Over at Economix, Catherine Rampell asks, “Does lowering the price of broadband increase its use?”
This provides a useful teaching moment. She collected data on broadband prices and adoption rates in different countries; and by linking lower prices with more broadband adoption, she’s trying to figure out the demand curve.
Unfortunately though, empirical economics isn’t that simple. Imagine instead that a supply-obsessed economist were interested in asking “Does increasing use of broadband raise its price?” Similar logic might lead him to examine data on broadband prices and adoption rates — yes, the same data — but he would expect to see more broadband correlated with higher prices because the supply curve is upward sloping.
So does a graph of broadband prices and quantities in different countries tell us about the supply curve or the demand curve? Unfortunately, it’s a mishmash. Let me explain. Prices and quantities are determined by both supply and demand. If both curves were the same in every country, broadband prices and use would be the same in every country. However that’s not what we observe. Prices and quantities differ across countries. But is this because their supply curves differ or their demand curves differ?
If the supply curve differs across countries — perhaps because it is more costly to lay cable in some places — then some countries will be on the leftmost part of the demand curve (high price, low quantity), and others will be on the rightmost part of the demand curve (low price, high quantity), with some in between. This is case 1 in my chart below; in this example, the cross-country data would be where the black crosses are. This must be what Rampell has in mind when she says “As Econ 101 would predict, the two measures are related: prices go down, subscription rates go up.”
But this isn’t all that Econ 101 suggests. What about case 2, where the demand curve varies across countries? People in rich countries like the U.S., Western Europe, and Scandinavia are probably willing to pay more for broadband access than people from Poland, Turkey, Mexico, or other poorer countries. My graph shows that poor countries will be on the leftmost part of the supply curve (low price and quantity), and rich countries will be on the right side (high price and quantity), with some in between. This is not just a story just about G.D.P. by the way — there are many other reasons demand may differ across countries. But whatever the reason, the black crosses representing the data would suggest that high prices are associated with high quantities, even though all demand curves are clearly downward-sloping.
So we have two cases, both of which have downward-sloping demand curves, but in one, the quantity of broadband subscribers is low in countries with high broadband prices; and in the alternative case, broadband subscriptions are high in countries with high broadband prices. In reality, the world is a mixture of both cases. The conclusion from this little example: when you plot real-world price and quantity data, you don’t learn the slope of the demand curve (unless you are strictly in case 1), and you don’t learn the slope of the supply curve (unless you are strictly in case 2). Instead you learn a combination of the slope of both demand and supply, and the extent to which variation is driven by these two forces.
The data that Rampell compiled are shown in the graph below. At first blush it looks like a mystery, as broadband price and quantity don’t look to be closely related, leading her to ask: “So what gives? Why isn’t there a stronger relationship between price and use?”
What gives is that the real world is serving up healthy doses of both case 1 and case 2: both supply and demand curves are different around the world. Consequently, we see low broadband prices yielding high adoption rates in case-1 countries (as Rampell expected) and low adoption rates in case-2 countries (check out all the poorer countries below her regression line, below). And the reverse pattern holds for high broadband prices. That is, you can’t infer supply or demand curves from simply looking at price and quantity data. For the technically minded, this is called the identification problem, and it is why econometrics is so darn difficult. This problem — the bane of most economists’ lives — arises precisely because prices and quantities are determined by both blades of the supply and demand scissors.
Armed with a little bit of economic theory, the poor fit between price and quantity isn’t such a mystery after all.
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Misterb,
You could argue that your “network effects” are in fact economic effects; think of economies of scale on the supply side… now with broadband you just have an economies of scale on the demand side as well.
A better fit would probably result from examining average price within a country. I particular in urban areas there are multiple broadband sources available and probably lower prices as DSL and Cable Modems use existing physical links. In other areas of the country there may be limited broadband and only more expensive options.
My guess is both demand and supply matter. But comparing countries adds too much noise.
misterb,
We don’t assume that the Demand Curves or Supply Curves are linear.
However the Gauss-Markov Theorem states that if we wanted to estimate the slope of either of these curves, then the best way to estimate them is with a Linear Regression line. A linear line won’t always give you the best fit, but it undoubtedly is one of the first tools in the Econometrics tool box to try. I’m assuming this is just an exploratory thought exercise of course
I assume if we had more data and a clearer understanding of the relationship we could try to fit a logarithmic or exponential line to the data.
I, too, am a non-economist. I consider broadband more of a utility requiring a lot of infrastructure before it can be offered. Does the same identification problem exist with consumer commodities that are not so much like utilities? Or is Adam Smith more on the mark in the more common case?
Uruguay is a relatively poor country with relatively high internet and broadband penetration, but with extremely expensive monthly subscription prices (max. broadband is 3 MB, 1024 kb cost about us$60).
One look at the R^2 and it is obvious that her estimation technique (or the model itself) is flawed.
Economics is more than just running regressions. Freakonomics failed to communicate this regarding abortions vs. crime rates in the book. The actual paper, which was a much more rigorous analysis, did. I hope this post illustrates why a Ph.D. in economics has value.
Applying the basic principles of economics to a complex world problem in exchange for a simple answer is like making an omelet without butter and a pan…messy.
This is a little basic. I think she was asking not only why her technique wasn’t picking up the expected relationship, but also why identification was proving difficult. If you look at the comments following her article, they are (for the most part) implicitly aimed at explaining variation in both supply and demand. Isn’t it the point of econometrics to dig a little deeper?
Anyway, while its true that each country might have unique supply and demand curves, shouldn’t broadly similar countries have broadly similar curves. That is to say, why is Norway so different from Sweden and Finland? Or France, Germany, and Austria? Is it due to idiosyncratic business environments, regulations, etc.? Maybe different subsidies? How would you propose getting around these confounding factors?
Hmm…
Upon a second reading, her model does only include price and quantity. If she wants to find the structural demand parameters, she should include an exogenous variable that affects the supply function, but not the demand function. Supply subsidies might be a quantifiable variable. Any others?