Kevin A. Bryan
13.3K posts
@Afinetheorem
Assoc. Prof. of Strategy, U Toronto Rotman | Chief Economist, CDL Toronto | Co-Founder, AllDayTA | Ars longa, vita brevis, occasio praeceps (especially now)
"Abduction and the Demand Curve" A new paper with @EconTraina The demand curve is the most basic object in economics. Hold everything else fixed, change the price, see what happens. Ceteris paribus. Day one stuff. Okay, maybe day 3. But what does "everything else" include? Unobserved quality, local tastes, recent advertising. Things the econometrician doesn't see. A market's demand curve holds those fixed. Now suppose you run a randomized experiment. Set a price, observe quantity, repeat. You've eliminated confounding. You have a causal effect. We love experiments. Perfect. Right? Right? Are these the same things? This maybe isn't well-known outside of IO, but the answer is no. When they aren't, what are you supposed to do? This paper connects the experimental literature with the structural IO demand estimation literature to make clear the interplay . In the experiment, you've averaged over all those unobserved conditions. You know what happens on average across markets when you set a price. You don't know what happens in THIS market, with THIS unobserved quality, at that price. The experiment gives the average demand response. Policy happens in a specific market. Two markets produce the same quantity at the same price. An experiment can't tell them apart. But at any other price, they diverge. The demand curve is a market-specific object. So what bridges the gap? Good ole' Berry (1994_ inversion. You observe a market's shares, prices, and characteristics. Inversion recovers the unobserved demand index, the δ*, that rationalizes what you see. It pins down WHERE on the demand function this particular market sits. Prior work treats this as a computational convenience. Berry (1994, p. 249) compares it to "taking logarithms of observed data." Berry and Haile (2021, p. 40) call it a "trick." They leave as an open question what happens when invertibility fails, "perhaps involving partial identification." We answer. Without inversion, even price-only counterfactuals are set-identified. The trick is not optional. Inversion is not just sufficient but necessary for recovering market-specific counterfactuals. But when exactly do you need it? Berry and Haile (2021) say experiments "generally" don't identify demand. Angrist, Graddy, and Imbens (2000) showed that when demand differs across markets beyond an additive shift, IVs identify a weighted average of derivatives, not any single market's response. Imbens even reiterates the point in his Nobel lecture. We first make "generally" exact beyond the linear case of AGI (2000). We characterize precisely when the experimental average price response equals every market's demand slope (if and only if additive separability holds, a knife-edge that every standard discrete-choice model violates). So outside of that case, what are we to do? That hasn't stopped IO economists. Are they just making stuff up? No! Berry inversion baby! Along the way, we can make a few more connections. @yudapearl asked whether ceteris paribus demand can even be formally defined in counterfactual language. We do that. The demand curve is the unit-level counterfactual Q_p(u) for a market with realized conditions held fixed. We also show the connection to Pearl's causal hierarchy. Experiments give Rung 2 (causal). The demand curve is a Rung 3 object (counterfactual). There's generically a gap between them. Berry inversion is what is called abduction in SCM to move between those rungs. The econometrics and CS frameworks are saying the same thing, and the demand curve is the natural, well-developed setting to see it.
This is just the economics of scarcity. Here is agriculture—same graph. Once something becomes plentiful (eg through automation), value reallocated to something that is scarce. We don’t eat less than before, if anything we eat way more. We don’t use computers less, we use them more. But neither is scarce, so their share of GDP decreases.
This is one of several reasons you shouldn't drop out of (or skip) college to start a startup at 18. Founders who do that tend to match the second paragraph rather than the first, because they haven't had time to have the "earned insight" he describes.
For whom?
Two kinds of people in the world. Those that believe like LKY that US econ dominance is based on being able to hire world's most ambitious, smartest people in a meritocratic space, and those that think if their friend worked at Google instead, US firms would still be dominant.
Turning to the astute public opinion analysis of "Roman Helmet Guy" to reach the conclusion that the only available options are stark binary in which tech entrepreneurs must embrace braindead nativism or else be murdered.
Today, we remember the passengers of the Komagata Maru and reflect on a painful chapter in Canadian history. In 1914, 376 passengers seeking opportunity and a better life arrived in Vancouver Harbour, only to be denied entry because of discriminatory immigration policies. Forced to remain aboard the ship for two months under difficult conditions, they were ultimately turned away. The boat returned to India and many of the passengers were imprisoned or killed. The incident remains a powerful reminder of the harm caused by prejudice and discrimination, and the importance of building communities grounded in respect and equal treatment for all.
