This work is distributed as a Discussion Paper by the

STANFORD INSTITUTE FOR ECONOMIC POLICY RESEARCH

SIEPR Discussion Paper No. 02-13 Social Influences and the Private Provision of

Public Goods: Evidence from Charitable Contributions in the Workplace

By Katherine Grace Carman

Stanford University January 2003

Stanford Institute for Economic Policy Research Stanford University Stanford, CA 94305

(650) 725-1874 The Stanford Institute for Economic Policy Research at Stanford University supports research bearing on economic and public policy issues. The SIEPR Discussion Paper Series reports on research and policy analysis conducted by researchers affiliated with the Institute. Working papers in this series reflect the views of the authors and not necessarily those of the Stanford Institute for Economic Policy Research or Stanford University.

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Social Influences and the Private Provision of Public Goods: Evidence from Charitable Contributions in the Workplace

Katherine Grace Carman∗

Stanford University

January 10, 2003

This paper investigates the factors that influence an individual’s decision to make voluntary contributions to a public good, focusing on the role of social influences. Measuring social influences is challenging due to several factors: group selection may be based on unobservable tastes, there may unobservable shocks that affect all group members, and the behavior of all group members is determined simultaneously. Proprietary data from the workplace giving campaign of a large national company are used. These data contain detailed information, which can be used to overcome the difficulties often associated with measuring social influences. This paper formulates the problem of measuring social influences as one of estimating the relationship between individual behavior and the behavior of peers by selecting appropriate instruments for group behavior. It also identifies the conditions under which various choices of instruments are appropriate. The results suggest that individual giving behavior is affected by social influences.

∗ kcarman@stanford.edu. I am grateful to the National Bureau of Economic Research and the Arrillaga Family Foundation for financial support of this research. Special thanks to my advisors, B. Douglas Bernheim, Tim Bresnahan, Antonio Rangel and John Shoven; members of the Public Economics Group at Stanford; and Alerk Amin, for able research assistance.

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1. Introduction

Privately provided public goods play a significant role in our society and economy. Many

important services are provided by charitably supported nonprofit organizations. Sectors that particularly

rely on voluntary contributions include higher education and health and human services organizations,

such as the Red Cross, the Salvation Army and the United Way. In 2000, individuals gave a total of $152

billion to nonprofits, representing 1.8 percent of total personal income. 1 If gifts from all sources are

considered, that amount rises to over $200 billion. Understanding the motivations behind the voluntary

provision of public goods has motivated many researchers in economics as well as other fields.

The private provision of public goods potentially suffers from the free rider problem described by

Samuelson (1954 and 1955). Warr (1982), Roberts (1984) and Bergstrom, Blume, and Varian (1986)

develop theoretical models where people only care about their consumption of private goods and the

overall level of provision of public goods. Bergstrom et. al. show that if preferences are identical, then

the poor will free ride on the contributions of the wealthy. Warr and Roberts show that government

contributions will fully crowd out voluntary contributions, when only contributors pay taxes towards the

public goods, while Bergstrom et. al. predicts only partial crowding out. In practice, there appears to be

much less free riding than predicted by these models and crowding out is only partial. Accordi ng to the

Independent Sector,2 70 percent of people contributed to non-profit organizations in 1998 and mean

contributions for contributing households were over $1000, representing 2.1 percent of their household

income.3 In addition individuals at all income levels make contributions. Experimental research further

confirms high rates of participation in the private provision of public goods (see Dawes and Thaler

(1988)). Empirical evidence suggests that there is less than full crowding out, for examples see Kingma

(1989), Andreoni (1993) and Ribar and Wilhelm (2002).

In an attempt to reconcile theory and behavior, various authors have formulated models in which

utility is derived from not only the consumption of private goods and the total provision of public goods,

but also from the act of giving. Andreoni (1988 and 1989), and Cornes and Sandler (1984) consider the

addition of a “warm-glow” term to the utility function. The predictions of these models are more in line

with actual behavioral patterns. While these models always predict less free riding, the exact nature of

the utility gained from personal contributions is not always articulated. 1 See Giving USA (2001). 2 The Independent Sector is an organization made up of nonprofits, foundations, and corporations that researches areas relevant to strengthening nonprofits and acts as an advocate for nonprofits in the political arena. 3 This amount represents the mean annual contribution and thus may be skewed by very large donations.

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One important possibility is that warm glow derives from social influences, although not all

social influences cause warm glow. Society may solve the free rider problem by developing norms or

social conventions that encourage sacrifice for the good of the group. While a number of theoretical

models investigate these forces, see Sugden (1984), Glazer and Konrad (1996) and Harbaugh (1998),

little empirical measurement is available. The existing empirical literature on social influences on giving

will be discussed in section 2. Social influences could also include coercion or pressure that causes

people to make contributions, but does not result in “warm glow”. Many people feel that this type of

pressure is particularly common in the workplace.

Suggestive evidence concerning social influences is provided by the behavior of donors and

fundraisers for nonprofit organizations. In a survey published by the Independent Sector in 2000 reports

that 77 percent of those who made donations to nonprofits did so because they were asked to give by

someone they knew well. Fundraisers often design their campaigns to leverage the power of social

influences. Rather than have a professional fundraiser ask for donations, they often have past donors

solicit additional contributions. Universities have alumni contact other graduates from the same class.

Countless organizations use bike-a-thons and walk-a-thons where participants must solicit sufficient

donations from their friends in order to participate in the event. This technique taps into peer networks

and the power of social connections. Other organizations recognize that individuals may be more likely

to contribute if they know that others are doing so as well. To facilitate the spread of this information

they publicly display thermometers that measure dollars raised towards their goal or the proportion of

people who have made contributions. These behaviors indicate a belief that social influences play a role

in individual contribution decisions.

This paper measures social influences in a context involving the private provision of public

goods: workplace fundraising for charities. Social influences occur when changes in the contribution

amount, observable characteristics, or unobservable characteristics and taste for making contributions of

one person cause others in the same social group to change their own contribut ion amount or participation

decision, assuming that all common factors affecting members of the group remain fixed. In this paper

social influences will be studies in the context of workgroups. These groups are defined by their physical

proximity and assignment to the same cost-center or work group, which allows identification of people

who know each other.

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The data for this study are proprietary records from a large national bank. The data contain

extensive information on the company’s employees and their participation in the company’s workplace

giving campaign in 2000 and 2001. These data are derived from the company’s payroll database, thus

they contain information on demographic characteristics, employment history, and annual income. Most

importantly, there is information on physical location and workgroup, making social groups identifiable.

Finally, the data include contribution amounts and the name of the recipient organization.

Difficulties measuring social influences are well known and are discussed extensively in the

literature on peer effects. These difficulties are discussed in more detail in sections 2 and 4 of this paper.

These problems are surmountable in the context of workplace giving for several reasons. First, social

groups are clearly defined and identifiable. Second, selection into groups is arguably based on criteria

other than the proclivity for contributing to public goods. Factors such as knowledge, productivity, and

experience are the primary criteria influencing group assignment. Third, changes in the composition of

social groups that are arguably exogenous can be related to changes in behavior. Public use datasets

typically do not provide rich information on social groups, making their use problematic in this context.

This paper formulates the problem of measuring social influences as one of estimating the

relationship between individual behavior and the behavior of peers by selecting appropriate instruments

for group behavior. It also identifies the conditions under which various choices of instruments are

appropriate. The instruments considered include peer characteristics and past behavior. Changes in

group composition over time are also exploited to measure the impact of social influences.

This analysis supports the hypothesis that social influences play an important role in the private

provision of public goods. Controlling for individual characteristics, a 10 percentage point increase in

group participation is associated with a 50 to 85 percentage point increase in the likelihood that an

individual participates, and a $10 increase in the mean contribution of the social group is found to be

associated with a an increase in individual contributions by $0.50 to $5.

In section 2, the relevant literatures on peer effects and the determinants of charitable giving are

discussed. Section 3 describes the data used for this study. Section 4 describes the general framework for

analysis. Section 5 discusses the results. Section 6 concludes.

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2. Literature Review

This paper is related to and informed by two strands of the literature. The first is the literature on

the measurement of peer effects in other contexts. The methodologies described and used in this strand of

literature inform the methods used in this paper. The second discusses the determinants of voluntary

contributions to charity, including the role of social influences.

2.1 The Peer Effects Literature

Recently, there has been a growth of literature that seeks to measure the relationship between

individual behavior and peer behavior, often referred to as social norms or peer effects. 4 These effects

have been studied in a variety of situations ranging from the classroom to the dorm room to the

workplace. In all of these settings, there are social groups that are clearly defined and observable.

Without these institutional settings, it is difficult to define social groups and it may not be possible to

identify any effects. The evidence for social effects is mixed, and depends on the exact situation being

studied.

The identification of social influences is complex and many potential pitfalls exist. Manski

(1993, 1995, and 2000), Brock and Durlauf (2000), Moffit (2000), and Durlauf (2001) develop formal

frameworks for measuring these effects and describe the difficulties associated with measurement. The

literature describes two types of social effects. First, there may be endogenous effects that cause an

individual’s behavior to change in response to changes in the behavior of his or her peers, hol ding peer

characteristics constant. Second, there may be contextual effects that cause an individual’s behavior to

change in response to changes in the characteristics of his or her peers, holding peer behavior constant.

The distinction between endogenous and contextual effects is not always clear and in many cases it is not

possible to separately identify these two types of effects. Therefore, endogenous and contextual effects

are considered jointly in many contexts. For my purposes, social influences include both endogenous and

contextual effects.

Several examples may help to clarify the distinction between the two types of social effects.

Manski provides an example that illustrates the difference between these two types of effects. In the

context of high school achievement, endogenous effects occur when individual achievement varies with

the average achievement of the group, contextual effects occur when individual achievement varies with

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the socio-economic background of the group. In the context of workplace giving, endogenous effects

occur when individuals give more because they observe their peers giving more. A very simple example

of a contextual effect would occur if individuals gave more when they had a peer who is in wheelchair

because it reminded of them of their duty to help those who are less fortunate. Alternatively, people who

have peers that are highly compensated may give more than those with less highly compensated peers, in

order to show off and fit in with their wealthy peers.

In the literature, the definition of contextual effects is often confused with a correlation between

group characteristics and exogenously determined characteristics of the environment. In the case of

workplace giving, if departments that include highly compensated executives receive additional

information on the quality of a particular charity, then individual donations may be correlated with the

average salary of the group. In this scenario the correlation between group characteristics and individual

behavior is not driven by a contextual effect.

Several confounding factors stand in the way of measuring the effects of social influences. First,

because groups make decisions simultaneously, it is difficult to tell the difference between the influencer

and the influenced. Manski refers to this as the reflection problem. The second potential problem is

selection; groups may be formed in a way that selects members with similar tastes. Third, there may be

correlated effects, which occur when there are external unobservable effects that cause members of the

group to behave similarly. For example, correlated effects exist when all members of a group change

their behavior in response to advertising. The existence of these external shocks can further complica te

the measurement of social influences if the external shocks are correlated with group characteristics.

These issues will be discussed more precisely, when a formal framework is introduced in section 4.

Manski shows that with prior knowledge of the makeup and characteristics of groups, it is

possible to separate correlated effects from social influences. In this paper, social influences are defined

to occur when changes in the behavior or the characteristics of an individual’s peers cause that individu al

to change his or her behavior, holding fixed external factors that affect all group members. Thus for the

purposes of this work, it is not necessary to separately identify endogenous effects and contextual effects.

Nevertheless, it is important to separate social influences from correlated effects.

Several other papers have studied various forms of social effects in the workplace. Because prior

knowledge of group makeup and characteristics are necessary to identify peer effects, departments in the 4 Eisenberg (2002) provides an excellent summary of this literature.

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workplace provide an excellent setting to study these phenomena. Ichino and Maggi (2000) look at

counterproductive behavior in a large Italian firm. They find evidence that Northern Italians who have a

proclivity towards counterproductive behavior are likely to move to the south, where attitudes towards

hard work are more lax. Similarly, highly productive Southern Italians tend to move to the north. Their

results suggest that selection drives some but not all of the similarity between individual behavi or and

peer behavior. Sorensen (2001) studies health insurance choices. He finds that the correlation between

individual behavior and peer behavior is stronger when observable characteristics are more similar. Duflo

and Saez (2000 and 2002) examine enrollment in tax-deferred savings accounts. In the first paper they

look at sub-groups within departments and find that own-group effects are larger than cross-group effects,

suggesting that common shocks and selection alone are not behind the correlation in behavior. In their

second paper, they use a randomized experiment to provide additional information to some employees. In

this experiment, they find evidence that information is shared within workgroups. Providing additional

information on tax-deferred accounts to only some members of a group caused all members of the group

to change their behavior. These papers provide empirical support for the existence of peer effects in other

workplace situations.

2.2 Voluntary Contributions to Charity

A number of empirical papers consider the role of social influences in determining individual

participation and contributions. Unlike this paper, none of these papers uses data where social groups are

observable. Experiments, surveys designed to focus on charitable giving, and publicly available datasets

such as the Consumer Expenditure Survey have been used to investigate the motivations behind voluntary

contributions to charity.

Several authors have developed surveys that allow them to investigate social influe nces on

giving. Long (1976) and Keating, Pitts and Appel (1981) use surveys that ask specifically about the

method of solicitation for gifts. Long’s results are mixed, but suggest that in some circumstances being

asked to contribute by someone you know may lead to increases in the amount contributed. Keating et al.

only consider contributions to the United Way, and find that people who work at companies that have

United Way campaigns are more likely to contribute. This result is not surprising because v ery few

people are asked to contribute to the United Way outside of the workplace, and other research suggests

that people who are not asked tend not to contribute.

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Schervish and Havens (1997) use the 1992 Survey of Giving and Volunteering in the United

States, which is produced by the Gallup Organization and the Independent Sector. While these data do

not contain social groups, the survey specifically asks about the motivations for giving. They find that

being asked to contribute by a friend or business associate has a significant positive effect on the

percentage of income given, but that being encouraged by an employer has a negative effect on

contributions. One drawback of this investigation is that they do not report results for regressions that

simultaneously control for demographic characteristics and the source of the invitation to contribute. In

addition, their results are based on self-reported motivations for giving

The role of social influences has also been explored in an experimental se tting. Fehr and Gächter

(2000 and 2002) designed experiments to highlight an additional motivation for mimicking the behavior

of others. In their experiments, they allow subjects to punish others who fail to contribute to the public

good. They find that punishment is common, even when it is costly. The possibility of punishment

significantly reduces the likelihood of free riding. If individuals are afraid of social sanctions from the

people they know, then a positive correlation between contributions within a social group and high

participation rates are likely. Dawes and Thaler (1988) summarize other experimental work and point out

that contributions are higher in experiments where discussions between subjects are allowed prior to

making contributions.

Andreoni and Scholz (1998) consider the same questions posed in this paper. They use the

Consumer Expenditure Survey to measure the effect of donations by an individual’s social reference

group on his or her own donations. They define social reference groups based on socio-economic and

demographic characteristics of each household. Andreoni and Scholz are able to identify subsets of the

population that value contributing more than others do. However, the data used by Andreoni and Scholz

do not allow them to measure social influences as defined in this paper because they are unable to observe

whether or not individuals actually know each other. Feldstein and Clotfelter (1976) use data and

methods similar to those used by Andreoni and Scholz and find no evidence of an effect of group

behavior on individual behavior.

This paper, in contrast to previous work, defines social reference groups to include only

individuals that actually interact with each one another. This makes it possible to identify h ow an

individual’s choices are affected by the behavior of other members of his or her social group. Andreoni

and Scholz point out that a workplace giving campaign or a university capital campaign might be a better

place to study the role of social influences in the private provision of public goods. While Fehr and

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Gächter (2000) and (2002), Dawes and Thaler (1988), and Andreoni and Scholz (1998) provide insight

into the motivations for giving, this paper will further our understanding by examining actual

contributions in a social setting, where extensive information is available concerning each member of

each group.

3. Data

In order to study the role of social influences on the private provision of public goods very

detailed data is needed. The ideal dataset would contain information on contributions, demographics,

income data, and information regarding social groups. Typical publicly available data uses a random

sample of individuals, making it impossible to observe social groups. Data from an emplo yer has the

potential to provide more of the necessary data. One of the primary benefits of using workplace data is

that it is easy to identify people who know each other. In addition, there is less likelihood of selection

bias. While people may choose their friends based on their charitable giving, it seems less likely that this

particular source of selection bias plays a significant role in the selection of employees. Factors such as

knowledge, productivity and past experience are probably more important.

Many companies operate workplace giving campaigns that allow the employees to make one time

or regular payroll deduction contributions to nonprofit organizations. Often contributions can only be

made to one nonprofit organization, typically the local United Way.5 Recently these restricted campaigns

have started to fall out of favor, and many employers have opened their campaigns to other nonprofits,

some are open to all registered 501(c)(3) organizations. Most contributions are made by regular p ayroll

deductions, although contributions by cash, check, credit card or stock grants are also sometimes allowed.

For a company to meet the requirements of this research, several characteristics were necessary.

First and foremost, the company needed to be able to provide information regarding each employee’s

department and physical location, in order to define social groups. Second, a large company with many

locations was needed. While the level of observation is the individual, a larger company with many

separate locations allows for more social groups. Third, a company with many employees engaging in the

same job was desirable. Similarity across locations could potentially avoid selection biases for some 5 The United Way operates like a franchise. Each local United Way is an independent due paying member of the larger organizations. The characteristics of United Ways can vary dramatically across locations.

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groups driven by the particular tasks of that group. Finally, a company with geographic diversity would

avoid any potential biases due to local United Way scandals or controversy. 6

The United Way of America assisted in the search for a company to participate in this project.

Their National Corporate Leadership group helps companies that operate in many locations coordinate

their campaigns and work with the local United Ways in each location. They helped identify several

potential companies. Obtaining legal permission to access employee databases proved to be an enormous

hurdle.

Access to proprietary data from a large national bank was secured. The bank operates both

traditional retail banking services and investment banking services. The bank has over 75,000 employees

and operates in over 20 states. The provider of the data for this project has asked to remain anonymous;

therefore detailed information regarding the company is not included. For confidentiality reasons, the

size of the sample is not divulged, however the sample size is large (generally in the tens of thousands)

and will not negatively affect statistical significance. The data contain information related to

employment, contributions and demographics for all employees as of September 2001. The data also

contain similar information for most of those employees from September 2000. The company allows

contributions to any registered 501 (c)(3) and the structure of the campaign is roughly the same across all

locations.

The campaign hierarchy contains several levels. There is a national campaign chair and co-chair

chosen from the upper levels of the company hierarchy who oversee the campaign and participate in

decisions regarding campaign marketing and goals. Below the chairs are the State Presidents (a job title,

not a campaign title) who are expected to play a motivational role in the campaign. There are also

division leaders selected from each of the major lines of business to ensure effective campaign

communication and coordination across different parts of the company. The bottom level within the

campaign hierarchy is the campaign team.

Each employee is assigned to a campaign team. Teams are based on location and line of business

and are normally intended to have between 5 and 75 employees (although the actual sizes range from 1 to

573). Over 50 percent of employees are on teams of 20 or fewer people. Campaign teams are intended to

overlap with actual work groups and are based on physical location and department. Team assignments 6 For example, during 2000 and 2001, funding of the Boy Scouts of America led to much debate. Also due to recent near bankruptcy by the Silicon Valley United Way, using a company near Stanford did not seem practical.

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are not based on other characteristics, such as demographics or giving history. Teams are also assigned a

team captain. For many teams, the workgroup manager serves as the captain. Captains are selected based

on officer status, level within the company, and contribution history. In addition, preference is given to

people who have served as a team captain in the past. However, serving as a team captain is voluntary,

thus an alternate may be chosen. In some cases a team may have no captain, if the first candidate refuses

or leaves the company and a replacement cannot be found.

At the beginning of the campaign, each employee receives an “ask letter”. These letters introduce

the campaign and suggest an amount that the employee could contribute. Ask amounts are a function of

income and past contributions. A common practice in fundraising is to annually ratchet up the amount

that each person is asked to give. For example, if you donated $2 per month year 2000, then in 2001, you

might be asked to donate $3 per month. There were 6 different letters sent to employees. Three of these

letters included a list of employees who contributed $1000 or more in the previous year. These lists are

sent to people who are capable of contributing at least $1000. People who make a donation of $1000 or

more are known as leadership givers. Those who received the list of the previous years leadership givers

may be able to directly observe the past behavior of some members of their social group.

In addition to the ask amounts, employees receive a giving guide. The materials given to the

employees clearly state, “This guide is based on previous associate giving. We know associates’

individual lifestyles will determine their gift amounts, and we suggest they use this chart only as a guide.”

Despite this statement actual gift amounts tend to be less than the giving guide would suggest. The

suggested and actual contributions as a percent of salary are listed in Table 1.

The campaign takes place in two stages. In the first stage (which lasts about 1 month) ,

“Pacesetters” are asked to make a contribution. Pacesetters, primarily employees at or above the senior

vice president level, represent roughly 4.4 percent of the company’s employees. 7 While Pacesetters are

asked to donate in the first stage, they have the option of donating during the second stage as well. In the

second stage, the remainder of the employees are asked to make donations. This two-stage structure is a

common campaign characteristic used in many types of fundraising campaigns. 8 In 2001, the first stage 7 Because the data for this project comes from a bank with numerous branches, the percentage of employees designated as Vice Presidents may be higher than in other organizations. 8 See Varian (1994) Andreoni (1998) and (2002), and Vesterlund (2002) for a discussion of these types of campaigns.

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took place in August and the second stage was in September. Employees were scheduled to receive their

campaign information on or around September 10.9

The data used in this research contain two types of information: campaign related ch aracteristics

and employee characteristics. Nearly all employees are included in this analysis. Those with

questionable salary information are excluded. This represents less than 0.5 percent of the sample. The

CEO is also excluded. Table 2 contains summary statistics of all variables. The campaign data include

pledge amount, non-profit designation, team assignments, team leader indicators, ask amounts, pace setter

indicator, and the letter version received. The participation rate is 57 percent, the m ean annual

contribution conditional on participation is $271, and the median is $72. In addition, using the workplace

zip code, it is possible to can identify which United Way is most likely to influence an individual either

through information provision directly through the workplace or through more general marketing and

publicity.

The employment data contains information on pay, hierarchy, line of business, location, tenure,

age and gender. There are two measures of annual pay: contractual pay and an estimate of actual pay that

takes into account bonuses and actual hours worked by hourly employees. The hierarchy information

includes information such as level, vice president, senior vice president, and executive vice president

indicators, and manager status. General line of business and specific cost centers are also included. The

location information includes city, state and work zip code as well as an internal mailcode. The mailcode

allows identification of people who sit within a small proximity of each other. Mailcodes range in size

from 1 to 537. Fifty percent of employees are in mail codes of 19 or fewer people. In addition, over 300

employees work primarily from home. These people have less regular contact with their fellow

employees thus we would expect that they are less affected by the people in their department.

The key benefit to studying workplace giving is the unique ability to observe social groups.

Social groups are defined in three ways: campaign teams, cost centers and mail codes. Each of these

groups is made up of people that actually know each other. People who sit near one another and work

together are the most likely to know each other, and therefore the most likely to discuss the giving

campaign, and in turn the most likely to influence each other’s contributions. Because teams are designed 9 Obviously the events of September 11 had some impact on employee giving in 2001. There is a slight increase in participation from 55.5 percent to 57.4 percent. But this exogenous shock merely acts as a time effect. Everyone in the country was affected by these events, although people in New York and Washington DC may have been more affected. Because employees are allowed to contribute to any 501 (c)(3), one might expect that many would make donations to the Red Cross disaster relief fund or the United Way’s September 11th Fund, or other fire fighter relief funds. In fact, less than 3 percent of employees gave to these organizations.

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to include people who work together (as defined by their physical proximity and their cost center), these

three social groups are likely to have significant overlap. Therefore, the t hree groups are studied

independently.

4. Framework

Because of the complexities inherent in measuring social influences, it is useful to consider this

problem in the context of a formal model. Each individual (i) is assigned to a group (k). The group

assignment process is not explicitly modeled. An individual's giving behavior in period t ( tiG , ) is a

function of their characteristics ( tiX , ), mean group behavior ( ktiG ,− ), mean group characteristics ( k

tiX ,− ),

unobservable common shocks that affect all members of the group in the same way, such as

environmental conditions ( ktη ), and unobservable shocks to and characteristics of the individual’s tastes

( ti ,ε ). In all analyses, group behavior is defined as the behavior of the other members of an individual’s

group, i.e. the individual is excluded. Here group behavior is defined as the mean contribution, but other

possibilities, such as the median contribution and the participation rate, are discussed below. An

individual’s behavior is given by the following:

tikt

kti

ktititi XGXG ,,,,, εηδγβα +++++= −− (1)

Here both γ and δ represent social influences. Following Manksi (1993), endogenous effects, captured

by γ , occur when the amount and individual gives is influenced by the amount given by their peers.

Contextual effects, captured by δ , occur when the characteristics (rather than the behavior) of an

individual’s peers influence their gift amount. Manski defines both endogenous and contextual effects as

social effects and shows that it is not possible to separately identify the two coefficients. Therefore, many

papers on peer effects proceed by assuming only one type of social effects exist. While this assumption

may not reflect reality, it is useful to consider the cases where only endogenous effects or only contextual

effects exist before considering the case where both effects are present.

4.1 The Case of Endogenous Effects: 0,0 =≠ δγ

In much of the literature on peer effects, contextual effects are ignored an only endogenous

effects are considered. In this case the equation describing individual behavior beco mes:

tikt

ktititi GXG ,,,, εηγβα ++++= − (2)

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The goal of this analysis is to estimate γ . An accurate measure of γ is interpreted as the effect

of a change in individual j’s gift, without allowing re-equilibration of the behavior of other group

members or external shocks, on the gift of individual i. It is useful in what follows to have reduced -form

expressions for tiG , and ktiG ,− . For individual i:

)())(1( ,,,,2

, tjkttj

ijti

kttiti XXG εηβαγθεηβαθγ ++++++++= ∑

≠

(3)

and the average behavior of all other members of the group is:

)()( ,,,,, tjkttj

ijti

ktti

kti XXG εηβαθεηβαγθ +++++++= ∑

≠− . (4)

In equation 3, θ is the weight placed on the characteristics of each of the other members, excluding i, of

group k, and is given by the following expression:

)1)(1(

1γγ

θ+−−

=N

(5)

From equation 4 we can see that the mean behavior of all others in group k depends on the characteristics

of individual i, unless 0=γ . As discussed by Manski (1993, 2000) and Moffit (2001), the coefficients

in equation 1 are difficult to identify due to several problems. In general, these problems arise because

the reduced form expression for ktiG ,− contains k

tη and ti ,ε .

An examination of equation 3 highlights the problems associated with using OLS to estimate

equation 1. If any of the components of equation 3 are correlated with the error terms in equation 1 then

the estimates of γ will be biased. Manksi has called the first problem the reflection problem. This refers

to the simultaneous determination of individual behavior and group behavior which causes the average

contributions of others, ktiG ,− , to be correlated with the individual’s unobservables, ti ,ε . Because of the

simultaneous determination of behavior, ti ,ε appears in equation 3. Therefore, OLS is only appropriate if

0=γ . The second problem is the group selection problem. This problem arises if individuals are

assigned to groups based on their unobservable taste for giving or factors that are correlated with the

unobservable taste. Within each group ti ,ε may be correlated with tj ,ε . If this is true, then ti ,ε and

ktiG ,− , which is a function of tj ,ε , will be correlated. This problem would disappear if group assignment

were random. The third problem is the existence of environmental factors and unobservable common

shocks within groups ( ktη ). The term k

tη picks up environmental factors that affect the entire group, as

opposed to ti ,ε , which represents individual unobservable characteristics. Because ktη enters into the

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equation for ktiG ,− , it is obvious that group behavior is correlated with the unobservables. These problems

all contribute to the biased estimates of γ obtained using OLS.

Estimation of the reduced form equation for individual gift amounts, tiG , shown in equation 3,

will identify the existence of endogenous effects and avoids these three problems. If the coefficient on

group characteristics is significant and it is assumed that there are no contextual effects, then endogenous

effects exist. However this method does not allow for measurement of the magnitude of the endogenous

effects.

Although the biased results from OLS are presented, this research focuses on the problem of

selecting suitable instruments for group behavior. An appropriate instrument will satisfy the following

three conditions. First, the instrument must be uncorrelated with all environmental factors that affect the

group in the current period, captured in ktη . Second, the instrument must be uncorrelated with individual

i's shock in the current period, ti ,ε . If group selection leads to a correlation between the unobservable

individual characteristics, ti ,ε and tj ,ε , then the instruments for ktiG ,− must be uncorrelated with tj ,ε for

all i and j. Finally, the instrument must be correlated with average group behavior, ktiG ,− . Many potential

instruments satisfy the final condition, that they be correlated with group behavior. However, it is

difficult to find instruments that will always satisfy the conditions that they be uncorrelated with the

unobservables. This is especially true when the selection of group members leads to a correlation

between the individual unobservable characteristics. The remainder of the paper proposes several

strategies for instrument selection, clarifies the conditi ons under which each is a valid instrument,

discusses problems that cannot be addressed with instrumental variables, and implements estimation.

A commonly used solution, to correct for the simultaneity problem and the possible correlation

between group behavior and unobservables, is to use the average characteristics of the other group

members, ktiX ,− , as an instrument for average group behavior, k

tiG ,− . This instrument was first proposed

by Case and Katz (1991). This methodology is only appropriate in the case where it is assumed that there

are no contextual effects. If there were contextual effects, group characteristics would be included in the

estimation equation and therefore could not be used as instruments. Group characteristics are only an

appropriate instrument when social influences include endogenous effects but not contextual effects.

Average peer behavior has the potential to be an appropriate instrument. Because the characteristics of

16

other group members enter into equation 4, group characteristics are likely to be correlated with group

behavior and to be uncorrelated with the individual unobservable characteristics and tastes, ti ,ε .

However, the use of group characteristics as an instrument requires the following assumptions about

unobservable common shocks:

0),( , =−kt

ktiXcorr η (6a)

0),( ,, =− tik

tiXcorr ε (6b)

Group characteristics are only an appropriate instrument if the characteristics of the team are uncorrelated

with the unobservable environmental conditions and the unobservable characteristics of each individual.

This is a reasonable assumption if unobservable common shocks are randomly distributed across groups.

For example, the average characteristics of the other members of a social group are valid instruments if

external shocks are due to the visibility of campaign posters. However, the company’s campaign could

target certain groups differently. For example, external pressure to contribute, from the CEO or other

executives, could cause different information to be provided to employees at the senior levels with in the

company. If this were the case then the average group salary could be correlated with unobservable

effects and the condition in equation 5 would be violated.

Another potential candidate for an instrument is the mean of the peers’ contribution in the

previous year, ktiG 1, −− :

)()( 1,11,1,11,1, −−−≠

−−−−− +++++++= ∑ tjkttj

ijti

ktti

kti XXG εηβαθεηβαγθ . (7)

Comparing equation 6 to equation 3, it is easy to see that the mean contributions in both periods are likely

to be highly correlated. Many of the observable characteristics that affect an individual’s taste for giving,

such as gender, salary and position in the firm, will not change much over time. However, appropriate

instruments must also be uncorrelated with the unobservables. Therefore, ktiG 1, −− is only an appropriate if

the terms in equation 6 are uncorrelated with ktη and ti ,ε . The following assumptions are needed:

0),( 1 =−kt

ktcorr ηη (8a)

kgroupiniallforXcorr ktti 0),( 1, =− η (8b)

kgroupiniallforXcorr titi 0),( ,1, =− ε (8c)

kgroupinjandiallforXcorr titj 0),( ,1, =− ε (8d)

0),( ,1, =− titicorr εε (8e)

17

kgroupinjiallforcorr titj ≠=− 0),( ,1, εε (8f)

The conditions listed in equations 7 a-f, place a number of restrictions on the characteristics of

unobservable shocks experienced by individuals and groups. In general, it is required that there be no

correlation between period t and period t-1. Equation 7a states that environmental factors and other

external shocks cannot be serially correlated over time. This condition would hold if, for example, the

group shocks were due to one member of the group being diagnosed with cancer, prompting the other

members to make contributions to the American Cancer Society. Equation 7b requires that observable

characteristics in period t-1 do not determine the common shocks in period t. Equations 7c and d state

that observable characteristics in period t-1 cannot determine the individual shocks in period t. Equation

7e requires that individual i's unobservable personal characteristics and tastes not be correlated over time.

Finally, 7f states that the unobservable characteristics of individual j in period t -1 cannot be correlated

with the unobservable characteristics of individual i in period t.

Due to the restrictive nature of the required assumptions, past group behavior may not be an

attractive instrument. However, what is required, in general, is that there be no correlation between

unobservables in period t and period t-1. This suggests that it may be constructive to look for an

instrument where there is likely to be little correlation between periods. Many people change jobs and

therefore change social groups within the company over time. The past behavior of people who have

recently joined an individual’s group may be a valid instrument for group behavior because the common

shocks that affected the two groups are likely to be different.

In order clearly discuss this instrument, it helps to define some new terms and introduce

additional notation. In these data there are two years of information. Groups can be made up of several

types of people. First, there are employees who remained in the same location in both years (stayers); the

average giving behavior of these employees in year t-1 is given by stayerktiG 1, −− , where k indicates their group

in year t. Second, there are employees who are newly hired into the firm (new) and therefore have no

giving behavior in the first year of data. Third, there are employees who move from one location to

another (movers); the average giving behavior of these employees in year t -1 is given by moverktiG 1, −− , where

k indicates their group in year t which is not the same as their group in year t -1. It is not necessary that all

of the employees in the third group (the movers) came from the same group in year t -1.

18

Again, examining the expressions for peer behavior can illuminate the relationship between peer

behavior and the unobservable terms. In this case, assume that several people, indexed by h , join group

k, and that each moves from a different group hl . Also assume that individual i is a stayer. Then, the

average behavior of all movers into group k is given by:

∑ −−−−−−− ++++=h

thlt

lthth

moverkti

hhGXG )( 1,11,1,1, εηγβα . (9)

In this case, it is not necessary to solve for the reduced form because the values of hltjG 1, −− are different for

each individual h. By using past data on mover behavior the simultaneity problem has been eliminated.

In this setting, moverktiG 1, −− is not a function of any characteristics of individual i and simply represents the

average gifts of the new group members in the previous period.

Given these definitions, it is now possible to evaluate the validity of using the p ast behavior of

movers, moverktiG 1, −− , as an instrument for the group’s average current behavior, k

tiG ,− . The past behavior of

movers, moverktiG 1, −− , is likely to be correlated with the current behavior of the group, k

tiG ,− , because the

movers are members of group k in period t. For moverktiG 1, −− to be a valid instrument, it must also be

uncorrelated with the unobservables. This will occur under the following assumptions:

0),( 1 =−kt

lt

hcorr ηη (10a)

0),( 1, =−ktthXcorr η (10b)

0),( 1, =−−kt

lth

hGcorr η (10c)

0),( ,1, =− tithcorr εε (10d)

0),( ,1, =− tithXcorr ε (10e)

0),( ,1, =−− til

thhGcorr ε (10f)

These assumptions are much less restrictive than those needed to use the past behavior of all group

members as an instrument. In general, to use the past behavior of movers requires that there be no

correlation across groups over time; whereas the past behavior of all peers requires that there be no

correlation within a group over time. First, 9a states that it is necessary that the external shocks to group

k in period t are different from those to group jl in period t-1. This seems reasonable, so long as the

characteristics of the movers do not impact the external shocks experienced by group k, as expressed in

equation 9b. 9d requires that there is no group selection based on unobservable individual characteristics

19

and tastes, expressed in 9d.10 In addition, 9c and f state that the past behavior of movers’ former peers

cannot be correlated with the unobservables, ktη and ti ,ε .

It is important to note that when mover behavior is used as an instrument for group behavior, the

sample will be limited to stayers who are in groups that contain movers. By excluding the movers from

the regressions, the simultaneity problem is avoided. It is also necessary to only include those employees

for whom we can calculate moverktiG 1, −− .

Using past behavior of all peers or of movers as an instrument for current peer behavior separates

endogenous effects, γ , from common shocks and group selection biases if there is no correlation between

unobservables, either across time or across groups, respectively. An alternative a pproach capitalizes on

the correlation between unobservables over time. Here the change in individual giving from year 0 to

year 1 is used as the independent variable.

0,00,0,0, ikk

iii GXG εηγβα ++++= − (11a)

1,11,1,1, ikk

iii GXG εηγβα ++++= − (11b)

1,0,1,01,0,1,0,1,0, ikk

iii GXG εηγβα ∆+∆+∆+∆+∆=∆ − (11c)

By differencing, the effects of common shocks, which affect all group members each year, can be

eliminated. For example, this method removes the effect of an office being located in the same building

as the Red Cross. Similarly, the group selection problem, which causes a correlation between ti ,ε and

tj ,ε can be eliminated. However, the reflection problem remains. Again looking at the reduced form for

kiG 1,0,−∆ is valuable.

( ) ( )∑≠

− ∆+∆+∆+∆+∆+∆+∆+∆=∆ij

jk

jik

iki XXcG 1,0,1,01,0,1,0,1,01,0,1,0, εηβαθεηβαγθ (12)

Where θ is given by equation 5. The reflection problem arises here because the error terms in equation

11c also appear in equation 12. To remedy this problem we can use the change in giving behavior of

movers as an instrument for the change in the average giving of the peer group overall. As in the

previous case, the sample is limited to new employees and stayers who are in groups that contain movers.

Intuitively, this is a good instrument because the changes experienced by movers are different from the

changes experienced by stayers but the two are correlated. The change in behavior of movers will be a

valid instrument if: 10 This assumption will be tested in the results section.

20

0),( ,1,01,0 =∆∆ stayerkmovercorr ηη (13a)

0),( ,1,01,0 =∆∆ stayerkmoverXcorr η (13b)

0),( 1,01,0 =∆∆ stayermovercorr εε (13c)

0),( 1,01,0 =∆∆ stayermoverXcorr ε (13d)

4.2 The Case of Contextual Effects: 0,0 ≠= δγ

In the previous section, we assumed that endogenous effects were the only form of social effect.

In this section, we focus on contextual effects. Individual behavior is now described by the following

equation.

tikt

ktititi XXG ,,,, εηδβα ++++= − (14)

In this case the goal is to measure the effect of average peer characteristics on individual behavior, δ . In

accordance with Manski (1993), if the terms in equation 3, the reduced form equation with endogenous

effects only, we can see that it is equivalent to equation 14. In estimating this equation it is not possible

to identify whether there are contextual or endogenous effects. However, if we assume that the re are only

contextual effects it is sufficient to estimate equation 14. In this case there is no simultaneity problem,

and if there is no correlation between the observable characteristics and the unobservables then the

estimate of δ will be unbiased.

4.3 The Case of both Endogenous and Contextual Effects: 0,0 ≠≠ δγ

If we assume that there are both endogenous and contextual effects, then we are in the case

described by equation 1. Again it may be valuable to consider the reduced form for ktiG ,− :

( )( ) ( )[ ]( )

++++

−−+−

+

++

−+

+−

−

=

∑≠

−−

ijti

kt

kti

tiktti

kti XN

X

NG

,

,

,,

, 1111

11

εηα

γδδβ

εηβγδ

αγ

γγ(15)

21

While this is quite a bit more complicated than in the case with endogenous effects only, all of the

variables appearing in equation 15 also appear in equation 4. In equation 15 the coefficient on ktiX ,−

contains both δ and γ . Whether we approach the problem of measuring social influences by assuming

0&0 ≠≠ δγ or 0&0 =≠ δγ or 0&0 ≠= δγ does not matter. Estimating an equation where

individual gifts are a function of individual and group characteristic will produce the same results

regardless of whether endogenous effects, contextual effects or both are assumed. In this framework , it is

not possible to separate endogenous and contextual effects. Any measurement of a coefficient on ktiG ,− or

ktiX ,− should be interpreted as an indication of a social influence, not an indication of only endogenous

effects or contextual effects. If the taste for giving is assumed to be a characteristic that is unobservable

to the econometrician, then the distinction between endogenous effects, where an individual is affected by

their peers’ behavior, and contextual effects, where an individual is affected by their peers’

characteristics, is even less clear. Therefore, any of the methods discussed above can be used to identify

social influences.

4.4 Additional Concerns

The framework and methods described above address many of the potential difficulties in

measuring social influences. Using instrumental variables addresses the problems of group selection,

unobservable environmental factors that are common within groups, and the simultaneity problem.

However four other important issues have not been addressed up to this point. First, the average giving

behavior of the other members of the group may not be observable to individual i. If this were the case,

the relevant measure of peer behavior would be the desire to give, not the amount given. Then, the

average amount given is a proxy for the relevant variable. This creates an errors -in-variables problem, in

addition to the problems discussed above. In this case, the framework developed above is still relevant

but an additional term must be introduced and terminology must be redefined. tiG , and ktiG ,− are

redefined to represent individual i's desire to contribute and the average desire to contribute of the other

members of group k. Actual gift amounts are denoted with tiG ,ˆ and k

tiG ,ˆ

− , where

ittiti zGG += ,,ˆ (16)

and ktiG ,

ˆ− is the average of tjG ,

ˆ for all ij ≠ :

kti

kti

kti zGG ,,,

ˆ−−− += . (17)

22

The difference between desired and actual contributions, the error in measuring the desired gift, is

denoted by itz . Substituting equations 16 and 17 into equation 1 gives an expression for actual gift

amounts:

ktititi

kt

ktititi zzGXG ,,,,,,

ˆˆ−− ++++++= εηγβα (18)

These changes to the framework will add the requirement that any instruments need to be uncorrelated

with itz and ktiz ,− .

Second, individual gift amounts are truncated. In order to address the truncation of the dependent

variable, tobit and probit regressions are used. Instrumental variables are implemented in the tobit and

probit setting following the method of Newey (1987). The probit regressions focus only on the decision

to participate or not. In practice, this decision may be more susceptible to social influences because many

companies focus on participation numbers when implementing their campaigns. How much each person

contributes is not as important as having many people contribute. 11

The institutional importance of the participation decision suggests that participation rates may

better capture the role of social influences. The third problem is that the mean contribution of an

individual’s peers may not be the best measure of the social influences that are experienced. To address

this issue, the median contribution and the participation rate of the group (excluding individual i) are also

considered. Using a different measure of group behavior does not change the underlying concepts

addressed in the framework, but formulating the framework in terms of the mean is more intuitive. In

addition, because of the median contribution and the participation rate are non -linear functions of the gift

amount, it may be possible to separate endogenous and contextual effects.

The fourth remaining issue is that the environmental factors and campaign characteristics that

affect each group may be correlated with the group’s characteristics. In order to address, average group

characteristics (including the individual) are included in the equation for individual behavior, when the

past behavior of movers is used as an instrument. This will control for any possible correlation between

external factors and group characteristics. However, one of the factors that led to the selection of this 11 Smith, Kehoe and Cremer (1995) suggest that past research into charitable contributions is flawed and that a two-stage selection model would be more appropriate. In the first stage, participation would be predicted, and contributions would be predicted in the second stage. They use past charitable behavior to identify participation, arguing th at it will not have an impact on the contribution amount. They acknowledge the concern that those who participated in other campaigns might contribute more than those who had not given before. A two-stage selection method is not used in the current paper because no suitable selection variable is available.

23

particular dataset was that the company maintains that campaign characteristics are the same across the

entire company. When group characteristics are included and the instrument is the past behavior of

movers, the assumptions regarding the correlation between characteristics and external and environmental

factors, in equations 9b and 9e, can be relaxed. In this case:

tikt

kt

ktititi XGXG ,,,, εηφγβα +++++= − (19)

With the addition of ktX , only conditions 10a, 10c, 10d and 10f are needed.

5. Results

Using the framework and methods developed in section 4, this section presents the results of the

estimation, both with and without the proposed instruments. Table 3 presents the estimation results of

equation 1, with groups defined by teams and group behavior defined by the mean gift of the other team

members.12 These results indicated that there is a positive and statistically significant correlation between

individual behavior and the mean behavior of other members of their team. However, the results

presented in Table 3 do not correct for the simultaneity problem, thus they are only indicative of the

strong correlation between the behavior of individuals and their peers and are not indicative of a causal

relationship. Using an OLS framework, a $1 increase in the team’s mean contribution is associated with

at $0.053 increase in individual contributions. Under a tobit, a $1 increase in the team’s mean

contribution is associated with at $0.079 increase in individual contributions. Focusing on the decision to

participate also indicates that a $100 increase in the mean contribution is associated with a 1.3 percentage

point increase in the probability of participating.

Table 3 also presents the coefficients on other control variables included in the regressions. The

signs of each coefficient are in line with expectations. Salary, age, and tenure with the company have a

significant positive effect on the amount given and the likelihood of participation. Team ca ptains and

pacesetters are more likely to give, and make larger contributions than those who are not designated these

roles. Interestingly, controlling for other factors, men tend to give less than women, although the estimate

of the coefficient is not significant in the OLS regressions, and are less likely to participate in the

campaign. Finally people who work from home are less likely to participate and give less. This provides 12 In this case, the analysis focuses only on behavior in 2001. The data for 2000 are not as complete as that for 2001. First, it does not contain all employees in 2000, only those who were also employe d by the company in 2001. In addition, the salary information is not as complete.

24

circumstantial evidence that in the absence of social interaction, contrib utions to the private provision of

public goods are less likely.

Table 4 presents a number of variations on the information contained in Table 3. For each

regression in Table 3 there are 27 counterparts in Table 4. Three definitions of social groups, 3

definitions of group behavior, and 3 treatments of location effects are considered. Only the coefficients

on group behavior are included in this table, but the same controls used in Table 3 were included in each

regression. In addition, fixed effects, defined by city or state, are considered in this table. Location fixed

effects control for some common environmental factors, ktη , that are geographically specific and

otherwise unobservable. These shocks could be at the state level or at a more specific local level (such as

the United Way). With the addition of these fixed effects, it is possible to more accurately measure the

causal relationship between individual behavior and the behavior of their peers.

Correlations within states or local areas could result from several factors. Each state has a

president who is expected to set the tone of the campaign for that state. In addition, all residents of a

given state may have similar giving behavior due to state tax laws, regional taste s for giving, or common

events. There may also be environmental factors that are prevalent at the city level. Within a given city,

there may be one senior level employee who oversees all of the employees in that location. Including a

local fixed effect may control for the environmental factors imposed by that leader. An additional cause

of location specific fixed effects may be the local United Ways. 13 The quality of United Ways can vary

dramatically.14 In addition to variation in quality, there may be differences in the marketing used by

United Ways. Some groups may have aggressive advertising campaigns or send representatives to local

workplaces to meet with employees to discuss their workplace campaigns. Given these factors, fixed

effects defined by the local United Way are also added.15 Because some areas have only a very small

number of employees, who could all be assigned to the same team, this analysis focuses on larger areas.

For example a small town might have only one branch of the bank, and all of the employees in that

branch could be assigned to one team. Location fixed effects are included in all additional tables.

13 Each United Way is an independently operated organization; United Ways are more like franchises than like branches of a single organization. Each United Way is assigned a territory, and these areas do not overlap, or cross state lines. 14 For example, the Silicon Valley United Way has undergone a number of high profile scandals (including near bankruptcy) over the past 5 years. Therefore, people who live in the San Francisco Bay Area may be more hesitant to participate in workplace giving campaigns. Other United Ways, such as Phoenix’s Valley of the Sun United Way, are extremely successful and popular in their communities, and have very high gift amounts and participation rates for all of their workplace giving campaigns. 15 In an ideal setting, both the local United Way that corresponds an individual’s home and workplace would be controlled for, but in order to protect individual confidentiality, information home addresse s is not included.

25

Table 4a presents the results of OLS estimation. A $1 increase in mean contribution of the other

members of a team, mailcode or cost center is associated with a $0.04 to $0.05 increase in individual

contributions. Median contributions are associated with a larger impact on individual giving than mean

contributions. Again, positive coefficients are found for all groups. Final ly, the last three columns look

at the correlation between group participation and individual gifts. Increases in the participation rate of

the group of 10% are associated with a $13 to $18 increase in individual contributions.

The results in this table suggest some general conclusions. First, the behavior of team members is

more highly correlated than the behavior of people who are in the same cost center or mail code. There

are several possible explanations. Campaign designers may do a good job of assigning teams to include

actual social groups. However, given the enormous size of the company and centralized team

assignments this would be surprising. It seems more likely that there are important unobservable shocks

occurring at the team level. This may be due to the presence of a team leader who is charged with

providing information and encouraging team members to participate in the campaign. Second, the

addition of location fixed effects decreases the magnitude of all of the coefficients, sugges ting that

common shocks based on location were biasing the coefficients on group behavior. Finally, the

relationship between individual and peer behavior is weaker for means than for medians. When peer

behavior is defined by means, the presence of outliers may introduce too much noise into the

measurement of γ .

Table 4b uses tobit to estimate equation 1. Qualitatively, the results from tobit estimation are not

different from those using OLS, although the coefficients are larger. Table 4c considers the individual’s

participation decision. Here, an $100 increase in the team’s mean gift is associated with only a 0.06

percentage point to 1.3 percentage point increase in the probability of giving. The correlation between

participation and the median contribution of an individual’s peers is larger, but still a $100 increase in the

team’s median gift is associated with only a 1.4 percentage point to 7.0 percentage point increase in the

probability of giving. Participation seems to be highly correlated within groups. A 10 percentage point

increase in group participation is associated with at 7.6 percentage point to 9.0 percentage point increase

in the probability of participating.

Table 5 presents the estimation results of the reduced form equation. These results suggest that

group characteristics are correlated with individual giving, however not always in the directions that one

might expect. A higher average group salary is associated with slightly lower individual contributions.

An increase in group average salary of $100, from $50,000 to $51,000, is associated with a 12 cent

26

reduction in gifts. A 10 percentage point increase in the number of mails on a team, is associated with a

$3 reduction in gifts. A 1 year increase in average tenure is associated with an increase in individual

contributions of $2.55. A 10 percentage point increase in the number of group members who are

pacesetters16 is associated with a $57 increase in individual contributions. And a 10 percentage point

increase in the proportion of employees who work from home within the group is associated with an $18

decrease in individual contributions.

While the results in tables 3, 4, and 5 suggest that there is a high correlation between behaviors

within groups and between group characteristics and individual contributions, using OLS (or tobits and

probits) does little to support an argument of social influences. We cannot rule out that these correlations

are driven by unobservable common shocks, simultaneity or selection. In order to avoid this problem, the

remainder of the results use instrumental variables.

Table 6 presents the results using group characteristics as instruments for group behavior. As in

Duflo and Saez (2000), the proportion of employees in each salary decile, tenure quartile and age quartile

are used jointly as instruments in all regressions. As in Table 4, each coefficient is from a different

estimation equation. Salary, salary squared, male, age, tenure, team captain, pacesetter, work form ho me

and United Way fixed effects are included in all regressions. Table 6a uses two-stage least squares and

finds a $1 increase in the group’s mean contribution is associated with a $0.07 to $0.09 increase in

individual contributions. A $1 increase in the group’s median contribution is associated with a $0.12 to

$0.22 increase in individual contributions. Finally a 10 percentage point increase in group participation is

associated with a $22 increase in individual contributions. As in Table 4, using a tobi t structure leads to

larger coefficients for each type of group behavior. In comparison to the results when instrumental

variables are not use, the use of group characteristics as an instrument for group behavior has mixed

effects on the magnitude of the coefficients. The coefficients on mean behavior increase, while the

coefficients on group participation in panel b decrease. The probit results in Table 6c are similar to those

found without instrumenting for peer behavior. Increases in group mean or median contributions have a

small effect on the likelihood that an individual will participate. A 10 percentage point increase in group

participation is associated with a 4.8 percentage point to 5.0 percentage point increase in the probability

of participating. Comparing the coefficients on the group participation rate in Tables 4c and 5c suggests

that the existence of unobservable common shocks or the simultaneous relationship between individuals

and the members of their social group may have been biasing the results in method 1.

16 Pacesetters are asked to make contributions 1 month earlier than others, and are asked to give at least $1000.

27

Table 7 presents estimation results using past group behavior as an instrument for current group

behavior. The results are similar to those found in Table 4. Individual contribution amounts and the

likelihood of participating increase with the mean contribution, the median contribution and the

participation rates of their social groups.

Table 8 uses the past behavior of movers as an instrument for the current behavior of all peers.

This method takes advantage of changes in group assignment from 2000 to 2001. The data on team

assignment is not consistent between these years. In addition, there was a change in the cost center

numbering between years. However, mail codes did not change. Therefore, it is only possible to est imate

this method by defining movers based on changes in physical location. An individual is designated as a

mover if their physical location changes, but if all employees in the location move together or their group

is subsumed into another location, they are classified as not moving.17

Table 8a presents the results using two-stage least squares. A $1 increase in the mean

contribution is associated with a $0.53 increase in individual contributions and a $1 increase in the

median contribution is associated with a $0.90 increase in individual contributions. A 10 percentage

point increase in participation is associated with a $30 increase in contributions by individuals. As in

previous methods, using a tobit structure increases the point estimates of the coefficients on group

behavior. In Table 8c, we again see that group gift amounts have little effect on individual participation

decisions, but a 10 percentage point increase in the group participation rate increases the probability of

individual participation by 7.8 percentage point.

Table 9 replicates the results for Table 8, but includes average group characteristics (including the

individual) as explanatory variables. Including average group characteristics controls for the possibility

that campaign characteristics may differ across groups based on the group’s observable characteristics.

The results in Table 9 are similar to those presented in Table 8.

Table 10 again uses the behavior of movers as an instrument for group behavior; however, these

results look at the change in giving between 2000 and 2001. Columns 1 and 2 show that a $1 increase

from 2000 to 2001 in the median peer contribution is associated with a 15.7 to 20 cent increase in

individual contributions from 2000 to 2001. Columns 3 and 4 show that a $1 increase from 2000 to 2001

in the median peer contribution is associated with a 33.8 to 45.9 cent increase in individual contributions 17 A number of different definitions of moving were tested. The results are robust to small changes in the definition of wh o moves and who stays in the same location.

28

from 2000 to 2001. Columns 5 and 6 show that an 10 percentage point increase, from 2000 to 2001, of

the group participation rate is associated with an increase in individual contributions of $5.

Robustness:

Throughout the course of this research, several other specifications were used. In general these

produced similar results, not reported here.18 In one specification, the sample was limited to employees

who work in bank branches. Investment banking, information technology, call center, administrative and

executives were excluded. This removes the possibility of common shocks that are related t o a group’s

role within the company. Again, positive and significant coefficients were found. In another

specification, within each group one person was selected as the group leader, based on their level in the

company, designation as a vice president, or manager status. The group leader’s behavior was also

included in the regression. These results suggest that the behavior of group leaders have a positive

marginal effect on their employees’ contributions and participation decisions. One common complai nt

about workplace giving campaigns is that individuals feel their employers coerce them to participate.

Table 11 presents results that suggest contribution amounts and participation decisions do not affect the

percent change in salary. Finally, the separation of social groups into sub-groups defined by

demographics and pay were also considered. There were no discernable patterns in the sub-group results.

Up to this point, little has been done to address the potential selection problem. If charitable

generosity is a factor in group selection, an individual’s unobservable taste for giving will be correlated

with the unobservable taste of others in their social group. If this is the case then we might also expect

that for an individual who moves from one location to another, their unobservable taste for giving will be

correlated with that of their group in both locations; thus the giving by other members of a mover's new

and old groups will be correlated. If charitable generosity were not a factor in gr oup selection, then we

would not expect the giving of other members of a mover’s new and old groups to be correlated. In order

to identify the existence of a selection problem, the 2000 behavior of the new groups members is

regressed on the behavior in 2001 of a mover's former group. If there is a selection problem then there

will be a significant relationship between the behavior of these two groups. As in all regression that

utilize changes in group composition over time, these results are limited to g roups defined by mailcode.

Table 12 presents the results of these regressions. These regressions suggest that there is a significant

(albeit small) correlation between the behavior of an individual’s new peers and their former peers.

However, individuals are likely to move to groups that are similar to those that they came from. Bank 18 Copies of tables for these additional specifications are available from the author.

29

tellers are unlikely to become executives over the course of one year. When group characteristics are

controlled for the correlation between the behavior of an individual’ s new peers and their former peers

decreases.

6. Conclusion

Using peer group characteristics and past behavior as instruments for peer behavior, this paper

finds evidence for the existence of social influences in the context of the voluntary provision of public

goods. The participation decision, to give or not to give, is primarily influenced by the participation of

peers. Both how many people give and how much they give are good predictors of gift amounts.

Individuals are also responsive to changes in the contribution behavior of their peers over time. However,

these effects are not necessarily due to the giving behavior of one’s peers. It is not possible to separately

identify endogenous and contextual effects. Individuals may respond to the same characteristics of their

peers that make their peers more charitable.

Social influences may cause contributors to feel warm glow, thus this paper provides additional

empirical support for theoretical models that include a warm glow or joy of giving term. The significance

of the social influences found in this paper, suggest one explanation for the lack of free riding seen in the

private provision of public goods. While free riding may still be likely in more anonymous situations, in

situations where the provision of public goods is organized around social groups, social influences may

provide an explanation for low levels of free riding. Social influences in giving could be caused by

information provision, social norms, or merely the power of suggestion. With the exception of

information provision each of these explanations for the existence of social influences could help to

overcome free riding. In order to identify which of these theories cause social influences a randomized

experiment is necessary.

While this paper only considers social influences on giving in the workplace, these forces are

likely to play a role in other settings. Social influences may help to explain why so many individuals

make contributions rather than free ride and why there is less than full crowding out in the private

provision of public goods. In addition, understanding the role of social influences may have policy

implications. If social influences work across income levels then changes in tax policy may have broader

implications than initially anticipated. Wealthy individuals are the most likely take tax deductions for

their charitable donations. Changes in tax deductibility of donations will directly impact the wealthy; the

resulting changes in their giving may also affect the giving behavior of other income groups.

30

Future research can provide insight into the distinction between the exact forms of social

influences and their existence in other settings. Extensions of this research might also include how group

members influence the decision of which type of organization to donate to, looking at the effects of group

composition and size, focusing on different segments of the workforce, or experimental work.

31

Table 1: Giving Guide

Salary

Suggested Giving as %

of Salary

Suggested Annual Gift

Amount

Mean Actual Giving as % of

Salary*

Median Actual Giving as % of

Salary* less than $15,000 0.60% $1 – 90 1.42% 0.42% $15,000 – 19,999 0.80% $120 – 160 0.34% 0.26% $20,000 – 29,999 1.00% $200 – 300 0.35% 0.21% $30,000 – 59,999 1.50% $450 – 900 0.39% 0.22% $60,000 – $124,999 2.00% $1200 – 2500 0.64% 0.40% $125,000 and above 2.50% $3125 + 1.38% 0.92% * for those who participate in the campaign Suggested amounts in this table comes from the corporate campaign materials provided to employees. Actual gifts as a percent of salary are taken from the data.

32

Table 2: Summary Statistics

Campaign Related Behavior Mean Stan. Dev. Min Max

Pledge in 2001 ($) 156 607 0 50,000 Participation in 2001 (%) 0.574 0.495 0 1 Pledge in 2000 ($) 149 588 0 31,050 Participation in 2000 (%) 0.555 0.497 0 1 Team Captain in 2001 (%) 0.061 0.239 0 1 Team Captain in 2000 (%) 0.046 0.209 0 1 Pacesetter in 2001 (%) 0.044 0.204 0 1 Pacesetter in 2000 (%) 0.034 0.181 0 1 Ask Amount in 2001 ($) 619 845 0 32,592 Received list of large donors (%) 0.149 0.356 0 1

Employment Related Information Mean Stan. Dev. Min Max

Contractual Pay ($) 39,485 30,906 0 1,250,000 Actual Pay ($) 46,266 121,196 300 8,380,105 Tenure as of 2001 (years) 8.289 8.324 -0.1397 53 Vice President 2001 (%) 0.167 0.373 0 1 Senior Vice President 2001 (%) 0.045 0.208 0 1 Executive Vice President 2001 (%) 0.001 0.037 0 1 Works from home (%) 0.002 0.048 0 1 Age in 2001 (years) 39 11.820 15 97 Male (%) 0.301 0.459 0 1

2000 2001

Number of Teams 6,853 8,555 Number of Mail Codes 10,895 10,499 Number of Cost Centers 14,051 14,810

33

Table 2: Summary Statistics (cont)

Social Group Variables Mean Stan. Dev. Min Max

Team size in 2001 43 67 1 573 Median Team Pledge (2001 Team, 2001 Pledge) 57 148 0 10,512 Median Team Pledge (2001 Team, 2000 Pledge) 51 159 0 10,512 Average Team Pledge (2001 Team, 2001 Pledge) 159 620 0 100,132 Average Team Pledge (2001 Team, 2000 Pledge) 144 296 0 12,670 Team Participation Rate (2001 Team, 2001 Participation) 0.574 0.261 0 1 Team Participation Rate (2001 Team, 2000 Participation) 0.551 0.253 0 1 Mail Code size in 2001 57 94 1 537 Median Mail Code Pledge (2001 Team, 2001 Pledge) 64 328 0 50,000 Median Mail Code Pledge (2001 Team, 2000 Pledge) 56 275 0 27,504 Average Mail Code Pledge (2001 Team, 2001 Pledge) 157 674 0 100,132 Average Mail Code Pledge (2001 Team, 2000 Pledge) 142 348 0 27,504 Mail Code Participation Rate (2001 Team, 2001 Participation) 0.573 0.268 0 1 Mail Code Participation Rate (2001 Team, 2000 Participation) 0.550 0.253 0 1 Cost Center size in 2001 34 62 1 531 Median Cost Center Pledge (2001 Team, 2001 Pledge) 76 324 0 25,000 Median Cost Center Pledge (2001 Team, 2000 Pledge) 68 299 0 15,004 Average Cost Center Pledge (2001 Team, 2001 Pledge) 152 747 0 133,510 Average Cost Center Pledge (2001 Team, 2000 Pledge) 136 366 0 16,877 Cost Center Participation Rate (2001 Team, 2001 Participation) 0.572 0.289 0 1 Cost Center Participation Rate (2001 Team, 2000 Participation) 0.548 0.274 0 1 Note: For Social Group Variables the level of observation is the individual, not the social group.

Changes Mean Stan. Dev. Min Max

Moved locations between 2001 & 2000 (%) 0.356 0.479 0 1 Difference 2000 & 2001 Gift ($) 24.716 1095 -25,000 350,000 Difference 2000 & 2001 Pay ($) 11082 114339 -125,000 8,180,105 Difference 2000 & 2001 Mean Gift for Mail Code -0.381 300 -37,432 19,741 Difference 2000 & 2001 Median Gift for Mail Code -67.708 3984 -250,000 19,965 Difference 2000 & 2001 Participation Rate for Mail Code 0.001 0.199 -1 1 Note: For Social Group Variables the level of observation is the individual, not the social group.

34

Table 3 The Effect of Individual Characteristics and Group Behavior on Individual Contributions

Dependent Variable Contribution Contribution Participation

Method OLS Tobit Probit

Independent Variables

Salary 2001 0.003** 0.003** 1.00E-07** (0.0000) (0.0000) (0.0000) Salary Squared -3.12E-10** -3.67E-10** -2.74E-14** (0.0000) (0.0000) (0.0000) Male -2.401 -53.463** -0.079** (3.1448) (4.8294) (0.0032) Age 2001 0.295* 4.634** 0.005** (0.1426) (0.2180) (0.0001) Tenure as of Oct 1 2001 4.161** 9.705** 0.008** (0.2042) (0.2999) (0.0002) Team Captain 2001 131.747** 231.287** 0.161*8 (6.0089) (8.5695) (0.0056) Pace Setter 2001 734.109** 853.911** 0.206** (7.9178) (10.9137) (0.0073) Mail To Home 2001 -347.483** -619.318** -0.336** (28.7569) (48.4584) (0.0241) Team Mean Gift 2001 (Excl.individual) 0.053** 0.079** 1.30E-04** (0.0023) (0.0004) (0.0000) Constant -57.316** -561.425** (5.0668) (8.0089) ** Significant at 1% level * Significant at 5% level Note: Standard errors in parentheses Probit results are probability scaled.

35

Table 4 Effects of Behavior of Social Group on Individual Contributions

Table 4a: OLS with 2001 Contribution as Dependent Variable Group Behavior Mean Contribution Median Contribution Participation Rate Fixed Effects None State United Way None State United Way None State United Way Group Definition Team 0.053* 0.048* 0.046* 0.456* 0.414* 0.400* 186.458* 159.775* 152.791* (0.0023) (0.0023) (0.0023) (0.0096) (0.0097) (0.0099) (5.3679) (5.5692) (6.1117) Mail Code 0.048* 0.043* 0.042* 0.090* 0.077* 0.073* 178.981* 152.307* 145.181* (0.0021) (0.0021) (0.0021) (0.0042) (0.0042) (0.0043) (5.2332) (5.4272) (5.9533) Cost Center 0.041* 0.038* 0.038* 0.156* 0.146* 0.143* 156.882* 136.606* 130.209* (0.0019) (0.0018) (0.0019) (0.0043) (0.0043) (0.0044) (4.7915) (4.9252) (5.3327)

Table 4b: Tobit with Individual’s 2001 Contribution as Dependent Variable Group Behavior Mean Contribution Median Contribution Participation Rate Fixed Effects None State United Way None State United Way None State United Way Group Definition

Team 0.079* 0.068* 0.064* 0.764* 0.677* 0.638* 899.671* 845.652* 826.232* (0.0004) (0.0004) (0.0005) (0.0043) (0.0046) (0.0138) (8.8273) (9.0348) (9.7917) Mail Code 0.069* 0.059* 0.056* 0.151* 0.127* 0.116* 871.840* 816.480* 795.761* (0.0004) (0.0028) (0.0029) (0.0059) (0.0059) (0.0060) (8.6271) (8.8310) (9.5584) Cost Center 0.059* 0.053* 0.051* 0.231* 0.209* 0.201* 797.401* 751.731* 730.460* (0.0003) (0.0004) (0.0004) (0.0061) (0.0060) (0.0061) (7.8937) (8.0110) (8.5754)

Table 4c: Probit with Individual’s 2001 Participation as Dependent Variable Group Behavior Mean Contribution Median Contribution Participation Rate Fixed Effects None State United Way None State United Way None State United Way Group Definition Team 1.30E-04* 1.04E-04* 8.75E-05* 7.01E-04* 6.15E-04* 5.51E-04* 0.903* 0.875* 0.835* (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0062) (0.0064) (0.0068) Mail Code 9.29E-05* 7.09E-05* 5.79E-05* 2.85E-04* 2.36E-04* 1.98E-04* 0.871* 0.842* 0.802* (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0060) (0.0062) (0.0066) Cost Center 9.96E-05* 8.58E-05* 7.92E-05* 1.80E-04* 1.56E-04* 1.41E-04* 0.830* 0.803* 0.763* (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0056) (0.0057) (0.0060)

* Significant at 1% level Note: Standard errors in parentheses Each coefficient represents the results of a separate regression. To save space, coefficients on salary, salary squared, male, age, tenure, team captain, pace setter, work from home and fixed effects are omitted from the table. Group behavior is always calculated excluding the individual. Probit results are probability scaled.

36

Table 5 The Effect of Individual Characteristics and Group Characteristics

on Individual Contributions (The Reduced Form Equation)

Independent Variables Salary 2001 0.003** (0.0000) Salary Squared -3.57E-10** (0.0000) Male 8.422** (3.2854) Age 2001 0.231 (0.1471) Tenure as of Oct 1 2001 3.406** (0.2145) Team Captain 2001 123.637** (6.1715) Pace Setter 2001 668.733** (8.1597) Mail To Home 2001 -249.087** (35.4761) Team Average Salary 2001(Excl.individual) -1.21E-03** (0.0000) Team Average Salary Squared (Excl.individual) -2.51E-12 (0.0000) Percent of Team that is Male (Excl.individual) -32.327** (7.4269) Team Average Age (Excl.individual) -0.632 (0.3599) Team Average Tenure (Excl.individual) 2.548** (0.4851) Percent of Team that is a Team Captain (Excl.individual) -15.241 (22.3885) Percent of Team that is a Pacesetter (Excl.individual) 568.550** (16.9139) Percent of Team that Work from Home (Excl.individual) -182.401** (52.7808) Constant -13.648 (11.2849) ** Significant at 1% level * Significant at 5% level Standard errors in parentheses

37

Table 6: Effects of Behavior of Social Group on Individual Contributions with Characteristics of Groups used as Instruments for Group Behavior

Table 6a: 2SLS with 2001 Contribution as Dependent Variable

Group Behavior Mean Contribution Median Contribution Participation Rate Fixed Effects United Way United Way United Way Group Definition Team 0.086* 0.218* 224.355* (0.0095) (0.0225) (17.0632) Mail Code 0.069* 0.120* 223.675* (0.0081) (0.0133) (16.5529) Cost Center 0.094* 0.139* 228.905* (0.0079) (0.0112) (15.2993)

Table 6b: Instrumental Variables Tobit with Individual’s 2001 Contribution as Dependent Variable

Group Behavior Mean Contribution Median Contribution Participation Rate Fixed Effects United Way United Way United Way Group Definition Team 0.284* 0.679* 717.032* (0.0141) (0.0329) (25.5699) Mail Code 0.221* 0.363* 692.565* (0.0120) (0.0195) (24.7341) Cost Center 0.263* 0.356* 695.396* (0.0118) (0.0162) (22.8694)

Table 6c: Instrumental Variables Probit with Individual’s 2001 Participation as Dependent Variable

Group Behavior Mean Contribution Median Contribution Participation Rate Fixed Effects United Way United Way United Way Group Definition Team 2.24E-04* 6.66E-04* 0.501* (0.0000) (0.0000) (0.0184) Mail Code 1.82E-04* 3.34E-04* 0.476* (0.0000) (0.0000) (0.0179) Cost Center 2.04E-04* 3.02E-04* 0.493* (0.0000) (0.0000) (0.0170) * Significant at 1% level Note: Standard errors in parentheses Proportion of group in salary decilies, tenure quartiles, and age quartiles used as instruments. Each coefficient represents the results of a separate regression. To save space, coefficients on salary, salary squared, male, age, tenure, team captain, pace setter, work from home and fixed effects are omitted from the table. Group behavior is always calculated excluding the individual. Probit results are probability scaled.

38

Table 7: Effects of Behavior of Social Group on Individual Contributions with Past Behavior of Groups used as an Instrument for Group Behavior

Table 7a: 2SLS with 2001 Contribution as Dependent Variable

Group Behavior Mean Contribution Median Contribution Participation Rate Fixed Effects United Way United Way United Way Group Definition Team 0.142* 0.331* 209.664* (0.0039) (0.0135) (10.6550) Mail Code 0.110* 0.078* 226.506* (0.0032) (0.0050) (10.7804) Cost Center 0.115* 0.169* 180.749* (0.0030) (0.0050) (9.4879)

Table 7b: Instrumental Variables Tobit with Individual’s 2001 Contribution as Dependent Variable

Group Behavior Mean Contribution Median Contribution Participation Rate Fixed Effects United Way United Way United Way Group Definition Team 0.208* 0.494* 894.094* (0.0053) (0.0195) (16.5404) Mail Code 0.151* 0.119* 872.248* (0.0044) (0.0070) (16.4609) Cost Center 0.156* 0.222* 768.790* (0.0038) (0.0070) (14.5213)

Table 7c: Instrumental Variables Probit with Individual’s 2001 Participation as Dependent Variable

Group Behavior Mean Contribution Median Contribution Participation Rate Fixed Effects United Way United Way United Way Group Definition Team 9.63E-05* 4.69E-04* 0.843* (0.0000) (0.0000) (0.0115) Mail Code 7.93E-05* 1.84E-04* 0.790* (0.0000) (0.0000) (0.0117) Cost Center 7.93E-05* 1.41E-04* 0.755* (0.0000) (0.0000) (0.0106) * Significant at 1% level Note: Standard errors in parentheses Each coefficient represents the results of a separate regression. To save space, coefficients on salary, salary squared, male, age, tenure, team captain, pace setter, work from home and fixed effects are omitted from the table. Group behavior is always calculated excluding the individual. Probit results are probability scaled.

39

Table 8: Effects of Behavior of Social Group on Individual Contributions with Past Behavior of Movers used as an Instrument for Group Behavior

Table 8a: 2SLS with 2001 Contribution as Dependent Variable

Group Behavior Mean Contribution Median Contribution Participation Rate Fixed Effects United Way United Way United Way Group Definition Team Mail Code 0.525 0.904 304.647 (0.0193) (0.0303) (30.9549) Cost Center

Table 8b: Instrumental Variables Tobit with Individual’s 2001 Contribution as Dependent Variable

Group Behavior Mean Contribution Median Contribution Participation Rate Fixed Effects United Way United Way United Way Group Definition Team Mail Code 0.676 1.105 903.214 (0.0261) (0.0415) (45.5176) Cost Center

Table 8c: Instrumental Variables Probit with Individual’s 2001 Participation as Dependent Variable

Group Behavior Mean Contribution Median Contribution Participation Rate Fixed Effects United Way United Way United Way Group Definition Team Mail Code 2.00E-04 0.001 0.776 (0.0000) (0.0000) (0.0341) Cost Center * Significant at 1% level Note: Standard errors in parentheses Each coefficient represents the results of a separate regression. To save space, coefficients on salary, salary squared, male, age, tenure, team captain, pace setter, work from home and fixed effects are omitted from the table. For this Table only group behavior defined by mail codes can be used. It is not possible to track changes in other social groups. Group behavior is always calculated excluding the individual. Probit results are probability scaled.

40

Table 9: Effects of Behavior of Social Group on Individual Contributions with Past Behavior of Movers used as an Instrument for Group Behavior with

Average Group Behavior included as Explanatory Variables

Table 9a: 2SLS with 2001 Contribution as Dependent Variable Group Behavior Mean Contribution Median Contribution Participation Rate Fixed Effects United Way United Way United Way Group Definition Team Mail Code 0.670* 0.969* 222.983* (0.0371) (0.0440) (39.0577) Cost Center

Table 9b: Instrumental Variables Tobit with Individual’s 2001 Contribution as Dependent Variable

Group Behavior Mean Contribution Median Contribution Participation Rate Fixed Effects United Way United Way United Way Group Definition Team Mail Code 0.836* 1.100* 838.635* (0.0491) (0.0601) (56.9002) Cost Center

Table 9c: Instrumental Variables Probit with Individual’s 2001 Participation as Dependent Variable

Group Behavior Mean Contribution Median Contribution Participation Rate Fixed Effects United Way United Way United Way Group Definition Team Mail Code 1.82E-04* 4.46E-04* 8.29E-01* (0.0000) (0.0001) (0.0434) Cost Center * Significant at 1% level Note: Standard errors in parentheses Each column represents the results of a separate regression. To save space, coefficients on salary, salary squared, male, age, tenure, team captain, pace setter, work from home and fixed effects are omitted from the table. For this Table only group behavior defined by mail codes can be used. It is not possible to track changes in other social groups. Group behavior is always calculated excluding the individual. Probit results are probability scaled.

41

Table 10: Effects of Changes in Behavior of Social Group, from 2000 to 2001 on Changes in Individual Contributions with the Change in Behavior of Movers used

as an Instrument for Group Behavior Column 1 Column 2 Column 3 Column 4 Column 5 Column 6 Change in Pay -2.19E-05** -1.11E-04** -2.54E-05** -1.13E-04** -2.16E-05** -1.11E-04** (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) Annual Pay 2001 0.001** 0.001** 0.001** (0.0001) (0.0001) (0.0001) Male -7.411** -6.895** -7.286** (2.7201) (2.5466) (2.5421) Age 2001 -0.253* -0.224* -0.244* (0.1148) (0.1075) (0.1073) Tenure as of Oct 1 2001 -0.887** -0.632** -0.622** (0.1695) (0.1537) (0.1534) Team Captain 2001 11.251* 9.787* 8.130 (5.1544) (4.8101) (4.7969) Pace Setter 2001 15.360* 9.717 9.943 (6.9697) (6.4659) (6.4554) Mail To Home 2001 -368.214** -424.952** -332.823** (125.0909) (117.8635) (116.7294) Change in Mail Code Average Gift (Excl.individual)

0.201**

0.157**

(0.0255) (0.0249) Change in Mail Code Median Gift (Excl.individual)

0.459**

0.338**

(0.0485) (0.0493) Change in Mail Code Participation Rate (Excl.individual)

50.332**

50.636**

(9.3032) (9.2244) Constant 8.176* -28.161** 5.390 -34.385** 8.683** -34.396 (3.7254) (5.9571) (3.3781) (5.4800) (3.3577) (5.4714) ** Significant at 1% level * Significant at 5% level Note: Standard errors in parentheses To save space, location fixed effects are omitted. For this Table only group behavior defined by mail codes can be used. It is not possible to track changes in other social groups. Group behavior is always calculated excluding the individual.

42

Table 11: The Effect of Campaign Participation on Changes in Pay

Percent Change in

salary Percent Change in salary Pledge Amount 2000 -0.001 (0.0015) Participated 2000 -3.629 (1.7766) Male 6.252 5.913 (1.9128) (1.8950) Age 2001 0.392 0.409 (0.0890) (0.0894) Tenure as of Oct 1 2001 -0.458 -0.433 (0.1237) (0.1242) Annual Pay 2000 Constant -10.694 -9.620 (3.2413) (3.2740) Note: Standard errors in parentheses. All Coefficients significant at the 1% level.

43

Table 12: Correlation Between the Behavior of the Former Peers and the New Peers of Individuals who Move from one Location to Another

Table 12a: Dependent Variable: Mean Contribution in 2000 by Movers’ New Peers Former Peers’ Mean Contribution in 2001 0.204* 0.094* 0.062* 0.077* (0.0057) (0.0050) (0.0094) (0.0082) Controls for Peer Characteristics None New Only Former Only Former and New

Table 12a: Dependent Variable: Median Contribution in 2000 by Movers’ New Peers

Former Peers’ Median Contribution in 2001 0.084* 0.035* 0.022* 0.043* (0.0056) (0.0053) (0.0077) (0.0072) Controls for Peer Characteristics None New Only Former Only Former and New

Table 12a: Dependent Variable: Participation Rate in 2000 by Movers’ New Peers Former Peers’ Participation Rate in 2001 0.155* 0.127* 0.129* 0.127* (0.0066) (0.0063) (0.0070) (0.0066) Controls for Peer Characteristics None New Only Former Only Former and New

* Significant at 1% level Note: Standard errors in parentheses Each column represents the results of a separate regression. To save space, coefficients on group characteristics (salary, salary squared, male, age, tenure, team captain, pace setter, and work from home) are omitted from the table. For this Table only group behavior defined by mail codes can be used. It is not possible to track changes in other social groups. In each regression the observations are individuals who moved. Group behavior is always calculated excluding the individual.

44

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