Method and Experimental Design

It is often difficult to identify causal relationships in observational data, especially if these are cross-sectional. Particularly in the environmental domain, survey data are also prone to all kinds of misreporting, such as with social desirability bias. Economic experiments or quasi-experimental data can help overcoming these challenges by observing people’s actions under actual incentives, and causality can then be established through the exogenous manipulation of factors of interest under controlled conditions (Parmeter and Pope 2013). It may be useful here to distinguish between four types of experiments in economics: (1) conventional lab, (2) artefactual field, (3) framed field, and (4) natural field (Harrison and List 2004). In natural field experiments, sometimes just termed ‘field experiments’, participant behaviour is observed in a context that is not artificially created or manipulated by the researcher. Typically, subjects also do not know that they are part of

an experimental study. When moving from the lab to the field or vice versa, researchers face trade-offs of control and external validity. Lab experiments allow for control of many factors that influence decision-making, whereas field experiments allow potentially greater external validity and obtaining of observational data in specific contexts. Under this typology, the experiment reported in this paper qualifies as a (natural) field experiment.

The experiment was run in September and October 2012 in Berlin. Sampling covered all twelve districts of the city, with the number of mailboxes roughly proportional to a district’s population size and, wherever possible, also proportional to size within sub-divisions (Stadtteile) of these districts. Data on population size were taken from official statistics (Amt für Statistik Berlin-Brandenburg 2012). It was not possible to obtain a full random sample of households or houses due to the unavailability of population data at this level. Within a district, a couple of arbitrarily selected streets were covered by the study.

Although this has resulted in some spatial clustering, we believe it is fair to say that the sample represents the population of Berlin relatively well. More importantly, using a between-subjects design, assignment to each treatment was randomized at the house level, thus ensuring an optimal experimental design.

As part of the study, households were supplied with a small sticker in coloured print that was 35 mm by 70 mm in size (Figure 5).

Figure 5: Sticker used in the study

Stickers did not display any brand or logo and avoided identification with any organization. However, they did have an environmental framing to trigger

pro-and reducing negative environmental impacts. To increase salience pro-and uptake, stickers were distributed in combination with small flyers stating “Attach the sticker – Save paper – Protect the environment” in German (Figure 6).

Figure 6: Flyer used in the study

These flyers, printed on black and white paper and 75 mm by 105 mm in size, also served as additional reminders, which have been found to be effective for achieving behavioural change in other studies (Cadena and Schoar 2011; Garner 2005; Karlan et al. 2010).

In the distribution phase, data were collected at the level of each house, including the total number of mailboxes and the number of mailboxes already equipped with a sticker. After twelve to sixteen days, houses were revisited to note down the main outcome variable: the number of households that had fully attached the sticker to their mailboxes. The study covered 125 houses with a total of 1,327 mailboxes. Out of these, 386, or 29.09 percent, were already equipped with a sticker. In total, then, 941 mailboxes did not have a sticker, and only these received one as part of our study in one of the treatments. In 57 houses the sticker was put into the mailbox (ACTIVE condition), while in 68 houses stickers were attached outside (FORCED condition).

5.4. Results

As a first step, it seems useful to compare the proportion of stickers in our sample with other samples. The 2012 figures from a large national survey on the adoption of “No junk

mail” stickers (IfD 2013) show that, at that time, from a sample of 26,063 people, 25.1 percent used a sticker. Although there is a statistically significant difference to our figure of 29.09 percent (Two-tailed two-sample test of proportions; z = -3.2602; p = 0.0011), it should also be pointed out that the same 2012 survey found 33 percent of respondents to use a sticker in cities with a population of more than 100,000 (Table 18). Taken together, it is fair to say that no substantial difference is to be found between the two figures and that the baseline adoption rate is similar to what others have found in survey-based research.

Looking at treatment effects and analyzing the data at the mailbox level reveals that 81 out of 507 (15.98 percent) of subjects attached the sticker in the ACTIVE condition, as compared to 94 out of 434 (21.66 percent) in the FORCED condition. The difference in proportions between treatments is statistically significant at the five percent level (One-tailed two-sample test of proportions; z = 2.2334; p = 0.0128), suggesting higher effectiveness of the FORCED treatment.

The following table displays selected variables of the collected data, disaggregated by neighbourhoods.

District Mailboxe

The columns on the relative frequencies of uptake refer to the number of mailboxes treated. It can be seen that uptake was particularly high in the inner city, headed by Friedrichshain-Kreuzberg, Germany’s stronghold of the Green Party²⁸, with about 35 percent of subjects attaching the stickers provided for them by the study. With the exception of Marzahn-Hellersdorf and Steglitz-Zehlendorf, at the end of the study more than 40 percent of sampled consumers were using a sticker in all districts. These figures indicate a substantial increase – in most cases of more than ten percent – in the use of stickers, compared to both our baseline of 29 percent or the large-scale survey baseline (IfD 2013) of 33 percent.

To better understand differences at the district level, Table 20 shows the lower and upper bounds of 95 percent confidence-level intervals (exact binomial distribution) of the proportion of stickers before and after treatment.

Table 20: Confidence intervals (95 percent) of sticker proportions before and after treatment by districts

District

Mitte .1375726 .3025688 .3087472 .5045677

Pankow .3103478 .4576965 .4855874 .6359152

Reinickendorf .2265634 .4542961 .3015212 .5388867

Spandau .1958658 .3899882 .31501 .5256721

Charlottenburg-Wilmersdorf .3768553 .5258808 .4359604 .5856539

Friedrichshain-Kreuzberg .0689121 .2273606 .3295502 .5530336

Schöneberg-Tempelhof .179633 .3408757 .3139321 .4942378

Marzahn-Hellersdorf 0 .052803 .0330725 .1822232

Neukölln .2268317 .3955711 .3237369 .5031819

28 In the 2013 general elections, Hans-Christian Ströbele, running in this district, received roughly 40 percent of the first vote, being Germany’s only candidate of the Green Party to gain a direct mandate for parliament.

In comparison, on average the Green Party received 8.4 percent of the second votes in Germany in the same elections.

Treptow-Köpenick .2990672 .4856416 .3830371 .5739031

Steglitz-Zehlendorf 0 .0445203 .1173315 .3008627

Lichtenberg .2740242 .4607895 .3248276 .5160541

Source: own calculations

Statistically significant differences at the 5 percent level between districts exist when there is no overlap between confidence intervals. It can be seen that, before treatment, especially Steglitz-Zehlendorf and Marzahn-Hellersdorf have substantially lower proportions of stickers that are also statistically significant from all other districts. Also Friedrichshain-Kreuzberg has a low ratio of stickers, which is statistically significantly different from proportions in Pankow, Charlottenburg-Wilmersdorf, and Treptow-Köpenick. Further, Charlottenburg-Wilmersdorf, the district with the highest proportion of mailboxes with stickers before treatment, is statistically different from Mitte, which has a comparatively low proportion.

After treatment, Steglitz-Zehlendorf and Marzahn-Hellersdorf are still statistically significantly different from all other districts and have a much lower proportion of stickers.

Yet the differences between all other districts have evened out following the treatment, with no further statistically significant differences remaining at the five percent level.

The data can also be analyzed at the house level, which could be important with respect to evaluating the treatment’s up-scaling potential, as landlords would appear to have an interest in reducing junk mail. In the following table, we present summary statistics and compare the proportion of stickers at the house level between treatments.

Table 21: Summary statistics of proportions at the house level by treatments

Variable N Median Mean SD Min Max

Proportion of stickers per house ACTIVE

68 .0732323 .1437085 .1784171 0 .6666667

Proportion of stickers per house 57 0 .2156595 .2930255 0 1

FORCED

Source: own calculations

Due to the high number of zeros in the sample – indicating houses where nobody attached a sticker – there is no statistical difference in medians between treatments (Rows 2 and 3 in Table 21; Wilcoxon–Mann–Whitney test; z = 0.595; p = 0.5517).

5.5. Discussion

First and foremost, the results demonstrate that people can be motivated to reduce paper waste from junk mail, flyers, and menus by a simple intervention – making stickers easily available. From the 1,327 mailboxes in our sample, 386 (29.09 percent) had a sticker attached before our intervention. After we distributed the stickers, this figure increased substantially by 175 to 561 stickers, meaning 42.28 percent of all mailboxes. This is equivalent to an absolute increase of 13.19 percent and a relative increase of 45.34 percent.

Clearly, this leads to acceptance of the first, more general, hypothesis H1.

The two treatments differed in terms of their uptake. With a difference in proportions of more than five percent – which is also statistically significant at the five percent level – we accept H2. In line with the literature on status quo bias, going with the forced choice is more effective as an intervention than simply distributing stickers for free. Our example of forcing consumers to decide either for or against a sticker is a relatively harmless choice that is easily reversible and reversible at low cost. Further, the consequences of the decision, at least on an individual level, are not very substantial. In our view, these criteria will also be useful when evaluating forced choice interventions in other contexts. If decisions can be made at low cost and they are easily reversible, making a deliberate choice by being forced to decide seems to be a preferable option, especially when the costs of the intervention are small (a simple sticker in our example) and the consequences of sticking with a default are large (30 kg of paper waste per household each year in our example).

Based on this estimate of 30 kg of junk mail per German household each year (WWF 2011), we calculate that about 5,250 kg – 175 new stickers x 30 kg – less junk mail will be distributed annually due to our intervention. Projecting these figures for all 1.9 million households in Berlin, reveals a large saving potential. Up-scaling the intervention for the

whole city, assuming that 13.19 percent, or roughly 250,000 households more, would adopt stickers, could reduce paper waste by up to 7,500 tons per year. This could also help consumers to save time and money on waste disposal. Handled properly, the advertisement industry could save costs on junk mail that is thrown away unread without having an effect on potential customers. In addition, society as a whole could enjoy environmental benefits, assuming that the production of ads is adjusted accordingly.

In this context, it is important to consider who could have an interest in applying the

“green nudge” presented in this paper. In Germany, landlords would perhaps have a great interest in preventing waste, with the aim of saving costs for cleaning up and disposal of discarded junk mail. In many instances, tenants receive “No junk mail” stickers along with their rental contracts. A key problem, however, is that the decision to attach the sticker can be postponed or the sticker can get lost somewhere among their files. It directly follows from our study that landlords should rather attach a sticker half-way to their new tenants’

mailboxes. They may even change the default status by attaching a sticker or using two-way signs. It would also be interesting to see how uptake of stickers would change if these were to be distributed under the landlord’s authority. Further, it would be important to look at variations of the interventions we have presented here. In particular, it could be interesting to develop an (experimental) test of interaction among neighbours. Clearly, in the way our intervention was designed, observations are not fully independent from each other. If many neighbours attach stickers to their mailboxes, this may motivate others to follow. Attaching stickers could then be perceived as a social norm. Ultimately, our design cannot control for such spillover effects and interactions among neighbours. With more resources available, one could treat only one mailbox per house to rule out such effects.