Post on 11-Apr-2017
The effects of resource scarcity on charitable giving Financial deprivation and the childhood environment influence donations to charity
FACULTY OF ECONOMICS AND BUSINESS
Lies Polet R0263272
Thesis submitted to obtain the degree of
MASTER IN DE TOEGEPASTE ECONOMISCHE WETENSCHAPPEN: HANDELSINGENIEUR
Major Marketing
Promoter: Prof. Dr. Efthymios Altsitsiadis
Assistant: Angelos Stamos
Academic year 2014-2015
The effect of resource scarcity on charitable giving
Financial deprivation and the childhood environment influence donations to charity
Generous people helping others in need are making a wonderful gesture; however, it is still not self-evident in
society that wealthier people share their resources with people in poor countries. Pro-social behavior is linked to
life history strategy, a theory dealing with the trade-off between investment in growing and maintaining knowledge
or skills versus investment in activities related to reproduction. Features of the childhood environment partly
determine individuals’ adult life history strategies. This means people will respond differently to resource scarcity
depending on characteristics of their childhood environment. This paper details an experiment investigating the
donation behavior of people with different financial backgrounds, both financially deprived and not. The result
suggests that, people from wealthy childhood backgrounds donate higher amounts than people from poor
childhood backgrounds when feeling financially deprived.
FACULTY OF ECONOMICS AND BUSINESS
I
Acknowledgements
I would like to express my gratitude towards my promotor Prof. Dr. Efthymios Altsitsiadis
who gave me the opportunity to learn something by giving me the chance to solve problems
on my own and by only intervening when I still had difficulties. Moreover, I would like to
thank his assistant Angelos Stamos for his patience when I had difficulties with
understanding something, his rapid and efficient responses on my questions and for taking
the time necessary when we had to meet. Next, I would like to thank Mike Watson, a native
English speaker, for carefully reading my text. His corrections and suggestions improved
the language of my thesis. I also want to thank my colleagues with whom I could share
questions or problems and all the respondents who filled in my online survey to be able to
collect my data. Finally, I would like to thank my friends and family for supporting me.
II
Table of Contents
Acknowledgements .............................................................................................................. I
General Introduction ............................................................................................................ 1
1 Literature ................................................................................................................... 3
1.1 Pro-social behavior ......................................................................................... 3
1.1.1 Definition............................................................................................... 3
1.1.2 Resource scarcity and pro-social behavior .......................................... 4
1.1.3 Donation behavior ................................................................................ 5
1.1.4 Relevance ............................................................................................ 5
1.2 Financial deprivation ....................................................................................... 5
1.2.1 Definition............................................................................................... 5
1.3 Life history strategy ........................................................................................ 6
1.3.1 Definition............................................................................................... 6
1.3.2 Life history strategy and financial deprivation ...................................... 7
1.3.3 Life history strategy and pro-social behavior........................................ 9
1.4 Hypothesis .................................................................................................... 10
2 Method .................................................................................................................... 12
2.1 Instruments ................................................................................................... 12
2.2 Data .............................................................................................................. 12
2.3 Participants ................................................................................................... 13
2.4 Procedure ..................................................................................................... 14
3 Results .................................................................................................................... 17
3.1 Preliminary data analysis .............................................................................. 17
3.1 Donation amounts......................................................................................... 17
3.1.1 Perceived childhood SES ................................................................... 17
3.1.2 General attitude towards donations ................................................... 20
General conclusion ........................................................................................................... 23
Appendices ....................................................................................................................... 26
List of figures ..................................................................................................................... 34
List of tables ...................................................................................................................... 35
Sources ............................................................................................................................. 36
1
General Introduction
Studies in pro-social behavior have greatly multiplied in recent years. If the terms pro-social
behavior and helping behavior sound familiar to you, you have probably heard of them
within a psychological context. Within a sociological context, the terms solidarity or solidary
behavior tend to be used more often. Economists prefer to use the terms cooperation or
cooperative behavior. However, in all three fields, people use the term altruism. An
interesting example of pro-social behavior is donation behavior; a well-known
contemporary topic. Numerous people donate to charity on a regular basis. Moreover, most
of the income of nonprofit organizations and charities comes from donations.
Consequently, it is useful to get more insight into donation behavior.
A central issue of our society today is financial security. Human beings do not want to worry
about their finances in the future. People experience financial deprivation when they feel
that their financial state is relatively inferior compared to other people. They will actually
change their moral behavior when people feel deprived. It would, thus, be of interest to
learn how financial deprivation is linked to other factors, for example, pro-social behavior.
Research has shown that deprivation of resources can create stress (Caplan & Schooler,
2007). Moreover, stress and resource deprivation threaten social engagement (Collins &
Feeney, 2004; McBride et al., 2006). Lim and Han (2003) found out that, in times of financial
crisis, something which is definitely linked to financial insecurity, it is assumed that social
life is threatened. However, a financial crisis turns out to enhance social solidarity (Kim,
2004; Lim & Han, 2003). How does donation behavior change when people feel financially
deprived? Does financial deprivation increase or decrease the size of donations? The
present study provides a more evolutionary perspective on the topic of charitable giving
combined with life history strategy; something which has received considerable research
attention because it has provided new perspectives on understanding human behavior.
To clarify the purpose of this paper, the concept of life history strategy has to be introduced.
Life history strategy has not only been useful in the study of animal behavior (Ellis,
Figueredo, Brumbach & Schlomer, 2009), it also has been important in human behavioral
ecology and child development (Belsky, Steinberg & Draper, 1991; Del Giudice, 2009).
Organisms have to allocate their time, resources and energy efficiently in order to survive
and reproduce. This theory deals with the trade-off between investment in somatic effort;
which means people, for example, invest in growing and maintaining knowledge or skills,
versus reproductive effort; which focuses on activities related to intra-sexual competition
and reproduction. In addition, features of one’s childhood partly determine the life history
strategies that adults enact later in life (Belsky et al., 1991; Kuzawa, McDade, Adair & Lee,
2010). Consequently, people respond differently to resource scarcity depending on their
2
childhood experience, captured in the socio-economic status (SES) of the individual’s
early-life environment. There has been much research about the relationship between
perceived childhood SES and life history strategies. However, little research has focused
on the link between life history strategies and pro-social behavior.
The aim of this paper is to investigate donation behavior in the context of life history
strategies and financial deprivation. Pro-social behavior, such as charitable donations, will
probably differ depending on whether people use different life history strategies. This
difference in donation behavior will probably only appear when people feel financially
deprived.
This paper is divided into four sections. The first section introduces the research about the
different concepts and formulates the hypothesis. Pro-social behavior is first described in
general. After that, resource scarcity is linked to pro-social behavior. The concept is
specified further by mentioning donation behavior, a well-known example of pro-social
behavior. The discussion of the concept ends with an explanation of why pro-social
behavior is such an interesting subject to study and with the formulation of the hypothesis.
Next, the second concept, financial deprivation, is defined. The literature section ends with
the third concept, life history strategy, which is first linked to financial deprivation and then
related to pro-social behavior. The second section provides an overview of the
methodology. The first part explains which instruments are used. In addition, this method
section describes data and participants and ends with a description of the procedure used.
In the third section, the results of the experiment are discussed. The final section contains
conclusions, implications and directions for further research. It also explains how the
findings of this present study can contribute to the literature and why the results are
relevant.
3
1 Literature
This first section will give an overview of some relevant, interesting research that has been
published on the topics pro-social behavior, financial deprivation and life history strategy.
1.1 Pro-social behavior
1.1.1 Definition
Batson (1998) defined pro-social behavior, which is mostly motivated by empathy, as the
broad range of actions intended to benefit one or more people other than oneself.
This kind of behavior is intriguing because it is influenced by many factors. It is suggested,
for example, that pro-social behavior increases with age. It is also very interesting to
investigate why some people behave pro-socially whilst others do not. Two possible
answers are the motives egoism and altruism. Egoism is caused by self-interest, whilst
altruism is preferring to benefit another rather than ourselves. There are a lot of forms of
pro-social behavior in our daily lives. They range from charitable donations to voluntary
work or simply helping other people. In this paper, the focus is on pro-social behavior and
more specifically on donations to charity. This can be explained with an example. Imagine
a workplace with members of work teams who have two main goals. They definitely want
to get their job done. Moreover, they like to maintain social relationships with their
colleagues. The outcome of group work may vary depending on whether the workers
handle the situation primarily in terms of task output, and then focus on social relations, or
vice versa. These workers have a mental model of their peer relations in terms of
“friendship” when they put priority on relationships with colleagues above tasks. However,
they could also hold a mental model of their peer relations in terms of “professional
colleagues”, when they think that their tasks are more important.
Does pure altruism actually exist? Is this human behavior sincere or do humans behave
differently when they are not rewarded for such behavior? Social psychologists started
focusing on the internal rewards resulting from pro-social behavior instead of only
considering the material rewards for helping others. Possible internal rewards are the warm
glow of being a moral person or the avoidance of feeling guilty. Batson (1991) and many
other empirical studies and experiments proved that true altruism does exist and is mostly
triggered by empathy.
The work of Piaget and Kohlberg gave rise to the investigation of the relationship between
the level of pro-social behavior and children’s and adolescents’ socio-cognitive
4
development (Eisenberg & Fabes, 1998). Because young people are often punished by
their parents when they behave in a certain way, children will base their moral behavior on
the consequences of their own behavior. As a result, the punished behavior will be judged
to be bad. When young people grow up, they will carry forward this moral evaluation of
their behavior without questioning whether such social rules are correct or not. In late
adolescence, people are capable of basing their moral judgment on abstract ethical rules.
The subjective evaluation of a situation, including motivational and cognitive aspects,
determines the magnitude of solidarity of a given person in a given situation. This
evaluation differs from person to person and implies goals, the perception of a situation, as
well as the mental model of the relationship. The last aspect is about questioning whether
the other person is a friend, an enemy or a competitor and, also, the expectations of the
other person. People are not necessarily aware of these questions. Kurt Lewin (1936)
defined behavior as a function of person and situation. Not only personal traits, skills,
abilities or an actor’s learning history will influence this subjective evaluation of a given
situation. There are also some situational factors, for example, the presence of others in
the situation. This example is a purely situational factor; however, there are more stable
factors which appear across situations like institutional and cultural influences. Both social
psychologists and sociologists study these factors, although social psychologists
investigate the circumstances and sociologists investigate more the structural and
institutional determinants.
1.1.2 Resource scarcity and pro-social behavior
Scarcity of financial resources causes financially dissatisfied people. These people mostly
think directly about acquiring financial resources to enhance their situation (Blalock, Just &
Simon, 2007; Bowles & Park, 2005; Callan et al., 2008; Haisley, Mostafa & Loewenstein,
2008; Neumark & Postlewaite, 1998). An example of this phenomenon is the fact that
financially dissatisfied people start participating in the lottery more frequently (Blalock et
al., 2007; Callan et al., 2008; Haisley et al., 2008). Besides this, when people start feeling
unhappy and financially dissatisfied, they become egoistic and try to improve their own
situation instead of helping others; which is confirmed by the fact that people want to
acquire more financial resources in order to enhance their personal situation. If people are
worrying about themselves, their worries could keep them from helping others. Indeed, this
could even lead to people harming others. Moreover, resource scarcity may awaken
antisocial behavior that damages welfare, for example, theft. On the other hand, as was
mentioned earlier, financial crisis, which is definitely linked to scarcity of resources, could
also enhance social solidarity (Kim, 2004; Lim & Han, 2003) despite all these negative
elements related to resource scarcity. So, whether resource scarcity results in more or less
5
pro-social behavior probably also depends on other factors. In this study, in a later
discussion, childhood environments will also be taken into account.
1.1.3 Donation behavior
What drives a human being to donate a sum of money to a charitable organization?
Donations can be seen as market transactions with a zero price. However, incentives for
these contributions may have adverse effects on voluntary contributions. Bénabou and
Tirole (2006) found that introducing explicit rewards for pro-social behavior could actually
discourage such behavior. This happens because others may perceive that the person is
possibly behaving pro-socially in order to get the rewards. It has actually been proven that
people behave more pro-socially when others can observe their actions because people
are concerned about how others perceive them. Besides this, there is also a significant link
between the amount donated and the probability that the amount would actually be
donated. Finally, Andreoni et al. (2011) proved that people take longer routes when leaving
a supermarket in order to avoid being asked for charitable donations.
1.1.4 Relevance
To conclude this section about pro-social behavior, some arguments why this behavior is
such an interesting subject to study are mentioned. Firstly, pro-social behavior from the
same individual varies between situations. Secondly, Ligthart (1995) proved that situational
factors can override or interact with the nature of a person even though personality reflects
pro-social behavior. Thus, the situation itself plays a major role in the pro-social behavior
story. Also, a person’s pro-social behavior not only varies depending on the situation, but
different motivations may determine that same individual’s behavior (Ross & Nisbett, 1991;
Smeesters, Warlop, Van Avermaet, Corneille & Yzerbyt, 2003; Van Lange, 2000).
1.2 Financial deprivation
1.2.1 Definition
An important goal for human beings is financial security. The way people think and feel
about their financial state is captured in the term ‘subjective financial wellbeing’. However,
people may compromise their moral behavior when they actually feel deprived. There is a
strong subjective influence, namely social comparison, on this subjective financial
wellbeing. When people feel that their financial state is relatively inferior compared to other
6
people, they experience financial deprivation. Both wealthier and poorer people can feel
financially deprived. This experience may shift moral standards.
Fratik (1985) proved that the relative rank of a person’s income determines how happy and
satisfied that human being feels. The feeling of being relatively financial dissatisfied causes
stress, bitterness and anger (Wilkinson & Pickett, 2009). Moreover, it encourages people
to improve themselves (Crosby, 1976; Kawakami & Dion, 1995). As was mentioned earlier,
if people start feeling relatively financially dissatisfied, they want to acquire more financial
resources to enhance their situation (Blalock et al., 2007; Bowles & Park, 2005; Callan et
al. 2008; Haisley et al., 2008; Neumark & Postlewaite 1998). Briers (2006) proved a similar
reasoning that financially dissatisfied people start consuming caloric resources or food
energy in order to meet their financial needs. This is possibly due to the fact that money
and food are related, exchangeable resources.
Christan and Morgan (2005) argued that relative financial deprivation leads to people
decreasing saving and increasing their consumption, mostly their attention-getting
consumption. These people are just trying to reduce the gap in consumption with the other
people to whom they compare themselves. Because people do not own an inherent “scale”
for an ideal desired amount of money, they rely on others to make a judgment about their
financial situation (Bazerman, Loewenstein & White, 1992; Hsee et al., 1999).
1.3 Life history strategy
1.3.1 Definition
Organisms have to make decisions about the allocation of their time, resources and energy
in an efficient way between various tasks to survive and reproduce, favoring allocation
strategies that optimize resource use over the life course and enhance fitness. (Schaffer,
1983; Williams, 1957). The laws of thermodynamics clarify that organisms who allocate
energy for a certain task cannot allocate this energy anymore for another task. For
example, people spending time gathering food, could not use this time sleeping; people
spending effort on parenting, could not use this time to acquire new friends (Kaplan &
Gangestad, 2005; Roff, 2002). As already mentioned in the introduction, this theory deals
with a trade-off between investment in somatic effort and reproductive effort. Somatic effort
refers to an investment resulting in growing and maintaining physiological systems and
embodied capital like knowledge or skills. Reproductive effort focuses on investment in
some activities that are related to intra-sexual competition and reproduction. Kenrick and
Luce (2000) used the analogy of a bank account. Somatic effort can be compared with
building up a bank account, whilst reproductive effort is similar to spending this account in
7
ways that will help to replicate the investor’s genes. In the same way that people do not
save money just to have a savings account, organisms will not invest in somatic effort just
to grow, maintain and learn. When people invest in somatic effort, they invest in future
reproduction. Humans save money now to improve their success in the future. In this way,
this trade-off between somatic and reproductive effort can be converted to a trade-off
consisting of choosing between spending resources on current reproduction or on future
reproduction. People who invest largely in reproductive effort can be categorized as people
following a faster strategy. By contrast, people investing in somatic effort, can be
categorized as people following a slower strategy.
Many researchers have found that people start decreasing spending, increasing savings
and becoming more cautious when they face resource scarcity (Carroll, Hall, & Zeldes,
1992). However, life history theory indicates that resource scarcity may produce other
responses. According to this theory, humans, and organisms in general face fundamental
trade-offs when they have to decide how they will allocate their limited energy and
resources. It provides a framework to understand and explain how, why and when people
make these trade-offs in their decisions; ranging from health decisions to economic
investments. Possible fundamental trade-offs arise, for example, when people have to
allocate energy and resources toward current versus future reproduction. So, life history
strategy actually explains how and why humans and other organisms allocate their
resources to different goals (Charnov, 1993; Daan & Tinbergen, 1997; Horn, 1978; Low,
200; Roff, 1992; Stearns, 1992). As a result, this theory has become increasingly useful in
explaining human behavior. Bearing in mind this theory, individuals can be placed along a
continuum, r to K. Low investment in a large number of offspring can be associated with r-
selected individuals. Likewise, high investment in a small number of offspring can be
associated with K-selected individuals (Promislow & Harvey, 1990). The “fast-slow”
continuum is the modern approach to this r-K continuum. These strategies consist of values
ranging from slow to fast (Figueredo et al., 2005).
1.3.2 Life history strategy and financial deprivation
Griskevicius (2013) found that when people are raised in resource-scarce environments,
they may be sensitized to adopt another life history strategy than when being raised in
resource-abundant environments. The resource availability is represented by the socio-
economic status (SES) (Belsky et al. 2012; Miller et al., 2009; Griskevicius et al., 2012). As
a consequence, lower-SES environments sensitize people to adopt a slow or fast life
history strategy, which is different from higher-SES environments. How are the features of
one’s childhood connected to adult ecologies which may lead to following slower versus
faster life courses? This interesting relationship was already briefly mentioned in the
8
introduction. Features of the childhood environment partially determine individuals’ adult
life history strategies (Belsky et al., 1991; Kuzawa et al., 2010). As a consequence, people
will respond differently to resource scarcity depending on the characteristics of their
childhood environment. This environment is captured in the socio-economic status (SES)
of one’s childhood. Socio-economic status (SES) is a modern indicator of resource
availability (Simpson, Griskevicius, Kuo, Sung, & Collins, 2012). The mortality rate and the
availability of resources in the local environment are possible cues in modern human
environments. The environmental factors linked to different life history strategies are
manifested in environments by these cues (Kaplan & Gangestad, 2005; Quinlan, 2007;
Worthman & Kuzara, 2005). Humans tend to be more impulsive and will engage in more
risk-seeking behavior if they grew up in a lower SES environment. These kinds of decisions
related to reproduction, where people start using fast or slow strategies, are laden with
risks. As argued earlier, the similarity with the financial world can be highlighted once again.
In this case, the risks associated with the decisions related to reproduction are similar to
those relevant in financial decision making (E. Hill, Ross, & Low, 1997). An investor, who
starts saving money for years, risks dying without reaping the benefits of these years of
investment. Similarly, when organisms decide to delay reproduction because they choose
to invest in growth and maintenance, they may end up not reproducing at all. In contrast,
some organisms spend their money generously, which may possibly cause financial
insolvency in the future. Similarly, if organisms do not invest in somatic effort and start
reproducing too quickly, they may die before their reproductive potential has been reached.
As a consequence, a slower life history strategy can be linked to a preference for less risk
and a faster life history strategy to a preference for more risk. Moreover, humans using
faster life history strategies give in to temptations more quickly and, for example, want to
start a family sooner. Thus, people will adopt faster strategies when their early-life
environments were characterized by higher levels of unpredictability and harshness. Their
future is very uncertain, therefore, people may not live long enough to reproduce if they
delay (Ellis et al., 2009). Organisms living under these conditions do not gain much from
an investment in somatic effort because this investment could easily be wiped out due to
forces that the organisms cannot control. Therefore, such organisms start evolving a faster
strategy. By contrast, organisms living under more predictable conditions can start
investing in somatic effort because they can resist threatening ecological challenges due
to their predictability. An unpredictable environment specifically results in faster
physiological and sexual development (Belsky, Houts, & Fearon, 2010; Ellis, 2004). Faster
sexual development, in turn, results in more offspring and reduced parental investment in
each offspring. Across cultures, in environments characterized by a higher mortality rate,
people are much younger when their first child is born (Griskevicius, Delton, Robertson, &
Tybur, 2011; Low, Hazel, Parker, & Welch, 2008; Wilson & Daly, 1997). This pattern is
consistent with a ‘fast life history strategy’. By contrast, humans who grew up in a higher
9
SES environment are less impulsive, more risk averse and want to delay starting a family.
They approach temptations more slowly; their future is more stable, whereby it is possible
to adopt slow strategies, resulting in delaying reproduction and investing in the future (Ellis
et al., 2009). This results in slower physiological and sexual development, leading to fewer
offspring and a greater parental investment in each child. This pattern is consistent with a
‘slow life history strategy’.
1.3.3 Life history strategy and pro-social behavior
Slow and fast strategies were already linked to certain features. To investigate the link
between life history strategy and pro-social behavior, these features are summarized below
(see Figure 1).
Figure 1: Illustration of correlates of fast and slow life history strategies (Griskevicius V.,
Ackerman J. & Cantu S., 2013)
In the context of mating, people enacting fast life history strategies have more sexual
partners and an earlier sexual debut. With regard to parenting, they have more children
and invest less effort in each child. Moreover, fast strategies are associated with earlier
physiological development and sexual maturity. Conversely, people enacting a slow life
history strategy have less sexual partners. Their sexual debut is postponed compared to
people enacting fast life history strategies. In the context of parenting, slow strategies are
10
associated with fewer children; however, their investment in each child is higher. At the
physiological level, slower strategies correspond with people who develop later
physiologically. However, the most interesting aspect amongst these features is the
difference in reward orientation. People enacting slow strategies tend to be less impulsive
and more risk averse compared to people enacting fast strategies. Moreover, slow
strategies are associated with a higher level of parental care and sociability (Réale et al.,
2010). Ellis (2009) also argued that these people invest more in the future. People showing
more pro-social behavior; more specifically donation behavior, generally think carefully
about their higher donation amounts and act less impulsively. Possibly, they reflect their
higher level of parental care by taking care of other people who are facing difficulties. After
all, people enacting slow strategies think more about the future. Since they care more about
the future, they will care more about the well-being and stability of society. Therefore,
people following slow strategies will behave more pro-socially. On top of that, contrary to
people following fast strategies, they want to invest in long-term stability, which can be
reached by helping others, thus stimulating high group cohesion. Conversely, the latter
group is satisfied with short-term benefits, which are not related to donating money. People
enacting fast strategies do not take into account long-term consequences. When you
donate to charitable institutions, you usually see the results of your monetary donation only
years later, or sometimes not at all. Since they place less value on group cohesion, they
do not care so much about other people and prefer to live individually. People enacting fast
strategies, due to their impulsiveness, risk-seeking behavior and need for short-term
benefits, also do not think so much about providing stability to people in inferior conditions
resulting more often in individualized expenditure decisions. Consequently, by combining
the arguments mentioned earlier slow strategies can be linked to more pro-social behavior
and fast strategies to less pro-social behavior.
1.4 Hypothesis
Human beings who have grown up in a poor environment will enact fast strategies when
they face financial deprivation. Consequently, as argued earlier, these fast strategies will
manifest themselves in less pro-social behavior. Conversely, human beings who have
grown up in a wealthy environment will enact slow strategies when feeling financially
deprived. As a result, these slow strategies will manifest themselves in more pro-social
behavior. More specifically, this research has chosen donating to projects of charitable
institutions as the representation of pro-social behavior. More pro-social behavior
corresponds to higher donation amounts, whilst less pro-social behavior corresponds to
lower donation amounts. Thus, the hypothesis states that people from wealthy
11
backgrounds will donate more to charity than people from poor backgrounds. However, this
donation behavior will only appear when they feel financially deprived.
12
2 Method
This second section will give an overview of the methodology used. After explaining which
instrument was used, the data and participants will be described. The section ends with the
procedure.
2.1 Instruments
An online survey is mostly used to investigate expectations, intentions, future behavior,
past behavior, average behavior, attitudes and motivation. Since our research was
performed to examine donation behavior, an online survey was distributed via the internet.
More specifically, for this research a self-administered online survey was used. A survey
was useful in this situation because all participants had to answer questions that were
formulated in the same way. The answer possibilities and scales were also equal. Besides
that, practicability and statistical analysis was uncomplicated compared to qualitative
methods. Moreover, participants could be divided into different subgroups which could be
compared in order to find any significant differences. This survey was taken online, which
proved to be a flexible method because of the fact that this survey could be sent quickly to
a lot of possible respondents and the resulting data was available online. The respondents
were asked to fill in the survey online, so that there was no mediator conducting interviews.
This kind of survey, namely a self-administered survey, offers three main advantages. Self-
administered surveys are low cost. The respondent also determines the speed of
completion of their own survey, which gives them control. Finally, respondents do not have
to worry about the judgment of the interviewer, which could distort results in the case of a
study like this one asking about childhood and current financial situations. One
disadvantage of a self-administered survey is the fact that respondents can decide not to
answer the survey. It is also possible that they do not react in a timely manner, do not
complete the survey or do not understand certain questions.
2.2 Data
As was mentioned earlier, data was collected using a self-administered online survey, that
took ten minutes to fill in. The survey was created in Qualtrics, a well-known online survey
software. This survey was distributed via social media and e-mail. Two hundred and twenty-
two participants filled in the online survey. After the survey was closed, some data from the
dataset containing answers such as ‘I don’t know’ to particular questions and some surveys
that were not filled in completely were deleted. Two hundred and seventeen complete and
13
useable surveys remained from which to start analyzing data in SPSS Statistics, a software
program used for statistical analysis. So, in total, five participants were omitted from the
sample due to incomplete observations. The fact that the survey contained open-ended
questions may have caused some people who started the survey not to complete it
because it took too much effort to fill in. However, without these open-ended questions, it
became difficult to financially deprive participants. Also, participants had to read three
descriptions of projects to measure pro-social behavior instead of just one. This is
explained further on. Participants, therefore, had to make more effort to read all the
descriptions in order to avoid distortion and to be able to create an average composite
view. Additionally, participants who were assigned to the manipulation condition may have
felt some discomfort about answering a question concerning their expenditure. Participants
wanted to explain why they only donated such a small amount of money to a particular
project. They always wanted to give a reason for their small donation. There was also an
important feature, namely social control, that may have affected the results. If participants
know their responses are being registered, they may behave differently because they do
not want people to think they are greedy, even if they are anonymous.
2.3 Participants
Two hundred and seventeen people (117 females, 100 males; mean age = 47.05 years,
SD = 1.68) participated in the experiment without being rewarded. Because donation
behavior is more likely for people who are financially stable, students, job-seekers and
people who are looking for a place to live were ignored; mostly people younger than 30
years old. Our participants were limited to people who were 65 years old, which is the legal
retirement age in Belgium, or younger. The typical individual donor profile has changed
over years. This donor profile consists of an individual in the middle age range, typically
between 35 and 64 years old. The age limitation applied in this study could create a
potential distortion in the experiment; however, most students would not able to give a
representative image of donation behavior because they are financially dependent on their
parents. As a consequence, not all of them are fully aware of the value of money.
This study made use of two hundred and seventeen participants, of which 46.1 % were
men and 53.9 % women, who were randomly assigned between 2 conditions. More older
people filled in the online survey, respectively, 24.0 % of the participants were between 30
and 45 years old and 66 % of the respondents were between 45 and 65 years old. 82.5 %
of the participants had a relatively positive general attitude towards donations, whilst only
17,5% had a relatively negative general attitude towards donations.
14
2.4 Procedure
In a between-subjects design, it is important that participants are randomly assigned to the
experimental conditions in order to isolate the effects of our manipulation of the
independent variable. One important advantage of a between-subjects design is that you
do not have to worry about counterbalancing, where you would normally have to avoid the
difficulties of standard repeated measures designs, characterized by subjects who are
exposed to all conditions. In addition, it is not possible for participants to affect each other’s
performances, since they are assigned to only one of the conditions. However, there are
also some disadvantages associated with between-subjects designs. This type of design,
for instance, requires a lot of participants, which is more time-consuming. If the number of
conditions increases, the number of participants will need to increase too. Furthermore, a
between-subjects design is less sensitive to the effects of the experimental manipulations
than a within-participants design.
This experiment had two between-subjects conditions: financial deprivation and control. In
the financial deprivation condition, participants were asked to list their expenditure over the
past 30 days (Zhou, Vohs & Baumeister, 2009). Additionally, in order to avoid distortion in
our results, participants assigned to the manipulation condition were told that they were
participating in two different experiments; one investigating expenditure in the beginning of
spring and another investigating donation behavior. A similar question was asked in the
control condition; however, participants had to describe the weather over the past days
instead of their expenditure. In order to achieve reliability, it is very important to guarantee
that the dependent variable is measured as accurately as possible, relying on participants
reporting their feelings precisely. In order to assess pro-social behavior, more specifically
donation behavior, three different hypothetical projects were described and participants
were asked to answer an open-ended question, “How much are you willing to donate to
this project”, by indicating a donation amount. (Kleber et al., 2013). Participants were
presented three different regional, humanitarian-aid projects from unfamiliar charitable
organizations. The three projects were all poverty-related and were all displayed each time
in a random order in order to avoid bias. Participants were given the hypothetical
opportunity to donate an amount of money to each project. After each description of the
three projects, they had to type in how much they would like to give to the specific project.
If they didn’t want to donate anything at all, they had to type in the amount zero euros.
One charitable organization was involved in helping children in developing countries. The
aim of their project was to improve the growth and development of children through
partnerships with local organizations. In the Philippines, this charitable organization was
working with the local partner, Virlanie Foundation. They were working around basic health
care, access to water and food problems. The second charitable organization dealt with
15
the lives of thousands of people in Eastern Europe who are impacted by poverty. This
organization’s project in Ukraine was providing material and emotional support but, also,
contributing to educational and spiritual development. The last charitable organization was
an open house for poor people. Their project consisted of a meeting place where people
could inquire, talk, support each other, learn budgeting or learn how to cook together.
By choosing different kinds of poverty-related projects, both domestic and cross-border,
possible distortions were taken into account due to emotional reactions towards particular
projects. Unknown projects were chosen to avoid differing emotional reactions between
participants towards certain humanitarian-aid projects. For example, if a participant’s family
had been confronted with cancer several times in the past, that participant might donate a
higher amount to a charity taking care of cancer patients. The potential disadvantage of
using unknown projects is the possibility that some participants might not donate at all to
these projects because they are not sure that their money will be well spent.
As a further step, participants’ general attitudes towards donations were measured to
investigate if this influenced their donation behavior and to verify if this interacted with the
other independent variables (Batson, 1991; Dickert & Slovic, 2009; Graziano, Habashi,
Sheese, & Tobin, 2007). Three statements (α = .86) were indicated using a 9-point scale
ranging from “don’t agree at all” (1) to “completely agree” (9): “I believe that donations help
to improve the lives of people in need”, “I would regularly donate, if I had enough money
available”, and “I believe that, in general, donations are a meaningful way to help people in
danger of starvation”. Responses were averaged into a composite ‘General attitude’ (α =
.86). Next, to assess the variable socio-economic status (SES), constituted measures from
Griskevicius, Delton and Tybur (2011) were used. Participants were asked to evaluate
three statements on a 9-point scale ranging from 1, strongly dis-agree, to 9, strongly agree.
The three statements (α = .88) used to measure perceived childhood SES were “My family
usually had enough money for things when I was growing up”, “I grew up in a relatively
wealthy neighborhood” and “I felt relatively wealthy compared to the other kids in my
school”. The mean score of perceived childhood SES was 6.22 (SD= 1.27) with scores
ranging from 1 to 9. 13.82% of participants scored below 4.0 and 41.94 % of participants
scored above 7.0, which means participants felt relatively wealthy when they were growing
up. Finally, the current SES of the participants was measured using three statements (α =
.70), namely “I have enough money to buy things I want”, “I don’t need to worry too much
about paying my bills” and “I don’t think I’ll have to worry about money too much in the
future”. Responses were combined in a ‘perceived childhood SES’ composite and a
‘current SES’ composite. The mean score was 6.46 (SD= 0.11) with scores ranging from 2
to 9. 9.22% of participants scored below 4.0 and 41.94% of participants scored above 7.0,
which meant that most of the participants do not worry that much about their financial
16
situation. Perceived childhood SES and current SES were moderately correlated (r= 0.52)
and the likelihood of such a correlation occurring by accident in a sample of 217 people,
was 0, which is much smaller than 0.05. This meant the null hypothesis “perceived
childhood SES and current SES are not associated” could be reject with great confidence
and perceived childhood SES and current SES were significantly associated (r= 0.52, p=0,
n=217).
17
3 Results
The results section consists of a preliminary data analysis followed by a report of the
donation amounts.
3.1 Preliminary data analysis
Responses on the three different projects were combined into a ‘Donation behavior’
composite. Participants donated on average 38.47 euros to a charitable project. A highest
donation amount of 500 euros was registered for this composite, 17.5 % of the participants
did not donate at all (Appendix A). To determine whether the variation of these donation
amounts is relatively high or low, the coefficient of variation was computed by using the
formula standard deviation divided by the mean, which resulted in a normalized standard
deviation of 1.53. Bearing in mind the rule of thumb, implying a coefficient of variation
greater than or equal to 1 indicates a relatively high variation, the conclusion was made
that, the data points were spread out over a wide range of values. Donation amounts were
positively skewed (z(g1) = 26.35, p <.05). As a consequence, the composite ‘Donation
behavior’ was log-transformed using the function lg10 (donation amounts + 1). Since zero
amounts cannot be log-transformed, the arbitrary one was added.
3.1 Donation amounts
3.1.1 Perceived childhood SES
A regression predicting donation amounts from condition, perceived childhood SES, and
their interaction revealed no condition x childhood SES interaction, t(213) = 1.08, p=.28,
β =0.086. Both coefficients and their significance are reported in Table 1.
Table 1: Regression analysis, donation behavior; condition, perceived childhood SES, interaction
condition x perceived childhood SES (SPSS)
Standardized coefficients (β) Sig.
Condition -0.077 0.29
Perceived childhood SES 0.14 0.072
Condition x Perceived childhood SES 0.086 0.28
18
As shown in Appendix B, a R2 of 0.014 was determined, which means that only 1.4 % of
the variability in donation behavior could be accounted for by this model, consisting of the
predictors condition, perceived childhood SES and their interaction condition x perceived
childhood SES.
The influence of condition and perceived childhood SES on donation behavior was further
investigated by eliminating the middle class of the variable perceived childhood SES.
Consequently, only the people characterized with a high childhood SES and a low
childhood SES were analyzed. Next, the continuous variable perceived childhood SES was
made categorical instead of continuous. Afterwards a two-way ANOVA was conducted with
the logged averaged composite donation behavior as dependent variable, the categorical
variables condition and childhood SES as predictors. As can be seen in Appendix C and
Table 2, there was no significant main effect for condition, F(1,54) =0.43, p=.52, or
childhood SES, F(1,54)=3.91, p=.053, and no significant interaction, F(1,54)=1.51,p=.22,
despite the fact that the effect for categorical childhood SES was close to significant.
Table 2: Two-way ANOVA, donation behavior; condition, categorical childhood SES,
interaction condition x categorical childhood SES (SPSS)
F Sig.
Condition 0.43 0.52
Categorical childhood SES 3.91 0.053
Condition x Categorical childhood
SES
1.51 0.22
Observing Figure 2, it would appear that people from high-SES backgrounds donated more
in the financial deprivation condition than people from low-SES background. However,
since the interaction was insignificant, people from low-SES backgrounds did not donate
significantly differently than people from high-SES backgrounds; neither in the control
condition, nor in the financial deprivation condition.
19
Figure 2 : Donation behavior as a function of condition and perceived childhood SES
The analysis was continued by verifying whether people from low-SES backgrounds
donated differently in the control condition and the financial deprivation condition. The
same analysis was executed for the people from high-SES backgrounds too. In order to
conduct these two analyses, the dataset was organized by groups based on the categorical
perceived childhood SES. Afterwards, a one-way ANOVA (Appendix D) was conducted
with the logged donation behavior as dependent variable and condition as the predictor.
As can be seen in Table 3, no significant result was determined for the people from low-
SES backgrounds, F(1,36)=0.19, p=.67, nor for the people from high-SES backgrounds,
F(1,18)=2.75, p=.11.
Table 3: One-way ANOVA, donation behavior; condition (SPSS)
F Sig.
Condition (People with low-SES background) 0.19 0.67
Condition (People with high-SES background) 2.75 0.11
Next, the difference in the control condition between people from low-SES backgrounds
and people from high-SES backgrounds and the difference in the financial deprivation
condition were investigated. The dataset was organized in groups based on condition. A
one-way ANOVA (Appendix E) was conducted with the logged donation behavior as a
dependent variable and the categorical perceived childhood SES as an independent
variable. As shown in Table 4, there was no significant result for the control condition,
1
1,1
1,2
1,3
1,4
1,5
1,6
1,7
Control condition Financial deprivation
Log1
0(D
on
atio
n a
mo
un
ts)
Condition
Donation behavior
low-SES background
high-SES background
20
F(1,31) = 0.33, p=.57. However, in the financial deprivation condition, people from low-SES
backgrounds donated significantly differently compared to people from high-SES
backgrounds, F(1,23)=4.36, p=.048.
Table 4: One-way ANOVA, donation behavior; childhood SES (SPSS)
F Sig.
Perceived childhood SES (control condition) 0.33 0.57
Perceived childhood SES (financial deprivation condition) 4.36 0.048
As can be seen in Figure 3, people from high-SES backgrounds donated significantly more
than people from low SES-backgrounds when feeling financially deprived.
Figure 3 : Donation behavior as a function of perceived childhood SES (financial deprivation)
3.1.2 General attitude towards donations
Did the general attitude towards donations influence the participants’ donation amounts?
Participants with a more positive attitude towards donations would probably have donated
a higher amount of money. In other words, the variable general attitude towards donations
might drive the donation amounts. As a consequence, the general attitude was used as a
control variable in our analysis to verify if this yields different results.
A regression (Appendix F) predicting donation amounts from condition, perceived
childhood SES, their interaction and general attitude towards donations revealed no
condition x childhood SES interaction, t(212)=0.873, p=.38, β =0.23. There was no main
effect of condition, childhood SES or an effect of their interaction, however a main effect of
1
1,1
1,2
1,3
1,4
1,5
1,6
1,7
low-SES background high-SES background
Log1
0(D
on
atio
n a
mo
un
ts)
Childhood SES
Donation behavior (Financial deprivation)
21
general attitude towards donations was determined, t(212)= 8.18, p < .001, β =0.50. Both
coefficients and their significance are reported in Table 5.
Table 5: Regression analysis, donation behavior; condition, perceived childhood SES, interaction
condition x perceived childhood SES, general attitude towards donations (SPSS)
Standardized coefficients (β) Sig.
Condition -0.26 0.19
Perceived childhood SES -0.17 0.35
Condition x Perceived childhood SES 0.23 0.38
General attitude 0.50 <
0.001
As can be seen in Appendix F, a R2 of 0.25 was determined, which means that 25 % of the
variability in donation behavior can be accounted for by this model.
After eliminating the middle class once again, a two-way ANCOVA was conducted with the
logged averaged composite donation behavior as dependent variable, the categorical
variables condition and perceived childhood SES as independent variables and the general
attitude towards donations as the covariate. By doing this, the fact that general attitude
towards donations differed between participants was catered for. Moreover, the possibility
that there was still a difference in donation behavior, taking into account the general
attitude, was investigated. As can be seen in Appendix G and Table 5, there was no
significant main effect for condition, F(1,53) =0.15, p=.70, or childhood SES, F(1,53)=0.29,
p=.59, and no significant interaction, F(1,53)=1.97, p=.17.
Table 6: Two-way ANCOVA, donation behavior; condition, categorical childhood SES, interaction
condition x categorical childhood SES, general attitude (SPSS)
F Sig.
Condition 0.15 0.70
Categorical childhood SES 0.29 0.59
Condition x Categorical childhood SES 1.97 0.17
General attitude 22.006 <0.001
22
Observing Figure 4, it would appear that people from high-SES backgrounds donated more
in the financial deprivation condition than people from low-SES background and that this
effect was reversed in the control condition. However, since the interaction was
insignificant, people from low-SES backgrounds did not donate significantly differently than
people from high-SES backgrounds; neither in the control condition, nor in the financial
deprivation condition. Comparing this figure with Figure 2 without the control variable
general attitude, people from low-SES backgrounds donated more, whilst people from high-
SES backgrounds donated less in both conditions when taking into account the general
attitude.
Figure 4 : Donation behavior as a function of condition and perceived childhood SES
1,05
1,1
1,15
1,2
1,25
1,3
1,35
1,4
1,45
Control condition Financial deprivation
Donation behavior
(General attitude = 0.0632)
low-SES background high-SES background
23
General conclusion
The hypothesis stated that human beings who have grown up in a poor environment will
enact fast strategies when feeling financially deprived, which will manifest itself in less pro-
social behavior, whilst human beings who have grown up in a rich environment will enact
slow strategies when feeling financially deprived, which will manifest itself in more pro-
social behavior. The purpose of this study was to extend our understanding of pro-social
behavior, more specifically donation behavior related to different financial childhood
backgrounds. Human beings enact different life history strategies, ranging from slow to fast
strategies. This difference in strategies could be related to differences in early-life
conditions (Belsky et al., 1991; Ellis et al., 2009). In this research, behaviors which were
consistent with a certain strategy and did not appear in the control condition, emerged
under the financial deprivation condition. In our experiment, resource scarcity gave rise to
different responses based on the participant’s perceived childhood SES. The conclusion
can be made that, in the financial deprivation condition, people from high-SES backgrounds
donated significantly more than people from low-SES backgrounds. This implies that
people who grew up wealthier behaved more pro-socially when feeling financially deprived
compared to people who grew up poorer. The financial deprived feeling enhanced social
solidarity. Besides this, the warm glow of being a moral person or avoidance of feeling
guilty could have triggered the donation behavior of people. The perceived childhood SES
can be associated with children’s socio-cognitive development. As was mentioned earlier,
children will base their moral behavior on the consequences of their own behavior which
could also influence pro-social behavior. On the other hand, few differences were found
between people from high-SES or low-SES backgrounds who were assigned to the control
condition and did not experience conditions of economic uncertainty. Only participants who
were exposed to resource-scarcity cues, donated differently depending on their perceived
childhood SES. Taking the general attitude of participants towards donations into account,
people from low-SES backgrounds donated more, while people from high-SES
backgrounds donated less in both conditions.
This is an interesting result for the prosocial behavior research and more specifically,
donation behavior. To be specific, more insight is gained into how people characterized by
different backgrounds donate. Moreover, once again this result confirms that life history
strategy theory provides additional perspectives in understanding human behavior.
Besides this, the conclusion can be made that, in times of resource scarcity, people from
rich backgrounds will donate more than people from poor backgrounds. In times of financial
crisis, charitable organizations could take this donation behavior into account in order to
target the group which will donate the most.
24
There are several important implications resulting from our findings. The current findings
highlight that differences in donation behavior are shaped by the combination of economic
uncertainty cues and different life history strategies of individuals. Moreover, donation
behavior that might appear illogical and incoherent from an economic perspective can be
logical and coherent from an evolutionary perspective (Kenrick et al., 2009). It is important
to mention that the motivation behind monetary donation is a relevant aspect to investigate.
Considering these findings, in times of resource scarcity, fundraisers could better
understand the effect of different financial backgrounds and, consequently, stimulate
donation behavior. Education affords skills and access to networks. Kaplan and Rauh
(2013) found that the wealthiest individuals in the US economy were those who could
access and apply education. Charity fundraisers could target educated people who
probably grew up wealthier and take into account the incentives for giving. After all,
donations are of crucial importance to charitable organizations. Besides this, the lower
income tax after donating is a possible incentive for individuals. The government could take
these findings into account by taking measures to make it more interesting for people from
wealthy backgrounds to donate. This is very topical given the present situation of collecting
donations for the victims of the earthquake in Nepal in a society currently characterized by
the consequences of the recent financial crisis.
The effects of perceived childhood SES might be even more impressive if the range of this
variable had been less limited. Participants coming from extremely poor environments,
which were not present in great numbers in this experiment, might have resulted in greater
life history effects. Additionally, participants were presented regional, humanitarian-aid
projects from relatively unknown charitable organizations. Although different kinds of
projects, both domestic and cross-border, were averaged to reduce the differences in
emotional reactions towards particular projects, this still forms a potential limitation. The
more projects the averaged composite donation behavior contains, the more representative
the variable donation behavior becomes. Furthermore, people who participated in this
experiment knew that their responses would be registered. Due to social control, donation
amounts may have been increased. People are concerned about how others perceive
them. As argued earlier, personal traits, the presence of others and cultural influences
could also influence the magnitude of solidarity (Lewin, 1936). Similarly, companies could
also donate to improve their good image and build reputation. Finally, only people between
30 and 65 years old were selected, in order to capture the picture of the typical individual
donor. The results should be interpreted in the light of these potential limitations.
In this study, the conclusion can be made that, people from poor childhood backgrounds
and wealthy childhood backgrounds enact different life history strategies and consequently
donate different amounts. One potential reason is the fact that people from different
25
backgrounds have different interpretations of pro-social behavior. Further research in this
area is needed to enhance our understanding of the psychological mechanisms driving
these differences in donation behavior.
26
Appendices
Appendix A: Preliminary analysis
Descriptive Statistics
N Minimum Maximum Mean Std. Deviation
Donation
behavior
217 ,00 500,00 38,4731 58,74293
Valid N (listwise) 217
Donation behavior
Frequency Percent Valid Percent Cumulative Percent
Valid ,00 38 17,5 17,5 17,5
Total 217 100,0 100,0
Appendix B:
Regression analysis (Donation behavior; condition, SES, condition x SES)
Model Summary
Model R R
Square
Adjusted R Square Std. Error of the
Estimate
1 ,134a ,018 ,004 ,66969
a. Predictors: (Constant), Interaction Condition*Childhood SES, Condition, Childhood SES
27
ANOVAa
Model Sum of Squares Df Mean
Square
F Sig
.
1 Regression 1,742 3 ,581 1,29
5
,27
7b
Residual 95,529 213 ,448
Total 97,271 216
a. Dependent Variable: Log10(Donation behavior)
b. Predictors: (Constant), Interaction Condition*Childhood SES, Condition, Childhood SES
Coefficientsa
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
B Std. Error Beta
1 (Constant) 1,035 ,209 4,949 ,000
Condition -,110 ,103 -,077 -1,061 ,290
Childhood SES ,049 ,027 ,136 1,807 ,072
Interaction
Condition*Childhood
SES
,084 ,078 ,086 1,081 ,281
a. Dependent Variable: Log10(Donation behavior)
28
Appendix C:
Two-way ANOVA (Donation behavior; condition, SES, condition x SES)
Between-Subjects Factors
Value Label N
Condition 1 control conditon 33
2 financial deprivation 25
Categorical childhood SES ,00
38
1,00
20
Tests of Between-Subjects Effects
Dependent Variable: Log10( Donation behavior)
Source Type III Sum
of Squares
df Mean
Square
F Sig.
Corrected Model 2,521a 3 ,840 1,674 ,183
Intercept 84,566 1 84,566 168,478 ,000
Condition ,215 1 ,215 ,429 ,515
Categorical childhood SES 1,960 1 1,960 3,904 ,053
Condition * Categorical
childhood SES
,759 1 ,759 1,511 ,224
Error 27,105 54 ,502
Total 115,806 58
Corrected Total 29,626 57
a. R Squared = ,085 (Adjusted R Squared = ,034)
29
Appendix D: One-way ANOVA (Donation behavior; condition)
Categorical childhood SES = ,00
ANOVAa
Log10(Donation behavior)
Sum of Squares df Mean Square F Sig.
Between Groups ,119 1 ,119 ,189 ,666
Within Groups 22,634 36 ,629
Total 22,752 37
a. Categorical childhood SES = ,00
Categorical childhood SES = 1
ANOVAa
Log10(Donation behavior)
Sum of Squares df Mean Square F Sig.
Between Groups ,684 1 ,684 2,752 ,114
Within Groups 4,471 18 ,248
Total 5,155 19
a. Categorical childhood SES = 1,00
30
Appendix E: One-way ANOVA (Donation behavior; SES)
Condition = control conditon
ANOVAa
Donation_behavior_log10
Sum of Squares df Mean Square F Sig.
Between Groups ,159 1 ,159 ,330 ,570
Within Groups 14,953 31 ,482
Total 15,112 32
a. Condition = control conditon
Condition = financial deprivation
ANOVAa
Log10(Donation behavior)
Sum of Squares df Mean Square F Sig.
Between Groups 2,302 1 2,302 4,357 ,048
Within Groups 12,152 23 ,528
Total 14,454 24
a. Condition = financial deprivation
31
Appendix F:
Regression (Donation behavior; condition, SES, condition x SES, general
attitude)
Model Summary
Mode
l
R R
Square
Adjusted R
Square
Std. Error of the Estimate
1 ,501a ,251 ,237 ,58627
a. Predictors: (Constant), General attitude, Condition, Childhood SES, Interaction
Condition*Childhood SES
ANOVAa
Model Sum of
Squares
df Mean
Square
F Sig.
1 Regression 24,403 4 6,101 17,750 ,000b
Residual 72,867 212 ,344
Total 97,271 216
a. Dependent Variable: Log10(Donation behavior)
b. Predictors: (Constant), General attitude, Condition, Childhood SES, Interaction Condition*SES
Coefficientsa
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
B Std. Error Beta
1 (Constant) ,685 ,426 1,609 ,109
Condition -,369 ,283 -,260 -1,304 ,194
Childhood SES -,060 ,065 -,169 -,934 ,351
32
Interaction
Condition*
Childhood SES
,038 ,043 ,229 ,872 ,384
General attitude ,175 ,021 ,503 8,179 ,000
a. Dependent Variable: Log1(Donation behavior)
Appendix G: Two-way ANCOVA (Donation behavior; condition, SES, condition x
SES, general attitude)
Between-Subjects Factors
Value Label N
Condition 1 control conditon 33
2 financial deprivation 25
Categorical childhood SES ,00 38
1,00 20
Tests of Between-Subjects Effects
Dependent Variable: Log10(Donation behavior)
Source Type III Sum of
Squares
df Mean
Square
F Sig.
Corrected Model 10,474a 4 2,618 7,246 ,000
Intercept ,077 1 ,077 ,212 ,647
General attitude 7,952 1 7,952 22,006 ,000
Condition ,054 1 ,054 ,149 ,701
Categorical childhood
SES
,105 1 ,105 ,290 ,593
Condition * Categorical
childhood SES
,711 1 ,711 1,966 ,167
33
Error 19,152 53 ,361
Total 115,806 58
Corrected Total 29,626 57
a. R Squared = ,354 (Adjusted R Squared = ,305)
34
List of figures
Figure 1: Illustration of correlates of fast and slow life history strategies (Griskevicius V.,
Ackerman J. & Cantu S., 2013) .......................................................................................... 9
Figure 2 : Donation behavior as a function of condition and perceived childhood SES .. 19
Figure 3 : Donation behavior as a function of perceived childhood SES (financial
deprivation)........................................................................................................................ 20
Figure 4 : Donation behavior as a function of condition and perceived childhood SES .. 22
35
List of tables
Table 1: Regression analysis, donation behavior; condition, perceived childhood SES,
interaction condition x perceived childhood SES (SPSS) ................................................. 17
Table 2: Two-way ANOVA, donation behavior; condition, categorical childhood SES,
interaction condition x categorical childhood SES (SPSS) ............................................... 18
Table 3: One-way ANOVA, donation behavior; condition (SPSS) ................................... 19
Table 4: One-way ANOVA, donation behavior; childhood SES (SPSS) .......................... 20
Table 5: Regression analysis, donation behavior; condition, perceived childhood SES,
interaction condition x perceived childhood SES, general attitude towards donations
(SPSS) .............................................................................................................................. 21
Table 6: Two-way ANCOVA, donation behavior; condition, categorical childhood SES,
interaction condition x categorical childhood SES, general attitude (SPSS) .................... 21
36
Sources
Books
Burns A.C. & Bush R. F. (2006). Principes van marktonderzoek. Toepassingen met SPSS.
Pearson Education Benelux, pp 542.
Clarke D. (2003). Pro-Social and Anti-Social Behaviour. Routledge New York. pp 172.
Field A. & Hole G. (2003). How to Design and Report Experiments. SAGE Publications
London. pp 384.
Lindenberg S. (2006). Prosocial behavior, solidarity, and framing processes. Solidarity and
prosocial behavior. Springer US. pp 23-44.
Articles
Artykhanov Y. (2013). Market economies and pro-social behavior: Experimental evidence
from Central Asia. Journal of Socio-Economics, 41(1), pp 16-24.
Briers B. & Laporte S. (2013). A wallet full of calories : The effect of financial dissatisfaction
on the desire of food energy. Journal of Marketing Research, 50(6), pp 767-781.
Burgoyne C., Young B. & Walker C. (2005). Deciding to give to charity : A focus group
study in the context of the household economy. Journal of Community and Applied Social
Psychology, 15(5), pp 383-404.
Casey T. & Poropat A. (2014). Beauty is more than screen deep : Improving the web
survey respondent experience through socially-present and aesthetically-pleasing user
interfaces. Computers in Human Behavior, 30, pp 153-163.
Cheung C., Ng S. (2012). Impacts of financial crisis on social engagement in Hong Kong.
The Journal of Socio-Economics, 41(5), pp 623-632.
Deck C. & Kimbrough E.O. (2013). Do market incentives crowd out charitable giving?
Journal of Socio-Economics, 47, pp 16-24.
Delton A., Griskevicius V., Robertson T. & Tybur J. (2011). Environmental contingency in
life history strategies : The influence of mortality and socioeconomic status on reproductive
timing. NIH Public Access, 100(2), pp 241-254.
Desmet P., Feinberg F. (2003). Ask and ye shall receive: The effect of the appeals scale
on consumers’ donation behavior. Journal of Economic Psychology, 24(3), pp 349-376.
DeVoe E. & Pfeffer J. (2011). Time is tight : How higher economic value of time increases
feelings of time pressure. Journal of Applied Psychology, 96(4), pp 665-676.
Ellis L. (1987). Criminal Behavior and r/K selection : an extension of gene-based
evolutionary theory. Deviant Behavior, 8(2), pp 149-176.
37
Figueredo A., Vasquez G. & Brumbach B. (2005). The K-factor : Individual differences in
life history strategy. Personality and Individual Differences, 39(8), pp 1349-1360.
Gladden P., Sisco M. & Figueredo A. (2008). Sexual coercion and life-history strategy.
Evolution and Human Behavior, 29(5), pp 319-326.
Gladden P., Figueredo A. & Jacobs W. (2009). Life history strategy, psychopatic attitudes,
personality, and general intelligence. Personality and Individual Differences, 46(3), pp 270-
275.
Griskevicius V., Ackerman J. & Cantu S. (2013). When the economy falters, do people
spend or save? Responses to resource scarcity depend on childhood environments.
Psychological Science, 24(2), pp 197-205.
Griskevicius V., Tybur J. & Delton A. (2012). The influence of Morality and Socioeconomic
status on risk and delayed rewards : a life history theory approach. Journal of Personality
and Social Psychology, 100(6), pp 1015-1026.
Kaplan S. & Rauh J. (2013). Family, education, and sources of wealth among the Richest
Americans, 1982-2012. American Economic Review, 103(3), pp 158-162.
Kleber J., Dickert S. & Peters E. (2013). Same numbers, different meanings : how
numeracy influences the importance of numbers for pro-social behavior. Journal of
Experimental Social Psychology, 49(4), pp 699-705.
Knutsson M., Martinsson P. & Wollbrant C. (2013). Do people avoid opportunities to
donate? A natural field experiment on recycling and charitable giving. Journal of Economic
Behavior & Organization, 93, pp 71.
Martin R., Randal J. (2008). How is donation behavior affected by the donations of others?
Journal of Economic Behavior and Organization, 67(1), pp 228-238.
Mesurado B., Mestre M., Mesurado B. (2014). Parental Expectations and Prosocial
Behavior of Adolescents From Low-Income Backgrounds: A Cross-Cultural Comparison
Between Three Countries--Argentina, Colombia, and Spain. Journal of Cross-Cultural
Psychology, 45(9), pp 1471-1488.
Newman G. & Shen J. (2012). The counterintuitive effects of thank-you gifts on charitable
giving. Journal of Economic Psychology, 33(5), pp 973-983.
Prediger S., Herrmann B., Vollan B. (2014). Scarcity and Antisocial behavior. Journal of
Public Economics, 119, pp 1-9.
Réale D., Garant D., Humprhies M., Bergeron P., Careau V. & Montiglio P. (2010).
Personality and the emergence of the pace-of-life syndrome concept at the population
level. Philosophical Transactions of the Royal Society B, 365(1560), pp.4051-4063
Sharma E. & Alter A. (2012). Financial deprivation prompts consumers to seek scarce
goods. Journal of Consumer Research, 39(3), pp 545-560.
38
Sharma E., Mazar N. & Alter, A. (2014). Financial deprivation selectively shifts moral
standards and compromises moral decisions. Organizational behavior and human decision
processes, 123(2), pp 90-100.
Simpson J., Griskevicius V. & Kuo, S. (2012). Evolution, stress, and sensitive periods : The
influence of unpredictability in early versus late childhood on sex and risky behavior.
Developmental Psychology, 48(3), pp 674-686.
Sims J. (2015). Hungry for money. Nursing times, 85(48), pp 20-21.
Vasquez G. & Schneider S. (2007). The K-factor, covitality, and personality. Human Nature,
18(1), pp 47-73.
White A., Li Y. & Griskevicius V. (2012). Putting all your eggs in one basket : Life history
strategies, bet-hedging, and diversification, Psychological Science, 24 (5), pp 715-722.
Zhou X., Vohs K. & Baumeister E. (2009). The symbolic power of money : Reminders of
money alter social distress and physical pain. Psychological Science, 20(6), pp. 700-706.
Internet
http://www.goedgeven.be/de-goede-doelen?c=14
http://www.qualtrics.com
http://onlinelibrary.wiley.com/doi/10.1111/j.1083-6101.2005.tb00259.x/full
FACULTY OF BUSINESS AND ECONOMICS Naamsetraat 69 bus 3500
3000 LEUVEN, België tel. + 32 16 32 66 12 fax + 32 16 32 67 91
info@econ.kuleuven.be www.econ.kuleuven.be