The high pace of reallocation across producers is pervasive in the U.S. economy. Evidence shows this high pace of reallocation is closely linked to productivity. While these patterns hold on average, the extent to which the reallocation dynamics in recessions are "cleansing" is an open question. We find downturns prior to the Great Recession are periods of accelerated reallocation even more productivity enhancing than reallocation in normal times. In the Great Recession, we find the intensity of reallocation fell rather than rose and the reallocation that did occur was less productivity enhancing than in prior recessions.

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Researchers use a variety of methods to estimate total factor productivity (TFP) at the firm level and, while these may seem broadly equivalent, how the resulting measures relate to the TFP concept in theoretical models depends on the assumptions about the environment in which firms operate. Interpreting these measures and drawing insights based upon their characteristics thus must take into account these conceptual differences. Absent data on prices and quantities, most methods yield ``revenue productivity" measures. We focus on two broad classes of revenue productivity measures in our examination of the relationship between measured and conceptual TFP (TFPQ). The first measure has been increasingly used as a measure of idiosyncratic distortions and to assess the degree of misallocation. The second measure is, under standard assumptions, a function of fundamentals (e.g., TFPQ). Using plant-level U.S. manufacturing data, we find these alternative measures are (i) highly correlated; (ii) exhibit similar dispersion; and (iii) have similar relationships with growth and survival. These findings raise questions about interpreting the first measure as a measure of idiosyncratic distortions. We also explore the sensitivity of estimates of the contribution of reallocation to aggregate productivity growth to these alternative approaches. We use recently developed structural decompositions of aggregate productivity growth that depend critically on estimates of output versus revenue elasticities. We find alternative approaches all yield a significant contribution of reallocation to productivity growth (although the quantitative contribution varies across approaches).

We explore current interpretations of firm-level dispersion in revenue-based productivity measures. Since revenue function estimates using proxy methods differ from factor elasticities, the residual emerging from this method remains a combination of demand and technical effciency shocks, and is not equal to the concept of revenue productivity that plays an important role in recent literature on misallocation. This has implications for applications where measured revenue productivity dispersion is used as an indicator of misallocation. Our empirical evidence suggests, under iso-elastic demand, measured dispersion may indicate either distortions or variation in demand shocks and technical effciency or all of the above.

We examine whether underlying industry innovation dynamics are an important driver of the large dispersion in productivity across firms within narrowly defined sectors. Our hypothesis is that periods of rapid innovation are accompanied by high rates of entry, significant experimentation and, in turn, a high degree of productivity dispersion. Following this experimentation phase, successful innovators and adopters grow while unsuccessful innovators contract and exit yielding productivity growth. We examine the dynamic relationship between entry, productivity dispersion, and productivity growth using a new comprehensive firm-level dataset for the U.S. We find a surge of entry within an industry yields with a lag an increase in productivity dispersion and then after a subsequent lag an increase in productivity growth. These patterns are more pronounced for the High Tech sector where we expect there to be more innovative activities. These patterns change over time suggesting other forces are at work during the post-2000 slowdown in aggregate productivity.