in college and family income categories.101010For variable
definitions and treatment averages, refer to Table A1. Through this, we match
the exact specification reported in BVWR. The coefficient corresponding to
Female is positive and significant, suggesting that the females volunteer
significantly more than men do. This gender gap is also clear when one looks
at the probability of investing over period in Figure 3(a).111111Additional
robustness checks with timeout as a control variable does not change the
result (not reported). Thus, we closely follow BRVW in terms of our
experimental design and empirical specification and replicate the gender gap
in the likelihood of volunteering.

######  Result 3.

Women volunteer significantly more than men in the absence of any form of
social recognition.

Figure 2: Gender gap in investment across treatments

The figure plot the probability of $Investment$, separately for each gender
across the treatments. In the baseline treatment, females invest 2.88 times,
while males invest 1.98 times ($t$-test, $p$-value=0.03). In Treatment ☺
females invest 2.86 times while males invest 2.62 times ($t$-test, $p$=0.60).
In Treatment ☹ females invest 3.41 times while males invest 2.39 times
($t$-test, $p$-value=0.13). In treatment ☺☹ females and males invest 2.92 and
2.94, respectively ($t$-test, $p$-value=0.97). The equality of probability of
$Investment$ between male and female is compared for each treatment. ***
p$<$0.01, ** p$<$0.05, * p$<$0.1,†p$=$0.13.

In the ☺ treatment, the picture is quite different. Females invest 2.86 times
while males invest 2.62 times (t-test, p-value=0.60). The median
$TotalInvestment$ for males and females are 2 and 3, respectively (Mann
Whitney test, $p$-value=0.51). The corresponding regression results are
reported in Columns (4)-(6) of Table 2, where the most stringent
specification, model (6), includes behavioral and demographic controls and
session dummies. The coefficient of females is consistently statistically
insignificant. The fact that the gender gap is closed is also clear from
Figure 3(b). This gives us our next result.

######  Result 4.

In the presence of positive social recognition for volunteers, there is no
difference in the likelihood of volunteering between men and women.

Thus, when volunteers receive positive social recognition, the erstwhile
gender gap disappears. In the ☹ treatment, females invest 3.41 times on an
average, while males invest only 2.39 times. While the raw gender gap is not
statistically significant at the 10% level (t-test, p-value=0.13), column (9)
of Table 2 presents the coefficient of Female after controlling for behavioral
and demographic variables and sessions. The coefficient of Female is positive
and significant at 5% level.121212The raw gender-gap is larger in the ☹
treatment than in Baseline, so is the standard error. The latter makes the
gender-gap statistically insignificant. Further, the median $TotalInvestment$
for males and females are 1 and 3, respectively (Mann Whitney test,
$p$-value=0.03). The mean and median comparisons suggest that negative social
recognition is unable to close the gender gap in volunteering, a finding which
is supported by Figure 3(c).

######  Result 5.

Women volunteer significantly more than men in the presence of negative social
recognition for non-volunteers.

Finally, in ☺☹ treatment, on average, the females invest 2.94 times while the
males invest only 2.92 times. Once again, the gender gap is statistically
insignificant (t-test, p-value=0.97), indicating that recognizing investors
positively and non-volunteers negatively close the gender gap in our setup.
The median $TotalInvestment$ for males and females are 2 and 3, respectively
(Mann Whitney test, $p$-value=0.91). The corresponding regression results
reported in col (10) - (12) of Table 2 show that the coefficient of Female is
statistically insignificant. This is further confirmed by Figure 3(d).

######  Result 6.

There is no difference in the likelihood of volunteering between men and women
when volunteers are positively socially recognized, and non-volunteers are
negatively socially recognized.

Note that our experiment is not sufficiently powered to answer, for instance,
whether males invest when positive social recognition is offered compared to
the baseline or whether females invest more under negative social recognition
than under positive social recognition.

How do the treatments affect volunteering decisions among men and women? A
cursory look at the data reveals that men invest more in ☺ than in Baseline,
however, the difference is not statistically significant (t-test,
p-value=0.13). They do not invest significantly more in the ☹ than in
Baseline, either. However, in the ☺☹ treatment, men invest significantly more
than in Baseline ($t$-test, $p$-value=0.04). These results are consistent with
the robust finding in the literature that, in general, men compete more than
women (Niederle and Vesterlund, 2007, 2011). In this case, men compete more,
not for monetary, but non-monetary rewards. Generally, competition for non-
monetary rewards have been found to affect behavior such as altruism (Duffy
and Kornienko, 2010). Ours is a case in the point. Women, on the other hand,
invest more in the ☹ treatment than in Baseline, but the difference is not
statistically significant ($t$-test, $p$-value=0.32). The difference in
volunteering for women between the other treatments with respect to Baseline
is minimal. Overall, the data offers some suggestive evidence that men react
more to positive social recognition, while women tend to respond more to
negative social recognition. The latter is consistent with earlier studies
which find a greater sense of shame among women (Ludwig et al., 2017). Put
together, the findings suggest that a combination of positive and negative
social recognition may be an optimal strategy to increase volunteering in
organizational settings. However, this is only suggestive and future research
should aim to uncover the precise within-gender effects of social recognition.
In addition, future research should also investigate the role of asymmetry in
preference for social recognition among males and females and examine if such
asymmetries can be used to plug gender-gaps in other areas.

Figure 3: Gender gap in investment across treatment over round

The figure plots the gender-gap across treatment over period.

Table 2: Gender Differences in Probability of Investment Across Treatment

| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | (12)
---|---|---|---|---|---|---|---|---|---|---|---|---
Female | 0.0904** | 0.0866** | 0.0940* | 0.0246 | 0.0261 | 0.0190 | 0.103 | 0.0835 | 0.137** | 0.00197 | -0.0364 | -0.0307
| (0.0427) | (0.0429) | (0.0514) | (0.0460) | (0.0457) | (0.0495) | (0.0650) | (0.0624) | (0.0584) | (0.0587) | (0.0580) | (0.0583)
Round | -0.00952* | -0.00959* | -0.00969* | 0.00124 | 0.00112 | 0.00113 | -0.00122 | -0.00137 | -0.00154 | -0.00404 | -0.00412 | -0.00416
| (0.00572) | (0.00569) | (0.00567) | (0.00526) | (0.00526) | (0.00526) | (0.00434) | (0.00449) | (0.00468) | (0.00490) | (0.00502) | (0.00510)
Female Share Session |  |  | 0.00495 |  |  | 0.135 |  |  | -1.580 |  |  | -0.687
|  |  | (0.201) |  |  | (0.304) |  |  | (1.494) |  |  | (0.802)
Observations | 750 | 750 | 750 | 750 | 750 | 750 | 600 | 600 | 600 | 750 | 750 | 750
Treatment | Baseline | Baseline | Baseline | ☺ | ☺ | ☺ | ☹ | ☹ | ☹ | ☺☹ | ☺☹ | ☺☹
Behavioral Controls | No | Yes | Yes | No | Yes | Yes | No | Yes | Yes | No | Yes | Yes
Demographic Controls | No | No | Yes | No | No | Yes | No | No | Yes | No | No | Yes
Session FE | No | No | Yes | No | No | Yes | No | No | Yes | No | No | Yes

  * †

Dependent variable: individual investment decision (1-invest, 0-don’t invest).
The table presents marginal effects. Standard errors are clustered at the
individual level and reported in parenthesis. Behavioral controls include
controls for preferences for risk , altruism, non-conformity and
agreeableness. Demographic controls include controls for age, religion, caste,
years in college and family income categories. *** p$<$0.01, ** p$<$0.05, *
p$<$0.1

##  4 Discussion and Conclusion

Babcock et al. (2017)’s influential work documents that women volunteer more
often than men for tasks that are less likely to influence their evaluation
and performance bonuses. Not only that, women also get asked to volunteer for
low-promotability tasks more often. A key question is how institutional
mechanisms can be put in place such that the gender gap in volunteering in
low-promotability tasks can be reduced.

We conduct a novel laboratory experiment to test whether socially recognizing
volunteers for their effort can reduce the gender gap in volunteering.
Besides, we also test the effectiveness of negative social recognition for
non-volunteers and positive and negative social recognition for volunteers and
non-volunteers, respectively. Our results indicate that positive social
recognition and both positive and negative social recognition close the gender
gap in volunteering; however, negative social recognition does not. Overall,
the interventions prove efficient: increase the likelihood that at least
someone volunteers for such tasks.

Our paper offers key policy recommendations to organizations. Any organization
has tasks which directly add value to the objective it primarily cares about,
and tasks which need to be done, but does not directly contribute to the
organization’s main objective. Consequently, the latter tasks are less
rewarding and inevitably attract fewer people. Non-monetary incentives, such
as social recognition, can increase the relative attractiveness of such tasks
without changing the monetary payoff structure and in the process help close
gender-gaps in volunteering for such tasks. Recent research indicates that
there are significant welfare effects of such non-monetary policy
interventions (Butera et al., 2019). Future research should investigate the
welfare effects through through the lens of gender.

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##  A Appendix

Table A1: Descriptive Statistics

Variable | Description | Baseline | Treatment ☺ | Treatment ☹ | Treatment ☺☹
---|---|---|---|---|---
|  |  |  |  |
Outcome |  |  |  |  |
TotalInvestment |  Total number of times an individual invested i.e. [0,10] | 2.39 | 2.73 | 2.88 | 2.93
Investment |  =1 if individual invested in any particular round | 0.239 | 0.273 | 0.288 | 0.293
GroupInvestment |  =1 if someone from the group invested in a round | 0.72 | 0.82*** | 0.87*** | 0.88***
Controls |  |  |  |  |
Female Share Session | Share of female subjects in any session | 0.453 | 0.48 | 0.483 | 0.48
Behavioral Controls |  |  |  |  |
Altruism | Index using question 9 to 11 in Survey | 4.18 | 4.06 | 3.99* | 4.11
Risk | Number of safe choices in Holt and Laury risk game | 5.45 | 5.79 | 5.37 | 5.33
Non-Conformity | Index using question 6 to 8 in Survey | 3.61 | 3.62 | 3.8 | 3.56
Agreeableness | Index using question 12 to 20 in Survey | 3.76 | 3.68 | 3.79 | 3.75
Demographic Controls |  |  |  |  |
Age | in years | 19.4 | 19.81* | 19.52 | 19.49
Female | =1 if subject is female | 0.45 | 0.48 | 0.48 | 0.48
Caste |  =1 General, =2 SC , =3 ST , 4 OBC , 5 if Prefer not to say | 1.47 | 1.44 | 1.52 | 1.84
Religion |  =1 Hindu , =2 Muslim , = 3 Christian , =4 Prefer not to say | 1.57 | 1.71 | 1.58 | 1.47
Family Income |  =1: EWS , =2: LIG , =3: MIG , =4: HIG , =5: Rich , =6: Super Rich | 4.05 | 4.31 | 3.93 | 3.83
Year in College |  =1 UG 1st , =2 UG 2nd , =3 UG 3rd , =4 UG 4th | 2.36 | 2.52*** | 2.23** | 2.43
Sample Size |  |  |  |  |
Sessions |  | 6 | 6 | 5 | 6
Subjects |  | 75 | 75 | 60 | 75
Rounds | Number of times the game is played | 10 | 10 | 10 | 10
Observations |  | 750 | 750 | 600 | 750
*** p$<$0.01, ** p$<$0.05, * p$<$0.1 |  |  |  |  |

  * †

This table reports the mean of each of our control variables. A t-test is used
to test statistical significance for difference between Treatment Baseline and
each of the recognition treatments.

(a) Probability of Investment over round

(b) Probability of Group Investment over round

Figure A1: Comparing Individual and Group Investment over round and across
treatment Figure A2: Distribution of Total Investment across Treatments Figure
A3: Screen with the Positive Social Recognition Figure A4: Screen with the
Negative Social Recognition Figure A5: Screen with the Positive and Negative
Social Recognition

##  B Experimental Instructions

See pages - of FinalInstructions.pdf See pages - of PostExperimentSurvey.pdf


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