Korea's Growth Experience and Long-Term Growth Model

This paper analyzes the Republic of Korea's rapid and sustained growth experience for the past six decades from the perspective of the neoclassical growth model (the workhorse model of the World Bank?s Long Term Growth Model (LTGM) project). Overall, the sources of Korea's growth were balanced among labor market and demographic factors, capital investment, human capital accumulation, and productivity growth. However, the main engine of growth evolved sequentially, e.g., labor and human capital factors in the 1960s, capital deepening in the 1970s, and then productivity growth for the following periods. The major sources of sustained growth over six decades were human capital accumulation and productivity growth rather than labor or capital investment. A counterfactual calibration of the model explains Korea's actual growth experience well, and shows why gaps between the model?s predictions and the data arise. This illustrates that an appropriate calibration of a simple neoclassical growth model provides useful lessons and tools for policy makers in developing countries in designing their national development strategies.


Policy Research Working Paper 8240
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. This paper analyzes the Republic of Korea's rapid and sustained growth experience for the past six decades from the perspective of the neoclassical growth model (the workhorse model of the World Bank's Long Term Growth Model (LTGM) project). Overall, the sources of Korea's growth were balanced among labor market and demographic factors, capital investment, human capital accumulation, and productivity growth. However, the main engine of growth evolved sequentially, e.g., labor and human capital factors in the 1960s, capital deepening in the 1970s, and then productivity growth for the following periods. The major sources of sustained growth over six decades were human capital accumulation and productivity growth rather than labor or capital investment. A counterfactual calibration of the model explains Korea's actual growth experience well, and shows why gaps between the model's predictions and the data arise. This illustrates that an appropriate calibration of a simple neoclassical growth model provides useful lessons and tools for policy makers in developing countries in designing their national development strategies.

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 experience 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 initiatives 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 current 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.
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 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). 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 Framework 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 where Y t denotes the aggregate output, X t the composite input (sometimes referred as "total factor"), and A t the total factor productivity (TFP) at period t. The composite input X t consists of capital K t and effective unit of labor L t such that 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 where I t 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 ∂K ≥ 0 and ∂F ∂L ≥ 0 for all K and L). The property of "diminishing returns" means that the marginal contribution of adding more inputs decreases as the amount of inputs increases (i.e., ∂ ∂K ∂F ∂K < 0 and ∂ ∂L ∂F ∂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 cK t , c L t = cF K t , L t for all c > 0). 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 h t and the employment size L t (measured by the number of workers) such that L t = h t L t , in other There is no direct data for the TFP variable, hence it is typically measured by the residual such that 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 where the only parameter β corresponds to the labor share in national income account, and A L,t denotes the labor-augmenting technology level. This can be re-expressed such that 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 where y t = Y t /L t and k t = K t /L t . Another way to represent the output per worker is From equation (6), we can derive the following (and typical) growth accounting formula where the "hat" notation denotes the growth rate of the corresponding variable, e.g., y t ≡ dyt/dt yt . From equation (7), we can derive another growth accounting formula From equation (5), the typical TFP growth rate A t is related to the labor-augmenting productivity growth A Lt as follows The equations (8)  purely from investment, we should use the second formula, which decomposes the growth of output per worker into pure productivity growth effect A Lt , human capital growth effect h t , and capital-deepening effect 1−β β K 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 ratio K Y increases, this would capture the genuine effect of capital growth due to the capital investment. This is the intuition behind considering the capital-deepening effect 1−β β K 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 y P,t ≡ Y t /N t (where N t is the total population size) rather than the GDP per worker y t ≡ Y t /L t above. GDP per capita differs from GDP per worker by the two demographic features of the labor market, (i) the labor force participation rate S E,,t ≡ L t /N L,t and (ii) the working-age population share S W,t ≡ N L,t /N t , where N L,t is the working-age population (age group of 15-64) size, and L t is the labor force size such that and in growth terms 3 Analysis of Korea's Economic Growth 5

Data
Equations (9)  (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 L t 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 S W,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 S W,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.      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  Figure 4. Figure 5 shows that the labor-augmenting

Features of Korean Economic Growth
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.  1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 Year AL Trend AL Actual AL Growth Trend AL Growth Actual .65 .7 . 75 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 Year Trend Actual .65 . 7 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 Year Trend Actual 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).

Decomposition Analysis
Applying our accounting framework of equations (9) and (12) (7) and (11), we express the GDP per capita such that 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 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. and the growth rate of this counterfactual measure is 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").    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.

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.

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) y and the gross growth rate of the GDP per capita between period t and t + 1 is where Λ t+1 = 1 + S W,t+1 1 + S E,t+1 1 + A L,t+1 1 + h t+1 , γ t is the investment rate at period t, and N t+1 , S W,t+1 , S E,t+1 , A L,t+1 , and h t+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) clarifies two things. First, the growth rate of GDP per capita increases in investment rate γ t , but this growth effect decreases in K t /Y t , i.e., the capital-output ratio of the base year. The latter decreasing growth effect from investment captures the diminishing returns property of the neoclassical 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 parameters (1 − β, δ) and to calibrate the growth rates of N t+1 , S W,t+1 , S E,t+1 , A L,t+1 , and h t+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 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 γ t+1 also. Typical neoclassical growth models assume that A L,t+1 and N t+1 are constant for all periods (we may make similar assumption on h t+1 ), but they are silent about the changing rates of γ t+1 , S W,t+1 , S E,t+1 and h t+1 . In fact, we cannot make such nonzero constant growth assumption for γ t+1 , S W,t+1 and S E,t+1 because they are "share" variables which are upper-bounded. Thus, we need to choose a way to predict the path for γ t+1 , S W,t+1 and S E,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.

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 N t+1 , A L,t+1 , h t+1 and the time-varying policy parameters (γ t+1 , S W,t+1 , S E,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 canonical neoclassical growth model, where the productivity and population grow at constant rates A L,t+1 = g A L , N t+1 = g N for all periods. We may make similar constant growth rate assumption for the human capital as well such that h t+1 = g h for all periods. The canonical neoclassical growth model also assume that investment rate is constant such that γ t+1 = γ t = γ 0 . This assumption 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.
way of calibrating the labor market demographic factors with this "steady-state assumption" is to choose that S W,t+1 = S W,t = S W,0 and S E,t+1 = S E,t = S E,0 (so that S W,t+1 = 0 and S E,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 g A L , g h , and g N 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- 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 γ 0 , S W,0 . and S E,0 are chosen. 12 For example, Korea's average PPP-adjusted real income level was $1,466 in 1960s Note. "g A L ": Annual growth rate of productivity of labor-augmenting technology, "g h ": Annual growth rate of human capital per worker, "g N ": Annual growth rate of population, "γ 0 ": Investment rate, "S W,0 ": Working-age population share, "S E,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 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. 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)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007), and then  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 participation 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).

Calibration 2: Time-varying Parameter Embedded Simulation
Approach Another way of using the LTGM is to evaluate the expected changes of income growth in response 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 variables are related to transitional growth. The changes of working-age population share, labor 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 evaluate 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 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.  Predicted GDP per capita allowing time-variation only for the labor market demography parameters, "Investment": 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 parameters.
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  (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.

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 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-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.