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Is there an easy cure for low growth?

Abstract The U.S. economy faces sizeable headwinds to keeping GDP growth even at 2% over the next decade. Demographics imply that labor force growth will be much slower than historical norms. The enormous twentieth century increase in average educational attainment is unlikely to be repeated. And the best guess for productivity growth is that it will continue to be modest--perhaps along the lines seen in the 1970s to early 1990s, or since 2004. There are no easy cures for low growth. We can hope for another wave of broadbased IT-linked innovation. But while there is enormous uncertainty, even worthwhile policy steps are unlikely to move the dial very much on their own.

Keywords Productivity * Potential growth * Economic policy

1 Introduction

The recovery from the Great Recession has been disappointingly slow. From the 2009 trough through 2016, GDP growth averaged only 2.1%. That rate is extraordinarily low compared with history. Hence, are we likely to do better than that going forward? Are there policies that can help?

The Survey of Professional Forecasters from February 2017 expects growth over the next decade to be about a quarter percentage point faster than what we've seen since 2009. Higher growth would help alleviate concerns about government budgets, and higher per capita income growth would imply faster increases in material standards of living. But even the disappointing 2.1% growth rate since 2009 was enough to bring the unemployment rate down from 10% to around 4 1/2% in early 2017. Hence, sustainable growth could plausibly be even lower than what we've observed recently.

My own modal estimate is that longer-run GDP growth--say, over the next 5-10 years--will be in the 1 1/2-1 3/4% range. (1) Relative to history, the biggest constraints on growth are headwinds from demographics and reduced gains in education. In particular, the labor force is projected to grow only slowly, and education will add less to labor quality and thus to labor productivity. Net of this labor quality slowdown, my benchmark forecast assumes that productivity runs at about its pace in the 1970s and 1980s, which is a little faster than what we've seen since 2004 and notably faster than it's been since 2011.

Of course, there is enormous uncertainty about any projection because low-frequency productivity trends are mysterious. In the last 45 years, the 1995-2004 period showed exceptional productivity gains. But neither professional forecasters nor newspaper articles in the early 1990s, predicted the pickup. And professional forecasters weren't expecting the productivity boom to end after 2004. (Muller and Watson 2016 discuss confidence intervals on long-run projections.)

2 Broad forces weighing on growth

The first headwind to growth is slow future labor force growth. Figure 1 shows the working age population, defined here as ages 15-64, along with the labor force. Census projections expect growth of the working age population to trend lower and lower over the next decade, until growth is barely above zero. The labor force grew enormously fast in the 1970s and 1980s, both in terms of population growth and increasing female participation. Going forward, labor-force growth will also be very low, around %% per year according to the Congressional Budget Office (CBO). It's expected to grow more rapidly than the 15-64 population because some older people still work.

The second headwind to growth is education. Figure 2 shows data from Goldin and Katz (2008) documenting the tremendous increase in the U.S. educational attainment in the twentieth century. The figure shows average years of schooling by birth year. The typical person born in 1880 had seven-and-a-half years of education. The typical person born in 1950 had about 13 years--a substantial increase. The pace of increase slows for cohorts born after that. Indeed, for cohorts born in the 1970s, educational attainment largely levels off. There is no additional increase. There's not much evidence that would suggest we are going to get a renewed surge in educational attainment going forward (Bosler et al. 2016).

Historically, this rising educational attainment was a notable force boosting productivity growth. For example, people who were retiring in the second half of the twentieth century had relatively low education; they were being replaced by newer cohorts who had more education. That is likely to be much less true going forward.

[FIGURE 1 OMITTED]

[FIGURE 2 OMITTED]

Now let me turn to productivity growth itself. Figure 3 shows GDP per hour for selected sub-periods since 1948. Once you get past the first bar, for most periods, the productivity growth shown has been pretty modest, i.e., in the 1-1%% range. In other words, since the early 1970s, this range is what's been normal. The exceptional period is the 1995-2004 period when productivity surged. Every analysis points to information technology as the source. Some of the improvement was going on within companies and tasks, but it was also happening within industries. Some companies were very good at taking advantage of information technology. The higher-productivity firms expanded, and the less-productive ones contracted.

Since 2004, even in the run up to the Great Recession, productivity growth was much more modest. The average growth in GDP per hour since then has been 1%. It was stronger in the immediate aftermath of the recession--the 2007-2010 bar in Fig. 3--and even weaker in the last 6 years. When I've dug more deeply into these figures, I've concluded that cyclical dynamics explain part of this weakness. In my opinion, the underlying trend is better captured by averaging over the period since 2004 (Fernald 2016).

[FIGURE 3 OMITTED]

But in thinking about underlying trends, I do want to separate out the effects of education and experience. The top portion of the bars show the growth-accounting contribution of education and experience, also known as labor "quality." Conceptually, labor quality is like hours in that it represents an increase in inputs.

Historically, labor quality added 0.4-0.5 percentage points per year to productivity growth. In the 2007-2010 period, it added almost a percentage point. The story is intuitive: Workers with fewer skills were more likely to lose their jobs. Hence, the people who had jobs were more productive. Once you remove this effect, even the 2007-2010 bar doesn't look that different from the other historical bars. Going forward, I expect labor quality will add only 0.1-0.2 percentage points per year to growth (Bosler et al. 2016). On its own, that's around a % percentage point drag on productivity growth.

Net of labor quality, my benchmark is that productivity growth will be similar to what it was during the 1970s, 1980s, and the early 1990s. That averaged about 0.9 percentage point. Adding two tenths for labor quality yields a benchmark forecast for GDP per hour of about 1.1 percentage points. Adding 0.5 percentage points for hours growth yields a GDP forecast of about 1.6% for longer-run growth.

Importantly, this extremely low forecast for GDP growth does not arise from unusually low innovation. We've seen long periods with productivity growth (net of labor quality) as low as I am projecting--namely, the 1970s and 1980s. What we haven't seen is that modest pace of productivity growth combined with a slow growth in hours and only modest growth in labor quality.

3 Why did productivity growth slow after 2004?

Still, even if the productivity projection underpinning my GDP forecast isn't unusually weak, many of the levers policymakers have work through productivity. Hence, it's important to assess the reasons why productivity growth slowed after 2004 in order to assess whether and how policy can affect it. I would highlight four main hypotheses to explain why the 1995-2004 productivity surge didn't last.

The first is rising mismeasurement. Maybe the surge never ended, but we just stopped measuring the gains? Maybe the statistics are missing most of the benefits of our smartphones, Google searches, and other IT-related hardware and software? I dug into that story in a paper last year (Byrne et al. 2016). The fundamental problem with the story is we've always had mismeasurement. And there is no evidence that mismeasurement has gotten worse. In fact, our point estimates were that IT-related mismeasurement was even larger in the late 1990s and the early 2000s. Hence, adjusting for mismeasurement made the post-2004 slowdown even worse.

A second hypothesis is that the Great Recession slowed investment in innovation or otherwise made the economy less efficient. Adler et al. (2017) discuss a range of channels through which a recession (financial or otherwise) or period of slow growth might endogenously slow innovation. For the United States, the fundamental problem with this story is that the slowdown in productivity growth predated the recession (Fernald 2015; Fernald et al. 2017). The bar chart in Fig. 3 shows that productivity growth in the 3 years preceding the Great Recession (2004-2007) was already far short of its 1995-2004 pace.

Figure 4 shows the same thing differently, by plotting the level of business-sector labor productivity. There is a clear kink a couple years before the recession, around 2004 or 2005. During and immediately after the recession, productivity actually rose above its 2004-2007 trend but then gradually returned towards that trend. In the background, there are dynamics related to capital deepening during and immediately after the recession, which have been unwinding in the last 5 or 6 years. But labor productivity is not actually all that different today than it would have been on if you continue the trend from the years right before the recession.

More broadly, looking back at Fig. 3, another way of thinking about this is that what we've been getting, for the last dozen years, and in fact since 2007, is productivity growth similar to what we've gotten for most of the period since 1973. The Great Recession was unusually traumatic. But the productivity experience of the last dozen years is not unusual.

[FIGURE 4 OMITTED]

A third story is declining dynamism, possibly caused by increases in regulation or other causes (e.g., Decker et al. 2016; Barro 2016). Both dynamism and regulation surely are related to productivity. The process of productivity growth is inherently dynamic, and in part involves reallocating resources from less-productive to more-productive uses. Across countries, regulatory barriers do seem to affect growth rates. For example, many studies have argued that labor- and product-market rigidities made it more difficult for the European economies to undertake business reorganizations necessary to benefit from information technology (e.g., Cette et al. 2016).

Frictions to entrepreneurship and reallocation can come from various sources. Starting with regulation, it is not clear for the United States that rising regulatory burdens are the first-order cause for why the 1995-2004 productivity surge did not last. First of all, a common story that, post-2008, the increases in regulation somehow caused the slowdown doesn't fit the timing. The slowdown started much earlier--in 2004 or 2005, if not 1973. Second, even for longer samples, it is challenging to find an empirical link between fluctuations in industry-specific Federal regulations and industry productivity dynamics. Fernald et al. (2017) look for such a link but do not find it.

In terms of dynamism more broadly, the relationship with productivity growth is a priori unclear. In some cases, technology may favor large firms so that reduced job and firm turnover is associated with productivity gains. That's a largely benign decline in dynamism. In other cases, innovation is associated with new establishments and firms, and may require substantial reorganization of jobs. In this case, barriers to dynamism are likely to impede productivity growth. Of course, declining dynamism could in some cases simply be a symptom of declining opportunities for startups. For example, in the late 1990s, the Internet caused a gold rush in Silicon Valley--startup activity and other measures of dynamism were very high. The gold rush ended, and while some firms were big winners, many others disappeared.

It is certainly not clear that the frictions that have reduced reallocation explain the post-2004 slowdown of some 1% percentage points per year in business-sector productivity. A challenge is to figure out what the efficient level of dynamism is, as well as to identify and quantify the importance of specific frictions and barriers that cause deviations from that efficient level. For now, I see declining dynamism as a symptom, but I don't yet know what it's a symptom of.

The fourth hypothesis, which is the one I have been pushing for a couple of years, is that we have returned to normal after an exceptional information-technology (IT)linked decade after 1995 (see also Gordon 2016). Every story from the early 2000s regarding the productivity surge pointed to IT. And much of that literature acknowledged that the gains were a sequence of one-off improvements. You didn't know ex ante how many Walmarts and Costcos you would get, at the expense of smaller, less efficient retailers. But nearly every job, and every industry, was transformed in some way between 1995 and 2005. For most of us, the changes have been much more incremental since then.

What this line of argument suggests is that modest productivity growth is the new normal. There are certainly upside surprises possible. We might well get another wave of IT-linked productivity gains, but we don't know when it will arrive.

4 Are there effective levers to boost growth?

A policy we could adopt towards low growth is to accept it, and adapt to it. Unfortunately, our policy levers to improve growth aren't all that powerful. Here's one way to think about that. If we had continued to grow at the 1995-2004 pace, rather than having a business-sector productivity slowdown of some 1% percentage points per year then, by 2016, the business sector would be about a quarter larger, which amounts to some $3.5 trillion. That's the order of magnitude you need to achieve, and it requires an enormous lever.

Still, there are things we can do that might push in the right direction, and might marginally improve efficiency in the economy. One is increasing spending on infrastructure investment. My reading of the literature is that a lot of infrastructure spending is likely to pass a cost-benefit test.

Now, even if we do a pretty large infrastructure program, the productivity benefits probably amount to only a few basis points per year on growth. For example, as a quick back-of-the-envelope calculation, suppose we spent $1 trillion on new infrastructure with a rate of return to market activities of 10% a year--both of which are large. Given that we're close to full employment today, I'm going to ignore any short-run effects from demand stimulus and focus on the long-run supply effects. The long-run effect would be to raise the level of GDP $100 billion ($1 trillion times 10%), which is on the order of 'A% of GDP. It if took 10 years to build the infrastructure and get those returns, then the gain is 5 basis points a year.

And even to get 5 basis points from infrastructure requires a large program with a high rate of return. Moreover, I have assumed that the extra spending doesn't push up interest rates and crowd out other investment in the long run. Hence, the actual gains from an infrastructure program would likely be even smaller than this example. This is simply an illustration that policy levers aren't all that powerful.

The logic of corporate tax reform is similar. Tax reform could improve efficiency. It could boost capital deepening. But the magnitude of the effect of revenue-neutral tax reform on productivity growth is, in most models, measured in basis points or, at most, tenths. Those effects don't fill in a growth shortfall of 13A percentage points per year.

Ultimately, the source of productivity gains is ideas. Hence, perhaps the most powerful steps are to seek to generate ideas and then make them easy to translate into productivity gains. Funding basic research is something that society should almost surely do more of, since there appear to be spillovers such that the social return is well above the private return. Basic research is a bit like buying a lottery ticket--you don't know whether the increase in research effort will ever pay off, but it might pay off in a big way. And then, coming back to the dynamism discussion, you need policies that help make markets flexible so that the ideas can be implemented appropriately by businesses.

I would note that ideas are global, and they spill across borders. It used to be that the ideas that mattered for the United States were developed in a small number of countries--the U.S., Canada, Western Europe, Japan (Fernald and Jones 2014). But China, India, and many other countries are today much closer to the research frontier. One way this shows up is in terms of R&D relative to GDP, shown in Fig. 5. India is not shown on the chart, but the rise of China and Korea is impressive.

Hence, one way the U.S. policymakers could encourage productivity growth is to continue to encourage emerging markets like China and India to grow and integrate further with the world economy. Such policies could include encouraging progress towards rule of law and the development of stable democratic institutions, as well as encouraging their active participation in international organizations and agreements.

[FIGURE 5 OMITTED]

5 Conclusion

To conclude, there's no easy cure for low growth. We can certainly hope for another wave of broadbased IT-linked innovation. Policies should primarily be aiming to help foster the creation and diffusion of ideas. But most of the specific policy steps we can take are small and incremental, even if worthwhile.

Disclaimer The views expressed here are my own and do not necessarily reflect the views of the Federal Reserve Bank of San Francisco or the Federal Reserve System.

DOI 10.1057/s11369-017-0042-4

References

Adler, Gustavo, Romain A. Duval, Davide Furceri, Sinem Kilig Qelik, Ksenia Koloskova, and Marcos Poplawski-Ribeiro, 2017. Gone with the Headwinds: Global Productivity. IMF Staff Discussion Note, SDN/17/04.

Barro, Robert J. 2016. The Reasons behind the Obama Non-Recovery. Wall Street Journal, http://scholar.harvard.edu/ files/barro/files/wsj_published_version_092116.pdf. Accessed 20 Sep 2016.

Bosler, Canyon, Mary C. Daly, John G. Fernald, Bart Hobijn. 2016. The Outlook for U.S. Labor-Quality Growth. Federal Reserve Bank of San Francisco Working Paper 2016-14. http://www.frbsf.org/economic-research/publications/working-papers/wp2016-14.pdf.

Byrne, David M., John G. Fernald, and Marshall B. Reinsdorft. 2016. Does the United States have a Productivity Slowdown or a Measurement Problem? Brookings Papers on Economic Activity 2016: 109-157.

Cette, Gilbert, John G. Fernald, and Benoit Mojon. 2016. The pre-Great Recession slowdown in productivity. European Economic Review 88: 3-20

Decker, Ryan A., John Haltiwanger, Ron S. Jarmin, and Javier Miranda. 2016. Declining Business Dynamism: What We Know and the Way Forward. American Economic Review 106 (5): 203-207.

Fernald, John G. 2014. A Quarterly, Utilization-Adjusted Series on Total Factor Productivity. Federal Reserve Bank of San Francisco Working Paper 2012-19 (updated March 2014).

Fernald, John G. 2015. Productivity and Potential Output Before, During, and After the Great Recession. NBER Macroeconomics Annual 29: 1-51.

Fernald, John G. 2016. Reassessing Longer-Run U.S. Growth: How Low? Federal Reserve Bank of San Francisco Working Paper 2016-18. http://www.frbsf.org/economic-research/publications/working-papers/wp2016-18.pdf.

Fernald, John G., Robert Hall, James Stock, and Mark W. Watson. 2017. The Disappointing Recovery of Output After 2009. Brookings Papers on Economic Activity, forthcoming.

Fernald, John G., and Charles I. Jones. 2014. The Future of U.S. Economic Growth. American Economic Review 104 (5): 44-19.

Goldin, Claudia, and Lawrence F. Katz. 2008. The Race between Education and Technology. Cambridge: Belknap Press.

Gordon, Robert J. 2016. The Rise and Fall of American Growth: The U.S. Standard of Living Since the Civil War. Princeton: Princeton University Press.

Muller, Ulrich K, and Mark W. Watson. 2016. Measuring Uncertainty about Long-Run Predictions. The Review of Economic Studies 83 (4): 1711-1740.

John Fernald is a senior research adviser at the Federal Reserve Bank of San Francisco and professor of economics at INSEAD Business School. He has a Ph.D. in Economics from Harvard University. His current research interests include understanding productivity growth as well as economic developments in China.

[1] Fernald (2016) provides further details on this projection.

Based on a presentation delivered at the session "Policies to Increase Long-Term Productivity Growth" at the NABE Economic Policy Conference, March 6, 2017.

[mail] John Fernald

john.femald@insead.edu

John Fernald [1](iD)

[1] Federal Reserve Bank of San Francisco, San Francisco, USA

[2] INSEAD Business School, Fontainebleau, France

Caption: Fig. 1 Slow future labor-force growth. Source Census, Bureau of Labor Statistics, and Congressional Budget Office (CBO). Labor-force projections are from CBO

Caption: Fig. 2 Educational attainment plateauing. Source Goldin and Katz (2008)

Caption: Fig. 3 Historically, labor quality "explains" a large growth. Source Bureau of Economic Analysis, Bureau of Labor Statistics, Fernald (2014)

Caption: Fig. 4 Productivity slowed before Great Recession. Source Bureau of Labor Statistics

Caption: Fig. 5 China, Korea, India, etc. closer to research frontier. Source OECD. "Europe" is unweighted average of France, Germany, and United Kingdom
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Date:Jul 1, 2017
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