According to the scientists at Predictably Irrational:

Finally, we wondered how less attractive individuals rationalized to themselves, their selection of less attractive others. Using a speed-dating study we found that more attractive people placed more weight on physical attractiveness in selecting their dates, while less attractive people placed more weight on other qualities e.g. sense of humor. Much like the famous line from Crosby, Stills, Nash, and Young, people find a way to love the ones they can be with.

My takeaway - ugly people have the same definition of attractive as hot people. They know they can’t get hot people, so they value hotness less, and therefore say they care about personality more. In other words, people care about personality because they have to, or at least because they think they have to.

Categories: Behavioral Economics, Dating, Information Asymmetries, Matching Mechanisms
1 Comment »

The best way to land a job or promotion is to work for free. It seems counter-intuitive, because we usually try to find new jobs or earn promotions to make more money, and working for free means not making any money (and has an opportunity cost), but working for free solves one of the biggest problem in the employment process: determining whether or not a candidate really is a good fit for the new role.

Most job seekers say the same thing “I’m a hard worker, I’m talented, and I’m motivated.” Of course, since everybody is saying it, managers have no way of knowing if it’s really the case. For most jobs, they can look at your past history and see if you’ve done the job successfully someplace else, but if you’re new to the job market, new to the industry, or looking for a promotion, you don’t have a history there. The manager is left with nothing but your word, and quite frankly, that isn’t worth much.

When you work for free, you show two things. First, you build a record of doing the job successfully. Second, you demonstrate that you’ve got a passion for what you’re doing. If you’re willing to work hard for no money, imagine what you’d do if you got paid for it. Employers are desperate for people passionate people who can do the job, and when they hire you, they’re usually taking a risk on whether or not you’re really as great as you say you are. The more you can prove it, the more likely an employer is to hire you and pay you a lot.

So how does this work for promotions? If you’re already in a job and you want a promotion, you could go up to your boss and explain that you’ve been a very successful worker, how you’ve been at the company a long time, and blabber about how great you are at your job. If your boss is smart, they’ll shoot right back and say “I know you’re great at this job, but that doesn’t mean you’d be great at the job we’re promoting you to.” A promotion usually requires added responsibility, and if it’s to management, a different set of skills. Instead, just start taking on extra responsibility. Take on leadership roles on projects, succeed, and then take on more. Don’t do it so much they start to take advantage of you, because at some point, you’re going to go to your boss and say “I’ve been doing the work of a manger for the past few months, and doing a great job, I think I’m ready to become one.” Even if they disagree, you now have proof that you can take to another company that you can do the work.

This isn’t just speculation that flies in the face of conventional wisdom. Every job that I’ve ever held, whether as a computer programmer, consultant, or web designer, was tied to some activity I had previously done free of charge for friends, family, non-profits, and myself.

While at a conference in Seattle last year, I saw a presentation by a woman who handled marketing and promotions for video games - a dream job.  While in high school, she started writing a music newsletter, with opinions about the industry, stories about local bands, and reviews of CDs.  She’d print off copies, take them to music stores, and leave them there for shoppers to look at.  Eventually, someone in the music industry found it, liked what she saw, and hired the presenter for a dream job in the music biz, from which she transitioned later to video games.  Through the newsletter, she demonstrated not only that she could write, but that she had a passion for the work.  It’s something that employers love, but that’s hard to screen for in interviews.  If you do the work for free, you prove both right off the bat.

If you only do exactly the work you get paid to do, you’re never going to grow.  Nobody is going to take on your risk for you, you need to do it yourself.  As long as you think of it as an investment in your future, and manage it accordingly, working for free can bring massive returns.

Categories: Business and Economics, Careers, Information Asymmetries
No Comments »

Jane Galt passes along another look at the power of digital beautification:

Categories: Information Asymmetries, Technology
No Comments »

An article on workforce.com says we need to ditch the HR Generalist:

They resist measurement. If you look within your firm, you’ll find that your generalists have no output or results metrics of any kind. They resist corporate-wide HR metrics and technology because once those are instituted, they’ll no longer be allowed to hide in their well-protected enclaves.

Most of the complaints seem to revolve around the same thing: Generalists protect information. Never mind the importance of transparency for governance in general, within a company, it is paramount. By hiding information, HR Generalists become the siloed barriers to change. At the same time, the more their role becomes “information conduit,” the easier it is to replace them with technology.

In all fairness to the HR generalist, people in all sorts of roles try to protect information and serve as barriers to change. I’ve seen companies where every single one of those complaints can describe anyone there, including senior management. It just so happens that HR generalists have these problems structurally built into their role.

Categories: Business and Economics, Careers, Information Asymmetries, Managing, Metrics
No Comments »

A new study from the Steinhardt Social Research Institute says there could be over seven million Jews. Why are there so many different numbers, and why are Jews so hard to count.

One of the biggest reasons is that it’s hard to determine just who is a Jew. People identify as Jewish on religious, ethnic, and cultural grounds, while others identify by birth, and still others were clearly born to Jews but do not personally identify as Jewish. This makes picking out who is Jewish in a survey extremely difficult. You have to ask the right questions, the right ways, and then figure out which answers mean someone is a Jew. Changes in how you define a Jew can cause swings of literally millions of people.

But once you come up with a definition for who’s Jewish and who isn’t, you’ve still got trouble counting people. That’s because no matter how you define it, there just aren’t that many Jews in the population. Think of a jar with 1000 marbles in it. Of those, somewhere between 975 and 985 are yellow, while the rest are green. The green marbles, between 15 and 25, represent the size of the Jewish population. Now imagine that you have to count them by taking a small sample, say 100 marbles, out of the jar. If the jar were half green and half yellow, this would work pretty well, but with only a small number of green marbles, your sample of 100 could give you one, two, or three, each of which would give you radically different estimate of the number of green marbles in the whole jar. Counting Jews works the same way - the statistical sampling methods used in normal social science break down when trying to reach a very broad but very small group.

Further, Jews are less likely than the population at large to respond to random digit dial phone surveys. In other words, when you try to take the marbles out of the jar and count them, some marbles won’t let you, and the green marbles are less likely to let you. Given the huge swings a few marbles can give you, this makes them even more difficult to count. Because Jews won’t talk to the survey interviewers, we don’t really know how much less likely they are to answer the phone, so we don’t really know how to weight things. We also don’t know the characteristics that make one person more likely to answer than another.

Each of these caveats makes counting Jews extremely difficult, and leads to the controversy around the numbers given by Jewish demographers. So what are the different techniques that are used? Random digit dial surveys, like the massive National Jewish Population Survey use a weighting technique, similar to what political pollsters use, to try and bring the numbers in line with what they know to be true about the population as a whole. These can be controversial, whether for counting Jews or voters, as they start to tinker with the underlying science of public opinion research in ways that not all social scientists or statisticians agree. The NJPS also needs an extremely large sample to cover the whole country, and this means it’s prohibitively expensive to conduct accurately.

Another technique, used by Dr. Ira Sheskin at the University of Miami, takes a collection of community studies and combines them all together to get a national account. Since most Jews live in urban areas, Dr. Sheskin’s community studies, of which he has done many, are more likely to target those numbers where Jews are concentrated. He also over-samples in “core” Jewish areas, increasing his response rate. However, this method has its own shortfalls. For instance, many elderly Jews are “snow birds,” with summer homes in the Midwest or Northeast and winter residences in southern areas like Florida and Arizona, making it likely that they’ll be double counted. The same thing happens with younger Jews who may go to college in one city while claiming residence in another. And finally, it ignores the “long tail” of Jewish communities, smaller cities that still have Jewish populations.

Researchers at Brandeis’s Steinhardt Social Research Institute used a third method. They took publicly available survey data, rather than data specific studies on the Jewish community, and conducted a “meta-analysis,” trying to merge it all together to come up with a good accounting. In other words, instead of taking 100 marbles out of the jar, they found twenty people who had each taken 100 marbles out of the jar for different reasons, and used their counting. Not only does this give them more samples, but it also allowed them to save money for expensive polls with large samples that are required for the other two techniques. Unfortunately, there are a number of risks here. Different studies use different samples, questions, methodology, making it complex to determine how to weight any specific data point, and restricting the available data to what those other studies are interested in. Nevertheless, it provides an innovative, and significantly more cost effective, technique for gathering data not only about how many Jews there are, but information about them as well.

So which of these methods works the best, and how many Jews are there? Because of the problems described above, there’s no way to really know. The best answer is that it’s somewhere between four and eight million, and that whatever number you pick is probably off by a million. And like with most things Jewish, there’s going to be a healthy debate around the issue so that any two Jewish demographers will probably give you three different numbers.

Categories: America, Community, Information Asymmetries, Jewishness, Metrics, Polling
No Comments »

Prizes stimulate innovation better than grants:

BACK in the 1700s, prizes were a fairly common way to reward innovation. Most famously, the British Parliament offered the £20,000 longitude prize to anyone who figured out how to pinpoint location on the open sea. Dava Sobel’s best-selling 1995 book “Longitude” told the story of the competition that ensued, and Mr. Hastings mentioned the longitude prize as a model at that meeting back in March.

Eventually, though, prizes began to be replaced by grants that awarded money upfront. Some of this was for good reason. As science became more advanced, scientists often needed to buy expensive equipment and hire a staff before having any chance of making a discovery.

The internet is changing the economics of innovation and discovery.  Science is no longer expensive like it once was, it is within the realm of dedicated and educated hobbyists.  Robin Hanson, who the article discusses, is everywhere you find interesting information economics problems.

Categories: Business and Economics, Incentive Centered Design, Information Asymmetries, Information Economics, Information Markets, Matching Mechanisms, Science, Social Software, Strategy, Technology, Users as Partners
No Comments »

In Slate:

But the more I thought about Cramer, the more I realized that pointing out that he gives terrible investment advice would be like pointing out that the sun rises. Worse, I would be dismissed as a wet blanket who didn’t get that the point of Mad Money was just to have a bit of ironic fun. I mean, of course Jim Cramer gives terrible investment advice—we all know that, right?—and we only watch the show because, well, because he does possess a certain bizarre type of market and entertainment genius—if there’s a pundit out there with more opinions about more stocks, I’ve never seen him—and he’s irreverent, madcap, and, yes, even brilliant, in an idiot-savant, freak-show sort of way. (Moreover, Cramer is mesmerizing reality TV. Admit it: You watch because you wonder if this is the night he finally has a heart attack, kills someone, or explodes in a tirade of expletive-laced slander.)

That is precisely why I watch Mad Money, and I love watching the show (though I can never stomach a full episode at a time). I think CNBC personalities are awesome, and although Jim Cramer is great in small doses, he’s nothing compared to personal finance guru Suze Orman, who will help you fix up your crappy finances and crappy relationships at the same time.

Categories: Business and Economics, Entertainment Media, Information Asymmetries, Information Markets, Television
No Comments »

Nevertheless, a small band of economists from universities, research institutions and the government are clearly expressing the blasphemy that many Americans could be saving less than they are being told to by the financial services industry — and spending more — while they are younger. The negative savings rate, they say, is wildly distorted.

According to them, the financial industry, with its ostensibly objective online calculators, overstates how much money someone will need in retirement. Some, in fact, contend that financial firms have a pointed interest in persuading people to save much more than they need because the companies earn fees on managing that
money.

From the NYTimes, via Marginal Revolution.

Categories: America, Business and Economics, Information Asymmetries
No Comments »

In BusinessWeek:

Acer also hopes to improve its position in the U.S., where it has just 1.8% of the market. The company sells lots of notebooks to small businesses, but among large corporate customers, “they don’t have the credibility” needed, says Elsa Opitz, research manager at IDC in London. Acer has raised its profile with U.S. consumers over the past two years through deals to sell its wares at Wal-Mart (WMT), CompUSA, and Circuit City (CC)—which could ultimately pay off with big companies, says U.S. sales chief Mark Hill. “More of a presence in U.S. retail,” he says, “will inevitably lead to better name recognition.”

The problem may also be that I’ve never seen an Acer that didn’t have huge quality problems - usually major hardware problems - and their customer service is usually horrible about trying to fix them. Corporate customers would be foolish to invest heavily in such unreliable machines that will save them on capital outlays but cost them orders of magnitude more on reurring service problems.

I think Acer is also overestimating the importance of the retail sector in attracting corporate customers.  Dell and pre-Lenovo IBM have/had no retail business and dominate the corporate market.  HP slowed down on retails sales when it started picking up corporate accounts.  Most importantly, though, Acer has no offerings to speak of in the server market, the real gem of the corporate accounts.

Categories: Business and Economics, Distribution, Information Asymmetries, Technology
No Comments »

My friend Katie recently pointed me to two sites with tons of data on money and politics.

http://www.opensecrets.org tracks political donations by industry, PACs, candidates, and issues.

And on the less well financed side of the hill, http://www.legistorm.com/ tells you the salary of every employee in the house/senate.  I was shocked at how much some, and little others, were making (remember that most of the data is either quarterly or semi-annual).

Categories: America, Governance, Information Asymmetries, Politics
No Comments »

I think that one of the most important changes we’re going to see in lots of professions over the next few years is the emergence of tools that close the gap between the middle and the top–that allow the decision-making who is merely competent to avoid his errors to be reach the level of good.

Malcolm Gladwell writing on his blog.  His musing lately have dealt with finding good basketball metrics, but this post was in response to his article about an algorithm that predicts movie box office receipts from scripts.

Gladwell is right that metrics and decision tools raise the quality of the average decision, but he seems to think it affects most the people right below the top or near the middle.  What it actually does is take the implicit knowledge of the best decision makers and make it explicit for everybody else.  I’m not sure the precise numbers, but if you asked a group of people if they were in the top 10% of their profession, something like 70% will say that they are, even though by definition, only 10% can be.  Everybody thinks that they can make better decisions without structure, advice, or metrics, but reality tells a different story.  This was one of the key findings I took away from Gladwell’s book Blink.  In practice, the biggest issue is that nobody wants to admit that their complex “art,” like picking hit movies or music, making business decisions, or medical services, can be reduced to a simple algorithm or decision tree.  But on average, that algorithm does better than the specialists.

There are two areas where I think you need to watch out with the movie algorithm.  First, movie revenues are heavily influenced by a number of factors that are decided by people today - opening size, marketing budget, timing.  The biases of the studio execs are all inside these factors, which means that they are biasing the results of the neural network.  IT could be argued that in fact, these things don’t matter, but I’m not sure if the current network controls for them.

The second issue is profitability.  Just because a movie will generate revenue, doesn’t mean it’s the most profitable investment.  A studio gets a better return with a $5 million film that generates $50 million in revenue than a $50 million film that generates $200 million in revenue, and if they can make ten of the cheaper films, then they’re able to make more profits with lower risk.  I see Hollywood moving, begrudgingly, in this direction, and entrenched organizational structures, distribution partners, and false preconceptions as the biggest osbstacles.

As an aside, the actual Gladwell article is worth reading for the description of Kamesian thinking and Dick Copaken alone.

Categories: Entertainment Media, Information Asymmetries, Information Economics
No Comments »

The Freakonomics Blog is also now talking about truth markets (is it information market day or something?):

An excerpt, which they excerpt from a letter:

I think the guy who talked about having a trustworthiness rating for individuals is on the right track. Except, anything that distracts users from making immediate changes (such as logging in, or a notion that I have to build my reputation to have an equal voice) could be the death of wikipedia. My approach to improve wikipedia would be to include an “information liquidity” metric along with each page, similar to a stock’s trading volume. Pages could be grey-scale coded based on the page change history, with high-volume pages appearing darker, more solid. Of course this can be gamed, but here gaming has visible artifacts. As far as accuracy goes, I think this would solve Stephen’s objection because the informational backwaters—pages with lower liquidity—would appear visually distinct from the heavily modified. In machine learning there’s a construct called a Boltzmann Machine (aka simulated annealing machine) which describes the dynamics of systems like wikipedia, but it requires a metric like volume/liquidity/energy. An alternative approach to social networking for solving accuracy/trust problems is this one, which I’m very intrigued by, but the system isn’t big enough yet to bear out the promises.

Categories: Incentive Centered Design, Information Asymmetries, Information Economics, Information Markets, Social Software
No Comments »

Online prediction markets have made it to Slate on the same day that my class on Information Aggregation and Predictions Market started.
From Bruce Reed on Slate:

After Bush signed the bill [banning online gaming] earlier this month, the online gaming giants shut down their American operations almost overnight. Sportingbet Plc, a British company, took a $391 million loss and sold its U.S. arm for $1.

Other companies are betting the law won’t stick. Trade Exchange Network, an Irish firm that runs Tradesports.com and Intrade.com, continues to welcome American customers. But here’s the real irony: At the same time the Republican Congress is trying to throw them out of the American market, the briskest business at Tradesports and Intrade is taking bets on whether Americans will throw out the Republican Congress.

In other news, I start the semester down $500 class bucks due to bad information about the Tigers’ victory prospects.

Categories: Information Asymmetries, Information Economics, Information Markets, Politics, Social Software
No Comments »

Via Tyler Cowen at Marginal Revolution:

Our argument can be briefly summarized: Attorneys are “knowledge workers,” who differ from other employees because they essentially carry around key firm assets in their brains. The knowledge assets these lawyers control—an understanding of the needs and interests of clients—are obviously of greatest value when used with specific clients. This specificity gives individual attorneys considerable leverage over their employers. By threatening to “grab and leave” with an important client, attorneys can leverage an increased share of their firm’s revenues. The up-or-out partnership system found in large law firms has evolved over time as a workable resolution to this particular problem. By forming partnerships and firing experienced attorneys who are not promoted to partnership positions, law firms limit the opportunity for experienced attorneys to grab and leave with the firm’s valuable clients. Grabbing and leaving is more important in legal partnerships than in conventional firms because law firms cannot readily establish property rights over the knowledge essential for serving particular clients.

He also has a different exerpt.

The argument applies to most professional services firms, like large management consulting firms. In these types of firms, partners handle client relationships - especially sales - while the actual work is left to the lower level associates. Partnership is the carrot at the end of the stick as well as a method for keeping experienced and talented associates from breaking out on their own. The up-or-out model keeps the number of partners small, their quality high, and incentivises low level associates.

Organizations in knowledge industries need to realize how fluid their customer relationships are, and how important it is to manage those relationships. Dworin Consulting helps firms and non-profits identify their stakeholders’ needs and develop customized strategies for engaging them. In addition, Dworin Consulting helps organizations design incentive schemes, like up-and-out promotion, to align employee motivations with group goals.

For those interested in the full paper, it’s available here, or here.

Categories: Careers, Incentive Centered Design, Information Asymmetries, Information Economics, Law, Matching Mechanisms
1 Comment »