WPS8240
Policy Research Working Paper 8240
Korea’s Growth Experience and Long-Term
Growth Model
Hyeok Jeong
Development Research Group
Macroeconomics and Growth Team
November 2017
Policy Research Working Paper 8240
Abstract
This paper analyzes the Republic of Korea’s rapid and sus- productivity growth for the following periods. The major
tained growth experience for the past six decades from the sources of sustained growth over six decades were human
perspective of the neoclassical growth model (the work- capital accumulation and productivity growth rather than
horse model of the World Bank’s Long Term Growth Model labor or capital investment. A counterfactual calibration
(LTGM) project). Overall, the sources of Korea’s growth of the model explains Korea’s actual growth experience
were balanced among labor market and demographic fac- well, and shows why gaps between the model’s predictions
tors, capital investment, human capital accumulation, and and the data arise. This illustrates that an appropriate cal-
productivity growth. However, the main engine of growth ibration of a simple neoclassical growth model provides
evolved sequentially, e.g., labor and human capital factors useful lessons and tools for policy makers in developing
in the 1960s, capital deepening in the 1970s, and then countries in designing their national development strategies.
This paper is a product of the Macroeconomics and Growth Team, Development Research Group. It is part of a larger
effort by the World Bank to provide open access to its research and make a contribution to development policy discussions
around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors
may be contacted at hyeokj@gmail.com.
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development
issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the
names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those
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its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
Produced by the Research Support Team
Korea’s Growth Experience and Long-Term Growth Model
Hyeok Jeong∗
Seoul National University
∗
This work was supported by the Global Facility on Growth for Development project between the World
Bank Group and Korea Development Institute [PO 7179114]. We appreciate the helpful comments from Steven
Pennings, Luis Serven, Jungsoo Park, audience from various conferences, and the policy-makers from Bangladesh
and Zambia. JEL Classiﬁcation: O11, O47, O53, J24. Keywords: Korea’s growth experience, Sustainable
development, Long-Term Growth Model, Growth policy, Human capital, Productivity, Capital deepening, Labor
market demography. Corresponding address: Seoul National University, Graduate School of International Studies,
Gwanak-ro 1, Gwanak-gu, Seoul 08826, Korea. E-mail: hyeokj@gmail.com.
1 Introduction
A casual observer of the Republic of Korea’s remarkable development experience, which Lucas
(1993) indeed called a ”miracle,” is often impressed by its rapid and compressed growth experi-
ence but often overlooks two important features of Korea’s development process: (i) how much
adverse Korea’s initial conditions were and (ii) the sustainability, not just the speed, of growth
which has continued for about 60 years, overcoming various kinds of adverse initial conditions.
In fact, this is exactly why Korea’s development experience is valuable for other developing
countries.
During the colonial era, Korea’s cultural heritage and various kinds of the autonomous initia-
tives of development were adversely aﬀected, which in turn resulted in the suppression of human
capital formation, entrepreneurship and its own capacity of nation building. After the end of
the colonial era, Korea again suﬀered from internal and external ideology ﬁghts, leading to a
massive civil war, the Korean War, for three years.1 After the end of the nationwide civil war,
Korea was divided into two regions, which has been limiting the scope of economies of scale for
national development. Korea tried to recover from the scar of the war and to reconstruct itself.
(Hereafter, we will simply refer to the Republic of Korea as ”Korea”.) However, various kinds of
corruption and disorder prevailed in Korea, which gave an excuse for the military to intervene in
politics and a military coup overturned the government, followed by a series of political turmoils.
In sum, the list of Korea’s initial conditions includes almost all sorts of barriers to development
such as colonial experience, civil war, corruption, a lack of physical and human resources, and
political instability, which are recognized as major hurdles to development for most of the cur-
rent developing countries. Korea was truly a devastated and poor nation when it ﬁrst engaged
in taking oﬀ to the path of miraculous growth, unaware of what would be coming.
Not all developing countries could achieve such performance of development after the end of
the Second World War when many developing nations became independent. Global environments
1
The entire peninsula of Korea was the war ﬁeld during the Korean War, which resulted 5.1 million casualties,
16% of residential structures and 40% of manufacturing factories and equipment were destroyed in the Republic
of Korea, and 74% of electricity facilities, 89% of energy factories, and 70% of chemical factories were damaged
in the Democratic People’s Republic of Korea, when the War ended in 1953.
1
have changed and each developing country faces diﬀerent kinds of challenges and development
goals in the context of its own history. Thus, Korea’s development experience per se would not be
of any help for current developing countries. However, understanding the underlying mechanisms
of such successful growth of Korea would be useful. This paper is an attempt to contribute to
such understandings in the context of World Bank’s recent initiative of the Long-Term Growth
Model (LTGM) project.2
The World Bank’s LTGM project aims to help the policy makers of developing countries
design their national macroeconomic development policies from the perspective of the neoclassical
growth model. By predicting the future growth paths stemming from the desired changes of
investment and/or labor market policies such as the promotion of labor force participation,
policy makers can better envision and quantify their development goals. This kind of quantitative
policy design would be a great help in articulating their policy goals and also in materializing
the actual changes. Furthermore, an explicit use of a structural growth model in doing this
kind of quantitative exercises is clearly beneﬁcial. At the same time, however, calibration of
the structural model is always a challenge, particularly for prediction purposes in response to
policy changes. Therefore, it would be useful to see if such an exercise can in fact be applied
to a previous development experience for a country which already achieved the development
goals that current developing countries are aiming for now. In this sense, the results of the
application of the LTGM to Korea’s development experience would deliver useful messages for
other developing countries. This is the goal of this paper.
We ﬁrst describe the canonical neoclassical growth model, which is the basis of the LTGM,
as our accounting framework of this paper in Section 2. This model will be applied to Korea’s
economic growth for the 1960-2014 period to identify the underlying sources of Korea’s GDP
per capita growth in Section 3 by implementing a counterfactual decomposition analysis. Based
on the analysis of Section 3, we calibrate Korea’s economic growth in two ways to evaluate the
performance of the LTGM in Section 4. First, we use the model as a tool for simulating Korea’s
2
The LTGM is an Excel-based tool that allows users to simulate future long-term growth for most of the
world’s developing and emerging economies, building on the neoclassical growth model. See Pennings (2017)
for a model description and contact LTGM@worldbank.org or Steven Pennings (spennings@worldbank.org) for
further information. The LTGM builds on earlier work by Hevia and Loayza (2012).
2
growth process and evaluate the ﬁt depending on the period of simulation as well as the policy
dynamics. Second, we evaluate the model as a prescriptive tool to identify the inﬂuences of
various sources of growth via the lens of Korea’s 55 years of growth experience. Both types of
calibration exercises illuminate the nature of the LTGM in analyzing the future growth process
of developing countries. Section 5 concludes.
2 Neoclassical Growth Model as an Accounting Frame-
work
We consider the standard neoclassical growth model based on the aggregate production function,
which was ﬁrst proposed by Solow (1956) postulating the relationship between inputs and output
at aggregate levels such that
Yt = At Xt (1)
where Yt denotes the aggregate output, Xt the composite input (sometimes referred as ”total
factor”), and At the total factor productivity (TFP) at period t. The composite input Xt consists
of capital Kt and eﬀective unit of labor Lt such that
Xt = F K t , L t .
where the production function satisﬁes the canonical properties of the neoclassical growth model,
i.e., (i) monotonicity, (ii) diminishing returns and (iii) constant returns to scale.3
The capital is accumulated according to the following law of motion
Kt+1 = It + (1 − δ ) Kt , (2)
where It denotes the capital investment and δ the depreciation rate of existing capital stock.
The diminishing returns property is the key property of the neoclassical growth model which
3
The property of ”monotonicity” means that addition of capital and labor contributes to increasing output (i.e.,
∂F ∂F
∂K ≥ 0 and ∂L ≥ 0 for all K and L). The property of ”diminishing returns” means that the marginal contribution
∂ ∂F ∂ ∂F
of adding more inputs decreases as the amount of inputs increases (i.e., ∂K ∂K < 0 and ∂L ∂L < 0). The
property of ”constant returns to scale” means proportional changes in all inputs at the same time induces the
same proportion of changes in output (i.e., F cKt , cLt = cF Kt , Lt for all c > 0).
3
stabilizes the growth dynamics to exogenous shocks. Owing to this property, the incremental
capital decreases over time unless there exists strong enough growth in TFP.
We can further decompose the eﬀective unit of labor into human capital per worker ht and
the employment size Lt (measured by the number of workers) such that Lt = ht Lt , in other
words Xt = F (Kt , ht Lt )
There is no direct data for the TFP variable, hence it is typically measured by the residual
such that
Yt
At = . (3)
F (Kt , ht Lt )
We do not need to assume any functional form on the production function F to perform the
standard growth accounting. However, to facilitate the measurement of the level of the TFP, we
have to choose a functional form for F . The most standard functional form for the aggregate
production function is the Cobb-Douglas form such as
Yt = Kt1−β (AL,t ht Lt )β , (4)
where the only parameter β corresponds to the labor share in national income account, and AL,t
denotes the labor-augmenting technology level. This can be re-expressed such that
Yt = At Xt ,
At = Aβ
L,t , (5)
Xt = Kt1−β (ht Lt )β ,
which has the same form of representation as in equation (1). In per worker terms, we can also
re-express the Cobb-Douglas production function such that
1−β β
yt = At kt ht (6)
where yt = Yt /Lt and kt = Kt /Lt . Another way to represent the output per worker is
1−β
yt = AL,t (Kt /Yt ) β ht . (7)
From equation (6), we can derive the following (and typical) growth accounting formula
yt = At + (1 − β ) kt + β ht , (8)
4
dyt /dt
where the ”hat” notation denotes the growth rate of the corresponding variable, e.g., yt ≡ yt
.
From equation (7), we can derive another growth accounting formula
1−β K
yt = ALt + + ht . (9)
β Y t
From equation (5), the typical TFP growth rate At is related to the labor-augmenting produc-
tivity growth ALt as follows
At = β ALt . (10)
The equations (8) and (9) show two diﬀerent ways of expressing the growth accounting of
output per worker. Each formula serves its own purpose of decomposing the growth of output
per worker. Equation (8) is to be used when we want to quantify the contributions of each
factor and the TFP to the growth of output per worker at surface level. However, this formula
has a limitation. The growth of capital per worker can be induced by the growth of the TFP
because the increase in TFP raises the marginal product of capital. Therefore, the observed
growth of capital is an outcome of two eﬀects, ﬁrst the pure investment eﬀect and second the
TFP-induced eﬀect. If we want to isolate the genuine contribution of capital accumulation
purely from investment, we should use the second formula, which decomposes the growth of
output per worker into pure productivity growth eﬀect ALt , human capital growth eﬀect ht , and
1−β K
capital-deepening eﬀect β Y t
. The capital-deepening eﬀect isolates the genuine capital
accumulation eﬀect because the increase in productivity would directly raise the output but
also the capital owing to the increase in marginal product of capital. Thus, the capital-output
K
ratio Y
increases, this would capture the genuine eﬀect of capital growth due to the capital
1−β K
investment. This is the intuition behind considering the capital-deepening eﬀect β Y t
as
the genuine capital accumulation eﬀect. We will use this version of growth accounting formula
as our benchmark framework in accounting for the growth of output per worker.
The conventional measure of the level of development or national welfare is the GDP per
capita yP,t ≡ Yt /Nt (where Nt is the total population size) rather than the GDP per worker
yt ≡ Yt /Lt above. GDP per capita diﬀers from GDP per worker by the two demographic
features of the labor market, (i) the labor force participation rate SE,,t ≡ Lt /NL,t and (ii) the
working-age population share SW,t ≡ NL,t /Nt , where NL,t is the working-age population (age
5
group of 15-64) size, and Lt is the labor force size such that
yP,t = SW,t SE,,t yt , (11)
and in growth terms
yP,t = SW,t + SE,,t + yt .4 (12)
3 Analysis of Korea’s Economic Growth5
3.1 Data
Equations (9) and (12) are our framework accounting for Korea’s economic growth (which can
be used for analyzing any country’s growth). To quantify these equations, we need the following
data series for our sample period 1960-2014, and their sources are in brackets as follows: (1)
total population size [World Development Indicators (WDI)] for Nt , (2) working-age population
share [WDI] for SW,t , (3) labor force participation rate [WDI] for SE,,t , (4) real GDP at constant
2011 national prices (in 2011 million US$) [”rgdpna” in Penn World Table version 9.0 (PWT
9.0)] for Yt , (5) capital stock at constant 2011 national prices (in 2011 million US$) [”rkna” in
PWT 9.0] for Kt , (6) human capital per worker [”hc” in PWT 9.0] for ht , (7) labor force size
[WDI] for Lt , (8) labor share [”labsh” in PWT 9.0] for β , (9) capital depreciation rate [”delta”
in PWT 9.0] for δ , (10) labor-augmenting technology level [calculated from equation (4)], and
(11) investment [calculated using investment rate data ”csh i” from PWT 9.0].6 The value of the
average labor share which we calibrate for the parameter β is 0.602. The value of the average
depreciation rate which we calibrate for the parameter δ is 0.053.
4
We use labor force data from WDI for Lt to maintain the consistency with the data use protocol of the
LTGM project so that there are possible diﬀerences in labor force participation rate between the national sources
and the WDI. Furthermore, using labor force instead of employment data may generate the diﬀerent growth rate
of SW,t . However, using the national source data, we ﬁnd that labor force participation rate and employment
rate tightly co-move with each other and the growth rates of SW,t between the two measures diﬀer only by 0.1%
for the sample period.
5
Part of the analysis of this section is based on Jeong (2016).
6
Original data source of the WDI labor variables such as working-age population, labor force participation
rate is the International Labor Organization (ILO) Statistics. The labor share and the capital depreciation rate
variables are time-varying in PWT 9.0 and we take the time-series averages during our sample period 1960-2014.
6
3.2 Features of Korean Economic Growth
Measuring the size of the Korean economy by the total GDP, Korean economy’s size increased
by 44 times for the 1960-2014 period. Our measure of GDP is the real GDP at constant 2011
national prices in 2011 million US$ value. Measuring the individual standard of living by the
GDP per capita, Korea’s standard of living increased by 22 times from $1,557 in 1960 to $34,300
in 2014, implying the annual average growth rate of 6% for about two generations. Thus, Korea’s
economic growth has been not only rapid but also sustained. The rapid growth of the Korean
economy is well-known. However, the sustaining feature of Korean growth is rather less so. The
labor productivity, measured by the GDP per worker, grew by 4.8% each year on average.
Figure 1 plots the path of Korea’s annual growth rate of the GDP per capita with the
quartic-ﬁt time trend, which shows a hump-shaped trend.7 That is, the growth of GDP per
capita accelerated for the 1960-1980 period and then gradually slowed down afterwards. There
are two noticeable dips in Figure 2: the ﬁrst one at 1980 and the second one at 1998. These
were the only episodes of negative growth during Korea’s development experience after the year
1960. There was a serious cold weather shock combined with the political turmoil during the
1979-1980 period, and the well-known Asian ﬁnancial crisis during the 1997-1998 period. At the
same time, Figure 1 also shows that the recovery from those adverse shocks was very fast in
Korea.
Figure 2 plots the path of Korea’s GDP per capita and that of GDP per worker, which shows
that the growth of Korea’s GDP per capita is mainly driven by the GDP per worker. There are
three sources of GDP per worker growth as in equation (7): (1) capital-output ratio, (2) human
capital per worker and (3) labor-augmenting technology (which is linked to TFP as in equation
(5)). Figures 3 to 5 display the growth of these three sources of growth of GDP per worker,
respectively.
Figure 3 plots the capital-output ratio (”K/Y”) and the investment rate (”IR”) together with
their quartic-ﬁt trends. This ﬁgure shows that Korea’s capital-output ratio almost monotonically
increased from 1.31 in 1960 to 3.88 in 2009, and then slightly decreased to 3.85 in 2014. The
7
By the ”quartic-ﬁt time trend,” we mean the ﬁtting regression line of the GDP per capita against the
fourth-order polynomial function.
7
Figure 1: Growth Rate of Korea’s GDP Per Capita
.15
.1
.05
0
-.05
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015
Year
Trend Actual
8
Figure 2: Role of GDP Per Worker for Korea’s GDP Per Capita Growth
40000
80000
30000
60000
GDP per worker
GDP per capita
20000
40000
10000
20000
0
0
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015
Year
GDP per capita GDP per worker
9
Figure 3: Korea’s Investment Rate and Capital-output Ratio
.5
4
.4
Capital-Output Ratio
Investment Rate
3
.3
2
.2
.1
1
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015
Year
IR Trend IR Actual
K/Y Trend K/Y Actual
average investment rate for the entire sample period is 31.5%. However, the investment rate
was not constant over time. It ﬁrst increased sharply from 11.7% to 43% in 1979, and then
gradually decreased to 30.4% by 2014 with ﬂuctuation. The sharp rise of Korea’s investment
rate in the 1970’s was mainly driven by the industrial policies in relation to export promotion
and establishment of heavy-and-chemical industries.
The human capital per worker (which is the rate-of-return weighted total years of schooling
index) monotonically increased throughout the sample period at the annual average growth rate
of 1.52%, but in a concave way, i.e. the growth rate of human capital has decreased over time from
10
Figure 4: Korea’s Human Capital Growth
.03
3.5
Growth Rate of Human Capital
.025
Human Capital Level
3
.02
2.5
.015
2
.01
1.5
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015
Year
HC Trend HC Actual
HC Growth Trend HC Growth Actual
2.7% in 1960 to 0.9% in 2014, as is shown in Figure 4. Figure 5 shows that the labor-augmenting
technology level (what we would call ”productivity” which has a one-to-one relationship with
the TFP) also almost monotonically increased by 2.8 times, implying the annual average growth
rate of 1.91%. Unlike the human capital growth, the path of the productivity growth rate does
not show much salient trends. It is just mildly hump-shaped.
There are two labor market demographic factors to the GDP per capita growth other than
the GDP per worker growth, i.e., the changes of working-age population share and those of labor
force participation rate, which are displayed in Figures 6 and 7.
11
Figure 5: Korea’s Productivity Growth
Growth Rate of Labor-augmenting Technology
3000 4000 5000 6000 7000 8000
.15
Labor-augmenting Technology Level
-.1 -.05 0 .05 .1
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015
Year
AL Trend AL Actual
AL Growth Trend AL Growth Actual
12
Figure 6: Korea’s Working-age Population Share
.75
.7
.65
.6
.55
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015
Year
Trend Actual
13
Figure 7: Korea’s Labor Force Participation Rate
.7
.65
.6
.55
.5
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015
Year
Trend Actual
14
The working-age population share was stagnant in the 1960s but increased rapidly in the 1970s
and 1980s, and then the increasing speed slowed down until 2014. Overall Korea’s working-age
population share increased from 55% in 1960 to 73% in 2014. The labor force participation
rate also increased during the sample period from 52% in 1960 to 68% in 2014.8 , 9
Thus, the
changes in these labor market demographic factors positively contributed to growth of the GDP
per capita as shown in equation (12).
3.3 Decomposition Analysis
Applying our accounting framework of equations (9) and (12) to the above data, we can decom-
pose Korea’s growth of GDP per capita for the 1960-2014 period by constructing counterfactual
GDP per capita measures as follows. Combining equations (7) and (11), we express the GDP
per capita such that
1−β
yP,t = SW,t SE,,t AL,t (Kt /Yt ) β ht . (13)
In order to isolate the contribution of productivity growth to GDP per capita growth, we ﬁx the
values capital-output ratio, human capital per worker, working-age population share and labor
force participation rate at the 1960 values and vary only the labor-augmenting technology level
as in the data. That is, the counterfactual GDP per capita measure due to the productivity
changes is
1−β
AL
yP,t = SW,1960 SE,,1960 AL,t (K1960 /Y1960 ) β h1960
8
Not surprisingly, this increase of labor force participation rate was due to the rise of the female labor force
participation rate from 39% in 1960 to 57% in 2014. The male labor force participation rate increased from
75% in 1960 only to 79% in 2014. Even in the year of 2014, there still exists a substantial gap in labor force
participation rate between men and women, although the gap dropped signiﬁcantly since 1960.
9
We observe a noticeable up and down in labor force participation rate between the mid-1970s and early 1980s.
Using the national data sources of population census and labor force survey data from the Korean Statistical
Information Service (KOSIS), we ﬁnd that this is an outcome of the combination of the WDI data issue and the
reality of the Korean economy. The WDI labor force data for Korea for the mid-1970s is a little overestimated,
which generates the more rapid rise of the labor force participation rate than the trend. (It seems to happen
because the WDI working-age population and labor force data are based on the estimates from the UN population
project and ILO, which can be diﬀerent from the ex-post national census and surveys.) However, the fall in the
labor force participation rate (as well as in the employment rate) for the early 1980s reﬂects the actual recession
of the Korean economy. In 1979, President Park was assassinated and there was a military coup in the following
year, which generated economic instability. Furthermore, there was unprecedented cold-weather damage in 1980.
15
and the growth rate of this counterfactual measure is
AL
yP,t = AL,t .
We can similarly construct counterfactual measures of GDP per capita due to the changes of
other components. Figure 8 plots those counterfactual GDP per capita measures for each of the
ﬁve components of productivity (labeled as ”AL”), human capital per worker (labeled as ”HC”),
capital deepening (labeled as ”K/Y”), working-age population share (labeled as ”WAP”), and
labor force participation rate (labeled as ”LFP”). Table 1 summarizes the growth rates of the
actual and the above counterfactual measures of GDP per capita for the entire period as well as
for each of the sub-period by decade (1960s, 1970s, 1980s, 1990s and 2000s) and the remaining
2010-2014 period.
Figure 8 and Table 1 reveal some interesting features of Korea’s economic growth, which have
not been recognized before. First, the largest contributing component is productivity growth
(1.9% each year on average) rather than factor growth during the entire sample period. The
second largest contributing component is human capital growth (1.5% each year on average),
and then the third one is the capital deepening eﬀect (1.3% each year on average). Second,
despite such contribution ordering, the magnitudes of contribution are all substantial for each
component, and they are more or less similar among these three components. That is, there has
been no single dominant component during the process of Korea’s economic growth. Further-
more, the contribution of the two labor market demographic factors, increases in working-age
population share and labor force participation rate combined, to increasing Korea’s GDP per
capita by 1.0% each year on average, which is not a small magnitude.
Comparing the contributions of the ﬁve growth components across sub-periods, major con-
tributing components are diﬀerent over time. In the initial development stage of the 1960’s,
human capital growth was the major driving force of Korea’s growth, increasing GDP per capita
by 2.2% each year on average. However, the human capital growth eﬀect gradually but mono-
tonically decreases afterwards. In the 1970’s, however, capital deepening was the main source
of growth, contributing to GDP per capita growth by 3% each year on average. The capital
deepening eﬀect dropped remarkably to 0.8% in the 1980s, surging back to 1.9% in the 1990s,
16
Figure 8: Counterfactual Measures of GDP per Capita
1500 2000 2500 3000 3500 4000 4500
Counterfactual GDP per Capita (2011 USD)
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015
Year
AL HC K/Y WAP LFP
Note (1) Each line represents the counterfactual path of GDP per capita from the isolated growth of each variable.
Note (2) ”AL”: Productivity growth of labor-augmenting technology, ”HC”: Human capital growth, ”K/Y”:
Capital deepening, ”WAP”: Changes of working-age population share, ”LFP”: Changes of labor force participa-
tion rate.
17
Table 1: Decomposition of Sources of Korea’s Growth of GDP per Capita (%)
Period Total WAP LFP AL HC K/Y TFP
1960-2014 5.9 0.5 0.5 1.9 1.5 1.3 1.1
1960-1970 5.0 -0.1 1.2 0.8 2.2 1.0 0.5
1970-1980 7.4 1.3 -0.3 1.2 1.9 3.0 0.7
1980-1990 8.6 1.1 1.1 3.7 1.7 0.8 2.2
1990-2000 6.0 0.3 0.2 2.3 1.2 1.9 1.4
2000-2010 3.9 0.1 0.2 2.2 0.8 0.5 1.3
2010-2014 2.5 0.1 0.8 0.5 0.9 0.3 0.3
Note (1) Each column represents the contribution of each variable to
the annual average growth rate of GDP per capita.
Note (2) ”Total”: Total growth of GDP per capita, ”WAP”: Contribu-
tion of changes of working-age population share, ”LFP”: Contribution
of changes of labor force participation rate, ”AL”: Contribution of pro-
ductivity growth of labor-augmenting technology, ”HC”: Contribution
of human capital accumulation, ”K/Y”: Contribution of capital deep-
ening, ”TFP”: Contribution of total factor productivity (which is equal
to the labor share times ”AL”)
18
and then diminished to 0.5% for the 2000s period and further to 0.3% for the 2010-2014 period.
For the rest of sub-periods of the 1980s, 1990s and 2000s, productivity growth was the main
engine of Korea’s growth, neither the capital deepening nor the human capital growth. In the
1980s, productivity growth only increased the GDP per capita by 3.7% per year on average.10
The contribution shares of the productivity growth out of the total growth of the GDP per capita
were 43%, 38% and 56% during the 1980s, 1990s, and 2000s, respectively. These contrasting
ﬁndings of the changing contributions between productivity growth and factor growth together
with the decreasing magnitudes of human and physical capital growth may signal that the forces
of diminishing returns have become stronger in the process of Korea’s economic growth.
4 Calibration of Korea’s Economic Growth
4.1 LTGM of the World Bank
The neoclassical growth model that we used as an accounting framework in analyzing Korea’s
economic growth can also be used as a simulation device for the future growth if we can make
a reasonable conjecture about the parameter values of the model that will govern in the future.
The other way of using the same model is to make inferences about the future policies regarding
the parameter values that are needed to reach the pre-set growth goal in the future. This way
of utilizing the neoclassical growth model is recently labeled as the ”Long-Term Growth Model
(LTGM)” approach by the World Bank for the purpose of helping policy makers in developing
countries to design their macroeconomic growth policies.
In terms of contents of the model, the World Bank’s basic LTGM is just the same as the
neoclassical growth model in Section 2. How to use such model for prediction or policy design
purposes depends on how to calibrate the model. This kind of calibration is not an easy exercise
because we need to calibrate the model to ﬁt the future that we do not observe at the moment
of calibration. The analysis of Korea’s economic growth in Section 3 can be utilized in ﬁnding
the right ways of calibrating the model in the following sense. Suppose there were policy makers
10
For the purpose of comparison of this productivity growth measure with the standard TFP growth, the last
column of Table 1 reports the standard TFP growth implied by our productivity growth measure as in equation
(10).
19
in the past in Korea, say in 1970, who wanted to predict what would happen to GDP per capita
growth after 1970 and the only available information set was the data for the 1960-1970 period.
Then, we may ask what would be the best way for them to calibrate the underlying parameters
of the model. We can answer this question because unlike the ﬁctitious policy makers in 1970, we
in fact know what actually happened after 1970 in Korea so that we can evaluate the calibration
method by evaluating the prediction performance against the actual data.
4.2 Objects of Calibration
We ﬁrst need to determine the set of parameters to calibrate. The GDP per capita at period t
is as in equation (13)
1−β
yP,t = SW,t SE,,t AL,t (Kt /Yt ) β ht
and the gross growth rate of the GDP per capita between period t and t + 1 is
Yt 1−β
yP,t+1 β
γt K t
+ (1 − δ )
= Λt+1 (14)
yP,t 1 + Nt+1
where
Λt+1 = 1 + SW,t+1 1 + SE,t+1 1 + AL,t+1 1 + ht+1 ,
γt is the investment rate at period t, and Nt+1 , SW,t+1 , SE,t+1 , AL,t+1 , and ht+1 are the growth
rates of population, working-age population share, labor force participation rate, productivity,
and human capital between periods t and t + 1, respectively. The growth equation (14) clari-
ﬁes two things. First, the growth rate of GDP per capita increases in investment rate γt , but
this growth eﬀect decreases in Kt /Yt , i.e., the capital-output ratio of the base year. The latter
decreasing growth eﬀect from investment captures the diminishing returns property of the neo-
classical growth model. Second, it increases in growth rates of working-age population, labor
force participation rate, productivity, and human capital but decreases in population growth
rate.
Now in order to simulate the growth path using equation (14), we need to select the pa-
rameters (1 − β, δ ) and to calibrate the growth rates of Nt+1 , SW,t+1 , SE,t+1 , AL,t+1 , and ht+1 .
When we substitute these growth rates with the actual data, we will get the precise growth rate.
For the purpose of simulation, we should choose a way to calibrate the growth rates of these
20
ﬁve growth variables at period t + 1 as well as the time-invariant parameters 1 − β and δ from
the observed data. Furthermore, to apply growth equation (14) to the next period at period
t + 2, we need to calibrate γt+1 also. Typical neoclassical growth models assume that AL,t+1 and
Nt+1 are constant for all periods (we may make similar assumption on ht+1 ), but they are silent
about the changing rates of γt+1 , SW,t+1 , SE,t+1 and ht+1 . In fact, we cannot make such non-
zero constant growth assumption for γt+1 , SW,t+1 and SE,t+1 because they are ”share” variables
which are upper-bounded. Thus, we need to choose a way to predict the path for γt+1 , SW,t+1
and SE,t+1 during the targeted future period for the simulation purpose. Furthermore, these
three variables are labeled as ”time-varying policy parameters” which would change depending
on demographics and policies.
4.3 Calibration 1: Status-quo Simulation Approach
To evaluate the neoclassical growth model as a simulation tool as the World Bank’s LTGM
project does, we would like to vary the calibration method and compare the patterns as well as
the performance of the prediction of the model to seek the best way to choose the calibration
objects, i.e., the future growth rates Nt+1 , AL,t+1 , ht+1 and the time-varying policy parameters
(γt+1 , SW,t+1 , SE,t+1 ), in order to simulate the growth path of GDP per capita. Regarding the
labor share and the depreciation rate parameters, we will ﬁx them at the same values as in the
decomposition analysis of the actual Korean economy in Section 3 in order to isolate the eﬀects
of the calibration method on simulation.11
The ﬁrst and the most straightforward way of calibration is to simply follow the canoni-
cal neoclassical growth model, where the productivity and population grow at constant rates
AL,t+1 = gAL , Nt+1 = gN for all periods. We may make similar constant growth rate assumption
for the human capital as well such that ht+1 = gh for all periods. The canonical neoclassical
growth model also assume that investment rate is constant such that γt+1 = γt = γ0 . This as-
sumption of ”constant rates” in fact can be a reasonable one when the economy is near the steady
state and the economy grows close to the balanced growth path, along which the growth rates
are determined mainly by the fundamental parameters of technology and preferences. Consistent
11
That is, 1 − α = 0.602 and δ = 0.053.
21
way of calibrating the labor market demographic factors with this ”steady-state assumption” is
to choose that SW,t+1 = SW,t = SW,0 and SE,t+1 = SE,t = SE,0 (so that SW,t+1 = 0 and SE,t+1 = 0)
for all periods.
Suppose that a policy maker in Korea made this set of ”steady-state assumptions” in 1970,
and then applied the benchmark growth model to simulate the GDP per capita for the future
period of 1971-2014. Suppose that the data available for this policy maker in 1970 are the 1960-
1970 period data. Once deciding to take the ”steady-state” approach, the best way to calibrate
the constant growth rates of gAL , gh , and gN would be to form an adaptive expectation and
the best ﬁts for the constant growth rate parameters would be the long-term average growth
rates, represented by the annual average growth rates of the corresponding variables for the data-
available period, i.e., the 1960-1970 period. In selecting the constant values for the investment
rate, working-age population share, and labor force participation rate, we may want to take
the average values for the past sample period to smooth out the shocks. However, if taking
the averaging period too long, the average values would not represent the true values of the
parameters for the simulation period. Thus, the average values for the initial ﬁve-year period
prior to the starting date of simulation, for example, the 1966-1970 period values for the 1970
simulated prediction, are to be used to calibrate the investment rate, working-age population
share, and labor force participation rate.
We can repeat the above simulation exercise by changing the starting year to 1980 (using
the 1970-1980 data) or to 1990 (using the 1980-1990 data) instead of 1970 using the same
calibration method. Comparison of the three sets of prediction results would be informative
because Korean economy has evolved from a transition economy toward a steady-state economy.
The calibrated values for the three sets of simulated prediction exercises, labeled as ”Pred 70”,
”Pred 80”, and ”Pred 90”, respectively for the 1970, 1980, and 1990 simulation, by the above
steady-state calibration method are summarized in Table 2. For the purpose of referencing with
other countries, in Table 2, we also indicate the average purchasing-power-parity adjusted real
GDP per capita level for each period when the parameter values of γ0 , SW,0 . and SE,0 are
chosen.12 For example, Korea’s average PPP-adjusted real income level was $1,466 in 1960s
12
Note that this real income measure is obtained from the ”rgdpe” in PWT 9.0 divided by the WDI population
22
Table 2: Calibrated Parameter Values from Status-quo Approach
Simulation gA gh gN γ0 SW,0 SE,0 PPP Real Income (2011 USD)
Pred 70 0.8% 2.2% 2.6% 0.27 0.54 0.56 1,466 (1960s)
Pred 80 1.2% 1.9% 1.7% 0.37 0.61 0.59 3,844 (1970s)
Pred 90 3.7% 1.7% 1.2% 0.35 0.68 0.61 7,688 (1980s)
Note. ”gAL ”: Annual growth rate of productivity of labor-augmenting technology, ”gh ”: Annual
growth rate of human capital per worker, ”gN ”: Annual growth rate of population, ”γ0 ”: Investment
rate, ”SW,0 ”: Working-age population share, ”SE,0 ”: Labor force participation rate.
when the investment rate was 0.27, working-age population share was 0.54 and the labor force
participation rate was 0.56.
Figure 9 compares the predicted paths of GDP per capita of the three simulations (similarly
labeled as in Table 2), overlaid with the actual path (labeled as ”Actual”). This comparison
illuminates important features of the LTGM as a simulated prediction device as follows.
First, notice that the ”Pred 70” simulation under-predicts the GDP per capita as shown in
Figure 9. It ﬁts only the very beginning-of-period data, i.e., for the 1971-1973 period. The
prediction diverges away below the actual one afterwards. This result is not a surprise, reﬂecting
Figures 3, 6, and 7, which show that the investment rate, working-age population share, and
labor force participation rate all increased during the 1960s, hence the 5-year average values
underestimate the future values. Furthermore, the investment rate and the working-age popu-
lation share further increased in the 1970s compared to the 1960s values. The investment rate
got stabilized after the early 1980s, and the increase of the working-age population share also
slowed down after the 1990s. The labor force participation rate continues to show increasing
trend except the substantial dip during the 1977-1986 period. Furthermore, Korea’s population
growth rate has fallen monotonically during the entire sample period from 3% in the 1960 to
0.4% in 2014. All these changes have increasing eﬀects of GDP per capita, which are not cap-
tured by the current calibration method. Figure 4 illustrated the declining growth rate of human
capital, particularly after the 1990s. Thus, current calibration method tends to over-estimate
data, hence is diﬀerent from our GDP per capita measure which is calculated from the ”rgdpna” in PWT 9.0. In
Table 2, we use the ”rgdpe” measure to facilitate the cross-country comparison of development level.
23
Figure 9: Comparison of Predictions from Diﬀerent Simulations
40000
30000
20000
10000
0
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015
Year
Actual Pred_70 Pred_80 Pred_90
Note (1) Each line represents the actual or the predicted path of GDP per capita at diﬀerent starting date of
simulation according to the LTGM using the prior data.
Note (2) ”Actual”: Actual GDP per capita, ”Pred 70”: Predicted GDP per capita in the year 1970, ”Pred 80”:
Predicted GDP per capita in the year 1980, ”Pred 90”: Predicted GDP per capita in the year 1990.
24
the GDP per capita after the 1990s and on. Figure 5 showed that the productivity growth rate
has been more or less constant during the sample period. Thus, current calibration method is
a reasonable one regarding productivity growth. In sum, the under-prediction of the Pred 70
using the steady-state cum status-quo approach calibration method seems to be mainly due to
the assumptions of the constant rates of investment, working-age population, and labor force
participation. Observing the ”Pred 80” simulation, we get similar results, although the ﬁtting
performance improves over the ”Pred 70” simulation. In contrast, the 1990 prediction, which
uses the 1980s data, ﬁts the data very closely during the 17-year period (1991-2007), and then
the model over-predicts the GDP per capita after 2008 with increasing gap. The main reason
behind the good ﬁt for the 1991-2007 period is that there were no clear trends for the investment
rate, though being subject to ﬂuctuations, so that the capital-deepening eﬀects are well captured
during this period. However, the over-prediction of the ”Pred 90” for the 2008-2014 period seems
to be caused by various reasons: (i) the gradual slowdown of human capital accumulation, (ii)
decreasing investment rate, particularly after 2005, (iii) the stagnation of working-age population
share after 2000, (iv) the sudden stagnation of productivity after 2010, which can be conﬁrmed
by Table 1 and Figures 3 to 7. Comparing the above patterns of predictions across Pred 70,
Pred 80, and Pred 90, we learn that the prediction performance of the LTGM would be good
when the economy grows in the stabilized environments, but the LTGM tends to under-predict
when the parameters of investment rate, working-age population share, and labor force partici-
pation rate are actively changing. The LTGM may over-predict the growth when the economy
is near the ﬁnal phase of transitional growth (and/or subject to negative productivity shocks).
4.4 Calibration 2: Time-varying Parameter Embedded Simulation
Approach
Another way of using the LTGM is to evaluate the expected changes of income growth in re-
sponse to the diﬀerent parameters of growth. For this exercise, we categorize the six parameters
of calibration of the LTGM in the following manner. The rates of productivity growth and
human capital growth are the determinants of the steady-state growth fundamentals. Thus,
we call these two growth rates as ”fundamental parameters.” The changes of the rest of the
25
variables are related to transitional growth. The changes of working-age population share, la-
bor force participation rate, and population growth rate aﬀect the growth via the demographic
changes in labor market, hence we call this set of variables as ”demography parameters.” The
change of investment rate aﬀects growth via the capital accumulation process and we call this
an ”investment parameter.” From this perspective, we can use the LTGM in order to evalu-
ate the roles of diﬀerent kinds of growth sources as follows. First, we simulate Korea’s GDP
per capita from the neoclassical growth model in Section 2 by calibrating the six parameters
varying over time as in the data, and consider this as the benchmark simulation. We label
this version of simulation as ”Simul.” Second, we simulate by ﬁxing all six policy parameters
by their time-invariant long-run averages, i.e., by the 1960-2014 period annual average growth
rates of productivity, human capital, population, and by the 1960-2014 period average values of
investment rate, working-age population rate, and labor force participation rate. We label this
version of simulation as ”Average,” which will capture the long-run growth eﬀects in the sense
that this simulation does not allow the time-varying patterns of the growth parameters. For
this ”Average” simulation, the six parameters are set by gAL = 1.9%, gh = 1.5%, gN = 1.3%,
SW,0 = 0.65, SE,0 = 0.61, and γ0 = 0.32. Figure 10 compares these two sets of simulations with
the actual data. The full simulation, ”Simul”, captures the growth path of the actual real GDP
per capita very well. The gap between the actual data and the ”Simul” is due to the diﬀerences
in the capital accumulation between the PWT 9.0 data (”rkna” variable) and the simulated
capital stock which is constructed as in the law of motion equation (2) of the model. The
”rkna” in the PWT 9.0 data is constructed by diﬀerentiating the capital goods into four kinds
of assets: structures (including residential and non-residential), machinery (including com-
puters, communication equipment and other machinery), transport equipment and other assets
(including software, other intellectual property products, and cultivated assets), with applying
diﬀerent depreciation rates and relative prices, while we apply the common average depreciation
rate and no relative price changes in our simulation.13 Thus, the gap between the ”Actual”
and the ”Simul” represents the compositional changes of heterogeneous types of capital assets
13
See Feenstra, Inklaar, Timmer (2015) and User Guide of PWT 9.0 for more detailed discussion about the
capital construction of the PWT 9.0 data.
26
over time in the process of Korean economic growth. It is interesting to notice that there are
virtually no gap until the mid-1980s and the gap started to emerge only after 1985 and gradually
widened afterward. This implies that the compositional changes in aggregate capital seems to
matter only after the mid-1980s. The comparison of the two simulations of ”Simul” and ”Aver-
age” in Figure 10 reveals another interesting feature of Korea’s growth process. The ”Average”
represents mainly the long-run average growth eﬀect holding the labor market demography and
investment rates ﬁxed. The simulated real GDP per capita of the ”Average” simulation is higher
than that of the ”Simul” simulation for the 1960-1985 period, which is revered for the 1985-2014
period. This shows that the transitional growth factors such as improvements of the labor force
participation and investment rate played a substantial role in Korea’s growth.
Third, to isolate the eﬀect of the demographic factors, we simulate Korea’s GDP per capita by
changing only the working-age population share, labor force participation rate, and population
growth rate according to the quartic-polynomial-ﬁt trends, and ﬁxing the rest of variables at the
long-run average values. Similarly, we can isolate the eﬀect of investment promotion by allowing
the time-varying investment rate only. The combined time-varying eﬀect of both demography
and investment parameters can be inferred by similar method also. The simulations labeled as
”Demography,” ”Investment,” and ”Both” in Figure 11 represent such eﬀects, respectively. It is
interesting to notice that using the nonlinear trends of labor market demography and investment
parameters, the model (simulation ”Both”) can ﬁt the data very well, even though we ﬁx the
”fundamental parameters” of human capital growth rate and productivity growth rate. In this
sense, the LTGM is a promising tool to predict what would happen in response to the changes
of labor market and investment policies and environments, with the appropriate selection of the
long-run growth rates of productivity and human capital.
Fourth, the tight-ﬁt of the model simulation to Korean economic growth by the time-varying
labor market demography and investment parameters does not imply that the main engine of
Korea’s growth is such changes in transitional growth parameters. Such ﬁtting performance is
based on the productivity and human capital growth rates of 1.9% and 1.5% every year in the
background. To evaluate the role of such fundamental parameters in the LTGM, we simulate the
model at the time-varying labor market demography and investment parameters, but turning
27
Figure 10: Comparison of Predictions from Fully Time-varying and Average Constant Simula-
tions
30000
20000
10000
0
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015
Year
Actual Simul Average
Note (1) Each line represents the actual or the predicted path of GDP per capita using diﬀerent calibration
methods.
Note (2) ”Actual”: Actual GDP per capita, ”Simul”: Predicted GDP per capita calibrating at fully time-varying
parameters, ”Average”: Predicted GDP per capita calibrating at constant parameters of average values during
the sample period.
28
30000
20000
10000
0 Figure 11: Labor and Investment Policy Eﬀects
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015
Year
Simul Average Demography Investment Both
Note (1) Each line represents the actual or the predicted path of GDP per capita using diﬀerent calibration
methods.
Note (2) ”Simul”: Predicted GDP per capita calibrating at fully time-varying parameters, ”Average”: Predicted
GDP per capita calibrating at constant parameters of average values during the sample period, ”Demography”:
Predicted GDP per capita allowing time-variation only for the labor market demography parameters, ”Invest-
ment”: Predicted GDP per capita allowing time-variation only for the investment rate parameter, ”Both”:
Predicted GDP per capita allowing time-variation for both labor market demography and investment rate pa-
rameters.
29
oﬀ the productivity growth, human capital growth, or both to zero. The simulated paths of the
real GDP per capita of these simulations, are labeled as ”No g h,” ”No g A,” and ”Neither,”
respectively, in Figure 12. This shows that Korea’s growth would have been much lower if the
promotion of investment and labor market demographic factors had been the only sources of
growth.
In the year of 2014, Korea’s real GDP per capita is $34,300 in 2011 USD using national
prices and $35,103 using PPP adjusted prices and population data in PWT 9.0. The Korea’s
PPP-adjusted real GDP per capita in 2014 is slightly lower than that of Japan ($35,358) and a
little higher than that of Spain ($33,864) in the same year. In 1960, Korea’s PPP-adjusted real
GDP per capita was $1,175 which was lower than those of Kenya, Tanzania, Bangladesh and
Haiti, while those of Japan and Spain were $5,351 and $5,741. Without human capital growth,
Korea’s 2014 real income level would have been $14,597 (close to level of Brazil in 2014). Without
productivity growth, Korea’s 2014 real income level would have been $12,178 (close to level of
South Africa in 2014). With neither of productivity and human capital growth, Korea’s 2014 real
income level would have been $5,970 (close to level of Bolivia in 2014). The above comparison
clearly illustrates that main backbones of Korea’s ”miraculous growth,” as is asserted by Lucas
(1993), are the productivity growth and human capital accumulation, although demographic
changes in labor market and investment promotion also played an important role. That is,
Korea’s growth experience shows that for successful and sustainable growth, the most critical
factors are productivity and human capital growth, i.e., the fundamental sources of long-run
growth rather than the sources of transitional growth, as most of the growth models assert.
5 Conclusion
Korea’s remarkable growth experience itself may inspire the developing world because Korea
started such development from a comprehensive set of adverse conditions (colonization, massive
civil war, corruption, a lack of physical and human resources, political instability and incessant
ideological conﬂicts etc.) that are often mentioned as critical barriers to development among
current developing countries. However, without knowing what is actually behind such a growth
30
30000
20000
10000
0 Figure 12: Long-run Growth Eﬀects
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015
Year
Simul Both No g_h No g_A Neither
Note (1) Each line represents the actual or the predicted path of GDP per capita using diﬀerent calibration
methods.
Note (2) ”Simul”: Predicted GDP per capita calibrating at fully time-varying parameters, ”Both”: Predicted
GDP per capita calibrating at constant fundamental parameters of human capital and labor-augmenting produc-
tivity growth, ”No g h”: Predicted GDP per capita with no human capital growth, ”No g A”: Predicted GDP
per capita with no labor-augmenting productivity growth, ”Neither”: Predicted GDP per capita with neither
human capital nor labor-augmenting productivity growth.
31
process, Korea’s development experience would be useless for other developing countries. This
paper attempted to provide such knowledge to shed light on the underlying mechanisms of
Korea’s growth from a macroeconomic perspective using the framework of the neoclassical growth
model, which is the workhorse of the World Bank’s LTGM project. From a counterfactual
decomposition analysis, we found that the most important source of Korean economic growth
for the 1960-2014 period was productivity growth, contributing to the growth of GDP per capita
by 1.9% each year on average for 55 years. The second largest contributing component was
human capital accumulation (1.5% each year), and the capital deepening eﬀect was the third
(1.3% each year). Demographic compositional changes such as the increases in the working-age
population share and labor force participation rate also contributed to GDP per capita growth
substantially by 1% each year. These results show that the underlying sources of Korea’s growth
were fairly balanced among diﬀerent growth components, and productivity growth was the main
driving force behind the scenes. This picture is diﬀerent from what many of the ﬁrst generation
of Korea’s development policy makers used to have in mind, who considered human or physical
capital accumulation as the main engine of Korean growth. In fact, that was the case in the 1960s
and 1970s. In the 1960s, human capital growth, based on rapid expansion of universal education
at primary and secondary levels of schooling, was the main engine of Korea’s growth. In the
1970s, capital deepening due to the increasing investment rate promoted by export-oriented
industrial policies indeed was the main engine of Korea’s growth. However, what bolstered
Korea’s sustained growth throughout, particularly for the 1980-2010 period, was productivity
growth, which has been rarely emphasized in most discourse about Korean economic growth. We
characterized the important features of the LTGM as a simulated prediction or policy prescription
tool, by applying the model to Korea’s growth experience ex post. We found that the model
under or over-predicts the growth performance when the economy is in transition, but the model
is calibrated from the steady-state cum status-quo approach, i.e., using constant growth rate
assumptions. This result itself may not be a surprise. The contribution of this paper, however,
is that we could quantify how big the discrepancy could be, and also show that the ﬁt of the
model becomes very good when the labor and investment policy parameters are stabilized. The
latter ﬁnding is a (pleasant) surprise because the model is not built to ﬁt the data in a reduced-
32
form way. We also found that the model ﬁts the data very well when the time-varying short-run
growth policies such as labor market demography and investment policies are embedded into
the model by nonlinear trends, together with calibrating the long-run growth policy parameters
such as the growth rates of productivity and human capital as constant numbers. This ﬁnding
suggests that we do not need to calibrate all variables as time-varying processes for the LTGM to
predict future growth in response to changes in targeted policies such as raising the investment
rate or promoting female labor force participation, conditional on ﬁxed values of productivity
and/or human capital growth rates.
33
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[4] Lucas, Robert E. Jr. (1993), ”Making a Miracle,” Econometrica, 61(2): 251-272.
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34