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Do parents invest into voluntary climateaction when their children are watching?Evidence from a lab-in-the-field experiment
Helena Fornwagner, Oliver P. Hauser
Working Papers in Economics and Statistics
2020-23
University of Innsbruckhttps://www.uibk.ac.at/eeecon/
https://www.uibk.ac.at/eeecon/
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University of InnsbruckWorking Papers in Economics and Statistics
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Do parents invest into voluntary climate action when their children 1
are watching? Evidence from a lab-in-the-field experiment 2
Helena Fornwagner (University of Innsbruck) 3
Oliver P. Hauser (University of Exeter)* 4
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This version: June 25, 2020. 6
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Abstract 8
Would parents do anything to enable a better future for their children – or only when they are seen 9
to do so? Here we study voluntary climate action (VCA), which are costly to today’s decision-10
makers but essential to enable sustainable living for future generations. We hypothesise that parents 11
will be most likely to invest in VCA when their own offspring observes their decision, whereas 12
when adults or genetically unrelated children observe them, the effect will be smaller. In a large 13
lab-in-the-field experiment, we observe a remarkable magnitude of VCA across conditions: parents 14
invest 82% of their €69 endowment into VCA, resulting in almost 14,000 real trees being planted. 15
Parents’ VCA varies across conditions, with larger treatment effects occurring when a parent’s own 16
child is the observer. In subgroup analyses, we find that larger treatment effects occur among higher 17
educated parents. Our findings have implications for researchers and policy-makers interested in 18
understanding voluntary climate action and designing programmes to encourage it. 19
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JEL-Classification: C99, Q51, Q54, H49, D19 21 22
Keywords: voluntary climate action, intergenerational cooperation, parents, children, 23
observability, lab-in-the-field experiment 24
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*Corresponding author: Correspondence and requests for materials should be addressed to Oliver 26 Hauser (email: [email protected]). 27
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1 Introduction 28
Reducing carbon dioxide (CO2) emissions—a major driver of climate change—is one of the 29
biggest challenges of humankind, to secure future “basic elements of life for people around the 30
world” (Stern, 2007). A number of different ways to cut CO2 emissions have been proposed, such 31
as changes in energy demand, adoption of clean power, as well as protecting and increasing forest 32
areas (see, e.g., Bastin et al., 2019). In addition to governmental regulations (Diederich and 33
Goeschl, 2014), the general population has a key role to play in reducing CO2 emissions (United 34
Nations, 2019). The importance of individuals taking action to tackle climate change has recently 35
been spearheaded by teenagers around the world through the “Fridays for Future” campaign. 36
Individual actions to reduce the harmful effects of climate change are referred to as voluntary 37
climate action (VCA). A VCA takes different forms on an individual level; however, one key 38
unifying aspect of VCAs is that they necessitate incurring a cost to the individual to provide a 39
benefit to the environment, a general public good that is largely consumed in the future (Fischer, 40
Irlenbusch and Sadrieh, 2004; Diederich and Goeschl, 2014; Hauser et al., 2014). Examples of 41
VCAs include investing in energy saving technology (e.g., solar panels), switching to CO2 friendly 42
purchasing habits (e.g., buying less red meat), or even engaging in small, everyday behaviours, 43
such as spending less time in the shower (Wynes and Nicholas, 2017). In our study, we are 44
interested in a VCA that has a long-lasting positive effect on the environment. We focus on CO2 45
offsetting, as such programmes have become increasingly widespread and available as means for 46
individuals to help reduce their “carbon footprint” (Kollmuss et al., 2010). 47
As individuals’ decisions to help reduce climate change become increasingly necessary (see, 48
e.g., World Bank, 2015), it is important to find ways to increase VCAs. Past research has examined 49
contextual changes (“nudges”) to the decision environment to motivate VCAs (Thaler and 50
Sunstein, 2009; Hauser, Gino and Norton, 2018). Bruns et al. (2018) implement a default option to 51
nudge experimental subjects in the lab to contributions to carbon offsetting reductions. Similarly, 52
Araña and León (2013) show that an opt-out condition for VCA programmes increases a VCA, 53
compared to an opt-in condition. Results from field studies suggest that if supporting a VCA is a 54
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pre-set default option, this also increases average contributions of experts in the field of 55
environmental economics (Löfgren et al., 2012). This effect is stable over longer time periods 56
(Kesternich, Römer and Flues, 2019). Stimuli like matching and rebate subsidies also have positive 57
effects on increasing a VCA (Kesternich, Löschel and Römer, 2016). Energy saving initiatives 58
(such as social norm nudges) have also been found to be effective in creating long-lasting effects 59
on a VCA (Allcott and Rogers, 2014; Jachimowicz et al., 2018). 60
Here, we propose a novel perspective on how to solve VCA dilemmas. VCAs contribute to an 61
intergenerational public good (IPG), in which the beneficiaries of the VCA (future generations) 62
are not the same as the decision-makers (DM) who pay the cost to invest in the VCA (current 63
generation). While extant research has focused on public goods within the same generation (see, 64
e.g., Fehr and Gächter, 2000; Milinski et al., 2006; Rand et al., 2009), or on cooperation between 65
different generations (Charness and Villeval, 2009), little research exists on intergenerational goods 66
where future generations cannot reciprocate the actions of the acting current generation and the 67
incentives to cooperate with the future are low (Fischer, Irlenbusch and Sadrieh, 2004; Hauser et 68
al., 2014; Kamijo et al., 2017; Ponte et al., 2017; Shahrier, Kotani and Saijo, 2017). However, this 69
does not imply that there exist no links to future generations: people—parents—who have children 70
are crucially (i.e. genetically) related to the next generation and they have an incentive to care for 71
their offspring’s wellbeing. We argue that this personal genetic link makes parents particularly 72
likely to engage in a VCA (that sustains the IPG and benefit their child in the future), especially 73
when their own child observes this action. 74
Through an innovative lab-in-the-field experiment, we propose to take advantage of parents’ 75
responsibility towards their children in fostering more long-term investment today for the benefit 76
of future generations. Specifically, we focus on parents1 as critical VCA contributors and 77
1 Having children is an essential dimension to test our hypotheses involving the intergenerational link: we acknowledge the potential for self-selection into who chooses to become a parent, but we argue this makes them more—not less—important to study in this context. According to Eurostat, one-third of all 220 million EU households have children (Eurostat, 2017). Therefore, considering parents is plausible and economically significant, as they form a major part of active participants in a society. As parents are in their adult life stage—working, producing and consuming—they can be considered one of the largest contributors to CO2 emissions, compared to children or elderly persons (Zagheni, 2011). Thus, getting parents to engage in any kind of VCA is likely to result in economically meaningful changes.
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exogenously vary to what extent future generations, including their own children, are salient to 78
them when they make a VCA decision. Specifically, we hypothesize that parents will be especially 79
likely to engage in a VCA when they are accountable to their offspring, relative to other observers. 80
While past work has shown the importance of observers to motivate costly cooperative behaviours 81
(see, e.g., Yoeli et al., 2013; Hauser et al., 2016), a parent’s offspring is critical here because they 82
are a representative of future generations who will benefit from the cooperative acts today. 83
Therefore parents, who have their children’s well-being at heart, be reminded of the benefits of 84
investing into the future when their children are present. Furthermore, parents want to be viewed 85
as role models by their own children (see, e.g., Knafo and Schwartz, 2001) and, more intimately, 86
their children are genetic beneficiaries of the VCA (Smith, 1977; Nowak, 2006). 87
Past work has shown that mechanisms such as direct and indirect punishment, direct 88
rewarding, as well as reputation building, foster contributions to public goods in the laboratory 89
(see, e.g., Rockenbach and Milinski, 2006; Milinski and Rockenbach, 2012) and in the field (see, 90
e.g., Balafoutas, Nikiforakis and Rockenbach, 2014). Observability in conjunction with 91
punishment (Fehr and Gächter, 2000), rewards (see, e.g., Hauser et al., 2016, Rand et al. 2009), 92
communication (Miller, Butts and Rode, 2002; Bracht and Feltovich, 2009; Balliet, 2010), and 93
framing (Andreoni, 1995; Rege and Telle, 2004) also positively influence cooperative behaviour 94
in the laboratory. Interestingly, when participants can choose, they only make high contributions 95
observable for others (Rockenbach and Milinski, 2011). Furthermore, a burgeoning literature using 96
field experiments has shown that being observed, even without the explicit mention or possibility 97
for punishment or reward, also increases cooperative behaviour (see, e.g., Bateson, Nettle and 98
Roberts, 2006; Ekström, 2012; Yoeli et al., 2013). The effect is typically stronger in the case of 99
“overt observability”, which means that actual identifying information (e.g., name and face) as well 100
as behaviour are revealed to the observer at or after the point of decision (Bradley, Lawrence and 101
Ferguson, 2018). 102
Most existing research has used adults (who are unrelated and strangers to the decision-103
makers) as observers. However, for observability to have the largest effect in an IPG, we argue that 104
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a link between today’s DM and the future generation needs to be established. Past research has 105
found that increasing the salience of the beneficiaries of an altruistic decision (the “identifiable 106
victim”) can lead to more giving (Small, Loewenstein and Slovic, 2007). Thus, we propose that an 107
observer who directly benefits from the public good, such as a representative of the future 108
generation (e.g., a child today), will be more influential on the DM’s decision than an observer 109
from the current generation (e.g., an adult). In addition, adults who are observed by a child may 110
also want to act as a role model by acting virtuously or in line with societal expectations (Adriani, 111
Matheson and Sonderegger, 2018). 112
Furthermore, the effect of an observer can be further increased by choosing a particularly 113
relevant representative of the next generation – specifically, a parent’s own child (e.g., Ben-Ner et 114
al. 2017). We are interested in parents’ behaviour when their own child is observing their decision. 115
Parents typically want to impart knowledge and good decision-making on their children (see, e.g., 116
Ben-Ner et al., 2017) and be viewed as role models by their own children (see, e.g., Knafo and 117
Schwartz, 2001). Indeed, children are influenced by the behaviour of their parents when it comes 118
to criminal behaviour (McCord and McCord, 1958), educational choices (Dryler, 1998), and career 119
development (Keller and Whiston, 2008). Moreover, there is a strong connection between parents 120
and children which can be used to transfer knowledge, attitudes or behaviours with respect to 121
climate change (Lawson et al., 2018). For example, Lawson et al. (2019) find that parents become 122
more concerned about climate change when this issue is brought to them and discussed by their 123
children. In addition, we expect one’s own offspring to be important, as parents have a vested 124
genetic interest in their children (Hamilton, 1964a, 1964b; Trivers, 1972; Rand and Nowak, 2013). 125
who benefit from the VCA. Furthermore, past work has found that parents are even more likely to 126
engage in actions which benefit their offspring, compared to a situation where they themselves 127
would benefit (Cassar, Wordofa and Zhang, 2016). Observation by their child will therefore likely 128
trigger the parent’s responsibility towards the child, increasing the likelihood that they engage in 129
the VCA, relative to any other observer. 130
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2 Methods 133
2.1 Voluntary climate action and study context 134
We carried out a novel lab-in-the-field experiment in Innsbruck, Austria. The experiment included 135
an incentive-compatible survey programmed in oTree (Chen, Schonger and Wickens, 2016) and 136
data were collected with tablets (see Supplementary Information, SI). Participation took no longer 137
than 20 minutes and our treatment conditions were randomly assigned to participants. Using a 138
neutrally-framed recruitment stand in public spaces, we recruited parents who were accompanied 139
by at least one of their own children aged between 7-14 years.2 At all times during the experiment, 140
only one parent (the main decision-maker) and one own child (who is an observer in one condition 141
and not involved in the experiment in the other conditions) were allowed to participate: in 142
conditions where the child was not an observer, s/he was asked to wait outside the study booth and 143
engage in various games and activities (supervised by the research assistants).3 In addition, for our 144
conditions with observers who are not related to the participant, we employed confederate adults 145
and confederate children who were introduced to the participant as “helpers from the community” 146
to act as observers. 147
Existing studies suggest that the type of VCA plays a role. For example, the general public 148
prefers investing in a VCA with local mitigation goals (Torres et al., 2015). The VCA to offset 149
CO2 takes the form of a local foresting programme, for which we have secured the collaboration 150
from the forestry office Innsbruck (“Forstamt Innsbruck”). We chose a foresting programme for 151
forest restoration, because such programmes are among the best climate change solutions available 152
today (Bastin et al., 2019). Participants were asked to choose between keeping money for 153
themselves or spending that money on planting trees. All trees that participants decided to plant are 154
planted in 2020 and 2021 on the “Nordkette” and “Patscherkofel”, mountain ranges in close 155
2 We chose this age range as our pre-experimental focus groups have shown that these children are old enough to understand the experimental setup but young enough so that parents still serve as role models (which may be less plausible for older teenagers) (Dudenredaktion (o. J.), 2019). 3 Similarly, if a parent had more than one child with them, they were asked to wait outside and engage in the games and activities prepared for them.
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proximity to Innsbruck, ensuring that the mitigation strategy is truly local. Moreover, this particular 156
area has a high suitability for the VCA, as it has a high net plant productivity with potential for 157
forest restoration (Bastin et al., 2019). 158
Following the experimental design by Goeschl et al. (2020), subjects received a short and 159
neutral description of the foresting program. In particular, they were informed that the foresting 160
programme has the following characteristics: (1) The trees are only planted if participants in our 161
study actually choose to spend their money on planting a tree. This ensures that the decision the 162
participants face is incentive-compatible and truly contributes towards reducing CO2 in the 163
environment. (2) The trees are selected in order to lead to a climate-friendly mixed forest, including 164
climate-efficient species of different fir trees or deciduous trees. These tree types would usually 165
not be planted as frequently due to their cost. (3) Each tree that is planted has an expected minimum 166
age of 120 years (estimate provided by the forestry office Innsbruck). This means that each three 167
that is planted by our participants lasts at least the equivalent of four average (human) generations 168
(following the Cambridge dictionary definition of a “generation”). (4) The trees will be monitored 169
and controlled annually to ensure they are healthy, and they will be listed in the governmental 170
database “Walddatenbank” to ensure a “paper trail” of the planting exists. (7) The trees are planted 171
in a forest which is certified with a PEFC certificate (for more information please visit 172
https://www.pefc.at), ensuring environmental sustainability. All these characteristics ensure a 173
maximum possible credibility of our CO2 offsetting program.4 174
Moreover, subjects were given information about greenhouse gas emissions and the role of 175
trees for CO2 reductions before deciding on the VCA. Since the general population has relatively 176
little prior knowledge about VCAs (Diederich and Goeschl 2014), we ensured that all participants 177
first gained a basic understanding of the VCA in this study. Whereas MacKerron et al. (2009), 178
Löschel, Sturm and Vogt (2013), and Goeschl et al. (2020) provided information as text on the 179
4 Another type of certification exists in the form of a CO2 certification. However, Tyrolean forests are not yet CO2 certified, which is a process that takes years to qualify for and is currently underway (but not yet complete) by the local authorities. In the meantime, the current certification programme fulfils all our required characteristics (such as longevity and investment into the future) to qualify as a VCA in our study.
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screen, our participants watched a short video5. The video informed about the public goods 180
character of CO2 reductions by explaining how planting trees removes CO2 from the atmosphere 181
and mitigates the effects of global climate change. In particular, the video highlighted that reducing 182
CO2 has an impact not only on current generations, but also on future ones. 183
184
2.2 Experimental conditions 185
The experimental treatments are summarized in Table 1. We implemented four conditions in a 186
between-subjects design, varying observability and the type of observer. In all conditions, a parent 187
receives a windfall endowment of €69 and is asked to decide how much of that money to keep for 188
themselves and how much to invest into the VCA (i.e. planting trees). Using their endowment, 189
participants may purchase between 0 and 46 trees, with each tree costing €1.50 (the average cost 190
of planting a tree in the foresting program).6 Any money not invested in planting trees is paid to 191
participants in cash at the end of the experiment. We also collected data on basic demographics 192
(e.g., gender, age, education, etc.) and included a short survey at the end of the experiment (see SI). 193
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Table 1. Experimental conditions, varying who observes the participant. 195 Condition Observer Max. Selfish Payoff Max. Public Good
NoObserver No observer €69 46 trees
StrangerAdult Adult (not related to the DM) €69 46 trees
StrangerChild Child (not related to the DM) €69 46 trees
OwnChild DM’s own child €69 46 trees
196
In our baseline NoObserver condition, the DM makes the decision in private without being 197
observed by anyone. In the StrangerAdult condition, the DM is observed by another adult who is a 198
hired actor (confederate) to act as the observer and who is unrelated to the DM (see detailed 199
5 We used a publicly available video by “youknow”, a leading provider of e-learning in the German-speaking world. The video is accessible here: https://www.youtube.com/watch?v=ZGXVq9obUms 6 An average Austrian citizen emitted in 2018 9.2 tonnes of CO2 equivalent per capita (Eurostat, 2018). According to the Tyrolian authorities, 46 trees, belonging to different climate friendly species, are needed to reduce 10% of the annual CO2 emissions of an average Austrian citizen.
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information about the observability procedure in the SI). This condition is similar to the standard 200
procedure used in observability experiments in the lab, where a DM is observed by another adult, 201
which helps establish a “general observability” effect. In the StrangerChild condition, the observer 202
is an actor who is a child between 7 and 14 years old and unrelated to the DM, which helps identify 203
whether the VCA can be encouraged by having an observer from the future (beneficiary) 204
generation. Finally, in the OwnChild condition, the observer is the child of the DM, in order to 205
understand whether the DM’s own child has an effect on the DM’s VCA behaviour. Detailed 206
information on the experimental design can be found in the SI. 207
3 Experimental sample 208
We ran the experiment with a total of 368 parents, 92 in each of the four treatment conditions. Data 209
were collected starting at the end of 2019 until early 2020, in three different locations in the city of 210
Innsbruck. In Table S1 in the SI, we provide background details on our participants based on the 211
post-experimental questionnaire. In Table S2, descriptive statistics are further broken down by 212
treatment, showing that randomisation worked: the randomly assigned participants are comparable 213
across a number of relevant characteristics. Across all treatments, 67% of our participants are 214
female (248 out of 368) and the average age is 42 years. Participants have on average 2.06 children 215
and the vast majority (96%) are currently employed. With respect to education, 86% received a 216
high school diploma (by completing an exam called “Matura”), which provides general access to 217
higher education and labour market qualifications.7 Out of those with high school diploma, half 218
(50%) have a university degree. The majority is married or in a registered relationship (66%) and 219
there is approximately an equal split between those living in the city (49%) versus those living in 220
the countryside. Our recruited sample is largely representative of the general population of 221
Innsbruck (Austria), where our trial took place (see SI for details). 222
We included a survey question asking participants how risk-seeking they are (see Falk et al., 223
2018). The mean reported value is 5.35 on a scale from 0 (not risk-seeking at all) to 10 (fully risk-224
7 The exam is called „Matura“ or “Reifeprüfung” in Austria. One is qualified to take the exam after a minimum of 12 years of schooling. It is comparable with a U.S. high-school diploma. or U.K. A-levels.
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seeking), and does not differ between treatments (Kruskal-Wallis test (kwallis), p = 0.255).8 225
Additionally, we asked the participants how patient they are, as a proxy of their time preferences. 226
The average reported score is 5.92, measured on a scale from 0 (not at all patient) to 10 (fully 227
patient). We do not find any treatment differences for the patience measure (kwallis, p = 0.397). 228
In three out of four treatments, the participant was observed. We therefore take a look at 229
observer characteristics as well. Stranger adult observers (who were hired by the experimenters as 230
confederates) are on average 39.89 years old, and observing children 11.33 years (StrangerChild: 231
12.23 years; OwnChild: 10.43 years; Wilcoxon-Mann-Whitney test (WMW), p < 0.001).9 The 232
majority (55%) of observers is female and the number of female observers does not differ across 233
treatments (Fisher's exact p = 0.231). We provide in Table S6 a summary of the gender matches of 234
participants and observers for the treatments with observers. Because both the participant sample 235
as well as the observers are made up of more women (F) than men (M), we have 99 FF matches, 236
88 FM, 54 MF and 35 MM matches. Gender matches are balanced across treatment conditions (χ", 237
p = 0.497). 238
4 Results 239
4.1 Total number of trees planted 240
Our first result is purely descriptive but nonetheless remarkable: across all conditions, the 368 241
participants chose to plant a total amount of 13,988 trees (our outcome measure, labelled VCA; out 242
of a maximum possible 16,928 trees). On average, participants invested 82.63% of their €69 243
endowment into the VCA, with 66.58% of participants choosing to invest their entire endowment 244
into planting all possible 46 trees (see Figure 1). The average VCA does not differ by the 245
participant’s gender (females: 37.79 trees, versus males: 38.47 trees; WMW, p = 0.724). 246
247
8 All p-values in the paper and in the SI are based on two-sided tests. 9 We had to select the children observers in the StrangerChild treatment ahead of the experiment: we chose confederates of ages 7 to 14. While the average age of children in the OwnChild treatment was less than 1.5 years lower (and significantly so) than in the StrangerChild treatment, we do not believe that this small difference between the two treatments is sufficient to affect our results in a meaningful way.
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248
Figure 1. Frequencies of amounts of trees planted across all participants in four conditions (N = 368). 249 250
4.2 Covariates that predict the VCA 251
We begin by examining which variables are predictive of VCA across conditions (see Table 2). Age 252
is a significant predictor of VCA (coeff = 2.23, p = 0.037), whereas gender (coeff = 0.50, p = 0.748) 253
and the participant’s number of kids are not significant (coeff = 0.91, p = 0.209). These results are 254
all consistent with past findings (see, e.g., Löschel, Sturm and Vogt, 2013 and Diederich and 255
Goeschl, 2014). 256
Having a high school diploma (“Matura”) as well as employment status both have a positive 257
effect on VCA. Higher education is associated with higher VCA (coeff = 10.77, p < 0.001), in line 258
with Diederich and Goeschl (2014). Employment is also positively associated (coeff = 11.37, p = 259
0.001), as one might expect that being employed implies greater disposable income (see also 260
Löschel, Sturm and Vogt, 2013). Meanwhile, neither risk nor patience is significantly associated 261
with VCA (Risk: coeff = 0.15, p = 0.598; Patience: coeff = -0.23, p = 0.355). Lastly, we find 262
variation by study location (see Table 7 Column 2).10 263
264
10 We discuss this variation by study (recruitment) location in more detail in the SI.
0
50
100
150
200
250
Freq
uenc
y
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46VCA: Number of trees planted
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Table 2. Regression results for the entire sample without treatments. 265 (1) (2) (3) (4) VCA VCA VCA VCA
Age 0.23**
(0.11) 0.20* (0.11)
0.51 (0.34)
0.43 (0.33)
Female 0.50 (1.56) 0.65
(1.55) 2.70
(4.78) 3.30
(4.72)
Nr. kids 0.91 (0.73) 1.12
(0.73) 2.63
(2.21) 3.20
(2.19)
Risk 0.15 (0.29) 0.21
(0.29) 1.16
(0.88) 1.33
(0.87)
Patience -0.23 (0.25) -0.17 (0.25)
-1.01 (0.77)
-0.80 (0.76)
High School Dipl. 10.77***
(2.03) 9.69*** (2.05)
24.65*** (5.67)
21.69*** (5.64)
Employed 11.37***
(3.43) 11.36*** (3.41)
24.37*** (9.28)
24.44*** (9.18)
Constant 6.64 (6.24) 9.07
(6.24) -13.69 (18.67)
-7.80 (18.46)
var(e.vca) 1063.18***
(168.38) 1024.69*** (161.91)
N 362 362 362 362 Location Fixed Effects No Yes No Yes
Notes: Ordinary least squares ((1)-(2)) and tobit regressions ((3)-(4); upper limit 46 and lower limit 0). Standard errors in 266 parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01. StrangerAdult, StrangerChild, OwnChild equals 1 for the respective treatments, 267 and 0 for the baseline NoObserver treatment. Herbstmesse and Sillpark equals 1 for the respective locations, and 0 otherwise 268 (baseline is the Rathausgalerien location). Age in years. Female equals 1 for female participants. The number of kids controls for 269 the respective variable for each participant. High School Dipl. is equal to 1 for participants who completed secondary education and 270 0 otherwise. Risk measures self-assessed risk attitudes with higher values indicating lower higher risk-seeking. Patience measures 271 self-assessed time preferences with higher values indicating higher patience. 272 273
4.3 Treatment effects on the VCA 274
Turning to our conditions, we first take a look at the raw VCA values (see Figure 2). We observe 275
the lowest VCA in NoObserver (mean = 37.12, 25th percentile = 35.00 and 75th percentile = 46.00) 276
and StrangerAdult (mean = 37.09, 25th percentile = 32.00, 75th percentile = 46.00). VCA is slightly 277
higher in StrangerChild (mean = 38.24, 25th percentile = 34.00, 75th percentile = 46.00) and it is 278
highest in OwnChild (mean = 39.60, 25th percentile = 43.00, 75th percentile = 46.00). 279
280
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281
Figure 2. VCA: Number of trees planted by treatment condition (N = 368 subjects). Each box plot shows the 282 average VCA of participants in each treatment. Box plots show the mean (indicated by black X signs), the 25th 283 and 75th percentiles, Tukey whiskers (median ± 1.5 times interquartile range), and individual data points. 284 Larger dots indicate a higher number of participants who invested the corresponding number of trees. 285 286
Next, we examine the effect of our treatments econometrically (see Table 3). Our analytical 287
strategy is twofold: First, we estimate the treatment effects on VCA using ordinary least squares 288
(OLS) regressions (in columns (1) and (2)). Second, we employ Tobit regressions (columns (3) and 289
(4)) to estimate treatment effects, taking into account that the dependent variable is the number of 290
trees planted (i.e. VCA), which is bounded by 0 trees on the lower end (if the participant keeps the 291
entire endowment for him/herself) and by 46 trees on the upper end (if the participant invests the 292
entire endowment into the VCA). For both models, we use the following specifications for columns 293
(1) and (3) which shows the main effects of the independent variables (treatment dummies) without 294
any control variables: 295
𝑉𝐶𝐴& = 𝛽* +𝛽,𝑆𝑡𝑟𝑎𝑛𝑔𝑒𝑟𝐴𝑑𝑢𝑙𝑡& +𝛽"𝑆𝑡𝑟𝑎𝑛𝑔𝑒𝑟𝐶ℎ𝑖𝑙𝑑& +𝛽9𝑂𝑤𝑛𝐶ℎ𝑖𝑙𝑑& + 𝜀& (1) 296
where i = 1, …, n indicates participant i, VCA is a continuous variable (ranging from 0 to 46) 297
measuring the number of trees a participant decided to plant, and the StrangerAdult, StrangerChild 298
and OwnChild dummies are 1 in the respective treatments and 0 otherwise, 𝜀& measures unobserved 299
scalar random variables (errors). 300
0
5
10
15
20
25
30
35
40
45
50
VCA:
Num
ber o
f tre
es p
lant
ed
NoObserver (N=92) StrangerAdult (N=92) StrangerChild (N=92) OwnChild (N=92) Treatment conditions
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14
In addition, we also report in columns (2) and (4) the same specification with a number of 301
control variables (see Section 4.2 above for a discussion on covariates): 302
𝑉𝐶𝐴& = 𝛽* +𝛽,𝑆𝑡𝑟𝑎𝑛𝑔𝑒𝑟𝐴𝑑𝑢𝑙𝑡& +𝛽"𝑆𝑡𝑟𝑎𝑛𝑔𝑒𝑟𝐶ℎ𝑖𝑙𝑑& +𝛽9𝑂𝑤𝑛𝐶ℎ𝑖𝑙𝑑& + 𝛽=𝑅𝑎𝑡ℎ𝑎𝑢𝑠& +303
𝛽@𝑆𝑖𝑙𝑙𝑝𝑎𝑟𝑘& + 𝛽C𝐻𝑒𝑟𝑏𝑠𝑡𝑚𝑒𝑠𝑠𝑒& + 𝛽G𝐴𝑔𝑒& + 𝛽H𝐹𝑒𝑚𝑎𝑙𝑒& + 𝛽J𝑁𝑟𝐾𝑖𝑑𝑠& + 𝛽,*𝑅𝑖𝑠𝑘& +304
𝛽,,𝑃𝑎𝑡𝑖𝑒𝑛𝑐𝑒& + 𝛽,"𝐸𝑑𝑢𝑐& + 𝛽,9𝐸𝑚𝑝𝑙𝑜𝑦𝑒𝑑& + 𝜀& (2) 305
where Rathaus, Sillpark and Herbstmesse are dummy variables for each of the three study locations 306
and 0 otherwise, Age is a continuous variable and Female an dummy variable for the participant’s 307
age and gender, NrKids is a continuous variable capturing the participant’s number of kids, Risk 308
and Patience are self-reported scale measure (scale from 0 to 10), High School Dipl. is a dummy 309
variable which is 1 if the participant completed secondary education (“Matura”) and Employed is 310
a dummy variable which is 1 if the participant is currently employed; all other variables are as 311
defined in Eq. (1). 312
As Table 3 shows, the largest coefficient relative to the baseline NoObserver is the OwnChild 313
treatment. Without control variables, the OwnChild coefficient is positive but not significant (OLS: 314
coeff = 2.48, p = 0.236; Tobit: coeff = 9.44, p = 0.149), whereas with control variables, the 315
OwnChild treatment leads to larger VCA at the 5% significance level (OLS: coeff = 3.68, p = 0.064; 316
Tobit: coeff = 11.79, p = 0.051). Neither the coefficient on StrangerAdult nor StrangerChild is 317
significant with or without control variables. In Section 4.4 we explore why the inclusion of control 318
variables may have affected the significance of our results in the OwnChild condition. 319
So far, these results suggest that parents may be affected by the presence of their own children 320
when making the VCA decision. To isolate the potential mechanisms at work, we explore three 321
explanations using non-parametric tests.11 First, we are interested in an general “observability” 322
effect previously studied in the literature (see, e.g., Yoeli et al., 2013 and Rogers, Ternovski and 323
Yoeli, 2016): we compare VCA in NoObserver with the average VCA, pooled across the three 324
treatments with observers (StrangerAdult, StrangerChild and OwnChild). Even though VCA is 325
11 We pre-registered the use of non-parametric tests for this analysis. However, an alternative specification testing the joint coefficients from Table 2 yields similar results.
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15
higher, as one might expect when the participant is observed, the difference between the pooled 326
observer conditions (38.31 trees planted) and the NoObserver condition (37.12 trees planted) is not 327
significant (WMW, p = 0.328). This suggests a general “observability” effect is weak in our setting. 328
329
Table 3. Regression results for the entire sample. 330 (1) (2) (3) (4) VCA VCA VCA VCA StrangerAdult -0.03
(2.09) 2.54
(2.00) 2.02
(6.35) 9.49
(5.99) StrangerChild 1.12
(2.09) 2.06
(1.96) 4.16
(6.37) 5.60
(5.79) OwnChild 2.48
(2.09) 3.68* (1.98)
9.44 (6.52)
11.79* (6.02)
Age 0.20* (0.11)
0.45 (0.33)
Female 0.69 (1.55)
3.64 (4.70)
Nr. kids 1.11 (0.73)
3.14 (2.20)
Risk 0.19 (0.29)
1.25 (0.87)
Patience -0.18 (0.25)
-0.82 (0.76)
High School Dipl. 10.02*** (2.06)
22.77*** (5.66)
Employed 11.78*** (3.42)
25.75*** (9.20)
Constant 37.12*** (1.47)
6.27 (6.42)
57.12*** (4.81)
-16.60 (18.86)
var(e.VCA) 1315.15*** (209.16)
1007.66*** (159.05)
N 368 365 368 365 Location Fixed Effects No Yes No Yes
Notes: Ordinary least squares ((1)-(2)) and tobit regressions ((3)-(4); upper limit 46 and lower limit 0). * p < 0.10, ** p < 0.05, *** p 331 < 0.01. StrangerAdult, StrangerChild, OwnChild equals 1 for the respective treatment, and 0 otherwise (baseline is the NoObserver 332 treatment). Herbstmesse and Sillpark equals 1 for the respective locations, and 0 otherwise (baseline is the Rathausgalerien location). 333 Age in years. Female equals 1 for female participants. The number of kids controls for the respective variable for each participant. 334 High School Dipl. is equal to 1 for participants who completed secondary education and 0 otherwise. Risk measures self-assessed 335 risk attitudes with higher values indicating lower higher risk-seeking. Patience measures self-assessed time preferences with higher 336 values indicating higher patience. 337 338
Second, we pool VCA across the two treatments, in which a child is the observer 339
(StrangerChild and OwnChild), and compare it with VCA in StrangerAdult. Here we test whether 340
observability by a child (who is a “representative” of the future beneficiaries generation) relative 341
to any observer is at work here. Again, as expected, the average VCA is higher (38.92 trees planted) 342
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16
when being observed by a child, but not significantly different from the average VCA (37.09 trees 343
planted) when being observed by an adult (WMW, p = 0.471). 344
Finally, we investigate whether the parent’s own child increases VCA, compared to being 345
observed by an unrelated child, thus holding constant the “observer’s generation” and focusing on 346
the link between a parent and their child. While VCA is higher, as one might expect, in OwnChild 347
(39.60 trees planted) than in StrangerChild (38.24 trees planted), this difference is also not 348
significant (WMW, p = 0.419). In sum, returning to Table 2, while the coefficient on OwnChild is 349
the largest treatment effect relative to the NoObserver baseline and suggests that parents are 350
particularly sensitive to their own child’s observation, we cannot distinguish this effect from the a 351
similarly-sized effect in StrangerChild when that participant is observed by a child who s/he is not 352
related to. Taken together, these three tests only suggest directional effects (in line with the 353
published literature) but do not reveal significant differences. 354
355
4.4 Treatment effects by education 356
357
Figure 3. VCA: Number of trees planted by location and education (N = 363 subjects). Each set of four box plots 358 shows the average VCA of participants for each education level. Respective treatment order for each education 359 level: NoObserver, StrangerAdult, StrangerChild, and OwnChild. Box plots show the mean (indicated by black 360 X signs), the 25th and 75th percentiles, Tukey whiskers (median ± 1.5 times interquartile range), and individual 361 data points. Larger dots indicate a higher number of participants with the corresponding number of trees. 362 363
02468
10121416182022242628303234363840424446
VCA:
Num
ber o
f tre
es p
lant
ed
NoObserver StrangerAdult StrangerChild OwnChild NoObserver StrangerAdult StrangerChild OwnChild
Treatment conditions
No Sec. Education (N=51) Sec. Education (N=312)
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17
Since education has been found to be a key determinant of the willingness to invest in a VCA 364
(Diederich and Goeschl, 2014) and secondary education completion differed substantially by study 365
location (Rathausgalerien: 91.56% completed secondary education; Sillpark: 75.44%; 366
Herbstmesse: 75.36%,Χ" p < 0.001), we examine our treatment effects in two sub-analyses 367
(pooling across locations).12 Specifically, we look at participants with versus without High School 368
Dipl.. Average VCA by treatment and high school diploma groups are summarized in Figure 3. 369
First, we observe a substantial main effect of having a high school diploma, pooled across 370
treatments, consistent with prior research (Diederich and Goeschl, 2014): whereas participants with 371
secondary education invest in 39.37 trees on average (25th percentile = 40.00, 75th percentile = 372
46.00), participants without secondary education invest at a significantly lower rate of 27.61 trees 373
(25th percentile = 10.00, 75th percentile = 46.00; WMW, p < 0.001). 374
We next turn to studying the effect of the treatments within each subgroup. We repeat our 375
empirical strategy (see Eqs. (1) and (2)). Participants with high school diploma form the majority 376
of our sample (312 of 368 participants, or 86%). Focusing on these participants first, we observe 377
consistent and sizeable effects of the OwnChild treatment: across all specifications, parents who 378
are observed by their own child are significantly more likely to invest in VCA (see OwnChild 379
coefficient all columns in Table 4). We do not find any evidence that being observed by a stranger 380
adult or stranger child leads to higher VCA. Furthermore, we also find evidence that parents invest 381
significantly more when being observed by their own child, compared to being observed by a 382
stranger child (WMW, p = 0.031). This rules out the possibility that, for these parents, the pathway 383
to more VCA is being observed by a future beneficiary; instead, they are uniquely influenced by 384
their own offspring’s observation. 385
386
12 In the SI, we analyse the treatment effects by each study location separately.
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18
Table 4. Regression results for parents with high school diploma. 387 (1) (2) (3) (4)
VCA VCA VCA VCA StrangerAdult -0.51
(1.98) 2.14
(2.00) 1.01
(6.70) 11.33 (6.94)
StrangerChild -0.62 (1.95)
0.70 (1.92)
-0.83 (6.50)
3.54 (6.32)
OwnChild 3.52* (2.01)
5.03** (1.97)
13.73* (7.25)
18.11** (7.07)
Age 0.05 (0.11)
0.12 (0.36)
Female 0.56 (1.52)
2.96 (5.21)
Nr. kids 1.52** (0.74)
4.53* (2.59)
Risk 0.18 (0.29)
1.31 (0.99)
Patience -0.20 (0.24)
-1.14 (0.85)
Employed 15.87*** (3.81)
38.32*** (11.67)
Constant 39.20*** (1.37)
18.73*** (6.54)
61.59*** (5.15)
7.98 (21.60)
var(e.VCA) 1180.56*** (217.36)
1030.96*** (189.84)
N 312 311 312 311 Location Fixed Effects No Yes No Yes
Notes: Ordinary least squares ((1)-(2)) and tobit regressions ((3)-(4); upper limit 46 and lower limit 0). errors in parentheses. p < 388 0.10, ** p < 0.05, *** p < 0.01. StrangerAdult, StrangerChild, OwnChild equals 1 for the respective treatment, and 0 otherwise 389 (baseline is the NoObserver treatment). Herbstmesse and Sillpark equals 1 for the respective location, and 0 otherwise. Age in years. 390 Female equals 1 for female decisions makers. The number of kids controls for the respective variable for each participant. Risk 391 measures self-assessed risk attitudes, with higher values indicating lower higher risk seeking. Patience measures self-assessed time 392 preferences with higher values indicating higher patience. 393 394
Turning to participants without a high school diploma (N = 51), we find that the treatment effects 395
look qualitatively different. Specifically, average VCA is low in the NoObserver condition (20.13) 396
and, remarkably, also in the OwnChild condition (23.94). The highest mean VCA is observed in the 397
StrangerChild condition (35.73), which is significantly different from the NoObserver condition 398
without covariates (see columns 1 and 3 in Table 7) but not significant with covariates (columns 2 399
and 4). The StrangerAdult condition (29.44) falls in the middle. In sum, we find that participants 400
with high school diploma are more likely to invest in the VCA when they are observed by their 401
own child, whereas participants without high school diploma are somewhat more likely to invest 402
in the VCA when they are observed by another (i.e. not their own) child. 403
404
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19
405
Table 5. Regression results for parents without high school diploma. 406 (1) (2) (3) (4) VCA VCA VCA VCA StrangerAdult 9.31
(7.72) 2.78
(7.70) 15.12
(12.74) 2.79
(11.33) StrangerChild 15.60*
(8.28) 11.95 (8.08)
27.94* (14.37)
18.95 (12.52)
OwnChild 3.81 (7.72)
-1.44 (8.05)
6.30 (12.58)
-2.48 (11.83)
Age 1.26*** (0.40)
2.18*** (0.68)
Female 4.28 (6.08)
9.47 (9.71)
Nr. kids -0.33 (2.38)
-1.40 (3.59)
Risk 1.19 (1.01)
2.19 (1.65)
Patience -1.67 (1.10)
-2.57 (1.70)
Employed 1.81 (8.75)
3.78 (12.79)
Constant 20.13*** (6.30)
-28.70 (20.17)
22.10** (10.15)
-65.04* (32.32)
var(e.VCA) 800.58*** (241.69)
550.50*** (164.17)
N 51 51 51 51 Location Fixed Effects No Yes No Yes
Notes: Ordinary least squares ((1)-(2)) and tobit regressions ((3)-(4); upper limit 46 and lower limit 0). Standard errors in 407 parentheses. p < 0.10, ** p < 0.05, *** p < 0.01. StrangerAdult, StrangerChild, OwnChild equals 1 for the respective treatments, and 408 0 for the baseline NoObserver treatment. Herbstmesse and Sillpark equals 1 for the respective location, and 0 otherwise. Age in 409 years. Female equals 1 for female decisions makers. The number of kids controls for the respective variable for each participant. 410 Risk measures self-assessed risk attitudes, with higher values indicating lower higher risk seeking. Patience measures self-assessed 411 time preferences with higher values indicating higher patience. 412
413
To investigate a potential mechanism that might explain this, we turn to the post-experiment 414
questionnaire. First, we asked how important it is for the participant to be a role model to their own 415
child(ren) and, second, how important it is for them to be a role model to other children (both on a 416
scale from 0 = not at all important to 10 = really important). While there were no significant 417
differences when comparing participants with and without high school diploma, directional effects 418
do emerge: parents with high school diploma rate being a role model to their own children (9.19) 419
higher than parents without high school diploma (8.70), whereas parents without high school 420
diploma rate being a role model to other children (9.45) higher than parents with high school 421
diploma (8.29). However, these differences are not large enough in our sample to suggest that 422
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20
parents with different education levels have distinctive preferences to be a role model to different 423
groups of children, although it offers an interesting perspective for future research. 424
5 Conclusion and discussion 425
Intergenerational public goods differ from standard public goods in that the beneficiaries (future 426
generations) are not the same as the decision-makers (current generation). Due to this unique 427
intergenerational structure of IPGs, we proposed that parents, who have a (genetic) link to the future 428
through their offspring, would be likely to invest into future generations through voluntary climate 429
actions. In our novel lab-in-the-field study, parents could choose between keeping money for 430
themselves or investing this money in purchasing trees which would be planted to offset CO2 431
emissions (with an expected lifetime of three human generations). We found a remarkable 432
willingness of parents to invest in the VCA: over 80% of all parents invested in the VCA, with 433
two-thirds of all participants investing their entire endowment into planting trees. This is far more 434
than the usual VCA contributions found in the literature: Bruns et al. (2018) report that participants 435
spent 35% of a default amount of money on a VCA, while Diederich and Goeschl (2014) find that 436
only 16% of subjects chose the emission reduction instead of a cash amount. 437
It is worth asking why participants were so willing to invest in the VCA opportunity we offered 438
to them. First, we designed the VCA opportunity with several features in mind that would make it 439
an attractive investment for participants. We built on the extension literature by Goeschl and 440
colleagues who have documented an array of appealing VCA properties, including that it meets 441
local mitigation goals (Torres et al., 2015; Baranzini, Borzykowski and Carattini, 2018), that it is 442
documented publicly that the VCA contributions have been used for the verified emission reduction 443
(Goeschl et al., 2020) and that it is explained in easy and clear language with examples (Löschel, 444
Sturm and Vogt, 2013). Individuals living in the area around Innsbruck (Austria) tend to spend 445
much time outdoors in the mountains and nearby forests, including the area where the trees in this 446
study would be planted, which would have likely increased the appeal of our VCA opportunity 447
further. An additional factor may have been the prominence and wide-spread news coverage of 448
“Fridays for Future”, an initiative spearheaded by teenagers across the world to draw attention to 449
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21
the need to reduce climate change. “Fridays for Future” was at its height during fall 2019 when 450
much of the study collection occurred and could have provided a boost to all climate-related studies 451
and initiatives. Nonetheless, the degree to which participants across all conditions were willing to 452
invest in the VCA was exceptional. In practical terms, the VCA from all our participants combined 453
translates into almost 14,000 trees being planted in the forests near Innsbruck, Austria. 454
We proposed that a VCA would be heightened when a parent is being observed by their own 455
offspring. The parent’s own child would serve multiple purposes, including a reminder to the fact 456
that a (genetic) link connects the parent (decision-maker) to their own child (future beneficiary). 457
Across our entire sample, we find some evidence for the hypothesis that parents give more when 458
their children are watching their VCA decision. Importantly, education plays a key role: as Diedrich 459
& Goeschl (2014) note, higher education increases the willingness to engage in a VCA and, in our 460
setting, our treatment effects are substantially larger in the subsample of participants with a high 461
school diploma. We find consistent evidence that parents with high school diploma are more likely 462
to invest in VCA when their own child is observing them, both relative to when no observer is 463
present as well as when another child (to whom they are not related) observes them. This latter 464
finding rules out the explanation that these parents engage in the VCA when a non-genetically 465
linked future beneficiary is present, providing some initial evidence that intergenerational 466
transmission of benefits is driven by parents’ realisation that a VCA today helps their own children 467
in the future. 468
Our study and its findings make several contributions to the existing literature. First, we 469
provide initial evidence that provision to IPGs can be increased by having parents be observed by 470
their own children when making VCA decisions. However, we also show that this is not true for 471
all parents equally: those with secondary education (see also Diederich & Goeschl, 2014) were 472
prompted to invest substantially more when their child was observing them, whereas parents 473
without secondary education showed no effect based on their own child’s presence. Second, we 474
show a remarkable amount of VCA in our setting, multiple times larger than previously 475
documented in the literature. The reasons for this may include the careful design of our VCA 476
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22
option, and the study materials more generally, based on prior literature (e.g., Diederich & Goeschl, 477
2014, Torres et al.) but may in part also be driven by the timeliness of our study alongside the 478
“Fridays for Future” campaign around the world. Third, we also contribute to the literature on 479
observability, showing that different types of observers play a role in affecting a VCA. Since a 480
VCA is a form of cooperation (in an intergenerational context, see Fischer et al. 2004 and Hauser 481
et al. 2014), we believe our findings offer promising avenues for researchers interested in testing 482
different observers for other cooperation scenarios. 483
Our findings offer practical implications for policy-makers and research questions for scholars 484
across a variety of domains. We focused on a VCA that involves planting trees. However, parents 485
make many important decisions in daily life that have consequences, if not always for future 486
generations, at least for years and decades to come that also shape the lives of the next generation. 487
Consider, for instance, voting: in many countries (including Austria), adults are not allowed to take 488
their children into the voting booth. Would parties (such as the Austrian Greens - a green political 489
party in Austria) that emphasize long-term investments in education and environmental protection 490
receive a greater voting share if parents had to choose under the watchful eyes of their own 491
children? While this is an open empirical question, one could imagine that voting systems may take 492
such considerations into account (e.g., see also proxy-voting on behalf of children, Kamijo et al. 493
2019). Even in more mundane activities, such as shopping for groceries (e.g., buying meat or 494
vegetarian alternatives), or choosing whether to take the bike to work or on the school run, 495
behaviour may be affected if the parent’s own children were present during the decision-making 496
process. 497
Of course, our study is not without limitations. Our results only speak to a certain segment of 498
society: adults with at least one child. We did not investigate how parents are different in their 499
VCA behaviour from adults who are not parents. We chose not to compare parents and non-parents 500
for several reasons. First, potential selection issues would complicate the interpretation of any 501
results: do non-parents choose not to have children that are related to intergenerational 502
considerations (e.g., environmental burden, overpopulation), or did they initially want to have 503
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23
children but were not able to have them for one reason or another? Second, there is no obvious 504
“kin” equivalent for non-parents who could act as the relevant observer: children are a parent’s 505
obvious connection to the future, whereas for non-parents other relatives (e.g., their own parents 506
or siblings) may not benefit from the IPG in the future. Other proxies (e.g., nephews or nieces) may 507
not be as close to the non-parent as a parent’s own child, and may thus resemble more a stranger 508
child. That said, to our knowledge, our paper is the first to investigate how the presence of a close 509
kin affects the parents’ economic decisions, relative to similar observers who differ in only one 510
other dimension (i.e. kin vs non-kin children). Future research should investigate to what extent 511
other observers who differ on theoretically relevant dimensions affect economic decision-making. 512
Another potential limitation is a ceiling effect of our results. By designing our VCA 513
opportunity with the most recent findings on VCAs design in mind, we may have ended up making 514
the VCA option “too attractive” to our participants across all our treatments. While other VCA 515
studies (and other donation-based studies with non-student samples, see Galizzi and Navarro-516
Martinez (2019) may, if anything, sometimes suffer from floor effects by not enticing sufficient 517
selfless behaviour, our design may have inadvertently led to a ceiling effect across conditions, 518
which may affect our ability to detect more subtle effects between conditions. Future experimental 519
designs could independently vary the attractiveness of VCA and the observer type to investigate 520
this further. 521
In conclusion, we hypothesized and demonstrated that different observers may differentially 522
affect parents’ costly investment into VCAs. We argue that, because of the intergenerational aspect 523
of VCAs, the parent’s own child appears to be a particularly effective observer to encourage 524
parental VCA behaviour, especially for parents with more education. As climate change continues 525
to accelerate, more research will be needed to understand how researchers and policy-makers can 526
encourage VCA – one pathway may be through the leverage and watchful eyes of children who 527
stand to gain from encouraging today’s investments into the future. 528
529
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24
Other information 530
Ethical Approval: This research has been approved by the ethics committee of the University of 531
Innsbruck (Certificate of good standing, 43/2019; July 29 2019). 532
Pre-Registration: The project has been pre-registered before data collection on aspredicted.org. 533
The pre-analysis plan is available on request. 534
Author Contributions: Both authors contributed equally to all aspects of the project including but 535
not limited to experimental design, project planning, implementation, manuscript writing, and data 536
analysis. 537
Competing Interests: The authors declare no competing interests. 538
539
540
Availability statements 541
Supplementary Information: Until the paper is published as a working paper or in a peer-reviewed 542
journal, the supplementary information is only available on request. 543
Data: The datasets generated and analysed during the current research will be made available 544
through the Open Science Framework after publication: 545
https://osf.io/2kdgz/?view_only=ae4b96704c8f41d8b66eebd1e5ce7bbf 546
Code: Custom code that supports the findings of the study will be made available through the 547
Open Science Framework after publication: 548
https://osf.io/2kdgz/?view_only=ae4b96704c8f41d8b66eebd1e5ce7bbf 549
550
551
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25
Acknowledgements 552
This research was funded by the Diligentia Foundation for Empirical Research. The funders have 553
no role in study design, data collection and analysis, decision to publish or preparation of the 554
manuscript. Additionally, we would like to thank for financial support by the SFB F63 "Credence 555
Goods, Incentives and Behavior". 556
We would like to thank Andreas Wildauer, head of the Forestry Office Innsbruck, for his 557
willingness and assistance in collaborating with us on this project. Moreover, we thank the City of 558
Innsbruck for supporting and implementing the foresting programme used in this study. We also 559
thank Loukas Balafoutas for his insightful feedback and Timo Goeschl for discussing the initial 560
idea with us. In addition, we are grateful for feedback from an anonymous referee solicited by the 561
Diligentia Foundation for Empirical Research when our grant application was reviewed and 562
approved. Thank you also to the reading group at the University of Innsbruck and the research 563
seminar at the University of Cologne for constructive discussions. 564
This project would not have been possible without the excellent assistance of our student 565
helpers and the confederates who spend many hours with us in the field. Moreover, we are grateful 566
to the research assistants who helped to develop the children entertainment program, build and run 567
the recruitment stand, and supported other aspects of the research program needed to successfully 568
run our experimental study. 569
570
571
572
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University of Innsbruck - Working Papers in Economics and StatisticsRecent Papers can be accessed on the following webpage:
https://www.uibk.ac.at/eeecon/wopec/
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2019-03 KatharinaMomsen,MarkusOhndorf:Whendopeople exploitmoralwiggle room?An experimental analysis in a market setup
2019-02 Rudolf Kerschbamer, Daniel Neururer, Matthias Sutter: Credence goods marketsand the informational value of new media: A natural field experiment
2019-01 Martin Geiger, Eric Mayer, Johann Scharler: Inequality and the Business Cycle: Evi-dence from U.S. survey data
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University of Innsbruck
Working Papers in Economics and Statistics
2020-23
Helena Fornwagner, Oliver P. Hauser
Do parents invest into voluntary climate action when their children are watching? Evi-dence from a lab-in-the-field experiment
AbstractWould parents do anything to enable a better future for their children - or only whenthey are seen to do so? Here we study voluntary climate action (VCA), which are costly totoday’s decision-makers but essential to enable sustainable living for future generations.We hypothesise that parents will be most likely to invest in VCAwhen their own offspringobserves their decision, whereas when adults or genetically unrelated children obser-ve them,