Growth and Productivity in Asean Countries

Michael Sarel

doi:10.5089/9781451851984.001.A001

Jump to Content Jump to Main Navigation

Growth and Productivity in Asean Countries

Author: Mr. Michael Sarel

Contributor Notes

Author’s E-Mail Address: msarel@imf.org

Publication Date:: 01 Aug 1997

ISBN:: 9781451851984

Language:: English

Keywords:: WP; growth rate; share; total factor productivity growth; growth rate of total factor productivity; share of Singapore; technological factor share; TFP growth in East Asia

Download PDF (1.2 MB)

This study examines the nature of the growth process in the ASEAN countries, and particularly whether it has been generated primarily by more inputs or by productivity gains. It uses internationally comparable data and explores an alternative method for estimating the capital and labor factor shares. The results, contradicting some previous studies, indicate a very impressive growth rate of TFP in Singapore, Thailand, and Malaysia, a relatively strong rate for Indonesia, and a negative rate for the Philippines. This study argues that the results of previous studies were driven mainly by the fact that they relied on national accounts data for measures of various variables and, in particular, the factor income shares of capital and labor.

Abstract

This is probably a fairly boring and tedious paper, but it is not intentionally so. This paper attempts to provide a new interpretation of the growth process in ASEAN economies to interest the historian, to derive new theoretical implications of the forces behind this process to motivate the theorist, and to draw new policy implications from the subtleties of government intervention in ASEAN economies to excite the policy activist. ²

I. Introduction

The phenomenal growth rates of many economies in East and Southeast Asia have been documented, dissected, analyzed, and discussed in a countless number of economic research papers, as well as in books and newspapers and at conferences. There is not much purpose in repeating the full set of stylized facts regarding these growth rates. It is enough to mention that many of these economies have continued, even in recent years, to grow at rates that exceed by far the normal historical and geographical experience. During 1980–95, for example, four ASEAN economies (Indonesia, Malaysia, Singapore, and Thailand) more than doubled their real income per person, compared with an increase of only 20 percent in the United States and other industrial countries and even less than that in many other regions of the world.³

A major question about the nature of growth in ASEAN economies is whether the growth process is mainly intensive or extensive.⁴ This seemingly inconsequential distinction plays a crucial role in determining how we judge the past performance of these economies and how we regard their future growth prospects. For example, one possible explanation for the collapse of the Soviet economy around 1990 is the “extensive growth” hypothesis.

William Easterly and Stanley Fischer (1994), among others, argue that the Soviet economy ran into inevitable diminishing returns after many decades of extensive growth, caused by a massive accumulation of capital not accompanied by technological progress. This extensive growth hypothesis, if true, raises serious concerns about other economies that invested heavily in past decades, such as many ASEAN economies. The comparison of the Asian economies with the Soviet economy was explicitly made in an essay provocatively entitled “The Myth of Asia’s Miracle,” by Paul Krugman (1994, p. 70):

“The newly industrializing countries of Asia, like the Soviet Union of the 1950s, have achieved rapid growth in large part through an astonishing mobilization of resources. Once one accounts for the role of rapidly growing inputs in these countries’ growth, one finds little left to explain. Asian growth, like that of the Soviet Union in its high-growth era, seems to be driven by extraordinary growth in inputs like labor and capital rather than by gains in efficiency.”

The view expressed by Krugman is primarily based on empirical findings reported in a series of papers by Alwyn Young (1992, 1993, and 1995). In these papers, Young decomposes the growth rates of several Asian countries into an “extensive” component, measured by the rate of factor accumulation, and an “intensive” one, measured by the growth rate of total factor productivity (TFP). His surprising conclusion is that growth rates of TFP in Asian economies are not at all as spectacular as their growth rates of output. Furthermore, he estimates their rates of TFP growth to be lower than in industrial countries, and, at least in Singapore’s case, to be virtually zero.⁵

Young’s findings, and Krugman’s subsequent Soviet analogy, have generated additional economic research and renewed interest in previous studies that analyzed the growth process in East Asia.⁶ In general, these other studies produced mixed results. Some of them found support for Young’s results (e.g., the study by Kim and Lau, 1994). Most studies, however, found support for the view that TFP growth in East Asia was much stronger than Young’s research suggests.⁷

Despite the impressive number of previous studies of the growth process in East and Southeast Asia, at present we still lack a full understanding of this issue. The methodology and the coverage of these previous studies limit the possibility of a meaningful cross-country comparison of the mechanics of economic growth. The main methodological problem of previous studies is that they have relied almost entirely on national accounts data for measures of real output, investment flows, estimates on capital stocks, labor force participation, composition of the labor force, and, most important of all, distributive shares of income distribution to capital and labor. It will be argued below that all these data are likely to be strongly affected by country-specific biases and measurement problems, including those relating to relative prices in the economy (especially the relative prices of investment goods), comprehensiveness of labor market data, demographic composition of the labor force (and also of the population outside the labor force), the government’s policies and tax incentives, the industrial structure of the economy (presence of monopolies and labor unions), and other factors that may affect the distribution of income.

With a few exceptions, previous studies have also had a coverage problem. Every study of growth and productivity necessarily makes some strong assumptions about economic structure and relationships among a set of economic variables. Of course, there are no strict rules for making such assumptions, and, in large measure, they depend on the discretion and the judgment of the authors. However, as will be argued below, the estimated values of key results of these studies, including the rate of TFP growth, are very sensitive to the particular assumptions. Therefore, in a study of a particular group of countries, it is crucial to include in the sample the full set of countries to be compared, in order to be able to draw meaningful conclusions on cross-country or cross-regional differences. The implication is that previous attempts to compare TFP growth in Southeast Asia with that in, say, the United States, are unlikely to be reliable because such comparisons are typically based on results from different studies that made different assumptions.

This paper addresses the major problems of previous studies and derives new (and, it is hoped, better) estimates of productivity growth in the ASEAN economies. Country-specific national accounts are used only for the most basic data, and common methodologies are employed to derive almost all of the key variables required. The results obtained are positive in terms of their implications for the nature of growth in ASEAN economies in the past and their growth prospects in the future. These results are markedly different from Young’s and do not support the pessimistic conclusions drawn by Krugman. However, most people familiar with the dynamic and vibrant Southeast Asian economies will not find them surprising.

The study conducted in this paper is, essentially, a growth accounting exercise. Accordingly, the structure of the paper follows the design of this exercise. Section II presents the growth accounting framework and prepares three of the building blocks of the exercise: it estimates the growth rates of output, capital, and labor. The next two sections deal with factor shares, the last (and crucial) building block of the exercise. Section III performs a sensitivity analysis with respect to the values of the factor shares. The results of this analysis show the crucial role played by the magnitude of these shares in the results of growth accounting exercises, especially in the case of fast-growing economies. Section IV discusses the main problems with previous attempts at estimating factor shares, develops a new procedure for estimating them, and reports results from applying this procedure. Section V constructs estimates of the marginal product of capital. Section VI puts together all the building blocks from the previous sections and estimates rates of total factor productivity growth. Section VII discusses the outlook for the future, and Section VIII concludes.

II. The Growth Accounting Framework and its Building Blocks

The growth accounting framework, which is the traditional framework used in numerous other similar studies of growth and productivity, is presented in Appendix I. Appendix II prepares the main building blocks of the exercise, estimating the growth rates of per capita output, capital, and labor in five ASEAN countries and the United States, during 1978–96 (trying to ensure, as much as possible, a uniform cross-country treatment). The estimated growth rates are presented in Figures 1-3.

III. A Sensitivity Analysis

This section presents a sensitivity analysis of growth rates and levels of total factor productivity (TFP) with respect to changes in values of capital shares. The purpose of this analysis is to demonstrate the high sensitivity of TFP estimates and to motivate the estimation of capital shares in each country using a consistent cross-country methodology.

For each of the six countries in the sample, the growth rate of TFP during 1978–96, as well as its level at the end of this period, is simulated, using the per capita growth rates of output, capital, and labor that were estimated in Section II. The sensitivity analysis is performed with respect to changes in the capital and labor factor shares (α and 1-α). We vary the value of α over the range [0.25-0.50], by increments of 0.01, covering the entire spectrum of most estimates of this parameter. Then, we calculate the TFP growth rate as a residual (the part of growth in output that is not accounted for by growth in inputs). We repeat this procedure for the level of TFP in 1996.⁸ The results are presented in Figure 4.

The top panel of Figure 4 describes the simulated rate of TFP growth. It demonstrates that the value of α has a large impact on this rate. For example, if α is similar across countries, then Singapore has, for each value of α, an annual rate of TFP growth that is significantly higher than in any other country in the sample. However, if the value for α is 0.45 in Singapore but only 0.30 in Malaysia and Thailand, then the resulting growth rate of TFP will be higher in these countries than in Singapore.⁹

Another interesting exercise is comparing Indonesia with the United States. If the value of α is relatively low in both countries, then the annual rate of TFP growth in Indonesia will be much higher than in the United States; if α is around 0.30, then this difference will exceed a full percentage point. However, if the value of a is relatively high in both countries (close to 0.50), then the rate of TFP growth in Indonesia will fall below the rate in the United States, and its value will even be negative.¹⁰

The bottom panel simulates the level of TFP in 1996. Here, the value of α has an even more dramatic effect. If α is similar across countries, then the order of the six countries, according to their TFP level, remains constant for every value of α: United States, Singapore, Malaysia, Thailand, Indonesia, and Philippines. However, if α differs across countries, then this order may change. Indeed, every possible permutation of the six countries can be a valid order, resulting from choosing the “right” value of α for each country.

IV. An Improved Method for Estimating Factor Shares

This section discusses the problems associated with estimating technological factor shares and develops a possible solution to these problems. Technological factor shares are derived from production functions and are defined as the elasticities of output with respect to each factor of production. For example, in a standard Cobb-Douglas production function, the technological factor shares are α (for capital) and 1-α (for labor).¹¹ Estimating these factor shares is a crucial step in the methodology used in growth accounting exercises. This was made clear in the previous section: even small variations in technological factor shares were shown to have powerful effects on TFP calculations. Unfortunately, there is no simple way to observe production functions and directly measure the factor shares. These shares need to be estimated using an indirect approach.

The two methods that are commonly used to estimate technological factor shares are the “regression” approach and the “national accounts” approach. Appendix III reviews these two methods and discusses their main weaknesses. In the remainder of this section, a possible way to improve upon these methods is suggested.

The following outline suggests a new method of estimating factor shares. It is based on the same principles as the national accounts approach, but has the advantage of being free of the problems associated with both the regression and the national accounts approaches. The main idea behind this approach is to ask a basic question: What are the possible reasons for cross-country differences in technological factor shares? In other words, why should the technological factor shares in Indonesia be different, say, from those in Canada?¹² In response to this question, two obvious candidates are:

differences in the structure of production,¹³ and
differences in the level of development.¹⁴

Of course, many more factors may affect the income shares, including, among others, government taxes, regulations, and imperfect competition. It is important to notice that all these factors affect the income shares, but not the technological factor shares.¹⁵ In other words, they do not affect the elasticity of output with respect to each factor of production; this elasticity is fully determined by the production technology.

Now, suppose a way is found to control for cross-country differences in both the structure of production and the level of development. It is assumed that, in this case, no additional systematic differences in factor shares would exist.¹⁶ This is not an innocuous assumption. However, it is reasonable, and extremely powerful: it immediately suggests a relatively simple and elegant method for estimating a panel of technological factor shares that are not subject to the problems identified with the two traditional methods. The estimation procedure is based on several specific assumptions:

Each economic activity is performed according to a standard Cobb-Douglas production function Y=AK^αL^1-α, where α is the elasticity of output with respect to the capital input. Different activities may use different technologies of production (i.e., they may have different α values).
The technologies (i.e., the α values) are not fundamentally different across countries and over time; where such differences exist, they can be fully captured by controlling for the average stock of capital per person across countries and over time.
The differences between technological factor shares and income shares are not systematic. In other words, some countries might reward one factor of production more than its technological share, while other countries might reward it less on account of government interventions and regulations, imperfect markets or information, and other reasons. But, on average across countries, the reward that each factor receives in each type of activity approximates its technological share in that type of activity.

The procedure is implemented in three steps:

For each major type of economic activity, collect data to construct a sample of observations and estimate the intrinsic technological factor shares (possibly as a function of the level of development).
For each country and for every year in the sample, estimate the relative intensity of each economic activity.
For each country and for every year in the sample, find the aggregate (economy-wide) technological factor shares.

A. The First Step

The first step, which estimates the technological factor shares of each major economic activity, involves a complex and tedious procedure. The main idea of the procedure is to use a sample of countries for which we have detailed information on both the national accounts and the composition of the labor force to estimate, for each major economic activity, “typical” income shares as a function of the level of development. The details of this procedure are fully described in Appendix IV. The main results are:

The capital shares were found to be significantly different across economic activities. The estimated shares are described in Table 1.
The level of development (measured by the average stock of capital per person) was not found to have a significant effect on the capital share in each economic activity.

Table 1.

Estimated Capital Share for Each Kind of Activity

Table 1.

Estimated Capital Share for Each Kind of Activity

	Kind of Activity	Capital Share (α)
1	Agriculture	0.275
2	Quarrying	0.601
3	Manufacturing	0.308
4	Utilities	0.538
5	Construction	0.189
6	Commerce	0.232
7	Transport and communication	0.320
8	Financial and business services	0.604
9	Government and other services	0.081

Table 1.

Estimated Capital Share for Each Kind of Activity

	Kind of Activity	Capital Share (α)
1	Agriculture	0.275
2	Quarrying	0.601
3	Manufacturing	0.308
4	Utilities	0.538
5	Construction	0.189
6	Commerce	0.232
7	Transport and communication	0.320
8	Financial and business services	0.604
9	Government and other services	0.081

B. The Second Step

The second step defines the relative intensities of the nine major economic activities as their relative shares in GDP, measured at factor cost. The value generated by each economic activity during 1978–91 is described in the National Accounts Statistics, which are published by the United Nations.¹⁷ The methodology that is used to define and measure each economic activity in this database is not directly comparable with the methodologies used in the various national statistics publications. Using the database from the UN’s National Accounts Statistics, we obtain better cross-country comparisons. However, for 1992–96, which is not yet covered by these statistics, we have had to estimate these values using a logarithmic extrapolation of data available for the preceding nine-year period.

C. The Third Step

The third step estimates the aggregate capital share in each country, for every year during the sample period 1978–96. It computes the weighted average of the capital shares in each major economic activity (estimated in the first step), using as weights the relative intensities of these activities (estimated in the second step).

D. The Results

The estimated values of the capital shares are depicted in Figures 5-7; and the averages for the most recent five-year period (1992–96) are described in Figure 8, panel (a). The results are revealing, given the large range of values of α that have been used in previous studies. The full panel of 114 estimates of α (for six countries, over the 1978–96 period) covers, perhaps surprisingly, a relatively narrow range: [0.28-0.35]. Singapore has the highest average estimate, 0.34, while Thailand and the United States have the lowest, 0.29. The results confirm that, as many other studies have estimated, the capital shares in ASEAN economies are, in general, larger than in the United States. The difference, however, is relatively small; and the estimated capital share of Singapore, despite being the highest in the sample, is still much lower than the estimate used by Young (close to 0.50). Another interesting result is the relative stability of the capital shares in each country during 1978–96.

V. Estimating the Marginal Product of Capital

In this section, we estimate the marginal product of capital (MPK), for each country and every year in the sample. MPK is not directly related to total factor productivity, but it is a key variable in determining rates of return to investment, and, as a result, growth rates of capital and output. MPK is particularly important in cross-country comparisons because capital tends to flow from place to place to take advantage of relative return opportunities.¹⁸

We estimated in the previous section the capital shares (α), and in Section II output per person and capital for each person (depicted in 1-2). The ratio of these two series, the output-capital ratio (Y/K) is depicted in Figures 5-7. The two variables (α and Y/K) enable us to estimate the MPK series.

Note that the marginal product of capital is equal, by definition, to the partial derivative of a production function Y(K,…) with respect to capital (K). This, assuming a Cobb-Douglas production function (as described, for example, in Appendix I), is equal to the expression α Y/K. The estimated MPK values are depicted in Figures 5-7. The average MPK values for the most recent five-year period (1992–96) are described for each country in the sample in Figure 8, Panel (b). This figure facilitates a cross-country comparison.

The estimated results indicate a large variation in MPK values across countries and also across time in each country; and the main source of this variation is the output-capital ratio, not the capital share.¹⁹ A significant factor that affects both the cross-country and the time-series variation is the level of development of the economy; the output-capital ratio and MPK tend to decrease during a country’s transition from middle income to high income. This process is a natural result of capital deepening and is in accord with the predictions of the standard neoclassical growth model (see, for example, Sarel, 1994). The only exception is Thailand, whose marginal product of capital is high for its level of development.²⁰

VI. The Mechanics of Growth in ASEAN Economies

In previous sections, we prepared all the tools needed to examine the mechanics of economic growth in the six economies covered by this study. For each country and for every year, we estimated per capita growth rates of output, capital, and labor, and factor shares.

Using the decomposition of a Cobb-Douglas production function, we now perform a simple two-step growth accounting exercise:

Calculate the growth rate of inputs, weighted by capital and labor shares:
- [α][growth of capital per person] + [l-α][growth of labor per person].
Calculate the growth rate of TFP as the difference between the growth rates of output and inputs:
- [growth of TFP] = [growth of output per person] - [growth of inputs per person].

The results of the growth accounting exercise are depicted, for each country, in Figures 9-11. A cross-country comparison of country averages for the full period and for the most recent five-year period are presented in Figures 12-13. Table 2 summarizes these results, describing contributions to growth during 1978–96 and 1991–96. The results suggest that:

Table 2.

Growth Accounting

Growth in percent per annum

(and contribution as percentage of growth of output per person)

Table 2.

Growth Accounting

Growth in percent per annum

(and contribution as percentage of growth of output per person)

	Output per Person	Capital per Person	Effective Labor per Person	TFP
	Average, 1978–96
Indonesia	4.74 (100)	8.97 (62)	0.93 (13)	1.16 (25)
Malaysia	4.54 (100)	6.86 (47)	0.58 (9)	2.00 (44)
Philippines	0.19 (100)	1.80	0.62	-0.78
Singapore	5.09 (100)	6.45 (42)	1.06 (14)	2.23 (44)
Thailand	5.24 (100)	7.32 (41)	1.51 (21)	2.03 (39)
United States	1.07 (100)	1.63 (44)	0.43 (28)	0.29 (27)
	Average, 1991–96
Indonesia	5.11 (100)	6.98 (44)	0.96 (13)	2.20 (43)
Malaysia	5.35 (100)	8.25 (50)	0.97 (12)	2.00 (37)
Philippines	1.63 (100)	1.15 (21)	0.87 (38)	0.67 (41)
Singapore	4.91 (100)	5.60 (40)	0.77 (10)	2.46 (50)
Thailand	6.51 (100)	11.13 (50)	1.37 (15)	2.25 (35)
United States	1.26 (100)	1.19 (27)	0.43 (25)	0.61 (49)

Table 2.

Growth Accounting

Growth in percent per annum

(and contribution as percentage of growth of output per person)

	Output per Person	Capital per Person	Effective Labor per Person	TFP
	Average, 1978–96
Indonesia	4.74 (100)	8.97 (62)	0.93 (13)	1.16 (25)
Malaysia	4.54 (100)	6.86 (47)	0.58 (9)	2.00 (44)
Philippines	0.19 (100)	1.80	0.62	-0.78
Singapore	5.09 (100)	6.45 (42)	1.06 (14)	2.23 (44)
Thailand	5.24 (100)	7.32 (41)	1.51 (21)	2.03 (39)
United States	1.07 (100)	1.63 (44)	0.43 (28)	0.29 (27)
	Average, 1991–96
Indonesia	5.11 (100)	6.98 (44)	0.96 (13)	2.20 (43)
Malaysia	5.35 (100)	8.25 (50)	0.97 (12)	2.00 (37)
Philippines	1.63 (100)	1.15 (21)	0.87 (38)	0.67 (41)
Singapore	4.91 (100)	5.60 (40)	0.77 (10)	2.46 (50)
Thailand	6.51 (100)	11.13 (50)	1.37 (15)	2.25 (35)
United States	1.26 (100)	1.19 (27)	0.43 (25)	0.61 (49)

During 1978–96, the rates of TFP growth were very strong in four of the five ASEAN economies (Indonesia, Malaysia, Singapore, and Thailand). Furthermore, TFP growth accounts for a significant portion of their phenomenal growth rates of output per person. The exception is the Philippines, which experienced a negative rate of TFP growth. Singapore appears to be the best performer in the region in terms of TFP growth rates, followed closely by Thailand and Malaysia.²¹
During 1991–96, the rates of TFP growth increased strongly (compared with 1978–91) in Indonesia and the Philippines. In these countries, the recent acceleration in the growth of output per person can be fully attributed to faster TFP growth.²² In the Philippines, the rate of TFP growth is still relatively low, although it improved significantly compared with the earlier period.

A question of particular interest is why our estimate of TFP growth rate in Singapore (2.2 percent) is so much larger than Young’s estimate (0.7 percent, assuming no quality-adjustments of labor). The difference reflects mainly the much lower estimate of capital share in the present study: 0.337, compared with Young’s 0.493. It is interesting to check what Young’s estimate would have been if he had used the same capital share as here. The answer is a rate of TFP growth of 1.8 percent. Therefore, it appears that the difference in capital shares accounts for more than 70 percent of the difference between our estimate of TFP growth and Young’s. The other factors that are responsible for the difference in the estimated growth of TFP include a different estimation period, the methods used for the measurement of capital stocks and labor supply, and the use of domestic and international prices.

VII. Outlook

Growth accounting exercises are valuable for understanding the sources of economic growth in the past. However, their usefulness for forecasting purposes is questionable. Whether the growth rate of total factor productivity estimated for the past continues in the future depends on many factors, not least on the economic policies being pursued. However, there are at least two questions that may be important in this context:

What are the TFP levels of the six economies at the end of the sample period?
Assuming that the economies will continue to experience the same rate of TFP growth as in recent years, what will be their growth rates of output per person in the long run?

A. Estimating TFP Levels

We assume a Cobb-Douglas production function and estimate the absolute levels of TFP as residuals. The estimated TFP levels for 1996 are presented in Figure 14, panel (b), and, for comparison, the output per person in levels is presented in panel (a) in the same figure. The results indicate that levels of TFP are closely related to levels of output per person, with two interesting exceptions.

The first exception is the much larger difference between Singapore and the United States in terms of TFP levels, compared with the difference between the two economies in terms of output per person: while Singapore in 1996 has an average income per person that was 83 percent of the level in the United States, its corresponding level of TFP was only 44 percent.

The second exception relates to the relative position of Indonesia and the Philippines. In terms of the level of output per person, the Philippines lags behind Indonesia in 1996 (by 30 percent), but in terms of the level of TFP it is ahead (by 11 percent). Indonesia compensates for its lower TFP level with a higher level of effective labor per person and, especially, a higher capital stock per person (66 percent higher than in the Philippines).

B. Estimating Long-Run Growth Rates

Assuming that recent TFP growth rates continue, it is possible in principle to estimate long-run growth rates.²³ To calculate long-run growth rates, it is assumed that the rate of capital accumulation is endogenously determined as in the standard neoclassical growth model, that effective labor per person and factor shares stay constant, and that TFP continues to grow at the same rate as during the five-year period that ended in 1996. The long-run growth rate of output per person in this case, according to the steady-state solution of the neoclassical growth model, is equal to the TFP growth rate divided by the share of labor in the production function (1-α) (see, for example, Sarel, 1994). The estimated long-run growth rates of output per person are presented in Figure 15, panel (b), and, for comparison, the growth rates during 1991–96 are presented in panel (a) in the same figure.

The results indicate interesting growth rate dynamics, both across time and across countries. The growth rates are expected to decline in all six economies in the sample, but in some the expected decline is much stronger than in others. The implied steady-state growth rate of Thailand, for example, is almost 3.3 percentage points lower than during 1991–96, when it was the fastest-growing economy in the sample. Relatively large declines are expected also in Malaysia (2.4 percentage points) and Indonesia (1.9 percentage points).

The growth rates in the ASEAN economies are expected to decline over time because past growth of output was partly driven by rates of capital accumulation that were faster than rates of output growth.²⁴ In the future, however, this tendency will diminish, as the output/capital ratio in these economies approaches a steady-state level. This effect is described in Figure 15.²⁵

VIII. Conclusions and Beyond

The growth accounting study that was conducted in this paper covered five ASEAN economies and the United States during 1978–96. An attempt was made to ensure consistency of the data across countries, by eschewing, as far as possible, the use of national data sources. This approach is particularly important in the case of data on factor shares, but it also has significant effects on the estimated capital stocks and labor inputs.

The results show an impressive growth rate of TFP during 1978–96 for Singapore (2.2 percent), Thailand (2.0 percent), and Malaysia (2.0 percent); a slower rate for Indonesia (1.2 percent); and a negative performance for the Philippines (-0.8 percent). The estimated rate of TFP growth for the United States during this period was 0.3 percent. The proportion of the growth rate of output per person that can be attributed to TFP growth is not systematically different from that in the United States. These results confirm the conclusions of many previous studies, but are in sharp contrast to the conclusions reached in the studies of Alwyn Young, especially regarding the TFP growth rate in Singapore.

One of the main determinants of the results here is the difference between the estimated technological factor shares and the income shares that have been previously derived from the national accounts data. This difference can explain why previous studies found lower rates of TFP growth for fast-growing Asian economies, and especially for Singapore. Perhaps more important, this difference points to an interesting possibility. Assuming that the income shares have been correctly measured in previous studies, that of capital is much higher in the fast-growing ASEAN economies than its estimated technological share in the production function. The significant difference between technological shares and income shares may partly explain the impressive investment rates in this region that fueled the growth rates commonly described as the Asian “miracle.” As far as these differences reflect taxes, incentives, or other government policies, they would imply that governments, by influencing the relative income shares of the factors of production, can affect the investment rate and the growth rate of the economy.

Other Publishers

Save
Cite
Email this content

Share Link

Copy this link, or click below to email it to a friend
Email this content
or copy the link directly:

https://www.elibrary.imf.org/view/journals/001/1997/097/article-A001-en.xml
The link was not copied. Your current browser may not support copying via this button.

Link copied successfully

Growth and Productivity in Asean Countries

Author: Mr. Michael Sarel

Volume/Issue:: Volume 1997: Issue 097

Publisher:: International Monetary Fund

ISBN:: 9781451851984

ISSN:: 1018-5941

Pages:: 50

DOI:: https://doi.org/10.5089/9781451851984.001

Keywords
I. Introduction
II. The Growth Accounting Framework and its Building Blocks
III. A Sensitivity Analysis
IV. An Improved Method for Estimating Factor Shares
V. Estimating the Marginal Product of Capital
VI. The Mechanics of Growth in ASEAN Economies
VII. Outlook
- A. Estimating TFP Levels
- B. Estimating Long-Run Growth Rates
VIII. Conclusions and Beyond

View raw image

Five ASEAN Countries and U.S.

Output 1978-96

View raw image

Five ASEAN Countries and U.S.

Output Growth Rates 1978-96

View raw image

Five ASEAN Countries and U.S.

Capital 1978-96

View raw image

Five ASEAN Countries and U.S.

Capital Growth Rates 1978-96

View raw image

Five ASEAN Countries and U.S.

Labor 1978-96

View raw image

Five ASEAN Countries and U.S.

Labor Growth Rates 1978-96

View raw image

Five ASEAN Countries and U.S.

Simulated TFP Growth 1978-96

View raw image

Five ASEAN Countries and U.S.

Simulated TFP Level 1996

View raw image

Indonesia

Alpha, Y/K Ratio, and MPK 1978-96

View raw image

Malaysia

Alpha, Y/K Ratio, and MPK 1978-96

View raw image

Philippines

Alpha, Y/K Ratio, and MPK 1978-96

View raw image

Singapore

Alpha, Y/K Ratio, and MPK 1978-96

View raw image

Thailand

Alpha, Y/K Ratio, and MPK 1978-96

View raw image

United States

Alpha, Y/K Ratio, and MPK 1978-96

View raw image

Five ASEAN Countries and U.S.

Capital Share (Alpha) 1992-96

View raw image

Five ASEAN Countries and U.S.

Marginal Product of Capital (MPK) 1992-96

View raw image

Indonesia

Growth of Output, Inputs, and TFP 1978-96

View raw image

Malaysia

Growth of Output, Inputs, and TFP 1978-96

View raw image

Philippines

Growth of Output, Inputs, and TFP 1978-96

View raw image

Singapore

Growth of Output, Inputs, and TFP 1978-96

View raw image

Thailand Growth of Output, Inputs, and TFP 1978-96

View raw image

United States

Growth of Output, Inputs, and TFP 1978-96

View raw image

Five ASEAN Countries and U.S.

Growth of Output per Person 1978-96

View raw image

Five ASEAN Countries and U.S.

Growth of TFP 1978-96

View raw image

Five ASEAN Countries and U.S.

Growth of Output per Person 1991-96

View raw image

Five ASEAN Countries and U.S.

Growth of TFP 1991-96

View raw image

Five ASEAN Countries and U.S.

Output per Person 1996

View raw image

Five ASEAN Countries and U.S.

TFP Level 1996

View raw image

Five ASEAN Countries and U.S.

Growth of Output per Person 1991-96

View raw image

Five ASEAN Countries and U.S.

Simulated Steady-State Growth

Table 2.

Growth Accounting

Growth in percent per annum

(and contribution as percentage of growth of output per person)

	Output per Person	Capital per Person	Effective Labor per Person	TFP
	Average, 1978–96
Indonesia	4.74 (100)	8.97 (62)	0.93 (13)	1.16 (25)
Malaysia	4.54 (100)	6.86 (47)	0.58 (9)	2.00 (44)
Philippines	0.19 (100)	1.80	0.62	-0.78
Singapore	5.09 (100)	6.45 (42)	1.06 (14)	2.23 (44)
Thailand	5.24 (100)	7.32 (41)	1.51 (21)	2.03 (39)
United States	1.07 (100)	1.63 (44)	0.43 (28)	0.29 (27)
	Average, 1991–96
Indonesia	5.11 (100)	6.98 (44)	0.96 (13)	2.20 (43)
Malaysia	5.35 (100)	8.25 (50)	0.97 (12)	2.00 (37)
Philippines	1.63 (100)	1.15 (21)	0.87 (38)	0.67 (41)
Singapore	4.91 (100)	5.60 (40)	0.77 (10)	2.46 (50)
Thailand	6.51 (100)	11.13 (50)	1.37 (15)	2.25 (35)
United States	1.26 (100)	1.19 (27)	0.43 (25)	0.61 (49)

Topics

Countries

Series

About

Resources

Contributor Notes

Abstract

I. Introduction

II. The Growth Accounting Framework and its Building Blocks

III. A Sensitivity Analysis

IV. An Improved Method for Estimating Factor Shares

A. The First Step

B. The Second Step

C. The Third Step

D. The Results

V. Estimating the Marginal Product of Capital

VI. The Mechanics of Growth in ASEAN Economies

VII. Outlook

A. Estimating TFP Levels

B. Estimating Long-Run Growth Rates

VIII. Conclusions and Beyond

Same Series

Other IMF Content

Other Publishers

Asian Development Bank

Inter-American Development Bank

International Labour Organization

Nordic Council of Ministers

The World Bank

Share Link

Table of Contents

Headings

Figures

Metrics