Monthly Archives: February 2016

Edit: Parts of this post are meaningfully incorrect. Specifically, the Fed prints on the order of 100s of billions (not 100s of millions). Will be updated soon.


In The Second Machine Age, Erik Brynjolfsson and Andrew McAfee capture the GDP effect of some technological progress as the price of a good falling from infinity to zero. This might get the supply-side tension right, but there are a few other problems in measuring long-term changes in standard of living. This post is a question more than answer and one that touches on interesting aspects of the debate on unmeasured consumer surplus, magnitude of economic wellbeing, and secular stagnation.

As a baseline, consider the BLS method of hedonic adjustment. To account for changes in product quality, they generate a regression of log price on various attributes (for example pixelation and screen size for television). They then consider improvements in these factors over time to make sure estimated inflation doesn’t overstate the truth.

This gets the year-on-year figures right, but makes long-term adjustment really hard. Specifically, consider your Internet service that’s probably too expensive. Hedonic adjustments will adjust for increases in speed, at least at a first order. Now of course, the consumer Internet didn’t exist in 1980 and therefore it’s theoretical price was infinite.

But suppose you could move the Internet back to 1980. Everything. Routers, telecom wires, transpacific cables, etc. The market price of this service would be next to nothing, since no one has computing technology to browse the web, since no engineers exist to create worthwhile content, and since building the necessary infrastructure from the state of science at that point in time is extremely hard.

In that sense, the shadow market price of the Internet that doesn’t exist yet is 0. This isn’t true for all technology. Shoes today are way better than shoes in 1980, and even if the technology didn’t exist to create them as we do now, the market price would still be far greater than that for the 1980 equivalent.

This framework as easily suggests there was massive deflation in the price of shoes (which is true) and a massive inflation in the price of Internet (which is false).

Our examples don’t need to be so extreme. An iPhone would be considered vaguely useful as a portable camera, but without technology to listen to music it wouldn’t be nearly as popular as it is today. Still it’s an incredible product and well-above its time in technology and should command a premium (this is the entire argument of a hedonic adjustment). And this of course assumes a stability in tastes and preferences.

This is intimately connected to the question of consumer surplus. It’s frequently said that productivity improvements are underestimated given the “explosion” of consumer surplus from web services like Netflix, Google, and Facebook – “how much would you pay for Facebook”, the question goes. Still it’s unlikely that this surplus actually goes unmeasured. I almost certainly wouldn’t pay $100/mo for LTE data on my iPhone if I couldn’t access Facebook, Google, or iMessage. Without considering the cross elasticities between free products, and the new industries they tempt, it’s hard to argue that GDP is underestimated relative to the true economic benefit.

Consider clean air. One day China will have a technology that cleans its air despite extremely intense energy consumption, and it will be extremely cheap relative to the life and economic savings it generates. Its creator will be praised for creating all sorts of consumer surplus unmeasured in China’s GDP. Unmeasured except for the boom in foreign investment, outdoor playgrounds, and botanical gardens that is.

Arguing that we didn’t have Netflix or Google in 1980 isn’t enough. We pay for Comcast and Apple, which are both prominent in GDP numbers. Of course this doesn’t say much about the distribution of that surplus – it might be that this benefits certain percentiles more than others, but this isn’t an easy claim to make without reference to the relevant cross elasticities of demand.

Does this mean inflation isn’t transitive? How would we model that? An error term that grows unreasonably when considering changes over decades or more?

It might be that the right answer is extremely large standard errors in estimates of long-term inflation. As certain markets grow large at the expense of others – cars versus cabs, for example – the statistical basis for making hedonic adjustments against an increasingly non-representative base challenges statistical conviction.

This is relevant in finance as well. 30 year Treasuries price in some expected degree of inflation. But the annualized inflation rate is likely very different from the true inflation, even though it’s correct on a year-on-year basis. Moreover, to the extent inflation is low from hedonic adjustment, it is because of a dramatic increase in the real growth rate.

Is there another premium in long-term bonds? A term premium yes, but also a “hedonic error premium” – which would include the expected inflation as measured (i.e. the breakeven rate) along with the error term that is larger in 30 years than it is in 10 years?

We could have a market for how much better 2015 is than 2014. We don’t have a market for how much better 2015 is than 1800. A diligent team of investors could answer the question “how much would you have to pay me to use 2010 healthcare instead of 2015 healthcare”. It’s not clear the same team could answer the question “how much would you have to pay me to use 1980 healthcare instead of 2015 healthcare” (a question posed by Larry Summers to suggest there is unmeasured productivity improvement since 1980).

Or it could just be that the existence of old age dating apps and the Internet makes being 85 much more tolerant – and the consumer surplus of the needless to say cheap apps is masked in elderly healthcare costs.

Now this isn’t to say technological progress is underrated in its contribution to humanity. If anything we probably owe earnings growth in legacy industries to technological surplus.


Most people agree GDP is probably a flawed tool. Results from a few papers I’ve recently read – which I’ll get to shortly – increase my curiosity. Ignoring its many other shortcomings, I want to focus on the difficulty of measuring realized GDP. My entire premise might be flawed, and definitely contact me if you can explain why. I might be very confused about the whole thing.

Consider an economy with households, firms, entrepreneurs, and banks. Households keep all their income as deposits in a bank, which invests in firms in the form of long-term debt, with residual business income accruing to entrepreneurs who hold equity.

Suppose tastes and preferences change and households no longer purchase widgets. Unfortunately at the beginning of this year firms had invested in large factories to scale their widget factory, financed largely by debt. Widget factories, having lots of unsecured debt, decide to default on the principal. Continuing operations is profitable for another year or two, however, and therefore they remain current on interest payments for another 2 years.

If widget factories decided to go bankrupt today, banks would have to writedown all their debt – taking a huge expense – and resulting in a sharp fall in production. Instead, widget factories will slowly default over the next decade, overstating ex ante GDP.

Now this effect might be moderated with a sensible requirement that banks immediately expense income for a loss reserve as they lend – with symmetric errors between reported and true earnings as borrowers default and recover. However my understanding is that the BEA uses current earnings excluding such accounting practices; and even if such were included the effect still remains large, especially when historical delinquency rates fail to capture the prevailing economic environment.

A working paper from Amir Sufi and Atif Mian prompted this question. In particular, they find that a one standard deviation increase in the household debt to GDP ratio over the last 3 years (6.2 percentage points) is associated with a 2.1 percentage point decline in GDP over the next three years, and outcome that seemed extremely large to me, especially given the econometric robustness they detailed.

Could it be that periods of substantive increase in household debt, and the rising delinquency rate that comes with it, make it harder to match economic revenues with economic expenses – perverting growth estimates? Even if the effect on GDP itself is small – which, if true, it cannot be given the meaningful role banks play in advanced economies – the effect on growth is meaningful. Consider valuation adjustments in financial inventories:

Screenshot 2016-02-02 00.48.50.png

Economic losses are unlikely to have been so concentrated. Non-recourse borrowers were economically delevered as soon as housing prices started to fall; and the ex post GDP in these years was much lower than that reported ex ante (not to be confused with revisions).

In this particular case, it is also likely the regression coefficient on HHD is also overstated. The increase in (Reported HHD / Reported GDP) is about 6% lower than that in (Economic HHD / Economic GDP) under standard calculations – where banks reserve losses at historical rates, growth is about 3% in the period of increasing debt, and the debt cycle gathered momentum over 10 years. In practice the effect is likely a little sharper as GAAP loss reserves are not included in the BEA estimate. In other words, the increase in the HHD/GDP ratio needed to achieve the same decrease in GDP is actually larger than estimated.

The particular problem I think this poses for economic analysis in general is from measurement error. Even if revised reported GDP is negligibly erroneous against perfectly reported GDP, both are meaningfully erroneous against perfectly reported economic GDP. There’s no good fix to this problem – it’s really hard to find out who has economically defaulted and who hasn’t. Doing a better job adjusting for loan reserves might help, but also open a host of other problems regarding standardized measures.

Of course the reported GDP eventually converges to the economic GDP, as losses must eventually be recognized. However, this process is subject both to institutional flaws, such as slow bankruptcy proceedings, as well as macroeconomic trends, such as low interest rates that allow borrowers to remain current on bad principal for a longer period of time.

Maybe more importantly, the measurement error attenuates regression coefficients and economic GDP turns out to be a lot more important than we gave it credit for.

I’ve worked out a back-of-the-envelope model to see the discrepancy that I’ll write about soon. In any case it generates an annualized 0.3% to 0.75% error in annualized growth rates under reasonable economic environments.

Some remedies might include a push towards using market, rather than face, value of debt. For example, sometime in 2007 the market value of subprime started collapsing even as no writedowns were realized – because investors realized the owners of these houses either had, or were going to, default and hence effectively unlevered.


The harder question is estimating the current, capitalized value of GDP. If there is any interesting writing on this topic, I’m interested.

By now you’ve heard that YC wants to learn all about it. I’m completely unqualified as an economic experimenter, but I have a few thoughts about the curious premise.

Very obviously any affordable study can only observe the individual rather than structural benefits of a basic income. There are genuine nonlinearities in the progression towards a world where (1) everyone has meaningful purchasing power, (2) where inefficiently-administered government welfare systems don’t create needless problems, and (3) where (1) and (2) are taken for granted.

But it’s also very likely that the move towards basic income will probably start with annual awards on the order of $1,000 to $5,000 in concert with a host of other legacy welfare technology rather than a complete revolution. Especially in the United States.

Therefore the reach of Sam’s fundamental question – “do people, without the fear of not being able to eat, accomplish far more and benefit society far more” – probably exceeds its grasp. But we can study the latter, something I’ll get to soon.

It’s also worthwhile being mindful of creating an observer effect. It certainly seems like a bad idea telling a young family with a newborn that it has a good fortune of a basic income, and don’t worry economists and sociologists will only study its developments every month. This effect would be particularly acute if the basic income is anything meaningful. Call this the “be careful not to create a lottery” bias.

If compelled I’d propose the following.

  1. Scrap the 5 year limit. It dramatically constrains the economic scope of a basic income and is unlikely to yield an interesting result. Individuals smooth income over a large period of time, and this is likely to suffer from a “lottery bias” as a result.
  2. Opportunity is one of the most important themes in American socioeconomic discourse, and opportunity is most elastic during childhood. Find a way to have a substantive answer to the question “how does basic income level the playing field for poor children”.
  3. Avoid the observer effect by finding ways to give people a basic income without their knowledge: though this is tricky for reasons I’ll outline below.
  4. Attempt to maintain general best practices for experiments (randomization, large sample for high statistical power, etc.)

The best way to satisfy (1), (2), and (4) might be something like an annual award on the order of $3,000 to $10,000 for couples that have just had a child for randomly selected people in randomly selected cities (ensuring some diversity in both type of people and type of city) for 20 years.

500 families in 10 cities at $5,000 would cost $25 million a year. That sounds doable for the Silicon Valley elite. Though there are plenty of ways to reduce this and still learn a lot. We can start with a single city. We can cut the study period to 10 years (and divide the group into early and late childhood – to see where the effect is strongly felt). I imagine the project should start in a single city – the administrative cost of studying 10 over 20 years isn’t negligible.

Though $5,000 isn’t a basic income, it is a little under what the average family spends on food. $10,000 to $15,000 is affordable, especially if we have only a single city. But at this order we sacrifice statistical power and also tempt strong observer effects.

For one, $10,000 to $15,000 awards will create envy. In neighborhoods where this program is most useful, this figure eclipses 100% of disposable income. The point of a basic income isn’t to create a table of special kids eating avocados amidst an inner city cafeteria serving mass frozen pizza. It would also be impossible to provide such awards without alerting the recipients.

Indeed doing so in general is hard. Working with employers is one option, though as economists know telling an employer you’re giving employee X an extra $5,000 means it can spend less to keep that employee than it would have to otherwise.

  1. It would be good to work with corporate echelons of a distributed employer that would randomly increase the salary without the knowledge of more immediately located hiring managers.
  2. Working with the IRS to randomly increase tax refunds to selected families could also work though this would need someone willing to work with the IRS.

The ability to precisely study beneficiaries is diminished if one is vigilant about a non-observable experiment. That’s a tradeoff YC will have to make. But the guilt, envy, awe, shock, and puzzle of receiving a lot of money will have large effects.

As a guiding principle, it may be cheapest to start an experiment that researchers can most quickly learn from and modify as needed (sequential hypothesis testing is valuable here). Building an apparatus where making modifications and adjusting the experiment doesn’t void the statistical validity of results already obtained is valuable.

Maybe the simplest advice is spend a lot of money by giving reasonable chunks of cash to a lot of people over a long period of time. Once collected data is made public the academic value of this data will be unbeatable. This is a landmark study if done correctly. It would directly challenge the government as both a provider of welfare, but philosophically demonstrate the capacity of Valley billionaires to produce meaningful research answering the hardest questions in social science.

I don’t know. Alex Tabarrok has an answer, though I’m not sure its sufficiently skeptical.

Of course, housing is overrated as a financial investment, largely because for most people it isn’t a financial investment, as much as a hedge against an increase in price of rent. You don’t get “a great investment and a place to live” primarily because you only get a place to live and very little investment. When the S&P500 shoots up, most of us can sell and live richer lives. When the housing market goes up most of us can sell… and buy another house a market that has already realized appreciation.

There are instances in which you might notice a genuine increase in wealth associated with a tighter housing market. If the price of large homes outpaces that of small homes, and you’re about to retire at that moment, you might be in luck. If a new job takes you away from San Francisco where prices have skyrocketed, you might be in luck.

But in general it’s not exactly easy to predict these trends – and it certainly shouldn’t be the province of homebuyers and the real estate agents that inform them. That doesn’t mean home ownership is a bad idea. In general, we don’t like cash flow volatility. A young couple working at Facebook, with enough cash for a downpayment and enough earning power for a good mortgage, might prefer to avoid the ups and downs of a tumultuous Bay Area rental market by purchasing a house. We’re all short the market to some extent.

In parts of the country without stratospheric home prices, a rental market is pretty nonexistent for affluent buyers. To the extent you want a nice family home with a pool in a safe, Iowa suburb, you may not have the option to rent. Indeed, “should you buy or rent a house” is question relevant only for affluent urbanites who have both the option to rent a decent place and credibility to borrow on good terms. The best argument to buy is that you probably live in a city where only kids at the local college rent.

The tax benefits of ownership are also questionable. While a mortgage interest deduction certainly exists, this is passed through to renters. In general it subsidizes building property since land is inelastic, and therefore is likely most beneficial for affluent midwesterners who like a lot of property on worthless land. This is not to say ignore the benefit in your calculus, as much as not to tell yourself that this benefit militates in your favor without actually looking at the numbers – more precisely, learning that there is such a deduction without any further information shouldn’t change your decision.

A thought experiment is useful. Imagine a town with some borrowers, owners, and a few landlords. If the government subsidizes interest on houses, market competition only increases the value of land relative to everything else but doesn’t change the relative price. Marginal investment is poured into building more property on top of that land. The supply of property-intense housing increases, and the price falls. Renters and owners both benefit equally, and because of competition not at all. The only obvious winners are landlords. The mortgage deduction doesn’t benefit the affluent, it benefits large landholders and their investors. Yet another tax debacle that benefits the superrich.

The pass through effect is muted somewhat by the asymmetry of potential buyers and renters in that some individuals might be “captive renters” as they can’t access affordable, long-duration credit on the principal at hand. Though this is almost tautologically irrelevant for markets where the question “should I buy or rent” persists; for any given house the individual who can pay the most, either capitalized or amortized, can afford to borrow.

Of course, accepting that housing is rarely an investment also challenges the associated wealth effects, either positive or negative, that many economists have observed. To focus the skepticism a particular theory, the Sufi-Mian leveraged loss multiplier, consider liquidity instead of solvency. The prevalence of non-recourse borrowing in the United States meant borrowers could pull residual equity into cash and leave if that value was above the collateral value.

Between 2003 and 2007 we had a bunch of people with high propensities to spend with access to non-recourse financing collateralized on a volatile asset. This was money that would be paid back only if prices kept rising. They weren’t leveraged precisely in the state of the world where that leverage would have been binding. It turns out that this was the state of the world that came to be, but the decline in spending wasn’t a question of solvency but loosing access to extremely cheap liquidity. Of course consumption and residential investment fell most in the leveraged areas where it increased most because that’s where closing the tap of free cash most dramatically affected ability to spend.

The best reason to rent might be that many educated people think housing is a good investment increasing the price of housing credit beyond what is justified – and an unlimited supply of conforming loans doesn’t really matter in this case since the people for whom this question is relevant are almost certainly buying a house above the conforming limit. People may also tell themselves that they get a tax credit one way and not the other (even though this is priced in both ways to begin with) which would further distort the market in favor of renters.

And even if housing was an investment, buying a house would infrequently be the best way to get there. The home you own faces plenty of idiosyncratic risk against the market as a whole – by region, by local policy, by construction style, etc. It would be foolish to expose yourself to this risk without relevant expertise – something both homebuyers and their real estate agents certainly lack.

There are other ways to express your beliefs about the housing market. If you already own a home, get a second mortgage against it and use that to buy an ICF index, your financial position might be closer to what many financial writers ascribed to middle-class Americans in 2005. Though, as you can see, that is probably far from where you actually were at the time.