New panel data estimates for the four East Asian Tigers show that the contribution of total factor productivity (TFP) to growth is much higher than past estimates. An extended production function with learning by doing implies that TFP is about 3.5% and these countries will grow at this rate in the long run.Asian Tigers, Systems Dynamic GMM, Growth Accounting, Factor Accumulation as Residual.

Artur Tamazian

B. Bhaskara Rao

Rup Singh

English

Research Papers in Economics

EERI
Economics and Econometrics Research Institute 
EERI Research Paper Series No 04/2010 
ISSN: 2031-4892 
Copyright © 2010 by B. Bhaskara Rao, Artur Tamazian and Rup Singh 
What is the Long Run Growth Rate 
of the East Asian Tigers 
B. Bhaskara Rao, Artur Tamazian and Rup Singh 
EERI
Economics and Econometrics Research Institute 
Avenue de Beaulieu 
1160 Brussels 
Belgium
Tel: +322 299 3523 
Fax: +322 299 3523 
www.eeri.euWhat is the Long Run Growth Rate 
of the East Asian Tigers? 
B. Bhaskara Rao 
School of Economics and Finance 
University of Western Sydney, Australia 
raob123@bigpond.com 
Artur Tamazian 
Department of Finance & Accounting 
University of Santiago de Compostela, Spain 
artur.tamazian@usc.es 
Rup Singh 
School of Economics 
University of the South Pacific, Fiji 
singh_r@usp.ac.fj 
Abstract 
New panel data estimates for the four East Asian Tigers show that the contribution of total factor 
productivity (TFP) to growth is much higher than past estimates. An extended production 
function with learning by doing implies that TFP is about 3.5% and these countries will grow at 
this rate in the long run.  
JEL:  O1,  N1 
Keywords:  Asian Tigers, Systems Dynamic GMM, Growth Accounting, Factor 
Accumulation as Residual. 1. Introduction 
This paper re-examines the decade old controversy on the East Asian miracle. To conserve 
space we limit to Hong Kong, Korea, Singapore and Taiwan i.e., the East Asian Tigers 
(EATs). We refer to excellent surveys by Seral (2000) and Jong-il Kim (2002) for 
bibliography. When this controversy started in early 1990s, the average GDP growth rate 
(1965-1990) of EATs was 8.8% compared to UK’s 3.7% and USA’s 4.0%; see Table 1 in 
Jong-il Kim (2002). The controversy was on whether EATs can continue to grow at this  
high rate in the long run, which we may say due to the confusion between the actual high 
growth rate and the long run or the steady state growth rate (SSGR). Since theoretical 
growth models imply that the SSGR equals growth of total factor productivity (TFP), a 
variety of growth accounting exercises was conducted to estimate TFP as the Solow 
residual. The general conclusion was much of the growth of EATs was due to factor 
accumulation (85%) and not due to TFP. Therefore, their growth rates will tapper off 
eventually to equal TFP, which was estimated between 0% to 1.5%. 
A neglected issue in this controversy is the effect of learning by doing (LBD) on TFP.
Jong-il Kim (2002) and Greiner, Semler and Gong (2005) recognise its importance by 
noting that developing countries go through 3 historical stages. At first LBD, stimulated 
by capital accumulation and openness to trade, play an important role in improving TFP.
In the second and third stages human capital formation and expenditure on R&D are 
important contributors to TFP.
In this paper we extend the standard Cobb-Douglas production function to take into 
account some LBD effects due to capital formation and trade openness (OT ). We also use 
an improved systems GMM dynamic panel data method of Arellano and Bover (1995) and 
Blundell and Bond (1998). Some of its advantages are: (1) it overcomes the weak 
instruments problem generally causing finite sample biases and (2) minimises time series 
problems, due to the persistence in the variables, because the model is estimated as a 
system in both the levels of the variables and their first differences.  This paper is organised as follows. Specification issues are in Section 2. Empirical results 
are in Section 3 and Section 4 concludes.  
2. Specification
1
Some externalities are ‘manna from the heaven’ type and do not need additional 
investments by firms. In the early stages of development two such important externalities 
are due to LBD of Arrow (1962) and OT.  These LBD effects may be due to a rapid capital 
accumulation through new investment in improved machines and exposure to international 
competition stimulates efficient methods of production. It is hard to say a priori the 
relative importance of these two LBD effects. The Cobb-Douglas production function with 
constant returns can be extended to include such effects as follows. 
Let a representative firm’s production function, where LBD effects are through the 
economy wide capital stock, be:  

(1 )
                                                            (1)
                                                                             (2)
it it it it it
it it t
YK A L
AB K
 





where Y is output, K is capital, L is employment and  it   is an error term such that 
2 ln (0, ). it N 	  B stands for the stock of knowledge which depends on autonomous 
factors. Therefore,  ln B 
 is the rate of growth of autonomous TFP. B may be constant 
 ln 0   B 
 or grow at a constant autonomous rate of g 0 (=  )
gt
t B Be where  0 B is the initial 
stock of knowledge.  ln B 
 thus captures the effects of trended variables affecting TFP.
Substitutions and aggregation give the aggregate production function: 
1 ( 1) ( 1) ( 1)
0                                                      (3)
gt
tt t t YB e K L
~~     
   
                                                
1 To conserve space we shall be brief. See Rao (2007) for details. where   
1/
1
n
n
ti   and 
2 ln (0, ). t N 	   Alternative assumptions about A in (2) are 
possible.   
If OT has an externality through LBD effects, which is important for the East Asian 
countries, A may be specified with the two LBD effects as: 
  12
0                                                            (4)
t gg O T
it t
t AB e K
  
The aggregate production function will be 
12 (1 )( ) 1( ( 1 ) ) ( 1 )
0                                  (5)
gg O T
tt t t
t YB e K L
 ~~    
    
This is the same as (3) except that g is now computed as (g1+g2 OT). The log-linear form 
of (5) where output and capital are measured in per worker terms is: 
01 2 ln (1 )ln (1 )( )
          ( (1 ))ln (1 ) ln                       (6)
tt
tt t
yB g g O T t
kL

    
  
    
where  (/) y YL  and  (/) . kK L   There is a steady state solution only when    1.    If 
 1    there is no steady state because there are no diminishing returns to k and  k 
 does 
not become zero, which is the definition of the steady state. Therefore, in the following 
derivation of SSGR it is assumed that   1,    and SSGR implied by (6) is: 
12                                                         (7)
(1 )
gg O T n
SSGR





where n is the rate of growth of labour force. The SSGR implied by (3) is a special case of 
(7) where  2 0. g   Note that in (7) SSGR increases with respect to  2 g and  if  1.  3. Empirical Results 
The dynamic systems GMM method in this paper essentially requires that (6) should be 
estimated in the levels and first differences of the variables forms as a system.
2 Estimates 
of (6) for the period 1972-2006 are in Table-1.  
Table 1 
GMM ESTIMATES OF PRODUCTION FUNCTION 
1972-2006 
Parameters (1) (2)  (3) 
Constraints  0   0
0.3




Intercept  3.90E
-03
[0.00]*
0.38E
-02
[0.00]* 
0.35E
-02
[0.00]* 
 0.230 
[0.01]* 
0.246 
[0.01]* 
-
1 g 0.034 
[0.00]* 
0.032 
[0.00] 
0.031 
[0.00]* 
2 g 5.50E
-04
[0.24] 
0.77E
-03
[0.05]* 
0.59E
-03
[0.12] 
 3.16E
-03
[0.32]
_ _
1 
(Level)  0.99 
[0.00]* 
0.99 
[0.00]* 
0.99 
[0.00]* 
__
2 R
Level
Difference
0.996 
0.094 
0.996 
0.109 
0.996 
0.092 
DW 
Levels
Difference
1.684 
[0.74] 
1.683 
[0.74] 
1.683 
[0.74] 
1.682 
[0.74] 
1.674 
[0.72] 
1.674 
[0.72] 
2
IV  26.418     
[0.19] 
26.237     
[0.24] 
27.767 
[0.23] 
Notes: p-values are in square brackets. * = 5% and ** = 10% significance. 
2
IV  is test 
for over-identifying restrictions of instruments. See Data Appendix for further details. 
                                                
2 The random effects model (REM) is used in such dynamic panel models.  However, we have conducted the 
Hausman test where the null is REM against the alternative of the fixed effects model. The null could not be rejected 
at the 5% level. The computed test statistic, with p-value in the square brackets is: 
3 2
3 16.996 0.7 . E 
   In column 1 estimate of (6), with a correction for first order serial correlation in the levels 
equation, is given. This serial correlation coefficient is close to unity highlighting the non-
steady nature of the level variables. It is set at 0.99 to achieve convergence.
3 Estimates in 
column (1) are good although the two LBD coefficients  2 and g  are insignificant. 
Estimate of  (share of profits) at 0.23, highly significant, implying that perhaps the 
contribution of TFP to growth has been underestimated in the previous growth accounting 
exercises. Estimate of 1 g implies that autonomous TFP is 3.4% . 
To increase the degrees of freedom and see if one and/or both LBD coefficients become 
significant, we reestimated the equation in column (1) with 3 alternative restrictions: (a) 
0;    (b) 2 0; g   and (c)  0.25.   The first of these restrictions gave marginally better 
results and is reported in column (2). Estimate of  and 2 g have slightly increased. There 
is a marginal improvement in 
2 R of the equation in the differences. In column (3) 
estimates of (2) with the constraint that  is equal to its stylised value of one third in 
many growth accounting exercises is reported. This has marginally decreased 
2 R of the 
equation in the differences and  2 g has now become insignificant even at the 10% level. 
The summary statistics of these 3 estimates are close and it is hard to select the best 
equation. Estimates of  1 g remained virtually stable in all the 3 equations. Our subjective 
preference is for the estimate in column (2) because its 
2 R s are marginally better. Using 
(2) and the sample means for (0.024) n and OT (1.47), the estimate of SSGR is 3.35%. This 
has slightly increased to 3.43% when the averages for the recent 5 year period (2002-
2006), with  (0.014) n and OT (2.55), are used. Both estimates imply that the SSGR of 
EATs is well over 3% and close to 3.5%. Using the sample average growth of output of 
                                                
3 It may be thought that there is no need for the levels equation and the systems estimation. However, when only the 
equation in first differences is estimated, a standard practice in many panel data estimates of growth equations, none 
of the parameters were significant. This may be due to some limitations noted in the text. 6.7% , the contribution of TFP is about 50%. This implies that factor accumulation and 
TFP equally contributed to growth of the EATs. 
4. Conclusions 
In this paper we have used a more efficient systems GMM technique to estimate the 
production function with LBD effects. Our estimates showed that the share of profits is 
about 0.25, which is less than the values used for ESTs in many past growth accounting 
exercises. Next, instead of estimating TFP as the Solow residual, it can directly be 
estimated with our extended production function. Consequently, it is possible to estimate 
the contribution of factor accumulation as a residual. This latter approach seems more 
convincing to us because TFP is estimated directly. Both approaches show that the 
contribution of TFP to growth of EATs is higher than past estimates and inspiration and 
precipitation seem to have played equal roles in the growth process of these countries.  
Needless to say there are limitations in our paper. The two LBD coefficients in column (1) 
were not significant and their significance is improved by imposing plausible constraints. 
Perhaps a larger sample may give better estimates. Our approach can be extended by 
identifying additional contributors to TFP, besides capital accumulation and OT.
Alternative measures of LBD due to capital accumulation are also possible. However, 
these are outside the scope of this short paper. Data Appendix 
Y is real output measured in 1990 US dollars. Data are obtained from the World Bank 
database: http://unstats.un.org.
K is real stock of capital computed using data on gross fixed capital formation with the 
perpetual method. Depreciation rate is 4% and the initial stock of capital is 1.5 times real 
output in 1969. These are close to the estimates of Bosworth and Collins (2003). 
L is labour force (or employment, whichever is available) obtained from the World 
Development Indicators (2007) and updated using the Asian Development Bank (ABD) 
database: http://www.adb.org/Documents/Books/Key_Indicators/2008. Where data is 
unavailable, working age population (15-64 years, inclusive) are taken from World Bank 
Indicators (2007). 
OT is openness of trade computed as the ratio exports plus imports to aggregate output. 
All series are in current US dollars derived from the World Bank database. References
Arellano, M. and S. Bond. (1998). “Dynamic panel data estimation using DPD98 for 
GAUSS: a guide for users.” Mimeo, Institute for Fiscal Studies, London. 
Arellano, M. and O. Bover. (1995). “Another look at the instrumental variable estimation 
of error-components models.” Journal of Econometrics, 68(1): 29-52. 
Arrow, K. J., (1962) ``The economic implications of learning by doing,'' Review of 
Economic Studies, 29(3): 155-173. 
Bosworth, B. and Collins, S. M. (2003). “The Empirics of Growth: An Update,” 
http://www.brookings.edu/~/media/Files/rc/papers/2003/0922globaleconomics_bosworth/
20030307.pdf 
Greiner, A., Semler, W., and Gong, G. (2004) The Forces of Economic Growth: A Time Series 
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Jong-il Kim (2002). “Total Factor Productivity Growth in East Asia: Implications for the 
Future,” Asian Economic Papers, 1(2): 50-70. 
Rao, B. B. (2007). “Estimates of  the Steady State Growth Rates for  Selected Asian Countries 
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th Conference of Australian 
Economists, Hobart, Australia. http://econpapers.repec.org/paper/pramprapa/2389.htm
Sarel, M., (1995). “Growth in East Asia: What we can and what we cannot infer from it,” 
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What is the Long Run Growth Rate of the East Asian Tigers?

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What is the Long Run Growth Rate of the East Asian Tigers?

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