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Other findings concerning age, size, productivity, relative growth, Pavitt technology classes and the macroeconomic environment are in line with the existing literature.
The method constructs a counterfactual which allows analyzing how size (productivity) of a firm would have evolved if it had not received a productivity (size) shock.
HGF only TFP (size) refer to high-growth firms in terms of productivity (size) but not in terms of size (productivity) —see the main text for more details.
(2017) find that high growth in productivity (size) increases the likelihood of high growth in size (productivity) but the magnitude of the effect from size to productivity is smaller than that of the effect from productivity to size.
The stocks of agents in each of the three states are constant over time as are the distributions of firm size, productivity amongst the self-employed, and the distribution of wages amongst the paid employees.
The intuition of this technique is to estimate the effect of productivity (size) growth on size (productivity) growth by matching high-growth-productivity (-size) firms with non-high-growth-productivity (-size) firms.
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Therefore, the productivity (size) shock considered in the baseline specification might include a size—(productivity-) shock component.
This finding supports the inverse farm size-productivity relationship, which posits that small farms are more productive than large farms (Chen et al. 2011).
Learning by growing represents an alternative rationale to explain the size-productivity nexus.
On the empirical front, little is known about the direction of causality in the size-productivity nexus at the firm level.
In addition to the descriptive statistics reported in Table 2, the size-productivity nexus is also illustrated by the correlation between (log ) employees and (log ) TFP, which is 0.33 (t-({ stat}=12.59)).
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