Two papers on the productivity slowdown

What Is Driving The TFP Slowdown? Insights From a Schumpeterian DSGE Model

By Marco Pinchetti

In this paper, I incorporate a Schumpeterian mechanism of creative destruction in a medium-scale DSGE framework. In the model, a sector of profit-maximizing innovators invests in R&D and endogenously generates productivity gains, ultimately determining the economy’s growth rate. I estimate the model using Bayesian methods on U.S. data of the last 25 years (1993q1-2018q4) in order to disentangle the key forces underlying the productivity slowdown experienced by the US economy since the early 2000s. In contrast with the previous literature, I exploit Fernald (2014) data on TFP, factor utilization and labour quality to discipline the production function, and find that the bulk of the TFP slowdown is due to a decrease in innovation’s ability to generate TFP gains. These findings challenge the view of a large part of the literature, according to which the recent TFP dynamics in the US are mostly driven by demand slumps and/or liquidity crunches.

Compositional nature of firm growth and aggregate fluctuations

By Vladimir Sirnyagin

This paper studies firm dynamics over the business cycle. I present evidence from the United Kingdom that more rapidly growing firms are born in expansions than in recessions. Using administrative records from Census data, I find that this observation also holds for the last four recessions in the United States. I also present suggestive evidence that financial frictions play an important role in determining the types of firms that are born at different stages of the business cycle. I then develop a general equilibrium model in which firms choose their managers’ span of control at birth. Firms that choose larger spans of control grow faster and eventually get to be larger, and in this sense have a larger target size. Financial frictions in the form of collateral constraints slow the rate at which firms reach their target size. It takes firms longer to get up to scale when collateral constraints tighten; therefore, businesses with the largest target size are affected disproportionately more. Thus, fewer entrepreneurs find it profitable to choose larger projects when financial conditions deteriorate. Using Bayesian methods, I estimate the model using micro and aggregate data from the United Kingdom. I find that financial shocks account for over 80% of fluctuations in the formation of businesses with a large target size, and TFP and labour wedge shocks account for the remaining 20%. An independently estimated version of the model with no choice over the span of control needs larger aggregate shocks in order to account for the same data series, suggesting that the intensive margin of business formation is important at business cycle frequencies. The model with the choice over the span of control generates an empirically relevant and non-targeted collapse in the right tail of the cumulative growth distribution among firms started in recessions, while the model without such a choice does not. The paper also discusses implications for micro-targeted government stimulus policies.

Two papers appearing in the same issue of the NEP-DGE report could look very promising if they were combined. Could we explain the productivity slow down by a change in the composition of innovating firms that now face more difficulties translating innovation?

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