Wednesday, April 29, 2015

Civilian Deaths and Maryland's Police Forces

With the unrest in Baltimore making headline news in recent days, I wanted to take a look at some background information on deaths in Maryland that are related to encounters with the state's various police forces.  A recent briefing paper by the ACLU looks at the statistics on deaths in police encounters in Maryland between 2010 and 2014.  Here are some of the highlights.

Let's open with this table showing the demographics of Maryland showing the ethnic and racial breakdown of the state:

Now, let's look at some of the statistics from the briefing paper:

1.) At least 109 people died in police encounters in Maryland over the years between 2010 and 2014.  During the same time period, four police officers died in civilian encounters including two in vehicle pursuits, one was shot in a raid and one was shot when off-duty and working as a security guard.  

As shown on this chart, these deaths were scattered widely throughout the state:

2.) The age range of those that died ranged from 15 to 78 with an average age of 35 years.  Nearly one-third of those who died were 25 years of age or younger.

3.) Of those who died in a police encounter, 75 were black (69 percent of the total), 30 were white and three were identified as Hispanic.  To put the number of blacks killed into perspective, blacks make up 28 percent of Maryland's total population.  When the racial composition is normalized, the rate at which blacks died per population size was five times that of whites.

4.) Of those who died, 86 people or 79 percent were killed by police gunfire.  Of those, 22 or 26 percent had no weapon of any kind.

5.) Of those who died, 23 people or 21 percent were not shot by police.  In those cases, police used handcuffs or other restraints, tasers or pepper spray.  

6.) Of those who died, 45 were not armed with a weapon of any kind, 38 had a gun, 11 had a knife, 8 had an airgun or fake gun and 7 had an object that was defined by police as a weapon, including a pen.

7.) Of those who died, 41 people or 38 percent were presented in a way that suggested either a medical or psychological health issue or that they were suffering from the use of an illicit substance.  Their behavior was often characterized as bizarre or erratic.

8.) In one case, a 26-year old white male with Down's Syndrome died as a result of "asphyxiation by homicide" on January 12, 2013 when off-duty sheriff's officers moonlighting as mall security guards attacked him because he didn't buy another $12.00 movie ticket so that he could watch "Zero Dark Thirty" for a second time.  The case went to a grand jury which declined to indict the three officers involved.  As well, the Frederick County police investigated themselves and found that they were not guilty of any wrongdoing.

Out of all 109 cases over the five year period, there were only two cases where a Maryland police officer faced criminal charges relating to the death of a civilian.  In both cases, the office was off-duty.  One officer was convicted and the other officer, charged in the choking death of a 17 year old boy in July 2012, was acquitted.

The data from this briefing paper shows us that civilian deaths during interactions with police in Maryland are not isolated events; over the past five years alone, an average of just under 22 civilians have died every year after an encounter with one of Maryland's police forces.  For a state with a population of just under 6 million people with its largest urban area that has only 622,000 people, that's a lot of civilian deaths at the hands of the police.

Tuesday, April 28, 2015

The Real Impact of Weather on Economic Growth

When the rate of economic growth slows down, particularly in the first quarter of the year, the mainstream media seems to focus on the "fact" that bad winter weather has had a negative impact on consumer spending which is responsible for about 70 percent of GDP.  A recent study by Justin Bloesch and Francois Gourio at the Federal Reserve Bank of Chicago looks at the connection between the fairly bad weather in the first quarter of 2014 and the slowing of the economy in the same quarter.

In case you've already forgotten, here is an example of the type of story that circulated in the mainstream media when Q1 2014 GDP growth stumbled:

Among the issues that connected the weather to the stalling economy were stories about employees who couldn't get to work, construction sector supply chains that were interrupted and households that delayed making retail purchases because they were spending more on heating costs and repairing storm damaged homes.

Here is a figure showing six economic indicators and how they were impacted with each of the three dots representing December 2013, January 2014 and February 2014:

While there was clearly a somewhat of a slowdown, it was unclear whether the slowdown was due to winter weather or another unrelated underlying trend.  At the time, the cause of the slowdown was particularly important because the Federal Reserve Open Market Committee had decided in December 2013 that it would start to taper its purchases of assets because the economy was showing some strength.  The challenge facing the FOMC was to disentangle how much of the economic weakness was weather related and how much was caused by outside forces.  This is more difficult than one might expect given that aggregate snowfall and temperature data are area weighted (i.e. they represent a geographic area) rather than being population weighted.  In other words, if winter temperatures are much colder than normal and snowfall amounts are much higher over less populated areas, the potential impact of harsh weather on the economy will be much lower than if the colder temperatures and increased snowfall amounts take place in a more populated region.  The goal of the research in this paper was to provide us with more robust statistical evidence regarding the effects of the weather on economic activity.

Let's start by looking at the weather side of the equation to see whether the winter of 2013 - 2014 was actually harsher than normal.  The authors' source of weather measurements data was taken from the U.S. Historical Climatology Network.  This data set has daily measures of temperature, snowfall and total precipitation from 1200 weather stations throughout the United States over the past 65 years.  The authors then calculated the monthly deviation or the difference between the monthly average temperature and the long-run normal temperature.  The temperature data is then averaged across all stations in a state and then aggregated the state data (weighted to their employment) to regional (i.e. Midwest, West, Northeast and South) and national weather indices.  The authors then did the same thing to the snowfall data.

These two figures show the entire national record of both temperature and snowfall deviation  from the late 1940s to 2014:

It is quite clear that on a weighted national basis, temperatures were lower and snowfall amounts were higher during the winter of 2013 - 2014.

Now, let's look at how much of an impact that the weather had on economic activity using the authors' state-level model as shown on this table:

1.) At its peak, the weather contributed negatively to the growth of non-farm employment over the period from November to March to the tune of -0.04 percent in the month of February or about 50,000 to 60,000 jobs.  By March, there was no impact of weather on non-farm employment and by April and May (plus 0.03 percent in each month), the weather effects on non-farm employment were completely reversed.

2.) The weather effect on the unemployment rate was negative in both November and December (-0.02 and -0.03 percent respectively and was positive in February, pushing the unemployment rate up by 0.06 percent.  By March, the impact of weather on the unemployment rate was back to zero percent.

3.) The peak impact of weather on the number of new unemployment insurance claims took place in February when new claims were up by 0.98 percent.  New claims dropped by 0.07 percent in January and 0.31 percent in March, again, showing that the weather effect on new U.I. claims was short-lived at best.

4.) At its peak, weather contributed negatively to housing permits during the month of February when the number of new permits declined by 1.04 percent.  Once again, by March, new housing permits had risen by 1.11 percent, completely reversing the losses of the previous month.

5.) The weather effect on housing starts was negative in February, dropping by 0.67 percent.  By March and April, this weather-related decline had been reversed when housing starts rose by 0.22 percent and 3.35 percent respectively.  It is interesting to note that the weather impact on housing starts was actually more significant in November 2013 (-1.71 percent) than it was during the depths of winter.

In addition, when using the national model, the authors found that the results were less clear.  The number of hours worked fell by 0.60 percent in February of which 0.19 percent is attributable to the weather.  Utility production grew by 3.3 percent in January of which 0.52 percent is attributable to the weather.  

The authors' conclusions show that weather does have an impact on economic activity, however, in the case of the winter of 2013 - 2014, the effect was very short-lived, generally negatively impacting economic growth for little more than a month.   In addition, the effect of weather is not very large, even though the winter weather in 2013 - 2014 was relatively severe when compared to historical records.  The authors' also note that the timing of the decline varied for many of the economic indicators with some indicators declining in December, others in January, February or March or a combination of months.  This tells us that the bad weather during the winter does not account entirely for the weak economy during that period and that other factors must have been at play, factors which include a correction in inventory levels and the impact of foreign trade. 

Monday, April 27, 2015

The Geological Setting of Nepal and Why it is Earthquake Prone

As a geoscientist, the occurrence of a significant earthquake is always of interest to me.  Since many people really don't understand why "the earth moves", I will use this posting to explain why the recent earthquake in Nepal that has killed thousands of people took place and why such an event is likely to be repeated in the future, just as it has in the past.

In the late 1960s and early 1970s, geologists and geophysicists discovered that the earth's crust, the outermost and thinnest layer of the globe, was formed of a number of plates that "float" on the earth's mantle.  These plates have moved throughout the hundreds of millions of years of geological time, creating and destroying continents and oceans.  When two or more of these plates collide, they can form a subduction zone where one plate is thrust over top of another plate.    There are two main types of subduction zone; the first occurs when a plate of oceanic crust is subjected under a plate of continental crust (oceanic - continental convergence) and the second occurs when two plates of continental crust collide (continental - continental convergence).  Here is a schematic section showing what an oceanic - continental convergence looks like:

The down-sliding plate will melt as it is subducted into the earth's mantle; as the melted continental crust rises, it forms a chain of volcanoes and a series of along the margins of the over-riding plate as it is crushed by the plate that is being subducted.  A prime example of this geological phenomenon can be found along the western margin of North America and is the driving force behind the most recent significant volcanic eruption of our lifetime, Mount St. Helens.

In the case of the Himalayan tectonic region, Nepal's major mountain terrain is being formed as two continental plates collide; in this case, the Tibetan/Eurasian plate is colliding with the Indian plate as the Indian plate moves northward as shown on this diagram:

The geological framework of the Himalayas can be simply thought of as two continents colliding with each other, resulting in one continent pushing up another.   As the stresses of the collision build, they are released as earthquakes along the multitude of faults that cross the region.  In many ways this process is similar to the process that formed the Cordillera of North America which most of us know as the Rocky Mountains.  In that case, a series of smaller island arcs located on the Pacific plate collided with the western margin of the North American plate, pushing the earth's crust up into a long mountain chain that runs the length of North America.

As the Indian plate is subducted under the Eurasian plate, it pushes the continental crust upwards, forming the formidable Himalayan plate as shown on this block diagram:

This collision results in significant shortening of the earth's crust as the earth's crust is thrusted upwards creating the Himalayan mountain chain; in the case of the Himalayan fold belt, a 2010 paper by Long et al suggests that there has been at least 344 to 405 kilometers of crustal shortening across the Himalayas.  This amounts to between 70 and 75 percent shortening.

Geologists believe that the Himalayan Range is one of the younger mountain systems in the world with the collision first taking place around 55 million years ago.  The process that formed the mountains is still occurring with the Indian plate moving northward at a rate of about 5 centimetres per year.  It is this forward drift and ongoing collision that creates the seismic activity which is manifested as earthquakes.  

The 2400 kilometer-long Himalayas can be divided into several geological or tectonic zones that are each separated by major thrust faults as shown on these diagrams:

Approximately one-third of the Himalayan range lies within the boundaries of Nepal.

Here is a cross section showing the physiography of the Himalayas:

As I noted above, the Himalayan mountains are crossed by a series of faults of the thrust (faults where one side moves vertically with respect to the other) and slip-strike faults (faults where one side moves laterally with respect to the other).  Some regions of the Himalayas are far more heavily faulted than others, particularly the central Himalayas found in Nepal.  In the Western and Central parts of Nepal, there are mainly thrust faults and in Eastern Nepal, there are mainly strike-slip and thrust faults. 

Let's close this brief posting with a map showing the seismic regions of Nepal that also shows the numerous earthquakes that have taken place since the early 1800s:

Nepal and the Himalayas in general are one of the world's most seismically active areas.  The complex tectonic setting of the region has made it a focus study area for the world's seismologists who are trying to gain a better understanding of the tectonic framework of the region so that, in the future, they may be able to predict the occurrence of significant earthquakes through changes in seismicity patterns.