The Wealth of Working Nations
Jesús Fernández-Villaverde, Gustavo Ventura, and Wen Yao∗
August 8, 2024

Abstract
Due to aging populations, the gap between GDP growth per capita and GDP growth per
working-age adult (or per hour worked) has widened in many advanced economies. Countries
like Japan, which have shown lackluster GDP growth per capita, have performed surprisingly
well in terms of GDP growth per working-age adult (or per hour worked). Many advanced
economies are also following similar balanced growth paths per working-age adult despite
significant differences in the levels of GDP per working-age adult. We calibrate a standard
neoclassical growth model to reflect changes in the working-age population for each economy.
This model aligns more closely with the data for all the economies in our sample when wematch GDP growth per working-age adult rather than when we match GDP growth per
capita, the “canonical” calibration target.
Keywords: Demographics, Growth, Developed Economies
JEL codes: E2, J1∗
Fernández-Villaverde, Department of Economics, University of Pennsylvania (email: jesusfv@econ.upenn.edu);
Ventura, Department of Economics, Arizona State University, USA (email: gustavo.ventura@asu.edu); Yao, Schoolof Economics and Management, Tsinghua University (email: yaow@sem.tsinghua.edu.cn). We thank Olivier Blanchard and John Cochrane for detailed suggestions, and many seminar participants for comments. We thank Hector
Cardozo and Jacob Shepard for their research assistance. Wen acknowledges financial support from the National
Natural Science Foundation of China (Grant No. 72073077). The usual disclaimer applies.

11

Introduction

As the populations of advanced economies age, output growth per capita is becoming a misleading
indicator for growth theory. Changing demographics mean that output growth per capita can
obscure important trends in output per working-age adult or output per hour worked, which are
more natural summary statistics for growth theory to focus on.
Take the case of output per working-age adult.1 This measure provides a stark metric of how
much an economy is exploiting its production possibilities, given social norms about work-life
duration. Also, output per working-age adult is straightforward to compute, since data on output
and working-age adults are readily available for many countries over long periods.
Japan exemplifies how useful it is to look at this metric. Between 1991 and 2019, GDP in
Japan grew at an annual rate of 0.83%, much lower than the 2.53% of the U.S. This seemingly
disappointing performance motivated a myriad of books and academic papers analyzing the origins
of Japan’s lackluster growth and presenting a multitude of policy remedies. Just as one example
among many, Pesek (2014), who popularized the term “Japanization,” writes:...few lessons are more timely or critical than those offered by Japan, a once-vibrantmodel for developing economies that joined the world’s richest nations, lost its way,and has been struggling to relocate it ever since.
In this book I explore what the world can learn from a Japanese economic funk thatbegan more than 20 years ago and has never really ended. That means exploring
where Japan went wrong, how it sank under the weight of hubris and political atrophy, and missed opportunity after opportunity to scrap an insular model based on
overinvestment, export-led growth, and excessive debt.
However, the outlook changes significantly when demographics are considered. From 1991 to
2019, Japan’s GDP per working-age adult grew annually by 1.39%. In comparison, the U.S. grewat 1.65% in the same measure, a difference of only 0.26%. Suddenly, there is nothing enigmatic
about Japan’s GDP growth. It is the consequence of an annual decline in working-age adults byabout 0.54%. Remarkably, from 1998 to 2019, Japan grew slightly faster than the U.S. in terms of
1
We follow the OECD’s standard definition of working-age adults as the population between 15 and 64 years
old. See Section 2.4 for a detailed explanation of the definition and possible alternatives. Section 2.5 discusses theuse of output growth per worker and TFP growth.

2GDP per working-age adult, with an accumulated growth of 31.9% compared to the U.S.’s 29.5%.
Even more strikingly, Japan’s growth in terms of GDP per working-age adult outperformed thatof all the other G7 countries plus Spain from 2008 to 2019.
Since output per working-age adult does not fully reflect variations in the length of work lives
or hours worked by an average worker, we also consider output per hour worked. Yet, output
per hour worked suffers from two serious problems. First, time series for hours worked are notavailable for many countries and periods and are subject to measurement challenges (Eldridge
et al., 2022). Second, the interpretation of output growth per hour worked is equivocal because
hours are an endogenous choice affected by many factors. For example, output per hour worked
in the U.S. (nonfarm business sector) grew 4.1% in 2009 and 5.2% in 2020, the only two years
where real GDP has fallen in the U.S. in the 21st century so far.
Thus, we will simplify the discussion by focusing on GDP growth per working-age adult, which
we find a more informative summary statistic than GDP growth per hour worked. However, since
we will report all data in both metrics, readers who prefer to think in terms of GDP growth
per hour worked can easily interpret our analysis in that context. In fact, our argument does
not depend on using either of the two metrics. Returning to the case of Japan, from 1991 to
2019, Japan’s GDP grew annually by 1.39% per working-age adult and 1.26% per hour worked.
In comparison, the U.S. grew at 1.65% per working-age adult and 1.53% per hour worked. The
difference between the U.S. and Japan is 0.26% when we use growth per working-age adult and
0.27% when we use growth per hour worked.
We start our investigation by presenting data on GDP growth and population for the G-7
countries plus Spain. This provides an overview of how demographic changes influence economic
growth. We find that while the working-age adult population of Canada and the United Statesgrew by about 29-31% between 1991 and 2019, the working-age adult population of Italy andGermany marginally declined by about 2%. Meanwhile, the working-age adult population ofJapan fell by about 14%. These are large differences.
Yet, output growth per working-age adult has behaved quite similarly over time for all economies
except Italy. Between 1991 and 2019, the annual growth rate in output per working-age adultaveraged between 1.33% (France) and 1.65% (U.S.). In comparison, Italy has lagged in terms ofgrowth per working-age adult. Indeed, most of the economies we study appear to be on parallel
trends, resembling the balanced growth paths of textbook growth models. Furthermore, these3trajectories are independent of diverging trajectories of the working-age adult population.
We then develop and calibrate a standard one-sector growth model with exogenous technological growth. The size of the working-age population varies according to the data for each economy.
In this context, we ask the extent to which the observed growth patterns of developed economies
are consistent with the predictions of basic theory.
The logic of the model is straightforward. The economy travels along a growth path determinedby the exogenous growth of technology, the discount factor, and total population growth. However,since the production function depends on labor, not total population, variations in the ratio oflabor over total population shift the slope of the growth path up or down and induce transitional
dynamics. In terms of the Euler equation characterizing the equilibrium behavior of the economy,
having a lower labor/population ratio is equivalent to having a negative technological shock in astandard real business cycle model (and with the same persistent and propagation effects).
This intuition illustrates that our key insight is that aging changes the ratio of working-age
adults over the total population. In the very long run, as the consequences of lower fertility rates
and longer life expectancy are worked out through the population pyramid, we might return to asituation where the labor/population ratio stabilizes. At that moment, output growth per capita
and output per working-age adult will again become roughly the same.
Our model does a very good job of tracking the observed output per working-age adult interms of the mean squared errors (MSE) between the model and the data. More importantly, ourmodel calibrated to match output growth per working-age adult tracks much better both output
per working-age adult and output per capita than the same model calibrated to match output
growth per capita, the “canonical” practice in the literature as prescribed by Barro and Sala-iMartin (2003) and Cooley and Prescott (1995). Our model reduces the MSE with respect to the“canonical” calibration by significant amounts for all eight countries in our sample, with as muchas 66% for France and 76% for Japan. Our interpretation of this result is not that our model is thebest possible one. Rather, we contend that growth theory must pay sharp attention to the right
output metric to match, even when working with the simplest models and having an interest in
output per capita. We also perform some extensions of the model and discuss their implications.
We finish by considering two large growing economies in the midst of a structural transformation: China and India. In the case of China, the combination of very fast economic growth and amoderate growth rate of population and working-age adults between 1991 and 2019 means that4

looking at total GDP growth, GDP growth per capita, or GDP growth per working-age adult gives
us roughly the same conclusions. Thus, China is a modern incarnation of the type of behavior
common in more advanced economies in the 1960s or 1970s, when modern growth theory wasdeveloped. Nonetheless, as China’s population ages over the next two decades, a gap between
GDP growth per capita and GDP growth per working-age adult will appear. In contrast, Indiais the mirror image of Japan: very fast growth of the working-age adult population means thatthe high rates of total GDP growth look less impressive in terms of working-age adults.
Policy implications. Our investigation underscores that evaluating fiscal and monetary policies (or other economic policies) requires considering them within the context of an economy’s
demographic evolution. For instance, criticizing Japanese monetary policy after 1998 for failing todeliver faster output growth per capita overlooks the fact that monetary policy has little influence
over demographic trends.2 Given that Japan’s output growth per working-age adult surpassed
that of the U.S. after 1998, it is hard to pinpoint what more the Bank of Japan could haveachieved.
Importantly, since Japan’s current demographic situation is a preview of what many otheradvanced and emerging economies will face in the future (Delventhal et al., 2021), economists needto learn to gauge growth experiences using the appropriate metrics. We argue for the importanceof output per working-age adult (perhaps with a redefinition of working age to adapt it to changing
retirement patterns) as an easily computable summary statistic of growth performance.
Nonetheless, we are cautious about how our results should inform economic policy. The firstcaveat is that the working-age adult population of a country is not an exogenous process. It
could be affected by migration and fertility policies (direct, e.g., a child tax credit, and indirect,
e.g., low fertility rates caused by high youth unemployment). In fact, immigration is a firstorder mechanism in the data to account for the relatively fast population growth in countries like
Canada and the U.S. Our analysis is silent about the economic impact of immigration except for
one point. At first sight, there seems to be little correlation between immigration and output
growth per working-age adult, with Japan, a low-immigration country, outperforming Canada, ahigh-immigration country. While this observation does not rule out a positive effect of immigrationon output growth per working-age adult, it shows that such a positive effect can be harder to2

For examples of negative assessments of Japan’s fiscal and monetary policy due to low GDP growth, see Itoand Rose (2007) and Hamada et al. (2010).

5

document than is sometimes hypothesized.
In contrast, we are less concerned with endogenous fertility. Changes in the native-bornworking-age adults were determined by fertility choices in previous decades. For example, Japan’s
fertility rate fell below the replacement rate (2.1 children per woman) in 1974, when the economy
was still booming.
The second caveat is that our argument does not imply that total output growth or output
growth per capita, are not relevant. For example, total output growth matters for public debt
and social security sustainability (Faruqee and Mühleisen, 2003; Kitao, 2015). Broadly, output
growth per capita gives us a sense of how fast the average resources available to each inhabitant ofan economy are changing. Similarly, Klenow et al. (2017) argue for the importance of considering
total population to evaluate social welfare growth. We are deliberately silent about welfare.
Related literature. In terms of the related literature, we highlight that we are not the firstto report data or calibrate models in terms of per working-age adult. While this practice is lesscommon than using per capita terms, many papers have followed it (see Klein and Ventura, 2021,
or several of the chapters in Kehoe and Nicolini, 2022).3
More in general, a very large literature explores the links between economic growth and population aging, going back to the pioneering work of Auerbach and Kotlikoff (1990), Cutler et al.
(1990), and Weil (1997). Instead of reviewing each aspect of this literature in detail, we highlighta few recent contributions that are closely related to us. Kotschy and Bloom (2023) have alreadypointed out the link between population aging and economic growth. The main difference between
that paper and ours is our emphasis on determining the right metric to assess GDP growth fromthe perspective of growth theory and our use of a standard neoclassical growth model to analyze
the data instead of empirical regressions. Jones (2022) has used endogenous growth models inwhich people discover new ideas to study the possible stagnation of living standards as the population shrinks. In comparison, we take technological progress as given.4 Our point is different:
researchers need to be careful about the object of measurement in the context of growth theory
when the number of working-age adults is falling or stagnating, regardless of our views on the
production function for ideas.
3

See, also, in the context of the public debate, https://archive.nytimes.com/krugman.blogs.nytimes.com/
2012/01/09/japan-reconsidered-2/.
4
See also Sasaki and Hoshida (2017). Sasaki (2019) explores ideas similar to those of Sasaki and Hoshida (2017)
and Jones (2022) in the context of a neoclassical growth model with a falling population.

6

Jaimovich and Siu (2009) show that demographic change accounts for approximately onefifth to one-third of the decline in output volatility in the U.S. Ferraro and Fiori (2020) make asimilar point about how the aging of the baby boomers considerably reduces the effects of tax
cuts on aggregate unemployment. Cravino et al. (2022) quantify how population aging increases
the share of services in total consumption. Acemoglu and Restrepo (2021) link automation andpopulation aging. Maestas et al. (2023) present evidence that population aging reduced U.S.
GDP growth per capita by 0.3% per year from 1980 to 2010. Hopenhayn et al. (2022) use
population aging to account for the recent evolution of firm concentration, entrepreneurship, andlabor share. Similarly, Karahan et al. (2019) argue that a slowdown in labor supply growth due todemographics caused two-thirds of the decline in the start-up rate in the U.S. since the late 1970s.
Finally, Aksoy et al. (2019) estimate a panel VAR to understand how demographic structures
affect macroeconomic trends across OECD countries.
The paper is organized as follows. In Section 2, we document the facts of interest. In Section
3, we present the standard neoclassical model we use in our quantitative analysis. In Section 4, we
present the calibration and solution of the model. Our quantitative results are in Section 5, while
Section 6 discusses them and extends the analysis to China and India. We conclude in Section 7.

2

Data and Facts

This section summarizes the data and facts used in our study. Our main data source is the WorldBank’s World Development Indicators (WDI) database, which compiles comparable statistics fora large number of countries and territories. Real GDP is the GDP in national constant prices.
Working-age adults are the population between 15 and 64 years old. Since the WDI does nothave hours data, we use the hours measure from the Conference Board reported by PWT 10.0 –“average annual hours worked by persons engaged.” For our set of mature economies, we consider
the G7 countries (United States, Canada, United Kingdom, Germany, France, Italy and Japan).
We also include Spain, the largest Western European economy that is not a member of the G7
and one that has undergone a swift demographic transformation.
We compute basic growth facts for the countries in our sample from 1991 to 2019. First, all
the relevant data before 1991 are not available in comparable form for all countries. Second, mostimportantly, the main point of the paper – relating population aging and GDP growth – only

7

became relevant in the 1990s.
Table 1: G7 plus Spain: Output and Population Growth Rates
1991-2019

CanadaFranceGermany

Italy

Japan

Spain

UK

U.S.

GDP
GDP per capita
GDP per working-age adultGDP per hour worked

2.47
1.40
1.48
1.23

1.61
1.10
1.33
1.28

1.38
1.25
1.47
1.31

0.70
0.52
0.79
0.71

0.83
0.76
1.39
1.26

2.05
1.35
1.41
0.67

2.08 2.58
1.53 1.63
1.62 1.65
1.37 1.53

Population
Working-age adults
Total hours worked

1.05
0.98
1.23

0.50
0.27
0.33

0.14
-0.09
0.08

0.18
-0.08
0.00

0.08
-0.54
-0.43

0.68
0.63
1.40

0.54
0.46
0.71

0.94
0.91
1.04

Table 1 reports output and population growth rates (Table 2 also reports GDP growth per
worker). All the variables are annual growth rates and are expressed in percentage points. As
in all the tables in the paper, we highlight a few numbers in red because they are salient to ourpresentation.

2.1

GDP Growth per Working-Age Adult

In the first row of Table 1, we see large differences in yearly GDP growth. While Italy (the worst
performer in terms of GDP growth) has only grown 0.70% a year for four decades, the U.S. (thebest performer) has grown 2.58%. This is a huge difference. In accumulated terms, the Italianeconomy has grown 121% since 1991, while the U.S. economy has grown 203%.
The second row of Table 1 shows that the differences in GDP growth become much smaller
in per capita terms. Italy, still the worst performer, has grown at a rate of 0.52%, while the U.S.
has grown at a rate of 1.63%. The fifth row, population growth, explains these differences: while
Italy’s population has grown at 0.18% a year, the U.S. population has been growing at 0.94%.
The third row of Table 1, where we report GDP growth per working-age adult, documents our
main argument. The relative performance of Italy vs. the U.S. does not change much, but thisis not the case for other countries. We compare first Japan and the U.S. (our numbers in red).
Japan’s GDP growth is much lower than the U.S., 0.83% vs. 2.58%, a difference of about 1.75
percentage points. However, in terms of per working-age adult, Japan becomes much closer tothe U.S. (1.39% vs 1.65%). In fact, Japan’s growth is ahead of Canada, France, Germany, andItaly. The reason appears in the sixth row of Table 1: while the population of working-age adults
8has fallen in Japan (-0.54%) due to population aging, it has grown in the U.S. at 0.91%.
If one drops the early 1990s from the sample (the years of the asset price collapse), Japan’s
performance is even better. From 1998 to 2019, Japan has grown slightly faster than the U.S.
in terms of working-age adults: an accumulated 31.9% vs. 29.5%. Suddenly, Japan’s economic
performance is transparent: there are fewer Japanese of working age, and a smaller labor input
leads to lower total GDP growth. Or, to put it differently, for Japan to match the GDP growthof the U.S., its GDP growth per working-age adult would need to have grown at 3.12% a year, an
outstanding feat once Japan had completed its neoclassical growth transition by the late 1980s.
A commonly cited fact about Japan is the high labor force participation rate of older adults.
While this observation holds, the participation rate for those aged 65 and over in Japan has notchanged much from 1991 to 2019. It stood at 25.3% in both 1991 and 2019, hitting a low of 19.8%
in 2006. Comparatively, in the U.S., the participation rate for adults aged 65 and over rose from
11.5% in 1991 to 20.2% in 2019. This suggests that Japan’s relative success cannot be attributed
to an increasing employment rate among its older population. Nevertheless, Japan’s aging has
had a less detrimental impact than it would have in a country with a lower participation rate
among older adults. We will return to this point below when we discuss output growth per hour
worked.
Figure 2.1 plots the underlying time series for all countries. Look at the evolution of the
population between ages 15 and 64 in each of the eight countries (bottom right panel). Compare
the evolution of working-age adults in the U.S. (dashed blue line at the top of the panel) with theevolution in Japan (dashed green line at the bottom).
A central message of the figure is that the wide dispersion in output paths that we observefor GDP per person across countries significantly shrinks. There is one exception to the rule –Italy – which we discuss further below. But excluding Italy, GDP growth per working-age adultranges from 1.33 (France) to 1.65 (U.S.). The low dispersion in growth rates is most interesting
because the level of the growth path of these economies is quite different (the figure normalizes
output per working-age adult to 100 in 1991). For example, Spain’s output per capita in PPP
terms is around one-third lower than that of the U.S., the highest-income country in the sample.
Yet, there is no evidence of convergence toward the U.S. and clear evidence of divergence in thecase of Italy.

9GDP Index (1991=100)

220

200

180

Population 15-64/Total Population

0.7

CanadaFranceGermany
Italy
Japan
Spain
UK
U.S.

0.68

0.66

160
0.64
140
0.62
120
0.6

100

80
1990

1995

140

2005

2010

2015

0.58
1990

2020

GDP/Population Index (1991=100)

160

150

2000

130

125

120

120

115

110

110

100

105

160

150

140

1995

2000

2005

2010

1995

2000

2015

100
1990

2020

GDP /(Population 15-64) Index (1991=100)

130
125
120
115

130

2010

2015

2020

2015

2020

2015

2020

CanadaFranceGermany
Italy
Japan
Spain
UK
U.S.

1995

2000

2005

2010

Population 15-64 Index (1991=100)

135

CanadaFranceGermany
Italy
Japan
Spain
UK
U.S.

2005

Population Index (1991=100)

135

CanadaFranceGermany
Italy
Japan
Spain
UK
U.S.

130

90
1990

CanadaFranceGermany
Italy
Japan
Spain
UK
U.S.

CanadaFranceGermany
Italy
Japan
Spain
UK
U.S.

110
120

105
100

110

95
100
90
90
1990

1995

2000

2005

2010

2015

85
1990

2020

1995

2000

Figure 2.1: G7 and Spain: 1991 - 2019

10

2005

2010

Indeed, the case of Italy is intriguing. Until the mid-2000s, Italy tracks the other countries inthe sample (although closer to the bottom of the pack). However, after the financial crisis, Italy
falls behind and cannot recover the level of output from before the crisis. Interestingly, Italy did
not have a financial collapse (like Ireland), nor was it under a memorandum of understanding
with the European Union (like Spain in July 2012). Italy’s problems seem deeper than digesting
the aftermath of a financial meltdown.
The data suggest that a common model can account for the experience of all these countries,
delivering roughly equivalent growth experiences in terms of per working-age adult but different
output growth path levels. This motivates us to postulate, in the next section, a simple neoclassicalgrowth model with country-specific technological processes. But let us look at hours worked first.

2.2

GDP Growth per Hour Worked

We focus now on the fourth row in Table 1, where we report GDP growth in terms of hours
worked. Comparing it with row three reveals that GDP per hour worked evolved very similarly to
GDP per working-age adult in all the other countries of our sample, with the exception of Spain.
For instance, the difference in GDP growth per hour worked between Japan and the U.S. (1.53%- 1.26% = 0.27%) is nearly the same as the difference in growth rates of GDP per working-age
adult (1.65% - 1.39% = 0.26%).
160

150

140

GDP /(Population 15-64) Index (1991=100)

160

CanadaFranceGermany
Italy
Japan
Spain
UK
U.S.

150

140

GDP per Hour Worked Index (1991=100)
CanadaFranceGermany
Italy
Japan
Spain
UK
U.S.

130
130
120
120
110
110

100

90
1990

1995

2000

2005

2010

2015

2020

100
1990

1995

2000

2005

2010

2015

2020

Figure 2.2: G7 and Spain: GDP per Working-age Adult and GDP per Hour Worked
This is even clearer in Figure 2.2, where we plot GDP per working-age adult in the left panel(this is the same plot as the bottom left panel of Figure 2.1) and GDP per hour worked in the11right panel. The paths of both variables are similar except for Spain. In Spain’s case, output per
working-age adult tightly closely follows that of the majority of countries. In contrast, output
per hour worked stagnated for several years until about 2005, then grew in parallel to othercountries, and subsequently stagnated again at the level of Italy around 2015. A conjecture forthis anomalous behavior relates to the disproportionate movements in immigration flows into
Spain of less skilled workers. Alternatively, measurement error in hours may have played a role.

2.3

A Growth Decomposition

We now provide a more formal decomposition of output changes that we have described above,taking into account all different margins. Define the identity Yt ≡ Nt · lt · et · ht · yt where:
1. Nt is total population at t.
2. lt is working-age adults, Lt , per person Lt /Nt .
3. et is total employment (regardless of the age of the worker), Et , as a fraction of working-age
adults, Et /Lt .
4. ht is total hours worked (regardless of the age of the worker), Ht , divided by total employment Ht /Et .
5. yt is output per hour worked Yt /Ht .
Table 2 reports the results of this decomposition plus the growth of GDP per worker, GDP
per working-age adult, total hours, and working-age adults.
We focus, just for the concision of exposition, on Japan. The key driver in Table 2 is theevolution of total hours worked, which fell 0.43% a year in Japan and increased by 1.04% in the
U.S. The evolution of total hours in Japan, -0.43%, is very close to the evolution of the workingage adults, -0.54%. A similar result holds for the U.S.: a 1.04% growth of hours and a 0.91%
growth of working-age adults.
While the population of working-age adults as a fraction of the total population has slightly
fallen in the U.S. (0.03% per year), it has fallen strongly in Japan (0.62%). Increases in theemployment rate in terms of working-age adults in Japan (0.74%) have been nearly exactly compensated by a fall in hours worked per worker (0.6%). The former increase is due to several
12

Table 2: Output Growth Decomposition
1991-2019

CanadaFranceGermany

Italy

Japan

Spain

UK

U.S.
2.58

GDP

Yt

2.47

1.61

1.38

0.70

0.83

2.05

2.08

Population
Working-age per person
Emp. rate per working-age
Hours worked per worker
GDP per hour worked

Nt
lt
et
ht
yt

1.05
-0.08
0.42
-0.17
1.23

0.50
-0.23
0.35
-0.30
1.28

0.14
-0.22
0.57
-0.40
1.31

0.18
-0.27
0.34
-0.26
0.71

0.08
-0.62
0.74
-0.61
1.26

0.68
-0.05
0.90
-0.14
0.67

0.54 0.94-0.08 -0.03
0.36 0.17-0.11 -0.04
1.37 1.53

GDP per worker
Yt /EtGDP per working-age adult Yt /Lt

1.05
1.48

0.98
1.33

0.90
1.47

0.45
0.79

0.65
1.39

0.53
1.41

1.25
1.62

1.49
1.65

Total hours worked
Working-age adults

1.23
0.98

0.33
0.27

0.08
-0.09

0.00
-0.08

-0.43
-0.54

1.40
0.63

0.71
0.46

1.04
0.91

Ht
Lt

factors, including higher female labor force participation. The latter decrease is mainly driven
by the fact that hours per worker have fallen from an average of around 2,000 hours for full-timeworkers to around 1,800 hours, closer to the standard in the U.S. and other advanced economies.
In the U.S., in comparison, the employment rate per working-age adult and hours worked per
worker have been flat. The main factor behind the differences in the U.S. total GDP growth and
GDP per hour worked is the growth rate of its population, 0.94% per year.

2.4

Redifining Working Age

So far, we have followed the standard definition used by statistical agencies worldwide of workingage adults as the population between 15 and 64 years old. Child labor is minimal in the economies
we consider (the G7 plus Spain). On the other hand, participation rates for adults 65 and older
are low but, in some cases, significant. In 2019 (the last year of our dataset), participation rates
of adults 65 and older ranged from 2.5% in Spain to 25.3% in Japan (OECD, 2024).
However, Oshio et al. (2018) document that many of those over 65 who are working in Japanare between 65 and 69. For example, around 50% of men and around 35% of women between
65 to 69 continue working, with participation rates falling drastically after 69. This observation
motivates a redefinition of working age to include all adults between 15 and 69 years old and arecomputation of our statistics to check for their robustness to the 64-year-old cutoff line.
Table 3 shows the new results in the third row. To ease comparison, we also include GDP
growth per working-age adult 15-64 in the second row (this is the same row as the third row of13

Table 3: GDP Facts: Different Working Age Definition
1991-2019
GDP
GDP per w.-age adult (15-64)
GDP per w.-age adult (15-69)

CanadaFranceGermany

Italy

Japan

Spain

UK

U.S.

2.47
1.48
1.39

1.61
1.33
1.25

1.38
1.47
1.40

0.70
0.79
0.74

0.83
1.39
1.21

2.05
1.41
1.38

2.08 2.58
1.62 1.65
1.60 1.58

Table 1). The main lesson from Table 3 is that our argument holds for alternative definitionsof working-age population. All countries have slightly lower GDP growth per working-age adult(15-69) than per working-age adult (15-64) since the 65-69 segment of the population is growing
in relative size with aging. However, their relative standing is roughly unchanged. For example,
growth falls by 0.07% in the U.S. and 0.18% in Japan, widening the gap between the two to 0.37%,
still a relatively small difference.

2.5

Why Not GDP Growth per Worker or TFP Growth?

There are possible alternative measures to GDP growth per working-age adult and per hour
worked that we do not report in Table 1. One metric is GDP growth per worker. We prefer GDP
per hour worked to GDP growth per worker because the former considers both the intensive andextensive margins of labor supply. Nonetheless, the key finding is very similar to our previous
arguments (line 7 of Table 2). While the level of the growth rates is lower (reflecting fewer hours
worked per worker across our sample), the relative performance of countries is roughly the sameas when we look at output per working-age adult or output per hour worked.
A second alternative metric is TFP growth. However, TFP growth is not the object of interestfor our analysis – getting a good summary statistic to gauge an economy’s performance in termsof economic growth – and we will not deal with it. TFP is informative about how efficiently inputs
are used, not about how many of the inputs are being used. A country can have an abysmal GDP
growth performance because it is using less of its available labor, and yet TFP can be growing at
a healthy rate. The best example is the U.S. from 1929 to 1941, a period of fast TFP growth thatField (2011) has called the “most progressive decade.” During the Great Depression, U.S. TFP
grew at 1.86% per year, better than any time between 1899 and 2007 except 1948 to 1973 (Crafts
and Woltjer, 2015). And yet, real GDP per capita was about 27% below trend in 1939 (Cole and14

Ohanian, 2004).5
From a practical standpoint, calculating TFP involves numerous assumptions, such as functional forms and methods for estimating stocks of physical and human capital. Even minor variations in these assumptions can lead to significant differences in TFP growth values. For instance,in our sample, Italy, Japan, and Spain have significantly increased the percentage of working-age
adults with tertiary education. This is problematic from the standpoint of the literature, which
has shown the challenges associated with converting additional years of higher education into
human capital and its quantitative implications for TFP estimates.6

3

A Standard Neoclassical Growth Model

We formulate a standard neoclassical growth model with exogenous technological change anddemographics. Each country is modeled as a different economy, without any interaction except
a possible common technology trend. This simple model will help us determine how much thegrowth experience of the eight countries in our sample fits standard theory. To ease notation,
we will work directly with the social planner’s problem. Since both welfare theorems hold in ourmodel, the solution to the social planner’s problem is also the market allocation.

3.1

Preferences and Technology

The economy is populated by an infinitely lived representative household of varying size Nt . Later,when we take the model to the data, we will equate Nt with the total population.
The preferences of the representative household over per capita consumption are represented
by:max
Ct /Nt∞Xtβ Nt log

t=0

Ct
Nt


,

where β is the discount factor and Ct is aggregate consumption.
5

In fact, GDP per capita below trend and an acceleration of TFP growth are fully compatible with the neoclassical growth theory we introduce in the next section. An acceleration of TFP growth causes a large positive wealtheffect. However, the economy will be more productive in the future, not today. Thus, a representative householdsmoothes utility by lowering labor supply and investment, both mechanisms that decrease output today. The key
point here is that the response of labor supply and investment to a change in the trend of TFP is the opposite of
the reaction to a temporary shock to TFP (the source of fluctuations in a standard RBC).
6
Cubas et al. (2016) and Schoellman (2011), among others, document significant disparities in education quality
across countries. As a result, comparing the human capital of an adult with 14 years of education in Spain to thatof an adult with the same level of education in Germany becomes challenging.

15

We do not have an endogenous labor choice. We abstract from it because we want to focus on
the growth properties of our economy, not its business cycle features (the frequency at which mostlabor fluctuations occur). The textbook formulation of the neoclassical growth model selects utility
functions that ensure the working-age population’s labor supply is constant along the balanced
growth path. Hence, in the interest of simplicity, we can directly drop the labor choice.7
Output is given by Yt = Ktθ (At Lt )1−θ where Kt is capital, and Lt is the working-age population.
At is the level of labor-augmenting technology, which grows at a constant rate g, At = A0 (1 + g)t .
Thus, in this economy, total factor productivity (TFP) equals At1−θ .
Output is used for consumption or investment It : Ct + It = Yt . Given a depreciation rate δ,
the law of motion for capital is Kt+1 = It + (1 − δ)Kt . Finally, Nt grows at an exogenously givenQtime-varying rate nt , so that Nt = ti=1 (1 + ni ), given N0 = 1.
Given the growth of technology g, we must normalize the variables. We use the country’s technology and population level to make the problem stationary. Specifically, let lt be the exogenously
given working-age population rate Lt /Nt . Then:
Ct
,
At Nt
Kt
kt =
,
At Nt
It
it =
,
At Nt

θ 
1−θ
Kt
At Lt
Yt
=
yt =
= ktθ lt1−θ .
At Nt
At Nt
At Nt
ct =

With these transformations, we can rewrite the social planner’s problem as follows:

max
ct

∞
X

β t Nt log ct

t=0s.t. yt = ktθ lt1−θ ,ct + i t = y t ,it = (1 + g)(1 + nt+1 )kt+1 − (1 − δ)kt .
7

Similarly, we drop the international aspect of the model. In an international model where the different
economies are on their balanced growth paths and have a common β (as we will use in our calibration), thisinternational aspect would not change any conclusion of importance to us.

16A standard Euler equation characterizes the solution to this optimization problem:

−1
θ−1
c−1
(lt+1 )1−θ + 1 − δ .
t (1 + g) = βct+1 θ(kt+1 )This Euler equation looks like the optimality condition of the textbook neoclassical growth modelwith population and trend technological growth except for the presence of a time-varying term
lt+1 .8 Consider the case that lt+1 = b
l is constant, i.e., the working-age population is a constantfraction of the total population. This is equivalent to a constant in front of the (normalized)
production function and, hence, irrelevant to the dynamics of the model.
Conversely, consider the case where, as in our calibration below, lt+1 changes. This is equivalent
to a shift in the level of the (normalized) production function as if we had a technological shock:a rise in lt+1 increases total production and hence investment and output; a drop in lt+1 lowers
total production and thus investment and output. In other words, changes in lt+1 have the same
effect as technological shocks in a real business cycle model without labor choice (and with thesame persistence and propagation).

4

Calibration and Solution

Our model is indexed by the parameters β, θ, and δ plus the exogenous values for g and N .
The parameter A0 is a scaling parameter that we will pick to match the initial value of GDP per
working-age adult in 1991. Furthermore, we assume that each economy is at its balanced growth
path in terms of per working-age adult at the start of the simulation.
We calibrate β, θ, and δ on an annual basis to match the data for 1991-2019, following
commonly used targets. See Table 4 for a summary of the calibration.
We select a discount factor β of 0.944 to replicate a 7.7% annual rate of return to capital
reported by the PWT 10.0 for the U.S. between 1991 and 2019 (given our model, we want tomatch the return on all capital goods, not on bonds or other financial assets). We pick thecapital share θ = 0.39 to match the average shares between 1991 and 2019 from PWT 10.0. The
depreciation rate is the average depreciation rate from PWT 10.0: δ = 0.04 for the U.S. These
values imply a capital/output ratio of about 3.34.
8

As such, shocks to Nt and At have the usual effects on output and investment.

17

Table 4: Calibration
Parameter

Value

Discount factor
Capital share
Depreciation rate
Labor augmenting technology growth rate, Canada
Labor augmenting technology growth rate, France
Labor augmenting technology growth rate, Germany
Labor augmenting technology growth rate, Italy
Labor augmenting technology growth rate, Japan
Labor augmenting technology growth rate, Spain
Labor augmenting technology growth rate, UK
Labor augmenting technology growth rate, U.S.

β
θ
δ
g
g
g
g
g
g
g
g

0.944
0.39
0.04
0.01480.01330.01470.00790.01390.01410.01620.0165

These three parameter values are common for all countries, even if our target values comefrom the U.S. Our presumption is that preferences, capital shares, and depreciation are roughly
the same across all G7 countries plus Spain. More importantly, by constraining our degrees of
freedom, imposing common parameter values limits the model’s flexibility to match each country’s
observations by varying the parameter values. Nonetheless, in extensive sensitivity analyses, wechecked that the model is robust when we allow for country-specific parameter values.
We show next how to calibrate the model for the U.S. case. Analogous steps are used forall other countries, and we skip their explanation in the interest of space. First, we select A0 to
match the level of U.S. GDP per capita in 1991. This is just a normalization. The population
growth rates match the observed data year by year in the U.S. Finally, we calibrate g = 0.0165
to match GDP growth per working-age population from 1991 to 2019 in the U.S.
To find the planner’s solution, we take the initial steady state in 1991 and its final steadystate in 2019 and compute the transition path between the two using the Euler equation and theinvestment and resource constraint equations using a nonlinear equation solver. More concretely,
k0 , the U.S. capital in 1991, satisfies the steady-state Euler equation:
1 + g = β θ (k0 )θ−1 (l0 )1−θ + 1 − δ ,where l0 is the U.S. working-age population ratio in 1991. To compute i0 = (1 + g)(1 + n0 )k0 −(1 − δ)k0 , we take population growth, n0 , to be the population growth observed in 1991.

18

The final steady-state capital of the U.S. kT is given by:(1 + g) = β θ (kT )θ−1 (lT )1−θ + 1 − δ ,where lT is the U.S. working-age population ratio in 2019. To compute investment at T , nT , weuse the population growth observed in 2019.

5

Quantitative Results

5.1

Benchmark Results

Figure 5.1 plots the evolution of GDP per worker (i.e., the model equivalent of a working-age
adult in the data) in each of the eight countries in the sample 1991-2019. In each panel, thedashed blue line represents the model, and the solid red line represents the log level data for one
country (normalized to be 1 in each case in 1991). While the dashed blue line might appear to bea straight line at first sight, it presents, in fact, small fluctuations due to varying growth rates of
the working-age adult population. However, since those variations within one country from year
to year are relatively small, they do not change the slope of the dashed blue line much.
The left panel in the top row is the U.S. The model captures well the main evolution of the
U.S. economy during the sample. The MSE between the model and the data in terms of output
per working-age adult is just 30 basis points. The most salient divergence is the drop in income
per worker after the financial crisis of 2007-2009, with a permanent change in the trend level ofaround 0.75 log points.
A similar picture of a permanent drop in the output level of around one log point holds for
Spain (the left panel in the bottom row) and the U.K. (the right panel in the bottom row). The
U.S., Spain, and the U.K. were economies where many researchers identified a large real estateboom in the early 2000s, which has left long-lasting scars. To explore the idea that there has beena trend change in income growth per worker, in Section 6.2, we explore an alternative specificationof the model for the U.S. with two different g’s, one before and one after 2007.
The right panel in the top row is Canada. The model does surprisingly well here, as well as inFrance (the left panel in the second row) and Germany (the right panel in the second row), withremarkably minor deviations between the data and the model.
19

GDP per Working-age Adult: U.S.

1.07

1.07

1.06

1.06

1.05

1.05

1.04
1.03

1.01

1.01
1

Model
Data

2000

2005

2010

2015

1995

GDP per Working-age Adult: France

1.08
1.07

1.07

1.06

1.06

1.05

1.05

1.04
1.03

2005

2010

1.03
1.02

1.01

1.01
1

Model
Data

0.99

2015

1.04

1.02

1

2000

GDP per Working-age Adult: Germany

1.08

Log Level

Log Level

Model
Data

0.99
1995

Model
Data

0.99
1995

2000

2005

2010

2015

1995

GDP per Working-age Adult: Italy

1.08
1.07

1.07

1.06

1.06

1.05

1.05

1.04
1.03

2005

2010

1.03
1.02

1.01

1.01
1

Model
Data

0.99

2015

1.04

1.02

1

2000

GDP per Working-age Adult: Japan

1.08

Log Level

Log Level

1.03
1.02

0.99

Model
Data

0.99
1995

2000

2005

2010

2015

1995

GDP per Working-age Adult: Spain

1.08
1.07

1.07

1.06

1.06

1.05

1.05

1.04
1.03

1.01

1.01

0.99

2010

2015

1.03
1.02

Model
Data

2005

1.04

1.02

1

2000

GDP per Working-age Adult: UK

1.08

Log Level

Log Level

1.04

1.02

1

GDP per Working-age Adult: Canada

1.08

Log Level

Log Level

1.08

1

Model
Data

0.99
1995

2000

2005

2010

2015

1995

2000

2005

Figure 5.1: Transitional Dynamics: 1991-2019
20

2010

2015

The left panel in the third row is Italy. This is the country for which our calibration of themodel misses the dynamics of the data. The Italian economy stopped growing in the early 2000s(see Fernández-Villaverde et al. 2023a, for details on Italy’s abysmal performance). The only way
the model can capture this observation is by being calibrated to a low level of growth over thewhole period 1991-2019, which leads to a large and persistent undershooting of the model from1991 to 2000. As we mentioned above, for the case of the U.S., a simple solution for this problemof the model would be to introduce two different trends in g for Italy. We will evaluate thismodification of the model in Section 6.2.
A similar picture holds for Japan, the right panel in the third row. The model outperforms
Japan’s observed output per working adult during the 1990s, as Japan was in a deep crisis after
the drop in asset prices after 1992. However, Japan recovered during the 2000s and ended thesample where the model predicted. The consequences of our findings for fiscal and monetary policy
are of the first order. Consider monetary policy: How can a researcher judge Japan’s fiscal andmonetary policy as a comparative failure if Japan has grown faster than the other G7 countries
since the early 2000s?
Finally, as in the data, there is no evidence of convergence of the different countries toward
the U.S., the highest-income country, with all countries traveling along their paths (with theirown levels).

5.2

The Importance of Demographics

The lesson we get from Figure 5.1 is not that our neoclassical growth model accounts for all
features of the data (or that the model is superior to other frameworks, such as an overlapping
generations model) but how, once we look at the data in terms of per working-age adult, there ismore agreement between theory and data than when using total or per capita terms.
To show this point, we recalibrate the model to match GDP growth per capita in each country
instead of GDP per working-age adult. Calibrating the model to GDP growth per capita is
the practice recommended, for example, in the textbook expositions of Barro and Sala-i-Martin(2003, p. 58) and Cooley and Prescott (1995, p. 20). Thus, we call this alternative procedurethe “canonical” calibration. Then, we compare the match of our baseline calibration and the“canonical” calibration to the data using the MSE between the data and the path generated by
the model.
21

Table 5: Percentage Reduction in MSE between “Canonical” and Baseline Calibration
1991-2019

CanadaFranceGermany

Italy

Japan

Spain

UK

U.S.

GDP per capita
GDP per working-age adult

27.54
27.61

65.82
66.00

31.52
33.59

50.21
50.43

72.93
76.19

3.96
3.41

19.84
19.86

5.85
5.80

Table 5 reports the results in terms of the reduction of the MSE between the model and thedata when we switch from the “canonical” calibration to our calibration. We do so in terms of
GDP growth per capita (the moment matched by the “canonical” calibration) and GDP growth
per working-age adult (the moment matched by the baseline calibration).
For all countries but the U.S., the MSE reductions are considerable, with around 50% for
Italy, 66% for France, and 73%-76% for Japan. That is, even if one only cares about GDP growth
per capita (the moment matched by the “canonical” calibration), one still wants to calibrate themodel GDP growth per working-age adult.
For the U.S., the improvement is smaller (5.85%-5.80%) because Lt /Nt does not change muchin our sample. Yet, a 5.85%-5.80% difference is significant in the context of the relative empirical
fit of DSGE models: it is about 40% of the improvement in the MSE one gets in a standard DSGE
model by introducing price and wage stickiness.
Recall that our calibration is quite simple. For instance, we assume that g is constant (see,though, next section). However, this is not important for the results in Table 5. If we let g change(e.g., to capture the slowdown of TFP growth observed in many countries), we improve theMSE of both calibrations but leave their relative performance roughly unchanged. In summary,considering demographics is a first-order task, even for the basic neoclassical growth model.

6

Discussion

In this section, we present several extensions of our study. More concretely, we analyze a casewith a common trend growth across countries and a case with a time-varying trend and extend
our sample to include China and India.99Many additional results, including further robustness analysis regarding the calibration and varying the sample,
are available in the working paper version of this paper, Fernández-Villaverde et al. (2023c).

22

6.1

Using a Common g

In the baseline calibration, we calibrate a country-specific g. The motivation was that technological progress in each country might be mediated by local institutions and social norms that
imply that not all scientific and engineering discoveries and business practice developments are
implemented equally across the economies (on the latter point, see Bloom and Van Reenen, 2010).
In our first robustness exercise, we instead impose that each country’s GDP growth rate per
worker is the same as in the U.S., 0.0165. The time-varying population growth rate and workingage population ratio remain country-specific. This exercise assesses what our model predicts foreach country if there are no differences in implementing the new technology.10 In particular,
this exercise controls for the possibility that different aging speeds in each country might lead todifferent g’s (for example, by slower adoption of new technologies by an aged workforce).
Figure 6.1 shows our results. The left panel in the top row, the U.S., is by construction thesame as in Figure 5.1. The model accounts well for the behavior of the UK (right panel in the
fourth row). This is not a surprise, since we know from Table 1 that the UK’s output growth rates
per working-age adult in 1991-2019 were only slightly below that of the U.S. Thus, substituting
their own g with the g of the U.S. makes little difference.
Conversely, we see large gaps appearing for the other countries, in particular, France and Italy,
which is evidence of problems for these countries in keeping up with the world technology frontier.

6.2

Changing Trends

Our findings in Section 5 suggested the importance of considering changes in the growth trend oftechnology. To explore this possibility, we split our sample between the periods 1991-2007 and2008-2019, or before and after the financial crisis.
Table 6 reports growth and population facts from 1991 to 2007. Similarly, Table 7 reports thesame statistics for the sample period 2008-2019. As expected, we see large drops in the rates of
GDP growth per working-age adult. In the U.S., the rate falls from 2.02% to 1.34%. In the caseof Italy, it even goes negative, from 1.55% to -0.11%. Interestingly, the country with the highest
growth rate of output per working-age adult in this later period is Japan, with 1.49%.

10
The U.S. GDP growth rate per working-age adult in 1991-2019 is the highest in our sample. Thus, as afirst-order approximation, we consider the U.S. g as a measure of the growth of the world’s technological frontier.

23

GDP per Working-age Adult: U.S.

1.07

1.07

1.06

1.06

1.05

1.05

1.04
1.03

1.01

1.01
1

Model
Data

2000

2005

2010

2015

1995

GDP per Working-age Adult: France

1.08
1.07

1.07

1.06

1.06

1.05

1.05

1.04
1.03

2005

2010

1.03
1.02

1.01

1.01
1

Model
Data

0.99

2015

1.04

1.02

1

2000

GDP per Working-age Adult: Germany

1.08

Log Level

Log Level

Model
Data

0.99
1995

Model
Data

0.99
1995

2000

2005

2010

2015

1995

GDP per Working-age Adult: Italy

1.08
1.07

1.07

1.06

1.06

1.05

1.05

1.04
1.03

2005

2010

1.03
1.02

1.01

1.01
1

Model
Data

0.99

2015

1.04

1.02

1

2000

GDP per Working-age Adult: Japan

1.08

Log Level

Log Level

1.03
1.02

0.99

Model
Data

0.99
1995

2000

2005

2010

2015

1995

GDP per Working-age Adult: Spain

1.08
1.07

1.07

1.06

1.06

1.05

1.05

1.04
1.03

1.01

1.01

0.99

2010

2015

1.03
1.02

Model
Data

2005

1.04

1.02

1

2000

GDP per Working-age Adult: UK

1.08

Log Level

Log Level

1.04

1.02

1

GDP per Working-age Adult: Canada

1.08

Log Level

Log Level

1.08

1

Model
Data

0.99
1995

2000

2005

2010

2015

1995

2000

2005

2010

Figure 6.1: Transitional Dynamics: 1991-2019, common g
24

2015

Table 6: G7 plus Spain: Basic Growth and Population Facts, 1991-2007
1991-2007

CanadaFranceGermany

Italy

Japan

Spain

UK

U.S.

GDP
GDP per capita
GDP per working-age adult

3.03
2.01
1.86

2.09
1.52
1.58

1.49
1.31
1.56

1.45
1.27
1.55

1.14
0.94
1.43

3.10
2.15
1.96

2.67
2.25
2.13

3.26
2.14
2.02

Population
Working-age adults

1.00
1.15

0.56
0.50

0.17
-0.07

0.18
-0.10

0.20
-0.28

0.94
1.12

0.41
0.53

1.10
1.21

U.S.

Table 7: G7 plus Spain: Basic Growth and Population Facts, 2008-2019
2008-2019

CanadaFranceGermany

Italy

Japan

Spain

UK

GDP
GDP per capita
GDP per working-age adult

1.79
0.65
1.07

1.03
0.61
1.11

1.27
1.16
1.35

-0.23
-0.36
-0.11

0.58
0.68
1.49

0.61
0.38
0.78

1.43 1.81
0.71 1.11
1.10 1.34

Population
Working-age adults

1.13
0.71

0.42
-0.07

0.11
-0.08

0.14
-0.12

-0.10
-0.90

0.23
-0.16

0.71
0.33

GDP per Working-age Adult: U.S.

1.07

1.07

1.06

1.06

1.05

1.05

1.04
1.03

1.04
1.03

1.02

1.02

1.01

1.01

1

GDP per Working-age Adult: Italy

1.08

Log Level

Log Level

1.08

1

Model
Data

0.99

0.70
0.46

Model
Data

0.99
1995

2000

2005

2010

2015

1995

2000

2005

2010

2015

Figure 6.2: U.S. and Italy: 1991 - 2019, change in trend
We illustrate the effects of a time-varying trend in our neoclassical growth model in Figure
6.2. In the interest of space, we only report the case of the U.S. and Italy (the results for theother countries are roughly similar). The growth of g from 1991 to 2007 is given by its value inTable 6, and the economy is transitioning along its balanced growth path. Then, in 2008 andin an unanticipated way, the growth of g drops to its value in Table 7 from that moment on,
and we compute the transition to a new balanced growth path in 2100 (sufficiently far in the25

future to ensure we have a complete view of the transition). The model now matches better theobservations for the U.S. and Italy, including the latter’s stagnation. However, our model is silent
about the sources of the change in g.
GDP per Working-age Adult: U.S.

1.07

1.07

1.06

1.06

1.05

1.05

1.04
1.03

1.04
1.03

1.02

1.02

1.01

1.01

1

GDP per Working-age Adult: Italy

1.08

Log Level

Log Level

1.08

1

Model
Data

0.99

Model
Data

0.99
1995

2000

2005

2010

2015

1995

2000

2005

2010

2015

Figure 6.3: U.S. and Italy: 1991 - 2019, drop in trend
A possible alternative to having two trends would be to have a shock to the level of the growthpath. In terms of our model, this would correspond to a sudden drop in A. As shown in Figure 6.3,
with this change in trends, the model fits the data even better. The improvement is particularly
salient when we let A fall in 2008 and 2009 and continue at its old growth rate. This suggeststhat the financial crisis permanently reduced the level of technology in our economies.

6.3

China and India

We now extend our analysis to China and India, the two most populated economies in the world.
This exercise illustrates when it is relevant to distinguish among different output growth rates in
economies with behavior different from that of the G7 and Spain.
Table 8: China and India: Basic Growth and Population Facts 1991-2019
1991-2019

China

India

GDP
GDP per capita
GDP per working-age adult

9.54
8.75
8.52

6.44
4.77
4.25

Population
Working-age adults

0.72
0.93

1.59
2.10

26

Table 8 reports growth and population facts for China and India. Both countries have experienced very fast growth since 1991: 9.54% and 6.44%, respectively. The different growth rates
for China do not change much whether we look at them in total, per capita, or working-age adultterms: 9.54%, 8.75%, and 8.52%. This similarity reflects the underlying strong growth of theeconomy after the start of the economic reforms in 1979 and the relatively moderate growth ofthe population and working-age adults.
GDP Index (1991=100)

1400

Population 15-64/Total Population

0.74

China
India

0.72

1200

China
India

0.7

1000

0.68
800
0.66
600
0.64
400

0.62

200

0
1990

0.6

1995

2005

2010

2015

0.58
1990

2020

GDP/Population Index (1991=100)

1100
1000

2000

1995

160

China
India

2000

2005

2010

2015

2020

2015

2020

2015

2020

Population Index (1991=100)
China
India

150

900
800

140

700
600

130

500
120

400
300

110

200
100
1990
1000
900

1995

2000

2005

2010

2015

100
1990

2020

GDP /(Population 15-64) Index (1991=100)

1995

180

China
India

170

800

2000

2005

2010

Population 15-64 Index (1991=100)
China
India

160

700
150
600
140
500
130
400
120

300

110

200
100
1990

1995

2000

2005

2010

2015

100
1990

2020

1995

2000

2005

Figure 6.4: China and India: 1991-2019

27

2010

In the case of India, however, we have a mirror image of Japan: the GDP growth rate per
working-age adult of 4.25% looks much less impressive than the high rate of total GDP growth,
6.44%, due to the fast growth rate of working-age adults (2.10%). For completeness, Figure 6.4
plots the time series for these two Asian countries. No matter in which terms we compute output
growth rates, the difference between China and India is staggering.
We do not include a simulation of our neoclassical growth model for these two countries andcompare it to the data. To understand China’s and India’s growth paths through the lenses of
the neoclassical growth model, we need to consider the process of convergence to the advanced
economies. This exercise would require the introduction of additional elements in the model. See,for example, Fernández-Villaverde et al. (2023b), where a neoclassical model similar to the one inthis paper is calibrated to capture China’s technological catch-up with the U.S.

7

Concluding Remarks

As Lucas (1988) famously put it, “Once one starts to think about them [the questions involving
economic growth], it is hard to think about anything else.” But to do so, we need the rightmeasurements. Historically, economists have looked at total and per capita output growth rates
to evaluate an economy’s performance and test their theories of growth (and the business cycle).
In this paper, we have argued that, as the population ages, total and per capita output growth
rates have become increasingly misleading since the early 1990s. The sharpest example is Japan:
once we correct for population aging by focusing on output growth rates per working-age adult(or per hour worked), Japan appears as a surprisingly robust economy since 1998, closely tracking
the output trajectories of other G7 countries.
Admittedly, looking at output growth rates per working-age adult is not without problems.
For example, more older individuals are remaining in the labor force, and the trend is likely tocontinue over the next several decades. However, for this paper, this trend is not too important.
As we have shown, any suitable redefinition of working age gives us roughly the same result. Whilestill a good measure of individual welfare, output growth per capita is fast becoming a source ofconfusion more than a help. Let us look for something better.

28

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