Posts this month
A blog on financial markets and their regulation
Last month, Jonas Heese published a paper on “Government Preferences and SEC Enforcement” which purports to show that the US Securities and Exchange Commission (SEC) refrains from taking enforcement action against companies for accounting restatements when such action could cause large job losses particularly in an election year and particularly in politically important states. The results show that:
All the econometrics appear convincing:
But then, I realized that there is one very big problem with the paper – the definition of labour intensity:
I measure LABOR INTENSITY as the ratio of the firm’s total employees (Compustat item: EMP) scaled by current year’s total average assets. If labor represents a relatively large proportion of the factors of production, i.e., labor relative to capital, the firm employs relatively more employees and therefore, I argue, is less likely to be subject to SEC enforcement actions.
Seriously? I mean, does the author seriously believe that politicians would happily attack a $1 billion company with 10,000 employees (because it has a relatively low labour intensity of 10 employees per $1 million of assets), but would be scared of targeting a $10 million company with 1,000 employees (because it has a relatively high labour intensity of 100 employees per $1 million of assets)? Any politician with such a weird electoral calculus is unlikely to survive for long in politics. (But a paper based on this alleged electoral calculus might even get published!)
I now wonder whether the results are all due to data mining. Hundreds of researchers are trying many things: they are choosing different subsets of SEC enforcement actions (say accounting restatements), they are selecting different subsets of companies (say non financial companies) and then they are trying many different ratios (say employees to assets). Most of these studies go nowhere, but a tiny minority produce significant results and they are the ones that we get to read.