What if senators represented people by income or race, not by state?

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What if the 100-member Senate were designed to mirror the overall U.S. population — and were based on statistics rather than state lines?

Imagine a chamber in which senators were elected by different income brackets — with two senators representing the poorest 2 percent of the electorate, two senators representing the richest 2 percent and so on.

Based on Census Bureau data, five senators would represent Americans earning between $100,000 and $1 million individually per year, with a single senator working on behalf of the millionaires (technically, it would be two-tenths of a senator).

Eight senators would represent Americans with no income. Sixteen would represent Americans who make less than $10,000 a year, an amount well below the federal poverty line for families.

The bulk of the senators would work on behalf of the middle class, with 34 representing Americans making $30,000 to $80,000 per year.

Imagine trying to convince someone — Michael Bloomberg, perhaps? — to be the lonely senator representing the richest percentile. And what if the senators were apportioned according to jobs figures? This year, the unemployed would have gained two seats. Think of the deals that would be made to attract that bloc!

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What about a Senate in which voters cast ballots for candidates campaigning to win over a certain age group? Thirteen senators would vie for 18-to-24-year-olds, who strongly support measures such as the cap-and-trade climate bill and marriage rights for gays. Nearly all of these senators would be Democrats.

Americans over 65 would control 16 seats — and would be mostly Republicans interested in protecting Medicare and the broader status quo. The baby boomer bubble would be largely in the eldest category, though its stragglers would round out the segment of voters, probably split between the parties, that is edging up on retirement.

Thirty-six senators would serve 25-to-44-year-olds, and 35 senators 45-to-64-year-olds — and would be likely to push the very issues now on the table, including health care, entitlement viability and tax breaks for the middle class.

A Primer on Information Theory and Privacy

If we ask whether a fact about a person identifies that person, it turns out that the answer isn’t simply yes or no. If all I know about a person is their ZIP code, I don’t know who they are. If all I know is their date of birth, I don’t know who they are. If all I know is their gender, I don’t know who they are. But it turns out that if I know these three things about a person, I could probably deduce their identity! Each of the facts is partially identifying.

There is a mathematical quantity which allows us to measure how close a fact comes to revealing somebody’s identity uniquely. That quantity is called entropy, and it’s often measured in bits. Intuitively you can think of entropy being generalization of the number of different possibilities there are for a random variable: if there are two possibilities, there is 1 bit of entropy; if there are four possibilities, there are 2 bits of entropy, etc. Adding one more bit of entropy doubles the number of possibilities.

Because there are around 7 billion humans on the planet, the identity of a random, unknown person contains just under 33 bits of entropy (two to the power of 33 is 8 billion). When we learn a new fact about a person, that fact reduces the entropy of their identity by a certain amount. There is a formula to say how much:

ΔS = – log2 Pr(X=x)

Where ΔS is the reduction in entropy, measured in bits,2 and Pr(X=x) is simply the probability that the fact would be true of a random person. Let’s apply the formula to a few facts, just for fun:

Starsign: ΔS = – log2 Pr(STARSIGN=capricorn) = – log2 (1/12) = 3.58 bits of information
Birthday: ΔS = – log2 Pr(DOB=2nd of January) = -log2 (1/365) = 8.51 bits of information

Note that if you combine several facts together, you might not learn anything new; for instance, telling me someone’s starsign doesn’t tell me anything new if I already knew their birthday.

In the examples above, each starsign and birthday was assumed to be equally likely. The calculation can also be applied to facts which have non-uniform likelihoods. For instance, the likelihood that an unknown person’s ZIP code is 90210 (Beverley Hills, California) is different to the likelihood that their ZIP code would be 40203 (part of Louisville, Kentucky). As of 2007, there were 21,733 people living in the 90210 area, only 452 in 40203, and around 6.625 billion on the planet.

Knowing my ZIP code is 90210: ΔS = – log2 (21,733/6,625,000,000) = 18.21 bits
Knowing my ZIP code is 40203: ΔS = – log2 (452/6,625,000,000) = 23.81 bits
Knowing that I live in Moscow: ΔS = -log2 (10524400/6,625,000,000) = 9.30 bits
How much entropy is needed to identify someone?

As of 2007, identifying someone from the entire population of the planet required:

S = log2 (1/6625000000) = 32.6 bits of information.

Conservatively, we can round that up to 33 bits.

So for instance, if we know someone’s birthday, and we know their ZIP code is 40203, we have 8.51 + 23.81 = 32.32 bits; that’s almost, but perhaps not quite, enough to know who they are: there might be a couple of people who share those characteristics. Add in their gender, that’s 33.32 bits, and we can probably say exactly who the person is.

An Application To Web Browsers

Now, how would this paradigm apply to web browsers? It turns out that, in addition to the commonly discussed “identifying” characteristics of web browsers, like IP addresses and tracking cookies, there are more subtle differences between browsers that can be used to tell them apart.

One significant example is the User-Agent string, which contains the name, operating system and precise version number of the browser, and which is sent every web server you visit. A typical User Agent string looks something like this:

Mozilla/5.0 (Windows; U; Windows NT 5.1; en-GB; rv:1.8.1.6) Gecko/20070725 Firefox/2.0.0.6

As you can see, there’s quite a lot of “stuff” in there. It turns out that that “stuff” is quite useful for telling different people apart on the net. In another post, we report that on average, User Agent strings contain about 10.5 bits of identifying information, meaning that if you pick a random person’s browser, only one in 1,500 other Internet users will share their User Agent string.

EFF’s Panopticlick project is a privacy research effort to measure how much identifying information is being conveyed by other browser characteristics. Visit Panopticlick to see how identifying your browser is, and to help us in our research.

Special effects

I suppose I should say at this point that I’m a Tarantino fan. And the reason I’m a Tarantino fan is that he pays attention to everything; what people say, how they look, and why they’re doing what they’re doing. Watch Kill Bill 2 and look at the sofa in Bill’s hacienda toward the end. It’s perfect.

Then there are the cars. Bill drives a De Tomaso Mangusta. (Italian for Mongoose. The only animal feared by a Cobra.) He would. It’s the only car he could have driven: American, and yet not.

Daryl Hannah uses a Trans Am with an eagle on the bonnet. Whereas Uma Thurman, who has two eyes, has a Karmann Ghia. You just know that His Quentin-ness spent hours, maybe even days, agonising over these tiny details, but it’s precisely that which makes (most of) his films so much more watchable than the when-in-doubt-blow-it-up blockbusters.

I can’t send or receive email: I’ve reached a sending limit

Google has a number of sending limits in place to prevent abuse of our system, and to help fight spam. If one of your mail accounts reaches an abuse limit, the account will be temporarily unable to send mail.

Each Standard Edition account can currently send to 500 external recipients per day. Premier and Education Edition users can send to 2000 external recipients per day. The email addresses can be distributed among the To:, Cc:, and Bcc: fields. Administrators can contact all user accounts within the domain by adding everyone in the domain to an email list.

Here are a few additional tips:

• Create multiple user accounts to send mail. For example, ‘Admin1′ and ‘Admin2′ can each send 500 messages to reach 1000 unique recipients. (On the Premier or Education Edition, two accounts can reach 4000 recipients.)
• Stagger mass communications over the course of two days. For example, send messages to the allotted number of recipients on day one, wait for 24 hours, and send messages to another group of recipients on day two.
• In all editions of Google Apps, an individual message can be sent to a maximum of 500 external recipients at one time.

In economic forecasting, it’s hard to beat the yield curve

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Since 1970, every time short rates have been higher than long rates – a condition called an inversion – the Standard & Poor’s 500 composite and the S&P/TSX composite index (formerly the TSE 300) have had negative or flat earnings growth. The curve showed relatively high short rates before the dot-com bust in 2000. Investors who heeded these warnings could have sold off their stocks ahead of the collapse.

The yield curve predicted the present economic malaise. In the 15 months leading up to the subprime housing that began last August, the U.S. Treasury curve was showing relatively high short-term rates. Says Anthony Crecenzi, author of the 2002 bestseller (in fixed income analysis circles, that is), The Strategic Bond Investor, “few indicators with such a stellar forecasting record have the yield curve’s simplicity.”

Its ability to predict economic growth or contraction 12 to 18 months ahead far exceeds the power of other analytical tools, including the analysis of far more esoteric macroeconomic indicators that track things like orders for tools that make other tools. As well, the yield curve beats folk tales about hemlines and Super Bowl winners.

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To make use of the yield curve, one needs to understand its nuances, which appear in the shapes it can take.

Normal slope: Investors expect the economy to grow and, with that growth, to generate inflation. Investors expect a higher yield in the future when inflation is higher and erodes their returns on the bond. In each successive period, the central bank will have to raise interest rates a little. A progression of higher interest rates and the risks of parking money for many years as well as giving up liquidity all justify rates rising over time.

Steep slope: Characteristic of the beginning of an expansion or the period when an economy comes out of recession. The curve will be at, say, three percentage points higher at 30 years than at 90 days. The steeper any yield curve, the greater is the incentive to park money in long-term bonds and the lower is the relative cost of borrowing short.

Flat curve: Shows all terms have similar yields. It is a mixed signal in which investors cannot agree on what lies ahead for the economy. In application, it means that there is very little for the investor to gain by buying a 30-year bond rather than a 30-day bond. At most, the flat curve offers yield protection, Mr. Jong notes. There is no premium for taking on more time risk, but the investor can at least lock in a return for as much as 30 years.

Higher rates for short than long bonds, often called an inverted curve: Predicts recession or shows a market crisis. The future in which interest rates, that is, the return on money, is lower than in the present is a clear signal of bad times to come.

In truth, Chinese workers were never as docile as the popular caricature suggested. But the recent strikes have been unusual in their frequency (Guangdong province on China’s south coast suffered at least 36 strikes in the space of 48 days), their longevity and their targets: foreign multinationals.

China’s ruling Communist Party has swiftly quashed previous bouts of labour unrest. This one drew a more relaxed reaction. Goons from the government-controlled trade union roughed up some Honda strikers, but they were quickly called off. The strikes were widely, if briefly, covered in the state-supervised press. And the ringleaders have not so far heard any midnight knocks at the door.

This suggests three things. First, China is reluctant to get heavy-handed with workers in big-brand firms that attract global media attention. But, second, China is becoming more relaxed about spooking foreign investors. Indeed, if workers are upset, better that they blame foreign bosses than local ones.

In the wake of the financial crisis, the party has concluded, correctly, that foreign investors need China more than it needs them. Third, and most important, the government may believe that the new bolshiness of its workers is in keeping with its professed aim of “rebalancing” the economy.

And it would be right. China’s economy relies too much on investment and too little on consumer spending. That is mostly because workers get such a small slice of the national cake: 53% in 2007, down from 61% in 1990 (and compared with about two-thirds in America). Letting wages rise at the expense of profits would allow workers to enjoy more of the fruits of their labour.

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In the wake of the financial crisis, things are different. Deflation is now a bigger threat than inflation. And with 47m workers unemployed in the OECD alone, labour is not holding back the global economy. What the world lacks is willing customers, not willing workers. Higher Chinese wages will have a similar effect to the stronger exchange rate that America has been calling for, shrinking China’s trade surplus and boosting its spending.

This will help foreign companies and the workers they have idled. A 20% rise in Chinese consumption might well lead to an extra $25 billion of American exports. That could create over 200,000 American jobs.

Your business wants my money? Good luck

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Question 1 is meant to be a quick killer: “Why will this business succeed?”

If you can’t answer that, then your hope of getting anyone other than mom and pop to throw you a financial bone is pretty slim. You say you have a unique idea? I doubt it. The only unique ideas I’ve heard pitched were complete eye-rollers — as in, “You have to be kidding.” One guy who came to see me during the Internet boom wanted to start a website that would help tenants trade their way out of building leases online. Could he have found anything more likely to fail?

Question 8: Does the product or service meet a need or a perceived need of the customer? Don’t tell me people are hungry and will eat in your new restaurant. It will have to provide more than food. It’s also about the experience, the price and the location. You have a plan for those three, too?

People looking for money also have the annoying habit of assuming that an investor with a lot of dough will throw little bits of it — say, $5,000 or $10,000 — at just about anything. Forget it. Managing a few big investments is way better bang for buck than funding a lot of small ones.

Question 40: Why will your business succeed when it must compete with larger companies? Another “Hunh?” for most start-ups. Any business that takes off grabs the attention of established players and other new ventures.

Ergo, Question 42: If you plan to take market share, how will you do it?

Question 60: What business experience does the management team have? No experience, no money — not even a hope.

And the clincher is Question 76: How will investors get their money out? We want our money back, you know, to invest in the next great thing. If you can’t tell me how and when I’ll get it out, then it’s not going in.

So, here’s some advice based on those cranky questions: Write a really good business plan. You can buy templates. I use one I modified from Planigent. It costs about $30, and includes spreadsheets for modelling cash flows, and PowerPoint for creating presentations to wow prospective investors. Don’t call anybody until you’ve finished the first point.