We usually think of "rich" as the opposite of "poor," and in some ways that makes a lot of sense. It's true, anyway, that rich people aren't poor, and poor people aren't rich. Thinking of rich and poor as opposites also feels natural because it introduces an obvious yardstick -- money -- for measuring how wealthy people are, and how much separates one person from another. Money is a convenient yardstick because it follows a simple rule: All other things equal, we'd always like to have another dollar. Think here of a line from The Simpsons, spoken by Montgomery Burns after Barney expresses admiration for his incredible wealth: "Yes, but I'd trade it all... for just a little more."
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Unfortunately, the word "poor" has lots of opposites, and not all of them have to do directly with money. "Healthy," "well-educated," "having access of clean water," and "nourished" are among the many opposites of "poor," and when we think about the relative merits of antipoverty programs, we have to weigh each of these things -- and more -- against each other. But how do we compare the importance of, say, health versus education versus housing? And how do we make tradeoffs between them? One approach is to apply our own values and priorities, but this ignores the preferences of the very people for whose benefit these programs are designed. This happens often in the world of development aid; a donor focusing on education, for example, might care more about classroom quality than hospital beds. But wouldn't it be better if we could instead ask the people receiving our help what they want?
This isn't just about trying to please. Development aid lore is rife with stories of well-intentioned outsiders missing the mark, offering people goods and services they don't really want. Recipients sometimes manage to extract some value from unwanted items by trading them for things they actually do want, or by jury-rigging them to serve other purposes (often with limited success). A mosquito net may get swapped for a machete, for example, or a kitchen set might be sold in order to just buy food. If we want to avoid these outcomes, we must answer the question: How can we best understand people's priorities and tastes?
Outside the poverty field, there are a growing number of ways of ascertaining and predicting what people like, and all of them are imperfect -- but they're getting better. Think about the ubiquitous taste-based suggestions on Amazon.com and Netflix, for instance: "Customers who liked this also liked _____." This "taste-matching" approach looks for other people whose preferences are similar to yours, then recommends things those people like that you haven't tried yet. It stands to reason that taste-matching methods improve over time; they look at thousands of consumers' feedback about thousands of products, and see what patterns emerge. As more wide-ranging data is amassed from more consumers to inform these suggestions, they become increasingly accurate.
A hazard of taste-matching, however, is that it has a hard time dealing with quirks. Suppose you like Mediterranean food but hate olives. If that quirk isn't shared by many other lovers of Mediterranean food, it's unlikely that it will be reflected in the food recommendations you would get from a taste-matching approach.
When it comes to fighting poverty, missing those quirks can be a deal breaker. Here's a real example from central Kenya, where farmers of Gichugu Division, at the foot of Mount Kenya, grew impressive crops but were hamstrung by isolation. Without information about foreign markets or access to exporters, most grew products like maize and kale, which they sold to local consumers. DrumNet, an ambitious program designed by the nonprofit organization PRIDE AFRICA, sought to help by setting up an export supply chain and encouraging the farmers to adopt crops that were both well-suited to the weather and soil of Gichugu Division and in high demand elsewhere. Specifically, the program pushed for French beans, a favorite of European consumers.
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