Why is productivity slowing down? (Forthcoming, Journal of Economic Literature)

With: Ian Goldin, Pantelis Koutroumpis and François Lafond

Abstract: We review recent research on the slowdown of labor productivity and examine the contribution of different explanations to this decline. Comparing the post-2005 period with the preceding decade for five advanced economies, we seek to explain a slowdown of 0.8 to 1.8pp. We trace most of this to lower contributions of TFP and capital deepening, with manufacturing accounting for the biggest sectoral share of the slowdown. No single explanation accounts for the slowdown, but we have identified a combination of factors which, taken together, accounts for much of what has been observed. In the countries we have studied, these are mismeasurement, a decline in the contribution of capital per worker, lower spillovers from the growth of intangible capital, the slowdown in trade, and a lower growth of allocative efficiency. Sectoral reallocation and a lower contribution of human capital may also have played a role in some countries. In addition to our quantitative assessment of explanations for the slowdown, we qualitatively assess other explanations, including whether productivity growth may be declining due to innovation slowing down.

Accounting for variety (Submitted)

Abstract: I introduce a general method to account for the distribution of underlying components (variety) in the growth of an aggregate quantity, using the notion of entropy. This accounting decomposition enables a number of insightful applications to index numbers in economics. The cross-entropy of GDP with respect to a benchmark captures the change in its distribution, and thus how well this benchmark matches data for price and volume indices across time. This 'error' changes demonstrably over time. Accounting of variety also lends itself to a decomposition of labour productivity growth by a technology component (how many more 'average' goods are produced per unit of labour?), and the allocation of labour (does the distribution of labour inputs converge to the distribution of outputs?) plus demand (does the distribution of expenditures diverge from the distribution of outputs?).

Social contagion and asset prices: Reddit's self-organised bull runs (Submitted)

With: Valentina Semenova

Abstract: Can unstructured text data from social media help explain the drivers of large asset price fluctuations? This paper investigates how social forces affect asset prices, by using machine learning tools to extract beliefs and positions of 'hype' traders active on Reddit's WallStreetBets (WSB) forum. We empirically document that sentiments expressed by WSB users about assets' future performances (bullish or bearish) are in part due to the sentiments of their peers and past asset returns. Our stylized model shows that information assimilation from peers can help explain return predictability and reversals, as well as bubble dynamics. The paper directly estimates the effect of WSB activity on asset prices. We document: that retail trader demand follows WSB discussions through using Trade and Quote data, the predictability of prices from retail trader discourse, the amplified market impact of idiosyncratic investor sentiment from viral content online, and the greater exposure of hype investors to bubbles in the markets.

Measuring productivity dispersion: a parametric approach using the Lévy alpha-stable distribution (R&R, Industrial and Corporate Change)

With: Jangho Yang, Torsten Heinrich, François Lafond, Pantelis Koutroumpis, and J. Doyne Farmer

Abstract: It is well-known that value added per worker is extremely heterogeneous among firms, but relatively little has been done to characterize this heterogeneity more precisely. Here we show that the distribution of value-added per worker exhibits heavy tails, a very large support, and consistently features a proportion of negative values, which prevents log transformation. We propose to model the distribution of value added per worker using the four parameter Lévy stable distribution, a natural candidate deriving from the Generalised Central Limit Theorem, and we show that it is a better fit than key alternatives. Fitting a distribution allows us to capture dispersion through the tail exponent and scale parameters separately. We show that these parametric measures of dispersion are at least as useful as interquantile ratios, through case studies on the evolution of dispersion in recent years and the correlation between dispersion and intangible capital intensity.