How do you boycott free?

I have a question in the wake of the Brendan Eich business.  I’ve never installed the Firefox browser on my laptop; Internet Explorer handles my browsing needs just fine, thanks.  I have an older desktop that’s still in service, but IE bogs down so we use Google Chrome instead.  I do have Firefox installed on my work computer; I have a couple of programs that require it, and my experience is that it is modestly more robust that IE on our crappy government networks.

But my question is, how do Mozilla and Google make money on free stuff?  Google supposedly makes money on targeted advertising and on selling consumer data for marketing purposes.  But while I regularly use Google for internet shopping, I have never – not once that I can recall – clicked on an embedded Google “targeted” advertisement and bought something.

And Mozilla?  They’re not even a search engine!  The only product they make that I know about is Firefox.  I assume they have an option to track my browsing history, but the user can opt out of that (as I have done at work).

So while I’ll be happy to help punish Mozilla for gross moral turpitude, I don’t actually understand the path a dollar takes from my wallet to Mozilla.

Any explanation would be appreciated.

The Whole Life MLM

Warning:  the following is for blogging purposes only.  Those looking to purchase life insurance of any kind should consult a qualified financial advisor.

In reading “the article” on MLMs I cited in my last post, I was disturbed to see one of my own life insurance companies listed among those whose sales practices and treatment of its interns show the same pattern of abuse for which MLMs have come under so much criticism.  Full disclosure:  my father-in-law was a highly successful career agent for this company.  He purchased and initially funded whole-life policies from this company for his children and later sold policies to me.  I can only assume he believes whole-heartedly in their products.

I’m disappointed to learn that this company now requires its interns to attempt to sell life insurance to their pre-existing contacts (a.k.a. “friends and family”).  As my father-in-law described it, his own internship in the early ‘60s required a lot of cold-calling, a soul-sucking enough activity at that.

My own experience is limited to that of a customer.  Based on that experience, I will offer some criticisms of this company, but before that I will offer the following qualified praise:

For a fixed monthly or yearly premium, and over a significantly long policy life, whole-life policies appear to offer tax-deferred returns significantly above most other interest-bearing savings vehicles, the cash values of which is as secure as the company itself.

Let me expand on these caveats.  Whole-life policies are, first, life insurance policies.  Like term life policies, they pay out to your beneficiaries when you die.  Unlike term policies, they accumulate what the industry calls “cash values” (and what other investments call “principal”).  In the first few years of a policy, these increases in cash values (what other investments call “returns”) are some fraction of your annual premiums (what other investments call “contributions”); in later years, they progressively exceed the annual premium.  These cash values do not count as income for tax purposes until the policy is “cashed out”; hence they are tax-deferred.  Like an equity investment, the  returns on the principal are not guaranteed (beyond some trivial level); however, unlike an equity investment, once the cash values increase, they can never go down.  Thus the “principal” is as secure as the company.

Let me give you an idea of what this looks like in practice.  To evaluate the “performance” of my whole life insurance policy, I follow what strikes me a simple formula:  subtract the annual premium from the annual increase in cash values and divide the result by the previous year’s total cash value.

For instance, I have a $100K policy started in 2000 that showed the following yearly increases for the years shown below.  These are representative; you may interpolate for the intervening years.

Year	2003	2004	2005	2007	2010	2014
Increase	$1144	$1245	$1356	$1636	$1945	$2377
Premium	$1295	$1295	$1295	$1295	$1295	$1295
Cash Value	$1991	$3135	$4380	$7232	$12473	$20940
Return	(7.5%)	(1.6%)	1.4%	4.7%	5.2%	5.2%

Again, while as a matter of law, life insurance is NOT an investment, I will use the language of investment to describe it:  steep negative returns in the early years for which premiums exceed earnings, followed by uninterrupted positive returns.  As you can see, the longer the life of the policy, the more attractive it becomes as an investment; by the ten-year point, they are paying returns that are well above anything else you would get at a comparable level of risk.  For instance, fourteen years in, I have paid $18,130 in premiums; my cash value is $23,317.  Like I said, 3.75% isn’t great, except compared to any other investment with a comparable level of risk.

Downsides

Now let me share the downside.

It’s not an investment.  By law, whole-life insurance policies are not investments, and are not to be marketed as investments.  This is downright weird:  for their face value, whole-life policies are an order of magnitude more expensive than term-life insurance, and wouldn’t make sense except as an investment.  But because they are not an investment, the insurance company doesn’t report their performance as actual investment companies do.  The customer is left to his own records to this out, as I have done above.

Obscurity.  Because they aren’t investments, there are some things about them that strike me as needlessly difficult to figure out.  For instance, every year, the insurance company reports the increase in my cash values (called “paid up additions” in the industry).  Some fraction of these increases are designated as “dividends”.  So, what is the difference between the “dividend” and “non-dividend” portion of my paid-up additions?  I asked this question years ago, and never did get a satisfactory response.  Indeed, the agent (who took over the servicing of my policy from my father-in-law upon his retirement) seemed not to know.  All he could do was mail me a blizzard of paperwork that didn’t really answer my question.

“It’s tax free!”  No, it’s not.  The policy owner has the option of “borrowing” from the policy up to the full amount of his cash values, and there are no taxes on loans.  But the premiums would still have to be paid, the rate of cash value increase falls, and some amount of interest on the loan has to be paid as well.  On balance, it amounts to a net negative return on the policy.  Now during the ‘90s, it was plausible to argue that you could invest your cash values in reasonably safe equities and still come out ahead.  This was pretty dumb advice then – what investor takes on more risk when he retires? – and utter nonsense today.  But it didn’t stop my father-in-law from suggesting it.

When the policy owner wants his money, he has two choices:  he can cash out the policy and take the entirety of his cash values.  The taxes on the policy are due that year.  I have been given to understand that the taxes only apply to the cash values in excess of the premiums paid, but I can’t claim to have seen this written anywhere, and don’t have any direct experience.  Alternatively, he can buy an annuity from the insurance company.  How taxes are calculated on the annuity, I have no idea.  And the annuity rates, last I looked at them, didn’t seem very good.

So that’s it, or at least as much as I think I understand.  On balance, and given my investment record since 2000, I’m pretty happy with my whole-life policies.

Why Short-Selling?

In reference to my recent posts on the dueling MLM moguls and their diversity minions, I have a question:  how does short selling work?

I know the textbook answer:  short-selling means “borrowing” stock and selling it, hoping the price goes down so it can be re-purchased at a lower price and returned to its owner.  But . . . what does it mean to “borrow” a stock?  Or more precisely, why would anyone “lend” me their stock?  I assume the lender charges some kind of rent for his loaned stock and a fixed lease time, but is that correct?  And why would I be enthusiastic about lending stock to someone so he can “bet” on a price decrease?  Especially (as in this case) when the short-seller is politically connected, and might be able to drive the price down irrespective of the stock’s value fundamentals, during which time I can’t sell it myself because I’ve loaned it out!

And why, as a regulatory matter, do we allow short-selling in the first place.  I get that, in theory at least, speculators aid in price discovery.  But short-selling is something beyond that, and it’s not really clear how it adds value to the market.

I would be grateful to anyone who can answer these questions.

Random Thoughts . . .

From Military.com:

Obama to Award Medal of Honor to 24 Army Vets

Associated Press | Feb 21, 2014

WASHINGTON - Seeking to correct potential acts of bias spanning three wars, President Barack Obama will award the Medal of Honor to 24 Army veterans following a congressionally mandated review to ensure that eligible recipients were not bypassed due to prejudice.

The Pentagon said the Army reviewed the cases of the 6,505 recipients of the Distinguished Service Cross from World War II and the Korean and Vietnam wars and found an eligible pool of 600 soldiers who may have been Jewish or Hispanic. The Army also worked with the National Museum of American Jewish Military History, the Jewish War Veterans of the USA and the American GI Forum, the largest Hispanic-American veterans group, to pinpoint potential medal recipients.

I don’t claim to understand, forensically speaking, the demarcation between the DSC and the CMH.  But how many of those 5905 white Gentile DSC recipients might have turned out to qualify for the CMH under the Pentagon’s current standards had their cases received the same reconsideration as the Jewish and Hispanic candidates?  I’ll bet a lot more than 24.

Foreign-Made American Flags Banned by US Military

Fox News | Feb 21, 2014

Under a new law signed as part of the 2014 omnibus appropriations bill, any flag purchased by the Defense Department is required to be 100 percent made in America. Rep. Mike Thompson, D-Calif., who wrote the legislation, said he did so for economic as well as symbolic reasons.

The legislation has been historically difficult to pass in part due to trade agreements, as well as the fact that flags made in China, the largest importer, cost significantly less than ones produced in the United States. An estimated $3.3 million worth of American flags are imported from Beijing each year.

Dale Coots, marketing manager for Annin Flagmakers, in Roseland, N.J., said the new legislation is a positive step, but says other issues regarding flag imports remain unresolved, including the Federal Trade Commission’s lack of enforcement on flag labeling.

"An American flag is considered a textile," she said. "And a lot of flags that sell online don't have any origin label, which is required under U.S. law."

Annin Flagmakers, which has produced American flags since the 1820s, will not benefit from the new law because the company employs more than 500 workers. The federal government only takes bids from small businesses in flag purchases, she said.

This is mostly meaningless in the big economic picture, but I’m curious whether or not the 500-worker cap on flag manufacturers has been applied to the Chinese companies with the same rigor it has been apparently applied to American companies.  But if the cost is a problem, wouldn’t lifting this cap be the way to go?

Obama Tells Pentagon to Plan for Afghan Pullout

Associated Press | Feb 25, 2014 | by Julie Pace

WASHINGTON - President Barack Obama has ordered the Pentagon to plan for a full American withdrawal from Afghanistan by the end of this year should the Afghan government refuse to sign a security agreement with the U.S, the White House said Tuesday.

Better late than never.  Yes, cutting your losses is hard.  Do it anyway.

Defense Budget Would Cut Troop Pay and Benefits

Feb 24, 2014 | by Brendan McGarry

The U.S. Defense Department is proposing limiting troop pay raises, reducing housing allowances and cutting funding for commissary stores because of automatic budget cuts, officials said.

The proposals to curb personnel costs, which the officials said consume a rising share of defense spending, include limiting troop pay raises to 1 percent, reducing housing allowances by an average of 5 percent,

According to my final leave-and-earnings statement, my housing allowance was almost 20% of my salary, although in ranged to almost 25% depending on my rank and where I was living.

cutting some $1 billion in commissary subsidies -- which will likely mean higher prices for troops and retirees

Commissary shoppers pay cost plus 5%.  But that 5% surcharge, if I understand correctly, may not be used for operating expenses.  It only funds construction.  That may have something to do with why our commissary seems perpetually under construction, but only three of the twenty registers are open at a time.

-- and higher health care fees for some retirees.

"The savings will enable us to sustain a well-trained, ready, agile, motivated and technologically superior force," Hagel said during a briefing Monday afternoon at the Pentagon. "Although these recommendations do not cut anyone's pay, I realize they will be controversial."

I’m not sure “not cut anyone’s pay” means what Hagel thinks it means.

Is the lottery a smart bet?

It is said that the lottery is a tax on people who are bad at math. What is intended by this is that the odds of winning the lottery as so miniscule that a rational cost-benefit analysis would recommend against participation. But is this always true? Since the jackpot “rolls over” and accumulates for successive drawings, is there some threshold at which the value of a lottery ticket exceeds the price?

Let’s consider the Megamillions lottery played Tuesday night. As has been well documented elsewhere, the probability of buying a winning number for a particular draw can be calculated thus:

Lottery Math

Number of balls in main cage:	75
Number of balls drawn from main cage:	5
Number of balls in Megaball cage:	15

Number of possible combinations of the six balls:

Probability of drawing correct number:

 

It follows that if the net present value of the lottery jackpot exceeds $N, then a $1 lottery ticket becomes a good investment. Correct?

First, consider that the net present value of the lottery jackpot is not the advertised jackpot. The one-time payout for last week’s $636M jackpot is only $340M, whose value is then diminished by federal, state, and local income taxes at (roughly) 45%. So the winner’s after-tax take is only $187M.

Yet not even this is the net present value. We must also consider the probability that, as happened last night, the jackpot is shared between multiple winners. What is that probability?

Number of Tickets sold: T

The probability that at least one winning lottery ticket (i.e., w ≥ 1) would be sold: p(T)

Each successive ticket t purchased adds to the total probability of a winning ticket p(t) the probability P multiplied by the probability that none of the earlier tickets were successful, which is 1 – p(t-1). Thus:

Last week’s drawing had 336M participants. Thus:

,

for P as calculated above.

We included in that that 73% probability the chance that multiple winning tickets were selected. But since multiple tickets means splitting the prize, then the expected value of the prize is the jackpot divided by the expected (e.g., the average over multiple trials) number of winners. Can this be calculated?

In general, the formula for the expected value of random variable W is

,

i.e., the sum, over all possible values of w, of the product of w and the probability of w. In this case, W is the random variable describing the number of winners, w is a specific number of winners, and p(w) is the probability of that w winners.

Given P as calculated above, let’s shift variables and designate p(T,w) as the probability of exactly w winners given T tickets sold.

The probability of one winner when one ticket is sold: p(1,1) = P

The probability of one winner when two tickets are sold:

The probability of one winner when three tickets are sold:

In our application, the number of tickets T is presumably fixed and known. We are also interested in knowing how the probability changes with w, the number of winners. Each ticket is an independent event, so:

The probability of two winners when two tickets are sold: p(2,2) = PP = P²

The probability of two winners when three tickets are sold:

“Wait a second!” the astute among you are saying about now, “that’s starting to look like the binomial theorem!” Indeed it does. In fact, we can generalize that for exactly k winners among T tickets sold, the probability is:

 ,

i.e., the product of the probabilities of all winners and losers and the number of ways we can combine them.

In our calculation of the expected value, it would be nice if we could apply the binomial theorem,

 ,

but we can’t, because we must weight the terms by as written, because we must weight the terms by w:

Unfortunately, I’m not good enough at math enough to figure out a closed-form solution to the summation above without knowing the answer in advance. And the usual way of evaluating , with factorials as show above, won’t work when T is hundreds of millions of tickets sold; no computer generates factorials that large. But the Wikipedia article suggests using iteration and logarithms to calculate for large T, which I have done in Matlab here:

%%
% Parameters
n = 75; % Number of balls in main bin
v = 5;  % Number of balls drawn from main bin
m = 15; % Number of balls in MegaBin
N = m*nchoosek(n,v); % Number of possible lottery combinations
P = 1/N; % Probability of receiving the winning number
%
% Calculate the binomial coefficients with logarithms
%
T = N;   % Arbitrarily set the number of number of tickets sold to 239E6; we vary this number over multiple runs
i = 1:T; % A vector containing all integers between 1 and T
logi = log(i); % Calculate the logarithm of all integers between 1
% and T
logu = 0;
logl = 0;
for k = i,
logu = logu + logi(T-k+1);
logl = logl + logi(k);
logBC(k) = logu – logl; % the natural log of the binomial coefficient, i.e. log[(T k)]
end;
% Calculate the expected value
logP = log(P);
logQ = log(1-P);
% natural log of the product of k wins and the probability of k wins
logwpw = logi + logBC + i*logP + (T-i)*logQ;
EW = sum(exp(logwpw)); % Calculate the antilog and sum
display(EW);

 

Fortunately, on a Xeon X5672 at 3.2GHz, this algorithm can be executed in a few minutes; note, however, that I optimized it for speed by minimizing the contents of the for loop.  The tradeoff is that I have several N-length floating-point vectors that managed to take up 18GB of memory.  Proceed at your own risk!

I ran the program for several values of T, and convinced myself that E[W] = T/N = PT. Now that I know the answer, I’d like a second crack at a closed form solution:

I will now resort to a bit of hand-waving:  given values of T in the hundreds of millions, the value for E[W] is not measurably diminished by conditioned on a given number of winners.  This is analogous to the probability of rolling double-sixes given that you already rolled one six.  (Answer:  1/6, not 1/36.)  So given that you won the jackpot, the number of additional expected winners is still PT.

Applying this to the problem at hand, it follows that the after-tax value of last night’s jackpot must be divided through by 1 (i.e., you) + 336/259 (the other winners), reducing the value of your jackpot share to $81M.

Now, it is theoretically possible that, given a high enough jackpot, that $81M could increase to the $259M necessary to make the expected value of the lottery ticket greater than the dollar you’re paying for it.  But remember that the number of tickets T for any given drawing will probably go up with the increasing jackpot, diminishing its value to an individual player.  And even if it didn’t, it wouldn’t follow that buying multiple tickets would be a good idea.  I will leave working the math out to the reader, but you would be assured of buying duplicate tickets, and thereby playing against yourself, after a few tens of thousands of tickets purchased, and the model would need to be extended to account for this.

(NOTE:  The foregoing was for entertainment purposes only.  Those wishing to invest their life savings in lottery tickets should consult a qualified dissipation professional.)

Housing Crisis: Making It Personal

Steve Sailer caps a series of posts on the housing crisis with some statistics from the California housing market.

Steve had written previously that an appropriate median home price / median AGI ratio is about 2.5. Oddly, this has been about the ratio I've targeted in my own home purchases, but I had never really considered looking at the community ratio to guage whether home prices were out of whack.

Along comes City Data, a website readers can look up the stats for their area by city or zip code, whichever is more meaningful. So I used the zip codes to compare the median house value of 2005 with the median AGI of 2004 (the most recent data, but fairly representative of the boom years) for my three real-estate holdings.

Gulf Coast: 4.1. Ouch! This is consistent with the $20K drop in the assessed value (courtesy of the tax assessor) from last year to this. The good news is that I bought it pre-boom, so the house is still almost twice as valuable now as it was when I purchased it (if the tax assessor is correct), and still rented out profitably.

Rocky Mountains: 3.9. The assessed value actually increased by $30K between last year and this, but I'm pretty sure the values were calculated a year in advance, so they probably reflect the 2007 value over the 2006 value. It also may reflect the $20K or so in improvements (added A/C, massive landscaping) I made during the three years I lived in it. I'll have a better idea come next year. I purchased this house in the middle of the boom, and the rental income only covers the payment when there is no maintenance. But . . . enter the magic of rental property depreciation, which saves me $1200 in taxes every year from this property alone.

Upper Midwest: 2.1. The housing boom never happened where I presently live, so the bust has largely skipped us as well. But here's the thing: in all three communities, while the median sale price has only slumped rather than crashed, the number of home sales has crashed, often dramatically. For instance, in my Gulf Coast zip code, the number of home sales fell from 350 during the second quarter of 2005 to only 75 for the second quarter of 2008. Ditto for the Rockies: 470 to 140. Ditto for the Midwest: 170 to 70. This could mean that the wave of speculative sales has ended; it could also mean that unsold inventories are accumulating until the prices adjust to reflect the new market realities.

The stock market, however, depresses the shit out of me. My portfolio is approximately (the exact number is too depressing to contemplate) $20K poorer than it was in January. I also looked at the average total returns of my 14 year investing life: two percent, as of the end of September. Two percent! Measily bank interest. So much for being a "long run" investor. I can take small satisfaction in watching the further decline of the stuff I dumped at the early stages.