Empirical Tests of What Can Induce Households to Recycle

Household garbage causes environmental damage, and landfills run out of space, but recycling technology is improving for more materials to be sold by municipalities to help defray costs of waste disposal. Yet households do not directly benefit from recycling, so they often do not recycle. The goal of this research is to test whether there are cost-effective ways to reduce household garbage and increase their recycling. This pilot study will inform the design of a larger study. As part of this work, we are interested in testing approaches to data collection.

For this pilot study of 150 households in Rantoul, Illinois, starting with a 6-week baseline period, we will measure the weight of each 96-gallon garbage cart and recycling cart. We will open each cart – but not reach inside – to record the percent full, the number of non-recyclable items visible at the top of the recycling cart, and the number of recyclable items visible at the top of the garbage cart. Then we keep 50 households in a control group with no change and invite 50 households to earn $50 to join each of two treatment groups. One is a recycling “Information Treatment” group (where 50 households take two quizzes to ensure they read it). The other 50 are invited to a recycling “Incentive Treatment” group that includes the same information treatment plus financial incentives to recycle. The Incentive Treatment group will get the same $50 to take the same quizzes, and they will get paid $10 per proper recycling cart. Then we take quantity and quality measures again for all 150 households for another 8-week period. The changes in weight of garbage and recycling for each treatment group can be compared to the changes for the control group to estimate exactly the effects of each treatment.

We will not only record the visual measures for each cart but also send a picture of the top of each cart via the app provided by “Zabble Zero-Waste” (https://www.zabbleinc.com/). It employs artificial intelligence (A.I.) to observe and estimate the quantity (% full) and quality (number of wrong items visible) in each garbage and recycling cart.

After we have finished collecting these 14 weeks of measures for garbage and recycling quantity and quality, we will invite a randomly selected subset of individuals from each treatment group to participate in a focus group, so that we can ask for feedback about their experiences in the study.

In addition, we have purchased data from Data Axle that provides an estimate of the socioeconomic characteristics of each dwelling’s occupants. These data include basic demographic characteristics, such as the age of the head of the household, whether the head of household is married, a measure of the household’s income, political affiliation, and the number of children residing in the household.

2023-05-06

2023-07-08

The primary outcomes for this analysis are: 1) garbage weight, 2) recycling weight, 3) quality of garbage in the cart, and 4) quality of the recycling in the cart. We are interested in these outcomes as measured directly by researchers in the field and by the “Zabble Zero-Waste” app.

We will estimate the effects of the “Information Treatment” and the “Incentive Treatment” using a difference-in-differences design such as:

Y_it=β_0+β_1 T_it^1+β_2 T_it^2+δ_t+λ_i+ε_it

where Y_it is the outcome of interest for household i in time t. In this equation, T_it^1 and T_it^2 are indicators for household i being assigned to treatment 1 or 2, respectively, in a period t after the intervention begins. A time fixed effect (δ_t) for each week controls for waste changes attributable to common shocks such as holidays and other seasonal trends, and household fixed effects (λ_i) control for unobservable, time-invariant determinants of a household’s waste behavior.

To test if the treatment effects are statistically distinguishable from one another, we will perform an F-test of the hypothesis that T_it^1=T_it^2.

We will also test whether treatment households are more likely to ask for a recycling cart than control households during the study period—i.e. was there an increase in the number of households participating in recycling. To see which part of the total treatment effect is driven by the intensive versus extensive margin, we will add two interaction terms between each treatment indicator and an indicator for having no cart during the 6-week baseline period to our differences-in-differences model above. The coefficients on these terms will estimate the differential treatment effect for those that initially had no cart. That increase in recycling would be driven by the extensive margin.

In addition, we are interested in the variance of the directly collected measures of waste quality and quantity versus those collected by Zabble, and how well the two data collection methods correlate with one another. This analysis will allow us to make determinations about which measure would be higher quality and yield more statistical power for the full study.

Similarly, we will measure the variance of, and correlation between, the directly collected demographic data versus the data purchased from Data Axle, to determine if the Data Axle information is high enough quality to use in our full study.

The data from the focus group will be qualitative in nature and will inform how we design our informational and outreach materials for the full study.

We will test whether treatment effects are heterogeneous along the following dimensions, using data purchased from Data Axle:

a. political affiliation
b. age of head of household
c. educational attainment
d. household income
e. family size
f. race or ethnic group

We will also attempt to detect heterogeneous treatment effects across other dimensions by implementing the causal forests procedure described by Wagner and Athey (2018).

For this pilot study of 150 households, we will measure the weight of each garbage can and recycling can for a 6-week baseline period and rate each household’s recycling quality by recording what garbage items in the recycling cart (and recyclable items in the garbage cart). Then we keep 50 households in a control group with no change and invite 50 to join a recycling “Information Treatment” group (and to take two quizzes to ensure they read the information). The remaining 50 are invited to a recycling “Incentive Treatment” group that includes both information and financial incentives to recycle. The Incentive Treatment group will be invited to take the same quizzes, and to get paid for proper recycling. Then we take quantity and quality measures again for all 150 households for another 8-week period. The changes in garbage and recycling for each treatment group can be compared to the changes for the control group to estimate exactly the effects of each treatment.

Not available

Randomization done in office by a computer.

First, a random selection of 40 households from each of the five truck routes of 600. That 200 households allows for attrition down to 150. Second, the total of about 150 will be divided randomly into three groups of about 50. One group is control, another 50 in the first "information treatment" group, and the last 50 in the "information and incentive" treatment group.

No

One cluster of 150 households.

150 households for 14 weekly measures including garbage weight, volume, and wrong items, plus recycling weight, volume, and wrong items.

50 households in the control group, another 50 in the first "information treatment" group, and the last 50 in the "information and incentive" treatment group.

We plan to use this pilot study of 150 households in order to undertake power calculations to determine the sample size for our full study.