Acoustics and your environment. The basics of sound and highway traffic noise. Hello, I'm here to talk to you about the basics of acoustics with a specific focus on highway traffic noise. I'd like to start by defining acoustics. Acoustics is the science of sound, including its production, transmission and effects. Applications of acoustics include medical ultrasonics, underwater acoustics, architectural acoustics, active or passive noise control, non-destructive evaluation, environmental noise and many, many more. It is the application of environmental noise produced by highway traffic that will be discussed here, starting with the basics of sound. Understanding acoustics requires the understanding of sound. Sound is a vibratory disturbance created by a moving or vibrating source. As the source moves or vibrates, surrounding atoms or molecules are temporarily displaced from their normal configurations, thus forming a disturbance that moves away from the sound source. In order to better understand sound waves, it's first useful to study this animated representation of waves. In this demonstration, the sphere in the middle pulsates in and out at equal intervals, forming waves that propagate away from the sphere or the source. One way to picture this type of wave is by imagining yourself tapping your finger on the surface of water. As you tap, waves propagate away from your finger in a circular pattern. Now we're going to look at a snapshot in time of the wave animation in order to get a better understanding of wave parameters. For simplicity, the outward propagating waves can be approximated with a trigonometric sine function. The pattern of the sine wave repeats itself periodically. The wavelength of these waves, represented by the Greek letter lambda, is defined as the repetition length. As a wave propagates through an unchanging medium, its wavelength remains constant. Another parameter of a wave is its amplitude. The amplitude determines the strength of the wave. Greater disturbances at the source lead to greater strengths during propagation. For sound waves, a higher amplitude equates to a higher volume. Waves also have an associated frequency. Frequency, abbreviated with a lower case F, is defined as the number of cycles of repetition per second. In other words, frequency is the number of wavelengths that have passed by a stationary point in one second's time. The unit of frequency used here is called Hertz, abbreviated HZ. It's defined as the cycles of the wave per second. When the number of Hertz exceeds 1000, it's common to write the amount in units of kilohertz. For example, 1000 Hertz equals 1 kilohertz. Frequency is inversely proportional to wavelength. An equation relating the two parameters is C0 equals F lambda. C0 is the speed of sound in the medium. In air at a temperature of 20 degrees Celsius, the speed of sound is 343 meters per second. This equation implies that longer wavelengths are associated with lower frequencies and shorter wavelengths are associated with higher frequencies. Sound can have a single frequency component, as we earlier illustrated. Or, it can have multiple frequency components at varying amplitudes, thus making each sound distinctive. This type of sound is complex. Most real-life sounds are complex. For convenience, the frequency components of a complex sound source are very often studied in terms of octave or fractional octave bands. These bands each cover a range of frequencies and are referred to by the center frequency of the band. When the sound is complex, it can be called broadband because it encompasses many frequency bands. For octave band analysis, the entire frequency spectrum is divided into one octave bands. A list of these bands is shown in the table. The center frequency of each band is one octave higher than the previous band. Notice the center frequency's associated wavelengths. For the frequency of 31.5 hertz, the wavelength in air is 10.89 meters, or 35.72 feet. And for the frequency of 8,000 hertz, the wavelength is 0.04 meters, or 0.14 feet. We've used animation to help describe various wave parameters. Now that these parameters have been explained, let's look at a demonstration of how sound waves operate. Sound waves must propagate through some medium since it's the medium's particles that support the wave. The example medium here is air. Now a source is introduced on the left side. It is producing a pure tone or single frequency sound wave in the air. The air particles are displaced as the waves propagate away from the source. The particles themselves are just oscillating back and forth, but it can be seen that the sound waves are propagating outward. As the air particles bunch up, this forms a high positive amplitude area. And when they are most spread apart, this forms an area of high negative amplitude. In their undisturbed configurations, the particles represent an area of zero amplitude. The amplitudes correspond to a change in pressure from its ambient value. This is termed the acoustic pressure. A sine wave is again shown to better understand the wave parameters. Notice how the darker areas of the acoustic wave line up with the peaks of the sine wave. We have now defined wave parameters, including frequency, and have illustrated the important characteristics of sound waves. The discussion has focused on the sound source. It's also important to understand the receiver of the sound. As an example, sound waves travel through the air and interact with the human ear. Perfect human hearing lies in the range of approximately 20 to 20,000 hertz. Frequencies below 20 hertz and above 20 kilohertz are heard by other living creatures, but not by humans. 20 to 20,000 hertz is called the audible sound range. In for sound includes frequencies below 20 hertz. And ultrasound includes frequencies above 20 kilohertz. In order to connect frequencies with sounds we recognize, let us first listen to the piano. A piano covers a frequency range of 55 to 8,360 hertz. A middle C is at 262 hertz. Human conversation is also recognizable. Nearly all information in human speech is contained in the frequency range of 200 to 6,000 hertz. It should also be noted that human hearing is most sensitive between about 1 and 6.3 kilohertz. Now that we better understand sound, it's suitable to introduce a more subjective part of sound, noise. Although sound can be measured as a physical quantity, noise requires the judgment of a human listener. Noise is defined as any unwanted sound. Unwanted sound may be defined differently depending on the listener. However, several types of noise are understood to be objectionable. These types include highway traffic. This type of noise will be discussed later in the presentation. Loud machinery or tools. And aircraft. A better understanding of noise involves quantifying its perception. Physical measurements of the strength of the wave are explained first, followed by perceptual measurements. We talked before about the amplitude of a wave. The amplitude of a sound wave can be quantified by measuring the associated pressure disturbance. In other words, we need to measure the change in pressure from its ambient value. Again, this change in pressure is termed the acoustic pressure. Sound can be represented in terms of a physical unit of pressure. One such unit is Pascals, abbreviated PA, a unit widely used in the acoustics community. Acoustic pressure amplitudes can range from the hundred-thousandths to the hundred-thousands in Pascals. Because of the wide range of amplitudes encountered when measuring pressure, it has become convenient and customary to plot the pressure data on the more compact logarithmic scale. On this scale, the unit is the decibel, abbreviated DB. And now instead of plotting the acoustic pressure, you plot the sound pressure level. The decibel values shown here correspond to measurements in the air. The equation for converting pressure to sound pressure level, abbreviated SPL, is SPL equals ten log P over PREF squared. In this equation, P is a time-averaged pressure, and PREF is the reference pressure, a quantity that depends on the medium in which the sound wave is propagating. In water, PREF equals one times ten to the negative six Pascals, which equals one micropascal. In air, PREF equals twenty micropascals. This is the pressure value which represents the threshold of unimpaired human hearing for a one kilohertz tone. In other words, the quietest audible sound at that frequency. To get an idea of how pressure and sound pressure level relate, here is an example calculation where air is the supporting medium. We have an acoustic pressure level of 0.036 Pascals. The sound pressure level is then calculated to be about 65 decibels. This is the approximate sound pressure level for normal conversation. That sounds great. It was fun. It was the first time I've been for a number of years. You've been to England often? Years ago. Family from England. Here are other examples of sound levels starting with a very quiet sound and working our way up. A quiet suburban neighborhood will exhibit an average sound level of about 40 decibels. A gas lawnmower 31 meters or 100 feet away will create a sound level of approximately 75 decibels. A diesel truck 15 meters or 50 feet away traveling at highway speeds will achieve a maximum level of 85 decibels. A jet aircraft at an altitude of 305 meters or 1000 feet will create a maximum level of about 90 decibels. The threshold of pain for humans is between 130 and 140 decibels. The sound pressure level is often represented by a capital L. We will now use L to help explain the combining of sound pressure levels. When you need to add sound pressure levels, it is not a simple matter of algebraically adding the numbers. 80 dB plus 80 dB does not equal 160 dB. As explained earlier in the presentation, the decibel scale is logarithmic. Therefore, to combine decibel values, each must be converted to a linear scale, added, and then converted back to a logarithmic scale. Here, L1, L2, up to Ln represent the n sound levels that are to be combined. For applications requiring only integer decibel accuracy, it is also possible to apply some simple steps to calculate an approximate total sound pressure level. First, find the decibel difference between two sound pressure levels. You then add an adjustment factor to the higher of the two sound pressure levels. If the decibel difference is 0 or 1, then you add 3 dB. 2 or 3, you add 2 dB. And between or equal to 4 and 9, you add 1 dB. If the difference is 10 decibels or greater, you can safely ignore the lower level source. Under these conditions, the higher level source is said to mask the lower one. Here's an example combining three sound pressure levels. The typical procedure is to combine the smallest values first, then work your way up. So we start by combining the 80 decibel levels. Their difference is 0, so we add 3 dB to 80 dB to get 83 decibels. Then the difference of 83 dB and 90 dB is 7, so we add 1 dB to 90 dB for a grand total of 91 decibels. Now that we understand how to quantify sound, we can move on to its perception. A sound's loudness is a subjective rather than an objective description of noise. It all depends on how the sound is perceived for a particular individual. When researchers determined there was a need to find a relationship between the sound pressure level of a noise and its subjective loudness, several experiments were performed. As a result of extensive human testing, objective descriptors of loudness were constructed and applied to human perception. We can take a look at examples of descriptive changes in perception and the corresponding sound level changes. When the sound pressure level increases or decreases, some humans in a normal living environment can detect a loudness change if the sound pressure level is altered by 3 dB or more. A change of 1 or 2 dB will usually go unnoticed. A 5 dB change, on the other hand, can easily be detected by most people. Also, sound will be perceived as being twice as loud if the decibel level increases by 10 dB or will be half as loud if decreased by 10 dB. And it's common to perceive a 20 dB change as 4 times as loud or 1 quarter as loud. Here's an example of a sound becoming twice as loud. We start with the first sound, complex noise. And here's the second sound, the same audio clip, but at a different sound level. The second audio clip should sound twice as loud as the first. There was a 10 dB difference. Listen to the sounds again. We can also look at human perception of sound according to frequency. Remember from before that the audible sound range for humans is from 20 to 20,000 Hz. This is shown plotted on a logarithmic scale. Even though perfect human hearing lies in this range, we do not hear equally well at all frequencies. Earlier it was stated that human hearing is most sensitive in the range of about 1,000 to 6,300 Hz. To describe sound levels in a manner which closely approximates normal human hearing, the actual sound level measurement is modified by applying a weighting. A weighting is a response function that spans the audible frequency range. This weighting assigns to each frequency a weight that is related to the sensitivity of the ear at that frequency. Frequencies to which the human ear is less sensitive are weighted less than those to which the ear is more sensitive. The A weighting curve emphasizes frequencies in the 1,000 to 6,300 Hz range and de-emphasizes frequencies out of that range. This is the A weighting plot in tabular form for octave bands. For each center frequency shown, a dB adjustment value is given. For example, for the 125 Hz octave band, a negative 16.1 dB adjustment should be applied. The 500 Hz band requires a negative 3.2 dB adjustment. The 1,000 Hz band, which can be thought of as the reference frequency for A weighting, has no adjustment. And the 2,000 Hz band requires a positive 1.2 dB adjustment in sound pressure level. A sound pressure level with A weighting applied is often stated in units of DBA. The A weighted sound level is the most widely used measure of environmental noise and is internationally accepted. Noise can be a steady continuous sound or it may tend to fluctuate between intensely loud and quieter periods. An example of the latter is the noise produced by road traffic. It peaks with the passage of a heavy truck and will have quiet intervals when there is little or no traffic. Because of the large array of noises and the need to understand them from different perspectives, there are several ways to describe noise. Many noise descriptors are available where each is appropriate for particular circumstances. A general noise descriptor is one we've already spoken about, the A weighted sound level. The symbol for this descriptor is L sub A. More specific descriptors are the sound exposure level, the maximum sound level, the hourly equivalent sound level, the day-night average sound level, the community noise equivalent level, and the 10 percentile exceeded sound level. While all of these A weighted noise descriptors can be applied to highway traffic noise, the LAE Q1H and L10 are most often used for traffic noise analysis. LAE Q1H, called the hourly equivalent sound level, is essentially the average A weighted sound level occurring during a one hour period. To illustrate the equivalent sound level, we are first showing you the sound pressure level recorded at one location over time. This time history reveals peaks and dips caused by loud and quiet sounds during the time period. Now applying the LAE Q1H noise descriptor, the sound pressure level averages and the plot flattens out. This sound descriptor should be applied to continuous sounds, such as relatively dense highway traffic. The other noise descriptor we will discuss is L10, the A weighted 10 percentile exceeded sound level. It's the sound pressure level exceeded 10 percent of a specific time period. Here's an example. Say 50 samples were taken during a period of time. You extract the highest 10 percent of the sound pressure levels, which in this case would be 5 samples. The fifth highest sound pressure level is the 10 percentile exceeded sound level. In the first part of this video, we discussed the basics of acoustics and reviewed some common noise descriptors which can be applied to highway traffic noise. Now we're going to move on to the acoustics involved in highway traffic noise. This includes the noise source or the vehicles driving on the highway, the receiver of the noise or the people in nearby communities, and the propagation path of the sound as it travels from the source to the receiver. Let's first talk about the sound source. We'll describe two basic sound source types and the relationship to highway traffic noise. A point source is defined as a source that's essentially concentrated at a single point from which noise propagates outward in all directions. In other words, sound radiates spherically from a point source. The sound levels measured from a point source decrease at a rate of 6 dB per doubling of distance. This is a decrease due to spherical spreading. A line source radiates sound cylindrically. It can be composed of multiple point sources arranged on the line located at a perpendicular distance relative to the observer. The sound levels measured from a line source decrease at a rate of 3 dB per doubling of distance. This is a decrease due to cylindrical spreading. Two theoretical sound source types have been described, but what about actual noise generated from a highway? Well, we can consider highway traffic noise as a line source or as point sources depending on the traffic density and the listener's proximity to the highway. Individual vehicles act as point sources. Categorizing vehicles into several different groups is important to predicting noise levels. Vehicles are typically divided into these categories. Automobiles, which includes all vehicles having two axles and four tires, designated primarily for transportation of nine or fewer passengers or for transportation of cargo. Medium trucks, which includes all cargo vehicles with two axles and six tires. Heavy trucks, which includes all cargo vehicles with three or more axles. Buses, which includes all vehicles having two or three axles and are designated for transportation of nine or more passengers. And motorcycles, which includes all vehicles with two or three tires with an open air driver and or passenger compartment. In general, the loudest vehicles are in the heavy truck category. As an example, at a distance of 15 meters or 50 feet, a single heavy truck traveling at normal highway speeds can produce a maximum sound level of around 85 dB a. The quietest vehicles are in the automobile category. For the same conditions as the example heavy truck, the maximum sound level of the automobile is around 75 dB a. The major noise sources in vehicles are for trucks, the exhaust stack, and for all vehicles, the engine and tire-pavement interaction. The sound levels of individual vehicle noise are influenced by how fast the vehicle is traveling. Whereas individual vehicles are here considered as point sources, highway traffic can be considered a line source. It is composed of several vehicles or point sources closely spaced. A community is a sufficient distance from the highway to see highway traffic as a line noise source. This implies that for highway traffic noise, the sound decreases due to cylindrical spreading at a rate of 3 dB per doubling of distance away from the road. Highway noise studies are born out of concern for people. People exposed to highway noise include those who live, go to school, or work in surrounding communities, or people who actually work on the highway itself. The concern lies in their safety and well-being. The most obvious negative effect of noise is physical damage of hearing. Transportation noise levels experienced by communities and the general public, however, are normally not high enough to produce hearing damage. Other effects are the interference of noise with certain activities, such as sleeping, relaxation, study, or conversation. Although most interruptions by highway noise can be considered merely annoying, some may be considered dangerous. An example of this is the inability to hear warning signals or verbal warnings in situations involving workers next to a noisy highway. Less obvious, but nevertheless real, are the stress effects of noise. There is ample evidence that noise can cause stress and thus may be a contributor to stress-related diseases such as anxiety or heart disease. The path the sound takes when traveling from the source to a receiver can be quite complicated when taking into account ground reflections, obstructions, and meteorological effects. The received sound level depends heavily on these parameters. We can start by talking about a simple propagation path, the path going directly from the source to a receiver. Besides geometrical spreading, which causes the sound level to decrease as it propagates toward the receiver, the propagation medium itself can possess inherent losses. Atmospheric absorption is the attenuation of sound during its passage through air. For propagation distances related to highway measurements, this attenuation is only significant at very high frequencies, greater than 5,000 Hz. It is in general small compared to attenuation caused by other propagation mechanisms and can usually be neglected. Meteorological conditions can also affect the direct propagation path. These conditions include vertical temperature and wind gradients. In general, daytime weather conditions permit the ground to be heated by solar radiation. The air nearest to the ground is warmest, becoming progressively cooler with increasing height. This is called a temperature lapse. The speed of sound increases with increasing temperature. This causes sound rays to bend upward away from the earth. In turn, the noise level at the receiver decreases. At night, the ground usually cools by radiation faster than the surrounding atmosphere, thus causing the air temperature to become warmer with increasing height. This is called a temperature inversion. Under these conditions, the sound rays bend downward toward the earth. For this case, the noise level increases at the receiver. Besides the refraction due to temperature gradients, wind can also cause the sound to deviate from a straight path. When sound is propagating upwind, the ray paths curve upward as with the temperature lapse, decreasing the received noise level. And when sound is propagating downwind, the ray paths curve downward as with the temperature inversion, increasing the received noise level. The importance of meteorological effects is that sound may reach the receiver with greater intensity if it is refracted downward toward the earth and with less intensity if the sound is refracted upward away from the earth than in cases where meteorological effects are negligible. It should be noted that while atmosphere conditions can have major effects on propagation of sound over distances greater than about 100 meters or 300 feet, typical highway studies are performed within 100 meters distance from the highway in areas where the meteorological effects would be less severe. As for some other meteorological conditions, effects of fog and precipitation are generally negligible. On the other hand, atmospheric turbulence which can be generated by moving vehicles or heated pavement could potentially cause fluctuations in received sound levels or can reduce soft ground attenuation, the next topic of discussion. In addition to a simple path from the source to a receiver, sound can also reflect off the ground during propagation. The ground reflected wave can interfere with the direct, non-reflected wave to produce a net increase or decrease in sound pressure level. Increases in sound pressure level can be attributed to an acoustically hard ground such as asphalt or water located between the source and the receiver. For practical highway applications, measurements have shown a 1 to 2 dB increase for the first and second row residences adjacent to the highway. It should be noted that theory indicates that in certain situations greater increases are possible. An acoustically soft ground such as grassland, plowed earth or snow can cause a significant broadband attenuation except at low frequencies. As a general rule of thumb, for each doubling of distance, the soft ground effect attenuates the sound pressure level at the receiver by 1.5 dB. This extra attenuation applies only to incident angles of 20 degrees or less. For greater angles, the ground becomes a good reflector and can be considered acoustically hard. Another consideration when discussing ground reflections is the terrain geometry. The sound may encounter terrain that's not flat but rather terrain with small hills. For a downwardly curving ground, the sound rays stray away from the ground's surface. For an upwardly curving ground, the sound rays are forced to encounter the ground's surface. We have so far discussed several aspects of the sound propagation path and have now arrived at the last topic covered in this presentation, obstructions in the propagation path. Natural terrain features such as hills and dense woods as well as man-made features such as buildings and walls obstruct the sound path between source and receiver and in most cases reduce or attenuate noise levels for nearby receivers. The amount of attenuation provided by these objects depends on the size of the object and frequency content of the associated noise source. The term noise barrier refers to any large object that blocks the line of sight between source and receiver including the ground itself if it protrudes upward through the line of sight. A noise barrier is commonly a wall or earth berm specifically constructed for the purpose of noise reduction. This type of obstruction is the most commonly used traffic noise abatement measure. When sound encounters a barrier, there are three possible paths it takes diffracted over or around the barrier, transmitted through the barrier, or reflected by the barrier. The diffracted sound is the most important path that reaches the receiver located on the other side of the noise barrier. This is the sound that bends over the top of the barrier into the barrier's noise shadow. The frequency content of the sound is important. As discussed earlier, there's a direct relationship between the frequency and wavelength of sound. Lower frequencies have longer wavelengths and higher frequencies have shorter ones. As a result, diffraction is not uniform over all frequencies. Longer wavelengths that approach the barrier height easily bend over the top of the barrier right down to the receiver whereas shorter wavelengths bend just slightly over the top, not reaching the receiver. Diffraction also occurs around barrier ends. In these instances, it's usually only important for receivers close to the end of a barrier. The noise shadow behind a barrier is not very well defined, much different than a shadow in optics. When light hits a barrier, there's a well-defined region of darkness or light absence behind the barrier. For sound, the shadow must be considered an area of noise reduction rather than an area of noise absence. The deeper or closer to the base of the barrier a receiver is positioned in the shadow zone, the greater the associated barrier attenuation. Barrier attenuation is usually represented by the more standardized term barrier insertion loss. Barrier insertion loss is defined as the sound level at a given receiver before the construction of a barrier minus the sound level at the same receiver after the construction of the barrier. The construction of a barrier usually results in a partial loss of soft ground attenuation, a loss apparent when compared to the no-barrier case. This is due to the barrier forcing the sound to take a higher path relative to the ground plane. Physically, barrier insertion loss is the net effect of barrier diffraction combined with this partial loss of soft ground attenuation. Another way sound from the source can reach the receiver on the other side of the barrier is by a rather direct although obstructed path. When the incident sound hits the barrier, some of the acoustical energy can be transmitted through the barrier material and continue to the source. The amount of transmission depends upon factors relating to the barrier material such as the weight, stiffness, or loss factors. The angle of incidence of sound and frequency spectrum of sound. The amount of sound reduction during transmission is called the transmission loss. Typically, the transmission loss improves with increasing weight of the material. Most common materials used in barrier construction provide a transmission loss of 20 dB or better. For materials such as concrete or masonry blocks, the transmission loss values are more than sufficient, exceeding 30 dB. In other words, the sound energy transmitted through these barriers is reduced by 30 dB, rendering it effectively negligible when compared with the diffracted sound. As a general rule, the transmitted sound must be at least 10 dB lower as compared to the diffracted sound in order for it to be ignored. The remaining noise is either absorbed by the noise barrier material or reflected. The ability of a barrier surface to absorb incident sound energy is characterized by its noise reduction coefficient, abbreviated NRC. Noise reduction coefficient values are based on an average of absorption coefficients at individual frequencies and theoretically range from zero to one. Zero indicating that the surface is totally reflective or that there's no absorption. And one indicating that the material is totally absorptive. NRC values for an absorptive barrier generally range from 0.6 to 0.9. A reflective barrier surface can cause noise to affect receivers on the opposite side of the highway. It is a common perception among such communities to hear a difference in the sound after the barrier is installed. Although theory indicates greater increases for a single reflection, practical highway measurements commonly show a 1 to 2 dB increase in sound pressure level from the no barrier case due to the sound reflected off the opposing barrier. Such increases can be considered typical for the first and second row residences. While this increase may not be readily perceptible, residents in the opposite side of the highway may perceive a change in the quality of the sound. The signature of the reflected sound may differ from that of the source due to a change in frequency content upon reflection. If we have parallel barriers, instead of just a barrier on one side of the highway, the situation becomes much more complicated. For parallel barriers, sound may reflect back and forth across the roadway many times before ultimately progressing outwards toward nearby receivers. The multiple reflections increase the sound level at nearby receivers and can reduce insertion loss provided by either wall alone by 2 to 6 dB. As an example, a barrier that achieves a 10 dB insertion loss may realize only a 4 to 8 dB insertion loss if a parallel barrier is placed on the opposite side of the highway. The reduction in barrier effectiveness due to multiple reflections from parallel barriers or retaining walls is called parallel barrier degradation. Research has shown that as a general rule, if the ratio of roadway width to average height of the parallel barriers is 10 to 1 or greater, the parallel barrier degradation is less than 3 dB. Remember that decibel changes less than 3 are typically not perceivable. Solutions to parallel barrier degradation include one, applying absorbing material to the face of the barrier and two, tilting the barrier out away from the highway. When tilting a barrier, one must consider tall structures on the opposite side of the highway, so as not to adversely affect them with the reflected sound. Several design considerations need to be addressed when constructing a barrier. Following is a brief discussion of some of the acoustical design parameters. As a general rule, a wall that breaks the line of sight between the source and receiver provides 5 dB attenuation. Thereafter, you can achieve about a half dB attenuation per incremental foot of height or one and a half dB attenuation per meter. Breaking the line of sight with a barrier and getting the 5 dB reduction is simple. With this, you can achieve a 68% reduction in acoustic energy, which equates to a 30% reduction in loudness. The actual insertion loss for such a barrier could be less than 5 dB because of the partial loss of acoustically soft ground attenuation as discussed earlier. It is also usually quite feasible to achieve a 10 dB reduction using walls or berms of reasonable height. Here you would get a 90% reduction in acoustic energy and a loudness reduction of 50%. Greater barrier noise reductions are not so easily attainable. A 15 dB reduction would be very difficult and a 20 dB reduction would be nearly impossible, noise barriers are usually designed with an insertion loss goal of 10 dB in mind. Remember that insertion loss includes the barrier reduction and the partial loss of ground attenuation. Actual barrier insertion losses of between 6 and 8 dB are quite common. Also, keep in mind that a barrier will provide no insertion loss until its attenuation exceeds any loss of excess ground attenuation due to construction of the barrier. Besides obstructions that are specifically constructed for noise abatement, other large objects can interfere with sound propagation. Specifically, we are referring here to rows of buildings or large areas of dense foliage. Buildings refer to houses, offices, apartments, and other similar structures. For rows of buildings, the amount of noise reduction varies with building sizes, their spacing, and site geometry. Typically, 4.5 to 5 dB attenuation is attainable for the first row of buildings and an additional 1.5 dB for each subsequent row up to a maximum of about 10 dB. To get an idea of how gaps or openings between buildings in a row can affect the amount of insertion loss, here are some percentages and the corresponding decibel reduction. If 40 to 65% of the first row is occupied by buildings, the expected insertion loss will be about 3 dB. For 65 to 90%, the insertion loss will be about 5 dB. Lastly, for percentages greater than 90, the buildings will provide attenuation comparable to that of a barrier of similar height. In addition to constructed noise barriers and rows of buildings, areas of dense foliage can provide attenuation. Such attenuation is caused by sound scattering into the sky from trunks and limbs, affecting the middle frequencies, and leaves, affecting very high frequencies. Sound absorption by leaves is generally not substantial. In addition, some low-frequency attenuation results from ground attenuation within the wooded area, where the roots of underbrush produce acoustically absorptive soft ground. To achieve any substantial amount of attenuation due to foliage, such as trees and bushes, foliage must be at least 30 meters or 100 feet deep, in other words, a strip of 30 meter width, and dense enough to block the line of sight between source and receiver. Further, the foliage area should have a height that extends 5 meters or 16 feet above the line of sight, typically as much as 5 dB attenuation is attainable. If the foliage area is doubled in width to 60 meters or 200 feet or extended beyond that, a reduction of 10 dB but no greater is attainable. Theoretically, using foliage to reduce the noise and communities surrounding highways is appealing. However, foliage of sufficient width and density to reduce noise is not usually found along highways. Vegetation planted as part of a highway project will not provide noise abatement. Phew, that was an ear fall. This presentation has highlighted some of the basics of acoustics and highway traffic noise. Of course, many details beyond what was covered here must be taken into account to accurately describe the sound in our communities. There are ongoing studies in this field of acoustics and we can look forward to progress and years to come.


Very good morning to you. Friday the 4th of June and of course non-farm payrolls is going to be Eagly anticipated today given the fact of the downside surprise that we saw last month and the sums couldn't reaction We had in today a few weeks ago So definitely that's the main focal point But certainly a few things for me to get you up to speed on so let's start off with how we closed on Wall Street and What I was looking at here was a graphic of the last kind of quarter and looking at an equity perspective, but the differential between the NASDAQ composite and blue and the S&P 500 in pink and What we can see here is pretty similar to how we closed on Wall Street yesterday, which was the NASDAQ 100 under performed We were down about 1.1% whereas the S&P was down about a third the Dow relatively flat and even though What we saw was equities generally moving on similar themes actually on the breakdown What we tended to see is in other asset classes particularly given the string of good data We had weekly jobless claims come in at below 400,000 for the first time since the onset of the pandemic We had a record high in the ISM services PMI and we had ADP national employment blowout Expectations even above the most optimistic estimate on the street Trifecta then of good data saw a fairly linear reaction in a sense of higher dollar and higher US yields So major currency pairs run a little bit of pressure We also saw tea notes fall and also precious metals came under some aggressive selling pressure as well yesterday Of course when you probably saw that moving gold and and also in sympathy in silver as well But on equity perspective What's interesting is that whole movement on the sector rotational basis We have tended to see in a higher yield environment and yesterday was pretty similar also with energy prices continue to remain fairly buoyant in the crude space is Energy and financials outperforming comparative to their technology peers for example So that continues to be the case and as you can see that divergence even probably more so more recently as we get closer to that point of eventual policy tightening from the Fed in the future But let's have a look at the charts and we'll kind of incorporate some of the news as well and few things to be aware of In the currency space as far as the dollar index is concerned this morning We're up about 0.1 percent. So we've really kind of held the moves from yesterday So let's start with euro dollar and then we'll look at cable. So as you can see from euro dollar here I'm just gonna shrink the chart so you can see it over my my camera feed Quite a nice Entry point if you are trading late in the US session continuation of that trend of dollar strength That was particularly prevalent yesterday But has been a bit of a theme throughout the week of course because euro dollar did start up on Monday Up at around the 122 50 mark and we're trading, you know, well over a full point lower than that at the moment But you can see that previous load we saw on Friday's session. So this time last week we broke down through that in the European exit yesterday through the fix came back up to that point on the retest late in the US session and then we've continued to drift south as Asia participants of kind of just continued that dollar strength trend in the overnight session that held on to those relative gains in the greenback So as such just keeping an eye on the euro here and we are trading down about 19 pips You can see going back here on the left hand side of the chart if we go back into May on the 13th and 14th It was an area of which we're now trading at the moment So be interested to see how we perform here technically speaking then Room toward the 121 handle just below there is the s1 on the daily pivots and then the overall Kind of mid-May low was seen down at around the s2 At 120 61 could we get down there today? Well, we could and would we get down there this morning? I probably doubt it. I'd say we probably need to see a really strong payroll figure to initiate Further dollar strength to bump us down to that degree But certainly that would be a potential scenario on the daily chart And what's been quite interesting is we've been watching this trend line for a while This goes back over the course of the last two months and you can see it was getting a little bit choppy But failed importantly to to close below that level despite a few excursions to the downside over the course of The last week or so. However, yesterday was a definitive break We also took out that horizontal level of an inflection point at around 121 65 and a half and You always just continue to trade heavy. So in actuality on the downside. It does remain susceptible I would say for further moves here on to to the downside and actually just looking at that double bottom that we had Going back to kind of middle and late May that would be around 120 Kind of 62 on the futures And so perhaps then the little space on the downside for continuation of a move lower today under under the right conditions Of course depending on payrolls, but definitely that's an area I'd keep an eye on and a little bit more bearish the underpriced action of the euro mid that research at dollar for cable Yeah, definitely been a theme throughout the week of Weakness overall. It's kind of under performed a little bit compared to its European peer one thing I would say is that I think trying to connect this idea of the potential delayed reopening on the 21st of June because of the current COVID situation on that and I think it's the Delta variant. They prefer to call it rather than the Indian Then we've seen a top in cable But I definitely think that's more tied to the technical move of had a decent running cable. Don't forget You know, we were trading back in The beginning of May so basically a month to the day We were trading down at 138th hand and we've managed to test up in a multi-year high in excess of 142 So I think that's nothing more than technical rejection up at a rose levels for the time being And I wouldn't really over interpret that at the moment if you're looking at cable here Again, I'll just move it up slightly so you can see but again going back to the same kind of time period as the euro You can see this area here That was around the 12 13th 14 17th on the left hand side That's about where we're trading at the moment at around 140 77 We've printed alert 81 so far and a bit of a Support found around that area due to those previous responses at that particular zone Otherwise a quick look in some of the other charts Let's have a look at gold definitely a real mover yesterday, of course given the The initiation of that positive data and dollar strengths and high yields gold Saw a Potential opportunity there on the break of the trend line from the 19th test on the 28th. It did come back up Quite nicely at one point to that level Before then the real aggressive momentum trade kind of took hold and Looking here at the bounce that we've seen this was the low in the overnight Asia pack session So it's a little bit choppy overnight and slightly more liquid conditions And we bounce snap back high on the back of that but that Rejection and this is what you tend to see when you see a Illiquid Momentum based fast money move a lot of those people who are short there would look to bail at the first nearest Clearest point and the pivot levels often in a fast moving market do do provide that kind of clarity But also you can see that previous low back on the 19th in proximity there around that kind of 1855 area We've we've adjusted so far So gold kind of now holding pattern. I would expect until payrolls as far as The S&P is concerned not too much in the way of interest of where we trade right now, but certainly What I found quite interesting about equities yesterday was Yields generally remained higher as did the dollar and gold remain lower on the back of those positive factors from the data releases But equities initially sold off So it kind of felt a little bit like people might get a little bit apprehensive about a knockout You know kind of payrolls then people start fearing about Fed tightening But then we just got countered by some aggressive dip buying So a really strong recovery And I think I guess not to try and curve fit too much rationale behind these moves But I definitely think it's an important practical point when you're trading payrolls later The moves and seem much more clear And so therefore much more actionable from an execution point of view in anything other than equities Because with equities, I think there's that balancing act between you know Is this good for equities? Is it bad in a sense of the implications on policy? Whereas the other products I think are moving in a lot more clearer and a more kind of linear way in that Respect so do bear that in mind later But perhaps then it's kind of like well strong economy The Fed is only ever going to be gradual and cautious. So net net after the initial kind of Correlated move on asset classes on the sell-off people's just rationale kicks in well look the Fed aren't ever gonna You know tighten Ridiculously quickly and equities kind of stabilize at that point I guess away from all of that on the technical perspective This is that daily chart. We've been obviously tracking for the last couple of weeks and that key level around forty one eighty and You can see we had a brief dip below there yesterday, but failed to close We've had another test of it as well this morning, but that that's gonna be quite interesting I think as far as the payroll the end of today and this week is concerned for just general sentiment being bullish or bearish over the short term for the S&P do we have Situation where we push back up and actually you finished a day with a really strong gain And we push up back up toward the high end of the range of the week Or do we break this down and actually close below it which could signal then some further? Gradual move lower albeit. I'd still expect it to be counteracted lower down At lower points by by people looking to to buy the dip Something to be aware of in the energy market Again, we did see a bit of a dip on the crude oil infantries yesterday. That was this kind of red candlestick here However, quite a nice response that we have been seeing ongoing and so certainly something I'm keeping an arm going forward Is this kind of trend channel going from the last two weeks? It's been fairly well Respected as we've continued to just grind our way higher in crude getting ever closer to 70 dollar marker We're pretty flat this morning, but something I'd be aware of from a technical perspective All right quick look at the news stories then get you up to speed a few other things first off talking about Biden President Joe Biden offered to scrap his proposed corporate tax hike during negotiations with Republicans according to two sources For me with the matter yesterday It would be a major concession of course by the Democratic president as he looks to kind of hammer out the Details around the infrastructure deal Biden offered to drop plans to raise corporate tax rates as high as 28 percent instead set a minimum 15 percent tax rate aimed at ensuring all companies pay taxes The reason why he's talking about all companies ensuring that they pay their tax as you will know Biden's 15 percent tax floor seeks to stop those large multinational companies like Amazon Starbucks all those ones that you read which have then Presence in say low tax areas like island in Dublin for example and pay little to know US taxes at all So it's trying to capture some of that and it's then seen as a brokering deal in order to get more Cross-party agreement given the fact that obviously the Republicans are going to be anti Tax rates jumping up as high as 28 percent to get his infrastructure deal over the line Other things I thought were quite interesting in the press You might not really recognize some of these faces, but this is feds Kaplan So let me just read out to you two fed speakers and remember this is the final week Of course in the final day before they go into the blackout period where they're not allowed to speak ahead of the June 16th meeting so the commentary at the moment I think is quite key and there's a real subtle observation I think that's been Evident this week through the number of speeches that we've had so Kaplan said quote I think it would be wiser sooner rather than later to begin discussions about adjusting our purchases With a view to taking the foot off the accelerator gently Gradually so we can avoid having to depress the break down down further down the road now A couple of things to bear in mind whenever you hear a federal reserve official speak Two questions. Are they a voting member of which Kaplan is not and then to what is their monetary policy view? In general and he is a hawk So the fact that he's talking about this idea Of adjusting purchases sooner rather than later and having those discussions kind of is in fitting with those those His stance, but let me just give you the context So new york feds williams who is a voter sits a little bit more Center if you like in his policy view He said quote while it makes sense for the fed officials to begin discussing their options for adjusting monetary policy The u.s. Economy is still far from the point at which the central bank might begin to withdraw its support And so for me What I think is quite clear and has been prevalent this week is the voting members have kind of towed the line Whereas the non voting members have been a little bit more Leaning on the side of talking about tapering And I think that's an important signal and I think that is a tactical approach from the fed in order to plant the seed To acclimatize us as investors To this notion of discussion on tapering to almost kind of control it in our minds and not freak out When those discussions in fact do materialize So all the time the official line is no it's too early yet We get told subtly on the side then from the non voting members that actually We we should start talking about tapering now So when it happens, it's like a good in between to get us like our behavioral Kind of psyche in the right place to minimize market impact. So this is quite classic. I'd say central bank communication tactics But something for the new guys to be aware of so let's to not get too spooked when you hear these guys talking Now another thing I just wanted to quickly have a look at You know through all my career. I never thought I'd be talking about heartbreak emojis, but here we are It's you know 2021 and Elon Musk I thought was being a little bit quiet this week Given his recent string of tweets and he hasn't let us down He's saved it for that Friday feeling and he tweeted overnight bitcoin heartbreak emoji and so he then had this meme talking about A breakup and quoting a Lincoln Park song that it didn't even matter in the first place So a couple things then to be aware of jokes aside Bitcoin's price first of all has reacted to his tweet This is one of the things I find very challenging to accept with some of the base bitcoin argument about it being Decentralized and you know away from intervention from a singular authority like a central bank. Well, I mean Elon Musk is like the central bank on steroids as far as his direct influence over this product. It's quite crazy So how you can have that view? I'm not quite sure but that aside Bitcoin has actually weakened over six percent since he made that tweet. He made that tweet here That's a price point. Um, just looking at it technically I've just been looking at the top end trend line, which has been containing price overall, but Rather than so much bitcoin of which if it does start to move lower, obviously Just keeping an eye on this trend line for the 19th 28th You've also got this kind of level here at around 35 780 in the futures, which was the air of support Through a couple of the recent trading sessions And then obviously just following further the decline and using the reference point of the previous price action at the last two weeks So 34 to 35 and you know how bitcoin moves we could quite easily see these types of levels Intra day be interested to see how the u.s. Come in given he tweeted it overnight If they're seeing it with fresh eyes But if bitcoin's remaining weak the one company that obviously suffers is his own in a slightly self-inflicting way and Tesla I think would be one to watch obviously payrolls is going to create some subsequent movement in U.s. Indices, but that aside We saw some selling pressure quite heavy in tesla In the latter part of the u.s. Session there were some reports about china orders of reportedly halved We also saw a kind of short-term kind of Two-week trend line break And we were looking at that horizontal level with the trend line 600 was a key And kind of 596 600 that zone was quite key for the test of the stock price And that trigger point and the catalyst of the news just saw the run lower And then we just kind of settled it around that low we printed on the 20th So here this was the close last night and obviously the tweet came overnight and subsequently bitcoin has now weakened 6 Plus if bitcoin starts to weaken further let's say 8 9 10 15 percent Just saying then definitely i'd be looking for a technical break here And that could act as a trigger point then for some further selling pressure to come into tesla shares So something to look out for that may low then would be down at 547 27 Should we get there but something to just keep an eye on later this afternoon? all right I'm not going to talk too much about payrolls now because I will be covering that Live later, so i'm going to leave that aside and just say One overall top level point Which is the data yesterday was very good I'm not going to include jobless claims specifically in that because it wasn't part of the reference week But generally claims jobless claims have been on a continuation of a positive employment trend The other data has been particularly the adp a lot of people will criticize adp saying it's not very accurate It wasn't accurate at all last month, but people still put a fairly heavy weighting to it Point being is market the way the market reacted yesterday Your dollar is still firmer the yields are still higher Gold has bounced off overnight lows, but it's still lower And so in my mind then the market is susceptible To anything other than a very strong number today you might see reversal in these prices is all i'm saying remember last month We were looking for knockout numbers One one and a half People talking about two million and it came in at two six six I'm not saying that necessarily happened again What I am saying is the take into consideration the market positioning from yesterday The bar is quite high now for an upside number. I think the headlines expected at six fifty for me I think if you get six fifty you actually see a bit of reversal some of yesterday's move It's just not good enough for how the market set itself up for this release. So Yeah, I'll go into it more details later ahead of the actual number, but that's it. I'm going to leave it there I'm not going to bother looking at too much else on the calendar because otherwise got factory orders 3 p.m fed share powell does speak as well At midday Speaking at a bis panel with christine legard dsp president and the pboc governor gang So it's something to just be aware of All right guys, that's it. I'll leave you to it. I wish you a good day ahead and good luck for payrolls and enjoy your weekend Thanks very much


I'm a research fellow at UNU Wider and I'm going to talk about the, what is now called the ICTD, UNU Wider Government Revenue Data Set. So the name seems to be getting longer as we go along and the title or the working title of my presentation is Toward Closer Cohesion of International Tax Statistics. So I've been working on the update of this of this data set and as I've gone along this year I've been making some notes and trying to put together a paper on some of the inconsistencies and issues I've covered. So one person that's read this paper so far described it as interesting in a very dry sort of way. So I hope it's, I hope it's at least interesting for some of you. So I'm going to start off with just a discussion about where the GRD fits into the bigger picture and discussions around taxation and development at the moment. And then thinking about the bigger picture, there's been a recent focus on domestic resource mobilisation from for example the Addis Action Plan and it's enshrined as one of the sustainable development goals, I'm not going to read it out, it's on the screen. There's a couple of SDG indicators that this kind of data could perhaps feed into the discussion surrounding. And indeed all of the, all of the sustainable development goals are certainly underpinned or it's been acknowledged that they need to be funded by an increase in domestic resource mobilisation. At the same time there's been issues around data quality and calls for in some corners a data revolution and so I think perhaps the GRD project is quite relevant and fits between these two current concerns in the development agenda. So thinking about the data set, it's a partnership with the International Centre for Tax and Development. So it began life there in around 2010, 2011 and after a lot of years of work it was finally launched at the back end of 2014. Since then it's been used in actually quite a lot of research studies. I did a Google scholar search for it a few days ago and I was personally surprised to see the pure number of people that had accessed this data and used it and the sorts of ways in which they had applied the data to their research questions and it's been used by economists at the UN, the World Bank and even some in the fiscal affairs department at the IMF which is really encouraging for us. But the management of the data set has moved over to UNU wider since the back of 2015, start of 2016. So we held a small event last year where a number of researchers came together and presented their findings having used the data set. And it fits as part of a broader program on taxation and development at wider so you might have been to some of the sessions that dealt with tax benefit micro simulation models or this morning some researchers were talking about the access to firm level data in South Africa. We kind of advertise this as a research data set. It's of course open to anyone to use but it came out of a need for or a perceived need for more open, reliable, comprehensive, maybe analytically accurate cross-country data for researchers to access for their studies. And this arose because a number of previous studies on links between taxation and development at the macro level had been based on ad hoc data sets perhaps created by individual researchers that sometimes weren't publicly available and at other times even if they were publicly available it was difficult to put the results in context with one another because the sources were different. There was also a case where or a number of cases where tax policy advice was being given to developing countries but the advice was being given based on or informed by knowledge that came from studies that had only used for example high income or OECD countries in their analysis and that was probably as a result of the lack of availability of tax data for developing countries. And so traditionally with cross-country data people would look to the IMS government finance statistics which has fairly limited country coverage and is not overly detailed in many cases. The same goes for the OECD's revenue statistics. Other problems with existing sources of of tax data and revenue data are that neither the IMF or OECD would systematically account for the presence of natural resource tax or natural resource non-tax in in total revenues. The two sources also differ quite a lot in the treatment of how social contributions are recorded and I'll say a bit more in fact probably a lot more about that in this talk because it's one of the things I've tried to focus on getting consistent in this version of the data set and traditionally these sources have had reason fairly poor coverage for developing countries. I should say that both the OECD and IMF in the in the kind of two years since I've been involved with this project and accessing the underlying data there have been improvements. The OECD released their revenue statistics in Africa for the first time last year for a limited number of countries and those statistics are quite detailed and we've been able to incorporate some of them this time around into the government revenue data set. The IMF's government finance statistics as well have improved their coverage for a number of developing countries over recent years so the signs are certainly in trending in the right direction for the availability of these types of statistics but there's still significant gaps and especially when you go back in time into the 1990s or 1980s. So to give an illustration of some of the challenges that exist in underlying data I'm going to show you some data that comes from the IMF's government finance statistics and why this presents real challenges for users trying to access and make sense of the data and make cross-country comparisons. So this series is for total taxation in Algeria between 1995 and 2010. This comes from the IMF's government finance statistics and you can see there's quite a lot of volatility and you might think that's probably due to resource revenue, hydrocarbon revenue and you would be right. If you try to disaggregate this from the IMF's government finance statistics into so I will just plot two categories taxes on income and taxes and goods and services you can see that for the first part of this period most of the volatility seems to be driven by income taxes which would suggest that there seems to be some sort of natural resource revenue tied up in that figure until 2002 but then what happens with the figure for with the graph for the rest of the time series I'm not entirely sure but it would appear that there's a massive reallocation away from taxes and goods and services toward or sorry from income taxes toward taxes and goods and services for 2003-45 and then it seems to jump back and then it jumps back again. In all honesty I don't know the reason for this. It looks like it's this portion of resource tax that at one point has been classed as an income tax and another time has been classed as tax and goods and services um but in truth I'm not sure what is the right way to classify this kind of data but hopefully this serves to illustrate that for a researcher wishing to use either the income tax ratio or the tax and goods and services ratio from this source you know it's very inconsistent and definitely presents some challenges there so this is the sort of thing that that crops up in underlying data and unfortunately there's not a lot of guidance provided with these sources to the to the end user as to how they should best deal with this sort of thing. So to take a little step back again we construct a cross-country dataset on government revenues and it runs from 1980 until 2015 and some data does exist for a number of countries into the 70s and even in 1960s but it starts to get very patchy the coverage so we draw a line under it at 1980 where the coverage generally starts to pick up and become quite complete. The sources we take this from are OECD's revenue statistics, IMF government finance statistics the economic commission for Latin America and the Caribbean sepal start initiative was previously quite a good source of data and the most recent update they haven't updated their data for quite a while so we might not be able to include any of that going forward. Mainly we're able to get data or access data for developing countries from IMF staff reports statistical appendices and on very rare occasions we can look to national data sources from revenue authorities for example to fill in some gaps if appropriate. So the data runs from 1980 to 2015 the data is there I think there are data points for almost every country over 180 I think and so we present revenue tax and subcomponents so that's direct tax indirect tax and the subcomponents of that which is income payroll property taxes and goods and services trade taxes and other taxes and we follow the classification of the IMF government finance statistics manual because it's fairly straightforward to follow and we express all of this as a percent of what we call a common GDP figure so sometimes if you've accessed data as a ratio of GDP from two different sources for the same country your observation not only might the tax figure be slightly different but the GDP figure that they've used may also be different so there's a very simple correction we just use one GDP figure and we take all of the data in common local currency and that GDP figure comes from the IMF's World Economic Outlook so compared to existing sources or underlying data sources I've identified what I think are the four main innovations or improvements that we're able to make and the first one is that we can achieve significant gains in coverage and consistency compared to other sources we can present revenues both inclusive and exclusive of social security contributions and that point is perhaps more relevant for OECD or high income countries a lot of developing countries or lower middle income countries don't at this point either collect or report social security contributions but it's kind of an important point that you should bear in mind if you're using this kind of data to make cross-country comparison we also were possible try to distinguish the part of revenue that comes from natural resources and so the Algeria example was just one where that's perhaps a problem and so we try to do that as consistently as possible for the largest number of countries as possible and perhaps most importantly we provide notes and extensive interpretations of the data and help to guide and try to guide users when they're using the data and so that they employ it in a more perhaps responsible manner so thinking about the gains in coverage what we offer is what we call a merge data set which incorporates data from both central and general government so ideally we would be able to incorporate data for the general government level for every country that's not possible because a large number of countries don't report at the general government level to the IMF and that's perfectly understandable so ideally we could have general government where we can't account for all revenue we try to just account for as much as possible so in some countries that might mean we some central government and state government together so India is one example where a lot of revenue is collected at both the central and the state level so we can sum those together in a large number of countries we're able to sum central government revenue with revenue collected at the at the social security funds level of government but similarly in and still quite a large number of countries data only really exists at the budgetary central level of government and so that's all we can really really report we also make available the underlying central government files and general government files should users wish to look at that data as well so I said before that for most of the for most of the developing countries the the data is lifted from IMF article four staff reports statistical appendices and this is just an example of one from Benin in the early 2000s and you can see that in this case the disaggregation is reasonably good direct taxes indirect taxes and trade taxes etc and so we're able to we're possible match those up with with the revenue headings that we have I should say that this is perhaps a rare example of of a statistical appendix which is quite detailed a lot of them are frustratingly frustratingly restrained in in what they in what they can report the second thing that I wanted to talk about was that across sources in both and across countries as well the way in which social security is funded social security contributions are reported and it differs widely and this if you're making a comparison of the tax ratio can you know can skew your results or your your graphs or your diagrams or if you're including this in an econometric approach and it's maybe something that you should you should be aware of so across countries some countries fund the social security via taxes some via more traditional social security contributions and in some countries employees are required to make compulsory contributions to the private sector and so those those contributions don't actually ever enter the general government so we don't see those numbers showing up in tax statistics and also across sources so between the IMF's government finance statistics and OECD revenue statistics we see social contributions classified in different ways sometimes and I'll show a few examples of where that's the case just not so if I plot this is for Denmark and Finland and this is taxes exclusive of social security contributions and this comes from the OECD's tax statistics so this exposition would suggest to you that Denmark collects a heck of a lot more tax than does Finland but if you have any sort of knowledge of those two countries you know that's perhaps not the case in truth this is kind of a definition of an issue and comes down to the way in which social security is funded in Denmark where it's funded almost entirely through personal income taxes when you then express this series as taxes inclusive of social contributions you see that the two lines are much closer together and so this gives a much more accurate picture of revenue collection in both in both countries and then you can see that the green line shows that what are what are reported as social security contributions in Denmark are actually only a couple of points of GDP whereas in Finland they're over 10% of GDP and this as I said is really a definitional issue and so I think personal income taxes in Denmark account for about or they are to the tune of about 25% of GDP which is more than a lot of countries collect in total taxes it's just due to the way that social security is funded but the wider sort of point is that if you use the variable taxes exclusive of social contributions you perhaps end up with a slightly invalid cross-country comparison of the tax ratio in these two countries how does the GRD overcome this kind of inconsistency it's fairly straightforward we present total taxes both inclusive and exclusive of social security contributions we also present direct taxes both inclusive and exclusive of social security contributions so there's a lot of columns in the data set but hopefully the fact that all those figures are available helps users to see these sorts of differences and make a better use of of these statistics a second issue that crops up and it doesn't it doesn't happen for a lot of countries but this is for this is a graph for the sum of payroll taxes and social security contributions in Sweden and you can see that if you take the data from the IMF government finance statistics they suggest that payroll taxes are close to 10 percent of GDP and social security contributions are very very low but if you take the exact same numbers from the OECD's revenue statistics you see the gray line doesn't really shift at all so they're capturing the same thing but the the mixture between payroll taxes and social security contributions has changed completely so if you were making inferences about the tax ratio in Sweden where payroll taxes are part of tax and social contributions are part of social contributions depending on where you got your data from you you may come up with an answer that's perhaps eight percent of GDP out depending on the source and again it's purely a definitional issue and to do with how the taxes are recorded in in the different sources but it's something that users should be aware of and so in cases like this where one source says one thing and another source says something totally different what we do is we annotate the data and say look we've selected data from the OECD's revenue statistics they suggest this about social security contributions but other sources suggest something slightly different so be aware. A couple of other inconsistencies that that come to mind I mentioned about private sector contributions so as far as I know in Switzerland employees are required to make or some employees are required to make compulsory social contributions to the private sector so those never actually enter the general government budget and so Switzerland's tax ratio ends up being somewhat lower than you might expect for for a high-income country like Switzerland and again this is not something we can account for in the data but it's somewhere where we guide users and say look compared to some other countries that collect all of their social security contributions at the general government level or at the social security funds level of government you might want to be be aware that this ratio is slightly lower. So thinking for a moment about natural resource revenues so researchers and policymakers are I think increasingly interested in in the non-resourced tax tech and certainly in this SDG context where there's a move toward trying to increase revenue mobilization in developing countries it's important that researchers are able to get at that data and most sources or most underlying sources are not very good at disaggregating the data in this way I should say that the the new africa tax statistics from the OECD actually do quite a good job at this for the limited number of countries where they report unfortunately the IMF's government finance statistics don't yet disaggregate their their tax revenues in this manner so in some cases we're able to go into individual country sources and and augment the the data from from OECD and I'll say a little bit more about that and so as an example of why this is maybe useful here is total government revenue in Nigeria as a percent of GDP so I think the underlying source here is an IMF country report and as you can see it's extremely volatile in the GRD we're able to break this down into the resource component and the non-resource component and you can see that the volatility is almost entirely driven by revenue from oil and then we can take the residual of that and you see that actually non-resource revenue has been fairly constant for the last 10 years or so at about four percent of GDP and can we do this for every country no we can't and Nigeria is a nice example and that's why I've put it up there and the data is less consistent for other countries there are an increasing number of countries where it is well reported and that's encouraging and but it's it's not always as consistent as this but we hope to give users of the data just an idea of of the amount of revenue that comes from from natural resources and what I should say is so that that variable there was what we've what we've called total resource revenue or total hydrocarbon revenue for Nigeria it's very it's not always possible to break that down into the part that comes from taxation and the part that comes from non-tax revenue indeed it's very difficult and the way in which these revenues are reported over time in the country reports is sometimes wildly inconsistent sometimes a report might suggest it's a non-tax revenue the next report would suggest that it's actually a tax revenue so it's really difficult for us to know in some cases some and they also differ across countries some countries will class for example a royalty as a non-tax payment other sources would class a royalty as you know an a tax payment and so we're not always entirely sure so the the total resource revenue figure is probably more consistent than figures on resource taxes or non-resource taxes um how accurate are these figures um well sometimes it's really difficult to know what we have done is we've I've tried to match up where we have a data point on natural resource revenue where with where the extractive industries transparency initiative also has a data point and so some of the researchers at the national resource natural resource governance institute um very usefully put all of the EITI data into excel form so um thank you to them for doing that but it's allowed me to come up with a bit of a comparison with how the figures from IMF's um country reports match up with EITI data and that's with the assumption that EITI is capturing you know as much resource revenue as possible um and so I was reasonably encouraged with with how close the figures were so our GRD figures are on the y-axis EITI data is on the x-axis and you know most of the the the points would cluster around the 45 degree line so I was I was fairly encouraged but there is there's certainly a tendency that our figures would would underestimate the total amount of resource revenue that's assuming that the the point in in the EITI is is the correct amount um but what this has also enabled us to do is enabled us to do I should say is annotate the data again so in cases where the EITI suggests that the country's collecting maybe there's some points that are at around 30% of GDP where the where where our data suggests it's much lower we're able to say look these figures come from the IMF country report they suggest around 10 to 15 percent of GDP come from natural resource revenues but the true figure may be much higher indeed so I've been able to use this data and just provide some more interpretation around the figures that we have contained in in the government revenue data set okay so the the fourth kind of innovation or addition we make and I've already said a little bit about this is that um we provide where possible interpretation of the data for users and try to provide guidance on what's actually going on in the data um and whether or not it's it's you know good research practice to include this data in your econometrics if you choose to do that um so we have a series of notes and comments and what we call flags in the data where it's just a dummy variable equal to one so if you were perhaps running a regression you could exclude that quite quite quickly and so the sorts of cases where we need to warn users are certainly when we when we can't account for natural resource revenues but we know that they're driving volatility or highly inflated um natural resource not sorry where they're driving volatility or inflated tax revenues um so we said to users look these figures seem high it's probably because there's natural resource revenue but we don't have a source which allows us to break that up um we also include comments and flags wherever wherever we have inconsistencies over the way that social contributions are reported so some of the some of the examples that I showed you we say look source x says this but source y says this be careful if you're making a cross-country comparison here and there are also there are also cases where we're able to to warn users that look we're not sure why this data is so high or so low but something looks incorrect um so quite often this is the case when a country has transitioned from a command economy to a to a market economy and you see that the composition of revenues has shifted starkly from perhaps non-tax revenue toward more traditional tax revenue or the makeup of tax revenue has changed has changed quite a bit and so we're able to provide that interpretation and and guidance for users and there are certainly cases where we suggest if you're doing econometrics maybe exclude this observation and see if your results hold and so we hope that users pay attention to that data and because we put a lot of a lot of effort into manually cleaning the data and looking for those those observations where we think that we can't explain what's going on in entirely but we think perhaps something is is causing a bias in the data um we also you know are quite okay with with cases where there are data points but they just look to be wildly wildly incorrect so it's not it's not often the case but there are some observations where we don't include any data because it just looks to be wrong and and in cases like that again we would suggest to users why we have not got a data point for that specific country here and and there are other there are other cases where we've flagged up the the data to users but hopefully um if you if you are interested in accessing the data you will pay attention to those um okay so I've spent the last probably three or four months with my head buried in excel trying to get this data set updated um and so just to talk for five or ten minutes about some of what I've been doing um so we've we've managed to improve coverage a little bit um so the small table on the right shows the percentage of total observations that we have for each of those um disaggregated categories um I should say the numbers there are probably off by a couple of percentage points because a lot of countries in the data set didn't exist before 1990 former soviet states example so it didn't make that small correction but compared with the with the last version of the data set we've been able to we've been able to improve coverage for income tax trade tax and goods and services taxes by about three three and a half percent of observations which when you're at the margin actually represents quite an improvement I think and most of that has been done to better disaggregation from the underlying sources such as the OECD the IMF um I've also tried really hard to fill in gaps in the time series so frustratingly in the older version of the data set there would be every now and again just if you had a time series from 1980 to 2012 there might be one or two missing observations just in the middle and if you're doing any sort of panel econometrics you know that country's probably going to get dropped from your estimation if there's a missing observation so um I filled in a lot of those gaps and I think there's only one or two countries where that's still the case so off the top of my head I think in 1996 or 7 Morocco changed from reporting its taxes on a calendar year basis to a fiscal year basis where which ran instead from January to December from July to June and so for 1996 there was only six months of data for Morocco so we we just don't have a full data point for that year and so we can't include it it's not comparable with the data that surrounds um the the data runs now up until up until 2015 and that's so a small number of countries have now reported at this stage um tax and revenue data for 2016 but um almost every country we have data up to 2015 which which is you know so usually the tax statistics will lie two years behind the the calendar year um and I'll say a little bit more about some of these some of these points so previously in the government revenue data set when we were taking data from article four consultations or statistical appendices it was sometimes unclear whether the trade tax figure included vat that's collected on imports or not um often vat collected on imports is collected by the customs authority so in the country report it might just be included under the trade tax revenue heading um the previous authors of the grd the will pritchard and others were quite open about the fact that they struggle to be consistent with how this data was was handled um I've fairly painstakingly gone through each and every country and tried were possible to remove the part of of sales tax or vat or excises on trade that should actually be classed as a tax and goods and service according to gfs m 2014 standards so this is again the the example from benin and this is a really nice example because you can see under taxes and international trade and transactions it shows the part of that that comes from vat um so it's quite simple to lift that and reclassify it as a tax and goods and service which brings it in line with benin's figures from the imf gfs unfortunately not many countries disaggregate this far and so it's sometimes quite it's sometimes quite tricky to to be able to make this correction um but i'm reasonably in fact i'm very confident that we've we've either identified the cases where it's not possible to make this correction and remove those data those data points because they're not comparable with other countries and there's only i think one or two countries in the data set where that's the case in every other case i've been able to make the correction um so if perhaps you have used the data set before to look at trade or goods and services ratios as a percent of GDP perhaps take another look and and see if those particular data points have have maybe changed this time around and so yeah i've corrected that in consistency um and the final thing i wanted to say is just about property tax so there's a very small literature on property tax in developing countries but there is a bit of attention at the moment um as of the government finance statistics manual 2014 um the imf have reclassified what was um included in property taxes um so historically this included um inheritance taxes non-recurrent and recurrent taxes on the movable property and taxes on financial and capital transactions which is a tax levied on the seal of of a property um in accordance with sna 2008 and esa 2010 guidelines this this item has been moved from a property tax to attacks and goods and services um which is fine so whenever we re re re accessed all of the data from the imf gfs the the correction had been made unfortunately oecd's revenue statistics team didn't follow suit and make that make that reallocation um they they weren't very forthcoming with a satisfying reason why they haven't done that but they promised me they'll revisit it revisit that decision again this year but for for now what we had to do was you know make that calculation ourselves for all of the oecd countries and were possible um for data that came from imf country reports so um this might seem kind of trivial because if you've ever looked at tax statistics property taxes are a very small make up a very small part of the overall revenue picture but if users are interested in using the property tax series from the grd um this probably will probably will have quite an impact because as a percent of property tax taxes on financial and capital transactions was often up to half or two thirds of that figure so the taxes on the movable property weren't actually that much um so if you've used the data for taxes on property then there there may be a small change in in the numbers that you see but um going forward this allows us to be more consistent with how the statistics are are classified um certainly by the imf okay so that's just a snapshot of some of the improvements that we've made uh this time around um so the dataset is freely open and available for anyone to use it's online at the wider website um under projects click on projects is the first one you see um looking forward we're hoping to visualize it and create an interactive tool so i appreciate always that researchers are happy to delve into this data file in the excel file um i know that i would always go straight there but other people would perhaps just want a snapshot of trends across countries or in certain in certain regions so we're hoping to implement that at some point uh in the future um certainly hoping to move to a more uh a more simple annual update cycle so this time around i took the chance to address a lot of these underlying issues that i knew needed to be corrected at some point in the data hopefully going forward the annual update will just add an extra year of observations and input any any uh revisions that have been made in the underlying sources and certainly if other sources come along or sources improve their coverage we can work to incorporate that in the dataset and hopefully improve coverage going forward this we've always been happy to collaborate with other researchers on this and collaborate with people in developing countries who might have specific country knowledge um please if you use the dataset get in touch tell us why you're using it just maybe purely selfishly it's nice to know what people have done with the dataset um but also please provide us with feedback so in a number of cases um people from various revenue authorities or who have worked in certain countries have looked at our data and said okay we know that this is from the underlying source the imf etc but i happen to suggest that it's out by x percent of tdp or something has been misclassified here or there so we're very happy to incorporate that sort of information and collaborate with other people on the dataset and that can only make it a richer resource um going forward okay so that's all i have to say on the dataset