STAT 3300 Homework #6Due Thursday, 03282019Note Answe.docx
Recycling rate paper
1. Factors Impact Recycling Rates of Canada:
2002-2012
Supervisor: Dr. Robert Petrunia
By Tong Lu
From the
Master of Arts Program
Faculty of Economics
Lakehead University
November 2015
2. 1
1. Introduction
Recycling has been an ordinary human activity for a long time. Early in 1980s,
most medium and large municipalities in most provinces in Canada possessed garbage
recycling system. From the available data from Statistics Canada, the residential
recycling rate1
of Canada in 2002 is 33.03 percent. Ten years later, the number boost
to 48.66 percent. For the non-residential recycling rate2
, the number fluctuates
between 24.64 percent and 24.53 percent from 2002 to 2012. We can see there is a
great jump in residential recycling rate, but the non-residential recycling rate remains
steady through these years.
The purpose of this paper is to examine the change of residential and
non-residential recycling rate for most provinces in Canada from 2002 to 2012.
Meanwhile, multiple regression models are built to analyze which factors potentially
impact both recycling rates. Panel data set are collected from CANSIM table of
Statistics Canada every two years during this period. We also consider the impact of
the various characteristics from the provincial government and business sector of
waste management industry on recycling rates.
This paper is divided into 8 sections. The next section focuses on literature
review, which examines the former study in this area. This is followed by data and
descriptive variables. This part shows the source of data set. In the fourth section,
several reasons for choosing independent variables are listed, and the reasons why
these variables could influence recycling rate are also shown. Section 5 indicates the
change of recycling rates during 2002 to 2012 in two ways: across provinces and
through time. Section 6 mainly talks about how to determine the recycling rates, a few
regression models are stated in this section. What seventh section shows are the
results of regression models. After that we discuss the results of regression and then
suggest some recommendations. Lastly, the research demonstrates the effect of all
variables on recycling rates.
1
Residential recycling rate: The percentage of residential sources of diverted materials accounts for residential
sources of waste for disposal.
2
Non-residential recycling rate: The percentage of non-residential sources of diverted materials accounts for
non-residential sources of waste for disposal.
3. 2
2. Literature review: Factors affecting recycling rate in Canada
For the purpose of determining factors affecting the recycling rates, it is crucial
to present the previous research in this area. Adams, Hong and Love (1993) examine
whether disposal fees and other factors affect households’ recycling activities and the
associated demand for solid waste collection service. The data are composed of 2300
households in Portland, Oregon, metropolitan area. The paper also considers monthly
household income, number of people in household, and education level of Canadian
residents, dummy variable of white and non-white, home renter or home owner as
determinants of recycling rates. The analysis finds that income is significant to the
demand for garbage collection services. The number of people in a household
(household size) has positive relationship not only with the frequency of participation
in curbside recycling, but also the demand for garbage collection services.
By using the panel data from 1996-2004 of counties in Minnesota, Sidique, Joshi
and Lupi (2010) demonstrate a series of new factors influencing the residential
recycling rate. In their model, independent variables include percentage of population
with access to curb recycling, number of drop-off recycling centers, cumulative
recycling education expenditure, income (measured in dollars), median age (years),
population density (the ratio of population and territory) and education (the
percentage of population with four or more years of college education). Furthermore
the model includes two dummy variables, which are counties’ implements variable
rate pricing structure and the ordinance requires residences to recycle. They assume
that age positively influences recycling given the results from previous study (Vining
and Ebreo (1990), Meneses and Palacio (2005), Saphores et al. (2006)). The results of
the pooled OLS model indicate the curbside recycling service, the number of drop-off
centers and the number of a country ordinance can positively impact the recycling rate.
The median age (years) and higher education (the percentage of population with four
or more years of college education) shows a positive significant relationship with the
recycling rate. However, a rise in income marginally lowers the recycling rate. For the
random effects model, the age, income and education become insignificant. Only one
of the policy variables, the number of a country ordinance stays significant, but the
4. 3
rest like curbside recycling service and drop-off centers are not significant anymore in
this model. The dummy variable implementation of a variable pricing maintains a
significantly influence on recycling rate.
Another research about household recycling rates is Abbott, Nandeibam and
O'Shea (2010). This study is the first to explain the regional and intra-regional
variation in household recycling rates in UK. Unlike the other studies, they analyze
both dry recycling rates and composting rates. From their econometric result, the
frequency of the residual waste collection is negatively related to the recycling rate,
which means the lower frequency of the residual waste collection leads to higher
recycling rate. This outcome is corresponded to what Callan (2006) and Thomas
(2006) showed in the past. Moreover, income has weak explanatory power, which
means it is insignificant to recycling rate.The population in their model has a
positive and statistically significant relationship with the recycling rate.
Starr and Nicolson (2015) analyze panel data from 2000 to 2012 of the
Massachusetts Department of Environmental Protection (MassDEP). The aim of this
study is to examine the impact of various demographic, economic and recycling
services, such as income per capita, population density, unemployment, trash services,
Pay as-you-throw (PAYT) program and meteorological data on household recyling.
They model the recycling rate as a function of a variety of variables by two periods.
Period one is from 2000 to 2008 and period two is from 2009 to 2012. The
independent variable is residential recycling rate. The dependent variables are:
dummy variable-service (drop off, curbside, both, subscription); dummy variable-the
amount of payment as you throw (PAYT); dummy variable-mandatory; dummy
variable-single stream; region, household size, population density, unemployment rate,
income per capita, median age, education (percentage of population with a Bachelors
degree or higher), political affiliation (ratio of Democrats to Republicans). The paper
provides estimates from both fixed and random effects models. The result shows that
for the fixed effects, the model has the lowest explanatory power through two periods.
5. 4
Pay as you throw (PAYT)3
is the only significant policy variable in both periods.
Household size, population density and age are the only significant economic or
demographic factors over time. The other two significant policy variables are dummy
variables: single and subscription. For the random effects model, it has higher
explanatory than the fixed effects. PAYT (pay as you throw) and Curbside are the two
significant policy variables in both two periods. Education is the only significant
contextual variable over two periods. Age is significant in Period 1, but not in Period
2. In this case, PAYT (pay as you throw) is still the significant policy variable in both
periods. Education, age and household size become significant this time. As a
conclusion, PAYT (pay as you throw) consistently increasing recycling. The other
policy variables have confined influence. Region education and age are statistically
significant and positive contextual factors.
Similarly, there is a study of the relationship between municipal promotion and
education and recycling rate performance in Ontario, Canada (Calvin Lakhan, 2014).
the data for Ontario’s residential recycling system was obtained from the Waste
Diversion Ontario (WDO) municipal data call, similiar variables were selected to built
the model. In this case, recycling rate was determined as a function of per household
expenditures on promotion and education of recycling4
, waste management policy,
income and demographic variables. The results for this model differ from the previous
finding. There is no statistically significant relationship between municipal per
household promotion and education expenditure and recycling rate. Even though the
author changed the expenditure spending on promotion and education at different
funding level and municipal groups, the result remains the same. However, with this
unexpected outcome, other independent variables like PAYT, curbside recycling, age,
education and population density are still positively related to the recycling rate.
Another area focusing on the recycling is how the cost of recycling and recycling
rate are connected. Kinnaman, Shinkuma and Yamamoto (2014) analyze the socially
3 Pay as you throw (PAYT): The concept of PAYT can be explained as each household are requested to pay extra
fee for every unit of garbage which surpassing a pre-determined quantity decided by the community.
4
Per household promotion and education investments: The fund that is used to raise the residents’ awareness of
municipal recycling activities for each family.
6. 5
optimal recycling rate using the data for 84 municipalities in Japan from 2005 to 2010.
They estimated the social cost of waste management as a function of the recycling
rate. The social cost of waste management is consistent for all municipal costs and
revenues, cost to recycling households, external disposal costs and external benefits of
recycling. In their regression model, the recycling rate is statistically significant to the
average social cost. The conclusion shows that social cost is minimized at the
recycling rate of10 percent. The authors also show the recycling rates with respect to
various types of waste material, like paper, glass, plastic. By the similar method,
Calvin Lakhan (2015) reports the varying portion of materials allowed in Ontario’s
residential program (Blue Box), and how these materials impacting the recycling rate
and provincial material management costs.
An interesting study examines how the recycling rate of solid waste affects air
pollution. By using the data from a waste municipality survey in Massachusetts from
2009to 2012, Eleftherios Giovanis (2015) employ an econometric model to estimate
the relationship between emissions and recycling rate5
. It turns out that the
recycling rate is significant at 1 percent level and it has negative relationship with
.The result implies a positive relationship between recycling rates and air quality.
3. Data and Descriptive Variables
The data for this research are collected from CANSIM tables of Statistics Canada.
CANSIM is Statistics Canada’s core socioeconomic Informix. The Data Appendix
provides the list of variables and their sources. The data of the residential recycling
rate (RRR) is calculated as the ratio of residential sources of diverted (recycled)
materials in Tonnes to residential sources of waste for disposal in Tonnes. By the
same method, we can measure the non-residential recycling rate (NRRR) by dividing
non-residential sources of diverted materials in Tonnes by the non-residential sources
of waste for disposal in Tonnes (CANSIM table 153-0041 and CANSIM table
153-0042). We obtain the residential and non-residential recycling rate every two
years from 2002 to 2012. CANSIM table 3845000 indicates the data forprovincial
5
PM, known as particular matter, is microscopic solid or liquid matter suspended in the atmosphere.
means the diameter of the microscopic matter is equal to or less than 2.5 micrometers. It has a negative effect on
health, climate and vegetation.
7. 6
population. The data of residential sources of waste for disposal, non-residential
sources of waste for disposal, residential sources of diverted materials, non-residential
sources of diverted materials for most provinces are available. However, the data for
Newfoundland and Labrador and New Brunswick are incomplete. No data are
available for Prince Edward Island, Yukon, Northwest Territories and Nunavut.
Therefore, this paper focuses on the residential and non-residential recycling rate in
Alberta, British Columbia, Manitoba, Nova Scotia, Ontario, Quebec and
Saskatchewan.
The demographic data are also acquired from the CANSIM tables of Statistics
Canada. Population density is the ratio of a province’s population to its area
(CANSIM table 3845000). CANSIM table 0510001 contains the median age in each
province. The data from CANSIM table 2820209 and CANSIM table 3845000
provide education variables such as the percentage of people who achieve bachelor’s
degrees and higher degrees. Household size (persons in one family) and household
income per capita is collected from CANSIM table 1110011 and CANSIM table
3845000 respectively. The data of unemployment rates are from CANSIM table
2820002. CANSIM table 1530043 contains quantities of different recycling materials
such as glass, white goods and plastics. At the same time, total number of employees
from 2002 to 2010 (every two years) is available for both local government sector and
business sector. CANSIM table 1530044 and 1530045 indicates business sector
characteristics of the waste management industry and local government characteristics
of the waste management industry respectively.
4. Recycling Rates
According to the figures in the appendix, we can see how the most provincial
residential recycling rates and non-residential recycling rates changed from 2002 to
2012. We cannot provide the figures of recycling rates of New Brunswick,
Newfoundland and Labrador, Prince Edward Island, Yukon, Northwest Territories and
Nunavut due to the missing data.
4.1 Across Provinces
8. 7
For Alberta, both recycling rates fluctuated from 2002 to 2012. The highest
residential recycling rate was 40 percent in 2008, and the lowest, which was 30
percent, happened two years later. The non-residential recycling rate remained under
20 percent and the lowest point stayed at 10.68 percent in 2008. Meanwhile, the
non-residential recycling rate kept at most 17 percent lower than residential recycling
rate through the decade (2002-2012).
In British Columbia, the residential recycling rate sharply increased from 53.46
percent in 2002 to 64.42 percent in 2004. Then it kept stable around 65 percent from
2004 to 2008. In 2010 it went up to 70.89 percent and barely changed in 2012. The
non-residential recycling rate for BC started at 41.04 percent in 2002. It reached its
lowest point at 33.38 percent in 2004 and began to go up. From 2008 to 2012, it
leveled out at 50 percent.
The residential recycling rate of Manitoba had a sharp decrease from 2002 to
2004. Then it had a rapid upward trend to 16.51 percent in 2006. The rest six years it
gently grew from 17.59 percent to 20.89 percent. The non-residential recycling rate
also plummeted in the first two years, from 28.08 percent to 18.03 percent, and it kept
consistently falling down from 2004 to 2012. It ended at 14.51 percent. Both
recycling rates for Manitoba were lower than 30 percent during this period.
Nova Scotia has the highest average residential recycling rate and non-residential
recycling rate among the provinces. Moreover, both recycling rates for Nova Scotia
experience huge shifts at the same time. The residential recycling rate hovered above
70 percent and its peak point was over 100 percent in 2008. It had a high start at 72.33
percent in 2002 and dramatically rose to 82.86 percent two years later. Another steep
jump happened from 2006 to 2008, increased by almost 20 percent. In 2010, the
residential recycling rate decreased to 94.08 percent and rarely shifted in 2012. There
was a rise for non-residential recycling rate from 31.56 percent to 72.25 percent in the
first four years. However, after its peak point at 72.25 percent, it dropped to 54.32
percent in 2012. Still, Nova Scotia had the highest non-residential recycling rate in
Canada over the years.
The residential recycling rate of Ontario had a consistent rise from 2002 to
9. 8
2010. More specifically, the recycling rate stood at 29.92 percent in 2002. Then it
jumped to 39.56 percent in 2004, 57.24 percent in 2008, peaking at 62.47 percent in
2010. In the last two years, it marginally dropped to 59.20 percent. On the other hand,
the non-residential recycling rate slowly declined from 2002 to 2010, from 19.93
percent to 12.57 percent respectively. However, it has a modest increase in 2012, to
15.37 percent. The non-residential recycling rate for Ontario remained under 20
percent over this period.
For Quebec and Saskatchewan, marginal change happened to residential and
non-residential recycling rates. Both recycling rates stabilize from 30 percent to 40
percent for Quebec. The residential recycling rate stayed approximately 30 percent
from 2002 to 2006. It moderately grew to 36.72 percent in 2008, and kept increasing
to 43.99 percent in the end. For the non-residential recycling rate, it gradually rose
from 2002 to 2008, reached its peak point at 42.99 percent in 2008. It then hovered at
40 percent for the rest time.
Saskatchewan has the lowest residential and non-residential recycling rate across
these provinces. The residential recycling rate fluctuated between 13.03 percent and
21.28 percent during the period. For the non-residential recycling rate, it had a
moderate decrease during the first 6 years, reached it bottom at 11.62 percent in 2008.
Then it grew back to just over 13 percent between 2010 and 2012.
4.2 Across Years
In another way, Nova Scotia had the highest residential recycling rate in 2002,
which was 72.33 percent. British Columbia is the second highest and it overtook 50
percent. It followed by Alberta and Quebec, which are 36.99 percent and 31.72
percent respectively. The residential recycling rate for Ontario, Manitoba and
Saskatchewan were all lower than 30 percent. Also in 2002, the non-residential
recycling rate for British Columbia ranked the first place, exceeded 40 percent (41.04
percent) among these seven provinces. Nova Scotia came after BC, which had 31.56
percent and stayed the second place. Manitoba and Quebec were kind of close,
approximately higher than 28 percent. Saskatchewan had the lowest non-residential
recycling rate, which was 14.9 percent. Therefore, Nova Scotia had the highest
10. 9
residential recycling rate and the biggest non-residential recycling rate happened in
British Columbia. Saskatchewan had both the lowest residential and non-residential
recycling rate.
In 2004, residential recycling rate for Nova Scotia climbed to 82.86 percent,
remained the first place of ranking. British Columbia fell to the second place compare
to 2002. Alberta, Ontario and Quebec stabilized between 30 percent and 40 percent.
Instead of Saskatchewan, Manitoba had the lowest residential recycling rate in 2004.
For non-residential recycling rate, Nova Scotia still stayed the first place, which was
41.37 percent. It followed by British Columbia and Quebec, which both above 30
percent. The non-residential recycling rate for Alberta, Manitoba, Ontario and
Saskatchewan remained stable at below 20 percent.
Compare to 2004, several changes of ranking happened in 2006. Nova Scotia
ranked first both in residential and non-residential recycling rates as before. British
Columbia had the second highest residential rate and the third highest non-residential
recycling rate. Exceeding 40 percent, made Ontario had the third highest residential
recycling rate in these provinces. Both recycling rates of Manitoba and Saskatchewan
were lower than 20 percent in 2006. The lowest residential recycling rate still
belonged to Saskatchewan. However, Alberta became the province which had the
lowest non-residential recycling rate.
Similar to 2006, Nova Scotia had the highest residential and non-residential
recycling rate in 2008, which are 101.28 percent and 67.90 percent respectively.
British Columbia had the second highest recycling rate for both residential and
non-residential. Ontario and Quebec had some advantage in contrast to the rest
provinces. Manitoba became the province with the lowest recycling rate this time,
only fall behind 0.25 percent to Saskatchewan. Alberta continued to have the lowest
non-residential recycling rate, which was only 10.68 percent.
In the year of 2010, Nova Scotia and British Columbia remained the provinces
with the highest and the second highest residential and non-residential recycling rates.
Ontario increases its residential recycling rate to over 60 percent. However, its
non-residential recycling rate dropped to 12.57 percent, which made Ontario the
11. 10
province with the lowest non-residential recycling rate in 2008. Quebec stayed around
the average place, and both recycling rates are close to 40 percent. Manitoba and
Saskatchewan still had very low recycling rates, all of them kept under 20 percent
during this year.
In 2012, Nova Scotia still had the leading position in both recycling rates, with
93.17 percent of residential recycling rate and 54.32 percent of non-residential
recycling rate. Similar to 2010, British Columbia followed after Nova Scotia. The
residential recycling rate of Ontario reached almost 60 percent though its
non-residential recycling rate remained very low. Quebec had stable recycling rates
compare to former years, which was 43.99 percent for residential and 39.25 percent
for non-residential. The residential recycling rate of Saskatchewan finally exceeded
20 percent in 2012, which stayed at 21.28 percent. Manitoba had the lowest
residential recycling rate and the second lowest non-residential recycling rate this year.
Like 2004, 2006 and 2008, the lowest non-residential recycling rate belonged to
Alberta, which was only 12.45 percent.
According to the figures in appendix, we can see the huge jump in residential
and non-residential recycling rate of Nova Scotia, and in residential recycling rate of
Ontario as well. The reason for the jump for Nova Scotia is that its government is
committed to maintaining higher recycling rate and low waste quantity for a long time.
For instance, Halifax, the largest city in Nova Scotia, the residents there are asked to
classify their daily waste into four categories: organics green carts, recyclables,
garbageand household special waste (HSW). Each type has very strict rules. Moreover,
the government of Nova Scotia implements huge expenditures on waste recycling.
The population of Nova Scotia is less than 1 million in the past years, which remains
about 10 percent of Ontario, 27 percent of British Columbia and 14 percent of Quebec.
However, the operating revenue of recycling from Nova Scotia government is even
much higher than other big provinces. Therefore, the recycling rate for Nova Scotia
kept increasing from 2002 to 2012. For the residential recycling rate of Ontario, the
main reason is still about boosting operating revenue of recycling from local
government. In 2002, Ontario had $272 million in operating revenue. The number
12. 11
went up to $893 millon in 2012, which is more than three times than it was in 2002.
5. Factors affecting Recycling Rate
The purpose of this paper is to examine the determinants of residential and
non-residential recycling rate for several provinces from 2002 to 2012 in Canada. A
few factors should be considered based on the pioneered study.
1. Population density may affect the recycling rate. On one hand, area with high
population density usually means a larger amount of waste and smaller living
space, it increases the difficulty of separating the waste, so the recycling rate
will fall. On the other hand, high population density leads to more recycling
activities. We examine the result of the influence of high density and low
density to residential and non-residential recycling rate.
The population density between rural and urban should also be considered in
this paper. However, according to the data from Statistics Canada, the
percentage of population density between rural and urban in Canada barely
changed from 2002 to 2012. Therefore, we will not add these two variables in
our model.
2. Income is another factor which may influence the recycling rate. On one hand,
people with higher income have higher consumption than the people with
lower income, whichresults in more waste. In this case, higher income could
lower the recycling rate due to higher amount of garbage. On the other hand,
people with higher income generally have better education background, they
have a better understanding of the importance of recycling to the environment.
Residents with a better comprehension of recycling not only engage in more
recycling activities themselves, but also encourage others to do the same.
Hence, people with higher income could lead to boosting of recycling rate. So
it is hard to conclude if the income has a positive or negative relationship to
recycling rate.
3. Basically, age seems to be positively related to the recycling rate. The reason
is elder people (particularly retirees) have more spare time in their daily life,
the chance they spend their time on classify the garbage into different types is
13. 12
bigger than younger people. However, over time with requirement and
development, schools or communities could teach young people about
recycling. From this point, age can influence the recycling rate in an opposite
way.
4. As mentioned above, people with higher education usually are conscious of
the necessity of protecting the living circumstance, so they pay more attention
to recycling.
5. Household size: The size of a family may have a small positive effect on
household recycling because the larger households normally produce more
garbage.
6. Unemployment rate: The unemployment people may have extra time to spend
on life activities like recycling, whereas they do not intend to do so. Another
point is with high unemployment rate, people who lost their jobs cannot show
great purchase power. In this case, they could not produce too much waste
volume. Therefore it is hard to jump to the conclusion.
7. The last part of the independent variables is the expenditure and labour data
from the waste management industry. The waste management industry
involves both local government and private firms. Variables from the waste
management business sector include: number of business (nbb), total
employees (teb), full-time employees (feb), part-time employees (peb),
operating revenues (orb), operating expenditures (oeb), capital expenditures
(ceb). The variables from the waste management government sector include
total employees (teg), full-time employees (feg), part-time employees (peg),
operating revenues (org), all current expenditures (aceg), collection and
transportation current expenditures (ctceg), tipping fees current expenditures
(tceg), operation of disposal facilities current expenditures (odfceg), operation
of transfer stations current expenditures (otsceg), operation of recycling
facilities current expenditures (orfceg), operation of organics processing
facilities current expenditures (oopfceg), contributions to landfills post
closure and maintenance fund current expenditures (clmceg), other current
14. 13
expenditures (oceg) and capital expenditures (ceg). For the record, all these
variables are united in per captia. We will exhibit how financial and labor
factors affect recycling rate and what relationship they have.
Table A1 in the Data Appendix shows the source of data and the definitions of
the variables. Unfortunately, some of CANSIM data from Statistics Canada are
missing. Next, Table A2 indicates the number of missing data for each variable and
how much percentage these missing data account for.
Table A3 reports the descriptive statistics of all variables included in our model.
The mean residential recycling rate is 40.88 percent and it range from 7.69 percent to
101.28 percent. The mean of non-residential recycling rate is much lower than
residential, which is only 28.61 percent. Age, population density and household size
have the most observations compared to other variables. These three variables cover
all data for each province in Canada every two years from 2002 to 2012. The mean
population density is 7.99 persons per square kilometer. More specifically, Yukon,
Northwest Territories and Nunavut has the sparsest population density in 2002. The
largest population density appears in Prince Edward Island in 2012. The mean
percentage of population with a bachelor’s degree or higher is 13.24 percent, it ranges
from 8.1 percent to 20 percent. The minimum unemployment rate happened in Alberta
in 2006 and the maximum belongs to Newfoundland and Labrador in 2002.
6. Demographic Determinants of the Recycling Rate
The recycling rate regressions are estimated for residential and non-residential
respectively. The following analysis is divided into three parts. The first part is the
multiple regression models for residential recycling rate, the second part is the
multiple regression models for non-residential recycling rate. The third part focuses
on the results of the regressions.
6.1 Residential Recycling Rate
Model 1
Model 1 is the basic model. Only population density, household income, median
age, house size and unemployment rate are included as the independent variables.
15. 14
Model 2
In model 2, we add squared median age and squared average household income to
the regression.
6.2 Non-residential Recycling Rate
Model 3
The same independent variables as in Model 1, only we select non-residential
recycling rate as our dependent variable.
Model 4
Based on Model 2, we add squared median age and squared average household
income to particularly present how age and income impact non-residential recycling
rate.
6.3 Results of Regression
Since the recycling rate is estimated for resident and non-resident separately, the
difference between them should be clearly different. Followings are the regression
results from STATA.
In Table 1, the result of basic models (Model 1 and Model 3) and also basic models
with squared age and squared income (Model 2 and Model 4) is indicated. In the
models, we choose demographic variables, income, household size, unemployment
rate, squared age and squared income as independent variables. Table 2A and 2B
shows the regression results of residential and non-residential recycling rate to each
demographic variable respectively. The reason we run regressions to single variable is
to demonstrate whether the collinearity exists among the demographic variables or
16. 15
not.
Table 1: Demographic Characteristics
Residential Recycling Rate Non-residential Recycling Rate
Variables Model 1 Model 2 Model 3 Model 4
den (.005) ( ) (.003)
.003
(.003)
Inc -7.23e-06
(6.51e-06)
-6.99e-05
(4.54e-05) (3.92e-06) (2.4e-05)
7.82e-10
(5.97e-10) (3.16e-10)
age .007
(.025)
-.513
(.413)
.020
(.015) (.219)
.007
(.005) (.003)
edu (1.273) ( )
.068
(.764) (.806)
house -.483
(.503)
-.548
(.499) (.302) (.264)
unempl -1.662
(1.417)
-2.624
(1.597) (.851) (.845)
_cons 1.256
(2.444)
12.541
(8.692)
3.156
(1.467)
16.329
(4.598)
N 48 48 48 48
0.665 0.688 0.742 0.813
Note: Standard errors are in brackets. *, **, and *** indicate ten percent, five percent and one percent level of
significance respectively.
Table 2A
Variables Residential Recycling Rate
den .031***
(.005)
Inc 6.19e-06
(5.92e-06)
age .061***
(.014)
edu 4.912***
(1.039)
house -1.179***
(.286)
unempl 2.228
(1.490)
_cons .173
(.044)
.206
(.197)
-1.963
(.550)
-.266
(.146)
3.861
(.838)
.253
(.110)
N 48 48 48 48 48 48
0.489 0.023 0.289 0.327 0.270 0.046
Note: Standard errors are in brackets. *, **, and *** indicate ten percent, five percent and one percent level of
significance respectively
17. 16
Table 2B
Variables Non-Residential Recycling Rate
den .014***
(.004)
Inc -5.57e-06
(4.01e-06)
age .0598***
(.007)
edu .939
(.855)
house -1.153
(.154)
unempl 2.208
(.992)
_cons .177
(.037)
.469
(.134)
-2.047
(.283)
.157
(.120)
3.660
(.450)
.132
(.073)
N 48 48 48 48 48 48
0.222 0.04 0.597 0.026 0.551 0.097
Note: Standard errors are in brackets. *, **, and *** indicate ten percent, five percent and one percent level of
significance respectively
From Model 1, we can clearly see that age, population density and education
have positive relationship to residential recycling rate. Education has higher
explanatory power than the other two demographic variables. Household size, income
and unemployment rate have negative contributions to the residential recycling rate.
Unemployment rate has the biggest minus coefficient in Model 1. Based on the
coefficients of income, higher income marginally decreases both the recycling rates.
Only population density and education level are significant variables, both at 1
percent level of significance. In Model 3, four independent variables, population
density, household size, income and unemployment rate significantly affect
non-residential recycling rate. Population density has the positive coefficient while
the others have negative coefficients.
Model 2 and Model 4 are based on the basic models, adding squared age and
income to observe how age and income affect both recycling rate. The coefficient of
age becomes negative while the coefficient of squared age is positive. Meanwhile,
impact from income and squared income to residential recycling rate is reverse. None
of these four variables is significant in this model. In Model 4, the coefficient on age
and still have different signs. The same situation happens to income and
squared income. Unlike Model 2, age, squared age, income and squared income all
significantly affect the non-residential recycling rate. More specifically, the age keep
18. 17
decreasing the non-residential recycling rate till it hits 36.375. It represents people
who are older than 36.375 make contribution to non-residential recycling rate. About
income, the turning point is 45403.47 Canadian dollars. Residents who earn less than
45403.47 dollars lower the non-residential recycling rate, and people with income
which is higher than 45403.47 dollars increase the recycling rate.Comparing the
results of regressions from Table 2A and 2B, the coefficients of each variable do not
greatly change. Meanwhile, the standard errors to each variable do not greatly
increase. These resuls suggest that collinearity among demographic variables does not
occur.
Given these findings regarding age, , income and , I include
and as determinants of non-residential recycling rate, but not as
determinants of residential recycling rates in the following analysis.
7. Demographic Plus Business and Government Waste Management Sector
Spending Determinants of Recycling
7.1 Residential Recycling Rate
Model 5
Based on Model 1, an extra independent variable, all current expenditures Local
Government (aceg), is added into Model 5. The new variable is from government
sector.
Model 6
Compared to Model 1, Model 6 is the regression of demographic independent
variables and the variables from government sector. The new variables are collection
and transportation current expenditures (ctceg), tipping fees current expenditures
(tceg), operation of disposal facilities current expenditures (odfceg), operation of
transfer stations current expenditures (otsceg), operation of recycling facilities current
expenditures (orfceg), operation of organics processing facilities current expenditures
(oopfceg) and other current expenditures (oceg). Plus, the sum of these variables is
19. 18
approximately equals to all current expenditures Local Government. In order to avoid
multicollinearity, I put them in Model 5 and Model 6 separately. Since there is not
enough data, so I drop contributions to landfills post closure and maintenance fund,
current expenditures Local Government (clmceg).
Model 7
As we can see in the Model 7, demographic variables and several business sector
variables are included in the model. From the result of this model, we can clearly
conclude the influence of number of business (nbb), total employees (teb) and
operating expenditure (oeb) to the residential recycling rate.
Model 8
Model 8 is the regression of basic variables plus number of business (nbb),
full-time employees (feb), part-time employees (peb), operating expenditure (oeb),
capital expenditure (ceb) from business sector.
7.2 Non-residential recycling rate
Model 9
Based on Model 4, we add all current expenditure (aceg) from government sector
into the new model.
Model 10
Based on Model 4, we add collection and transportation current expenditures
20. 19
(ctceg), tipping fees current expenditures (tceg), operation of disposal facilities
current expenditures (odfceg), operation of transfer stations current expenditures
(otsceg), operation of recycling facilities current expenditures (orfceg), operation of
organics processing facilities current expenditures (oopfceg) and other current
expenditures (oceg) into Model 10. Since there is not enough data, so I drop
contributions to landfills post closure and maintenance fund, current expenditures
Local Government (clmceg).
Model 11
Based on Model 4, we add various business sector variables into Model 11, such as
number of business (nbb), total employees (teb) and operating expenditures (oeb).
Model 12
Based on Model 4, new independent variables from business sector are estimated in
Model 12. They are number of business (nbb), full-time employees (feb), part-time
employees (peb), operating expenditure (oeb) and capital expenditure (ceb).
7.3 Discussion of Results
Table 3 indicates the result of Models 5-8 investigating the residential recycling
rate.
21. 20
Table 3
Residential Recycling Rate
Variable Model 5 Model 6 Model 7 Model 8
den 7.9e-03
(.007)
0.02*
(.010)
.017***
(.006)
.011*
(.006)
inc -1.95e-05*
(9.18e-06)
6.4e-05
(3.93e-05)
4.73e-06
(1.12e-05)
-2.2e-05
(1.36e-05)
age -7.2e-05
(.0262)
.0718**
(.032)
.054*
(.029)
.010
(.031)
edu 2.49*
(1.434)
-9.722*
(5.276)
6.938***
(1.989)
10.463***
(2.736)
house 0.06
(.523)
1.021
(.620)
.636
(.582)
.320
(.586)
unempl -0.858
(1.367)
8.070*
(3.85)
-2.457
(1.409)
-4.194**
(1.821)
aceg .007***
(.002)
ctceg -0.015
(.011)
tceg .005
(.007)
odfceg .008*
(.004)
otsceg .021
(.012)
orfceg -0.007
(.012)
oopfceg -0.026
(.025)
oceg .013*
(.007)
nbb 6845.73***
(1961.954)
9180.432***
(2324.299)
teb 40.574
(146.774)
oeb -0.003
(0.002)
3.127e-04
(0.002)
feb -331.730
(287.519)
peb 1182.831
(883.862)
ceb -0.004
(.006)
_cons .067
(2.494)
-6.470
(3.302)
-4.714
(2.929)
-1.812
(3.057)
N 40 23 40 35
0.734 0.981 0.757 0.772
Note: Standard errors are in brackets. *, **, and *** indicate ten percent, five percent and one percent level of
significance respectively.
In Model 5, Income, age, and unemployment rate have negative coefficients
while population density, education level and household size have positive ones.
Education remains the variable which has the highest positive explanatory power. All
current expenditure from local government (aceg) affects the residential recycling rate
22. 21
in a slightly positive way. Moreover, income, education level and all current
expenditures from local government (aceg) are significant in this model.
The result of Model 6 indicates that education is the only variable with negative
coefficient among the basic variables. Unemployment rate has the largest positive
coefficient. It means with 1 percent of increasing unemployment rate, the residential
recycling rate improves 8.07 percent. In the financial variables from local government,
only three variables, collection and transportation, current expenditures local
government (ctceg) operation of recycling facilities current expenditures of local
governmment(orfceg) and operation of organics processing facilities current
expenditures of local government (oopfceg), impact residential recycling rate in a
negative way. What worth mentioning is, all the financial variables from local
government have low explanatory power, regardless of plus or minus. Age, population
density, education level, unemployment rate, other current expenditures local
government (oceg) and operation of disposal facilities, current expenditures local
government (odfceg) are significant in this case.
In Model 7, all the variables from basic model show the positive relationship
with residential recycling rate except unemployment rate. Only operating
expenditures from business sector (oeb) has positive coefficient while total employees
from business sector (teb) and the number of business from business sector (nbb)have
negative coefficient. The number of business (nbb) has the largest coefficient in the
business sector variables. In this model, population density and education level are
both significant at 1 percent the level of significance. Age is also significant, at 10
percent the level of significance.
According to the result of Model 8, unemployment rate and income are the
two variables with negative coefficients in the basic variables. Half of the business
sector variables have the positive relationship with residential recycling rate, like
number of business of business sector (nbb), part-time employees of business sector
(peb) and operating expenditures of business sector (oeb). In contrast, full-time
employees from business sector (feb) and capital expenditure from business sector
(ceb) have the negative coefficient. In this model, 50 percent of the demographic
23. 22
variables are significant. Education level is significant at 0.01-level. Unemployment
rate is also significant, but only at 5 percent level of significance. Population density
shows significance at 10 percent level of significance. None of the business sector
variable is significant in this model except the number of business from business
sector (nbb). More specifically, number of business from business sector (nbb) is
significant at 1 percent level of significance.
Table 4 demonstrates the OLS regression result for Models 9-12, which
examine non-residential recycling rates. Again, business sector characteristics and
local government characteristics of the waste management industry are added as
independent variables.
Table 4
Non-residential Recycling Rate
Variable Model 9 Model 10 Model 11 Model 12
den -0.003
(.003)
-0.005
(.012)
.002
(.003)
.002
(.003)
inc -5.06e-05**
(2.38e-05)
1.781e-04
(1.38e-04)
-6.6e-05*
(3.82e-05)
-1.389e-04***
(4.43e-05)
3.83e-10
(3.35e-10)
-3.82e-09
(2.26e-09)
6.66e-10
(5.16e-10)
1.51e-09**
(5.83e-10)
age -0.626***
(.218)
.405
(.921)
-0.969***
(.275)
-1.298***
(.365)
.008***
(.003)
-0.006
(.012)
.013***
(.004)
.017***
(.005)
edu .237
(.749)
3.908
(6.297)
1.484
(1.227)
3.444*
(1.746)
house -0.710***
(.210)
-2.354**
(,880)
-0.854***
(.311)
-1.252***
(.352)
unempl -3.235***
(.649)
-6.741
(4.294)
-3.961***
(.791)
-5.137***
(.999)
aceg .004***
(.001)
ctceg .018
(.013)
tceg -0.013
(.008)
odfceg 7.575e-04
(.004)
otsceg .018
(.014)
orfceg 8.1e-04
(.014)
oopfceg .037
(.040)
oceg .004
(.007)
nbb 945.192
(1219.686)
671.650
(1455.23)
teb -0.188
(86.753)
oeb 2.473e-04
(8.749e-04)
.001
(.001)
24. 23
feb -177.262
(152.288)
peb 290.940
(468.420)
ceb -0.004
(.004)
_cons 15.183
(4.522)
-2.602
(17.658)
22.155
(6.088)
31.371
(7.766)
N 40 23 40 35
0.919 0.968 0.884 0.895
Note: Standard errors are in brackets. *, **, and *** indicate ten percent, five percent and one percent level of
significance respectively.
In Model 9, age and squared age affects the non-residential recycling rate in an
opposite way. It means a rise in age will cause a drop in non-residential recycling rate
while an increasing squared age will increase the non-residential recycling rate. It
means increasing age decrease the non-residential recycling rate, but it will increase
the recycling rate when it is over 39.125. The result also show income and squared
income also impact recycling rate reversely, which means non-residential recycling
rate falls with higher income (before 66057.44 dollars), but it goes up with increasing
income (greater than 666057.44 dollars). Among the rest of demographic variables,
only education level has the positive coefficient while household size, population
density and unemployment rate have the negative ones. Population density and
education level are insignificant in this model and the rest independent variables all
significantly impact the recycling rate. One unit rising in all current expenditures
from local government (aceg) leads to an increase of non-residential recycling rate by
0.004%.
Model 10 demonstrates age and income remains positive to recycling rate, but
squared age and squared income mean an opposite way. Similar to Model 9,
population density and education level keep impacting non-residential recycling rate
reversely. Meanwhile, household size and unemployment rate has the negative
relationship with recycling rate. Age and squared age are no longer significant. The
turning point of income in this Model is 23311.52 dollars. The non-residential
recycling rate keep falling before the income of 23311.52 while it changes into rising
with income higher than 23311.52. In addition, household size shows significance at 5
25. 24
percent. None of the local government characteristics are significant in this model.
Model 11 indicates age, income, household size, unemployment rate and total
employees of business sector (teb) are the variables with minus coefficients.
Unemployment rate has the strongest negative effectiveness to non-residential
recycling rate. One percent rise in unemployment rate means 3.961 percent drop in
non-residential recycling rate. Meanwhile, age, squared age, income, household size
and unemployment rate are significant. None of the business sector is significant in
this model. In this model, age and squared age are significant while income and
squared income are not. Age has the negative relationship with recycling rate till it
reaches 37.26, after that their relationship becomes positive.
Model 12 illustrates the same results as Model11 with respect to age, squared age,
income and squared income. Population density and education level remains the
positive effectiveness to non-residential recycling rate. Household size and
unemployment rate each has the negative coefficient. Specifically, education level has
the strongest explanation power in demographic variables. In another word, 1 percent
increasing in education level generates 3.44 percent increasing in non-residential
recycling rate. Except population density, the rest of the demographic variables are all
significantly impact the non-residential recycling rate. An uptrend age will generate a
drop in non-residential recycling. However, after 38.18 years old, age starts to boost
the recycling rate. Same as age, inhabitant with lower income, which is less than
45993.38 dollars, will result in decreasing the recycling rate, whereas people with
income, which is higher than 45993.38 dollars, will be beneficial to recycling rate.
For business sector variables, same as Model 11, none of them shows significance.
8. Discussion and Recommendation
According to the results , we can see that for residential recycling rate,
population density and education level generally shows significance. It indicates
higher population density and education level comes with higher residential recycling
rate. All current expenditures from government is also significant. If the local
government increases expenditures into recycling, then the recycling rate rises. The
number of business per capita is the only significant business sector variable. An
26. 25
increasing number of business of waste management means more firms are engaged
in recycling rate, which can bring higher recycling rate. For non-residential recycling
rate, income, age, household size and unemployment rate are the significant in the
demographic variables. The household size is not relevant to non-residential recycling
rate.
After 12 OLS regressions, we can see that some problems emerge in the results.
Firstly, my database is not really big. Approximately, the number of observations is
less 70, which means my regression results are very sensitive. If there are more
sources of data, I could run the regression more accurately. In addition, policy is
essential to residential recycling rate. The reason Nova Scotia has high recycling rate
is based on the local policy. Therefore the best way to analyze the recycling rate is to
narrow the research to a municipality since different municipality has their own
garbage recycling policy. In that case, we can add the policy variables in the model.
Moreover, when the object of study is a city, then we have more policy details such as
the garbage recycling policy and the punishment of breaking policy. Further, we can
distinguish the role of government and business sectors separately on recycling rates.
9. Conclusion
This paper examines which factors can impact residential and non-residential
recycling rates across provinces in Canada. Income, household size, unemployment
rate, demographic variables, local government and business sector characteristics are
modeled in this study. After utilizing the OLS regression method, we can clearly see
how these variables affect recycling rates.
For residential recycling rate, population density and education level are
significant, and they have positive relationship with residential recycling rate. Higher
age, income and unemployment rate reduce the recycling rate, but they do not
significantly impact the residential recycling rate. The only business sector
characteristic of waste management industry is the number of business (nbb), which
means increasing number of business slightly increases the recycling rate. Three
characteristics from local government of waste management industry are significant,
27. 26
but all of them have limited effectiveness.
On non-residential recycling rate, population density is the significant variable
among demographic variables, which shows positive influence to recycling rate. Half
of the regression results indicates age is significant while the other half not. Increasing
household size, education, income and unemployment rate decrease the recycling rate,
and all of them significantly impact the recycling rate. Generally, age and income are
linearly corresponding to non-residential recycling rate and be significant in rising
recycling rate.
28. 27
References
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30. 29
Data Appendix
Table A1 Data Source
Variable Definition Data Source
rrr
Residential recycling rate
CANSIM table 1530041
CANSIM table 1530042
nrrr
Non-residential recycling rate
CANSIM table 1530041
CANSIM table 1530042
age Median age CANSIM table 0510001
den Population density per square
kilometer
CANSIM table 3845000
edu Percentage of population with
a bachelor degree or higher
CANSIM table 2820209
CANSIM table 3845000
house The number of people in one
family
CANSIM table 1110011
inc Household income per capita CANSIM table 3845000
unempl Unemployment rate CANSIM table 2820002
newsp Newsprint CANSIM table 1530043
cb Cardboard and boxboard CANSIM table 1530043
mp Mixed paper CANSIM table 1530043
allpf All paper fibres CANSIM table 1530043
gla Glass CANSIM table 1530043
fm Ferrous metals CANSIM table 1530043
ca Copper and aluminum CANSIM table 1530043
mm Mixed metals CANSIM table 1530043
wg White goods CANSIM table 1530043
elec Electronics CANSIM table 1530043
pla Plastics CANSIM table 1530043
tir Tires CANSIM table 1530043
crd Construction, renovation and
demolition
CANSIM table 1530043
org Organics CANSIM table 1530043
om Other materials CANSIM table 1530043
nbb Business Sector: Number of
businesses of the waste
management industry
CANSIM table 1530044
teb Business Sector: Total
employees of the waste
management industry
CANSIM table 1530044
feb Business Sector: Full-time
employees of the waste
management industry
CANSIM table 1530044
peb Business Sector: Part-time CANSIM table 1530044
31. 30
employees of the waste
management industry
orb
Business Sector: Operating
revenues of the waste
management industry
CANSIM table 1530044
oeb
Business Sector: Operating
expenditures of the waste
management industry
CANSIM table 1530044
ceb
Business Sector: Capital
expenditures of the waste
management industry
CANSIM table 1530044
teg
Local Government: Total
employees of the waste
management industry
CANSIM table 1530045
feg
Local Government: Full-time
employees of the waste
management industry
CANSIM table 1530045
peg
Local Government: Part-time
employees of the waste
management industry
CANSIM table 1530045
orlg
Local Government: Operating
revenues of the waste
management industry
CANSIM table 1530045
aceg
Local Government: All current
expenditures of the waste
management industry
CANSIM table 1530045
ctceg
Local Government: Collection
and transportation, current
expenditures of the waste
management industry
CANSIM table 1530045
tceg
Local Government: Tipping
fees, current expenditures of
the waste management
industry
CANSIM table 1530045
odfceg
Local Government: Operation
of disposal facilities, current
expenditures of the waste
management industry
CANSIM table 1530045
otsceg
Local Government: Operation
of transfer stations, current
expenditures of the waste
management industry
CANSIM table 1530045
orfceg
Local Government: Operation
of recycling facilities, current
CANSIM table 1530045
32. 31
expenditures of the waste
management industry
oopfceg
Local Government: Operation
of organics processing
facilities, current expenditures
of the waste management
industry
CANSIM table 1530045
clmceg
Local Government:
Contributions to landfills post
closure and maintenance fund,
current expenditures of the
waste management industry
CANSIM table 1530045
oceg
Local Government: Other
current expenditures of the
waste management industry
CANSIM table 1530045
ceg
Local Government: Capital
expenditures of the waste
management industry
CANSIM table 1530045
Table A2 Missing Ratio of Data
Name shorten name
Total
number
Missing
number
Missing
Ratio
All sources of waste for disposal tw 66 12 18.1818%
Residential sources of waste for disposal rwd 66 14 21.2121%
Non-residential sources of waste for
disposal
nrwd 66 14 21.2121%
Residential sources of diverted materials rdm 66 18 27.2727%
Non-residential sources of diverted
materials
nrdm 66 18 27.2727%
Residential Recycling rate rrr 66 18 27.2727%
Non-Residential Recycling rate nrrr 66 18 27.2727%
Median Age age 66 0 0.0000%
Population pop 66 0 0.0000%
Land of area den 66 0 0.0000%
Population density loa 66 0 0.0000%
Bachelor Degree or above bda 66 6 9.0909%
high education percent edu 66 6 9.0909%
Household size house 66 0 0.0000%
Income inc 66 0 0.0000%
Unemployment Rate unempl 66 6 9.0909%
Newsprint newsp 66 35 53.0303%
Cardboard and boxboard cb 66 35 53.0303%
33. 32
Mixed paper mp 66 37 56.0606%
All paper fibres allpf 66 50 75.7576%
Glass gla 66 34 51.5152%
Ferrous metals fm 66 25 37.8788%
Copper and aluminum ca 66 42 63.6364%
Mixed metals mm 66 27 40.9091%
White goods wg 66 35 53.0303%
Electronics elec 66 36 54.5455%
Plastics pla 66 19 28.7879%
Tires tir 66 35 53.0303%
Construction, renovation and demolition crd 66 19 28.7879%
Organics org 66 19 28.7879%
Other materials om 66 23 34.8485%
Number of businesses Business Sector nbb 66 11 16.6667%
Total employees Business Sector teb 66 11 16.6667%
Full-time employees Business Sector feb 66 15 22.7273%
Part-time employees Business Sector peb 66 15 22.7273%
Operating revenues Business Sector orb 66 11 16.6667%
Operating expenditures Business Sector oeb 66 11 16.6667%
Capital expenditures Business Sector ceb 66 22 33.3333%
Total employees Local Government teg 66 21 31.8182%
Full-time employees Local Government feg 66 22 33.3333%
Part-time employees Local Government peg 66 22 33.3333%
Operating revenues Local Government orlg 66 20 30.3030%
All current expenditures Local
Government
aceg 66 21 31.8182%
Collection and transportation, current
expenditures Local Government
ctceg 66 22 33.3333%
Tipping fees, current expenditures Local
Government
tceg 66 24 36.3636%
Operation of disposal facilities, current
expenditures Local Government
odfceg 66 24 36.3636%
Operation of transfer stations, current
expenditures Local Government
otsceg 66 29 43.9394%
Operation of recycling facilities, current
expenditures Local Government
orfceg 66 21 31.8182%
Operation of organics processing
facilities, current expenditures Local
Government
oopfceg 66 31 46.9697%
Contributions to landfills post closure and
maintenance fund, current expenditures
Local Government
clmceg 66 49 74.2424%
Other current expenditures Local oceg 66 32 48.4848%
