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Economic News, Data and Analysis

Spam, email viruses, and economics

If you’re like
me, you are starting to dread the Monday morning inbox on your email program.
I routinely get very familiar with my delete key as I weed the unsolicited
junk email, aka spam, from my system. This routine has gotten a bit more
interesting as of late with the latest round of email viruses such as the
famous “love bug.”

The phrase “You
may already be a winner” has now been replaced by “Make $$$ from home”
and “lose 50 pounds in 2 weeks!”.

It’s not that
much of a pain to delete messages, but certainly the cumulative time, day
after day, and across millions of computer users, certainly adds up. (Say,
2 minutes a day times 93 million users times 10$ an hour times 260 week-days
/ year = $8.06 Billion/year or about 0.1% of GDP.)

I was thinking
about ways that I could protect myself from junk email and the next round
of email viruses. I do use a spam filter which catches the worst offenders
on one of my email addresses, and my virus protection does a decent job,
but there are holes.

Most of the solutions
that I have seen involve a reaction to an email that was sent – for example,
by contacting the ISP of the offending email address. On the virus side,
a virus protection program helps, but I still have to deal with the emails
set to be from the people I know who are not protected.

Both of these
solutions to the spam problems, however, have a large downside – they involve
non-trivial costs. A good virus protection program costs around $40; and,
if I spent all of my time tracking down spammers and complaining, I wouldn’t
have time to complain about them here.

The Free Rider
Problem

The problem with
these “solutions” to the spam and email virus problem is that the costs
to any individual of stopping the latest junk email far outweigh the benefits
to that individual. However, if a spam email is stopped, the benefit is
then multiplied by a large number of users.

Therefore, it
may be the case that stopping spam or email viruses may have aggregate
benefits that are greater than the costs of prevention, but no individual
has the incentive to combat the spam; and so the spamming goes on unabated.

This description
of the emails and viruses problems is a subset of a larger class called
“free-rider” problems in economics. The free rider problem arises whenever
there is an incentive for someone to enjoy the benefits of a good or service
without having to pay for it. In this case the “good or service” is “no
spam.”

For a free rider
problem to exist, the good must have some characteristics of a public
good
. Namely, it must be impossible (or at least difficult) to prevent
others from receiving the benefit once the good has been provided (non-excludable).
Public goods also have the property that one person’s use of the good does
not preclude or diminish other’s use of the good (non-rival).

Anti-spam measures
and email virus protection falls neatly into both of these categories.

A Solution?

When goods exhibit
one or both of these goods there then is a the potential for the government
to come in and improve over the outcome that would be achieved by the market
alone.

When goods do
have the properties of public goods mentioned above, the market tends to
provide too little of the good – there is not enough anti-spam activity
in this case. The government may then be able to come in and improve the
outcome.

One way some (primarily
state) governments have tried to do this has been to pass anti-spam laws
– for example, Washington state two years ago passed a law allowing spam
recipients to collect $500 in damages per email message. However, even
with strong anti-spam laws, there may still be a free-rider problem. Pursuing
legal action against those that send the email still takes time, and the
benefits of successful litigation or police action still go to a large
number of people, most of whom are still free-riding.

For those pesky
email viruses, there is an easier solution – the government should enact
policies to increase the use of virus protection programs. One way to do
this would be to provide a subsidy for purchases of anti-virus software.

Unfortunately,
there is still no low-cost magic bullet for individuals to use against
email spam, and until there is, there may be a role for an active government
in this fight.

Web
Links


  Coalition
Against Unsolicited Commercial Email


  The
Spam Site
– as in Hormel – not email.

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Filed under: Economics

Unemployment Rate: Behind the Numbers

The rate of unemployment
for the month of April fell to a 30 year low of 3.9% – the lowest rate
since January 1970. The employment-to-population ratio was also at a high
of 64.9%.

Is the unemployment
rate really a good measure of the labor market? In a recent speech, Alan
Greenspan used the term “pool of available workers” repeatedly rather than
referring directly to unemployment. The “pool” is meant to represent a
broader measure of people who would like jobs rather than focusing only
on those who fit the exact definition in the Department of Labor’s monthly
survey.

This feature looks
a little more closely at the numbers behind the numbers.

Past unemployment

The graph below
looks at the unemployment rate and the employment ratio over time. It’s
pretty easy to spot the most recent major recessions – there are nice spikes
in 1958, the early and late 1970’s, early 1980’s, and early 1990’s.

 



 

Is “Unemployment”
a good measure?

Here’s how the
Bureau of Labor Statistics defines unemployment:

“They
had no employment during the reference week; they were available for work
at that time; and they made specific efforts to find employment sometime
during the 4-week period ending with the reference week.  Persons
laid off from a job and expecting recall need not be looking for work to
be counted as unemployed.”

The official number
misses several categories of workers including discouraged workers, and
“under-employed” – people with part time jobs who would like to work full
time. It might be better to look a broader measure of the “pool of available
workers” – which includes both the unemployed as well as people who are
out of the labor force, but who would like jobs.

The BLS also defines
loosely attached, of “marginal” workers, about 1.2 million people, are
those who wanted work and had looked for a job in the prior 12 months (but
not the prior 4 weeks as required to be counted as unemployed). Discouraged
workers, people who are not looking for work because they don’t think there
are any jobs for them, represent 330,000 workers as was actually up from
245,000 a year earlier. Overall there are 4.3 million people who are not
in the labor force, but who would like a job.

If we were to
add this group to the unemployed, we would find closer to 7% of the current
workforce available for work.

Composition
of Unemployment

Unemployment is,
by necessity, only a single number. But underlying the 3.9% is a wider
range of unemployment experiences.

Race/gender/education

The following
table shows the employment rates for various groups. One of the more dramatic
changes in the unemployment rate for black teenage men: A fall from last
year’s rate of over 30% down to 22%.

 

Hispanics 5.4%
Whites 3.5%
Blacks  7.2%
Women 3.5%
Men 3.2%
Teenagers 12.7%
College Graduates 1.5%
< High School
Diploma
6.1%

Source: Department
of Labor

Duration

The Average duration
is 12.4 weeks while the median duration is only 6 weeks. This means that
half of unemployment spells are relatively short, yet there are still many
people who are unemployed for a long period of time. This would suggest
that there are different “kinds” of unemployment – for some unemployment
is simply a transition between jobs, while for others unemployment is a
persistent problem.

 

Total unemployed 100.0
   
Less than 5 weeks
44.1%
   
5 to 14 weeks
33.5%
   
15 to 26 weeks
12.0%
   
27 weeks and over
10.4%

Source: Department
of Labor

Web
Links

   
DOL, BLS: Employment
Data Release


   
BLS defines
unemployment

 
Greenspan’s
speech
with the “pool of available workers”

Graph constructed
by J. S. Irons. All data is from the US
Department of Labor, BLS
.

Filed under: Data

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