### What kind of polyethylene material can be recycled?
Polyethylene (PE) or polyethylene is the most common plastic. The annual global production is around 80 million tons. Its primary use is in packaging (plastic bags, plastic films, geomembranes, containers including bottles, etc.).
Expanded polyethylene (EPE) can often be made from polyethylene. EPE is a closed-cell, non-crosslinked polyethylene foam composed of LDPE (low-density polyethylene) resins. Due to its excellent thermal insulating qualities and water resistance, EPE foam finds extensive use in packaging. It is produced via an extrusion technique and is often offered in a variety of shapes and sizes, including sheets, rolls, pipes, rods, L, C, and U sections, each of which has a specific set of applications. The most common forms of utilization are rolls and sheets.
EPE is a recyclable material, and many companies today can offer Polyethylene foam recycling solutions. The method of EPE recycling is relatively simple. In fact, hot melting is the easiest first step to recycle EPE. The recycled EPE can be used to granulate PE pellets.
The hot melt machine can be used to achieve melted EPE, and GREENMAX from INTCO, which has a decent reputation, currently offers the hot melt machine at a reasonable cost. A device that is capable of processing a variety of foam materials is the GRRENMAX foam densifier. The EPE material can be perfectly melted by it. Its technology of screw crushing can break the EPE material into fragments. Its hot melt technology can then be used to melt these EPE bits into ingots.
This melting approach has several advantages, such as reducing the volume, easy to transport, reduce transportation costs and so on. And the configuration and service of GREENMAX foam densifier is very good. When the foam densifier is shipped to the guests, there will be a dedicated engineer alongside to help text the operation. As long as the operation is proper, the hot melted EPE ingots will be of good quality.
If you also want to participate in the recycling of this recyclable EPE, GREENMAX foam densifier will be a very good choice.


Osmosis (, US also ) is the spontaneous net movement or diffusion of solvent molecules through a selectively-permeable membrane from a region of high water potential (region of lower solute concentration) to a region of low water potential (region of higher solute concentration), in the direction that tends to equalize the solute concentrations on the two sides. It may also be used to describe a physical process in which any solvent moves across a selectively permeable membrane (permeable to the solvent, but not the solute) separating two solutions of different concentrations. Osmosis can be made to do work. Osmotic pressure is defined as the external pressure required to prevent net movement of solvent across the membrane. Osmotic pressure is a colligative property, meaning that the osmotic pressure depends on the molar concentration of the solute but not on its identity.
Osmosis is a vital process in biological systems, as biological membranes are semipermeable. In general, these membranes are impermeable to large and polar molecules, such as ions, proteins, and polysaccharides, while being permeable to non-polar or hydrophobic molecules like lipids as well as to small molecules like oxygen, carbon dioxide, nitrogen, and nitric oxide. Permeability depends on solubility, charge, or chemistry, as well as solute size. Water molecules travel through the plasma membrane, tonoplast membrane (vacuole) or organelle membranes by diffusing across the phospholipid bilayer via aquaporins (small transmembrane proteins similar to those responsible for facilitated diffusion and ion channels). Osmosis provides the primary means by which water is transported into and out of cells. The turgor pressure of a cell is largely maintained by osmosis across the cell membrane between the cell interior and its relatively hypotonic environment.


Solar-cell efficiency is the portion of energy in the form of sunlight that can be converted via photovoltaics into electricity by the solar cell.
The efficiency of the solar cells used in a photovoltaic system, in combination with latitude and climate, determines the annual energy output of the system. For example, a solar panel with 20% efficiency and an area of 1 m2 produces 200 kWh/yr at Standard Test Conditions if exposed to the Standard Test Condition solar irradiance value of 1000 W/m2 for 2.74 hours a day. Usually solar panels are exposed to sunlight for longer than this in a given day, but the solar irradiance is less than 1000 W/m2 for most of the day. A solar panel can produce more when the Sun is high in Earth's sky and produces less in cloudy conditions, or when the Sun is low in the sky. The Sun is lower in the sky in the winter.
Two location dependent factors that affect solar PV yield are the dispersion and intensity of solar radiation. These two variables can vary greatly between each country. The global regions that have high radiation levels throughout the year are the middle east, Northern Chile, Australia, China, and Southwestern USA. In a high-yield solar area like central Colorado, which receives annual insolation of 2000 kWh/m2/year, a panel can be expected to produce 400 kWh of energy per year. However, in Michigan, which receives only 1400 kWh/m2/year, annual energy yield drops to 280 kWh for the same panel. At more northerly European latitudes, yields are significantly lower: 175 kWh annual energy yield in southern England under the same conditions.


Several factors affect a cell's conversion efficiency, including its reflectance, thermodynamic efficiency, charge carrier separation efficiency, charge carrier collection efficiency and conduction efficiency values. Because these parameters can be difficult to measure directly, other parameters are measured instead, including quantum efficiency, open-circuit voltage (VOC) ratio, and § Fill factor. Reflectance losses are accounted for by the quantum efficiency value, as they affect external quantum efficiency. Recombination losses are accounted for by the quantum efficiency, VOC ratio, and fill factor values. Resistive losses are predominantly accounted for by the fill factor value, but also contribute to the quantum efficiency and VOC ratio values.
As of 2024, the world record for solar cell efficiency is 47.6%, set in May 2022 by Fraunhofer ISE, with a III-V four-junction concentrating photovoltaic (CPV) cell. This beat the previous record of 47.1%, set in 2019 by multi-junction concentrator solar cells developed at National Renewable Energy Laboratory (NREL), Golden, Colorado, USA, which was set in lab conditions, under extremely concentrated light. The record in real-world conditions is held by NREL, who developed triple junction cells with a tested efficiency of 39.5%.

## Factors affecting energy conversion efficiency
The factors affecting energy conversion efficiency were expounded in a landmark paper by William Shockley and Hans Queisser in 1961. See Shockley--Queisser limit for more detail.


## Factors affecting energy conversion efficiency
### Quantum efficiency
When a photon is absorbed by a solar cell it can produce an electron-hole pair. One of the carriers may reach the p--n junction and contribute to the current produced by the solar cell; such a carrier is said to be collected. Or, the carriers recombine with no net contribution to cell current.
Quantum efficiency refers to the percentage of photons that are converted to electric current (i.e., collected carriers) when the cell is operated under short circuit conditions. The two types of quantum that are usually referred to when talking about solar cells are external and internal. External quantum efficiency (EQE) relates to the measurable properties of the solar cell. The "external" quantum efficiency of a silicon solar cell includes the effect of optical losses such as transmission and reflection. Measures can be taken to reduce these losses. The reflection losses, which can account for up to 10% of the total incident energy, can be dramatically decreased using a technique called texturization, a light trapping method that modifies the average light path.
The internal quantum efficiency (IQE) gives insight into the internal material parameters like the absorption coefficient or internal luminescence quantum efficiency. IQE is mainly used to aid the understanding of the potential of a certain material rather than a device.
Quantum efficiency is most usefully expressed as a spectral measurement (that is, as a function of photon wavelength or energy). Since some wavelengths are absorbed more effectively than others, spectral measurements of quantum efficiency can yield valuable information about the quality of the semiconductor bulk and surfaces.
Quantum efficiency is not the same as overall energy conversion efficiency, as it does not convey information about the fraction of power that is converted by the solar cell.

### Maximum power point
A solar cell may operate over a wide range of voltages (V) and currents (I). By increasing the resistive load on an irradiated cell continuously from zero (a short circuit) to a very high value (an open circuit) one can determine the maximum power point, the point that maximizes V×I; that is, the load for which the cell can deliver maximum electrical power at that level of irradiation. (The output power is zero in both the short circuit and open circuit extremes).
The maximum power point of a solar cell is affected by its temperature. Knowing the technical data of certain solar cell, its power output at a certain temperature can be obtained by


Reflex syncope is a brief loss of consciousness due to a neurologically induced drop in blood pressure and/or a decrease in heart rate. Before an affected person passes out, there may be sweating, a decreased ability to see, or ringing in the ears. Occasionally, the person may twitch while unconscious. Complications of reflex syncope include injury due to a fall.
Reflex syncope is divided into three types: vasovagal, situational, and carotid sinus. Vasovagal syncope is typically triggered by seeing blood, pain, emotional stress, or prolonged standing. Situational syncope is often triggered by urination, swallowing, or coughing. Carotid sinus syncope is due to pressure on the carotid sinus in the neck. The underlying mechanism involves the nervous system slowing the heart rate and dilating blood vessels, resulting in low blood pressure and thus not enough blood flow to the brain. Diagnosis is based on the symptoms after ruling out other possible causes.
Recovery from a reflex syncope episode happens without specific treatment. Prevention of episodes involves avoiding a person's triggers. Drinking sufficient fluids, salt, and exercise may also be useful. If this is insufficient for treating vasovagal syncope, medications such as midodrine or fludrocortisone may be tried. Occasionally, an artificial cardiac pacemaker may be used as treatment. Reflex syncope affects at least 1 in 1,000 people per year. It is the most common type of syncope, making up more than 50% of all cases.


## Signs and symptoms
Episodes of vasovagal syncope are typically recurrent and usually occur when the predisposed person is exposed to a specific trigger. Before losing consciousness, the individual frequently experiences early signs or symptoms such as lightheadedness, nausea, the feeling of being extremely hot or cold (accompanied by sweating), ringing in the ears, an uncomfortable feeling in the heart, fuzzy thoughts, confusion, a slight inability to speak or form words (sometimes combined with mild stuttering), weakness and visual disturbances such as lights seeming too bright, fuzzy or tunnel vision, black cloud-like spots in vision, and a feeling of nervousness can occur as well. The symptoms may become more intense over several seconds to several minutes before the loss of consciousness (if it is lost). Onset usually occurs when a person is sitting up or standing.
When people lose consciousness, they fall down (unless prevented from doing so) and, when in this position, effective blood flow to the brain is immediately restored, allowing the person to regain consciousness. If the person does not fall into a fully flat, supine position, and the head remains elevated above the trunk, a state similar to a seizure may result from the blood's inability to return quickly to the brain, and the neurons in the body will fire off and generally cause muscles to twitch very slightly but mostly remain very tense.
The autonomic nervous system's physiological state (see below) leading to loss of consciousness may persist for several minutes, so
If patients try to sit or stand when they wake up, they may pass out again
The person may be nauseated, pale, and sweaty for several minutes or hours

## Causes
Reflex syncope occurs in response to a trigger due to dysfunction of the heart rate and blood pressure regulating mechanism. When heart rate slows or blood pressure drops, the resulting lack of blood to the brain causes fainting.

### Vasovagal
Typical triggers include:
Prolonged standing
Emotional stress
Pain
The sight of blood
Fear of needles
Time varying magnetic field (i.e. transcranial magnetic stimulation)

### Situational
After or during urination (micturition syncope)
Straining, such as to have a bowel movement
Coughing
Swallowing
Lifting a heavy weight

### Carotid sinus
Pressing upon a certain spot in the neck. This may happen when wearing a tight collar, shaving, or turning the head.


## Logging
The datatype is the transferred datatype, it will be converted from internal type to transferred type before transfers:
- float
- uint8_t and int8_t
- uint16_t and int16_t
- uint32_t and int32_t
- FP16: 16bit version of floating point, allows to pack more variables in one packet at the expense of precision.
The logging cannot be started until your are connected to a Crazyflie:
              # Callback called when the connection is established to the Crazyflie
        def connected(link_uri):
            crazyflie.log.add_config(logconf)

            if logconf.valid:
                logconf.data_received_cb.add_callback(data_received_callback)
                logconf.error_cb.add_callback(logging_error)
                logconf.start()
            else:
                print "One or more of the variables in the configuration was not found in log TOC. No logging will be possible."

        def data_received_callback(timestamp, data, logconf):
            print "[%d][%s]: %s" % (timestamp, logconf.name, data)

        def logging_error(logconf, msg):
            print "Error when logging %s" % logconf.name
The values of log variables are transferred from the Crazyflie using CRTP packets, where all variables belonging to one logging configuration are transferred in the same packet. A CRTP packet has a maximum data size of 30 bytes, which sets an upper limit to the number of variables that can be used in one logging configuration. If the desired log variables do not fit in one logging configuration, a second configuration may be added.
              crazyflie.log.add_config([logconf1, logconfig2])

## Synchronous API
The synchronous classes are wrappers around the asynchronous API, where the asynchronous calls/callbacks are replaced with blocking calls. The synchronous API does not provide the full flexibility of the asynchronous API, but is useful when writing small scripts, for logging for instance.
The synchronous API uses the python Context manager concept, that is the
    with
keyword.
A resource is allocated when entering a
    with


Documentation Version
You're reading the documentation for a stable version of MoveIt that is not being developed further. For information on the recommended version, please have a look at Main.

# MoveIt Setup Assistant
## Overview
The MoveIt Setup Assistant is a graphical user interface for configuring any robot for use with MoveIt. Its primary function is generating a Semantic Robot Description Format (SRDF) file for your robot. Additionally, it generates other necessary configuration files for use with the MoveIt pipeline. To learn more about the SRDF, you can go through the URDF/SRDF Overview page.

## Getting Started
MoveIt and ROS
- Follow the instructions for installing MoveIt first if you have not already done that.

- If you haven't already done so, make sure you have the Franka description package for Noetic:

  > sudo apt install ros-humble-franka-description

- If you have the

      panda_moveit_config

  package already git-cloned from the Getting Started page, be sure to delete that now since this tutorial will teach you how to create it from scratch:

  > cd \~/ws_moveit/src rm -rf panda_moveit_config catkin clean panda_moveit_config

## Step 1: Start
- To start the MoveIt Setup Assistant:

  > roslaunch moveit_setup_assistant setup_assistant.launch

- This will bring up the start screen with two choices: Create New MoveIt Configuration Package or Edit Existing MoveIt Configuration Package.

- Click on the Create New MoveIt Configuration Package button to bring up the following screen:
<!-- -->
- Click on the browse button and navigate to the panda_arm_hand.urdf.xacro file installed when you installed the Franka package above. (This file gets installed in /opt/ros/noetic/share/franka_description/robots/panda_arm_hand.urdf.xacro on Ubuntu with ROS Noetic.) Choose that file and then click Load Files. The Setup Assistant will load the files (this might take a few seconds) and present you with this screen:


# MoveIt Setup Assistant
## Step 2: Generate Self-Collision Matrix
The Default Self-Collision Matrix Generator searches for pairs of links on the robot that can safely be disabled from collision checking, decreasing motion planning processing time. These pairs of links are disabled when they are always in collision, never in collision, in collision in the robot's default position or when the links are adjacent to each other on the kinematic chain. The sampling density specifies how many random robot positions to check for self collision. Higher densities require more computation time while lower densities have a higher possibility of disabling pairs that should not be disabled. The default value is 10,000 collision checks. Collision checking is done in parallel to decrease processing time.
- Click on the Self-Collisions pane selector on the left-hand side and click on the Generate Collision Matrix button. The Setup Assistant will work for a few second before presenting you the results of its computation in the main table.

## Step 3: Add Virtual Joints
Virtual joints are used primarily to attach the robot to the world. For the Panda we will define only one virtual joint attaching the panda_link0 of the Panda to the world world frame. This virtual joint represents the motion of the base of the robot in a plane.
- Click on the Virtual Joints pane selector. Click on Add Virtual Joint

- Set the joint name as "virtual_joint"

- Set the child link as "panda_link0" and the parent frame name as "world".

- Set the Joint Type as "fixed".

- Click Save and you should see this screen:

## Step 4: Add Planning Groups
Planning groups are used for semantically describing different parts of your robot, such as defining what an arm is, or an end effector.
- Click on the Planning Groups pane selector.

- Click on Add Group and you should see the following screen:
Add the arm
- We will first add Panda arm as a planning group

  - Enter Group Name as panda_arm

  - Choose kdl_kinematics_plugin/KDLKinematicsPlugin as the kinematics solver. Note: if you have a custom robot and would like a powerful custom IK solver, see Kinematics/IKFast

  - Let Kin. Search Resolution and Kin. Search Timeout stay at their default values.
<!-- -->


# MoveIt Setup Assistant
## Step 4: Add Planning Groups
- Now, click on the Add Joints button. You will see a list of joints on the left hand side. You need to choose all the joints that belong to the arm and add them to the right hand side. The joints are arranged in the order that they are stored in an internal tree structure. This makes it easy to select a serial chain of joints.

  - Click on virtual_joint, hold down the Shift button on your keyboard and then click on the panda_joint8. Now click on the \> button to add these joints into the list of selected joints on the right.
<!-- -->
- Click Save to save the selected group.
Add the gripper
- We will also add a group for the end effector. NOTE that you will do this using a different procedure than adding the arm.

  - Click on the Add Group button.

  - Enter Group Name as hand

  - Let Kinematic Solver stay at its default value; None.

  - Let Kin. Search Resolution and Kin. Search Timeout stay at their default values.

  - Click on the Add Links button.

  - Choose panda_hand, panda_leftfinger, and panda_rightfinger and add them to the list of Selected Links on the right hand side.

  - Click Save

## Step 5: Add Robot Poses
The Setup Assistant allows you to add certain fixed poses into the configuration. This helps if, for example, you want to define a certain position of the robot as a Home position.
- Click on the Robot Poses pane.

- Click Add Pose. Choose a name for the pose. The robot will be in its Default position where the joint values are set to the mid-range of the allowed joint value range. Move the individual joints around until you are happy and then Save the pose. Note how poses are associated with particular groups. You can save individual poses for each group.

- IMPORTANT TIP: Try to move all the joints around. If there is something wrong with the joint limits in your URDF, you should be able to see it immediately here.

## Step 6: Label End Effectors
We have already added the gripper of the Panda. Now, we will designate this group as a special group: end effectors. Designating this group as end effectors allows some special operations to happen on them internally.


# MoveIt Setup Assistant
## Step 6: Label End Effectors
- Click on the End Effectors pane.

- Click Add End Effector.

- Choose hand as the End Effector Name for the gripper.

- Select hand as the End Effector Group.

- Select panda_link8 as the Parent Link for this end-effector.

- Leave Parent Group blank.

## Step 7: Add Passive Joints
The passive joints tab is meant to allow specification of any passive joints that might exist in a robot. These are joints that are unactuated on a robot (e.g. passive casters.) This tells the planners that they cannot (kinematically) plan for these joints because they can't be directly controlled. The Panda does not have any passive joints so we will skip this step.

## Step 8: 3D Perception
The 3D Perception tab is meant to set the parameters of the YAML configuration file for configuring the 3D sensors sensors_3d.yaml.
e.g. point_cloud parameters:
For more details about those parameters please see perception pipeline tutorial
In case of sensors_3d.yaml was not needed, choose None.

## Step 9: Gazebo Simulation
The Simulation tab can be used to help you simulate your robot with Gazebo by generating a new Gazebo compatible urdf if needed.
You can use the generated robot urdf to spawn the robot in Gazebo in the following way.
- Use rosrun to start gazebo empty world:

  > roslaunch gazebo_ros empty_world.launch paused:=true use_sim_time:=false gui:=true throttled:=false recording:=false debug:=true

- Use rosrun to spawn the robot:

  > rosrun gazebo_ros spawn_model -file \</path_to_new_urdf/file_name.urdf\> -urdf -x 0 -y 0 -z 1 -model panda

## Step 10: ROS Control
ROS Control is a set of packages that include controller interfaces, controller managers, transmissions and hardware_interfaces, for more details please look at ros_control documentation
ROS Control tab can be used to auto generate simulated controllers to actuate the joints of your robot. This will allow us to provide the correct ROS interfaces MoveIt.
- Click on the ROS Control pane selector.
<!-- -->


# MoveIt Setup Assistant
## Step 10: ROS Control
- Click on Add Controller and you should see the following screen:

- We will first add Panda arm position controller

- Enter Controller Name as arm_position_controller

- Choose position_controllers/JointPositionController as the controller type

- Next you have to choose this controller joints, you can add joints individually or add all the joints in a planning group all together.

- Now, click on Add Planning Group Joints.
<!-- -->
- Choose panda_arm planning group to add all the joints in that group to the arm controller.
<!-- -->
- Click Save to save the selected controller.

## Step 12: Generate Configuration Files
You are almost there. One last step - generating all the configuration files that you will need to start using MoveIt
- Click on the Configuration Files pane. Choose a location and name for the ROS package that will be generated containing your new set of configuration files. Click browse, select a good location (for example, your home directory), click Create New Folder, call it "panda_moveit_config", and click Choose. "panda_moveit_config" is the location used in the rest of the documentation on this wiki. This package does not have to be within your ROS package path. All generated files will go directly into the directory you have chosen.

- Click on the Generate Package button. The Setup Assistant will now generate and write a set of launch and config files into the directory of your choosing. All the generated files will appear in the Generated Files/Folders tab and you can click on each of them for a description of what they contain.
<!-- -->
- Congratulations!! - You are now done generating the configuration files you need for MoveIt

## What's Next
The MoveIt RViz plugin
- Start looking at how you can use the generated configuration files to play with MoveIt using the MoveIt RViz Plugin.
Setup IKFast Inverse Kinematics Solver
- A faster IK solver than the default KDL solver, but takes some additional steps to setup: Kinematics/IKFast

## Additional Reading
- See the URDF and SRDF page for more details on the components of the URDF and SRDF mentioned in this tutorial.


Hebbian theory is a neuropsychological theory claiming that an increase in synaptic efficacy arises from a presynaptic cell\'s repeated and persistent stimulation of a postsynaptic cell. It is an attempt to explain synaptic plasticity, the adaptation of brain neurons during the learning process. It was introduced by Donald Hebb in his 1949 book The Organization of Behavior. The theory is also called Hebb\'s rule, Hebb\'s postulate, and cell assembly theory. Hebb states it as follows:
The theory is often summarized as \"Cells that fire together wire together.\" However, Hebb emphasized that cell A needs to \"take part in firing\" cell B, and such causality can occur only if cell A fires just before, not at the same time as, cell B. This aspect of causation in Hebb\'s work foreshadowed what is now known about spike-timing-dependent plasticity, which requires temporal precedence.
The theory attempts to explain associative or Hebbian learning, in which simultaneous activation of cells leads to pronounced increases in synaptic strength between those cells. It also provides a biological basis for errorless learning methods for education and memory rehabilitation. In the study of neural networks in cognitive function, it is often regarded as the neuronal basis of unsupervised learning.


Hebbian engrams and cell assembly theory\[edit\]
Hebbian theory concerns how neurons might connect themselves to become engrams. Hebb\'s theories on the form and function of cell assemblies can be understood from the following:
The general idea is an old one, that any two cells or systems of cells that are repeatedly active at the same time will tend to become \'associated\' so that activity in one facilitates activity in the other.
Hebb also wrote:
When one cell repeatedly assists in firing another, the axon of the first cell develops synaptic knobs (or enlarges them if they already exist) in contact with the soma of the second cell.
\[D. Alan Allport\] posits additional ideas regarding cell assembly theory and its role in forming engrams, along the lines of the concept of auto-association, described as follows:
If the inputs to a system cause the same pattern of activity to occur repeatedly, the set of active elements constituting that pattern will become increasingly strongly inter-associated. That is, each element will tend to turn on every other element and (with negative weights) to turn off the elements that do not form part of the pattern. To put it another way, the pattern as a whole will become \'auto-associated\'. We may call a learned (auto-associated) pattern an engram.
Work in the laboratory of Eric Kandel has provided evidence for the involvement of Hebbian learning mechanisms at synapses in the marine gastropod Aplysia californica. Experiments on Hebbian synapse modification mechanisms at the central nervous system synapses of vertebrates are much more difficult to control than are experiments with the relatively simple peripheral nervous system synapses studied in marine invertebrates. Much of the work on long-lasting synaptic changes between vertebrate neurons (such as long-term potentiation) involves the use of non-physiological experimental stimulation of brain cells. However, some of the physiologically relevant synapse modification mechanisms that have been studied in vertebrate brains do seem to be examples of Hebbian processes. One such study reviews results from experiments that indicate that long-lasting changes in synaptic strengths can be induced by physiologically relevant synaptic activity working through both Hebbian and non-Hebbian mechanisms.


Auction format. The best-known feature of a Treasury auction---the single-price format---was introduced in 1992.
The change from the earlier multiple-price format was part
of a major overhaul of the auction process that followed several violations of auction rules in 1991.
The Treasury first adopted the multiple-price format
when it initiated bill auctions in 1929 and it continued to use
that format when it introduced auctions of coupon-bearing
securities in the early 1970s. However, when the auction
process came under scrutiny in 1991, public officials became
interested in alternative formats that might appeal to more
investors and that might lead to lower financing costs.
Several academics had suggested earlier that single-price
auctions might reduce financing costs (see Carson \[1959\],
Friedman \[1960, 1963\], and Smith \[1966\]). In a single-price
auction, a participant can bid its actual reservation yield for
a new security, that is, the minimum yield at which it is willing to buy the security. The bidder certainly has no reason to
bid a lower yield, but if the auction stops at a higher yield it
will get the full benefit of buying at that higher yield. In contrast, the multiple-price format encourages a participant to
bid higher than its reservation yield in hopes of getting the
security on more favorable terms.
Whether the Treasury would be better off selling securities in a single-price format or a multiple-price format was a
matter that could only be resolved by empirical analysis.7 In
September 1992, the Treasury announced that, in an experiment, it would begin to auction two-year notes and five-year
notes in the single-price format. It subsequently produced
two empirical studies analyzing the results of the experiment (see Box 2). Although the evidence was not unambiguous, the Treasury decided in October 1998 that it justified
extending the single-price format to all auction offerings.
Restrictions on auction awards to competitive bidders.
In the interest of fostering a liquid post-auction secondary
market for a new issue, the Treasury limits the maximum
auction award to a single bidder to 35 percent of the offering,
less the bidder's "reportable net long position" in the security. A bidder's net long position is the sum, as of one-half
hour before the close of bidding, of
(a) when-issued, forward, and futures contracts for the
security and for principal STRIPS to be derived from the
security (STRIPS---an acronym for Separate Trading
of Registered Interest and Principal of Securities---
are single-payment claims for the respective interest
and principal payments from a coupon-bearing
security) and
www.newyorkfed.org/research/current_issues 3
On Wednesday, August 25, 2004, the Treasury auctioned
\$24 billion of two-year notes that would be issued on
August 31 and that would mature on August 31, 2006. It
received noncompetitive bids for \$994,798,000 of the notes,
and competitive bids for \$51,580,904,000.
After allocating enough notes to satisfy the noncompetitive bids, the Treasury had \$23,005,202,000 notes remaining to be sold to competitive bidders. Accepting bids in
order of increasing yield, the Treasury stopped at a yield of
2.494 percent. All bids specifying yields below 2.494 percent were accepted in full; a bid at 2.494 percent was allocated 32.34 percent of the amount specified. (The allocation
at the stop is the ratio of (a) the quantity of securities that
remain to be sold at the stop to (b) the quantity bid at the
stop, rounded up to two digits to the right of the decimal


not materialize, they would end up owning more securities
than they wanted. In the interest of encouraging broad public participation in its auctions, the Treasury announced in
July 1990 that it would limit the total bids by a given participant at a given yield or discount rate to 35 percent of the
amount offered to the public.
Granularity of bidding. When the Treasury introduced bidding on notes and bonds in terms of yields in 1974, it specified
that bids should be expressed to a whole basis point.11When it
introduced bidding on bills in terms of discount rates in 1983,
it made a similar stipulation.12 Bidding to a whole basis point
continued until 1995, when Treasury officials specified that
bids on notes and bonds should be expressed to 1/10 of 1 basis
point. The greater precision was intended to "increase participation in Treasury auctions and to conform the auctions to
market practice for when-issued trading."13 Officials refined
the bidding increment to 1/2 of 1 basis point in late 1997 for
thirteen-, twenty-six-, and fifty-two-week bills and in April
2002 for cash management bills, saying that they expected the
change "to promote more efficient and aggressive bidding and
lead to marginally higher revenue." 14
4
The Treasury produced two empirical studies of the results
of its experiment with a single-price auction format: Malvey,
Archibald, and Flynn (1995) and Malvey and Archibald
(1998). The studies calculated---for both single-price and
multiple-price auctions---the difference between the auction
yield of a security and the yield at which the same security
was trading in the when-issued market at the time of the
auction. A positive difference indicated that the securities
had been auctioned at a yield higher than the one at which
they were trading in the when-issued market. For securities
auctioned in a multiple-price format, the average difference
was statistically significantly greater than zero. For securities
auctioned in a single-price format, however, the studies were
unable to reject the hypothesis that the average difference
was zero. These results suggest that moving to a single-price
format would lead to lower financing costs. Nevertheless,
the studies were also unable to reject the hypothesis that
changing auction formats would leave the average difference
unchanged. The apparent inconsistency arose because the
differences between auction yields and when-issued yields in
single-price auctions were quite volatile. The studies could
neither reject the hypothesis that the average difference in
single-price auctions was different from zero, nor could they
reject the hypothesis that the average difference was different from the (positive) average difference that characterized
multiple-price auctions.
The studies also examined whether single-price auctions
reduced the concentration of auction awards. The studies
found that the single-price format did not materially affect
the distribution of awards between dealers and others but
that it did lead to a lower concentration of auction awards to
the largest dealers.
Box 2


variable. Indeed, when the stock market was weak in 1994, fewer executives exercised
their options and total compensation took a dip. However, most executives still enjoy generous base pay packages and in fact in 1994 a record number earned more than \$1 million.12
Moreover, while CEOs might face the unhappy prospect of their stock options slipping
"underwater,\" employees have their very livelihoods at risk, since layoffs are often the first
response to economic downturns.
B. International Analysis
During the 1990s, executive pay in other countries, though increasing, doesn't begin to
approach the outrageous levels in the United States. The slight increases in foreign
compensation levels can be attributed to two factors:
• Stock options are increasingly permitted as part of compensation in other countries
• Mergers between U.S. and foreign firms have resulted in foreign executives receiving
pay hikes to lift them toward the level of their U.S. colleagues.
Still, CEOs at many globally competitive foreign firms earn modest amounts relative to comparable U.S. firms. We requested compensation information from the more than
90 foreign firms that had net income levels over \$1 billion in 1997. Because most governments outside the United States do not require that corporations report such information,
response to our requests was spotty. However, as the chart on page 5 reveals, none of the
corporations that responded pay their top executive anywhere near the average total compensation of \$10.6 million earned in 1998 by the average U.S. CEO. These seven foreign
firms successfully compete in the global marketplace while paying their top executive
between 4 percent and 27 percent of the amount earned by average U.S. executive.
No Japanese firms responded to our inquiry, but Towers Perrin, a U.S.-based consulting firm, has studied Japanese compensation levels and estimates that chief executives
of companies with revenues between \$250 million and \$500 million in that country make
on average \$420,855.13 Pay levels for Japanese executives have not risen much during the
decade. Towers Perrin estimated in 1990 that executives of large Japanese companies
received between \$270,000 to \$400,000 per year.14
6
Olav Sabo (D-MN) points out that"a company doesn't exist solely for the benefit of those
running it. It has a relationship with shareholders, consumers, communities and workers---all of which are essential to the company's success. Those who work on the factory
floor should be as important as those who work in the executive suite."16 Likewise, William
Bennett, one of the nation's leading conservatives and a self-described ethics expert, chastised members of the American Compensation Association in May 1999, describing some
pay packages as "ridiculous" and posing the rhetorical question "How much do people
actually need?\"
These arguments are nothing new. At the turn of the century, finance magnate J.P.
Morgan espoused the opinion that CEOs should not make more than 20 times the compensation of the average employee.17 In 1912, Theodore Roosevelt told the Ohio Constitutional
Convention:"I hold it to be our duty to see that the wage worker, the small producer, the
ordinary consumer, shall get their fair share of the benefit of business prosperity."18
The other camp of critics focuses on economic concerns. They include money managers and other economic analysts---including even Federal Reserve Chairman Alan
Greenspan---who see excessive executive compensation as an obstacle to shareholder profits and U.S. corporate competitiveness. The following section lays out the five major economic arguments against excessive CEO pay.
A. Short-termism
Exorbitant stock options grants have generated much of the explosion in executive pay
in the past decade. Promoters of stock options bill them as a way to get managers to
think like owners, by linking their wealth directly to firm value. However, a study by the


Will an oil price cap manage to reduce the flow of
revenue to Russia?
In early September 2022, the finance ministers of the G7 countries confirmed their intention to implement a price
cap on purchases of Russian oil and related products. Their objective is to reduce Russia's ability to fund its
invasion of Ukraine while limiting the war's impact on global energy prices. A survey by the Initiative on Global
Markets asked economists for their thoughts on price caps. Romesh Vaitilingam summarises the results.
We invited our European and US experts to express their views on this proposal, asking both panels whether they
agree or disagree with the following statements, and, if so, how strongly and with what degree of confidence:
a) A price cap imposed by the G7/EU countries on purchases of Russian oil and oil-related products (and which
applies to all importers of Russian oil using Western trade infrastructure, shipping, and insurance) would be an
effective measure to reduce the flow of revenues to Russia.
b) The oil price cap imposed by the G7/EU countries will not have a substantial effect on the world oil price (such as
the Brent crude benchmark).
The price cap and Russian revenues
Of our 43 US experts, 39 participated in this survey; of our 49 European experts, 38 participated -- for a total of 77
expert reactions. On the first statement, over two-thirds of the panellists agree or strongly agree with the statement
(among whom European experts were more likely to say that they strongly agree), most of the rest are uncertain, and a few disagree. Weighted by each expert's confidence in their response, 16% of the European panel strongly agree, 57% agree, 20% are uncertain, 4% disagree, and 4% strongly disagree (totals don't always sum to 100 because of rounding). Among the US panel (again weighted by each expert's confidence in their response), 5% strongly agree, 59%
agree, 28% are uncertain, and 8% disagree. Overall, across both panels, 11% strongly agree, 58% agree, 23% are
uncertain, 6% disagree, and 2% strongly disagree. More details on the experts' views come in the short comments that they are able to include when they participate
in the survey. Among those who agree or strongly agree with the statement, Maristella Botticini at Bocconi argues:
'Price caps (and floors) typically generate distortions. True. But in extraordinary times, like during a war after a
pandemic, policy recommendations based on EC101 models should become more sophisticated. A temporary price
cap together with other policy measures can help a lot.' Richard Portes at London Business School (LBS) points out that: 'Russia is already selling oil to India and China at
substantially discounted prices. And the oil price is responding to demand, not significantly affected by speculation. Russia will have few alternative major buyers at prices above the cap, provided that is set sensibly.' Olivier
Blanchard at the Peterson Institute also refers to the importance of where the cap is set: 'A well designed price cap,
just such as to give Russia the incentive to sell at the cap rather than just stop selling. Want to affect revenues, not
quantities sold. Hard to do right.' David Autor at MIT also considers implementation difficult to get right: 'It's a great idea in theory but hard to enforce
in practice. The cap will likely depress Russian oil revenue somewhat, in part by enabling China, Iran, and North
Korea to negotiate a greater discount (though above the price cap).' But Kenneth Judd at Stanford is optimistic:
'Exercising monopsony power will push down revenues to Russia. They may find other buyers but the mechanics of
moving oil will limit that leakage. Hopefully diplomatic efforts will convince potential buyers to not increase their
purchases.'
LSE Business Review: Will an oil price cap manage to reduce the flow of revenue to Russia? Page 1 of 3
Date originally posted: 2022-09-28
Permalink: <https://blogs.lse.ac.uk/businessreview/2022/09/28/will-an-oil-price-cap-manage-to-reduce-the-flow-of-revenue-to-russia/>
Blog homepage: <https://blogs.lse.ac.uk/businessreview/>
Several experts who agree with the statement wonder how large the effect could be. Ricardo Reis at the London
School of Economics (LSE) says: 'Effective to reduce the flow of revenues to Russia, but unclear how large. Depends, in the short run on response of non-G7/EU government, and in the long run on entry into market of non- G7/EU traders/shippers/financiers.' He links to a useful overview of the issues at The Economist.
Judith Chevalier at Yale replies: 'While leakage will be large, the sanctions plus price cap should improve the
bargaining position of non-G7 countries vis-à-vis Russia and reduce Russian revenues. There appears to be some


when inflation is low. The reasoning behind
this practice is that increasing interest rates
reduces spending, "cools" the economy and
reduces inflation, while reducing interest
rates increases spending, "heats up" the
economy and increases inflation.
Neo-Fisherism
But what if central banks have inflation
control wrong? A well-established empirical regularity, and a key component of
essentially all mainstream macroeconomic
theories, is the Fisher effect---a positive
relationship between the nominal interest
rate and inflation. The Fisher relationship,
named for Irving Fisher, is readily discernible in the data. Look at Figure 1, for example, which is a scatter plot of the inflation
rate (the four-quarter percentage change
in the personal consumption deflator---the
Fed's chosen measure of inflation) vs. the
fed funds rate for the period 1954-2015. In
Figure 1, a positively sloped line would be
the best fit to the points in the scatter plot,
indicating that inflation tends to rise as the
fed funds rate rises.
Many macroeconomists have interpreted
the Fisher relation observed in Figure 1 as
involving causation running from inflation to the nominal interest rate (the usual
market quote for the interest rate, not
adjusted for inflation). Market interest rates
are determined by the behavior of borrowers and lenders in credit markets, and these
borrowers and lenders care about real rates
of interest. For example, if I take out a car
loan for one year at an interest rate of 10
percent, and I expect the inflation rate to be
2 percent over the next year, then I expect
the real rate of interest that I will face on
the car loan will be 10 percent -- 2 percent =
8 percent. Since borrowers and lenders care
about real rates of interest, we should expect
that as inflation rises, nominal interest
rates will rise as well. So, for example, if the
typical market interest rate on car loans is


10 percent if the inflation rate is expected to
be 2 percent, then we might expect that the
market interest rate on car loans would be
12 percent if the inflation rate were expected
to be 4 percent. If we apply this idea to all
market interest rates, we should anticipate
that, generally, higher inflation will cause
nominal market interest rates to rise.
But, what if we turn this idea on its head,
and we think of the causation running
from the nominal interest rate targeted by
the central bank to inflation? This, basically, is what Neo-Fisherism is all about.
Neo-Fisherism says, consistent with what
we see in Figure 1, that if the central bank
wants inflation to go up, it should increase
its nominal interest rate target, rather than
decrease it, as conventional central banking
wisdom would dictate. If the central bank
wants inflation to go down, then it should
decrease the nominal interest rate target.
But how would this work? To simplify,
think of a world in which there is perfect
certainty and where everyone knows what
future inflation will be. Then, the nominal
interest rate R can be expressed as
R = r + π,
where r is the real (inflation-adjusted) rate
of interest and π is future inflation. Then,
suppose that the central bank increases the
nominal interest rate R by raising its nominal interest rate target by 1 percent and uses
its tools (intervention in financial markets)
to sustain this forever. What happens?
Typically, we think of central bank policy as
affecting real economic activity---employment, unemployment, gross domestic
product, for example---through its effects
on the real interest rate r. But, as is widely
accepted by macroeconomists, these effects
dissipate in the long run. So, after a long
period of time, the increase in the nominal
interest rate will have no effect on r and will


be reflected only in a one-for-one increase
in the inflation rate, π. In other words, in
the long run, the only effect of the nominal
interest rate on inflation comes through the
Fisher effect; so, if the nominal interest rate
went up by 1 percent, so should the inflation
rate---in the long run.
But, in the short run, it is widely accepted
by macroeconomists (though there is some
disagreement about the exact mechanism)
that an increase in R will also increase r,
which will have a negative effect on aggregate economic activity---unemployment will
go up and gross domestic product will go
down. This is what macroeconomists call a
non-neutrality of money. But note that, if
an increase in R results in an increase in r,
the short-run response of inflation to the
increase in R must be less than one-for-one.
Neo-Fisherism says, consistent with what we see in
Figure 1, that if the central
bank wants inflation to go up,
it should increase its nominal
interest rate target, rather
than decrease it, as conventional central banking wisdom
would dictate. If the central
bank wants inflation to go
down, then it should decrease
the nominal interest rate target.
The Regional Economist \| www.stlouisfed.org 7
However, if inflation is to go down when R
goes up, the real interest rate r must increase
more than one-for-one with an increase in
R, that is, the non-neutrality of money in the
short run must be very large.
To assess these issues thoroughly, we need
a well-specified macroeconomic model. But
essentially all mainstream macroeconomic
models predict a response of the economy
to an increase in the nominal interest rate
as depicted in Figure 2. In this figure, time


is on the horizontal axis, and the central
bank acts to increase the nominal interest
rate permanently, and in an unanticipated
fashion, at time T. This results in an increase
in the real interest rate r on impact. Inflation π increases gradually over time, and
the real interest rate falls, with the inflation rate increasing by the same amount
as the increase in R in the long run. This
type of response holds even in mainstream
New Keynesian models, which, it is widely
believed, predict that a central bank wanting to increase inflation should lower its
nominal interest rate target. However,
as economist John Cochrane shows, the
New Keynesian model implies that if the
central bank carries out the policy we have
described---a permanent increase of 1
percent in the central bank's nominal interest rate target---then the inflation rate will
increase, even in the short run.4
The Low-Inflation Policy Trap
What could go wrong if central bankers do not recognize the importance of
the Fisher effect and instead conform to
conventional central banking wisdom?
Conventional wisdom is embodied in the
Taylor rule, first proposed by John Taylor
in 1993.5
Taylor's idea is that optimal
central bank behavior can be written
down in the form of a rule that includes
a positive response of the central bank's
nominal interest rate target to an increase
in inflation.
But the Taylor rule does not seem to
make sense in terms of what we see in
Figure 2. Taylor appears to have thought,
in line with conventional central banking wisdom, that increasing the nominal
interest rate will make the inflation rate go
down, not up. Further, Taylor advocated a
specific aggressive response of the nominal
interest rate target to the inflation rate,
sometimes called the Taylor principle. This
principle is that the nominal interest rate


Littering in Context: Personal and Environmental
Predictors of Littering Behavior
P.Wesley Schultz1
, Renée J. Bator3
, Lori Brown Large2
, Coral M.Bruni2
, and
Jennifer J.Tabanico2
Abstract
This article reports the results from a large-scale study of
littering behavior. Findings are reported from coded
observations of the littering behavior among 9,757 individuals
at 130 outdoor public locations in the United States. The focus
was on littering behavior of any item, but a separate sample is
also reported on the littering behavior of only smokers. For
smokers, the observed littering rate for cigarette butts was 65%.
Results from the general littering observations showed that of
all the disposal behaviors observed, 17% resulted in litter.
Statistical analyses using multilevel modeling showed that age
(negatively) was predictive of individual littering. At the level
of the site, the presence of existing litter (positively) and the
availability of trash receptacles (negatively) predicted littering.
Supplemental analyses showed that among individuals who
disposed of an item, distance to the receptacle was positively
predictive of littering.Implications for litter prevention
strategies are discussed.
1California State University, San Marcos, USA, 2Action Research, Inc., CA,
USA, 3SUNY Plattsburgh, NY, USA
Corresponding Author:
P. Wesley Schultz,California State University, 333 SouthTwin Oaks
Valley Road, San Marcos, CA 92096, USA, Email: <wschultz@csusm.edu>
Keywords: Littering, field study, behavioral observation
2
Litter is any piece of misplaced solid waste (Geller, 1980). This
can range from small items, such as cigarette butts or candy
wrappers, to abandoned automobiles, appliances, and even
spacecraft. Most commonly, litter refers to items that are
discarded by an individual, but it can include any item that is in
an unacceptable location, regardless of the origin. This could
not only include the candy wrapper dropped on the ground but
also the newspaper that blows out of a trash can. The
distinction here is between litter (the item) and littering (the
behavior). Although the exact percentage of litter attributed to
improper disposal behavior by individuals is unknown, there is
evidence to suggest that a large majority of litter is linked with
individual disposals (MSW Consultants, 2009). A recent
analysis of the sources of litter along roadsides attributed 70%
to individuals (52% to motorists and 18% topedestrians). In
comparison, 21% came from unsecured loads, 5% from the
vehicles themselves (e.g., tires and vehicle debris), and 3%
came fromunsecured containers in the nearby vicinity.
Similarly, at transition points such as bus stops, 88% of the
small littered items were attributed to individuals, as was 90%
of large items (69% to pedestrians and 21% to motorists).
These findings underscore the importance of the individual as a
source of litter.
Litter poses a number of important environmental, social,
and aesthetic problems. As an environmental problem, litter is
a substantial source of contamination. Misplaced plastics,
Styrofoam, paper, glass, and many other commonly used
consumer materials accumulate in the environment, posing a
number of harmful environmental consequences. The social
problems related to litter include safety hazards, fire hazards,
human health hazards, and indirect health hazards from
bacteria, rats, roaches, and mosquitoes that are attracted to
litter. In addition, litter is predictive of changing crime rates in
a community (Brown, Perkins, & Brown, 2004), and there is
experimental evidence showing that the presence of litter
results in an increase in othersocial transgressions like theft
(Keizer, Lindenberg, & Steg, 2008). There are aesthetic issues
3
with litter, as there is near unanimous agreement that litter is
unsightly (Pandey, 1990). Indeed, the presence of litter in a
residential com- munity decreases property value, and litter in
commercial areas reduces sales and attracts fewer customers


(National Association of Home Builders, 2009; Skogan, 1990).
Finally, there are the direct costs of litter cleanup, which
conservatively tops US\$11 billion annually in the United States
(MSW Consultants, 2009).
Given the myriad of problems that result from litter, it is not
surprising that a sizable amount of research has focused on
understanding and preventing it. Litter was one of the first
environmental problems to lend itself to systematic behavioral
research, with studies going back more than 40 years. In an
early 1968 study, Keep America Beautiful (KAB) reported on
the attitudes, beliefs, and self-reported behaviors among a large
national sample (Public Opinion Surveys, Inc., 1968).
Subsequently, studies throughout the 1970s were used as a
basis for creating litter prevention programs (Burgess, Clark, &
Hendee, 1971; Cone & Hayes, 1980; Geller, Winett, & Everett,
1982). In the section below, the three dominant approaches to
understanding litter and littering behavior are summarized.
Prior Studies of Litter
Who litters? One approach to understanding littering focuses
on the demo- graphic and personal qualities of the type of
person who litters---the "litter bug." Although much of these
data come from surveys in which people self- report littering
rates, a few studies have conducted observations (e.g.,
distributing a marked flyer or handbill under varying
conditions and monitoring to see which accumulate as litter).
The widely accepted conclusions from these studies are that
littering is more common among males, younger adults, and
individuals living in rural communities more than cities.
However, the research results on these characteristics of the
"litter bug" are far from conclusive and many studies have
failed to find significant demographic predictors (Beck, 2007;
Finnie, 1973; Geller, Witmer, & Tuso, 1977). As a result, there
4
is little consistent evidence for demographic characteristics of
the "litter bug."
How often do people litter? Given the volume of litter that
accumulates nationally and worldwide, it is important to
understand the littering behavior of individuals. One way to
address this question is by watching the behavior of individuals
in public spaces(Geller et al., 1977; Heberlein, 1971).Although
only a handful of studies have utilized observational methods,
the results are instructive. An early study by Finnie (1973)
reported observations of individuals in four outdoor spaces in
Philadelphia as they ate hot dogspurchased from street
vendors. Of the 272 observed individuals, 91 littered the
wrapper (33%). Littering was more common in sites that were
already littered and in sites without trash receptacles. Similarly,
Cialdini, Kallgren, and Reno (1991) and Cialdini, Reno, and
Kallgren (1990) placed flyers on the windshields of parked cars
and observed the percentage of individuals who littered. In one
illustrative finding, they found that 14% of the individuals
littered when the environment was litter free, whereas 32%
littered into an already-littered environment. In an interesting
extension of these findings, Keizer et al. (2008) found that
participants were more likely to litter into "disordered" settings (those with graffiti or fireworks or shopping carts left
unreturned). These findings illustrate the importance of
understanding the role of the physical context in facilitating or
discouraging littering behavior, andsimilar results have been
reported in other studies (Williams, Curnow, & Streker, 1997).
Collected litter. By far, the most commonly used method for
litter research is to count and characterize the types of litter
collected from different locations (KAB, 2007). Litter cleanups
happen on a regular basis, including the KAB (2007) Great
American Cleanup, regular Adopt-a-Highway cleanups, and
the Ocean Conservancy's International Coastal Cleanup. In
addition, states and local governments regularly conduct "litter
surveys" to identify the types and sources of materials found
along roadways throughout the country. These events remove
millions of pounds of litter annually from roadways, parks,
5
shorelines, and natural areas worldwide. In the 2007 Coastal
Cleanup, the Ocean Conservancy collected 6 million pounds of
materials, including cigarette butts (1,971,551 or 27% of all
collected items), food wrappers (10% of collected items), caps
and lids (9%), bags (8%), plastic beverage bottles (7%), plastic


utensils (5%), and glass beverage bottles (5%; Ocean
Conservancy, 2007).
The current study involved making unobtrusive observations
of disposal behavior of pedestrians at outdoor sites in which we
simultaneously examined demographic characteristics of the
participants as well as contextual variables such as the
presence, characteristics, and placement of receptacles. As an
extension of prior littering studies, the current work examined
both person-level and context-level predictors of observed
littering behavior using a multilevel modeling framework.
Current Project
Although there is a long history of research on litter and
littering, a number of fundamental questions remain to be
answered. In the current article, the results from a nationwide
study of littering behavior are reported. This research
investigation had three goals: (a) to conduct an observational
study of littering behavior across a diverse sample of sites and
locations; (b) to develop a set of observational methodologies
for observing littering (including a modified protocol for
observing smokers) that could be replicated over time and in
different locations; and (c) to utilize a multilevel approach in a
way that would allow for the simultaneous analysis of
personal- and contextual-level determinants of littering. At the
level of the individual, we examined the effects of variables
found to predict littering in past research: gender (males
littering more than females), age (younger littering more than
older), and distance from a receptacle (greater distance at the
time of disposal predicting higher littering rates). We also
explored new potential predictors, including time of day, and
whether being in a group might be associated with lower
littering rates because of social disapproval. At the level of the
6
context, past research led us to expect that littering would
occur more often: in sites that were high in existing litter, in
sites with fewer receptacles, and in sites with no existing
signage about littering. We also explored several less widely
studied variables, including rural versus urban locations,
cleanliness, landscaping, infrastructure, and the number of
people within the location.
Method
Sites and Participants
During the spring of 2008, systematic observations of
individuals were con- ducted in a wide range of outdoor public
locations across the United States. The research design was
developed as a multilevel model, with random samples of
individuals "nested" within site (see Raudenbush & Bryk,
2002). At each location, random samples of individuals were
selected, and their behavior was unobtrusively monitored as
they moved through the site. A modified protocol was
developed for monitoring the behavior of smokers, which
included the various means by which smokers typically dispose
of their butts.
Observations were conducted in 10 states (Arkansas,
California, Georgia, Illinois, Kentucky, Nevada, New Mexico,
New York, Utah, and Vermont), selected to represent a variety
of regions across the country. Within each state, an urban,
rural, and suburban city was selected using U.S. Census
statistics. Finally, within each of those cities, specific
observation sites were randomly selected from a list of all
possible sites of each type: city center, fast food, recreation,
gas station, and rest stop. Three additional site types were
selected for observations of cigarette smokers: medical,
bars/restaurant, and retail.
The final data set included observations of 9,757 individuals
from 130 locations: 86 general litter and 44 focused on
cigarette disposal. Of these, 30 were recreational, 24 city center,
22 fast food, 12 retail, 12 bars/restaurants, 11 gas stations with
7
convenience stores, 11 rest stops, and 8 medical facilities.
Procedure
Systematic observations were made by pairs of observers
following a strict protocol that was developed after
considerable training. The protocol and code sheets are
available on request from the authors. On arrival at the research
site, the field team first defined the physical boundary of the


observation area. This would be an area that the team could
clearly observe from an unobtrusive lookout point (e.g., in a
public seating area or inside a parked car). This lookout point
was typically at the back border of the observation area. This
enabled the research team to remain forward facing (which was
necessary when they were inside a parked car) and increased
their ability to remain unobtrusive. These boundaries were
typically areas of about 2,000 square feet that allowed for
unobstructed and unobtrusive observations of individuals. This
observation area always included the most heavily trafficked
part of the site (e.g., the entrance/exits to the nearest building).
Before observing any participants, the research team used a
detailed codebook to record a variety of characteristics related
to the setting.
Setting characteristics. The codebook provided a variety of
categorical and continuous measures for the research team to
record for each setting. The research team specified the site
type---recreational, city center,fastfood,retail, bar/restaurant,
gasstation with convenience store, reststop, or medical facility
(categorical measure); identified whether the location was
categorized asrural, urban, or suburban (categorical measure);
recorded the time of day as before noon, afternoon, or after 4:00
p.m. (categorical measure); rated the amount of existing litter in
the location from 0 = not at all littered to 10 = extremely littered
(continuous measure); indicated whether or not each of 9
different types of litter (e.g., paper, food wrappers, cans, bottles,
etc.) was present or absent (categorical); rated the amount of
cigarette butt litter present from 0 = not at all littered to 10 =
extremely littered (continuous); counted the number of cigarette
8
buttsin the observational area (continuous); rated the overall
cleanliness of the site from 0 = not at all clean to 10 =
extremely clean (operationalized as free from bad smells, litter,
unkempt infrastructure, and objects that do not belong in the
location; continuous); judged the landscaping (operationalized
as the presence and care of foliage; continuousfrom 0 = not at
all landscaped to 10 = extremely landscaped); and rated the
overall infrastructure from 0 = low infrastructure to 10 = high
infrastructure (operationalized as the placement of physical
objects within a location as a means to increase the aesthetics,
walk- ability, cleanliness, and landscaping of the area). This
included planters, paved walkways, benches, and trash
receptacles(continuous).The research team also recorded the
number oftrash receptaclesfor each offive receptacle types:
trash can, ashtray, ash/trash combination, dumpster, and
recycling (continuous). For analytic purposes, these were
summed to produce a single score of the total number of
available receptacles. The team recorded whether or not there
was littering signage present (dichotomous, 1 = yes, 0 = no).
Finally, they rated the crowdedness of the location from 0 = not
at all crowded to 10 = extremely crowded, operationalized as the
inability to move freely. It was defined for the observational
team asthe combination of the number of people in the location,
given the features of the location (continuous). These measures
of the setting were made to examine the impact of contextual
variables on participants'littering behavior.
Participant characteristics. After recording the details of the
setting, the research team randomly selected a participant by
taking the Nth person to enter the space, with N determined
using the crowdedness of the location and ranged from 1 to 6.
Random selection of the individual participant at each site is a
key aspect of this research protocol, and it provides data that
can be used to calculate a littering rate for each site as well as
the data needed to analyze the personal and contextual-level
predictors of littering.
The research team recorded each participant's gender,
approximate age, whether the selected participant was alone or
9
with one or more others, and noted one of three possible disposal
options: the participant did not have an item to dispose, the
participant had an item to dispose but left the site carrying the
object, or the participant disposed of the object. No other
observations were made for participants who had no object to
dispose or who left the site with the object.
The research team made additional recordings for only those


participants who disposed of an object. They recorded whether
the object was disposed of properly or improperly. Proper
disposal was operationalized as any disposal that resulted in the
object being placed in a receptacle, including ashtray, trash can,
or recycling bin. Items placed in the wrong receptacle (e.g.,
trash in a recycling container or cigarette butts in a trash can)
were not coded as litter. For analytic purposes, these disposals
were coded as "proper." Also coded as proper were pocketing
the item or handing it to another person. Improper disposals
included disposals on the ground, planters, bushes or
shrubbery, or disposals on or around receptacles. The research
team also recorded the type of object disposed using a code
sheet with 13 options, including an open- ended option for
"other."
Of those who were observed to have littered, the researchers
recorded the person's intent to litter using eight categories,
drawing on prior work by Williams et al. (1997): drop without
intent, drop with intent, flick, shoot and miss, inch away,
wedge, sweep, or 90%. All but the first coded category were
classified as "with intent." Drop with intent was a subjective
classification made by the coder and required one of two
specific actions: the individual visually inspected the item
either at the point of disposal or immediately fol- lowing, or
there was an observable hand movement indicating an
intentional discard (e.g., flick, toss, fling, wedge, and sweep).
The disposal strategies of drop, flick, and shoot and miss
involved the intentional placement of the item in an improper
location; sweep strategies involved brushing items from a flat
surface unto the ground; and "90%" codes included instances
where the individual collected other items for proper disposal
10
but intentionally left one or more objects behind. More detailed
descriptions of these eight intentional disposal strategies can be
found in Williams et al.
Finally, the research team recorded littering participants'
distance (in feet) from receptacles at the time they littered. Any
discrepancies were resolved through discussion. Observations
within each site continued until 30 participants were observed
making a disposal (proper or improper) or until the conclusion
of an 8-hour observation period.A minimum of 4 hours of
observations were conducted at each site.
Results
Reliability Measures
During training, the research team conducted multiple sessions
duringwhich they practiced coding both the settings and
individuals. These training sessions were conducted until the
pairs of raters achieved a minimum of 80% agreement for the
categorical variables of the setting and r = .70 for continuous
variables of the setting. The reliabilities reported below are the
average percentage agreements and correlations across pairs of
team members on the final day of training:
• Did the person have an item for disposal in his or her
hand (dichotomous, 1 = yes or 0 = no)? Percentage of
agreement, 93%.
• Did the person dispose of an item while within the
observational boundary (dichotomous, 1 = yes or 0 = no)?
Percentage agreement, 95%.
• Did the disposalresult in litter(dichotomous, 1 = yes or 0 =
no)? Percent- age of agreement, 97%. This variable served
asthe primary outcome.
11
• Type of item disposed? (categorical: 13 options plus
"other"). Percentage of agreement excluding "other,"
97%.
• For individuals who were observed littering, was there
clear intent (dichotomous, 1 = yes or 0 = no)? Percentage of
agreement, 98%.
• For individuals who were observed littering, what was
the distance to the closest receptacle at the point of
littering, in feet (r = .94).
During actual data collection, additional reliabilities were
computed using data from 127 observed individuals at three
sites obtained from three pairs of the field team. The following
percentage agreement was found: gender(95% agreement),
approximate age (r = .94), whether the individual was alone


(96% agreement), and time of day (100% agreement).
The data were analyzed as a multilevel model, which
allowed for both individual- and context-level predictors of
littering behavior (Hox, 2002; Raudenbush & Bryk, 2002). The
summary below begins with basic descriptive statistics from
the observations and then proceeds to report the results from
the hierarchical linear model. Results from the multilevel
models are reported following convention, with β representing
unstandardized Level-1 coefficients (in these analyses, personlevel predictors) and γ representing unstandardized Level-2
coefficients (in these analyses, context-level predictors).
Measures of variability are also reported, with σ representing
variance at Level 1 (person-level) and τ for variance at Level 2.
Descriptive Statistics
Across the 130 sites, 118 of them (91%) had at least one trash
receptacle. These included 64 sites with uncovered trash cans,
58 sites with lidded trash cans, 43 sites with ash receptacles, 16
sites with recycling bins, 18 sites with combined trash can/ash
receptacles, and 12 sites with dumpsters. Many of the sites had
several types of receptacles, so the total exceeds 130. Of the
12
130 sites, only 2 had no visible litter within the observation
boundary. By count, the most frequently observed visible litter
included cigarette butts and miscellaneous paper. The number
of sites with various types of litter is shown in Table 1. "Other"
items included diapers, dog waste, fishing gear, clothing, and
children's toys. These findings indicate that although trash
receptacles are quite common in public spaces, ash receptacles,
and (particularly) recycling bins are less common.
Observations of littering in general were made at 86 sites
across 10states. A total of 8,990 general observations were
made; an additional sample of 767 smokers is reported
separately below. The general observations were evenly
divided across rural (33%), suburban (34%), and urban areas
(33%). Observations were made throughout the day, with 27%
made in the morning before noon, 58% in the afternoons
between noon and 4:00p.m., and 16% in the evening after
4:00p.m. Of the observations, 56% of the observed targets were
male, and 44% were female. Observed ages ranged from 1 to
82 (M = 38, SD = 16), and 50% of the observed individuals
were alone.
Of the 8,990 people who were observed, 2,472 left the site
with no object for disposal (28%), 4,534 left the site with an
object (50%), and 1,962 dis- posed of an object while on site
(22%). Among these disposals, there were 342 instances of
littering observed. That is, of all 8,990 individuals that were
observed moving through a diverse range of sites, 4% littered.
In addition, of all the disposal behaviors that were observed (N
= 1,962), 342 (or 17%) were improperly disposed by littering.
The remaining proper disposals included trash receptacle
(60%), pocketing the item (9%), handing the item to another
person (6%), ashtray (6%), and recycling bin (1%).
13
Table 1. Percentage of Sites (out of 130) With that Litter Type
Present and Percent of Participants who Disposed of that Item
Improperly
Litter type % sites with this
type of litter
present
% participants who
disposed of item
improperly
Cigarette butts 82 (fo = 106) 57 (n = 194)
Paper 67 (fo = 87) 7 (n = 20)
Food wrappers 45 (fo = 58) 14 (n = 14)
Confections 34 (fo = 44) 0 (n = 0)
Napkin/tissue 34 (fo = 44) 8 (n = 9)
Miscellaneous
plastic
33 (fo = 43) 0 (n = 0)
Food remnants 24 (fo = 31) 20 (n = 16)
Beverage cup 16 (fo = 21) 3 (n = 5)
Beverage bottle:
plastic
11 (fo = 14) 5 (n = 5)
Food containers 9 (fo = 12) 2 (n = 1)


Plastic bags 8 (fo = 11) 5 (n = 2)
Beverage can 6 (fo = 8) 12 (n = 8)
Beverage bottle:
glass
5 (fo = 6) 0 (n = 0)
Yard waste 5 (fo = 6) 0 (n = 0)
Combination/mixed
trash
0 (fo = 0) 4 (n = 12)
Other 27 (fo = 35) 37 (n = 46)
Unknown 0 (fo = 0) 8 (n = 10)
14
Of the 1,962 coded disposals, the most frequent were
cigarettes (N = 340), mixed trash (N = 337), and paper (N =
272). Table 1 also shows the types, frequencies, and littering
rates for the disposed objects. The table shows the frequency of
proper and improper disposals, along with the percentage of
each type of material that was littered (computed as improper /
proper + improper). The "other" category includes a number of
low-frequency disposals, including pet waste, candy and other
confections, matches and cigarette lighters, diapers, straws,
chewing tobacco, and miscellaneous product pack- aging like
price tags, foil wrappers, and twist ties.
The 342 acts of littering were coded into discrete disposal
strategies, along with coded intent. The most frequent littering
strategy was to drop with intent (N = 183, 54%). That is, the
person committed a clear and deliberate act of littering. Other
litter strategies included flick (N = 68, 20%) and drop without
intent (N = 42, 12%). The behaviors were also coded into
littering strategies found in prior research (Williams et al.,
1997): inch away (N = 8), shoot and miss (N = 8), wedge (N =
4), sweep (N = 3), and 90% (N = 2). When combined, an
estimated 81% of observed littering occurred with intent.
The observation team coded the distance (in feet) from the
disposer to the nearest receptacle (trash, recycling, or ashtray).
Although there were several instances of littering that occurred
immediately adjacent to a receptacle, most littering occurred at
a considerable distance (mean distance to a receptacle at time
of littering was 29 feet).
Multilevel Modeling
Finally, a series of statistical analyses were conducted to
examine the individual and contextual variables that were
predictive of littering. The analysis was conducted using only
data from observations where a disposal (either proper or
improper) occurred (N = 1962). Multilevel modeling is a
statistical technique that allows for "nested" data structures (in
15
this case, individuals nested within site). In addition, the
multilevel approach does not require balanced data (i.e., equal
numbers of observations per site) and instead utilizes all
available information to estimate the underlying effects. The
approach makes it possible to simultaneously model
individual-level and contextual- level variables and to estimate
the percentage of total variance in the out- come measure that
results from each (quantified as the IntraClass Correlation
Coefficient \[ICC\]). The analysis was conducted as a two-level
model, with person at Level 1 and context at Level 2. The
analyses were conducted in SPSS 19 using MIXED LINEAR.
This analysis assumes a continuous and normal dependent
variable (which was violated). A parallel set of analyses were
also performed using the SPSS 19 GENLINMIXED procedure
and specifying a logistic link function and binomial
distribution. For ease of interpretation, we have presented the
results in the original probability units from the continuous
SPSS MIXED multilevel model (0 = no littering and 1
= littering), rather than log-odds units. The conclusions are the
same as those obtained using a logistic link function.
The initial random effects model showed that the overall
littering rate was .17. That is, of all disposals, 17% were
improper, t(74.86 = 9.87, p \< .001). Acrossthe 1,962 disposals,
σ = .12, Z = 30.66, p \< .01, and the 86 locations, τ00= .022, Z =
5.71, p \< .01, there was considerable variability in the littering
rate. The ICC was .15. This statistic is directly interpretable,
and it indicates that 15% of the variance in littering behavior
resulted from site-level variables, whereas 85% resulted from


individual variability. This finding shows that on a national
level, the large majority (85%) of littering behavior results from
individual-level variables (e.g., age, gender, attitudes, and
motivation). This is not saying that physical context does not
matter, and in fact, these resultsshow that 15% of the variance in
observed littering behavior was due to some aspect of the
context (e.g., existing litter, lack of convenient receptacles, etc.).
The second set of analyses focused on individual-level
predictors of littering behavior: age, gender, time of day, and
whether the individual wasalone. Results showed that age, β =
16
−.001; df = 1943, t = −2.15, p \< .05, and gender, β = −.06; df =
1943, t = −3.42, p \< .05, were the only significant predictors,
with older individuals littering less than younger and males
littering more than females. Time of day and being alone were
not significantly predictive of littering. For clarification, age
was coded into demographic categories. The highest rate of
observed littering occurred for younger individuals, aged 18 to
29 years, for whom the littering rate was 26%. For adults 30
years and older, the littering rate remained steady at
approximately 15%. Children and adolescents (younger than 18
years) had a littering rate of 13%. The gender effect in the
general littering observations showed that men (21%) littered
more than women (15%). No other individual-level variables
were predictive of littering. However, the variability in the
Level-1 equation remained statistically significant, indicating
that other variables are required tofully explain individual
variability in littering. Combined, age and gender explained
less than 1% of the residual variance at Level 1.
Using the hierarchical structure of these data, the contextual
predictors of littering behavior were analyzed: site type (e.g.,
city center and fast food), location type (rural, urban, and
suburban), amount of existing litter present, beautification
efforts in the area (included ratings for cleanliness,
landscaping, and infrastructure), availability and number of
receptacles(summed number across trash, ash, and recycling),
posted signage about littering, and crowdedness of the location.
Given the relatively large number of predictor variables,
particularly with the dummy coded categorical variables of site
type and location type, the multilevel model was conducted
through a building process as recommended by Raudenbush
and Bryk (2002). The analysisstarted by testing each dummycoded categorical variable, sequentially, and removing
nonsignificant predictors. The continuous predictors were then
examined, again removing nonsignificant predictors. The
cumulative results from these analyses revealed two uniquely
and statistically significant predictors: availability of disposal
receptacles and amount of litter present. The first was the
17
number of disposal receptacles. As part of the site observations,
the team counted the number of receptacles (trash, recycling,
cigarette, and dumpster), along with the distance from the
person at the time of disposal. The average was 5.8 bins per
location, with a range from 0 to 19. The analysis for presence
of receptacles revealed the expected finding that littering rates
were higher when no receptacle was present. But more relevant
to the current research questions, among sites with at least one
receptacle, the statistical analysis showed that locations with
more receptacles had a lower littering rate, γ = −.01, df =
73.42; t = −2.52, p \< .05. Thisstatistical coefficient can be
interpreted directly, such that for every added trash receptacle,
the littering rate decreased by 1% (from the overall rate of
17%).
The second statistically significant predictor of littering
behavior was the presence of litter in the site. Locations with
more litter were associated with a higher littering rate. The
statistical analyses showed that the presence of existing litter
(rated by the observers on a scale from 0-10) was predictive of
littering behavior, γ = .02, df = 82.01; t = 2.40, p = .018. This
indicates that for every unit increase in the amount of existing
litter (from 0-10), the observed littering rate increased by 2%.
With both predictors in the model, the variance at Level 2
remained statistically significant, indicating that more variables
are needed to fully explain the variability across site. Combined,
the two variables explained 9% of the Level-2 variance, but the
variability in littering rates across the sites remained


statistically significant, τ = .019; Z = 4.60, p \< .001.
Supplemental analyses were also conducted using the Level1 predictor of distance to the nearest receptacle. This analysis
was performed using the multilevel framework but only for
observations at sites with at least one receptacle (of any type).
Results showed that distance to a receptacle at time of disposal
was strongly related to the likelihood of littering, β = .007; df =
1926, t = 17.95, p \< .001. Distance to the receptacle
explained 11% of the
18
residual variance at Level 1, σ = .108, Z = 30.55, p \< .001. Note
that distance was coded in feet, so that for each added foot of
distance from a receptacle at the time of disposal, the
probability of littering increased by .007. For clarification, we
calculated littering rates for disposals at seven different
distances, each of 10-foot increments. For disposals that
occurred within 0 to 9 feet of a receptacle, littering rates were
12%. At the largest distance (60 or more feet), littering rates
were 30% of disposals.
Observations of smokers. In addition to the large number of
general littering observations, the field team also conducted a
smaller number of observations of smokers. The separate focus
on smokers was based on two considerations. First, unlike the
general littering observations of individuals moving through a
public space, all smokers have something to litter---a cigarette
butt. Second, cigarette butts constitute the most frequently
collected litter worldwide.
The observations were made using the same protocol
described above, with a few modifications. First, only
individuals who were (estimated) over the age of 21 were
utilized. This qualifier was imposed to provide consistency
across our observational protocol and to respond to the
possibility of local restrictions on tobacco use for individuals
younger than 21 years. Second, a measure of existing litter was
included at each site that focused on the number of cigarette
butts within the observational boundary. Third, the measure of
existing receptacles focused on only ashtrays (or trash/ash
combinations). As with the previous study, disposals of
cigarette butts were coded as proper if they reached any type of
receptacle and not necessarily an ashtray.
In total, observational data were obtained from 767 smokers
from 44sites (11 recreational, 12 bars/restaurants, 12 retail, 8
medical, and 1 city center). There were 412 males and 344
females, ranging in age from 21 to 72 (M = 40, SD = 13; 11 not
coded). Of the 767 observed individuals, 206 (27%) left the
observation area still smoking, and the disposal behavior of 31
19
smokers could not be clearly established. Of the remaining 530
smokers, 187 properly dis- posed of the butt (35%) and 343
improperly disposed (65%). When the butt was littered, drop
with intent was the most frequently used strategy (35%),
followed by flick (27%), stomp (27%), and "other" (1%,
including placing the butt on or near a receptacle). Although
there were several instances of littering near a receptacle, most
littering occurred at considerable distance from a receptacle
(average distance at time of littering was 31 feet).
The data analytic strategy followed the multilevel model
approach used above, in which the individual and contextual
predictors of littering were examined simultaneously. The
reported results were calculated using the MIXED command in
SPSS version 19, and the results are reported in original
probability units. The analysis was performed on 530 cases
(187 proper disposals and 343 littered). The results from the
multilevel model showed that across the 530 individuals, σ =
.135, Z = 15.70, p \< .001, and the 44 locations,
τ00 = .081, Z = 4.73, p \< .01, there was considerable variability
in the littering rate. The ICC was .38, indicating that 38% of the
variance in cigarette littering resulted from contextual variables,
whereas 62% resulted from individual variability. This is a
considerably higher clustering effect than that observed for
general littering behavior, and it suggests that cigarette butt
disposal is more affected by contextual-level variables than are
general disposals (see recommendations section below).
At the level of the individual (Level 1), only age emerged as
a statistically significant predictor, with older individuals
littering less than younger, β = −.004, df = 518.49; t = 2.93, p


\< .01. The highest littering rates occurred for smokers in their
20s (66% littering rate) and 30s (72%), compared with smokers
in their 40s (58%), 50s (66%), and 60s (50%). Age explained
less than 1% of the Level-1 variance, and the variability across
individuals remained statistically significant, σ = .134, Z =
15.59, p \< .001, suggesting the need for additional predictors.
Neither gender, time of day, nor being part of a group were
related to cigarette butt littering.
At the level ofsite (Level 2), analyses utilized the contextual
20
predictors used in the analyses of general litter (with minor
modifications noted above). The resultsshowed three uniquely
predictive variables:site type, existing litter, and presence of ash
receptacles. One ofthe strongest predictors of cigarette littering
wasthe number of ash receptacles, γ = −.09, df = 31.91, t =
−2.13, p \< .01). The parameter estimate from the analysis is
directly interpretable, and it indicates thatfor every added ash
receptacle,the littering rate for cigarette butts decreased by 9%
(from the initial base littering rate of 65%). The second
significant predictor of cigarette litter was the amount of
existing litter, γ = .05, df = 39.01, t = 2.24, p = .03, with more
littered environments attracting more cigarette butt litter. Note
that the existing litter is of any type and not just cigarette butts.
Results also showed an effect for site type, where retail locations
were associated with the lowest rate of littering (58%),
followed by city centers (58%). Bars and restaurants were third
(62%), whereas recreational (74%) and medical/hospital sites
(75%) had the highest littering rates. Combined, these three
variables explained 24% of the variance at Level 2, although the
variability in littering rates across site remained statistically
significant, τ = .061, Z = 3.21, p \< .01,suggesting the need for
additional predictors.
Finally, supplemental analyses were conducted to examine
distance to an ash receptacle at time of disposal. Commensurate
with the previous analyses, this Level-1 predictor was
examined within the multilevel framework but only using
data from sites with at least one ash receptacle. Results showed
that distance to the nearest receptacle was strongly predictive of
littering, β =
.005, df = 292.79, t = 6.93, p \< .001. Although a few instances
of littering were observed immediately adjacent to an ash
receptacle, the average distance for litterers was 31 feet away.
Discussion and Conclusions
The results from these litter observations support a number of
conclusions. First, the overall littering rate was 17%. That is, of
all the disposals observed across the country, 17% were
21
improper. In addition, of the individuals sampled from 86
locations nationwide, 4% littered as they passed through the
site. For cigarette butts, the littering rate obtained from the
focused observations was 65%. This is a strikingly high
number, despite the strong norm favoring proper disposal that
has emerged over the past 40 years (see Bator, Bryan, &
Schultz, 2011).
Importantly, this littering rate was generated from a random
sample of individuals across a range of different locations and
not just a few isolated observations or of one type of location.
In addition, the results showed that in the majority of instances
(81%), the littering occurred with intent.
The results from these analyses underscore findings from
studies con- ducted nearly 40 years ago (Burgess et al., 1971;
Finnie, 1973; Geller et al., 1977). Although much has been said
about litter and littering over the years, no study has afforded
the opportunity to simultaneously test the degree to which it is
affected by personal and contextual variables. In fact, to our
knowledge, this is the first article to examine the same behavior
across a large number of contexts---a procedure which allows
for a quantitative analysis of "personal" and "environmental"
influences on behavior. The results of the current research
indicate that 15% of general littering acts result from contextual
variables, and 85% result from personal qualities. This finding
is particularly instructive because it indicates that given the
same infrastructure and opportunities to properly dispose,
individuals will vary tremendously. Note that if the trend had
been reversed, such that 85% of the variance was due to the


situation, it would indicate that while individuals vary across
settings, within a setting they act similarly (e.g., littering or
not).
The results from the analyses of littering behavior identified
only a couple of significant predictors. Interestingly, gender
was not a consistent predictor of littering behavior. Gender was
a significant predictor of littering in the general observations,
with males littering more than females. However, gen- der was
not a significant predictor of littering for cigarette butts. This
22
second finding runs contrary to prior data showing that men are
more likely to litter than women (Meeker, 1997; Torgler,
García-Valiñas, & Macintyre, 2008) but is consistent with other
observational studies showing no gender effects (Finnie, 1973;
Geller et al., 1977; Williams et al., 1997).
At the individual level, the results did show a consistent and
statistically significant effect for age, with young adults (18-
29) more likely to litterthan older adults. The negative
relationship between age and littering has been documented in
several survey studies of littering behavior (Beck, 2007), with
researchers reporting that younger people tend to litter more
often than those who are older (e.g., Durdan, Reeder, Hecht,
1985; Finnie, 1973; Heberlein, 1971; Krauss, Freedman, &
Whitcup, 1978). Krauss et al. (1978) also found that younger
participants were more likely to litter. They considered that
normative control requires both internal controls and cognitive
information, both of which develop through the socialization
process.
At the level of the location, presence and number of trash
receptacles, along with the amount of litter present were
significant predictors oflittering behavior. These findings are
consistent with previous studies (Cialdini et al.1990; Meeker,
1997), although Roales-Nieto (1988) reported results show- ing
that adding more receptacles did not result in reductions in
litter. This latter finding suggests that a raw count of
receptacles is probably an overly simplistic consideration.
Indeed, the current study shows that convenience (i.e., distance
to a receptacle) plays an important role. One well-placed
receptacle is likely to produce a larger reduction in littering
than several inconveniently placed receptacles.
To this end, it is tempting to ask about the "optimal" spacing
between receptacles. Although these data do not speak directly
to this issue, there is evidence that the lowest littering rate
occurs when receptacles are available and close at hand. This
effect was consistent for both general littering and disposals of
cigarette butts. Further inspection of the data showed that
aggregated observed general littering rates were low (and
23
relatively flat at 12%) for receptacles less than 20 feet away.
The littering rates increased linearly between 21 and 60 feet
and then remained relatively flat at 30% for receptacles 61 feet
away and beyond. It is also important to point out that
"optimal" spacing will vary by location, and the key
consideration is the distance to the receptacle when the
individual has an item for disposal. The current results showed
that the lowest rate of littering occurred when a receptacle was
fewer than 20 feet away. To deter littering, we encourage
thoughtful placement of receptacles so they are in the most
easily accessible location depending on where pedestrians are
likely to be when they are in need of making a disposal.
The observations ofsmokersrevealed similar findings. First,
with regard to cigarette butt litter, results showed an average
national littering rate of 65%. Thisissubstantially higher than
that found for littering in general and corroborates the high
number of cigarette butts collected in cleanups worldwide. As
with general litter, younger individuals were more likely to
litter than older, although the overall rate of improper cigarette
disposal was above 50% for all age groups. With regard to the
multilevel analyses, resultsshowed a clustering effect of .38,
indicating that a substantial amount of variability in littering
behavior results from contextual variables. Subsequent analyses
revealed that the lack of convenient ash receptacles, and sites
with high levels of existing litter (of any type, not just cigarette
litter), were predictive of higher litter rates. Although the
littering rates reported in this article are based on a large,