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Overcoming Free-Riding in Information Goods:

Sanctions or Rewards?

Matthew J. Hashim mhashim@email.arizona.edu

Jesse C. Bockstedt bockstedt@email.arizona.edu

September 24, 2015

Abstract

Many environments involving information goods suffer from an extensive free-riding

problem. For example, social loafing and lurking on content and discussion forums, leeching on

file-sharing networks, or pirating of digital goods. Despite the use of incentive-based

interventions in practice, it is not always clear which interventions result in the best outcomes for

all players involved. We conduct a lab experiment using a public goods game to explore the role

of rewards and sanctions in mitigating free-riding behavior at both individual and group levels.

Our results provide interesting insights and non-obvious consequences regarding the use of

negative and positive incentives to improve total welfare. Sanctioning only the worst free-rider in

the group results in a significant decrease in free-riding for the worst free-rider and marginal

decreases in free-riding for all other group members. Rewarding only the highest contributor in

the group results in a significant increase in free-riding for that individual and everyone else in

the group. Sanctioning the group results in an increase in free-riding, whereas rewarding the

group results in a decrease in free-riding. Overall, our research offers insights for the design and

implementation of interventions and incentive schemes in information goods environments

having a free-rider problem.

1. Introduction

Free-riding is a common problem in many economic environments involving information goods

and services. For example, peer-to-peer (P2P) file sharing networks have been notoriously

plagued with free-riders, increasing the burden of supporting the network to those that choose to

share content (Johar et al., 2011; Krishnan et al., 2004). User-generated content platforms such

as Wikipedia also have a very large ratio of free-riders to contributors (Gardner, 2013). A similar

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problem surfaces as social loafing and lurking in online discussion forums (Chidambaram and

Tung, 2005), where very few members of the forum actually contribute to the threads therein.

Concerns regarding the free-riding of information goods are not just limited to content

sharing platforms but may also affect other technology-enabled contexts. Virtual team

performance can suffer due to free-riders (Furst et al., 1999), the effectiveness of group decision

support systems can be reduced by free-riding (Reinig and Shin, 2002), and information security

risks can also increase as result of software and security free-riding (August and Tunca, 2008).

Fortunately, in these contexts an information system is a fundamental component that

enables the information good. Accordingly, controls or interventions may be implemented by

the information system, making it is possible for a moderator, legal body, or platform to

introduce interventions to combat free-riding. In fact, an important characteristic of information

goods, information services, and online platforms, is that interventions can be easily

administered, monitored, and tested through software or analysis of network communications.

For example, in many modern P2P file sharing systems, a user’s download capabilities are

directly correlated to the file contributions they make to the network. Also, in the context of

piracy, the Recording Industry Association of America (RIAA) monitors network

communications and actively pursues legal actions against individuals and universities for illegal

copying and sharing of licensed content. However, despite the common use of interventions in

practice and the ability to monitor the effects of these interventions, little research has evaluated

the effectiveness of interventions suitable for information goods contexts. Thus it is not clear

what interventions result in the best outcomes for all stakeholders involved.

We explore the use of interventions to address the information goods free-rider problem

by drawing primarily upon the experimental economics literature. Several studies have examined

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the effects of sanctions on free-riding behavior, however, there has been little research that

addresses the impact of rewards on free-riding behavior (see Section 3 for a review of the

literature). Furthermore, we are unaware of any research that compares the differences in free-

riding outcomes for rewards and sanctions as they are enforced by an exogenous system (i.e.,

information system) to different groups of market players, and in response to the players’

behavior. The ability to monitor free-riding and exogenously apply incentives to shape behavior

is unique to the information systems context, and has generally not been studied in the prior

economics of free-riding literature. In this paper we attempt to fill this gap in the literature while

simultaneously leveraging the unique characteristics of information systems applications (Goes,

2013). We address the following research questions: Which exogenously-applied intervention

reduces free-riding more effectively, rewards or sanctions? To be most effective in reducing free-

riding, should exogenously-applied rewards and/or sanctions be applied at the individual or

group level?

To address our research questions we perform a laboratory experiment using a public

goods game. We implement our research using this setup for several reasons. First, the types of

information goods motivating our study share the fundamental characteristics of public goods

(Varian, 1998; McConnell, 2011), as described in detail in Section 2 of this paper. Second,

similar to free-riding of information goods, there is extensive experimental research on public

goods that has recognized the existence of free-riding behavior (see e.g., Davis and Holt, 1993,

chap. 3, for an overview). Third, although an abstract laboratory game is a simplification of

reality, that simplification allows us to identify the underlying mechanisms driving behavior, and

free of potentially confounding factors. That is, the underlying mechanism driving behavior

remains the same in an abstract game and reality, so our findings are meaningful to real contexts.

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Thus, simplification allows us to directly assign experimental manipulations to the associated

results.

Our laboratory experiment allows us to study the causal effects of exogenously-enforced

free-riding interventions in information goods through randomization of assignment of

participants to treatments. Given our research questions and the motivation provided by the

information goods context, we implement individual- and group-level interventions in our study.

Individual-level interventions include sanctioning a chronic free-rider and rewarding a generous

contributor. For example, in a real-world IS context, an industry group such as the RIAA may

file a lawsuit (i.e., sanction) against an individual for piracy. Also, a user-generated content

platform may give status, recognition, or perks (i.e., reward) to an individual for adding content.

Alternatively a sanction/reward can be applied to an entire group rather than an individual. For

example, seeders of content in P2P networks may enable a software setting that limits (i.e.,

sanctions) the upstream bandwidth to all users of the network due to chronic leeching. Further,

contributions from a few individuals to a platform such as Wikipedia benefits (i.e., rewards) the

consumers of content due to an increased availability of knowledge and resources to all.1

The results of our experiment provide interesting insights on the behavior of free-riding

and the use of negative or positive incentives to improve outcomes. Specifically, we find that

rewarding all players in a group leads to a reduction in free-riding on average across all group

members. Conversely, sanctioning all players in a group leads to a significant increase in free-

riding on average across all group members. Interestingly, applying interventions at the

individual level has non-obvious consequences. Sanctioning only the worst free-rider in the

group results in a significant decrease in free-riding for the worst free-rider, and marginal

1 The literature has also established individual contributions as a proxy for ‘work effort’ towards provision of a good

(see e.g., Dickinson, 2001; Dickinson and Isaac, 1998).

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decreases in free-riding for all other group members. Rewarding only the highest contributor in

the group results in a significant increase in free-riding for that individual and everyone else in

the group. There are significant implications of these results for the design and implementation

of interventions and incentive schemes in information goods environments that suffer from the

free-rider problem.

The remainder of our paper is organized as follows. Section 2 describes the background

and setup for the public goods game used in our experiment, and also provides a theoretical

foundation for the sections that follow. Accordingly, our hypotheses and treatments are

developed based upon a discussion of the relevant literature in Section 3. The experimental

design and implementation is detailed in Section 4. Analysis and results are presented in Section

5, and we conclude with a discussion of implications and policy recommendations in Section 6.

2. Background on Public Goods Games

We first provide a background on public goods games, which is a common approach for studying

the free-rider problem in economics (e.g., Samuelson, 1954; Groves and Ledyard, 1977;

Grossman and Hart 1980). After providing this background, we discuss how we apply this

approach to the unique context of information systems. The free-rider problem occurs when

those who receive positive utility from resources, goods, and services do not pay for them, which

can result in an under provisioning, overuse, or degradation of the resource, good, or service.

Free-riding is most commonly observed in public goods, which are goods that are both non-

excludable and non-rivalrous. In other words, individuals cannot be effectively excluded from

using a public good, and the use of the public good by one individual does not reduce its

availability to other individuals. Certainly, not all information goods and services can be

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modeled as public goods, but there are many examples that align well with the definition of a

public good (Varian, 1998). For example, prior research has presented Wikipedia (Zhang and

Zhu, 2011), open source software development (Baldwin and Clark, 2006; Johnson, 2002;

Bessen, 2006), online discussion forums (Lakhani and Von Hippel, 2003; Rafaeli et al., 2004),

peer-to-peer file sharing networks (Hashim et al., 2014; Krishnan et al., 2003), and even the

Internet itself (Huberman and Lukose, 1997; Nissenbaum and Introna, 1999; Introna and

Nissenbaum, 2000) as public goods. Additionally, each of these examples commonly suffer

from, or are vulnerable to, the free-rider problem. Therefore, the insights drawn from using a

public goods framework will be applicable in many information systems contexts.

The primary mechanism for studying the free-rider problem empirically is the public

goods game (e.g., Andreoni, 1988).2 In the standard public goods game from experimental

economics, the players independently choose how many tokens in their private endowment to

contribute to a public account (the public good). The sum of tokens in the public account is

multiplied by a factor and then the new total is split among the players. The game is designed to

test the free-riding problem since it provides no incentive to contribute to the public account,

assuming rational self-interested players. That is, players are strictly better off by deviating to a

lower contribution level than the other players in their group. We use the standard setup for a

public goods game in our experiments with the exception of our manipulation of the

multiplication factor. Specifically, we vary the marginal per capita return (MPCR) in the latter

part of the experiment based on treatment conditions (described further in Section 3).

2 Often referred to as a voluntary contribution mechanism (VCM).

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2.1 Game Setup and Parameters

We base our game on the standard public goods game, which considers a group of n symmetric

individuals concerned with the value derived from the provision of a public good. Each

individual i is endowed with an amount of tokens y, which can be held in a private account or

contributed to the public good (i.e., a public account). Note y is the same for all individuals.

Individuals simultaneously, independently, and privately choose to contribute gi tokens from

their endowment to the public good (0 ≤ gi ≤ y). An individual’s earnings from the public good

are determined by summing the total contributions by all individuals in the group, and

multiplying the summed contributions by the MPCR ai. The standard voluntary contribution

mechanism (Isaac et al. 1984), or payoff function, for individual i is linear and given by:

𝜋𝑖 = 𝑦 − 𝑔𝑖 + 𝑎𝑖 ∑ 𝑔𝑗𝑛𝑗=1 ,

where 0 ≤ ai ≤ 1. Our game and payoffs closely follow the prior literature (e.g., Faillo et al.,

2013; Fehr and Gächter, 2000). Where our game deviates from the prior literature is the manner

in which ai is manipulated in each treatment during the latter part of the experiment. Note that

our treatment manipulations (i.e., possible heterogeneous values of ai under certain treatments)

can result in asymmetric payoff functions for the players, but the manipulations do not have an

effect on the equilibrium play of the game as long as ai > 1 / n.

In our experiment, we set n = 4, with each player having an initial endowment y = 50

tokens. In the baseline game before any treatment manipulations are introduced, we set the

initial MPCR 𝑎𝑖0 = 0.5 ∀ 𝑖. In our treatment manipulations ai is always in [0.3, 0.7], but may be

heterogeneously set among individual players. Given we use ai > 1 / n in all conditions, the

standard prediction of the public goods game is not affected. Alternatively, assuming

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coordinated play, if ai = 1 / n then players would be indifferent to contributing to the public good

and if ai < 1 / n then players would be strictly worse off by doing so.

2.2 Standard Prediction and Nash Equilibrium

The group’s total payoff is maximized when all players contribute all of their tokens to the public

account. However, the standard prediction of Nash equilibrium play requires that we assume

individuals are rational and selfish, and care only about their own monetary payoff. In a public

goods game, an individual under these assumptions is strictly better off by free-riding on the

contributions of other players and will always contribute nothing to the public good (Fehr and

Gächter, 2000). The free-riding prediction remains valid any time the individual benefits from

holding onto their endowment as a private investment rather than contributing their endowment

to the public account. That is, the MPCR is less than the return from the private investment (i.e.,

ai < 1).

Pure Nash equilibrium play is rarely observed in a standard single-period public goods

experiment, as players tend to contribute something to the public good. In a single-period game,

free-riding is typically minimal and players tend to contribute to the public good at levels

halfway between the Pareto efficient level and the free-riding level (Marwell and Ames, 1981).

However, in iterated or repeat-play public good games, it is typical to observe a decay in

cooperation as the contribution to the public account declines over time, and thus a gradual

decline in contributions toward Nash equilibrium play is observed (e.g., Isaac et al., 1985; Isaac

and Walker, 1988; Andreoni, 1995; Levitt and List, 2007; Chaudhuri, 2011). Furthermore, the

introduction of punishments (i.e., sanctions) and rewards to players has been shown to increase

cooperation and reduce free-riding behavior in certain conditions (e.g., Andreoni et al., 2003;

Sefton et al., 2007).

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As we will discuss further in Section 4, our implementation of the public goods game

uses repeat-play with matched partners, and the introduction of sanctions and rewards through

exogenous manipulation of the MPCR at the individual or group level (i.e., symmetric versus

asymmetric payoff functions). To the best of our knowledge, this is the first public goods game

experiment that studies the combination of these two factors: (1) sanctions vs. rewards, and (2)

asymmetric vs. symmetric payoff functions.

3. Hypotheses and Treatment Design

Section 2 describes the standard single-period public goods game as having a Nash equilibrium

of full free-riding by individuals. In this standard game, no individual-specific contribution

information is shared among the group of players and there are no direct consequences to the act

of free-riding other than the impact it has on the calculated payoff. When sanctions and rewards

are introduced to the game, however, we expect individual contributions and thus the total group

welfare to depend on reactions to these incentives. For example, applying a sanction or reward

to a player not only changes their payoff, but should also impact their expectations of future

sanctions and payoffs. Making the information about a sanction that has been applied to an

individual public to all players in a group is expected to change the expectations of future

payoffs and sanctions of all group players. We now review some of the related literature to

support our hypotheses and treatment design.

Although the idea of applying sanctions as a mechanism to mitigate free-riding behavior

is not a new one (e.g., Ostrom et al., 1992; Yamagishi, 1986), a recent seminal paper by Fehr and

Gächter (2000) found that punishment is particularly effective at increasing contributions to

public goods. Faillo et al. (2013) replicate Fehr and Gächter (2000) but with a refinement that

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allows pro-social players to punish anti-social players (i.e., ‘legitimate’ punishment). Many

other experimental economics papers followed that study these various sanctioning mechanisms

involving players punishing other players (see e.g., Bochet et al., 2006; Carpenter, 2007a,b;

Masclet et al., 2003; Nikiforakis, 2008). An interesting result from this body of work is that

players would usually rather incur a cost themselves than sanction another player for free-riding.

Rewards have also been introduced in similar settings but have generally been less effective than

sanctions at mitigating free-riding behavior (Andreoni et al., 2003; Sefton et al., 2007; Walker

and Halloran, 2004). In all of the previously cited papers, the sanctions and rewards are applied

from within the team itself and not from an exogenous social planner or information system. In

contrast, Dickinson (2001) and Dickinson and Isaac (1998) both implement an exogenously-

determined reward mechanism (Dickinson, 2001, adds sanctions as well) that incentivizes

individuals based upon their absolute and/or relative contribution levels to the public good.

Exogenously applying sanctions or rewards is representative of how incentives are applied in

contexts where information systems provide the platform in which the free-riding occurs.

The prior public goods research has shown it is possible to manipulate the MPCR so that

it may be used as a mechanism to apply sanctions and rewards. That is, it follows that a decrease

in the MPCR is viewed as a sanction by participants in an experiment, whereas an increase in the

MPCR is viewed as a reward. The early research on free-riding in public goods found that

decreasing (increasing) the MPCR for players resulted in more (less) free-riding (Isaac and

Walker, 1988). Follow-up studies have manipulated MPCR in a heterogeneous manner within

groups of players and have shown a similar result. To be precise, those players with a lower

MPCR contribute less to the public good and those with a higher MPCR contribute more (Fisher

et al., 1995). Both of these studies assigned MPCRs to players at the start of the experiment,

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rather than having the MPCRs assigned as per an exogenously enforced reward/sanction due to

player behavior. Fischbacher et al. (2014) conducted a public goods experiment with

heterogeneous MPCR values and a possible uncertainty of MPCR. The authors predicted and

found that treatments with a higher MPCR should result in higher contributions in comparison to

lower MPCR resulting in lower contributions. They also found that heterogeneous MPCRs

between players does not necessarily affect conditional cooperation. Therefore on aggregate,

contributions are similar to those for players in groups with homogeneous MPCRs. In contrast to

our paper, Fischbacher et al. (2014) assign heterogeneity of MPCR based upon a treatment

selection and/or by the roll of a die after playing the game (i.e., uncertainty), rather than using

any kind of carryover of behavior from one stage to another.

As regards the research on economics of free-riding in information goods, most studies

have not used the experimental economics methodology. There is a growing literature on the

economics of piracy, and many of the studies take a free-riding perspective. In this literature

stream, pricing decisions are often studied in conjunction with other factors (Chen and Png,

2003; Khouja and Park, 2008), such as the use of technology deterrence (Sundararajan, 2004),

quality levels (Lahiri and Dey, 2013), patch management (August and Tunca, 2008; Kannan et

al., 2013; Lahiri, 2012), and bundling (Bhattacharjee et al., 2009; Gopal and Gupta, 2010).

Sampling of digital goods with pricing considerations have been modeled (Chellappa and

Shivendu, 2005), and word-of-mouth effects with optimal software pricing (Liu et al., 2011)

have also been considered. However, the explicit introduction of interventions to address free-

riding in public goods games has not been used extensively in the economics of piracy literature.

We know of one paper using a framed lab experiment to gain insights about the specific digital

piracy context, and another abstract lab experiment motivated by piracy behaviors. Regarding

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the former, Hashim et al. (2014) use a modified volunteer’s dilemma game (i.e., The Piracy

Game) to study the role of the source of advice on piracy levels. In the latter paper, Hashim et

al. (2015) implement an abstract multi-threshold public goods game to understand the role of

targeted information on contribution decisions. Both of these papers are different from ours in

terms of the game as well as the intent of the research. Also, neither of those papers considered

sanctions or rewards for the players, but instead were focused on the provision of information

about contribution behaviors. Our paper complements the prior work and extends to the broader

impacts of free-riding in information goods generally. All of the work in this area, including the

present study, provides policy guidance for vendors as well as social planners in the context of

piracy as well as information goods in general. The current study is also differentiated from the

prior information systems free-riding research by making a contribution to the public goods

game literature in experimental economics.

As regards our paper, we implement sanctions and rewards in a much different manner

than the prior literature because information systems and online platforms provide the ability to

monitor and exogenously apply interventions. We carryover behavior from a previous stage in

the public goods game and allow that behavior to determine which player(s) the system will

sanction or reward in a future stage of the game. For example, imagine a digital pirate

downloading unlicensed software, being monitored by the software vendor, and then one day

being hit with a surprise lawsuit for his pirating actions. Our paper is most similar to Cadigan et

al. (2011), and Dickinson (2001). Cadigan et al. (2011) utilize a two-stage public goods game

where the first-stage is conducted to establish a baseline of contribution behavior. Subsequently,

earnings from the first-stage carryover to become either the endowment or the groups’ MPCR in

the second-stage, depending on the treatment condition. Like Cadigan et al. (2011), we also

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carryover the contribution behavior from the first-stage of our game, and then apply sanctions or

rewards in the second-stage. However, in contrast to their study, we consider individual-level

and group-level incentives, and we do not manipulate endowments, which more closely emulates

real-world free-riding in information systems. Regarding Dickinson (2001), they employ a

within-subjects design and participants are assigned heterogeneous endowments. From there,

they focus on individual incentives (bonuses or deductions of tokens) but do not consider group-

level impacts or manipulation of the MPCR. Their experimental design also provides immediate

feedback regarding high and low contribution amounts and establishes focal points (i.e., anchors)

for contribution behavior. We further differentiate our paper from the others because our

carryover of sanctions and rewards are unknown to the players. That is, the players do not

explicitly know their MPCR may change in a future stage based upon individual behavior, and so

the first-stage behaviors observed in our experimental design are likely to represent the

participants’ true contribution strategies. Therefore, the participants’ strategies in our experiment

should be free of priming, endogenously-enforced consequences, heterogeneous endowments,

and contribution focal points.

In that regard, we have integrated the idea of ‘exogenously-enforced’ sanctions and

rewards with the possibility for heterogeneous MPCRs within a group (unknown to the players).

This unique aspect of our game is particularly relevant in the context of information systems.

Information systems, information services, software, and platforms provide owners new

capabilities to act as virtual social planners and the option to apply exogenously-enforced

sanctions and rewards. Furthermore, our research is a new contribution to the economics

literature on public goods games (see Section 4 for further details about the implementation of

our experiment).

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To summarize the findings of prior literature: under the presence of heterogeneous

MPCRs within a group, the overall contribution behavior to a public good should be similar to

the contribution behavior for those groups with homogeneous MPCRs. That is, the predicted

result of increasing a particular player’s MPCR is an increase in contribution to the public good

(e.g., giving a forum contributor recognition – a reward – should result in increased utility for the

contributor). Accordingly, the sum of the groups’ contributions will increase also. Conversely,

reducing a player’s MPCR is expected to reduce a players contribution to the public good (e.g.,

litigating a pirate – a sanction – should result in less piracy by that pirate), and also reduce the

sum of the groups’ contributions. Therefore, the standard prediction of equilibrium play and the

findings from the prior literature can be extrapolated to the scenario where sanctions and rewards

are applied at either the individual or group level. Specifically, this prediction leads to the

following hypotheses:

Hypothesis 1. In a group where all players’ MPCRs are identically adjusted, lowering (raising)

a player’s MPCR will result in a lower (higher) level of contribution to the public good by that

player.

Hypothesis 2. In a group where only one player’s MPCR is adjusted, lowering (raising) that

player’s MPCR will result in a lower (higher) level of contribution to the public good from that

player; however, other group members’ contributions will not change.

As can be inferred from these hypotheses, there are two factors of interest (1) the

selection of the intervention as either a sanction (i.e., decrease in MPCR) or reward (i.e., increase

in MPCR), and (2) the application of the intervention (sanction or reward) to either an individual

or an entire group. To test these predicted hypotheses and explore these issues, we modify the

standard single period public goods game by using an iterative game and introducing a payoff

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intervention that combines the two factors of interest midway through the multi-round game. In

our design, players are matched with the same partners for all rounds, they receive the same

starting endowment at the beginning of each game, and are randomly assigned to one of four

treatment groups.

In our experiment, the four treatments proxy the interventions motivated in the

Introduction: All Increase, reward the entire group; All Decrease, sanction the entire group; One

Increase, reward only an individual; One Decrease, sanction only an individual. Formally, we

define the treatment groups as follows for a public goods game with n players who are indexed

by i = 1,…, n. Let aR and aS be the MPCRs for the reward and sanction treatments respectively,

let a0 be the initial MPCR for all players in round 1, and let aS < a0 < aR. In treatment 1, All

Increase, a reward is applied equally to all members of the group, i.e., the MPCR is

homogeneously increased for all group members: ai = aR ∀ i. In treatment 2, All Decrease, a

sanction is applied equally to all members of the group, i.e., the MPCR is homogeneously

decreased for all group members: ai = aS ∀ i. In treatment 3, One Increase, a reward is applied to

only one member of the group, i.e., the MPCR of the most generous contributor is increased

only: ahighest_contributor = aR and ai = a0 for all i such that i ≠ highest contributor. In treatment 4,

One Decrease, a sanction is applied to only one member of the group, i.e., the MPCR of the

worst free-rider is decreased only: alowest_contributor = aS and ai = a0 for all i such that i ≠ lowest

contributor.

4. Implementation and Parameterization

We implemented the public goods game as defined in Section 2. A total of 30 rounds were

played during each session of the experiment and the four treatments were tested. Treatments

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differed by the MPCR assigned to the participants during the second stage of the game, and are

detailed in Table 1. The MPCR assignment is based upon the discussion in Section 3 described

earlier, and we set aR = 0.7 and aS = 0.3.

Table 1: Experimental Treatments

Treatment Name MPCR in Rounds 1-15 MPCR in Rounds 16-30

All Increase 0.5 0.7

All Decrease 0.5 0.3

One Increase 0.5 0.7 for one player; 0.5 otherwise

One Decrease 0.5 0.3 for one player; 0.5 otherwise

In the experiment, groups of participants were randomly and anonymously assigned by a

computer using a partners matching protocol established before the start of the first round

(Andreoni and Croson, 2008; Croson, 1996). The use of partners matching was required because

of the two-stage design of our experiment. That is, the consequences of contribution behavior

from the first stage carryover to the second stage. Therefore, re-matching of participants using a

strangers protocol would not allow for sanctioning (rewarding) of low (high) contributors as the

members of the groups would change over time. However, participants are never informed of

who is in their group or about individual contributions of their group members. Participants

remain in the same group for the entire session.

At the beginning of the experiment the participants are informed they will play 30

rounds. Participants are informed that at the conclusion of the experiment, the computer will

randomly choose three of the 30 rounds for payment, and pay each participant their average

payoff over these rounds at a conversion rate of 10 tokens per 1 US dollar. Randomly selecting a

few rounds for payment avoids wealth effects because the earnings from each round are valued

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independently of each other, reducing the ability for accumulated wealth to affect decisions in

later rounds.

During each round of the experiment participants are shown their payoff function, the

amount of their endowment y = 50 tokens, and their MPCR ai. The participants always know

their own MPCR as well as the MPCR of the other players in their group. Participants then

individually and simultaneously choose their individual contribution to the public good, gi. For

gi, participants are allowed to only enter integers in [0,y]. Once all of the participants input their

contributions, the sum of the group contributions to the public good is shown, along with the

individual payoff for the round (Croson, 2001). Participants are never informed about their

group members' individual contributions to the public good and participants receive a new

endowment at the beginning of each round and the aforementioned procedures are repeated for

every round of the experiment.

At the beginning of Round 16, all participants are halted by the computer and shown a

message corresponding with their treatment. The participants are never ex ante told if or when

the MPCR would change prior to receiving the message. After the participants read the message

the experiment was subsequently restarted by requiring them to input an unlock code (provided

by the experiment administrator). Table 1 describes the changes to MPCR based on the

treatment, and Table 2 provides the content changes that are substituted in the following message

that is received by participants:

“Due to the [content 1] contribution levels of one of your group members, [content 2] in

your group now [content 3] a group account factor of [content 4] for the rest of the experiment

unless you are otherwise notified.”

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Table 2: Message Content by Treatment

Treatment Content 1 Content 2 Content 3 Content 4

All Increase high ALL PLAYERS have 0.7

All Decrease low ALL PLAYERS have 0.3

One Increase high THAT PLAYER has 0.7

One Decrease low THAT PLAYER has 0.3

The experiment was conducted at a large university in the Southwest region of the United

States in the fall of 2013 and the spring of 2014. Participants were recruited by email using the

laboratory's on-line recruitment system, and participation was limited to a single session.

Participants with prior public goods experience were excluded from recruitment to control for

biased contribution behavior as a result of their prior experience.3 Upon entering the laboratory,

participants were randomly assigned to individual computers and communication between

participants was not allowed during the session. Copies of the experiment instructions were

provided to each participant and were read aloud by the experiment administrator. The

experiment was computerized using the z-Tree v.3.3.6 software package (Fischbacher, 2007).

A total of 116 individuals participated in our experimental sessions. Each session

consisted of multiple groups of 4 participants, with a total of between 12 and 16 participants in

the room during a session. Each individual participated in only one treatment. The breakdown of

participants to treatments are as follows: 28 participants in All Increase, 32 participants in All

Decrease, 28 participants in One Increase, and 28 participants in One Decrease. Each session

lasted approximately 45 minutes and participants were compensated $11.64 US on average

which included their experiment earnings plus a $5 US show-up fee. All participants were paid

3 Public goods games utilize a standard setup, so subjects with prior public goods experiment experience may ex

ante fix a strategy without consideration for the treatment manipulations in the experiment. To account for this

possibility, we randomly recruit subjects from the pool of subjects that do not have public goods experience.

19

in cash privately and individually at the conclusion of the experiment after completing a short

demographic questionnaire.

5. Results

The results section presents the experimental data and empirical tests, followed by a summary of

our hypothesis testing in Section 6. First, we validate the random assignment of participants to

treatments by comparing contributions prior to our manipulation of MPCR and discuss our

aggregate results in Section 5.1. We then present our between-subject analyses in Section 5.1.1

and within-subject analyses in Section 5.1.2 to determine the impact of the message and

associated changes to MPCR on contribution behavior. Lastly, we conduct sub-sample within-

subject analyses to understand the effects of sanctions and rewards, which are presented in

Section 5.2.

5.1 Aggregate Results

Table 3 shows the average contributions for the four treatments, and are aggregated by groups of

rounds pre- and post-MPCR manipulation. The average contributions for all four treatments are

similar across Rounds 1-15 as expected because the treatment manipulations have not been

introduced until Round 16. In fact, we cannot statistically distinguish contributions under any

one treatment from another in Rounds 1-15. We begin our analysis by taking the average

contribution per participant over Rounds 1-15 and using those averages in a Kruskal-Wallis test

(χ2 = 0.490, p = 0.92). Although the Kruskal-Wallis test does not control for correlations

between contributions for each participant, the non-parametric test assists in building the story

for contribution behavior in our experiment. We follow the non-parametric tests by conducting

random effects panel data Tobit regressions, as shown by Table 4. Random effects Tobit models

20

are appropriate for our analysis due to the panel data nature of our data (i.e., multiple participants

with observations per participant), as well as the potential for censorship of the outcome variable

at 0 or 50 tokens. Also, random effects are appropriate for our analysis because the participants

are randomly selected from the population, randomly assigned to treatments, and our controlled

laboratory experiment design limits the impact of omitted variables. Therefore, our data do not

suffer from those types of biases of concern in most secondary data analyses. Further, our use of

a between-subject design results with treatment effects being perfectly collinear with participant

fixed effects. The robustness of the result comparing contributions between treatments for

Rounds 1-15 are confirmed, and we cannot distinguish contribution levels between treatments

for Rounds 1-15.

Table 3: Average Contribution by Treatment

Treatment Obs. Rounds 1-15 Rounds 16-30

All Increase 420 18.57 (16.87) 20.82 (18.79)

All Decrease 480 16.98 (16.61) 8.03 (10.94)

One Increase 420 17.49 (15.26) 13.66 (15.73)

One Decrease 420 18.66 (17.63) 17.80 (17.01) Standard deviations in parentheses.

As an additional test of baseline contribution behavior and random assignment of

participants to treatments, we conduct comparisons of contributions between treatments in

Round 1, and the results are consistent with those from Rounds 1-15 (Kruskal-Wallis test, χ2 =

1.97, p = 0.58). Therefore, we are confident in the random assignment of participants to each

treatment and setup of our experiment and because the pre-intervention contribution behaviors

are statistically indistinguishable between each of the treatments.

21

(a) Rounds 1-15

(b) Rounds 16-30

Figure 1: Average Contribution per Round by Treatment

We also present a graphical representation in Figure 1 of the aggregate results in Table 3,

but instead displayed on a per round basis. The result discussed in the prior paragraph regarding

22

Rounds 1-15 is given by sub-Figure 1(a). Consistent with the prior result, we are unable to

visually distinguish average contributions in any one treatment from another treatment. In

contrast, sub-Figure 1(b) provides the graphical equivalent of the aggregate figures provided in

the Rounds 16-30 column of Table 3. Similar to the table, there is a clear segregation of average

contributions provided by the values in the sub-figure. Contributions in All Increase appear to be

the highest, followed by One Decrease, then One Increase, and with All Decrease having the

lowest average contributions. In the next subsection we provide statistical tests for these

observations.

5.1.1 Between-Subject Treatment Effects

In this section we present between-subject analyses of the treatments after MPCR manipulation

(i.e., Rounds 16-30). It is apparent from the averages displayed in Table 3 and sub-Figure 1(b)

that there are significant differences in contributions between the treatments. A Kruskal-Wallis

test using the average contribution by participant provides initial indications that the

contributions between all of the treatments over Rounds 16-30 are not equal (χ2 = 17.26, p <

0.01).4 To tease apart the differences between treatments, we further explore our comparisons of

contributions in Rounds 16-30 using panel data regressions.

Results from random effects Tobit regressions are presented in Table 4 support the

findings in Table 3 and sub-Figure 1(b), in that contributions by participants are significantly

different between treatments. The results provide evidence that there are significant differences

between treatments in the latter half of the experiment. The regressions in Table 5 use All

Decrease as the baseline treatment, with the same relationship of contribution levels by treatment

4 As with the non-parametric comparison of contribution behavior in Rounds 1-15, the Kruskal-Wallis comparison

between treatments of Rounds 16-30 does not take into account correlations between contributions for participants.

23

emerging, regardless of baseline treatment used for the regression. The following pattern

emerges after ordering the average contributions from highest to lowest: All Increase, One

Decrease, One Increase, All Decrease.

Table 4: Random Effects Panel Tobit Regressions for Individual Contributions

Variable Rounds 1-15 Rounds 16-30

All Increase 4.44 (4.36) 18.92*** (0.09)

One Increase 3.84 (4.35) 9.08** (4.43)

One Decrease 1.22 (4.38) 11.78*** (4.43)

Round -0.91*** (0.10) -1.16*** (0.09)

Constant 20.51*** (3.08) 29.37*** (3.69)

Observations 1740 1740

Participants 116 116

Obs. Censored at 0 378 429

Obs. Censored at 50 175 176

Wald χ2 92.42*** 173.62*** The dependent variable is the amount contributed to the group account

by the individual i in round t, between 0 and 50 inclusive. Modeled as a

Random Effects Tobit with censoring at zero and fifty. Standard errors

are shown in parentheses.

*** p < 0.01; ** p < 0.05; * p < 0.10

The results presented to this point illustrate the effect of manipulating MPCR on

contribution behavior between treatments. However, we have not addressed a major question

thus far in our analysis. That is, how does the baseline behavior in Rounds 1-15 compare to the

treated behavior in Rounds 16-30 after an exogenous intervention? We address this question in

the next subsection.

5.1.2 Within-Subject Treatment Effects

It is a well-known stylized fact that contributions decrease over time in public goods games (i.e.,

over time participants approach the Nash outcome by free-riding on the contributions of others).

Accordingly, many of the studies cited in Section 3 attempt to deal with the problem in order to

24

maintain (or improve) levels of contributions over time. In this section we conduct within-

subject analyses to assess the effect of our treatment manipulations on free-riding behavior.

To assess the extent of the within-subject effects associated with the MPCR change at

Round 16, we conduct random effects Tobit regressions to detect differences in contribution

levels within each treatment. Results by treatment are shown in Table 5, and include a dummy

variable ‘After Intervention’ that is assigned a value of 0 for Rounds 1-15 and a value of 1 for

Rounds 16-30. Comparing contributions in Rounds 1-15 vs. Rounds 16-30, we find that

contributions slightly increase after the intervention for All Increase and remain consistent in

One Decrease (i.e., free-riding is mitigated to some extent for both treatments). However,

contributions significantly decrease when comparing Rounds 1-15 vs. Rounds 16-30 for All

Decrease and One Increase, suggesting an increase in free-riding behavior in both treatments in

comparison to the contributions in the rounds before the intervention.

Table 5: Random Effects Panel Tobit Regressions for Individual Contributions by Treatment

Variable All Increase All Decrease One Increase One Decrease

After Intervention 3.08** (1.42) -10.91*** (1.09) -5.16*** (1.13) -0.36 (1.51)

Constant 17.93*** (3.03) 14.11*** (2.38) 17.21*** (2.63) 14.47*** (3.36)

Observations 840 960 840 840

Participants 28 32 28 28

Obs. Censored at 0 159 262 145 241

Obs. Censored at 50 128 45 87 91

Wald χ2 4.73** 100.70*** 20.99*** 0.06 The dependent variable is the amount contributed to the group account by the individual i in round t, between 0 and 50

inclusive. Modeled as a Random Effects Tobit with censoring at zero and fifty. Standard errors are shown in

parentheses.

*** p < 0.01; ** p < 0.05; * p < 0.10

5.2 Sub-Sample Analysis of Sanctioned / Rewarded Participants

In the individual intervention conditions, not all participants’ MPCR values were adjusted. To

further analyze the implication of heterogeneous MPCR manipulations, we now conduct within-

25

subject sub-sample analyses of the One Increase and One Decrease treatments. This analysis

will inform of us of behavior differences for those participants who are and are not sanctioned or

rewarded. That is, we create sub-samples for those participants that were and were not flagged

for treatment in Rounds 16-30, and carry the flag for each participant in the sub-sample back to

Rounds 1-15. Accordingly, Table 6 provides the average contributions in Rounds 1-15 and

Rounds 16-30, by sub-sample. In the One Increase treatment, contributions appear to decrease

by several tokens for both groups of participants, regardless if they are rewarded or are not

rewarded, consistent with the within-subject result presented in the prior subsection. However,

in the One Decrease treatment, we see a different result. Individual participants that are

sanctioned actually increase their contributions in Rounds 16-30 in comparison to Rounds 1-15.

In contrast, participants that are not sanctioned decrease their contributions only slightly on

average.

Table 6: Effects of Sanctions and Rewards on Average Contribution

Treatment Obs. Rounds 1-15 Rounds 16-30

One Increase Not Rewarded 315 14.00 (13.36) 10.28 (13.25)

Rewarded 105 27.96 (15.87) 23.82 (18.10)

One Decrease Not Sanctioned 315 22.41 (16.82) 20.61 (16.91)

Sanctioned 105 7.43 (15.10) 9.38 (14.38) Standard deviations in parentheses.

Table 7 presents random effects Tobit regressions by sub-sample to reinforce the results

shown by the average contributions in Table 6. As with the analysis used to generate the results

shown by Table 5, a dummy variable ‘After Intervention’ is assigned a value of 0 for Rounds 1-

15 and a value of 1 for Rounds 16-30. In One Increase, contributions significantly decrease for

both groups as indicated by the negative and significant coefficient on After Intervention,

whether the participants are or are not rewarded by an increase in MPCR. However, in One

26

Decrease, contributions increase in Rounds 16-30 only for those participants that are sanctioned,

whereas those participants who are not sanctioned appear to maintain their level of contributions.

Table 7: Sub-Sample Treatment Random Effects Panel Tobit for Individual Contributions

One Increase One Decrease

Variable Not Rewarded Rewarded Not Sanctioned Sanctioned

After Intervention -5.25*** (1.23) -4.59* (2.64) -2.21 (1.46) 13.10* (6.78)

Constant 12.85*** (2.11) 31.16*** (6.15) 21.47*** (3.14) -23.08*** (8.86)

Observations 630 210 630 210

Participants 21 7 21 7

Obs. Censored at 0 131 14 108 133

Obs. Censored at 50 37 50 75 17

Wald χ2 18.25*** 3.04* 2.29 3.74* The dependent variable is the amount contributed to the group account by the individual i in round t, between 0 and 50

inclusive. Modeled as a Random Effects Tobit with censoring at zero and fifty. Standard errors are shown in

parentheses.

*** p < 0.01; ** p < 0.05; * p < 0.10

6. Discussion and Conclusion

6.1 Summary of Findings

We observed some significant differences in contributions to the public good, and thus

implications for free-riding behavior, due to the sanction and reward interventions. We first

focus our attention on the group-level interventions. In the All Increase condition the individual

contributions increased slightly after the reward was applied to all group members. Although it

may seem obvious, the result is actually quite encouraging. The Nash equilibrium behavior

predicts free-riding should continue to increase over time, thus continuously decreasing

contributions should be observed. Since there is an increase in contribution levels between the

first and second half of the experiment, rewarding all players may have staved off the inevitable

reduction in contributions to the public good that are typically seen in these games. In contrast,

we observed a significant decrease in contributions in the All Decrease treatment, as would be

expected. These two results provide support for the predictions from theory outlined in

27

Hypothesis 1. Specifically, increasing payouts reduced free-riding and decreasing payouts

increased free-riding.

In terms of the individual sanction and reward conditions, we observe some non-obvious

consequences from the introduction of the individual-level interventions. In One Increase, the

rewarded individual significantly decreased her contributions as compared to the first fifteen

rounds and the rest of the group also significantly decreased their contribution behavior. In the

One Decrease condition, the sanctioned individual significantly increased their contribution as

compared to the initial fifteen rounds and the rest of the group contributions are not statistically

different from the prior rounds. These results contradict Hypothesis 2, which is based on the

existing theory on public good games and Nash equilibrium behavior. Our results provide strong

evidence that introducing sanctions and rewards that do not impact the entire group creates new

dynamics in free-riding behavior.

6.2 Implications for Policy and Practice

Of all of the conditions, One Decrease provides the most interesting incentive strategy for

reducing free-riding. It is the only sanctioning condition where a significant reduction in free-

riding is observed. Nash equilibrium behavior would predict that with no intervention, all

players should eventually free-ride in all treatments. However, in One Decrease we see the

sanctioned individual significantly reducing their free-riding and contributing more to the public

good. Additionally, the rest of the group, on average, does not significantly change their

behavior from the first 16 rounds. In other words, punishing only the worst free-rider appears to

correct the behavior of the free-rider and also sends a signal to the rest of the group.

28

Punishing only the worst free-riders may be the best strategy for reducing free-riding,

especially in contexts such as digital piracy and social loafing where free-riders of the

information goods can be identified and dealt with by the (a) system. Furthermore, public

awareness of these sanctions seems to be a good deterrent to prevent other users from increasing

free-riding. This result has interesting policy implications for the management of information

goods, information services, and online platforms that suffer from free riding. It should be a

straight forward task to incorporate mechanisms that monitor free-riding behavior information

systems and platforms. For example, online discussion forums could use visitor’s IP addresses

to track users that do not contribute. With data on free-riding behavior, online platforms and

system owners could then implement usage restrictions or reduced feature sets for the heaviest

free-riders and also make these sanctions public knowledge to other users. Information systems

and software provide a flexible and customizable environment for implementing these types of

sanctions. In the context of information goods piracy, the results of this experiment suggest that

the policy of identifying and prosecuting heavy offenders may be an effective deterrent. This is

in-line with the current practices of the Recording Industry Association of America (RIAA),

Motion Picture Association of America (MPAA), and other industry associations.

The All Increase condition also provides some evidence that rewarding everyone for

good behavior potentially prevents increases in free-riding. Further, the results of our

experiment suggest that rewards should only be applied to all group members and not to the

individual contributor. Although it may seem difficult in practice, this type of reward could

easily be applied in the context of user-generated content environments such as Wikipedia and

online discussion forums by providing more content and features for all users if certain

contribution goals are met by the community. For example, the Executive Director of the

29

Wikimedia Foundation has noted: “The average donor is paying for his or her own use of

Wikipedia, plus the costs of hundreds of other people (Gardner, 2013).”

Interestingly, the One Increase condition had counterintuitive implications. On average,

the total contributions of participants decreased significantly after a single member of the group

was rewarded. Further, the sub-sample analysis suggests both the rewarded individual and the

rest of the group increased their free-riding behavior after the intervention. This mechanism of

only rewarding the best contributors may signal to the receiver of the reward that they are over-

contributing and signal to the other group members that they are underappreciated. The results of

the experiment suggest that individual-based rewards commonly used in many discussion

forums, question and answer websites, and other information systems context may actually be

counterproductive. Indeed, recent research has shown that upon receiving such rewards, a user’s

contributions to question and answer websites tend to drop significantly (Goes et al., 2014).

Generally, information systems owners should be aware that free-riding behavior can be

reduced through the proper interventions. In standard public goods, government regulation and

taxation are often used as a way to curb free-riding behavior. Given the nature of information

systems and online platforms, owners have the capability to act as a social planner in these

environments by monitoring free-riding behavior and implementing regulations via rewards and

sanctions on their users dynamically.

6.3 Limitations and Future Research

Since we observe different free-riding behaviors between the sanctioned and rewarded

individuals and their non-sanctioned and non-rewarded group counterparts, the expected change

in total group welfare is non-obvious. Specifically, in the One Decrease condition, the increase

30

in the sanctioned individual’s contribution may or may not significantly impact the group’s total

welfare. Therefore, one potential extension of this work is to explore how the group size impacts

the effects of sanctions and rewards. Prior research has shown that the group size does not

typically change contribution behavior, except for very large groups (Isaac et al., 1994).

However, the prior research has not considered sanctions and rewards applied asymmetrically

within the group. We fixed the group size at 4 in this study; larger or smaller groups may have

different free-riding dynamics with asymmetric sanctions and rewards. Similarly, the sizes of

the sanction and reward, both absolute size and the size relative to the endowment, are likely to

impact changes in free-riding behavior. A future experiment could utilize treatments that vary

group, sanction, and reward size.

6.4 Conclusion

The general implication of the study is that how and to whom a free-riding intervention is

applied has a significant impact on resulting free-riding behavior. Our paper has provided initial

insights on how sanctions and rewards impact free-riding behavior at the individual and group

levels. In the context of information goods, the results of this experiment have implications for

incentive and mechanism design in environments where free-riding is common. Information

systems and online platforms provide unique capabilities for monitoring free-riding behavior and

implementing interventions to address this problem.

31

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37

Appendix A: Experiment Instructions

Thank you for participating in this economic experiment. You will be paid in cash for your

participation, and the amount of money you earn depends on the decisions that you and other

participants make in individual rounds. Your final payment will be determined by three random

draws done by the computer at the conclusion of the experiment. Each draw will correspond to

one round of the experimental session. The average earnings over these three randomly selected

rounds will be used to calculate your final payment. All earnings in this experiment will be

presented to you in tokens and converted to US dollars at the conclusion of the experiment. A

show-up fee of $5 US dollars will also be provided to each participant.

The conversion rate, which is identical for everyone, is: 10 tokens per 1 US dollar.

You will never be asked to reveal your identity to anyone during the experiment. You should

never offer to reveal your identity to anyone during the experiment. Your name will never be

associated with any of your decisions. In order to keep your decisions private, please do not

reveal your choices or your identity to any other participant.

You are welcome to ask questions at any time by raising your hand. Please wait for an

experimenter to come to your seat before asking your question. While the experiment is in

progress, please do not speak or communicate with other participants. This is important to the

validity of the study.

Specific Guidelines

You will participate in 30 rounds in a group with three other participants. You will not know

who is in your group. In each round you will receive an endowment of 50 tokens. The

endowment is identical for everyone. You and every member in your group have to individually

decide how much of this endowment to allocate to a group account. Before you make your

contribution decision, you will be asked to enter your estimate of the average tokens contributed

by each group member. Your contribution must be a whole number, between 0 and 50 tokens.

All decisions are made simultaneously and without communication. No other group member

will ever know how much you choose to contribute to the group account.

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Your earnings in each round are determined by combining what is left of your endowment after

your contribution, plus the earnings from the group account. The earnings equation is presented

below.

Your earnings = your endowment – your contribution + group account earnings

The earnings from the group account depend on the total group contributions. The earnings are

calculated by summing together the contributions for your group, then multiplying the summed

contributions by a group account factor. So, if the group account factor is 0.5, and your total

group contributions are 100, you will have earned 0.5 * 100 = 50 tokens as your group account

earnings.

You will always know the group account factor before making your contribution decision.

Unless otherwise specified, all players in your group will always have the same group account

factor as you.

Examples:

If your combined group account for a round is 70 tokens and the group account factor is

0.5, then you will have earned 35 tokens from the group account earnings.

If your average payout for the three randomly chosen rounds is 75 tokens, you will earn a

payment of $12.50 in cash at the end of the experiment ($7.50 in earnings + a $5.00

show-up fee).

Are there any questions?