Whorfian Economics: The Interaction of Language and Economic Decisions

Does the way in which the language we speak describes the future have an impact our intertemporal choices? Apparently so:

Whorfian Economics, by Keith Chen: Mark and Geoffrey were kind enough not only to write thoughtful columns on a recent working paper of mine here and here, but to invite me to write a guest post explaining the work. In the spirit of a non-linguist who’s pleased to be discovering this blog, I wanted to use Mark and Geoffrey’s insightful posts as a springboard to explain my work.

In a nutshell: I find a strong correlation between how a language treats future-time reference (FTR), and the choices that speakers of those languages make when thinking about the future. Specifically, in large data sets that survey families across hundreds of countries, I find a strong and robust negative correlation between the obligatory marking of FTR in the language a family speaks, and a whole host of forward-looking behaviors, like saving, exercising, and refraining from smoking.

[...For those readers unfamiliar with this typological distinction, English is considered a strong-FTR language because of observation that unlike most Germanic languages, English generally requires speakers to grammatically mark future events, primarily with either the de-andative construction “be going to” or the de-volative construction “will”. It is this generally tendency toward obligatory grammatical marking of future time that characterizes the strong vs. weak FTR distinction. ...]

These correlations hold both across countries and within countries, even when comparing effectively identical families born and living in the same country. While the data I analyze don’t allow me to completely understand what role language plays in these relationships, they suggest that there is something really remarkable to be explained about the interaction of language and economic decision making. These correlations are so strong and survive such an aggressive set of controls, that the chances they arise by random lies somewhere between one in 10,000 and one in 10^32.

Starting with Mark’s post: Mark illustrates beautifully an idea that is really the central concern of all work done in modern econometrics: it can often be difficult to tell the difference between strong correlations produced by causal relationships, and correlations which arise through non-causal factors. Since questions of the connection between language and behavior have historically generated considerable controversy, it seems important to think hard about what exactly these correlations actually suggest. Towards this, I’ll discuss briefly why my analysis suggests that a non-causal story is unlikely, and that a language’s structure is causing its speakers to behave differently. ...

In short, I believe the data suggest a strong and robust relationship between linguistic and economic data, a relationship that bears explaining. Where this leaves us is what I think is an exciting place: one where Economists have a lot to learn from Linguists.

Much, much more here, including a discussion of potential weak points in the results.