
doi: 10.2139/ssrn.4368609
handle: 10419/269204
Industry concentration and markups in the US have been rising over the last 3- 4 decades. However, the causes remain largely unknown. This paper uses machine learning on regulatory documents to construct a novel dataset on compliance costs to examine the effect of regulations on market power. The dataset is comprehensive and consists of all significant regulations at the 6-digit NAICS level from 1970-2018. We find that regulatory costs have increased by $1 trillion during this period. We document that an increase in regulatory costs results in lower (higher) sales, employment, markups, and profitability for small (large) firms. Regulation driven increase in concentration is associated with lower elasticity of entry with respect to Tobin's Q, lower productivity and investment after the late 1990s. We estimate that increased regulations can explain 31-37% of the rise in market power. Finally, we uncover the political economy of rulemaking. While large firms are opposed to regulations in general, they push for the passage of regulations that have an adverse impact on small firms.
Concentration, 340, 330, Competition, ddc:330, L11, 338, Machine Learning, Market Power, ddc:340, L51, D4, Regulations, C45
Concentration, 340, 330, Competition, ddc:330, L11, 338, Machine Learning, Market Power, ddc:340, L51, D4, Regulations, C45
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