total factor productivity growth in east asia: a two ... · total factor productivity growth in...

European Journal of Economics, Finance and Administrative Sciences

ISSN 1450-2275 Issue 14 (2008)

© EuroJournals, Inc. 2008

http://www.eurojournals.com

Total Factor Productivity Growth in East Asia: A Two

Pronged Approach

Ammara Mahmood

Ph.D Scholar at School of Management, Yale University

Tel: +203-7816239

E-mail: [email protected]

Talat Afza

Professor and Dean, Faculty of Business Administration

COMSATS Institute of Information Technology

Lahore Campus

Tel: +92-321-4476024; Fax: +92-42-9203100

E-mail: [email protected], [email protected]

Abstract

This paper is an attempt at explaining the East Asian growth miracle. Employing the DEA

approach and the Malmquist productivity index the Total Factor Productivity of eight East

Asian countries have been calculated over the period 1980-2000. Comparison of the

sample countries reveals that the frontier was defined by Malaysian and Indonesia while

South Korea caught up with the frontier countries in later years. In the second stage the

paper analyzes the factors responsible for the surge in total factor productivity experienced

by the sample countries. Based on a panel regression with random effects of the countries

over the time period 1980-2000, for the sample as a whole, secondary education was the

only variable that had a positive impact on TFP growth and efficiency growth while it was

insignificant for technical change. On the contrary, trade openness and foreign direct

investment were seen to be inconsequent as determinants of TFP growth and its

components. This highlights the fact that internal technological improvements as opposed

to foreign capital and technologies were the main determinants of TFP growth and

improvements in efficiency. However, our indicator for internal R&D, the number of

scientific and technical journal articles, does not help in explaining the observed

phenomenon. Overall, the explanatory variables chosen in our model help explain

efficiency change but are inconsequent in explaining shifts in the production frontier.

Keywords: Total Factor Productivity, DEA Approach, Malquist Productivity Index, East

Asian Economies

1. Introduction After suffering a setback to its popularity in the early 1960s due to the failure of the ‘formula’ theories

to achieve substantive growth in the Third World (for example, Rostow, 1960; Lewis, 1954; and

Solow, 1956), interest in the field of long-term growth has resurfaced on economists’ research agenda

since the 1980s (see, for instance, Romer, 1986; and Lucas, 1988). One of factors responsible for this

change has been empirical evidence of convergence in per capita incomes and output across nations

94 European Journal of Economics, Finance and Administrative Sciences - Issue 14 (2008)

(Martin & Sunley, 1998). This encouraging finding has led to a surge in the re-orientation of growth

theory, with particular respect to treating as endogenous to the growth process those factors that the

traditional neoclassical growth model classifies as exogenous; in particular, human capital and

technological change.

The birth of endogenous theory, combined with observed evidence of cross-country

convergence in per capita incomes and output, has channeled much of the recent work in the field of

new growth empirics in the direction of regional growth patterns and testing for convergence. At the

same time, the spectacular growth of the East Asian economies over the past three decades has held

policy makers around the globe spellbound.

The first wave of growth in Japan was followed by a wave of growth in the four East Asian

“Tigers”; namely, Hong Kong, South Korea, Singapore and Taiwan. The next wave of growth was

witnessed in Indonesia, Malaysia, and Thailand. Since 1960, these eight economies together have

grown about twice as fast as the rest of East Asia and the industrial economies, about three times as

fast as Latin America and South Asia, and about five times as fast as sub-Saharan Africa (Day, Smith,

Thomas & Yeaman, 2005). The East Asian growth “Miracle”, as described by the World Bank (1993),

has led to the penning down of vast and diverse literature on the debate regarding the determinants of

growth (see, for instance, Young, 1995; Kim and Lau, 1994; World Bank, 1993; and Pack and Page,

1994).

However, few researchers have studied the impact of growth in total factor productivity (TFP)

as a potential factor influencing economic performance of the East Asian economies over the past few

decades. And yet we see that improvements in efficiency have been significant in determining the

growth trajectories of many developed economies (Zachariadis, 2004). Owing to this crucial nature of

productivity growth, numerous authors have made improvements in TFP the locus of their studies. For

instance, Fare, Grosskopf, Norris and Zhang (1994) have calculated TFP growth in seventeen OECD

countries. Savvides and Zachariadis (1994) delve into a detailed study of the determinants of TFP

growth, and their role in enhancing the overall productivity of OECD countries. Zachariadis (2004)

also analyses the role of research and development expenditure on productivity growth and overall

growth in output for a sample of OECD economies.

Due to their concentration on TFP growth, the above mentioned studies have provided valuable

insights into the dynamics of growth in OECD countries. In the case of East Asia, similar studies are

seriously lacking. 1 With an attempt to bridge this gap in literature, this paper studies total factor

productivity in five high-growth East Asian economies; China, Indonesia, Malaysia, South Korea and

Thailand 2 over the period 1980-2000, following a two-pronged approach, whereby we first calculate

the growth in TFP, and then proceed to analyze the factors responsible for this growth.

The paper proceeds as follows: section II provides an overview of the current literature on

productivity growth and convergence and the determinants of TFP growth. Section III will continue by

providing an in-depth explanation of the methodology used to calculate TFP growth, and then

explicates the model that evaluates the simultaneous contribution of various factors to growth in TFP.

Section IV goes on to describe the data, while section V discusses the results of the two stages. Finally,

section VI concludes the paper.

2. Literature Review 2.1. Productivity Growth and Convergence

Several issues related to productivity growth have gained attention in recent years. The main focus has

been on the productivity slowdown in the U.S and European countries during the 1960s and 1970s.

1 Young (1995) is perhaps one of the few researchers who have examined productivity growth in the East Asian region. But Young’s analysis

underscores the role played by factor accumulation in the Miracle, rather than growth in productivity. 2 These countries are part of the “Miracle” series of economies identified by the World Bank (1993). Further details regarding the selection criteria are

given in section III.


Simultaneously, countries like Japan were seen to report high levels of productivity growth (Fare et al.,

1994). The increase in productivity of newly industrialized countries was attributed to the convergence

phenomenon of the neo-classical growth framework. According to Martin and Sunley (1998),

economists’ interest has largely focused on two measures of convergence: beta and sigma convergence. 3 There have been numerous attempts to measure the speed of cross-country beta-convergence (see, for

example, Baumol, 1986; Romer, 1986; Barro and Sala-i-Martin, 1992a, 1992b, 1995). The general

conclusion from these studies is that only when attention is restricted to the set of richer OECD

countries is there some support for absolute convergence4.

Similarly, Fare et al. (1994) criticize the studies conducted by De Long (1988) and Baumol

(1986), contending that the latter present a strong case for convergence primarily due to their

incorporation of partial productivity measures, namely labor productivity. By focusing on TFP growth

and breaking it up into technical and allocative components, Fare et al. (1994) make insightful

contributions to exiting literature on growth theory. Our study has also been largely inspired by the

framework employed by Fare et al. (1994).

2.2. Studies regarding determinants of TFP growth

Recent studies on TFP have also sought to analyze the determinants of growth in productivity.

Zachariadis (2004), in his study of ten OECD countries, regresses TFP growth on R & D intensity 5

using the Aghion and Howitt (1998) framework, according to which, internal R & D strongly

determines the level of productivity growth in an economy. These findings are also supported by Keller

(2002), who estimates that up to 80% of growth in productivity originates in domestic R & D. Jones

(1995) contends that the number of scientists engaged in R & D provides an adequate measure of the

level of domestic R & D in an economy.

However, domestic R & D alone does not provide an adequate estimate for growth in TFP.

Keller (2002) states that the remaining 20% of an economy’s productivity growth is brought about via

a channel of imports and foreign technology transfers. It is believed that foreign technologies can shift

a country’s domestic technology frontier outwards by introducing new inputs and production

techniques not previously available (Keller, 2002). A number of papers, including Coe and Helpman

(1995), Crespo, Martin and Velazquez (2002) and Griffith, Stephen and Van Reenen (2000), have

shown that foreign sources of technology have been an important source of productivity growth for

developed economies.

Savvides and Zachriadis (2005) state that less developed countries often carry out little R & D

of their own, and therefore, for these economies, technology diffusion across international borders

assumes a crucial role as a propellant of growth in TFP. However, despite the vital importance of

international technology diffusion as a channel of TFP growth in the context peculiar to low and

middle income countries, only a handful of empirical studies have been carried out, such as Coe and

Helpman (1995) and Mayer (2001).

3 Beta-convergence among a group of economies is said to exist if the beta value for the regression of the growth rate of regional relative per capita

income over a given period on the level of regional relative per capita income at the beginning of the period is negative (Martin & Sunley, 1998). A

negative value of beta reveals a tendency for per capita incomes to equalize across economies; therefore, beta measures the speed of convergence. A

group of economies is said to be characterized by sigma-convergence if the dispersion (variance) of their relative per capita income levels decreases

over time. 4 Absolute convergence refers to the long-term tendency for per capita GDP to equalize, or converge, across economies. The value of beta measures the

speed of the convergence process (Martin & Sunley, 1998). 5 R & D intensities for each economy are constructed as the fraction of output (gross domestic product) devoted to research and development

expenditures by an economy. See Zachariadis (2004) for further details on R & D intensity and its role in promoting growth in productivity.


According to Wolff (1996), if an economy can increase its pace of technological progress by

means of capital imports that embody the latest technology, and by cross boarder transfer of

knowledge, its TFP growth will be higher. Zachariadis and Savvides (2005) argue that technology is

embodied in capital and intermediate goods, so that the direct import of these goods is a channel of

transmission of foreign technology, and hence eventual growth in TFP. This channel is consistent with

the models of Grossman and Helpman (1991), Francesco and Wilson (2004), and Eaton and Kortum

(2001). Moreover, according to Zachariadis (2004), openness can also have a positive impact on an

economy’s productivity growth through other channels, such as increased competition in the domestic

market.

Savvides and Zachriadis (2005) suggest that foreign direct investment (FDI) by multinational

corporations (MNCs) might be another channel for the international transmission of technology.

Besides the import of capital goods by the subsidiaries of MNCs, FDI frequently involves the

movement of employees and managerial talent across countries, as well as links between MNC

subsidiaries and local firms; all potential channels for the transfer of new technologies (Savvides and

Zachriadis, 2005). The issue of FDI spillovers has also been investigated by Borensztein, de Gregorio

and Lee (1998) and Xu (2000), to yield similar results.

A third approach to growth in productivity could be that of enrichment of human capital.

Papageorgiou (2002) underscores the great importance of human capital in the growth process. As

outlined by Nelson and Phelps (1966), and later built upon by Benhabib and Spiegel (1994), the

secondary enrollment ratio provides a valuable insight to the level of education within an economy,

which is crucial in the development of new technologies domestically and the effective absorption of

foreign technologies. Hence, gross secondary enrollment ratio (GSER) is an important determinant of

TFP growth. In their study of the manufacturing sectors of 32 economies over the period 1965 to 1992,

Savvides and Zachariadis (2005) have employed GSER as a variable in the determination of TFP.

3. Data Description Our dataset comprises the five economies of China, South Korea, Thailand, Malaysia and Indonesia

over the period 1980-2000. Japan has been excluded because it does not form part of the remarkable

East Asian “Miracle” that the region witnessed 1960 onwards, owing to its treading an individual,

separate trajectory altogether, in the growth process (World Bank, 1993). Moreover, a striking feature

of East Asian growth in recent years has been the increasing role of China’s economy in regional

economic activity and the significant shift in the pattern of East Asia’s trade (Day et al., 2005). China’s

economic rise is increasingly affecting East Asian growth dynamics, and we therefore feel it wise to

include China as part of our sample. Hong Kong has been left out on grounds of being a semi-

autonomous economy which is constitutionally within the realm of China, whereas Singapore and

Taiwan have not been included due to lack of availability of data.6

In order to calculate total factor productivity for the first part of our paper, we use data for

capital stock and labor as inputs in the Malmquist productivity index, and gross value added is taken to

be the output.

The primary data regarding capital, labor and gross value added have been obtained from the

Penn World Tables (PWT) Mark (6).7 PWT provides purchasing power parity and national income

accounts converted to international prices for 168 countries, for some or all of the years from 1950-

2000. All data is normalized to 1996 values8.

6 There was insufficient data regarding education and journal article publications for the observed time period for Taiwan and Singapore. Since these

variables are crucial to our study, we have excluded these economies from our study. 7 The European Union or the OECD provide more detailed purchasing power and real product estimates for their countries and the World Bank makes

current price estimates for most PWT countries at the GDP level. For further detail, refer to Heston, Summers and Aten (2002). 8 PWT 6.0 uses a substantially larger and more recent benchmark relative to PWT 5.6 that relates to 1996. The dataset consisted of 32 heading parties and

expenditure shares that were put together by the World Bank for 115 countries from various regional UN ICP comparisons. The underlying dataset

combined the comparisons of the EU, OECD, and other European and formerly Soviet countries for 1996, a total of 52 countries. The data then

combine International Comparison Program benchmark comparisons in different regions for either 1993 or 1996, with the former being brought

forward to 1996 (Heston, Summers & Aten, 2002).


Owing to non-availability of reliable data on capital stock, investment was used as a proxy,

after employing the perpetual inventory method to discount for depreciation. This approach is in

conformity with that employed by Dimmerman (2003) and Dean (1964). Figures for investment were

obtained from the PWT. We use a ten percent rate of depreciation 9 to discount the annual investment

as given by the PWT.

Data for labor comprises the total working population of each of the five sample countries.

Similarly, GDP at constant prices was taken as a proxy for gross value added, in the absence of reliable

figures for the latter.

Table 1 shows the means and standard deviations for gross value added, capital and labor for

each country in the sample over the period 1980-2000.

Table 1: Summary Statistics: Gross Value Added, Labor and Capital 1980 – 2000

Country Gross Value Added Labor Capital

China 2252.1 0.658 1929.0

(1.417) (0.1046) (1363.)

Indonesia 483.92 0.078 380.02

(276.0) (0.016) (250.0)

Malaysia 118.62 0.007 134.70

(72.7) (0.0001) (9307)

South Korea 397.36 0.0019 631.07

(237.7) (0.0038) (426)

Thailand 245.49 0.0031 376.65

(143.4) (0.059) (251.2)

Average: 699.50 0.159 690.29 Note: The values for gross value added and capital are given in billions of constant 1996 US dollars, while the figures for labor represent billions of

people. Numbers in parentheses indicate standard deviations.

For the second part of our paper, we have employed a host of variables on which TFP has been

regressed. These variables are; gross secondary enrollment ratio (GSER), trade openness, number of

technical and scientific journal articles published, and gross foreign direct investment (GFDI).

According to the literature reviewed, these variables have been observed to have a significant impact

on growth in TFP.

The figures for GSER have been obtained from the database of World Development Report

(2005) and statistics of the UNESCO (2002). 10

These figures have been compiled for the period 1980-

2000. Data for Thailand and Malaysia were unavailable for a few years, and hence we used linear

interpolation/extrapolation techniques to fill in the gaps11

.

To gauge the extent to which an economy engages in international trade, we have obtained

figures for trade openness 12

from PWT version 6.1. PWT expresses import and export figures in

national currencies from the World Bank and United Nations data archives.

Publication of scientific and technical journal articles has been used to measure indigenous

research and development. These figures have been obtained from the World Development Report

(2005) for the period 1980-2000. FDI incorporates the import of capital goods by multinational

corporations (MNCs), and the transfer of managerial and technical skills resulting from the link

between parent companies and local subsidiaries of MNCs. The figures for FDI over the period 1980-

2000 have also been obtained from the World Development Report (2005).

9 For the manufacturing sectors of Pakistan and India, Burki, Khan and Bratsberg (1997) use rates of depreciation in the range 5 - 15 %. Since our study

focuses on all sectors of the economy, we feel that a ten percent rate of deprecation should be reasonable. 10 For further details, see the United Nations Educational, Social and Cultural Organization’s website www.unesco.org. 11 For Malaysia, data was missing for 1997, while for Thailand, data was missing for 1999.

12 Trade Openness = . CGDP

Exports Imports + See Heston, Summers and Aten (2002) for further details.


Table 2 shows the mean and standard deviations for GSER, trade openness, scientific and

technical journal article publications and gross foreign direct investment for each country in the

sample, and the entire sample, over the observed time period. We see that South Korea has the highest

mean GSER. In case of journal articles, China has the highest rate of publication. Malaysia has the

highest gross FDI, and also appears to be the most open economy in terms of foreign trade.

Table 2: Regressor Statistics

Variable Means/(Standard Deviations)

Entire Sample China Indonesia Malaysia Thailand South Korea

Gross Enrollment Rate 57.291 51.916 44.701 55.175 41.381 91.923

(1.609) (9.557) (8.090) (8.488) (15.671) (6.592)

Journal Articles 1978.689 6628.815 95.432 247.789 309.933 2523.823

(203.988) (5144.101) (40.136) (104.561) (139.067) (2879.716)

Gross FDI 2.355 2.719 1.470 4.405 2.009 1.088

(0.0689) (2.441) (1.480) (2.248) (1.556) (1.0158)

Openness 64.093 29.725 47.844 122.429 69.735 49.779

(0.512) (11.791) (6.535) (40.432) (20.894) (15.160) Note: Values in parentheses indicate standard errors.

4. Methodology 4.1. The Productivity Index

Given the crucial role of productivity in economics and the limitation of reliable data on prices and

quantities, economists often calculate productivity indices to measure productivity change, rather than

estimating technology and shifts in technology directly (Fare & Grosskopf, 1992). Following on this

practice, we, in our paper apply Malmquist DEA methods to panel data to calculate indices of total

factor productivity change, technological change, technical efficiency change and scale efficiency

change. These methods are discussed in greater detail by Fare et al. (1994).

According to Coelli (1996), DEA programs and an (input or output-based) Malmquist TFP

index may be used to measure productivity change, and to decompose this productivity change into

technical change and technical efficiency in the presence of panel data. The methods implemented in

the program are based upon the work of Fare, Grosskopf and Lovell, (1985). We use seminal software

developed by Coelli (1996), 13

which has the added advantage of breaking technical efficiency further

into ‘pure’ technical effects, and scale efficiencies.

This break-up of technical efficiency is a way of further refining the components of

productivity growth in order to compensate for possible scale effects that may apply to the productivity

growth of any particular country. Thus, we can now talk about how, while a country’s productivity

growth may solely consist of ‘catching-up’, whether even this catching up is because of changed

returns to scale for the country, or whether there is actual improvement in the efficiency of its

productive processes. This was originally proposed by Banker, Charnes and Cooper (1984), to consider

for a variable returns to scale situation.

A more generally used index for measuring growth in total factor productivity is the Tornqvist

index. The Tornqvist productivity index assumes a translog production function and gives exact results

for the translog specification. Since the translog form is flexible, Diewert (1976) regards the Tornqvist

index as a “superlative” means of determining productivity growth (see Fare et al. 1994). Since the

Tornqvist index assumes efficient production, it has the drawback of not differentiating productivity

growth into the two possible components of changes in performance and changes in technology. The

13 Dr Tim J. Coelli is an Associate Professor in the School of Economic Studies at the University of New England, Australia. Dr Coelli has been actively

involved in the development of efficiency and productivity measurement software. His software has been distributed free of cost for a number of years,

and his programs are now very widely used all around the globe (Singh, Fleming & Coelli, 2000).


Malmquist index, on the contrary, does not require any assumptions regarding efficiency and

functional form, and is therefore able to distinguish between the factors causing changes in

productivity14

.

Given that we are analyzing a certain set of countries that have been specifically chosen for

their remarkable growth over the past two decades, the Malmquist index is a better way of deciding

how much of this growth is attributable to the ‘catching-up’ effect. Consequently, we calculate

productivity change as the geometric mean of two Malmquist productivity indices. This measure was

originally introduced by Caves, Christensen, Laurits and Diewert (1982a, b), and named after Sten

Malmquist, who had earlier proposed the idea of constructing quantity indices as ratios of distance

functions (Coelli, 1996). According to Fare et al. (1994), “distance functions are function

representations of multiple-output, multiple-input technology which require data only on output and

input quantities”. Fare et al. (1994) advocate the usage of a distance function to see whether the growth

in productivity is attributable to the above-mentioned catching up effect, described as improvements in

performance, or whether this growth is due to a shifting out of the technological frontier at a country’s

given set of inputs.

Since the Malmquist index gives measures of productivity changes over time, over every period

1,0, =tt ….T, the production technology tS is defined such that the inputs Nt Rx +∈ into output

Mt Ry +∈ . Hence, tttt xyxS :),{(= produces }ty . Although the Malmquist productivity index is

frequently used as an input-oriented index which determines the impact changes in inputs have over

productivity, it can also be defined as an output-based index. Fare and Grasskopf (1992) have set up

the input based Malmquist productivity index, while Fare et al. (1994) use the productivity based

Malmquist productivity index to determine TFP growth. Using an equivalent method developed by

Fare et al. (1989, 1992, 1994), we establish the output based productivity index. This index firstly

captures relative changes over time, using the ratio of distances from the productive frontier across

consecutive time periods. Pioneered by Shephard (1970), and later used by Fare (1988), the distance

function is defined as:

})/,(:inf{),(0

ttttttSyxyxD ∈= θθ

1}))/,(:(sup{ −∈= ttt Syx θθ

This function is defined as the reciprocal of the “maximum” proportional expansion of an

output given certain input combinations. When 1),(0 =tttyxD , it implies that ),( tt yx lies on the

boundary of the production frontier. In this case, the output is technically efficient. However,

1),(0 ≤tttyxD implies technical inefficiency. Similar results can be obtained for the input distance

function utilized by Deaton (1979), where the input distance function is defines as

}),/(:sup{),( tttttt

i SyxyxD ∈= λλ under constant returns to scale.

Under constant returns to scale production technology, maximum feasible output is only

achieved when average productivity ( xy / ) is maximized. The maximum, as will be shown in the next

section, is the benchmark country with the highest level of productivity. For the rest of the countries in

the sample, decreasing distances imply catching up to the maximum productive frontier, thus,

improving technical efficiency. This maximal level that is observed on the frontier is determined using

programming techniques that are explained later on.

To explain growth trends across periods of time the Malmquist productivity index gives

distance functions with respect to time. These are defined as follows:

})/,(:inf{),( 1111 tttttt

O SyxyxD ∈= ++++ θθ

),( 11 ++ ttt

O yxD measures the maximal proportional change in outputs to ensure that ( 11 , ++ tt yx ) is

feasible. At time period t, ( 11 , ++ tt yx ) is unattainable. 15

The maximal proportional change in output

14 For further details, see Fare et al. (1994).


required to ensure the feasibility of ( tt yx , ) at t + 1 is denoted by ).,(1 ttt

O yxD+ Fare et al. (1994) define

the productivity index in period t as:

),(

),( !1

ttt

O

ttt

Ot

yxD

yxDM

++

=

Similarly, the Malmquist index for period t +1 is defined as follows:

),(

),(1

!111

ttt

O

ttt

Ot

yxD

yxDM

+

++++ =

Keeping in line with common practice and to avoid setting an arbitrary benchmark, we define

the Malmquist productivity index as the geometric mean of two Malmquist indices defined according

to the parameters in Caves et al. (1982a, b): 2/1

1

11111

11

),(

),(

),(

),(),,,(

=+

+++++++

ttt

o

ttt

o

ttt

o

ttt

otttt

OyxD

yxD

yxD

yxDyxyxM

This technique uses the ratios of two distance functions year on year in order to evaluate

productivity growth. This enables us to avoid setting any arbitrary benchmark, and we are able to use

two separate time-period distance functions to see how technological change can be shown using the

country’s distance functions16

.

Given the above specification, the level of the Malmquist index shows how productivity overall

is growing. An index value greater than 1 indicates growth in productivity, while values less than unity

show deterioration in the level of total factor productivity. Without looking at the efficiency and

technical change components, however, it is not possible for us to draw a comprehensive conclusion

about any changes in the level of productivity.

By rewriting the productivity index according to the method prescribed by Fare et al. (1989,

1992) we can distinguish between efficiency and technical change:17

=++ ),,,( 11 tttt

O yxyxM

2/1

1!11

11111

),(

),(

),(

),(

),(

),(

×

++++

+++++

ttt

o

ttt

o

ttt

o

ttt

o

ttt

o

ttt

o

yxD

yxD

yxD

yxD

yxD

yxD

Efficiency change × Technical change

Where: Efficiency change = ),(

),( 111

ttt

o

ttt

o

yxD

yxD+++

, Technical change =

2/1

1!11

11

),(

),(

),(

),(

++++

++

ttt

o

ttt

o

ttt

o

ttt

o

yxD

yxD

yxD

yxD

The value for efficiency change measures the overall change in relative efficiency, and is a

measure of the distance between observed production and the maximum possible production level

between the two time periods t and t + 1. The component for technical change, calculated as the

geometric mean of two ratios, measures the shift in production technology. This ratio represents the

relative change in the input technologies over the time period t and t + 1 (i.e. change in tx and 1+t

x ).

The availability of different indices for technical and efficiency change in the Malmquist index

make it even more appealing in the context of this paper. The sample countries all share the

characteristic of having exhibited exemplary growth over the past few decades (Day et al., 2005). It is,

therefore, quite insightful to make comparisons on the basis of technical change and efficiency change.

Thus, we will use the Malmquist index not only to evaluate net productivity growth, but also to see

15 Only when (

11 , ++ tt yx ) is obtained can it be concluded that technical change has taken place. 16 According to Fare et al. (1994), this form is typical to Fisher Ideal Indices. 17 Farrell (1957) developed a direct measure for calculating the distance functions of the Malmquist productivity index based on the relationship between

technical and efficiency change components. This approach has been adopted in this paper and is somewhat different from the manner in which Caves

et al. (1982a, b) employed the Malmquist index. For further detail, see Fare et al. (1994).


whether this growth comes via the concept of improved utilization of existing resources (“catching-

up”) or through an outward shift in the production frontier. Although the Malmquist index can be used

for any type of returns to scale, for the purpose of analysis we define a constant returns to scale

production technology following Fare et al. (1994).

4.2. Determinants of TFP growth

After calculating TFP growth for the sample countries over the time period 1980-2000, it is the prime

objective of our study to examine the importance of several channels through which growth in TFP

occurs. Following up on previous studies, we incorporate three avenues of TFP growth simultaneously

in order to gauge the relative importance of alternative mechanisms: research and development (R &

D), international technology diffusion and human capital.

The number of scientific and technical journal publications by domestic scientists has been

employed as proxy for indigenous R & D as suggested by Nelson (1996). In addition to internal R&D

recent literature stresses the importance of foreign sources of technology as determinants of TFP

growth (see, for instance, Coe and Helpman, 1995; Crespo, Martin and Velazquez, 2002; Griffith,

Stephen and Van Reenen, 2000; Zachariadis, 2004; and Savvides and Zachriadis, 2005). Therefore, we

have incorporated both FDI and trade openness as explanatory variables in our model. Similarly,

secondary education has been seen to have a crucial role in explaining growth in TFP. Nelson and

Phelps (1966), Benhabib and Spiegel (1994) and Savvides and Zachriadis (2005) incorporate

secondary enrollment ratio in their studies of TFP. In light of this research practice, we have utilized

gross secondary enrollment ratio as a determinant of TFP growth in our study.

Hence, in the second stage of our paper, we regress values for growth in TFP measured using

the Malmquist index on gross secondary enrollment ratio, trade openness, the number of technical and

scientific journals published, and gross foreign direct investment. More specifically, we estimate the

following models: 20

1

4

20

1

3

20

1

2

20

1

1 itit

t

it

t

it

tt

itiit JOURNOPENFDIEDUCGTFP εααααββ ++++++= ∑∑∑∑====

(1)

......(20

1

4

20

1

3

20

1

2

20

1

1 itit

t

it

t

it

tt

itiit JOURNOPENFDIEDUCTECHCH εααααββ ++++++= ∑∑∑∑====

(2)

......(20

1

4

20

1

3

20

1

2

20

1

1 itit

t

it

t

it

tt

itiit JOURNOPENFDIEDUCEFFCH εααααββ ++++++= ∑∑∑∑====

(3)

We first look at the overall impact of the explanatory variables on TFP growth and then

proceed to look at the impact on the technical and efficiency change components of the Malmquist

productivity index. In these equations, iβ represents country-specific dummies which are included so

as to capture variation not attributable to the afore mentioned three avenues. itGTFP refers to growth in

TFP, itTECHCH is the growth in innovation over time, itEFFCH is the improvement in efficient

utilization of available inputs. itEDUC is the gross secondary school enrollment ratio, itFDI is the

gross foreign direct investment, itOPEN is the economy’s openness to foreign trade, and itJOURN is

the number of technical and scientific journal article publications taking place in the economy. The

intercept term β represents the estimated value of the dependant variable when all the independents

have a value of 0, while itε is the error term.


5. Empirical Results 5.1. Results for TFP Growth

Given a single output (gross value added) and two inputs (capital and labor), we calculate technical

efficiency and total factor productivity for the five East Asian countries; China, Indonesia, Malaysia,

Thailand and South Korea, over the time period 1980-2000.

Technical efficiency in our context is defined with respect to the maximum attainable output

levels, while technology throughout the sample period is defined in terms of the output distance

function (see Fare et al., 1994). The best practice frontier constructed from the countries in the sample

represents the maximum level of output that can be obtained given the inputs. The frontier is based on

the assumption of constant returns to scale production technology. Given a single output and two

inputs, the best practice frontier is the equivalent of the production function of the most efficient

country in the sample.

Table 3 shows the results for technical efficiency for the sample countries over a five year

interval. 18

The distance from the frontier can be used to measure its levels of inefficiency.

Table 3: Technical Efficiency Across the Years

Technical Efficiency: Selected Years

(Constant Returns to Scale)

Year

Country 1981-85 1986-90 1991-95 1996-2000

China 1.151 1.099 1.070 1.282

Indonesia 1.000 1.000 1.000 1.000

Malaysia 1.000 1.001 1.000 1.000

Thailand 1.000 1.364 1.490 1.489

South Korea 1.179 1.000 1.000 1.000

Mean: 1.066 1.093 1.112 1.154

In the best practice frontier based on our sample of five East Asian countries, we see that over

the observed period, Indonesia and Malaysia defined the frontier and remained on the frontier

throughout. In 1990, South Korea caught up with the best practice frontier countries; as post-1990,

South Korea has maintained a technical efficiency figure of 1.00. Similarly, in 1995, China had a

figure of 1.00 for technical efficiency, and joined South Korea, Malaysia and Indonesia on the best

practice frontier. Thailand, on the contrary, has consistently been lying below the frontier, implying

that the country has been unable to attain technical efficiency in line with other countries in the region.

China also fell below the frontier in 2000. Overall, Indonesia and Malaysia have been the two most

efficient economies defining the frontier, followed by South Korea.

18 A value of 1 implies that the country lies on the best practice frontier, while a value greater than 1 suggests technical inefficiency, and that the country

lies below the frontier.


Figure 1:

Technical Efficiency Trends

0.000

0.200

0.400

0.600

0.800

1.000

1.200

1.400

1.600

1981-85 1986-90 1991-95 1996-2000

Year

Level

China

Indonesia

Malaysia

Thailand

South Korea

Figure 1 graphs the technical efficiency frontier. It is apparent that Thailand and China are both

straying quite far from the efficient level of production. Values greater than unity indicate inefficient

production, with 1 being the optimum production point. Once again, we see that efficiency is defined

by South Korea, Malaysia and Indonesia, all of which have been lying on the productive frontier for

the recent past. Thus, these results corroborate the break-up of the productivity determinants analysis,

whereby Malaysia, South Korea and Indonesia were the very countries that were defining level of

productive efficiency for the region.

We now present Malmquist productivity indices, along with the indices for efficiency change,

technical change and scale change for the five economies in our sample. 19

A summary description of

the average performance of each economy over the period 1980-2000 is revealed by Table 4. As

mentioned earlier, if the value of the Malmquist index or any of its components is less than unity, this

denotes a deterioration in performance, whereas values greater than unity denote improvements in the

relevant performance. Also note that these measures capture performance relative to the best practice in

the sample.

Table 4: Individual performances over time

Average Annual Changes

Malmquist index Technical

Change

Efficiency

Change

Pure Efficiency

Change Scale Change

Country (MALM) (TECHCH) (EFFCH) (PEFFCH) (SCH)

China 0.985 0.979 1.006 1.000 1.006

Indonesia 0.998 0.998 1.000 1.000 1.000

Malaysia 1.029 1.303 1.000 1.000 1.000

S. Korea 1.011 1.003 1.008 1.001 1.007

Thailand 1.075 1.072 1.003 1.000 1.003

Mean: 1.019 1.016 1.003 1.000 1.003

The last line of Table 4 shows that for the entire sample on average, productivity increased

noticeably over the twenty years studied: the growth in TFP as given by the Malmquist productivity

index was 1.9 percent for our sample as a whole. 20

This growth was largely due to technological

innovation (TECHCH), which improved by 1.6 percent, rather than improvements in efficiency

(EFFCH), which registered an increase of only 0.03 percent. These results are in line with those

19 Since the Malmquist index is an index based on discrete time, each country will have an index for every pair of years. This involves calculating the

component distance functions using methods that we have described earlier. 20 Subtracting 1 from the number reported in the table gives average increase or decrease per year for the relevant time period and relevant performance

measure.


obtained by Fare et al. (1994) for their sample of seventeen OECD countries for the time period 1979-

1988.

Moving on to the country-by-country results, we note that South Korea has the highest TFP

change in the sample, at 7.5% per annum on average, most of which can be attributed to improvements

in innovative ability. Indeed, South Korea’s rate of innovation was the highest in the five economies

studied. On the other hand, China exhibits a decrease in TFP of 1.5%, which is even lower than

Indonesia, where TFP fell by 0.2%. The other two countries in sample, i.e., Malaysia and Thailand

display positive, albeit lower, values for increase in TFP, of 2.9% and 1.1%, respectively.

Our small sample size, and the Korean economy’s considerably high value for technical change

(at 7.2%), allow us to conclude that South Korea, along with Malaysia, has played a significant role in

shifting the frontier outwards over time.

Figure 2:

Malmquist Index Trends

0.850

0.900

0.950

1.000

1.050

1.100

1.150

1981-85 1986-90 1991-95 1996-2000

Time

TF

P

China

Indonesia

Malaysia

Thailand

South Korea

For the purpose of meaningful and in-depth analysis, we have graphed the trends of total factor

productivity growth, efficiency change and technical change over five year intervals for all the

countries in our sample. Figure 2 illustrates that while overall productivity for the entire sample over

the observed period has increased, productivity for South Korea was the highest in 1980-85. Despite its

high economic growth in subsequent years, we see that growth in productivity has been continuously

declining over the observed period. However, despite the decline, TFP growth in South Korea has been

higher than all the other countries in the sample. China, on the other hand, has had the lowest level of

TFP growth. China’s TFP growth was at its minimum in the period 1986-90, after which productivity

showed a rising trend, and converged to the productivity levels of all other countries in the sample.

This stands to show that China’s focus on expanding the industrial and export base has simultaneously

resulted in productivity improvements. As for Malaysia, Indonesia and Thailand, their TFP growth has

remained in the vicinity of each other and followed similar trends, with Malaysia registering the

highest TFP growth. For these economies, average TFP growth peaked over the period 1986-90;

thereafter, it declined, and showed signs of recovery again in 1996-2000. Perhaps the beginning of the

financial crisis in these economies resulted in a slowdown in productivity growth in the early 1990s.

Since these graphs present average trends over five year intervals, it is not possible to pinpoint country

specific factors affecting TFP growth.


Figure 3:

Efficiency Change Trends

0.900

0.920

0.940

0.960

0.980

1.000

1.020

1.040

1.060

1981-85 1986-90 1991-95 1996-2000

Time

Effic

iency C

hange

China

Indonesia

Malaysia

Thailand

South Korea

Figure 3 illustrates efficiency change over the five year intervals for the sample economies.

Through out the sample period we see that Indonesia and Malaysia maintained an efficiency index of 1

and did not register any improvements in efficiency. Since these countries already defined the

efficiency frontier we see no catching up for these two countries. After 1990 we see that South Korea

also joined these two efficient economies while Thailand shows a u-shaped pattern of efficiency

growth over the observed period. China is perhaps the most interesting economy as China’s efficiency

change pattern exhibited a zigzag shape over the observed time period; overall China has been unable

to catch up with the best practice countries.

Lastly, figure 4 shows the technical change component of the productivity index. It is

interesting to note that technical change in all five economies follow similar patterns as TFP growth as

a whole. This strengthens our earlier claim that TFP growth has been caused primarily due to technical

change that is innovation rather than improvements in efficiency. Perhaps the only difference that has

emerged is the fact that technical change rose steeply relative to TFP growth for Indonesia and China

in the period 1996-2000.

Figure 4:

Technical Change Trends

0.850

0.900

0.950

1.000

1.050

1.100

1981-85 1986-90 1991-95 1996-2000

Time

Technic

al C

hange

China

Indonesia

Malaysia

Thailand

South Korea

5.2. Results of Panel and OLS Estimation

The results for OLS and panel regressions with fixed and random effects are given in Table 5 while

Table 6 illustrates the results of panel regression on technical change and efficiency change.


Table 5: Parameter estimates and summary statistics Panel and OLS regression of explanatory variables on

total factor productivity

Parameter estimates

Variable Standard OLS Fixed Effects Random Effects

Intercept 0.0098 0.8958

(0.000272679)** (0.054835)**

Gross Enrollment Ratio 0.1217 0.1463 0.1537

(0.031715)** (0.039032)** (0.041737)**

Openness -0.0578 -0.6727 -0.0605

(0.033375)* (0.045088) (0.034712)*

Gross FDI 0.0028 -0.0100 -0.0066

(0.010174) (0.010823) (0.010055)

Scientific and technical journal articles -0.0003 0.0397 0.0291

(0.000225098) (0.022003)* (0.025965)

Continuous time dummy -0.0041 -0.0041

(0.00196753)** (0.00187043)** 2R 0.1912 0.3642 0.2037

F-test of fixed effects 5.3791

(0.0006)**

Wu-Hausman test statistic 1.6658

[0.4348]

Note: Values in parentheses show standard errors, while those in square brackets are p-values. A single-asterisk Indicates statistical significance at α =

0.10 and double-asterisk indicates statistical significance at the 0.05 level.

Table 6: Parameter estimates and summary statistics for panel regression of explanatory variable on technical

change and efficiency change

Variable Parameter estimates

TECHCH EFCH

Intercept 0.9451 0.9685

(0.059923)** (0.046458)**

Gross Enrollment Ratio 0.0466 0.0776

(0.045619) (0.034340)**

Openness 0.0002 -0.0606

(0.037930) (0.029529)**

Gross FDI 0.0069 -0.0114

(0.010987) (0.00856910)

Number of scientific and technical journals 0.0065 0.0286

(0.028371) '(0.022165)

Continuous time dummy -0.00257167 -0.0017

(0.00204354) (0.00161659) 2R 0.0714 0.0903

Note: Values in parentheses show standard errors, while those in square brackets are p-values. A single-asterisk indicates statistical significance at α =

0.10 and double-asterisk indicates statistical significance at the 0.05 level.

In order to analyze the determinants of TFP growth, we regressed total factor productivity

growth for the entire sample against GSER, GFDI, journal articles and openness. The results are shown

in Table 5. The first column shows the ordinary least square estimates for GSER, GFDI, journal

articles and openness, along with t-values at the 10 % and 5 % significance levels. The second column

shows the results after taking into account country specific effects through the inclusion of country

dummies. The next two columns show the results for fixed effect and random effect 21

parameter

21 According to Greene (1997), panel data may have group effects, time effects, or both. These effects are either fixed effect or random effect. A fixed

effect model assumes differences in intercepts across groups or time periods while taking coefficients to be constant, whereas a random effect model

explores differences in error variances.


estimates respectively. The panel estimates of the impact of GSER, GFDI, journal articles and

openness on technical change and efficiency change are given in Table 6.

Table 5 reveals that the fixed effects estimates yield the highest value for R-squared. 22

It is

interesting to note that OLS estimates which included dummies and a continuous time trend also give

the same value for 2R23

.

However, the Wu-Hausman test24

favors usage of the random effects model over the fixed

effects model. A glance at the test-statistic and the p-value indicates that the random effects estimators

are consistent and efficient. 25

Based on these statistics, it can be established that the random effects

specification best suits the sample countries.

These results are in tandem with Greene (2005), who highlights the problems that a fixed

effects specification can create. Fixed effects models tend to deplete degrees of freedom, which may

lead to increased standard errors. Also, fixed effects eliminate cross-sectional variance in the

independent variables, which again increases standard errors (and might make some standard errors

infinite, as in the case of variables that do not vary temporally). Moreover, they can exacerbate

problems of measurement error if the reliability of time-series variation in the explanatory variables is

poor (Greene, 2005).

For the regression on technical change and efficiency change 2R is higher with efficiency change

as the dependent variable as compared to technical change as the dependent variable. This shows that the

explanatory variables chosen in the model are more significant for the determination of efficiency change

relative to technical change. Overall Table 6 highlights the fact that despite the importance of technical

change in determining TFP growth, the explanatory variables that we have chosen are not adequate

determinants of technical change. On the contrary the variables that explain TFP growth provide a better

explanation of efficiency change.

We now proceed to interpret the parameter estimates for each variable under the random

specification for TFP growth and for its two components technical change and efficiency change.

For TFP growth gross secondary enrollment ratio has a positive value which is significant at the

5% level of significance.26

This implies that the level of secondary education is an important factor in

improving production technologies. Zachariadis (2005) in his study of TFP growth in OECD countries

obtains a similar positive relation between secondary enrollment and TFP growth. This result is also in

line with empirical literature on human capital which stresses the importance of education as an

essential component in improving production techniques, and hence productivity, especially in middle

and low income countries. Rodriguez-Claire (1997), Eicher (1996) and Restuccia (1997), all develop

models highlighting the importance of human capital in the adoption of new technologies and as a

determinant of productivity growth.

In the case of our sample countries, education has been a high priority, and there is a general

trend of encouraging vocational training. This is especially true for South Korea, as pointed out by

Holliday (2000), that the Korean state established a “productivist” welfare system, whereby education

was particularly promoted as a means of enhancing productivity and growth. Similarly, Davis (2003)

shows that during the period 1990 to 2000, China revamped its educational infrastructure. This was a

great improvement over the education system that prevailed during the 1980s, as our results show that

over the same period, China registered significant productivity improvements. Similar policies were

undertaken by the other countries in the region. It is therefore not surprising to see that governments’

objective of improving education has been directly correlated with productivity growth. This clearly

22 The fixed effects model includes country dummies as it implicitly takes into account country differentials, and therefore exhibits the highest 2R

(Ghura & Goodwin, 2000)

23 2R = 3.462

24 The Hausman specification test compares the fixed versus random effects under the null hypothesis OH that the random effects estimator is consistent

and efficient, while the fixed effects estimator is consistent, but not efficient (Greene 1997). 25 The Hausman test-statistic = 1.6658, p-value = 0.4348 26 The p-value for GSER is 0.000, which shows that the coefficient of GSER is highly significant.


shows that improved education in our sample countries was important in improving the quality of

workers and their ability to utilize resources efficiently. Secondary enrollment ratio has not registered a

significant impact on technical change as table 6 shows the value is insignificant at the 5% level of

significance. On the other hand secondary enrollment ratio has had a significant positive impact on

technical change for the sample countries over the entire period. This clearly shows that secondary

education is an important variable in improving efficiency but does not directly lead to greater

innovation. Education has not played an important role in shifting the production function of the

sample countries; on the contrary secondary education has assisted relatively inefficient countries to

catch-up with the benchmark countries.

Helpman (1991), Caselli and Wilson (2004) and Eaton and Kortum (2001) hypothesized that

the direct import of capital and intermediate goods is a channel of transmission of foreign technology,

and hence eventual growth in TFP. However, for our sample trade openness has a statistically

significant negative value, 27

implying that high levels of imports and exports negatively impact TFP

growth for the sample countries over the period under observation. FDI is also observed to have

negative impact on productivity growth. Openness also has a significant negative value for efficiency

change but is insignificant in explaining technical change. The coefficient of GFDI appears to be

statistically insignificant for TFP growth as a whole, technical change and efficiency cahnge. Although

the coefficient of openness is negative, it is quite low, as only 6% of the growth in TFP can be

attributed to openness.

Similarly, the negative coefficient for FDI only explains 0.6% of the growth in TFP. This

means that FDI and trade openness are not the main factors affecting TFP. This is in line with

Zachariadis’s (2005) observation that FDI and trade do not significantly impact TFP growth; instead,

they have a stronger relationship with total value-added. Perhaps as Mayer (2001) points out, for

certain middle-income countries, the basic conditions for proper utilization of imported machinery or

capital may not exist, thereby hindering efficient production, and hence negatively impacting TFP. FDI

is crucial for capital accumulation, but it does not guarantee improvement in productivity. The negative

impact of trade openness on efficiency change contradicts the generally held belief that growth in East

Asia was primarily export-led and that these countries, especially South Korea, prospered by opening

up to the world market. On the contrary, internally developed technology and production methods

coupled with local policy initiatives have been a more important determinant of productivity growth

relative to the role played by foreign technology transfers and competition in the international market.

Internal research and development, as measured by the proxy, the number of scientific and

technical journal articles published, has a positive coefficient. However, this coefficient appears to be

statistically insignificant. 28

The coefficient for the components of TFP growth: technical change and

efficiency change, also register no significant relation with the number of scientific and technical

journal articles. This invalidates the choice of the proxy for research and development, as scientific and

technical journal articles do not have any substantial affect on the method of production. On the

contrary, actual expenditure on research and development in the form of R&D intensities has been

observed to be a more suitable variable explaining TFP growth. Zachariadis (2004), using R & D

expenditure as a fraction of GDP for 10 OECD countries over 1971-1995, shows that R & D has

significantly impacted TFP growth. Due to the unavailability of data on R&D expenditure for our

sample countries, we employed journal articles as proxy; however, as the results demonstrate, this was

not an appropriate proxy.

As Table 5 shows, the coefficient of the time trend has a statistically significant negative value. 29

This shows that TFP growth has shown a negative trend over time, as growth has tapered down over

the period. The slowdown in TFP growth over time could be attributed to the financial crisis that most

of these countries went through during the 1990s. According to Cho and Rhee (n. d.), Thailand, South

27 The p-value for trade openness is 0.081 which is significant at the 10% level of significance. On the other hand the coefficient of GFDI has a p-value of

0.511 which is statistically insignificant. 28 The value for the coefficient of the number of scientific and technical journal articles is 0.029 and the p-value is 0.263. 29 The estimated value for the coefficient of time trend in -0.0041


Korea and Indonesia were particularly badly hit. Also in the case of South Korea, the large chaebols

have been criticized for the mismanagement arising from large scale production over time. As for the

components of TFP growth no significant relation with time has been observed.

Conclusively, our results for the determinants of TFP growth substantiate the literature on

human capital theory, as secondary enrollment is the most important determinant of productivity

growth over time. Foreign transfers of technology through increased trade and capital imports have not

had a significant impact on TFP growth. Thus, TFP growth can be attributed to internal technological

developments due to the highly educated labor force; however, over time, TFP growth has slowed

down. Incase of the components of TFP growth we observe that accumulation of human capital and

foreign capital transfers and trade have a significant impact on efficiency change, however as far as

technical change is concerned none of the variables in our model have been adequate in explaining

shifts in the frontier. Thus, the determinant variables in the second stage of our model are satisfactory

in explaining catching up among the sample countries however, our model does not provide insight

into the factors responsible for innovation responsible fro shifting the production frontier outwards,

6. Conclusion In light of the high growth in the East Asian economies, combined with the observed evidence of

cross-country convergence in per capita incomes and output, much of the recent work in the field of

new growth empirics has been directed towards the study of regional growth patterns and testing for

convergence. Similarly, there has been growing focus on the role of productivity growth in influencing

overall growth patterns of developed economies. Given the lack of research on the dynamics of

productivity growth in East Asia, this paper used the Malmquist productivity index to first calculate

TFP growth for China, Indonesia, Malaysia, South Korea and Thailand over the period 1980-2000, and

then proceeded to analyze the determinants of TFP growth.

Given the assumption of constant returns to scale production technology, the Malmquist

productivity index was used to construct the best practice frontier and to separate the technical and

efficiency components of TFP growth. In the best practice frontier based on the sample of five East

Asian countries, it was seen that over the observed period, Indonesia and Malaysia defined the frontier,

and remained on the frontier throughout. In 1990, South Korea caught up with the best practice frontier

countries, while Thailand remained the most technically inefficient economy.

For the entire sample on average, productivity increased noticeably over the twenty years. This

growth was largely due to technological innovation, rather than improvements in efficiency.

Improvement in the efficient utilization of resources was slower compared to the technological

innovation, as indicated by the larger value of the technical change component of the Malmquist index.

Following on with our two-pronged approach, the second stage of our study analyzed the

determinants of TFP growth and its components, namely efficiency change and technical change.

Based on a panel regression with random effects of the countries over the time period 1980-2000, for

the sample as a whole, secondary education was the only variable that had a positive impact on TFP

growth and efficiency growth while it was insignificant for technical change. On the contrary, trade

openness and foreign direct investment were seen to be inconsequent as determinants of TFP growth

and its components. This highlights the fact that internal technological improvements as opposed to

foreign capital and technologies were the main determinants of TFP growth and improvements in

efficiency. However, our indicator for internal R&D, the number of scientific and technical journal

articles, does not help in explaining the observed phenomenon. Overall, the explanatory variables

chosen in our model help explain efficiency change but are inconsequent in explaining shifts in the

production frontier.

After studying TFP growth and its possible determinants for the case of East Asia, the question

arises of what we have learnt from this exercise. And most importantly, can we evaluate government

policy in any country’s case, or suggest more suitable policy for the State to implement? According to

Felipe (1997), the results of growth accounting exercises or estimation of productivity values do not


allow us to make an appraisal of the industrial policy and government intervention in any country.

Performing a TFP growth exercise with the aim of decomposing overall economic growth is not the

same as explaining the ultimate causes of growth. Therefore, most explanations about the growth of

countries stemming from increased productivity advanced by literature are unwarranted (Felipe, 1997).

As observed in our sample TFP growth levels in the chosen countries have little relationship to the

overall GDP growth. Thus we need to emphasize that in the catch-up phase of development for low-

and medium-developed countries governments should have a GDP growth-oriented focus rather than

worrying about the level of TFP growth.

However, the importance of education as an important determinant of TFP growth and overall

GDP growth cannot be undermined. The results of our study highlight the importance of education in

the development of innovative production technologies. Therefore, it can be advocated that

governments should focus more on domestic policy, especially education rather than having an

outward oriented approach in their efforts to improve TFP. It is important to keep in mind that different

models are applicable only under different assumptions, before drawing any conclusion from the

results. This, however, does not imply that studies on TFP growth have yielded dismissible results so

far. Thus, our study is a preliminary attempt to study the important determinants of TFP growth

without a focus on policy proposals. In future, we would like to further study in the area of technology

transfer on the lines of Savvides and Zachariadis (2005) and research the interaction between human

and physical capital more intensively as suggested by Felipe (1997).


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