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The fight for 15

September 16, 2015 37 comments

Whenever I hear an argument about the possibility of raising the minimum wage to $15 per hour, it sounds like this. Person A, who is for it, makes the case that it’s too difficult to live on minimum wage earnings, and it doesn’t make sense for someone working full time to struggle so much to feed their kids. Person B, who’s against it, says that 15 is too high, that too many employers will be unwilling to pay for unskilled workers at that rate, and they will replace such people with machines instead of doing so. Essentially, they argue the bad will outweigh the good.

Full disclosure: I am often Person A. I once figured out that if you take someone’s hourly wage in dollars, and you multiply by 2, then you get their yearly wages in thousands of dollars. That means an income of $100K per year is $50 per hour. That means an income of the current New York minimum wage, $8.75 per hour, is a measly $17.5K per year, which would be absolutely crazy to try to live on, according to my reckoning. In other words, I think about what I could theoretically live on, if I had a minimum wage job, and I have extreme sympathy for people who try to.

Let’s get back to Person B’s argument. It’s weird because it sounds like Person B is arguing for the sake of the poor, but they’re ignoring the vital question of what is a living wage. Let me give you an analogy.

You have a sick population, and they all need 3 pills per day to stay well. The pills are expensive, though, and so the people in charge of pill distribution give most people 2 pills per day. They argue that, if they gave out 3 pills per day to everyone, some people would have no pills. For the sake of those theoretical people, then, they give out only 2, and everyone remains sick.

In other words, for the sake of holding on to crappy jobs that pay below living wages, and where the employees need food stamps to survive, we don’t raise the bar so they can actually sustain someone in a basic way. It’s almost like we’re desperate to hold on to them because otherwise our unemployment rate would be higher.

I say, figure out what a living wage is, and raise the minimum wage to that level. I actually don’t know what the magic number should be, exactly. Is 15 big enough? Maybe it is, in some places, but maybe in others it’s actually smaller. It doesn’t have to be the same throughout the country. But for as long as we live in a country where the model is that a job is supposed to support you, we should make sure it actually does.

Categories: economics

The Chef Shortage, Explained

This is a guest post by Sam Kanson-Benanav, a chef who has managed restaurants in Minnesota, Wisconsin, and New York City. He spent two years in the studying global resource marketplaces in the Amazon rainforest, and his favorite food is a french omelet. 

Despite my desperate attempts at a career change, I’ve become fairly inured to the fact I work in one of the most job secure industries in America. And I’m not a tenured professor.

I am a professional restaurant person – cook, manager, server, and bartender (on nights when a bartender doesn’t show up). As a recent Washington Post article highlights: it has become increasingly more difficult for kitchens to staff their teams with proper talent. We could ponder a litany of reasons why talented cooks are not flocking to the kitchens, but if you prefer to stop reading now, just reference Mathbabe’s entirely accurate post on labor shortages.

Or, we could just pay cooks more. As it turns out, money is a very effective motivator, but restaurants employ two cannibalizing labor models based on fundamentally contrasting motivators: tipping and wages. I’ll take these on separately.

Tipping servers suppress wages for the kitchen                 

We already know tipping is a bad system, which bears less correlation to the actual quality of service you receive than to the color or gender of your server. It’s an external rewards based system akin to paying your employees a negligible wage with a constant cash bonus, a historically awful way to run a business.

In other words, restaurant owners are able to pass off the cost of labor for employing servers onto their consumers. That means they factor into their menu prices only the cost of labor for the kitchen, which remains considerable in the labor-intensive low margin restaurant world. Thankfully, we are all alcoholics and willing to pay 400% markups on our beer and only a 30% markup on our burgers. Nevertheless, the math here rarely works in a cook’s favor.

For a restaurant to remain a viable business, a cook (and dishwasher’s) hourly wage must be low, even as bartenders and servers walk away with considerable more cash.

In the event that a restaurant, under this conventional model, would like to raise its prices and better compensate its cooks, it cannot do so without also raising wages for its servers. Every dollar increase in the price of line item on your receipt increases a consumers cost by $1.20 , the server happily pocketing the difference.

Unfair? Yes. Inefficient? Certainly. Is change possible? Probably not.

Let’s assume change is possible

Some restaurants are doing away with this trend, in a worthy campaign to better price the cost of your meal, and compensate cooks more for their work. These restaurants charge a 20% administration fee, which becomes part of their combined revenue—the total pool of cash from which they can pay all their employees at set hourly rates.

That’s different then an automatic service fee you might find at the end of your bill at a higher end restaurant or when dining with a large group. It’s a pre tax charge that repackages the cost of a meal by charging a combined 30% tax on the consumer (8% sales tax on 20% service tax) allowing business owners to allocate funds for labor at their discretion rather than obligate them to give it all to service staff.

Under this model cooks now may make a stunning $15-18 an hour, up from $12-$13, and servers $20-30, which is yes, down from their previous wages. That’s wealth redistribution in the restaurant world! For unscrupulous business owners, it could also incentive further wealth suppression by minimizing the amount a 20% administration fee that is utilized for labor, as busier nights no longer translate into higher tips for the service staff.

I am a progressive minded individual who recognizes the virtue of (sorry server, but let’s face it) fairer wages. Nevertheless, I’m concerned the precedents we’ve set for ourselves will make unilateral redistribution a lofty task.

There is not much incentive for an experienced server to take a considerable pay cut. The outcome is likelier to blur the lines between who is a server and who is a cook, or, a dilution in the level of service generally.

Wage Growth

Indeed wages are rising in the food industry, but at a paltry average of $12.48 an hour, there’s considerable room for growth before cooking becomes a viable career choice for the creative minded and educated talent the industry thirsts for. Celebrity chefs may glamorize the industry, but their presence in the marketplace is more akin to celebrity than chef, and their salaries have little bearing on real wage growth of labor force.

Unlike most other industries, a cook’s best chance and long term financial security is to work their way into ownership. Cooking is not an ideal position to age into: the physicality of the work and hours only become more grueling, and your wages will not increase substantially with time. This all to say – if the restaurant industry wants more cooks, it needs to be willing to pay a higher price upfront for them. This is not just a New York problem complicated by sky high rents. It’s as real in Wisconsin as it is Manhattan.

Ultimately paying cooks more is a question of reconciling two contrasting payment models. That’s a question of redistribution.

But “whoa Sam – you are a not an economist, this is purely speculative!” you say?

Possibly, and so far at least a couple of restaurants have been able to maintain normal operations under these alternative models, but their actions alone are unlikely to fill the labor shortage we see. Whether we are ultimately willing to pay servers less or pay considerably more for our meals remains to be seen, but, for what its worth, I’m currently looking for a serving job and I can tell you a few places I’m not applying to.

Guest post: Open-Source Loan-Level Analysis of Fannie and Freddie

This is a guest post by Todd Schneider. You can read the full post with additional analysis on Todd’s personal site.

[M]ortgages were acknowledged to be the most mathematically complex securities in the marketplace. The complexity arose entirely out of the option the homeowner has to prepay his loan; it was poetic that the single financial complexity contributed to the marketplace by the common man was the Gordian knot giving the best brains on Wall Street a run for their money. Ranieri’s instincts that had led him to build an enormous research department had been right: Mortgages were about math.

The money was made, therefore, with ever more refined tools of analysis.

—Michael Lewis, Liar’s Poker (1989)

Fannie Mae and Freddie Mac began reporting loan-level credit performance data in 2013 at the direction of their regulator, the Federal Housing Finance Agency. The stated purpose of releasing the data was to “increase transparency, which helps investors build more accurate credit performance models in support of potential risk-sharing initiatives.”

The GSEs went through a nearly $200 billion government bailout during the financial crisis, motivated in large part by losses on loans that they guaranteed, so I figured there must be something interesting in the loan-level data. I decided to dig in with some geographic analysis, an attempt to identify the loan-level characteristics most predictive of default rates, and more. The code for processing and analyzing the data is all available on GitHub.

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The “medium data” revolution

In the not-so-distant past, an analysis of loan-level mortgage data would have cost a lot of money. Between licensing data and paying for expensive computers to analyze it, you could have easily incurred costs north of a million dollars per year. Today, in addition to Fannie and Freddie making their data freely available, we’re in the midst of what I might call the “medium data” revolution: personal computers are so powerful that my MacBook Air is capable of analyzing the entire 215 GB of data, representing some 38 million loans, 1.6 billion observations, and over $7.1 trillion of origination volume. Furthermore, I did everything with free, open-source software.

What can we learn from the loan-level data?

Loans originated from 2005-2008 performed dramatically worse than loans that came before them! That should be an extraordinarily unsurprising statement to anyone who was even slightly aware of the U.S. mortgage crisis that began in 2007:

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About 4% of loans originated from 1999 to 2003 became seriously delinquent at some point in their lives. The 2004 vintage showed some performance deterioration, and then the vintages from 2005 through 2008 show significantly worse performance: more than 15% of all loans originated in those years became distressed.

From 2009 through present, the performance has been much better, with fewer than 2% of loans defaulting. Of course part of that is that it takes time for a loan to default, so the most recent vintages will tend to have lower cumulative default rates while their loans are still young. But there has also been a dramatic shift in lending standards so that the loans made since 2009 have been much higher credit quality: the average FICO score used to be 720, but since 2009 it has been more like 765. Furthermore, if we look 2 standard deviations from the mean, we see that the low end of the FICO spectrum used to reach down to about 600, but since 2009 there have been very few loans with FICO less than 680:

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Tighter agency standards, coupled with a complete shutdown in the non-agency mortgage market, including both subprime and Alt-A lending, mean that there is very little credit available to borrowers with low credit scores (a far more difficult question is whether this is a good or bad thing!).

Geographic performance

Default rates increased everywhere during the bubble years, but some states fared far worse than others. I took every loan originated between 2005 and 2007, broadly considered to be the height of reckless mortgage lending, bucketed loans by state, and calculated the cumulative default rate of loans in each state:

Screen Shot 2015-06-18 at 6.18.41 PM

4 states in particular jump out as the worst performers: California, Florida, Arizona, and Nevada. Just about every state experienced significantly higher than normal default rates during the mortgage crisis, but these 4 states, often labeled the “sand states”, experienced the worst of it.

Read more

If you’re interested in more technical discussion, including an attempt to identify which loan-level variables are most correlated to default rates (the number one being the home price adjusted loan to value ratio), read the full post on toddwschneider.com, and be sure to check out the project on GitHub if you’d like to do your own data analysis.

The market for your personal data is maturing

As everyone knows, nobody reads their user agreements when they sign up for apps or services. Even if they did, it wouldn’t matter, because most of them stipulate that they can change at any moment. That moment has come.

You might not be concerned, but I’d like to point out that there’s a reason you’re not. Namely, you haven’t actually seen what this enormous loss of privacy translates into yet.

You see, there’s also a built in lag where we’ve given up our data, and are happily using the corresponding services, but we haven’t yet seen evidence that our data was actually worth something. The lag represents the time it takes for the market in personal data to mature. It also represents the patience that Silicon Valley venture capitalists have or do not have between the time of user acquisition and profit. The less patience they have, the sooner they want to exploit the user data.

The latest news (hat tip Gary Marcus) gives us reason to think that V.C. patience is running dry, and the corresponding market in personal data is maturing. Turns out that EBay and PayPal recently changed their user agreements so that, if you’re a user of either of those services, you will receive marketing calls using any phone number you’ve provided them or that they have “have otherwise obtained.” There is no possibility to opt out, except perhaps to abandon the services. Oh, and they might also call you for surveys or debt collections. Oh, and they claim their intention is to “benefit our relationship.”

Presumably this means they might have bought your phone number from a data warehouse giant like Acxiom, if you didn’t feel like sharing it. Presumably this also means that they will use your shopping history to target the phone calls to be maximally “tailored” for you.

I’m mentally tacking this new fact on the same board as I already have the Verizon/AOL merger, which is all about AOL targeting people with ads based on Verizon’s GPS data, and the recent broohaha over RadioShack’s attempt to sell its user data at auction in order to pay off creditors. That didn’t go through, but it’s still a sign that the personal data market is ripening, and in particular that such datasets are becoming assets as important as land or warehouses.

Given how much venture capitalists like to brag about their return, I think we have reason to worry about the coming wave of “innovative” uses of our personal data. Telemarketing is the tip of the iceberg.

Sharing insurance costs with the sharing economy

One consequence of the “sharing economy” that hasn’t been widely discussed, at least as far as I’ve seen, is how the externalities are being absorbed. Specifically, insurance costs.

Maybe because it’s an ongoing process, but for both Uber and AirBnB, the companies tell individuals who drive that their primary car insurance should be in use, and they tell individual home- or apartment-dwellers that their renters insurance should apply.

In other words, if something goes wrong, the wishful thinking goes, the private, individual insurance plans should kick in.

When people have tried to verify this, however, they responses have been mixed and mostly negative. The insurance companies obviously don’t want to cover a huge number of people for circumstances they didn’t expect when they offered the coverage.

So, if an Uber driver gets into an accident while ferrying a passenger, it’s not clear whether their primary insurance will cover it. It’s even less clear if the driver is using the Uber app and is on their way to get a passenger. Similarly, if an AirBnB guest falls because of a broken staircase, it’s not clear who is supposed to pay for the damages to the person or the staircase. What if the guest burns down the house?

So far I don’t think it’s been fully decided, but I think one of two things could happen.

In the first scenario, the insurance companies will really refuse to cover such things. To do this they will have to have a squad of investigators who somehow make sure the customer in question was or was not hosting a guest or driving a customer. That would involve suspicion and some amount of harassment, which customers don’t like.

In the second scenario, which I think is more likely given the above, the insurance companies will quietly pay for the damages accrued by Uber and AirBnB usage. They won’t advertise this, and if asked, they will discourage any customer from doing stuff like that, but they also won’t actually refuse to pay the costs, which they will simply transfer to the larger pool of customers. It doesn’t really matter to them at all, in fact, as long as they are not the only insurance company with this problem.

That will mean that the quants who figure out the costs of insurance will see their numbers change over time, depending on how much more the insurance is being called into action. I expect this to happen a lot more for Uber drivers, because if you are an Uber driver 40 hours a week, that means you’re always in your car. So our insurance costs will go up in proportion to how many people become Uber drivers. I expect this to happen somewhat more for AirBnB renters, because the house or apartment is in constant use; if it’s being rented by rowdy partiers, all the more. Our renters insurance will go up in proportion to how many people are AirBnB renters.

That reminds me of a story my dad used to like telling, whereby a friend of his rented out his Cambridge house to a Harvard professor, and when he came back it was totally trashed, including what looked like a bonfire pit in the living room. The professor in question was Timothy Leary.

Anyhoo, my overall conclusion is that the new “sharing economy” businesses really will end up sharing something with the rest of us soon, namely the cost of insurance. We will all be paying more for car insurance and home- or renters-insurance if my guess is accurate. Thanks, guys.

Categories: economics, rant, statistics

Fingers crossed – book coming out next May

As it turns out, it takes a while to write a book, and then another few months to publish it.

I’m very excited today to tentatively announce that my book, which is tentatively entitled Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, will be published in May 2016, in time to appear on summer reading lists and well before the election.

Fuck yeah! I’m so excited.

p.s. Fight for 15 is happening now.

Predatory credit score-based insurance fees

I’ve been looking into who uses credit scores – FICO scores or other alternative scores – and I’ve found that the insurance industry is a major user.

Homeowners insurance rates, for example, varies wildly by state depending on what kind of credit score you have, often more than doubling for people with poor credit versus people with excellent credit. This is in spite of the fact that homeowners insurance applies not to the payments of mortgages but rather to the contents of an apartment or home.

Similarly, auto insurance rates vary by credit score, even though someone with a poor credit score isn’t obviously a bad driver. For example, in Maryland, people with bad credit scores can be charged 40% more just for having bad credit scores.

Statistics like this make me wonder, how much of this price discrimination comes from the insurance companies trying to understand and account for actual risk, and how much comes from their understanding that poorer people have fewer options and will simply pay predatory rates?

And just in case you’re a believer in free markets and fair competition, and think such predatory behavior would be whisked away in a competitive market, insurance companies actually target people who don’t shop around and charge them more. In other words, it’s not a free market if not everyone actually has good information.

Tell me if you have more examples like this, I’m a collector!