October 2001 // Volume 39 // Number 5 // Research in Brief // 5RIB1

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An Examination of Rural Recycling Drop-Off Participation

Recycling education programs have constituted an important component of Extension outreach in the past decade. The study reported here used a two-stage statistical modeling procedure to predict the characteristics of residents who participate in a recycling program and to explain frequency of visiting the drop-off sites. The results reveal that older respondents in large households in more remote sections of the community are more likely to use the drop-off centers than others. Those participants who show more satisfaction with the convenience and quality of the drop-off sites visit more frequently. An unintentional consequence was a spirited competition among the seven townships involved to claim the highest participation rate.

Thomas W. Blaine
Extension District Specialist, Community Development
Ohio State University Extension, Northeast District
Wooster, Ohio
Internet Address: blaine.17@osu.edu

Kimberly D. Mascarella
Environmental Planner
Eastgate Regional Council of Governments
Youngstown, Ohio
Internet Address: kmascarella@eastgatecog.org

DeAnna N. Davis
Former Recycling Coordinator
Ohio State University Extension
Canfield, Ohio

Introduction and Purpose

Over the past decade, Extension agents and specialists across the U.S. have become increasingly involved with recycling education programs (Guy & Rogers, 1999; Owen, 1994; Hustedde, 1993; Hammer, 1990). This involvement has been the result of several factors. In Ohio, the passage of House Bill 592 in 1988 required communities to begin planning the management and reduction of solid waste generated locally. To comply with this legislation, communities began the creation of Solid Waste Management Districts (SWMD's), typically at the county level.

In Mahoning County, Ohio the SWMD, in cooperation with the County Commissioners, applied for and received a grant from the USDA Rural Development Administration for the purpose of developing, planning, and conducting a Rural Recycling Education & Awareness Program (RREAP). The program began in April of 1995 and was housed at the Ohio State University Extension Office in Mahoning County.

The mission of the RREAP is to provide the less densely populated rural communities in Mahoning County with an aggressive and comprehensive educational awareness program that will integrate the concepts of pollution prevention, recycling, and solid waste reduction into the daily activities of the rural population. Rural recycling is often a more difficult challenge to communities than urban or suburban recycling because in densely populated areas curbside pickup of waste is feasible, while in rural areas residents must take their materials to drop-off sites. The targeted area includes seven townships in the southern part of the county. These townships are 5-mile square geographic areas, with one drop-off site per township, usually located near the geographic center of the township.

One of the USDA's requirements for funding was that the RREAP conduct a survey of residents in order to identify characteristics of those who use the drop-off sites and what residents' views are concerning these sites. The specific objectives of the survey we conducted were to learn:

  1. Whether the respondents were using the drop-off sites;
  2. For those who did, their frequency of use;
  3. User ratings of the sites; and
  4. General household, occupation, age, gender, and location information, along with relationships among these variables and those listed in objectives 1-3.

Survey Method and Procedure

In order to obtain a random stratified sample of residents in the area, we obtained enlarged township maps from the County Engineer's Office. We required a total of approximately 350 responses in order to obtain a plus/minus 5 percent margin of error for the seven-township area (Krejcie & Morgan, 1970). Thus, we decided to survey 50 residents per township. Each township was divided into four quadrants, with approximately 13 residents surveyed per quadrant.

We adopted a door-to-door survey procedure, with a total of five volunteers undergoing training on survey practices, including appropriate one-on-one interview techniques. The volunteers were instructed on how to introduce themselves as well as the RREAP program and on the intent of the survey. General surveying etiquette and safety tips were included in the volunteer training sessions. Interviews lasted an average of about 15-20 minutes. At the conclusion of each interview, the volunteers gave respondents a magnet with the local township's recycling center's site location and hours of operation, along with an information booklet on recycling.

Respondents were asked whether they had ever used a local drop-off site and if they had, their frequency of use. Next they were asked to rank three characteristics of their local site on a 1-5 scale, with 1 indicating poor and 5 indicating excellent. These characteristics were: convenience of location, convenience of hours, and quality of assistance. Respondents were also asked about how they heard about their local site and what suggestions they had to increase participation. Finally, they were asked to provide some basic socio-demographic information including number of members in the household, age, and occupation.

Results and Discussion

User versus Non-user

A total of 52% of the respondents stated that they had used one of the drop-off facilities at least once. Of that group, the average number of visits was 22 times per year, or slightly less often than every other week. Of those who had never used a center, only 33% knew of the location of the center in their township. Participation rates varied significantly by township, ranging from a low of 34% to a high of 72%. Our first research goal was to determine whether we could identify characteristics of users versus non-users of the drop-off sites.

A logistic (logit) regression is a procedure that estimates a dichotomous variable (such as user or non-user) as a function of a set of independent variables (McFadden, 1973). This procedure is not new to Extension educators (Israel & Ingram, 1991). The result yields an equation that is capable of predicting whether someone with a given set of characteristics is more or less likely than someone else to use or not use a facility. We coded non-users as a zero and users as a one.

We expected that five components of the survey might play a role in a household's decision to use or not use the drop-off facility. These were:

  • Number of people in the household,
  • Age of the head of the household,
  • Location of residence (rural or rural residential),
  • Type of residence (single versus multiple family housing), and
  • Distance to the nearest recycling drop-off site.

Table 1 (A) shows the results of the logit regression that specifies use as a function of the five variables stated above. The results demonstrate that three of the variables are significantly associated with use. Larger households with an older head of household located in more rural settings are more likely to have used one of the facilities (p<.10). Type of dwelling and distance to the nearest site played no significant role. These two variables were discarded from the equation, which was re-estimated. The results of the second estimation are listed in Table 1 (B). Note that the parameter estimates and the standard errors are not sensitive to the different estimation.

Table 1
Results of Logit Regression (Dependent Variable: User/Non-User)

Table Two: Predictions of who uses and doesnÄt use drop-off facilities

One of the advantages of using a logistic procedure is that, based on the characteristics of the members of the sample, we can use the equation to predict who uses the facility and who does not, and then compare our predicted outcomes to the actual use patterns of the members of the sample. Table 2 shows the results of this comparison.

Table 2
Users and Non-Users: Predicted Versus Observed

 Success Rate

This table reveals that our equation predicts that, of the 144 households who use the facilities, 99 would be predicted to do so on the basis of their characteristics. This yields a predictive success rate of 69%. The success rate in predicting non-users is smaller (60%), indicating that we know less about why respondents do not participate than why they do participate. The overall success rate in predicting use is 65%, somewhat typical to high for this type of analysis.

We most likely could have produced a higher prediction success rate had we included demographic variables such as income and education. But we excluded these from the survey because they tend to reduce response rate, and we did not believe that this information would be of specific use in tailoring Extension programs on recycling education.

Frequency of Use

Next we set out to identify and measure the effects of selected variables upon frequency of visitation to a drop-off site. In this analysis we used frequency of visits as a dependent variable in an ordinary least squares (OLS) regression equation. In addition to the three independent variables that were found to be statistically significant in the logistic regression equation, we added a variable that combined the ratings of the three categories upon which the respondents were asked to evaluate the sites. These categories were: convenience of location, convenience of hours of operation, and quality of assistance. These characteristics were only evaluated by users and thus could not be included in the logit model. The combined variable was calculated as the mean of the three ratings and is referred to in Table 3 as Convenience/Quality.

Table 3
Respondents' Opinions on Convenience/Quality

Table Three: Percentage Ratings of Convenience and Quality on a 5-point scale

This table demonstrates that the overwhelming majority of users are highly satisfied with the convenience/quality of the recycling centers. A total of 92% ranked this variable between good and excellent.

Table 4 (A) includes the results of the initial OLS estimation. Two of the five independent variables were found to be statistically significant (p<.10). The other three were discarded from the equation, which was re-estimated. The results of this estimation are presented in Table 4 (B).

Table 4
Results of Regression on Frequency of Use (Dependent Variable: Frequency of Use)

Table Four: Parameter estimates for factors influencing Frequency of Use

People who ranked Convenience/Quality higher visit more often than others. For every one point increase on the 1-5 scale in Convenience/Quality, respondents average visiting nearly six more times per year. Thus, a person who ranked Convenience/Quality at a 1 (poor) would average one visit per month, while someone ranking it a 5 (excellent) would average three visits per month.

The age variable was as important in determining frequency of use as it was in determining participation in the previous model. The regression parameter in this result indicates that for every 10 years increase in the age of the head of household, respondents average visiting roughly two more times per year. Thus a person in his/her seventies would tend to make an average of 10 more visits per year than someone in his/her twenties. Recall that participation rates were higher for older respondents as well.


This study revealed several interesting phenomena. Older respondents are more likely to have visited one of the centers and visit more frequently than younger users. People in larger households in rural settings are more likely to have visited, but they do not visit more frequently than others. Those who are more satisfied with the location, hours, and quality of assistance associated with their local sites visit more frequently.

As a result of this study, Extension began an effort to ensure that more residents would be aware of the location of the drop-off site in their township. A modified edition of RREAP's newsletter was distributed to 17,000 households through a local circular delivery service in the target area. This special issue featured a highlight of the locations of drop-off sites and pointed out benefits to the community associated with recycling.

Based upon the profiles of users/non-users explained above, we assumed that families with young children represented a substantial portion of the population not using the drop-off sites. Working on this assumption, RREAP initiated a program to acquaint primary school students with the recycling sites. Each child received an information packet to take home. The packet contained local drop-off site locations, operating hours, and a list of items that were and were not accepted for recycling.

Perhaps the most significant impact on the community was the prompting of a local controversy, something that had been totally unexpected when RREAP began the project. When the survey results were released, the local media published the results. They tended to focus on participation rates by township.

Some of the township recycling coordinators working in townships with reported low participation rates began to debate the issue vigorously, citing statistics on volume of recycled waste as refuting the survey results showing they had low participation rates. The survey had not attempted to measure volume, and in any case, although the sample size was large enough to generalize about the seven-township area, it was not large enough to distinguish participation rates by township. The sample size would have to have been roughly four times what it was (n=1440) to have accomplished this.

The positive outcome of this event was that it spirited a lively competition among townships to increase participation for "bragging rights." To the extent that this occurred, the survey was a tremendous success. Future survey work may be able to quantify the magnitude of this impact.


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This article is online at http://joe.org/joe/2001october/rb1.html.