Something has been bothering me about the way evidence is (sometimes) used in economics and econometrics: theories are assumed throughout interpretation of the data. The result is that it’s hard to end up questioning the model being used.
Let me give some examples. The delightful fellas at econjobrumours once disputed my argument that supply curves are flat or slope downward by noting that, yes, Virginia, in conditions where firms have market power (high demand, drought pricing) prices tend to go up. Apparently this “simple, empirical point” suffices to refute the idea that supply curves do anything but slope upward. But this is not true. After all, “supply curves slope downward/upward/wiggle around all over the place” is not an empirical statement. It is an interpretation of empirical evidence that also hinges on the relevance of the theoretical concept of the supply curve itself. In fact, the evidence, taken as whole, actually suggests that the demand-supply framework is at best incomplete.
This is because we have two major pieces of evidence on this matter: higher demand/more market power increases price, and firms face constant or increasing returns to scale. These are contradictory when interpreted within the demand-supply framework, as they imply that the supply curve slopes in different directions. However, if we used a different model – say, added a third term for ‘market power’, or a Kaleckian cost plus model, where the mark up was a function of the “degree of monopoly”, that would no longer be the case. The rising supply curve rests on the idea that increasing prices reflect increasing costs, and therefore cannot incorporate these possibilities.
Similarly, many empirical econometric papers use the neoclassical production function, (recent one here) which states that output is derived from the labour and capital, plus a few parameters attached to the variables, as a way to interpret the data. However, this again requires that we assume capital and labour, and the parameters attached to them, are meaningful, and that the data reflect their properties rather than something else. For example, the volume of labour employed moving a certain way only implies something about the ‘elasticity of substitution’ (the rate at which firms substitute between labour and capital) if you assume that there is an elasticity of substitution. However, the real-world ‘lumpiness‘ of production may mean this is not the case, at least not in the smooth, differentiable way assumed by neoclassical theory.
Assuming such concepts when looking at data means that economics can become a game of ‘label the residual‘, despite the various problems associated with the variables, concepts and parameters used. Indeed, Anwar Shaikh once pointed out that the seeming consistency between the Cobb-Douglas production function and the data was essentially tautological, and so using the function to interpret any data, even the word “humbug” on a graph, would seem to confirm the propositions of the theory, simply because they follow directly from the way it is set up.
Joan Robinson made this basic point, albeit more strongly, concerning utility functions: we assume people are optimising utility, then fit whatever behaviour we observe into said utility function. In other words, we risk making the entire exercise “impregnably circular” (unless we extract some falsifiable propositions from it, that is). Frances Wooley’s admittedly self-indulgent playing around with utility functions and the concept of paternalism seems to demonstrate this point nicely.
Now, this problem is, to a certain extent, observed in all sciences – we must assume ‘mass’ is a meaningful concept to use Newton’s Laws, and so forth. However, in economics, properties are much harder to pin down, and so it seems to me that we must be more careful when making statements about them. Plus, in the murky world of statistics, we can lose sight of the fact that we are merely making tautological statements or running into problems of causality.
The economist might now ask how we would even begin to interpret the medley of data at our disposal without theory. Well, to make another tired science analogy, the advancement of science has often not resulted from superior ‘predictions’, but on identifying a closer representation of how the world works: the go-to example of this is Ptolemy, which made superior predictions to its rival but was still wrong. My answer is therefore the same as it has always been: economists need to make better use of case studies and experiments. If we find out what’s actually going on underneath the data, we can use this to establish causal connections before interpreting it. This way, we can avoid problems of circularity, tautologies, and of trapping ourselves within a particular model.