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/

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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: o.hauser@exeter.ac.uk). 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

194

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

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.

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

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)

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.

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

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

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

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

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

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

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

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

26

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724

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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,