The Neoclassical Growth of China
Jesús Fernández-Villaverde
University of Pennsylvania, NBER, and CEPRLee Ohanian

Wen Yao

University of California, Los Angeles

Tsinghua University∗

September 9, 2023
Abstract
This paper studies China’s four-fold increase in per capita GDP relative to the U.S.
between 1995 and 2019. We first show that China’s growth pattern is very similar tothat of several other East Asia economies that initially grew very quickly. We find thata minimalist Ramsey-Cass-Koopmans model, endowed with a parsimonious TFP catch-up
process, captures the TFP paths of all these economies very closely and accounts for China’s
real GDP growth and the growth paths of the other East Asia economies. We use the modelto address China’s future growth prospects and find that China’s growth will likely slow
substantially in the future, to the point where the U.S. growth rate will likely exceed China’s
in 20 years. We also find that China’s income per capita will converge to roughly 44% of
the U.S. level around 2100 and that the size of the overall U.S. economy will ultimately
overtake China’s economy, reflecting faster projected U.S. population growth and muchhigher projected U.S. productivity.
Keywords: China, East Asia, economic growth, Ramsey-Cass-Koopmans model, TFP
catch-up.
JEL codes: E10, E20, O4∗
jesusfv@upenn.edu, ohanian@econ.ucla.edu, yaow@sem.tsinghua.edu.cn. We thank Kaiji Chen, Kjetil
Storesletten, Zheng Song, Fabrizio Zilibotti, Tao Zha, and participants at many seminars for useful comments.

1

1

Introduction

China’s real per capita GDP surged from 6.6% of the U.S. per capita GDP level in 1995 (whenChina became a middle-income country) to 25% in 2019 (the last year of data in Penn World
Tables 10.0, PWT 10.0), representing a nearly four-fold catch-up to the U.S. in just 25 years.1
China’s economic growth has been one of the most transformative events in economic history, as
China’s economy is now the largest in the world in PPP terms.
Understanding China’s growth is an active research topic, and the future growth of China is
an important open question. This paper conducts a neoclassical analysis of China’s economic
growth between 1995-2019 and applies it to evaluate China’s future growth prospects. Our firstmain finding is that China’s real GDP catch-up is remarkably similar to those of several other
East Asia countries that grew rapidly. Our second main finding is that a minimalist RamseyCass-Koopmans model, endowed with a parsimonious TFP catch-up process, very accurately
accounts for China’s real GDP growth and the growth paths of the other East Asia economies.
Our third main finding is that projecting the TFP catch-up process into the future forecasts that
China’s growth will slow substantially, with the U.S. growth rate overtaking China’s in 20 years.
We begin by comparing China’s growth experience to the experiences of other East Asian
growth miracles (Korea, Taiwan, Japan, Hong Kong, and Singapore).2 We document that China’s
catch-up growth pattern is remarkably similar to that of the other growth miracles at the samestage of development. All had rapid initial growth, followed by a significant decline in their growthrate. This similarity leads us to develop a minimalist Ramsey-Cass-Koopmans one-sector growthmodel augmented with a parsimonious TFP catch-up process governed by three parametersthat specify: (i) the initial TFP level of an economy, (ii) the asymptotic catch-up TFP boundrelative to the U.S., and (iii) the speed of catch-up. We calibrate standard model parameters toconventional aggregate targets and estimate the parameters of the TFP catch-up process to fit
the observed patterns of TFP growth. While we are agnostic about the specific mechanisms that
may be driving TFP catch-up in China or the other growth miracles, our approach provides asimple and transparent evaluation of the growth process in these economies.
We find that the model matches China’s income per capita growth from 1995 to 2019 surprisingly well. We emphasize surprisingly because the model is so simple; it does not include
factors studied within the literature, including sectoral reallocation, financial market developments, industrial policies, foreign direct investment, or trade policies. We do not dismiss theimportance of these or other factors. Rather, this analysis suggests that many of the important
effects of these and other factors influencing China’s economic growth are manifested within a
1Unless otherwise noted, all per capita measures are from the PWT 10.0 and adjusted for purchasing power
parity (PPP). We set the threshold for classifying a country as a middle-income nation at $3,000 in 2017 PPPadjusted dollars, a commonly used benchmark.
2
To conserve space, we refer to the Republic of Korea; China, Taiwan Region; and China, Hong Kong SAR,
respectively, as Korea, China, Taiwan, and Hong Kong.

2

simple productivity catch-up process operating within a standard one-sector framework.
Our model has more difficulty matching China’s measured investment rates, but we will
make two arguments to reconcile the data with the model. First, many indications suggest that
China’s official investment rates are mismeasured. When we use the corrected investment series
from Chen et al. (2019), our model matches investment rates extremely well. Second, we will
present evidence that investment efficiency in China is low, creating a wedge between investment
and output growth.
We use the same minimalist model (with some country-specific recalibration) to analyze
Japan, Korea, Taiwan, Hong Kong, and Singapore and find that the model also accounts forthese growth episodes. This is a key validation for the model, particularly since the analysis
for these other growth miracles covers different institutional and policy environments and many
more years than the analysis of China. The much longer period of study for these other four
growth miracles has important implications for China’s future, as TFP growth has continued to
decline in all four economies over time after their growth miracle stage.
The common growth experiences of China and the other growth miracles, and the quantitativemodel’s success, lead us to assess China’s future growth prospects. We use the model and thefitted TFP catch-up process to predict that China’s relative per capita GDP level will asymptote
to about 41% of the U.S. level around 2050, reflecting a substantial slowdown in China’s observed
TFP catch-up in recent years. Moreover, given China’s forecasted demographics, which we takefrom the UN, the model predicts that U.S. output will grow faster (2.29%) than China’s (2.27%)
by 2043 and that, by 2088, the U.S. economy will be larger than China’s in PPP terms.3 Slowing
TFP growth and a declining population suggest a far-from-booming Chinese economic future.
This analysis builds on much previous research. Most importantly, given our neoclassicalapproach, Barro (2016) has argued that, from the perspective of conditional convergence, China’s
GDP growth rate since 1990 has been surprisingly high and that, consequently, China’s growthis bound to fall back toward historical averages. We show that Barro’s findings can be reconciledwith neoclassical growth by introducing a process of TFP catch-up. Furthermore, our projectionof China’s future growth supports Barro’s view that China’s growth will decline and provides aquantification of China’s economic future compared to that of the U.S.
Pritchett and Summers (2013) elaborate on the regression to the mean in terms of growthacross many economies. Also, they argue that rapid growth episodes are frequently followedby discontinuous drop-offs in growth. The authors conjecture that the salient characteristicsof China (high levels of state control, corruption, and authoritarian rule) make a discontinuous
decline in growth relatively likely. We reach a similar conclusion, reflecting a rapid decline in
TFP catch-up, which in turn may indeed be related to the institutional aspects emphasized by3
For our statement regarding 2043, there is very little demographic uncertainty left. Given current birth anddeath rates in China, around 85% of people who will be alive in China were already born by the end of 2022.

3

Pritchett and Summers (2013).
Song et al. (2011) construct a growth model consistent with China’s economic transition.
The key behind their dynamics is that firms that use more productive technologies must financeinvestments through internal savings due to financial market imperfections. The most intriguing aspect of their model is that the downsizing of financially integrated firms forces domestic
savings to be invested abroad, generating a foreign surplus. Since we do not deal with an open
economy, we do not explore this aspect of China’s growth experience. Thus, we see our paper ascomplementing Song et al. (2011). Chen and Zha (2023) highlight the importance of the gradualist approach to China’s reforms and compare the path under gradualism with a laissez-fairecounterfactual. The authors focus much attention on the trade-offs between active government
interventions and long-term growth.
Our emphasis on the low investment efficiency of China is shared by Bai et al. (2020), who
focus on the distortions created by loans given by local authorities to unskilled labor-intensive
firms after 2008. Similarly, Jiang et al. (2022) analyze the role of local governments using landsale revenue to fuel infrastructure investment and how such a policy leads to a declining capital
return. Xiong (2019) studies the agency problem between the central and local governments,
which might lead to over-investment and unreliable economic statistics.
Our paper is also related to studies on China’s business cycle fluctuations, such as Changet al. (2019) and Yao and Zhu (2021). In particular, Chang et al. (2016) explore the policy
implications of subsidized investments in capital-intensive industries facing a collateral constraint
within a distorted banking system and find that these subsidies increased China’s growth.
Finally, our paper is linked with the literature on the growth experience of Japan, the first
East Asian growth miracle. Christiano (1989) argues that the neoclassical growth model cannot
account for Japan’s postwar saving experience. Interestingly, he gets much better results when heslows down convergence to the model’s balanced growth path (BGP) by introducing a minimum
consumption level. Our assumption of TFP catch-up with respect to the U.S. yields the exact
mechanism (slower convergence to the BGP, as there is a strong incentive to accumulate further
capital only once TFP has caught up). This idea is corroborated by Chen et al. (2006), whoshow how using actual Japanese TFP growth rates in a standard growth model generates saving
rates close to those in the Japanese data between 1956 and 2000. Parente and Prescott (1994)
explore how barriers to technological adoption slow down convergence to a BGP and account forlarge differences in income per capita.
The rest of the paper is organized as follows. Section 2 describes the data. Section 3 compares
China’s growth experience with that of other Asian growth miracles. Section 4 presents the modeland Section 5 calibrates it. Section 6 reports our quantitative results for China. Section 7 extends
our model to the other East Asian growth miracles. Section 8 concludes. An Appendix offers
additional details about the data and more quantitative exercises, including growth accounting.
4

2

Data

This section summarizes the data used in our study. Data for China, Taiwan, Hong Kong, Japan,
Korea, and Singapore are from the Penn World Tables (PWT) 10.0 (see Feenstra et al., 2015, fordetails). The Appendix presents a full description of the construction of these data.
Real GDP is denoted as GDP, and we focus on the production definition of real GDP, whichis RGDP O in PWT 10.0. PWT provides two different GDP measures for this variable for China.
One is based on GDP growth constructed by Wu (2014), who has produced a measure of Chinese
real GDP growth by modifying the official Chinese GDP data. Wu’s measure of real GDP grows
more slowly than the official real GDP from China’s national accounts, the other measure of
production-side GDP in the PWT. We focus on the official data because they conform wellto other measures of China’s economic growth, including energy consumption and construction
activity, and because many other researchers agree with this assessment and use the official
series, including Brandt and Zhu (2010), Zhu (2012), Song et al. (2011), Fernald et al. (2013),
and Chang et al. (2016). Moreover, our main findings regarding China’s growth performance
would be strengthened if we used Wu’s measure, as described below.
The variable population corresponds to pop in PWT 10.0. The population data from PWT
10.0 are slightly lower than those from the China Statistical Yearbook 2021, published by China’s
National Bureau of Statistics (NBS).4 However, this difference is not quantitatively important.
The variable investment rate corresponds to the variable cshi (or cshi + cshx + cshm ) in
PWT 10.0. The investment rate is nominal investment divided by nominal output. We follow
Whelan (2002) in using the nominal-to-nominal ratio, as he notes that this measure avoids someof the measurement difficulties involved in using ratios of chained (real) data and thus may
be considerably more accurate. The nominal investment rate from China’s National Bureauof Statistics is higher than that in PWT 10.0. These differences are plotted in Figure B.2 inAppendix B. We discuss the implications of this difference in the analysis below.
The variable labor share corresponds to the variable labsh. Finally, our variable TFP isconstructed using variables RT F P N A and CT F P in PWT 10.0. Due to data limitations, andalthough we use RGDP O as our GDP measure, the TFP data come from using RGDP N A .5
However, given the dynamics of the growth model that we will present later on, the quantitative
differences for the arguments we want to make about the main patterns of the Chinese growthexperience are likely to be small. We present a comparison of different measures of output in
Figure B.1 in Appendix B.
4

http://www.stats.gov.cn/sj/ndsj/2021/indexeh.htm
PWT 10.0 constructs TFP only using RGDP N A , the real GDP at constant national prices. The real capitalstock reported by PWT 10.0 is also at constant national prices, which prevents us from estimating TFP directly
using RGDP O .
5

5

3

GDP Growth and the Investment Rate

This section compares China’s GDP per capita growth and investment rate to those of other
East Asian economies when they were at similar stages of development to provide a benchmark
for interpreting China’s growth performance. The data will show that China’s per capita growthand investment rate have been similar to those of the other economies, suggesting a commonality
among the factors driving growth in East Asia.

3.1

China’s GDP Growth, 1995-2019

We analyze China’s growth performance for the 25 years from 1995 to 2019. We begin in 1995
because China’s per capita GDP of $2,825 (2017 PPP-adjusted dollars) at that time places itat the threshold of a middle-income country ($3,000). This per capita income level is roughly
consistent with the World Bank’s decision to classify China as a middle-income country in 1997.6
Moreover, China’s income per capita in 1995 was close to Japan’s income per capita in 1950, thefirst year for which Japan’s data are available in the PWT 10.0.
To compare China’s performance to that of other East Asian economies that grew quickly, we
find the country-specific year in which Japan, Korea, and Taiwan had approximately the samelevel of per capita income ($2,825 in 2017 PPP dollars). We then examine the evolution of GDP
and investment dynamics for each of those economies from that time onward. In Appendix C,
we extend the analysis to include Hong Kong and Singapore, which also had rapid growth. We
do not include them in the main text to avoid cluttering the graphs and because their dynamics
could reflect some factors that may be specific to the fact that they are city-states.

Log (RGDPO per capita)

10.5

10

9.5

9

China
Japan
Korea
China, Taiwan Region

8.5

8
10

20

30

40

50

60

70

Year

Figure 3.1: GDP per capita, normalized at China’s GDP per capita in 1995
We are less interested in the growth pattern of countries below the threshold of $3,000 because growth in lowincome countries tends to be heavily dependent on issues that are specific to the very early stages of development,such as agricultural policies (e.g., collective farms vs. private/privatized farms), and are beyond the scope of ourinvestigation. Nonetheless, the main findings are fairly robust to choosing a lower threshold income level.
66Figure 3.1 shows real GDP for China, Japan, South Korea, and Taiwan, in which each economy
begins at approximately the same per capita income level. The most striking aspect of these datais that the time path of China’s per capita GDP is remarkably similar to those of the three other
East Asian miracle economies. After 25 years of becoming middle-income economies, China,
Japan, and Taiwan achieved nearly the same per capita real GDP level. The one exception is
Korea, which was somewhat ahead of the other three. These data show nothing unusual about
China’s real GDP growth rate relative to the experiences of these other economies.
10.5

Log (RGDPO per capita)

10

9.5

9

8.5
China
Representative Asian Economy

8
5

10

15

20

25

30

35

40

45

Year

Figure 3.2: GDP per capita, normalized at China’s GDP per capita in 1995
Figure 3.2 compares China’s GDP per capita to the average of the other three economies (a“representative Asian economy”). China’s real GDP path is marginally lower than the averageof Japan, Korea, and Taiwan.
10.5

10

Log (RGDPEper capita)

Log (RGDPNAper capita)

10.5

9.5

9

10

9.5

9

8.5
China
Japan
Korea
China, Taiwan Region

8

China
Japan
Korea
China, Taiwan Region

8.5

8
10

20

30

40

50

60

70

10

Year

20

30

40

50

60

70

Year

Figure 3.3: GDP per capita, alternative definitions
Figure 3.3 shows a very similar pattern between China and the other East Asian economies
when per capita real GDP is measured using two alternative measures of GDP: the national
accounts approach GDP (RGDP N A ) and the expenditure approach GDP (RGDP E ).7 To check7

We note that Japan’s first year (1950) RGDP N A per capita is $3237, which was around 15% higher than7

for robustness, Figure 3.4 uses 2000 as the base year for a normalization point in time since thatwas the year when China had GDP (RGDP N A ) per capita ($3350) that was closest to Japan’sin 1950.
These figures suggest that China’s future growth pattern will slow substantially, just as growthslowed substantially in Japan, Korea, and Taiwan after their first 25 years of becoming middleincome economies.

Log (RGDPNAper capita)

10.5

10

9.5

9

China
Japan
Korea
China, Taiwan Region

8.5

10

20

30

40

50

60

70

Year

Figure 3.4: GDP per capita, alternative definitions, alternative base year
A possible shortcoming of the previous figures is that the world technological frontier was at
a different point in time when China had (roughly) the same levels of income as the other East
Asian economies. To control for this mechanism, we examine the evolution of GDP per capita
relative to the U.S. level for each of those economies. This exercise assumes that the U.S. GDP
per capita is a fair proxy for the GDP per capita achievable given the world technological frontier
and prevailing social institutions.
0.7

RGDPO per capita relative to the U.S.

RGDPOper capita relative to the U.S.

0.8
0.7
0.6
0.5
0.4
0.3
China
Japan
Korea
China, Taiwan Region

0.2
0.1
10

20

30

40

50

60

0.6

0.5

0.4

0.3

0.2
China
Representative Asian Economy

0.1
70

5

Year

10

15

20

25

30

35

40

45

Year

Figure 3.5: GDP per capita relative to the U.S., normalized at China’s GDP per capita in 1995
China’s RGDP N A in 1995. Japan started its industrialization in the late 19th century, much ahead of all theother East Asian economies, and the destruction of World War II did not erase this early advantage. See Fukao
and Settsu (2021) for a summary of Japan’s growth experience.

8

The left panel of Figure 3.5 shows how China’s performance is less impressive than the performance of the other economies. This result is not a surprise: Figure 3.1 shows that China’s
growth performance was approximately the same as those of other East Asian economies. However, China’s growth started much later, when U.S. GDP per capita was much higher. Thismeans China benefited from an even higher technological frontier for its catch-up. This notionof distance to the technological frontier will play a key role in our model in Section 4. The rightpanel of Figure 3.5 replicates Figure 3.5 but now compares China’s GDP per capita relative to the
U.S. to the average GDP per capita relative to the U.S. of the “representative Asian economy”
defined above.

3.2

China’s Investment Rate

Figures 3.6 and 3.7 plot the investment rate for China, Japan, Korea, and Taiwan for theirrespective 25-year periods after becoming middle-income economies. The left panel presents
gross capital formation (GCF). In contrast, the right panel of the figure presents an alternative
concept of investment, which is the sum of gross capital formation and net exports. Variables
are all in nominal terms in the figures. These data show that the nominal investment rate of
China is qualitatively very similar to the investment rate in the other Asian miracle economies.
Quantitatively, China’s investment rate is somewhat higher.
China
Japan
Korea
China, Taiwan Region

0.45

0.45
0.4

(GCF+NX)/CGDPO

GCF/CGDPO

0.4

0.35

0.3

0.35
0.3
0.25

0.25
0.2

China
Japan
Korea
China, Taiwan Region

0.2
0.15
0.15
10

20

30

40

50

60

70

10

Year

20

30

40

50

60

70

Year

Figure 3.6: Investment rates
Figure 3.8 plots the return to capital across economies. Bai et al. (2006) construct the returnto capital in China from 1978 to 2005, and Chen et al. (2019) extend their results to 2016.
According to Chen et al. (2019), China’s return to capital fell sharply after 2008 and became
substantially lower than that of the other economies, suggesting low investment efficiency.8 Thisobservation will play an important role when we discuss how our model matches the observed
8

PWT 10.0 also reports rates of return to capital for China. We believe the data from Chen et al. (2019) aresuperior. In any case, the results with data from PWT 10.0 are very similar and available upon request.

9

0.45
0.45
0.4

(GCF+NX)/CGDPO

GCF/CGDPO

0.4
0.35

0.3

0.35

0.3

0.25

0.25

0.2
China
Representative Asian Economy

0.2

China
Representative Asian Economy

0.15
5

10

15

20

25

30

35

40

45

5

10

15

20

Year

25

30

35

40

45

Year

Figure 3.7: Investment rates
investment data.
0.16

China
Japan
Korea
China, Taiwan Region

0.16

China
Representative Asian Economy

0.15
0.14

Return to Capital

Return to Capital

0.14

0.12

0.1

0.13
0.12
0.11
0.1
0.09

0.08

0.08
0.06
0.07
0.06

0.04
10

20

30

40

50

60

70

5

10

15

20

Year

25

30

35

40

45

Year

Figure 3.8: Return to capital

3.3

Taking Stock

We have documented two significant similarities between the growth experiences of China andthose of Japan, Korea, and Taiwan:
1. China has grown at about the same rate as these other East Asian economies. The difference
between China and the other economies is not in its economic growth rate but rather inthe size of China’s population compared to the other economies.
2. China’s investment rate has been qualitatively similar to those of the other economies andquantitatively has been somewhat higher, suggesting capital accumulation contributed more
to growth in China. Also, we documented a lower rate of return on capital in China.
The following section uses a minimalist growth model to interpret these facts.
10

4

A Minimalist Neoclassical Growth Model

This section presents a deterministic discrete-time Ramsey-Cass-Koopmans framework with twocountries. One country is at the technology frontier, and its technology grows at a constantrate. The other country has a lower level of technology but catches up deterministically to somefraction of the technological frontier. There is no connection between the two countries other than
the technological diffusion from the frontier to the catch-up country. These are all the elements
needed to make our points and thus allow us to abstract from complicated specifications of theevolution of technology, uncertainty, multiple sectors, and other complexities. More broadly, we
will see that our results suggest that to the extent that these and other complexities impacted
China’s growth, they may well be reasonably captured by our TFP catch-up process.

4.1

Preferences and Technology

The economy consists of two countries. The first country (China) is populated by an infinitely
lived representative household of varying size Nt and whose preferences over per capita consumption are:max
Ct /Nt∞Xtβ Nt log

t=0

Ct
Nt


,

where β is the discount factor and Ct is aggregate consumption.
China’s output is given by Yt = Ktθ (At Lt )1−θ where Kt is capital, At is China’s level of laboraugmenting technology, and Lt is employment (which we will assume is an exogenously given
but time-varying fraction of China’s population). Recall, when we present results in the next
sections, that TFP is equal to A1−θ.
tOutput is used for consumption or investment It , which induces a law of motion for capital
Kt+1 = It + (1 − δ)Kt , where δ is China’s depreciation rate. China’s resource constraint is given
by Ct + It = Yt . Finally, we assume that China’s population Nt grows at a time-varying rate nt ,Q
so that Nt = ti=1 (1 + ni ), given N0 = 1.
The second country, which we will call the U.S., is also populated by an infinitely lived
representative household of varying size Nt∗ (when a variable or a parameter does not have asuperscript star, it denotes either a parameter for China or a parameter common to the U.S. andChina) with the same preferences:max

Ct∗ /Nt∗∗∞X

β Nt∗ log
t

t=0

∗



Ct∗
Nt∗


,

and technology Yt∗ = (Kt∗ )θ (A∗t L∗t )1−θ . This is the same technology as China’s, except for four
elements.

11

First, the capital share in the U.S. is θ∗ , and, second, the depreciation rate in the U.S. is δ ∗ .
The differences between China and the U.S. in the elasticity of output with respect to capital andthe depreciation rate might be due to differences in industry composition, production techniques,or maintenance decisions by firms that we do not model.9 Third, the U.S. population grows at
Qa time-varying rate of n∗t : Nt∗ = ti=1 (1 + n∗i ), given N0∗ = 1. We will only compare China andthe U.S. in terms of relative per capita income. Thus, population size does not matter in our
exercise, and normalizing N0 = N0∗ = 1 is irrelevant for our purposes. The fourth is the U.S.
technology A∗t , which we consider as the world technological frontier, and which grows at theexogenously given rate g, that is, A∗t = (1 + g)t .

4.2

Specifying Technological Catch-Up

There are two regimes for China’s technology, At . In the first regime, t ≤ 0, At is a small constantfraction 0 < γ0 ≪ 1 of the U.S. technology. This regime stands in for China’s pre-reform economy:
At = γ0 A∗t = γ0 (1 + g)t .
In the second regime, t > 0, China’s technology is governed by a catch-up process. This regime
stands in for China’s post-reform economy:
At = γαt A∗t = γαt (1 + g)t .
The catch-up process is a simple one, but it captures important common features thatcountries experience during the process of catch-up growth, including (i) the level to which
they ultimately catch up, (ii) the speed of their catch-up, and (iii) how their catch-up speed
changes over time.10 Three parameters characterize the process we use. There is a constant, γ(0 < γ0 ≪ γ < 1), the asymptotic fraction of the U.S. technology that China can achieve. We
interpret γ as a measure of institutional quality (and, therefore, the ability to adopt new technologies). However, other interpretations are possible (e.g., it can be interpreted as an index ofhuman capital or allocative efficiency). The second term, 0 < αt < 1, measures China’s distanceto the technology frontier γ at any point in time.
Given an initial value α1 such that γ0 < α1 γ (i.e., technology is better after economic reform
begins), we assume that ln(αt ) is declining at rate η, which is the third parameter:ln(αt ) = (1 − η) ln(αt−1 ),9See, on maintenance and repair, McGrattan and Schmitz (1999). Below, we will use data from PWT 10.0 todiscipline our choices for θ and δ.
10
Appendix D documents, using growth accounting, that TFP growth accounts for 75% of China’s output
growth from 1995 to 2019. Thus, a process of TFP catch-up is key for understanding China’s aggregate dynamics.

12

(i.e., αt is growing in levels). This produces a concave path for αt that asymptotes to 1. This
captures the common experience among low-income economies that grow very quickly during theearly stages of modernization, with a rapid adoption rate (e.g., “low-hanging fruit”). Still, therate slows down as it approaches the frontier when improvements become harder.

4.3

The Stationary Problem

Given the model has continuous growth, we follow standard practice and normalize the variables.
We use the growth rates of U.S. technology and China’s population to make the problem stationary in China. We use the U.S. growth rate of technology as a scaling factor because before
reform At = γ0 A∗t and after reform At → γA∗t . Specifically, let:Ct,
A∗t NtKt
kt = ∗ ,At Nt
Itit = ∗ ,
At Nt

θ 
1−θ
Yt
Kt
At Lt
yt = ∗= ktθ (γαt lt )1−θ ,=At Nt
A∗t NtA∗t Ntct =where lt denotes the exogenously given employment-to-population ratio Lt /Nt .
Therefore, we rewrite the social planner’s problem for post-reform China as follows:
max
ct

∞
X

β t Nt log ct

t=0

subject to:yt = ktθ (γαt lt )1−θ ,ct + i t = y t ,it = (1 + g)(1 + nt+1 )kt+1 − (1 − δ)kt .
A standard Euler equation characterizes the solution to the optimization problem:

−1
θ−1
1−θ
c−1
+1−δ .
t (1 + g) = βct+1 θkt+1 (γαt+1 lt+1 )There is an equivalent stationary problem for the social planner in the U.S., where we use
the U.S. growth rate of technology and population to scale the variables, but we skip this in the13interest of space. The Euler equation for this normalized problem is given by:(c∗t )−1 (1 + g) = β(c∗t+1 )−1

5



θ

∗

θ∗ −1
∗
kt+1

1−θ∗
∗
lt+1
+ 1 − δ∗.

Calibration

We calibrate the nine parameters of our model on an annual base using data for 1995-2019 (thesame period as in Section 3). For the first six parameters, we follow commonly used values andtargets. See Table 1 for a summary of the calibration, and, in Subsection 6.2, we will discuss how
robust our quantitative results are to changes in our calibrated values.
Table 1: Calibration
Parameter

Value

Discount factor
Capital share, China
Capital share, U.S.
Depreciation rate, China
Depreciation rate, U.S.
Technology growth rate, U.S.
Initial technology, China
Catch-up boundCatch-up rate

β
θ
θ∗
δ
δ∗
g
γ0
γ
η

0.948
0.5
0.39
0.054
0.040
0.02
0.03
0.22
0.08

We pick a discount factor β of 0.948 to replicate a 7.6% annual rate of return to capital
reported by PWT 10.0 for the U.S. between 1995 and 2019 (notice that our model matches thereturn on all capital goods, not on bonds or other financial assets). We choose the capital shareθ∗ = 0.39 for the U.S. to match the average shares from PWT 10.0. For China, in comparison,
we pick θ = 0.5 instead of using the capital income share from the PWT (0.42). Bai et al. (2006)
defend this higher value by using data on provincial capital income share from China’s National
Bureau of Statistics. Indeed, the share of 0.5 is widely used in the literature on China, such as
Song et al. (2011) and Brandt et al. (2008). Nonetheless, in Appendix F, we report our resultswith θ = 0.42.
Similarly, the depreciation rate is the average depreciation rate from PWT 10.0: δ = 0.054
for China and δ ∗ = 0.040 for the U.S. We calibrate g = 0.02 to match the long-run productivity
growth of the U.S. economy during the 20th century. The population growth rates match theobserved data year by year in China and the U.S. These parameters yield a steady-state K/Yratio in the U.S. of 3.39, which is close to the average value of 3.24 (including government capitalstocks) over the 1995-2019 period.

14

To specify the three catch-up parameters for China, we assume that the U.S. was on its BGP
in 1995 or, in the transformed stationary problem, that the U.S. was at its steady state withcapital k0∗ . More concretely, k0∗ satisfies the steady-state Euler equation:
∗∗
1 + g = β θ∗ (k0∗ )θ −1 (l0∗ )1−θ + 1 − δ ∗ ,(1)where l0∗ is the employment-to-population ratio in the U.S. in 1995. Solving this equation withthe calibrated parameters above yields k0∗ = 3.59. To compute i∗0 = (1 + g)(1 + n∗0 )k0∗ − (1 − δ)k0 ,we assume that population growth, n∗0 , is the population growth observed in 1995.
Similarly, we assume that in 1995 (t = 0), China was at the initial steady state implied by
γ0 . Thus, we have the Euler equation:
1 + g = β θ(k0 )θ−1 (γ0 l0 )1−θ + 1 − δ .

(2)where l0 is China’s employment-to-population ratio in 1995. To compute investment in thissteady state i0 = (1 + g)(1 + n0 )k0 − (1 − δ)k0 , we assume that n0 is equal to the population
growth rate in China in 1995.
From PWT 10.0, we know that, in 1995, China’s PPP-adjusted per capita output was 0.066
of the U.S. per capita output:
Y0 /N0= 0.066.
Y0∗ /N0∗
That is,y0(k0 )θ l01−θ 1−θ
=γ= 0.066.
y0∗(k0∗ )θ∗ (l0∗ )1−θ∗ 0(3)Equations (2) and (3) are a system of two equations in two unknowns: γ0 and k0 (given k0∗ ).
This system yields γ0 = 0.03 and k0 = 0.27. These numbers imply that the ratio in per capita
output between the U.S. and China in 1995 (around 15) is accounted for by a relative TFP ofaround 5.8 (≈ 1/(γ01−θ ) = 1/(0.030.5 )) and a ratio of capital of around 13.4, but which is partially
offset by a Chinese employment-to-population ratio that is around 17% higher.
Next, we assume that an institutional reform occurs in 1996 (t = 1), in which γ0 jumps to γ:
AT −1
AT
A0
A1
= γ0 , A= αT −1 γ, A
∗ = α1 γ, ..., A∗
∗
A∗0
1
T −1
T

= γ. The last equation only holds approximately for alarge T , which we pick to be 105, as αT asymptotes to 1. As we will see momentarily, T is givenby the furthest UN population projections into the future, which we use to render the problem
stationary.
Finally, we choose the institutional level γ and convergence rate η to minimize the distance(using the least squares metric) between the generated TFP for China in the model and the TFP
in the data. This process yields γ = 0.22 and η = 0.08. The parameter α1 therefore evolves asln(α1 γ) = (1 − η) ln (γ0 ).

15

We pick this particular form of TFP convergence process because it is simple, easy to replicate
and use in other applications, has just three parameters, and captures the spirit of the underlying
mechanisms that the large literature on convergence has discussed, including relatively fast growthin the early stages of catch-up and a limit to convergence (e.g., conditional convergence), which
may reflect institutional factors. Our analysis below requires forecasting the future path of TFP,
and our process captures past data extremely well based on just a few parameters.11
TFP relative to the U.S.
0.5

0.45

0.4

0.35

0.3

0.25

0.2
Model
Data

0.15
2000

2010

2020

2030

2040

Figure 5.1: China’s TFP relative to the U.S.
Figure 5.1 plots China’s TFP relative to the U.S. in the data and our calibration to illustrate
how the former matches the latter. The estimated process starts somewhat lower but fits thedata very closely for 2000 to 2019, inducing the “right” neoclassical growth dynamics for mostof the observed series, as shown below. Choosing a value of γ = 0.22 implies a final TFP of
46.7% of the U.S. level, indicating serious structural/institutional limitations in China’s long-run
productivity level. While our simple process abstracts from those limitations and their specificeffects, it captures the implicit impact of those factors on TFP up through 2019 well.
We stress that specifying that the institutional reform began in 1996 is immaterial for thenumerical results. It is simply a convenient benchmark. The assumptions we make are that China
was on the BGP implied by γ0 in 1995 and that the technological gap with the U.S. began toclose at a constant rate from that moment. An analogous interpretation is that γ0 is already the
product of a reduction in the technological gap between the U.S. and China that started with thefirst wave of reforms that followed the Third Plenary Session of the 11th Central Committee of
the Chinese Communist Party in December 1978.12 Given the low level of γ0 that we infer, 0.03,
11

The alternative of directly feeding China’s observed TFP into our model does not allow us to extrapolate
future values for China’s TFP. Our fitted TFP curve makes forecasting future TFP transparent and simple.
12
See Naughton (2018, Ch. 5) for a chronology of economic reform in China since 1978. The main economic
reforms came in two waves: from 1979 to 1989 and 1993 to 1999. For example, China unified its dual foreignexchange regime at the end of 1993, there was a large fiscal reform in 1994, and inflation was tackled in 1996.
Thus, our benchmark year of 1996 is in the middle of the second reform wave, making it a reasonable point for
our analyses and its focus on China’s growth after China became a middle-income country. Chen and Zha (2023)
emphasize the gradualist approach of China’s reforms, which fits well with our idea of a smooth TFP catch-up.

16whether China was at its BGP in 1995 or still converging to it, given the first wave of reforms
from 1979 to 1989, has minimal effects on our simulations below.
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. employment-to-population ratio in 2019. To compute investment at T , weassume that population growth in the limit, n∗T = 0.024%, is the total population growth rate
predicted by the UN in 2100, the furthest projection as of early 2023.
Similarly, for China, the final steady-state capital kT is given by:(1 + g) = β θkTθ−1 (γlT )1−θ + 1 − δ ,where lT is the employment-to-population ratio in China in 2019. To compute investment at T ,we assume that population growth in the limit, nT = −1.19%, is the growth rate of the total
population in 2100 predicted by the UN.
To compute the planner’s solution for the U.S., we take the initial steady state in 1995 andits final steady state in 2100 and compute the transition path between the two using the Euler
equation and the investment and resource constraint equations using a nonlinear equation solver.
For China, we take the initial steady state in 1995, assume that the reform started in 1996, andcompute the transition path toward the final steady state in 2100, where A2100 = γA∗2100 using
the same set of equations and the same nonlinear equation solver.

6

Quantitative Results

This section presents the quantitative findings. First, we report our benchmark results anddiscuss how they compare with the data. Second, we explore the robustness of the results toalternative calibrations and assumptions regarding how investment is determined in the model.

6.1

Benchmark Results

Figure 6.1 compares the model results to the data on the level of income per capita and its
growth rate. The left panel shows that the model closely matches the evolution of China’s
income per capita relative to the U.S. income per capita from 1995 to 2019. This finding suggeststhat the minimalist growth model captures the main mechanisms underlying China’s growthexperience. Put differently, China’s growth during this period conforms to that generated by asimple, deterministic, one-sector optimal growth model with a smoothed, exogenous TFP path.

17

Income Per Capita relative to the U.S.

0.45

Income Per Capita Growth Rate

0.14

Model
Data

0.4
0.12
0.35
0.1
0.3
0.25

0.08

0.2
0.06
0.15
0.04
0.1

Model
Data

0.05

0.02
2000

2010

2020

2030

2040

2000

2010

2020

2030

2040

Figure 6.1: Transitional dynamics (model and data): Income per capita
Catch-up. The first main implication of the analysis is that China’s catch-up process willslow substantially as China’s technology level approaches its catch-up bound (γ = 0.22). Since
China has differences in its capital share, depreciation rate, population growth, and employmentto-population ratio, its relative income per capita asymptotes to 0.44, which is lower than its
relative TFP of 47%. The UN forecasts that, by 2100, China’s population will be around 1.95
times larger than that of the U.S. (766.7 million vs. 394 million). Given this forecast, the model
implies that the U.S. economy will be around 17% larger than China’s by the end of this century.
The sharp slowdown in income per capita growth is seen in the right panel of Figure 6.1,
which compares income per capita growth rates in the model and the Chinese data. The model
replicates the hump shape of these patterns and their slowdown over time. Since our model has
no shocks, we do not track the year-to-year fluctuations. Nonetheless, there is a clear similarity
between the model and the data in terms of longer-run movements.
Investment. The left panel of Figure 6.2 draws investment as the model’s forecast against two
different investment series in the data: PWT is the investment rate reported by PWT 10.0. NBS
is the investment series from China’s National Bureau of Statistics (the investment rates are in
nominal ratios, which avoids the need to compute relative prices). The right panel of Figure 6.2
shows the rate of return on capital from Chen et al. (2019) and compares it with the model.
Our model undershoots in terms of investment at the end of the period. But this raises thequestion of why China has not grown more, given this higher investment. Four factors can help
reconcile the model with the data. The first factor is that much of China’s investment has had alow marginal productivity due to distortions. For example, König et al. (2022) have documented
that the return to the productivity of R&D investments is lower in China than in Taiwan due
to pervasive output wedges. This explanation fits well with the right panel of Figure 6.2, whichshows that the rate of return to capital in the model is higher than in the data. This argument
also reconciles the data with the three stylized facts from Section 3: while investment rates in
18

I/Y

Return to Capital

0.25

Model
Data

0.5

0.2
0.4

0.15
0.3

0.1

0.2

0.05

0.1
Model
Data: PWT
Data: NBS

0
2000

2010

2020

2030

0
1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045

2040

Figure 6.2: Transitional dynamics (model and data): Investment, and return to capital
China have been somewhat higher than in other East Asian economies, the growth rate has been
about the same and the rate of return on capital lower.
The second factor is the mismeasurement of investment. Chen et al. (2019) have argued thatthe investment rate in 2016 was about 7 percentage points lower than officially reported (see also
Xiong, 2019, for a model of how flawed statistics come about in China due to the agency problem
between central and local governments). Figure 6.3 plots the results of our model against thenominal investment rate in Chen et al. (2019, p. 106, Figure 10).13 With the series from Chenet al. (2019), the model does an excellent job of replicating investment. This explanation has the
additional advantage that, by lowering the total amount of investment, it increases the rate of
return on capital, helping with that aspect of the model as well.
I/Y
0.5

0.4

0.3

0.2

0.1

Model
Data: corrected by Chen et al. (2019)
Data: NBS

0
2000

2010

2020

2030

2040

Figure 6.3: Corrected investment by Chen et al. (2019)
The third factor is that much of the investment in China has reflected political priorities. Herd
(2020) has shown that business investment has the same hump shape we document. Compare
13

For this comparison to be precise, we would need to recalibrate China’s TFP process to be consistent with theoutput data in Chen et al. (2019). Since we aim to point out that the official investment series may mismeasure
the extent of capital formation, we skip this additional step.

19

Figure 6.B in his paper with our right panel in Figure 6.2; the difference between the modeland the data can be attributed to the increase in investment in housing and infrastructure. Bai
et al. (2020) and Jiang et al. (2022) make related points based on the policies of local authorities,
which have taken advantage of the local government financing vehicles to borrow and invest
in low-rate-of-return projects. However, notice that investment in infrastructure in China has
fluctuated between 7% and 10% of output since the early 2000s (Jiang et al., 2022). While thisis a high rate compared to other middle-income countries, investment in infrastructure can only
account for part of the difference in total investment.
To explore this third factor more thoroughly, in Subsection 6.2, we will document that if we
fix investment exogenously, à la Solow, at a higher level, the model cannot match the data on
per capita income and per capita income growth. This factor fits particularly well with Figure
6.2: the rate of return on capital in the data is lower than in the model, while the investment
rate is higher than in the model, especially after 2008, because many investment decisions during
this period were made for reasons other than maximizing profits. Therefore, endogenous saving
decisions must be a key part of the analysis.
Finally, a fourth factor is that our calibration might yield too little investment. We will revisitand dismiss this explanation in Subsection 6.2.
Future income growth rates. Our second main result is that, according to our model, by2043, U.S. output will grow faster (2.29% ) than China’s (2.27%) (recall the model forecast
that, by 2100, the U.S. economy will be larger than China’s in PPP terms; in 2019 according
to PWT 10.0, China’s economy is larger than the U.S. economy). This can be seen in the left
panel of Figure 6.4, which plots the (total) income growth rates for China and the U.S. This
sharp prediction is based on three elements. One is China’s unfavorable demographics, with afalling population.14 Second is the stability of the employment-to-population ratios (right panelof Figure 6.4). The third is the slowdown of technological catch-up (Figure 5.1).
The decomposition of these three elements helps us gauge possible caveats to the model’s
forecast. Let us start with demographics. This is the least uncertain of the three elements.
Absent a particularly deadly epidemic or massive migration flows, the population as far out as2043 is roughly already determined: around 85% of the Chinese people who will be alive in 2043
have already been born (and nearly all workers: a person born in 2023 will only be 20 years old in
2043, and thus either studying or just entering the labor force). Demographic (and social norms)
changes might have a greater impact on the employment-to-population ratio but by only a few
percentage points up or down. If anything, the right panel of Figure 6.4 suggests that China’s
employment-to-population ratio will fall due to aging, accelerating the date at which the U.S.
14

According to China’s National Bureau of Statistics, China’s total population started falling in 2022.

20

Income Growth Rate

Employment-to-population Ratio

0.58

0.12
China
USA

0.56

0.1

0.54
0.08
0.52
0.06
0.5
0.04
0.48
0.02

0.46
China
USA

0

0.44
2000

2010

2020

2030

2040

2000

2010

2020

2030

2040

Figure 6.4: Transitional dynamics: Total income and employment-to-population ratio
would overtake China’s output growth rate.15
The main source of uncertainty in the model forecast of per capita income growth rates isthe evolution of relative TFP in Figure 5.1. Our calibrated process matches the data, and theevidence of other East Asian economies presented in Section 3 suggests that avoiding a slowdown
in the convergence process is a tall order. Even if TFP growth falls in the U.S. (lowering total
U.S. income growth), this reduction in TFP might hurt China through international technologicaldiffusion or just postpone the takeover by the U.S. in terms of the income growth rate for a decade.
The logic of the neoclassical growth model is straightforward and powerful in terms of its
implications. With a population growth rate in the long run of −1.19% projected by the UN(which assumes a recovery in fertility from the current 1.08 children per woman to 1.48), TFP
needs to grow 1.19% a year just to keep total income constant (given constant employmentto-population and capital-worker ratios). Delivering a growth rate of 3% would require a TFP
growth rate of 4.19% in an economy with a middle-high income already. While not out of therealm of possibility, given the upside potential of AI and automation, the historical record is notsanguine about the likelihood of a long-run TFP growth rate of 4.19%. A shrinking populationand slowing TFP catch-up are substantial headwinds facing China.16
15

Nonetheless, there might be a counterbalancing effect as the average human capital of Chinese workers increases as younger, more educated cohorts enter the job market. We will explore this possibility in future research.
Still, the experience of southern European countries, such as Italy and Spain, that have experienced a similar increase in average human capital over the last 25 years, is not very optimistic. In the absence of fast TFP growth,
many highly educated workers are allocated to jobs for which they are overqualified, thus substantially reducing
the potential contribution of higher human capital.
16
There are many other possible headwinds (financial crises, adverse political developments, etc.), but sincethose are absent from our model, we remain silent about them. Suffice it to say that considering them will only
make our main argument stronger.

21

6.2

Robustness

We conduct an extensive battery of robustness exercises with our model by varying the parametervalues. In the interest of space, we will report the three most interesting experiments. In these
exercises, we recompute the institutional levels γ0 and γ and the convergence rate η to minimize
the distance between TFP in the model and the data to make all results easily comparable.
Changing the elasticity of intertemporal substitution. In our first experiment, we change
the utility function from a log to a general CRRA form. If, for instance, we lower the elasticity ofintertemporal substitution to 1/2, we deliver a model-implied investment rate much lower thanthat in the data (bottom right panel of Figure 6.5) and, with it, we miss the growth of income
per capita (top left panel of Figure 6.5).
The converse exercise of increasing the elasticity of intertemporal substitution over one delivers
a better match of investment to the data (which we argue may reflect significant mismeasurement), but too much income growth relative to the data.
Income Per Capita relative to the U.S.

0.4

Income Per Capita Growth Rate

0.14

Model
Data

0.35

0.12

0.3
0.1
0.25
0.08
0.2
0.06
0.15
0.04

0.1
Model
Data

0.05

0.02
2000

2010

2020

2030

2040

2000

2010

TFP relative to the U.S.

2020

2030

2040

I/Y

0.5
0.5
0.45

0.4

0.4

0.35

0.3

0.3
0.2
0.25
0.1
Model
Data: PWT
Data: NBS

0.2
Model
Data

0.15

0
2000

2010

2020

2030

2040

2000

2010

2020

2030

2040

Figure 6.5: Transitional dynamics (model and data): IES = 1/2

From Ramsey-Cass-Koopmans to Solow. In our second experiment, we fix the investment
rate exogenously at its 2009 level (0.45) to capture the evidence in the left panel of Figure 6.2. We
22

can think of this exercise as moving from a Ramsey-Cass-Koopmans model, where consumptionsaving decisions are chosen endogenously, to a Solow model, where consumption-saving is fixed
exogenously. We interpret this experiment as addressing the possibility that our model misses key
mechanisms behind investment or because investment is determined by political forces outside
the model.
Figure 6.6 reports these results. Here China continues growing much faster for a longer
period. However, the model can no longer match the recent per capita income and income
growth observations. While a simple Solow model can deliver fast economic growth from thehigh investment rates of China for decades to come, the data do not support the hypothesis that
China is getting much out of this high investment rate. Our previous explanation –a combinationof low investment efficiency and mismeasurement– matches the data better.
Income Per Capita relative to the U.S.

0.6

0.5

0.12

0.4

0.1

0.3

0.08

0.2

0.06

0.1

Income Per Capita Growth Rate

0.14

Benchmark Model
Data
Solow Model

0.04

Benchmark Model
Data
Solow Model

0

0.02
2000

2010

2020

2030

2040

2000

2010

TFP relative to the U.S.

2020

2030

2040

I/Y

0.5
0.5
0.45

0.4

0.4

0.35

0.3

0.3
0.2
0.25
Benchmark Model
Data: PWT
Data: NBS
Solow Model

0.1
0.2
Model
Data

0.15

0
2000

2010

2020

2030

2040

2000

2010

2020

2030

2040

Figure 6.6: Transitional dynamics (model and data): Investment rate fixed at 0.45

Changing the depreciation rate. In our third experiment, we experiment with alternative
depreciation rates. In Figure 6.7, we set the depreciation rate to 0.1 for both China and the
U.S. The simulation results illustrate that the model’s prediction is robust to this alternative
calibration. Remember that, in our benchmark calibration, the depreciation rate is 0.04 for the
U.S. according to PWT 10.0. Hence, we also experiment with setting both countries’ depreciation23

rates to 0.04. Given that the model’s predictions are similar, we do not report them.
Income Per Capita relative to the U.S.

0.45

Income Per Capita Growth Rate

0.14

Model
Data

0.4
0.12
0.35
0.1
0.3
0.25

0.08

0.2
0.06
0.15
0.04
0.1

Model
Data

0.05

0.02
2000

2010

2020

2030

2040

2000

2010

TFP relative to the U.S.

2020

2030

2040

I/Y

0.5
0.5
0.45

0.4

0.4

0.35

0.3

0.3
0.2
0.25
0.1
Model
Data: PWT
Data: NBS

0.2
Model
Data

0.15

0
2000

2010

2020

2030

2040

2000

2010

2020

2030

2040

Figure 6.7: Transitional dynamics (model and data): δ = δ ∗ = 0.10

7

The Asian Miracle Economies

We have shown that a minimalist Ramsey-Cass-Koopmans model can account for the growthexperience of China since 1995 and that it projects a substantial slowdown in China’s middle-run
growth prospects. But can this model account for the experience of other Asian miracles? In thissection, we will argue that the answer is yes, and that the results for these other economies will
provide important support for our assertion that the model captures important aspects of China’s
catch-up experience. This validation exercise is particularly valuable because, as we explainedin the introduction, the model should account for longer experiences (for example, in the caseof Japan, since the late 1950s) than China’s, pushing the model to its explanatory limits. This
provides additional confidence for projecting China’s TFP process into the future.
More concretely, we examine the experiences of Korea, Japan, and Taiwan.17 In all cases, wecalibrate the model as in Section 5. The discount factor and the three U.S. parameters remainunchanged; we pick the capital share and depreciation rates to match the PWT 10.0 data, the17

We also show the growth experience of Hong Kong and Singapore in Appendix F.

24

parameter values for TFP to match the observed TFP using a quadratic metric, and the reported
population growth rates. We will make an exception for Taiwan regarding θ. PWT 10.0 imputes
self-employed labor compensation, assuming the self-employed earn the same average wage as
employees, which greatly overestimates labor share and leads to a θ as low as 0.27 for Taiwan.
Instead, we choose an alternative labor share for Taiwan from PWT 10.0, which only includes
the labor compensation of employees; this gives θ = 0.47. Nonetheless, we report the results for
Taiwan with θ = 0.27 in Appendix F.18
As an initial year for our calibration, we pick the same year we use for each economy in Figure
3.1. If TFP data are unavailable from the initial year, we start from the first year when TFP
data are available. The long-run relative TFP (γ) ranges between 0.45 for Korea and 0.67 for
Taiwan. Table 2 summarizes the calibration.
Table 2: Calibration: The Asian miracle economies
Economy

δ

γ

η

θ

Initial Year

TFP Available From

Japan
Korea
Taiwan0.037 0.51 0.17 0.40
0.045 0.45 0.10 0.45
0.050 0.67 0.06 0.47

1950
1972
1962

1954
1972
1962

China

0.054 0.22

1995

1995

0.08

0.50

Japan. First, we consider Japan. In the top left panel of Figure 7.1, we see that the model
does a fair job of tracking Japan’s experience until the late 1980s, when output grows for several
years well above the predictions of the model (top right panel of Figure 7.1). This divergence
between the model’s predictions and the data is compatible with the notion that Japan went
through an asset price bubble (typically dated from 1986 to 1991) that pushed the economy
above its neoclassical transitional dynamics. Furthermore, the return of per capita income to thelevel predicted by the model at the end of the top left panel of Figure 7.1 accounts for the poor
performance of Japan’s economy since 1992, as the effects of the previous decade were undone.
The plot for investment (middle right panel of Figure 7.1) reinforces this view, as investment
rates were substantially higher in the 1980s and 1990s than the model’s predictions. The evolution
of TFP (middle left panel of Figure 7.1) suggests that Japan’s convergence process with the U.S.
has finalized with a per capita income of around 60% of the U.S. level. Finally, the bottom panelof Figure 7.1 shows that the observed rate of return on capital is below what the model predicts
by the end of the period. Nonetheless, the model captures the hump shape of this rate of return,reflecting the interaction between productivity, investment, and labor.
18

See https://www.stlouisfed.org/on-the-economy/2018/january/measuring-labor-share-asian-tigers
and Appendix E about the difficulties of measuring labor shares in East Asian economies.

25

Income Per Capita relative to the U.S.

0.9

Income Per Capita Growth Rate

0.15

Model
Data

0.8
0.1
0.7
0.05

0.6

0.5

0

0.4
-0.05
0.3
Model
Data

0.2

-0.1
1960

1970

1980

1990

2000

2010

1960

1970

1980

TFP relative to the U.S.

1990

2000

2010

I/Y

0.75
0.5

0.7
0.65

0.4
0.6
0.55

0.3

0.5
0.2
0.45
0.4
0.1
0.35

Model
Data

Model
Data

0.3

0
1960

1970

1980

1990

2000

2010

1960

1970

1980

1990

2000

2010

Return to Capital

0.25

Model
Data

0.2

0.15

0.1

0.05

0
1960

1970

1980

1990

2000

2010

Figure 7.1: Japan: Transitional dynamics
Korea. Figure 7.2 plots the results for Korea. We can see in the top left panel that the modelhas done quite well in accounting for growth at the start (from around 1970 to 1985) and theend of the sample (from around 2000 to 2019). The model misses, however, the very high growth
rates between 1985 and 1996 that pushed observed output well above the predicted level andthe great Asian financial crisis of 1997. Interestingly, and in contrast with Japan, Korea has not
returned to the path forecast by the model. However, the data at the end of the sample in the
top right panel of Figure 7.2, where growth seems to underperform the model’s forecast, suggest

26that such a return might start appearing.
Nonetheless, the central message of the model, namely, that growth slows down after a country
catches up, is vindicated by the data: Korea has been growing recently exactly as the RamseyCass-Koopmans model predicts, if not slightly more slowly (top right panel of Figure 7.2). The
model also matches TFP relative to the U.S. (middle left panel), the hump shape of investment(middle right panel), and the rate of return on capital (bottom panel).
Income Per Capita relative to the U.S.

0.7

Income Per Capita Growth Rate

0.2

Model
Data

0.15

0.6

0.1
0.5
0.05
0.4
0
0.3
-0.05
0.2

-0.1
Model
Data

0.1

-0.15
1980

1990

2000

2010

2020

1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025

TFP relative to the U.S.

I/Y

0.7
0.5

0.65
0.6

0.4
0.55
0.5

0.3

0.45
0.2
0.4
0.35
0.1
0.3

Model
Data

Model
Data

0.25

0
1980

1990

2000

2010

2020

1980

1990

Return to Capital

0.25

Model
Data

0.2

0.15

0.1

0.05

0
1980

1990

2000

2010

2020

Figure 7.2: Korea: Transitional dynamics

27

2000

2010

2020

Taiwan. Our next case study is Taiwan. The top left panel of Figure 7.3 shows that per capita
income growth is faster in the data than in the model until the early 2000s, when convergencewith the U.S. stops. After several decades of fast growth, Taiwan’s growth has returned to levels
below those forecasted by the model (top right panel of Figure 7.3). Interestingly, the model doesa good job with investment without underestimating it (middle right panel of Figure 7.3).
Income Per Capita relative to the U.S.

0.9

Income Per Capita Growth Rate

0.14

Model
Data

0.12

0.8

0.1
0.7
0.08
0.6

0.06

0.5

0.04
0.02

0.4

0
0.3
-0.02
0.2

Model
Data

-0.04

0.1

-0.06
1970

1980

1990

2000

2010

1970

1980

TFP relative to the U.S.

1990

2000

2010

I/Y

0.9
0.5
0.8
0.4
0.7
0.3
0.6

0.2
0.5

0.1

0.4
Model
Data

Model
Data

0.3

0
1970

1980

1990

2000

2010

1970

1980

1990

Return to Capital

0.25

Model
Data

0.2

0.15

0.1

0.05

0
1970

1980

1990

2000

2010

Figure 7.3: Taiwan: Transitional dynamics

28

2000

2010

Taking stock Our minimalist model accounts reasonably well for the experience of Japan,
Korea, and Taiwan, particularly for the first 25 years of data in each economy. Later, wider
divergences appear, with exceptionally fast growth in the late 1980s/early 1990s, a decade thatsome economists have referenced as reflecting a bubble, both in terms of growth exceeding theneoclassical benchmark, followed by a severe financial crisis. Interestingly, the growth above themodel’s predictions has been undone in the case of Japan and Taiwan, though it has yet to beundone in the case of Korea.

8

Summary and Conclusion

Since China became a middle-income country in 1995, its growth performance has been remarkably similar to that of other East Asian growth miracles at the same stage of development. We
find that a neoclassical framework with a simple three-parameter TFP catch-up process accounts
for the growth patterns in these economies. More broadly, we find that the many factors viewed
as central to understanding these growth miracles, ranging from industrial policies to financial
market modernization to trade and FDI policies, manifest themselves within a simple TFP catchup process in a one-sector framework. We, therefore, view this paper as a complement to thosepapers in the literature that have developed detailed models with these mechanisms. We interpret our model’s minimalism as a fundamental virtue; increased modeling complexity comeswith empirical challenges, including the very difficult task of trying to project these mechanisms’
future course, which is needed to forecast China’s future growth.
China has been growing because it is accumulating capital and is catching up with the world’s
technological frontier. But China’s technology catch-up is slowing considerably, both in absoluteterms and relative to the other East Asian growth miracles. This fact, combined with a declining
population, means that China’s growth can only slow down, perhaps significantly. Our futureforecast for China’s economy, which assumes a continued comparatively high savings rate andemployment-to-population ratio, might be too optimistic should either of these factors decline.
On the other hand, our forecast may be too pessimistic if China implements new institutional
reforms that improve economic efficiency to reverse its declining TFP catch-up rate.

29

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32

Appendix
The following appendices provide further details about the data employed in the paper andoffer additional information regarding Hong Kong and Singapore and additional experiments.

A

TFP Construction

We use two different TFP measurements from PWT 10.0 to construct our TFP:
• RT F P N A :– TFP at constant national prices (2017=1), constructed using RGDP N A .
– RGDP N A is based on the national account GDP growth rate.
– PWT reports the growth rate of RT F P N A , normalizes the RT F P N A to 1 in 2017 forall economies, and backs out the TFP level for other years for each economy.
• CT F P :– TFP level at current PPPs (USA=1), constructed using CGDP O .
– PWT computes each economy’s CT F P level and uses U.S. CT F P to normalize them.
TFP

We construct China’s real TFP relative to the U.S., T F PCN,t, as follows:
U S,t• Take the value of CT F PCN,2017 and assume T F PCN,2017 = CT F PCN,2017 .
• The relative TFP in any year, take 2019, for example, isNA
T F PCN,2019
CT F PCN,2017 RT F PCN,2019=A
T F PU S,2019
CT F PU S,2017 RT F PUNS,2019= CT F PCN,2017Note that CT F PU S,2017 = 1.
The constructed TFP is plotted in Figure 5.1.

1

NA
RT F PCN,2019.
A
RT F PUNS,2019

B

Different Measures of China’s per Capita GDP

As discussed in the main text, there are two GDP series for China in PWT 10.0: one is basedon GDP growth adjusted by Wu (2014), denoted by “adjusted” in Figure B.1, and the other isbased on the GDP growth rates from China’s official national accounts. Figure B.1 plots thedifferences in levels (left panel) and growth rates (right panel).
log China's per capita GDP

17

Growth of China's per capita GDP
0.15

16.5
0.1

16
15.5

0.05
15
0

14.5

PWT GDPNA Adjusted

PWT GDPNA Adjusted

PWT GDPO Adjusted

14

PWT GDPO Adjusted

PWT GDPE Adjusted
PWT GDPNA

13.5

PWT GDPE Adjusted-0.05

PWT GDPNAPWT GDPOPWT GDPOPWT GDPEPWT GDPE

13

-0.1
1980

1985

1990

1995

2000

2005

2010

2015

1980

1985

1990

1995

2000

2005

2010

2015

Figure B.1: GDP per capita: log-level and growth rate
We also report the investment rates from China’s National Bureau of Statistics and comparethem with those from PWT 10.0 in Figure B.2.19
Investment Rate
0.55

0.5

0.45

0.4

0.35

0.3
PWT Nominal: I = GCF
PWT Nominal: I = GCF+NX
NBS Nominal: I = FCF + Inven + NX
NBS Nominal: I = FCF + Inven
NBS Nominal: I = FCF

0.25

0.2
1995

2000

2005

2010

2015

Figure B.2
19

PWT denotes the investment rate reported by PWT 10.0, where GCF is gross capital formation, and NX
is net exports. NBS denotes the data from the National Bureau of Statistics in China, where FCF is the fixed
capital formation, and Inven denotes changes in inventories. The investment rates are in nominal ratios, which
avoids the need to compute relative prices.

2

C

Facts including Hong Kong and Singapore

We exclude Hong Kong and Singapore from our benchmark facts because their population are
relatively small. We add them now. Hong Kong’s first year in the data is 1960, with a GDP per
capita level of $6004. China’s GDP per capita level reached $5953 in 2004. Hence, we plot thefigure from 2004 below.
China
China, Hong Kong SAR
Japan
Korea
Singapore
China, Taiwan Region

Log (RGDPO per capita)

11

10.5

10

9.5

9

5

10

15

20

25

30

35

40

45

50

55

60

Year

Figure C.1: GDP per capita, normalized at China’s GDP per capita in 2004
Figure C.1 shows that the fact on output growth is robust if we include these two cities.
Hong Kong and Singapore performed well during the early 2000s, leaving the other East Asian
economies behind, but their growth rate eventually slowed down, as in all the other economies.

10.5

China
China, Hong Kong SAR
Japan
Korea
Singapore
China, Taiwan Region

China
China, Hong Kong SAR
Japan
Korea
Singapore
China, Taiwan Region11

Log (RGDPEper capita)

Log (RGDPNAper capita)

11

10

9.5

10.5

10

9.5

9
9
8.5
10

20

30

40

50

60

5

Year

10

15

20

25

30

35

40

45

50

55

60

Year

Figure C.2: GDP per capita, alternative definitions
Similarly, alternative measures of GDP (Figure C.2) do not change the picture much.
Figure C.3 plots the investment rates. Hong Kong’s and Singapore’s investment rates differ
from those of other East Asian economies. Look at Singapore’s case, where investment reachedlevels as high as 70% in terms of gross capital formation over GDP, but with negative investment
discounting net exports.
3However, this is not a surprise. Singapore was the recipient of large foreign direct investment(FDI) flows. Given the size of its economy earlier in the sample, a few large FDI projects can
induce wild fluctuations in measured investment, as shown in Figure C.3. Recall, for instance, that
Mobil started building a large oil refinery in Jurong (Pioneer Road) in 1966, while Esso started
another one at Pulau Ayer Chawan (now part of Jurong Island) in 1970, making Singapore arefinery hub for the region. These observations reinforce our choice of leaving Hong Kong andSingapore out of the main text’s discussion.
China
China, Hong Kong SAR
Japan
Korea
Singapore
China, Taiwan Region

0.6
0.55

0.45
0.4
0.35

(GCF+NX)/CGDPO

0.65

GCF/CGDPO

0.5
0.45
0.4
0.35
0.3

0.3
0.25
0.2
0.15
0.1

China
China, Hong Kong SAR
Japan
Korea
Singapore
China, Taiwan Region

0.05

0.25

0

0.2

-0.05
0.15
5

10

15

20

25

30

35

40

45

50

55

60

5

10

Year

15

20

25

30

35

40

45

50

55

60

Year

Figure C.3: Investment rates

D

Growth Accounting

An interesting complement to our quantitative experiments is undertaking a standard growthaccounting exercise for China. This exercise shows that, indeed, TFP growth is a key driver ofChina’s GDP growth and, therefore, that as TFP growth is bound to slow down, so is China’s
GDP growth.
Let us assume a production function of the form Yt = Ktθ (At Lt )1−θ , where T F P = A1−θ.
tFollowing Hall and Jones (1999), we can decompose GDP per capita into:
Yt
= At
popt



Kt
Yt

θ
 1−θ

Lt
popt

where popt is total population. Hence, the growth of GDP per capita can be expressed as:

∆

Yt
popt



 


θ
Kt
Lt
= ∆At +
∆
+∆
1−θ
Yt
pop
  t 

1
θ
Kt
Lt
=
∆T F P t +
∆
+∆
1−θ
1−θ
Yt
popt

where ∆ denotes a growth rate.
4

Table D.1 shows the growth accounting exercises with the national account approach TFP,
capital, and GDP (RGDP N A ). Since the contribution of total factor productivity growth isweighted by 1/(1 − θ), the growth contribution of TFP is 2 × 3.11/8.30 = 75% of the growth in
GDP per capita.
Table D.1: Growth accounting in the data, national accounting approach
Data
GDP per capita TFP(RGDP N A )Aver. annual growth rate
Cont. to per capita GDP growth

8.30
100.003.11
74.83

Capital/output Emp/pop
ratio
ratio
2.09
25.21

-0.05
-0.56

Unfortunately, data limitations preclude us from undertaking this growth accounting using
production-based GDP, the concept that we use in our quantitative model, in a consistent way(see our discussion in Section 2 of the main paper).

E

Discussion of Labor Income Shares

E.1

Labor Shares from the PWT 10.0

Computing the labor share from income data is challenging because we need to calculate thelabor compensation of self-employed workers, which includes both a labor and a capital income
component. Gollin (2002) discusses possible treatments of self-employed income. PWT 10.0
estimates the labor share in the following five ways, among which Adjustment 1-3 is from Gollin(2002):20• Naive share: Labor income only includes labor compensation of employees, which does
not include self-employed income.
• Adjustment 1: Treat all self-employed income as labor compensation.
• Adjustment 2: Self-employed workers use labor and capital in the same proportion as therest of the economy. Although adjustment 2 is considered the best measure, only Japan in
our sample reports mixed income.
• Adjustment 3: The self-employed earn the same average wage as employees, but thisgreatly overestimates labor share in East Asian economies, where self-employed workers
are mostly low-skilled. PWT 10.0 considers adjustment 3 as the best measure for Taiwan.
20

See the details in Feenstra et al., 2015, Appendix C.

5• Adjustment 4: Assume that all self-employed work takes place in the agricultural sector.
Thus, the entire value added in agriculture is added to labor compensation. PWT 10.0
considers Adjustment 4 the best measure for Korea, Hong Kong, and Singapore.
Table E.2 shows the labor shares PWT 10.0 constructs for each economy. PWT 10.0 chooses
what it considers the best measure of labor income in each economy from the five adjustments,
and uses it to construct the TFP.
Table E.2: Labor shares reported by PWT 10.0
Different Adjustment Methods
EconomySample Period

Naive
(1)

Adj 1
(2)

Adj 2
(3)

Adj 3
(4)

Adj 4
(5)

U.S.
China
Japan
Korea
TaiwanHong Kong
Singapore

1995-20191995-2019
1950-20191972-20191962-20191960-20191960-2019

0.56
0.58
0.51
0.46
0.53
0.48
0.42

0.64

0.61

0.63

0.6

0.6
0.61
0.68

0.57
0.7
0.54
0.55

0.73
0.54
0.52

Best Labor Share
Method

Value

Adj 2
Naive
Adj 2
Adj 4
Adj 3
Adj 4
Adj 4

0.61
0.58
0.6
0.55
0.73
0.48
0.43

0.48
0.43

Figure E.1 plots the different labor shares from PWT 10.0. In a blog entry, Restrepo andReinbold also report Adjustment 4 for Taiwan, which is very similar to the naive share.21 However,
we did not find such information in PWT 10.0 to replicate their computations in a modelconsistent way.

E.2

Discussion of Adjustment of Labor Income Shares

China: China’s National Bureau of Statistics provides annual data on the labor share for eachprovince but not for the aggregate economy. Bai et al. (2006) construct the aggregate labor shareas the average of the provincial labor shares weighted by each province’s GDP share. This gives
an average labor income share of 0.5.
Before 2004, the National Bureau of Statistics counted all self-employment income as labor
income, corresponding to Adjustment 1 by PWT 10.0’s definition. Thus, the estimates of 0.5
from Bai et al. (2006) overstate the true labor share and understate the true capital share. In
2004, the National Bureau of Statistics, for the first time, explicitly excluded the imputed capitalincome of self-employed workers from the estimates of labor income. Unfortunately, the National
Bureau of Statistics does not report the magnitude of this adjustment, and we cannot adjust ourlabor share estimates for the years before 2004.
21

https://www.stlouisfed.org/on-the-economy/2018/january/measuring-labor-share-asian-tigers

6

PWT Labor Share:CHN

0.9

0.8

0.8

0.7

0.7

0.6

0.6

0.5

0.5

0.4

0.2
1995

0.4

Best
Naive
Adjustment 1
Adjustment 2
Adjustment 3
Adjustment 4

0.3

2000

2005

2010

2015

0.2
1950

2020

0.8

0.7

0.7

0.6

0.6

0.5

0.5

0.2
1970

1980

1990

2000

2010

0.2
1960

2020

0.7

0.5

0.5

0.4

0.4

0.3

0.3

1980

1990

2000

1970

2010

1980

0.2
1960

2020

2010

2020

1990

2000

2010

2020

PWT Labor Share:SGP
Best
Naive
Adjustment 1
Adjustment 2
Adjustment 3
Adjustment 4

0.7

0.6

1970

2000

0.8

0.6

0.2
1960

1990

Best
Naive
Adjustment 1
Adjustment 2
Adjustment 3
Adjustment 4

0.9
Best
Naive
Adjustment 1
Adjustment 2
Adjustment 3
Adjustment 4

0.8

1980

PWT Labor Share:TWN

0.3

PWT Labor Share:HKG

0.9

1970

0.4

Best
Naive
Adjustment 1
Adjustment 2
Adjustment 3
Adjustment 4

0.3

1960

0.9

0.8

0.4

Best
Naive
Adjustment 1
Adjustment 2
Adjustment 3
Adjustment 4

0.3

PWT Labor Share:KOR

0.9

PWT Labor Share:JPN

0.9

1970

1980

1990

2000

2010

2020

Figure E.1: Labor shares reported by PWT 10.0
PWT 10.0 uses the naive labor share (0.58) for China as the best labor share, which meansthat labor income only includes the labor compensation of employees and does not include selfemployed income at all. However, as explained above, this number is not reported in China’s
official data; it is unclear whether this is a label mistake or some adjustment has been made.
Taiwan: PWT 10.0 uses Adjustment 3 for Taiwan as the best labor share, which gives alabor share of 0.73. The “same-wage” assumption may not be too far off the mark in advanced
economies, where the share of employees in the total number of persons engaged (employees +self-employed) is 85-95%. However, this share is below 50% in many emerging economies and as7

low as 4%. In those economies, using the information on the wages of employees will overstate
the labor income of the self-employed. Hence, the labor share of Taiwan is likely to be too high.
Singapore: PWT 10.0 follows Adjustment 4 by adding the entire value added in agriculture
to labor compensation, assuming that all of the self-employed work in Singapore takes place inagriculture. This gives a labor share of 0.43, which is still too low as the labor income of theself-employed outside agriculture is largely ignored. Moreover, the effect of such an adjustment
is minimal. The adjusted labor share is very close to the labor share before adjustment, which
is 0.42. In the exercises in Subsection F.4, we pick a more conventional value for labor income
share of 0.6, nearly the same as the one in the U.S.

F

Transitional Dynamics in China, Taiwan, Hong Kong,and Singapore

F.1

China: Alternative capital income share

In this section, we set China’s capital income share to 0.42, the value from PWT 10.0. Figure F.1
shows the new transitional dynamics. Now the model delivers income growth that is a bit lower
than in the data (top left panel) but matches the observed TFP much better (bottom left panel).
In comparison, the model does a much worse job matching the observed investment rates. The
explanation is transparent: with θ = 0.42, the production function is more concave in capital.
Thus, the social planner does not want to invest that much in capital, and the resulting growth
is lower.

F.2

TaiwanIn this section, we show the results for Taiwan when we use the capital income share directlyfrom PWT 10.0. Figure F.2 shows that the model hugely underestimates observed growth, as
the marginal productivity of capital in the model falls very rapidly.
Figure F.2 justifies our choice of disregarding the value of θ reported in PWT 10.0 for Taiwan:
the behavior that it would imply for growth in our model (or, for that matter, any other commonlyused growth model) is sharply counterfactual.

8

Income Per Capita relative to the U.S.

0.55

Income Per Capita Growth Rate

0.14

Model
Data

0.5
0.12

0.45
0.4

0.1

0.35
0.3

0.08

0.25
0.06

0.2
0.15

0.04
Model
Data

0.1
0.05

0.02
2000

2010

2020

2030

2040

2000

2010

TFP relative to the U.S.

2020

2030

2040

I/Y

0.7
0.5
0.6
0.4
0.5
0.3
0.4

0.2
0.3

0.1

0.2

Model
Data: PWT
Data: NBS

Model
Data

0.1

0
2000

2010

2020

2030

2040

2000

2010

2020

2030

2040

Figure F.1: China: Transitional dynamics with θ = 0.42

F.3

Hong Kong

Our next set of results deals with Hong Kong, where we use the capital share, θ = 0.51, directlyfrom PWT 10.0. In the top left panel of Figure F.3, the model forecasts well Hong Kong’s income
per capita, although there is a growing gap at the end of the sample, where it seems that theeconomy of the city is falling behind what one would expect. The model also does a good job
matching TFP relative to the U.S.
The main divergence between the data and the model is in investment, which seems much
lower in the data than in the model. However, since Hong Kong is heavily dependent on tradeand services, our model might need to be modified to capture all the peculiarities of Hong Kong’s
data on investment. For example, investments in factories close to but not in Hong Kong (i.e.,
the Chinese hinterland of Hong Kong) might not be properly recorded in Hong Kong’s statistics.

9

Income Per Capita relative to the U.S.

0.8

Income Per Capita Growth Rate

0.14

Model
Data

0.12
0.7
0.1
0.6

0.08
0.06

0.5

0.04
0.4

0.02
0

0.3

-0.02
0.2
Model
Data

-0.04

0.1

-0.06
1970

1980

1990

2000

2010

1970

1980

TFP relative to the U.S.

1990

2000

2010

I/Y

0.9
0.5
0.8

0.4

0.7

0.6

0.3

0.5
0.2
0.4
0.1
0.3
Model
Data

Model
Data

0.2

0
1970

1980

1990

2000

2010

1970

1980

1990

2000

2010

Figure F.2: Taiwan: Transitional dynamics with θ = 0.27

F.4

Singapore

We close our investigation with Singapore, with results in Figure F.4. As in previous cases, themodel does a fair job of matching income per capita (top left panel and TFP relative to the
U.S. (bottom left panel). In the case of income per capita, Singapore outperforms the modelfrom around 2000 to 2010. However, after 2010, growth relative to the U.S. stopped. Strikingly,
Singapore seems to have converged very quickly to 90% of the U.S. TFP (with convergence tothis level completed by the mid-1980s). See Young (1995) for an early paper that noticed thispattern.
Also, the model does not do a great job with investment (bottom right panel), at least until
the early 2000s. In contrast with the Hong Kong case, now the model underestimates the extent
of investment in the data for many years. Young (1992) documented, several decades ago, someof the big differences between the growth process of these two economies despite their apparent
similarities.

10Income Per Capita relative to the U.S.

1.3

Income Per Capita Growth Rate

0.15

Model
Data

1.2
0.1

1.1
1

0.05

0.9
0.8

0

0.7
-0.05

0.6
0.5

-0.1
Model
Data

0.4
0.3

-0.15
1970

1980

1990

2000

2010

1970

1980

TFP relative to the U.S.

1990

2000

2010

I/Y

0.7
0.5
0.65

0.4

0.6

0.55

0.3

0.5
0.2
0.45
0.1
0.4
Model
Data

Model
Data

0.35

0
1970

1980

1990

2000

2010

1970

1980

1990

2000

2010

Figure F.3: Hong Kong: Transitional dynamics

F.5

Taking stock

To sum up, our minimalist model has more difficulties in the cases of Hong Kong and Singapore,but even for these two economies, the model can capture the slowdown in growth that we forecast
for China.

11

Income Per Capita relative to the U.S.

1.4

Income Per Capita Growth Rate

0.35

Model
Data

0.3
1.2
0.25
1

0.2
0.15

0.8

0.1
0.6

0.05
0

0.4

-0.05
0.2
Model
Data

-0.1

0

-0.15
1970

1980

1990

2000

2010

1970

1980

TFP relative to the U.S.

1990

2000

2010

I/Y

1

0.7

0.6

0.9

0.5
0.8
0.4
0.7
0.3
0.6
0.2
0.5

0.1
Model
Data

Model
Data

0.4

0
1970

1980

1990

2000

2010

1970

1980

1990

2000

Figure F.4: Singapore: Transitional dynamics θ = 0.4

122010