What is a Bitcoin?
Nothing. They don't exist. Not even in the sense you're thinking they do.
Bitcoin is actually a system of recording transactions, and not a unit of currency. Bitcoins do not have serial numbers, are absolutely indistinguishable from each other, and can be broken down into any smaller unit of value without having to exchange one object for another (like a dollar for four quarters). It's important to keep this in mind.
How do I get a Bitcoin?
Again, you don't.
You can create a wallet, which is an address in the system, that can be credited a payment of a certain amount of bitcoins, but you never actually receive a bitcoin. While functionally, you have the authority to spend all the bitcoins that have been credited to the address, so in simple terms, you "have" those bitcoins.
The issue is that the bitcoins cannot be taken from that address. Bitcoin relies on a complicated encryption system that means a transaction must be voluntary (i.e. you must provide the password [actually a private key] to the account for each transaction). That also means if anyone gets your password, they have your bitcoins. There's no such thing as "cold storage" because every address is part of the network at all times.
That being said it's safer to keep your private keys away from machines connected to the internet unless you're authorizing a transaction.
How much are they worth?
Well, as Buffett said, price is what you pay, value is what you get.
Don't be coy. It doesn't suit you.
You are correct imaginary interlocutor, so in an effort to answer your question the only way I know how, here's math.
Bitcoins have a fixed supply, and a fixed velocity. There can be no more than 21 million of them, and each one can trade hands roughly once an hour. (This is not 100% accurate, but for now we'll use it). What that means is that there can be at most 184 billion bitcoins worth of transactions a year.
That sounds like a lot.
No it doesn't. The United States GDP was roughly $16.4 trillion last year and that's about a quarter of the global GDP. That means that each bitcoin is worth roughly $356 2012 USD, if you assume BTC will replace 100% of global currency. Scale to your liking depending on the anticipated adoption rate.
Why is there an extra number there?
Because USD are inflationary, in that more are issued each year. We do this because economics tells us that is how to run a healthy economy. Generally inflation is used to prevent wealth accumulation which prevents trade and destroys economies. Bitcoins, however, are deflationary, in that there's a fixed supply of them.
This is very, very bad. It is also why no country has used a non-fiat currency in decades. That being said, the relationship between USD and Bitcoins is such that the value of a Bitcoin goes up every year, or more precisely, the value of a dollar goes down.
Are there other ways to get Bitcoins?
Yes, you can mine them. In practical terms, you cannot mine them. What you can do is purchase mining power from a commodity exchange like CEX. Mining power is measured in GH/s or billions of equations solved per second.
That sounds confusing.
Indeed.
What are GH/s worth?
Well, to give you the straight dope on that you need to know what today's risk-free rate of return is. If we assume that to be 3%, than a GH/s is worth roughly 13.9 BTC until next year, then it goes down by about 30% every two weeks as the difficulty increases.
Caveat Lector
This is economics, not trading advice
Now, on to the more important caveat. You cannot buy a BTC for $356 USD. You cannot sell a GH/s for 13 BTC. These things exist in thinly traded, ill-informed, wildly volatile and incredibly speculative markets.
The equations above tell you the value, not the price. I make absolutely no prediction as to what the price of any commodity will be at any given time, because "Markets can remain irrational a lot longer than you and I can remain solvent."
Friday, December 20, 2013
Monday, December 16, 2013
Induced Economic Effects Part 2 - Template Entities and "Buck Rot"
So last week we introduced the concept of money flowing into, through, and out of an economy based on an initial investment and subsequent transactions.
We also promised to update, and failed to deliver.
Of course I'm using the royal "we", in both senses, but that's neither here nor there.
Today, I want to introduce the concept of iterated transactions, because when we talk about how much money remains in an economy after a given period of time based off of an initial investment, what we're really trying to capture is a sequence of iterated transactions.
One of the fundamental truths of economics is that money itself has no value, and only its use gives it utility in the traditional economic/utilitarian sense. This is true whether we're talking about USD, BTC, or CNY. It's the ability to spend money that gives it its value, and consequently, the velocity of money is a core concept in economic analysis.
What is "the velocity of money"? It's very similar to the physical concept of velocity, in that it is the number of hands a unit of currency moves through in a given period of time. So in the case of economics, it is helpful to think of money (or value) as "mass", people (or entities) as "distance", and time as ... well time.
One of the derivations of this, and I won't go into too much detail in this post on it is the "stickiness" of prices in the New Neoclassical Synthesis, which is that prices are not perfectly fluid, because it takes a shock of sufficient "force" (similar to Newtonian F=ma) to move prices, and that they have a proportional rate of change equal to their momentum (big transactions that have been happening frequently for a long time change more slowly than little transactions that have been happening infrequently for a relatively short period of time).
When we talk about induced economic effects, and attempt to quantify the size of the residual impact after a given time, we need to talk about how much of each dollar stays in the economy after each transaction. To do that its useful to discuss template entities. The reason we have template entities, is that in practical terms, it's very difficult to quantify each individual transaction, but in aggregate, we can take averages, and develop a theorem with strong statistical validity as long as there's a solid average.
Last week I used a 7-11 as our example entity, and discussed the purchase of a candy bar. If we assume that every entity has a similar structure in terms of money that stays in the economy vs money that leaves the defined region, we can come up with a formula.
So again, for simple discussion, let's assume in each transaction, roughly 50% stays in the geographic region. That number is of course a placeholder, and there's a substantial body of work to calculate the actual value, primarily by the US Bureau of Economic Analysis through their Regional Input/Output Multiplier System, known colloquially as RIMS II.
What our simplified assumption tells us, is that for every dollar spent in the region under analysis, fifty cents stays in the region. Combining that with a velocity of money, which we'll assume to be at 1.5 per quarter, or 6 per year (which is close enough to the actual value for this simplified analysis) we can see that within a year, each dollar is spent 6 times, each time, half of it leaves the economy, or that our residual is equal to:
.gif)
We also promised to update, and failed to deliver.
Of course I'm using the royal "we", in both senses, but that's neither here nor there.
Today, I want to introduce the concept of iterated transactions, because when we talk about how much money remains in an economy after a given period of time based off of an initial investment, what we're really trying to capture is a sequence of iterated transactions.
One of the fundamental truths of economics is that money itself has no value, and only its use gives it utility in the traditional economic/utilitarian sense. This is true whether we're talking about USD, BTC, or CNY. It's the ability to spend money that gives it its value, and consequently, the velocity of money is a core concept in economic analysis.
What is "the velocity of money"? It's very similar to the physical concept of velocity, in that it is the number of hands a unit of currency moves through in a given period of time. So in the case of economics, it is helpful to think of money (or value) as "mass", people (or entities) as "distance", and time as ... well time.
One of the derivations of this, and I won't go into too much detail in this post on it is the "stickiness" of prices in the New Neoclassical Synthesis, which is that prices are not perfectly fluid, because it takes a shock of sufficient "force" (similar to Newtonian F=ma) to move prices, and that they have a proportional rate of change equal to their momentum (big transactions that have been happening frequently for a long time change more slowly than little transactions that have been happening infrequently for a relatively short period of time).
When we talk about induced economic effects, and attempt to quantify the size of the residual impact after a given time, we need to talk about how much of each dollar stays in the economy after each transaction. To do that its useful to discuss template entities. The reason we have template entities, is that in practical terms, it's very difficult to quantify each individual transaction, but in aggregate, we can take averages, and develop a theorem with strong statistical validity as long as there's a solid average.
Last week I used a 7-11 as our example entity, and discussed the purchase of a candy bar. If we assume that every entity has a similar structure in terms of money that stays in the economy vs money that leaves the defined region, we can come up with a formula.
So again, for simple discussion, let's assume in each transaction, roughly 50% stays in the geographic region. That number is of course a placeholder, and there's a substantial body of work to calculate the actual value, primarily by the US Bureau of Economic Analysis through their Regional Input/Output Multiplier System, known colloquially as RIMS II.
What our simplified assumption tells us, is that for every dollar spent in the region under analysis, fifty cents stays in the region. Combining that with a velocity of money, which we'll assume to be at 1.5 per quarter, or 6 per year (which is close enough to the actual value for this simplified analysis) we can see that within a year, each dollar is spent 6 times, each time, half of it leaves the economy, or that our residual is equal to:
.gif)
or the initial investment times the one minus the amount left after each transaction raised to the number of transactions we expect to have occurred in the time period. In our simplified example, this is effectively a half-life.
Barring outside reinvestment, the money in an economy decays at a predictable rate, hence the term of art I like to use, "buck rot".
In this example world, every year ~98.5% of our initial investment decays out of the economy, which is why reinvestment and exports play a crucial economic role.
Which we'll talk about soon. Before that though, there's a few posts I've been meaning to publish on the emerging Bitcoin phenomenon.
Monday, December 9, 2013
Induced Economic Effects
A lot of people ask me what I do for a living.
Well, mostly the people paying me, but they ask it a lot, so I figured I'd take a blog post to talk about something relevant to my interests.
These days I spend a lot of time studying and quantifying the economic impacts of investments in different communities based on a concept called "Induced Economic Effects".
What it boils down to, is that for every dollar invested into an economy, a certain number of cents can be expected to stay circulating in that economy.
So, for example, if you were to buy a candy bar from your local 7-11, and for some strange reason, it were to cost exactly $1.00, my job is to figure out where all one hundred pennies wind up, and how quickly they get there.
I didn't say it was a good job.
Econometric Analysis has not led to nearly the debauchery I was promised, as both the booze and the bitches have been mysteriously over-represented in the brochure.
That being said, tracking a dollar is hard work. Tracking millions of them is even harder, but because this is a blog, and you free-loading readers aren't actually paying for this, we'll stick to the example of the dollar at the 7-11.
So let's follow the money.
Assuming you live in a civilized state like New Jersey (and not some communist VAT utopia like the EU), your state has a sales tax. Sales tax can range anywhere from 0 to 25%, but in this example, we'll assume it's going to be 6% because you're not the kind of person to shop in one of those sketchy low sales tax HUD areas.
That means that six pennies have disappeared to the coffers of the state, so we need only follow ninety four more. (Actually, that's a bald faced lie that we'll return to in a bit, but for now pretend to believe it.)
Of our remaining ninety-four cents, let's assume that the retailer marked the product up 100%, as is their custom. This means that forty-seven pennies disappear out of our economy back to the manufacturer. If you're keeping track at home, we've now accounted for nearly half of the first generation of pennies in just two short hops.
And this is where things get hairy. Of the forty-seven remaining pennies, some percentage went to overhead. Traditionally in retail we anticipate approximately 30 percent of Net Revenue to be consumed by overhead. In this simplified example, we'll assume that ~30% of the net revenue from every sale covers the SG&A expenses of the store since 7-11 generally runs a pretty tight ship. that takes fourteen more pennies out of the equation to the bank, the power company, and whatever other expenses the shopkeep has. This leaves thirty-three pennies for labor.
Not bad.
Well, mostly the people paying me, but they ask it a lot, so I figured I'd take a blog post to talk about something relevant to my interests.
These days I spend a lot of time studying and quantifying the economic impacts of investments in different communities based on a concept called "Induced Economic Effects".
What it boils down to, is that for every dollar invested into an economy, a certain number of cents can be expected to stay circulating in that economy.
So, for example, if you were to buy a candy bar from your local 7-11, and for some strange reason, it were to cost exactly $1.00, my job is to figure out where all one hundred pennies wind up, and how quickly they get there.
I didn't say it was a good job.
Econometric Analysis has not led to nearly the debauchery I was promised, as both the booze and the bitches have been mysteriously over-represented in the brochure.
That being said, tracking a dollar is hard work. Tracking millions of them is even harder, but because this is a blog, and you free-loading readers aren't actually paying for this, we'll stick to the example of the dollar at the 7-11.
So let's follow the money.
Assuming you live in a civilized state like New Jersey (and not some communist VAT utopia like the EU), your state has a sales tax. Sales tax can range anywhere from 0 to 25%, but in this example, we'll assume it's going to be 6% because you're not the kind of person to shop in one of those sketchy low sales tax HUD areas.
That means that six pennies have disappeared to the coffers of the state, so we need only follow ninety four more. (Actually, that's a bald faced lie that we'll return to in a bit, but for now pretend to believe it.)
Of our remaining ninety-four cents, let's assume that the retailer marked the product up 100%, as is their custom. This means that forty-seven pennies disappear out of our economy back to the manufacturer. If you're keeping track at home, we've now accounted for nearly half of the first generation of pennies in just two short hops.
And this is where things get hairy. Of the forty-seven remaining pennies, some percentage went to overhead. Traditionally in retail we anticipate approximately 30 percent of Net Revenue to be consumed by overhead. In this simplified example, we'll assume that ~30% of the net revenue from every sale covers the SG&A expenses of the store since 7-11 generally runs a pretty tight ship. that takes fourteen more pennies out of the equation to the bank, the power company, and whatever other expenses the shopkeep has. This leaves thirty-three pennies for labor.
Not bad.
Or, if you're one of my clients, "What the hell are we paying you for, any idiot could have told us that?!"
True, and the difference is, this is where an idiot leaves the discussion. Because in reality, this is where things are just starting to get interesting.
If we assume that the region or economy under analysis is the state, how much money is left in the economy?
The correct answer is: None.
"Wait... what? I know I'm bad at math, but if we start with a dollar, how'd you get to none?" I can hear you asking now, because I, like the FBI, can turn your computer microphone on at will in direct contravention of any perceived rights or liberties you may have.
It's a gift.
What's actually going on is that after the first generation of transactions, a portion of each of these expenses stays in the state. At this point, to make things easier we'll have to create a template entity and assume that all of the recipients of our pennies behave the same way. While we know this is not true, we can make the assumption that on average they'll all even out to something.
What is that? Well stayed tuned, as tomorrow's post will detail template entities and the time-decay of microeconomies, or as I like to call it, "buck rot".
Friday, December 6, 2013
Income Inequality and The Cost of Education
What's the value of a good education?
Hard to say in America, where the quality of education is falling faster than the cultural relevance of my refences, but what we can quantify is what's the value of an education.
Let's look at some numbers.
Below is the average weekly income of a high school graduate versus the average income of a college graduate (bachelor's degree any field).
Hard to say in America, where the quality of education is falling faster than the cultural relevance of my refences, but what we can quantify is what's the value of an education.
Let's look at some numbers.
Below is the average weekly income of a high school graduate versus the average income of a college graduate (bachelor's degree any field).
While this only tells half the story, it's a compelling half already. What we can see from this graph is that the average college graduate earns roughly $23,000 per year more than the average high school graduate, and that that gap is widening by about 1% a year.
Whew, that's problematic if you're that guy without a degree.
But wait, it gets worse.
According to this chart that I totally did not make up (with numbers sourced from 11 years of BLS data), there's a roughly 16 point difference in labor force participation, meaning a college graduate is almost 28% more likely to be employed at that higher wage than a high school graduate is at the lower.
What does this say about the cost of education?
Well, if we assume that 18-year-olds behave in an economically rational manner and that unicorns are a thing, we can calculate what's called a "Net Present Value" of a college degree by taking a discounted future return on the increased earnings and probability of earnings to yield a "value" that a rational consumer should be willing to pay for the privilege of being roughly 30% more likely to be employed at a salary roughly $20,000 higher than they would be without it.
Summing up the tedious math of probability weighted present values of future returns, we can safely say that the "value" of a college education in the United States in 2003 was roughly $188,495. Projecting forward at that growing with that 1% annual gap for the next ten years, someone entering college today would rationally be willing to pay over $200,000, and still come out ahead of their diploma-only counterpart.
Now of course this simplifies the equation quite a bit by ignoring the likelihood of dropping out, which is staggeringly high in the US, but even assuming there's only a 50% chance of graduating, $25,000 per year is where tuition should be based off the economic factors.
Not surprising since the average published college tuition for a four year school ranged from $15,130 to $30,094 for the 2013-2014 school year.
Funny how economics works out like that.
Raw Data (Note Q4 '13 was projected from an average of the previous three quarters since data was unavailable at the time of writing)
Wednesday, December 4, 2013
Unemployment and Gender
A quick post to follow up a discussion from Google+.
A big factor in labor dynamics over the last four decades has been a shift in demographics.
One of the problems with gender equality is that in many cases it means a woman can do a job just as well as a man. In fact, statistically speaking, roughly 50% of the time, she can do it better.
What this means is that often when a woman joins the workforce, as thankfully they have done in droves over the last half century, they are often replacing a man.
Labor force participation is a zero-sum game, in so far as it has held steady between 56 and 64% since 1976. An unfortunate side effect of this is that men have seen steadily increasing unemployment since for as long as we have reliable data.
That being said, from a strictly economic standpoint, any time a woman replaces a less qualified man, society benefits from increased productivity, so we shouldn't exactly be mourning the progressive improvement of American society.
Especially, as the data indicates, we still have a long way to go.
Data used in this analysis were from the United States Bureau of Labor Statistics, specifically Series:
LNS10000000 - Civilian noninstitutional population
LNS11000000 - Civilian labor force
LNS10000001 - Civilian noninstitutional population, male
LNS11000001 - Civilian labor force, male
LNS10000002 - Civilian noninstitutional population, female
LNS11000002 - Civilian labor force, female
Raw data available here.
A big factor in labor dynamics over the last four decades has been a shift in demographics.
One of the problems with gender equality is that in many cases it means a woman can do a job just as well as a man. In fact, statistically speaking, roughly 50% of the time, she can do it better.
What this means is that often when a woman joins the workforce, as thankfully they have done in droves over the last half century, they are often replacing a man.
Labor force participation is a zero-sum game, in so far as it has held steady between 56 and 64% since 1976. An unfortunate side effect of this is that men have seen steadily increasing unemployment since for as long as we have reliable data.
As shown in the graph, unemployment among women declined from 1976 to 1997, and plateaued, not rising until the recession of 2009 (Black Arrow), while men had faced rising unemployment at about a quarter point per year, until 2009.
Same chart, Googlified
Especially, as the data indicates, we still have a long way to go.
Data used in this analysis were from the United States Bureau of Labor Statistics, specifically Series:
LNS10000000 - Civilian noninstitutional population
LNS11000000 - Civilian labor force
LNS10000001 - Civilian noninstitutional population, male
LNS11000001 - Civilian labor force, male
LNS10000002 - Civilian noninstitutional population, female
LNS11000002 - Civilian labor force, female
Raw data available here.
Effects of Minimum Wage
I've seen a lot of chatter lately regarding raising the federal minimum wage. Some strongly in favor, some strongly opposed.
As always, I have no opinion on the matter, only data.
The three charts below show Minimum Wage (inflation adjusted to 2013 USD) vs. Unemployment (Real Unemployment, not those claiming benefits), a normalization of the two for easier comparison, and finally Minimum Wage vs. GDP growth.
The results are largely what you'd expect. Raising minimum wage has a measurable but small impact on labor participation. conversely, it has a significant measurable impact on the velocity of money, and hence GDP.
The take away is that a healthy economy is not always one in which everyone is employed. In fact, at no point in US history has "employment" exceeded 70% (it wasn't until WWII that it exceeded 50% with the new acceptance of women in the workplace). The notion that everyone should be able to go to work is a modern one, and indeed, apparently false.
If we truly follow the division of labor concept laid out by Adam Smith, there is as much, if not more productivity from a stay-at-home dad than from a father who splits his time between work and home. The decrying of the "housewife" and the emasculation of the husband "unable" to provide for their family are again new societal constructs, and without grounding in economics. People should do what they like, focus on it, and be good at it. Social stigmatization does not increase productivity, any more than yelling at a dog will keep them from soiling the carpet if you don't let them out.
Economics can incent behavior, but only so far, as eventually the marginal utility of further rewards declines to zero, as does the marginal disutility of further punishment.
These wasted resources, incentivizing desired behavior, and discincentivizing undesired or stigmatized are better allocated to production in an ideal world.
As the Department of Labor cautions and I learned to my great cost some years back, "The employer [of an intern] derives no immediate advantage from the activities of the intern; and on occasion its operations may actually be impeded;" or in layman's terms, some workers productivity will always be below their wage, no matter how low the wage.
In short the data dictates the economy as a whole benefits most when the most productive members are less encumbered by those acquired most cheaply. This lends support to notions of basic income and the importance of social safety nets, while clearly arguing for a higher minimum wage.
A strong consumer base builds a strong and vibrant economy, but despite what we told little Jenny and Johnny growing up, some people simply should not be neurosurgeons or astrophysicists, or heaven forfend economists, lest they wind up doing more harm than good. (I'm looking at you, Ron Paul).
While everyone thinks they want to lower unemployment, in reality we benefit most by increasing per capita nominal income (the amount you take home) and per capita GDP (the value of those dollars) in equal measure, providing the most productivity and the highest quality of life is the purpose of economics, and we shouldn't let political talking points frame our discussion of the data.
As always, you're welcome to take a look at the data, the methodology, and the sources to come to your own conclusions.
As always, I have no opinion on the matter, only data.
The three charts below show Minimum Wage (inflation adjusted to 2013 USD) vs. Unemployment (Real Unemployment, not those claiming benefits), a normalization of the two for easier comparison, and finally Minimum Wage vs. GDP growth.
Inflation Adjusted Minimum Wage vs. Unemployment
Inflation Adjusted Minimum Wage vs. Unemployment (Normalized)
Inflation Adjusted Minimum Wage vs. Change in GDP
With Trendlines from Excel
The take away is that a healthy economy is not always one in which everyone is employed. In fact, at no point in US history has "employment" exceeded 70% (it wasn't until WWII that it exceeded 50% with the new acceptance of women in the workplace). The notion that everyone should be able to go to work is a modern one, and indeed, apparently false.
If we truly follow the division of labor concept laid out by Adam Smith, there is as much, if not more productivity from a stay-at-home dad than from a father who splits his time between work and home. The decrying of the "housewife" and the emasculation of the husband "unable" to provide for their family are again new societal constructs, and without grounding in economics. People should do what they like, focus on it, and be good at it. Social stigmatization does not increase productivity, any more than yelling at a dog will keep them from soiling the carpet if you don't let them out.
Economics can incent behavior, but only so far, as eventually the marginal utility of further rewards declines to zero, as does the marginal disutility of further punishment.
These wasted resources, incentivizing desired behavior, and discincentivizing undesired or stigmatized are better allocated to production in an ideal world.
As the Department of Labor cautions and I learned to my great cost some years back, "The employer [of an intern] derives no immediate advantage from the activities of the intern; and on occasion its operations may actually be impeded;" or in layman's terms, some workers productivity will always be below their wage, no matter how low the wage.
In short the data dictates the economy as a whole benefits most when the most productive members are less encumbered by those acquired most cheaply. This lends support to notions of basic income and the importance of social safety nets, while clearly arguing for a higher minimum wage.
A strong consumer base builds a strong and vibrant economy, but despite what we told little Jenny and Johnny growing up, some people simply should not be neurosurgeons or astrophysicists, or heaven forfend economists, lest they wind up doing more harm than good. (I'm looking at you, Ron Paul).
While everyone thinks they want to lower unemployment, in reality we benefit most by increasing per capita nominal income (the amount you take home) and per capita GDP (the value of those dollars) in equal measure, providing the most productivity and the highest quality of life is the purpose of economics, and we shouldn't let political talking points frame our discussion of the data.
As always, you're welcome to take a look at the data, the methodology, and the sources to come to your own conclusions.
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