A few months before leaving office, former President Barack Obama wrote about the challenges that his successor would have to tackle. Recent innovations, he claimed, “have not yet substantially boosted measured productivity growth.” In fact, since 2004, productivity growth slowed across nearly all advanced economies. Productivity being the most important determinant of economic growth—over 60 percent of cross-country differences in income can be explained by productivity. Understanding this importance, Obama concluded, “Without a faster-growing economy, we will not be able to generate the wage gains people want, regardless of how we divide up the pie.”
There can be several explanations for the productivity slowdown, from mismeasuring to the “drying out” of productivity-enhancing innovations. But there isn’t any consensus on the explanation for the slowdown. Most researchers agree that in order to understand productivity, we must look at the behavior of the smallest possible economic unit driving changes in aggregate productivity: the firm.
My most recent paper looks at millions of firms concentrated in more than 40 countries to answer a simple question: Is there productivity convergence? Convergence, originally, is the name given by economists to the process through which poor countries grow at rates faster than those in rich countries, generating catch-up. Examining the question at the firm level and with a focus on productivity, I ask: Are the low-productivity firms improving their productivity at a faster rate than the high-productivity firms?
Intuition would say the answer is yes. Imagine the life of a newly established small firm producing footballs. In its beginning, it is likely that the firm is unproductive relative to the costs of making footballs, with each football produced requiring more worker-hours than an already established, large firm. But the small firm can improve quickly by simply imitating good practices at the larger firms by, for example, hiring a manager that used to work at a larger firm or by buying a football machine that bigger firms use. So these small firms can initially be expected to improve their productivity quite fast. However, for the already highly productive firms to improve their productivity requires more effort than simply copying productive practices. This requires innovating, which in itself is risky and costly. Yet, if they want to stay at the top, the large firms must keep innovating, even though they know that it is likely that the smaller firms will eventually benefit from those innovations.
My paper finds evidence of a “U-shaped” convergence curve for short-term productivity growth. That is, fast productivity growth is concentrated not just at the bottom but also at the top of the productivity distribution (see Figure 1). The analysis compares firms within the same country and in the same narrowly defined industry group (using the six-digit North American Industry Classification System). This result is robust across all sectors of the economy. In simpler terms, the small, low-productivity firms grow fast, but so do the large high-productivity ones.
The results are particularly salient because they point to a “middle productivity trap,” where firms that reach average levels of productivity will lag behind those at the very top. I find that these middle-productivity firms are not especially different from the high-productivity ones in terms of size: take a look at the distribution of sales with respect to productivity in the left panel of Figure 2. Rather, they differ in terms of how efficiently they use their inputs: see distribution of combined inputs used for production with respect to productivity in the right panel of Figure 2.
Figure 2: Firms’ sales (left) and aggregate inputs used (right) by productivity percentiles
These dynamics are a plausible explanation for two important facts. First, a widening productivity dispersion—a phenomenon that has been happening in the U.S. since the mid-1990s—and, second, the increasing market share of “superstar” firms. Increasing productivity dispersion negatively correlates with overall future productivity growth.
This trap suggests that there may be market failures that hinder the diffusion of technologies and knowledge from firms at the top of the distribution to firms in the middle. In fact, I find evidence consistent with this hypothesis when looking at convergence dynamics for industries of different knowledge intensity. The U-shaped convergence dynamics are driven by knowledge-intensive industries (see Figure 3), for which presumably the costs of technology adoption are the highest.
Productivity is the most important determinant of economic growth and, in turn, of living standards more generally. As Paul Krugman, Nobel Laureate in economics, once put it, “Productivity isn’t everything but, in the long run, it is almost everything.” These findings suggest that a crucial challenge for development economists who focus on productivity will be to deepen our understanding on how the diffusion and adoption of knowledge and technology within and across countries can be accelerated.