Total factor productivity growth in the Singapore manufacturing industries during the 1980's

Total Factor Productivity Growth in the Singapore Manufacturing Industries During the 1980’s FOT-CHYI WONG and WEE-BENG GAN

This paper evaluates the TPP growth of 27 manufacturing industries in Singapore during the 1980’s. The average TPP growth for the overall manufacturing sector was 1.6 percent per annum during the period. Although material and capital inputs accounted for larger shares of the manufacturing output growth, the contribution of TPP growth was quite significant. There was also an improvement in TFP performance between the first and second halves of the 1980’s. In addition, structural change had been found to be an important element of manufacturing TPP growth. The observed procyclical fluctuations in TPP growth had also been found to be the result of supply-side, rather than demand-side, shocks. ( EL 047).

Total factor productivity (TFP) growth is the difference between the growth of output and the growth of a composite of all factor inputs. It therefore represents the efficiency with which an economy’s productive inputs-labor, capital, energy and materials-are jointly put to use.’ In this connection, TFP growth assumes particular significance in a resource-scarce economy like Singapore. The growth of an economy is governed by the growths in its labor force, capital and TFP. Given the trend decline in growth of the labor force in Singapore, from an average of 4.7 percent per annum in the 1970’s to an average of 3.1 percent per annum in the 1980’s, a higher rate of economic growth can only be achieved through either an increase in capital or an improvement in efficiency in the use of all inputs (i.e., TFP). However, increasing the use of capital without a commensurate increase in the use of labor (i.e., a rising capital-to-labor ratio), is unsustainable as it would lead to a decline in the marginal productivity of capital and, therefore, its rate of return. As such, TFF growth would constitute an important source of sustainable long-term economic growth for a mature economy like Singapore. This study evaluates the TFP growths of 27 manufacturing industries in Singapore classified according to the 3-digit level of the Singapore Standard Industrial ClassifiFat-Chyi

Wow

l Senior Economist, The Monetary Authority of Singapore, Economics Department, 10 Shewon way, MAS Build0207. l Wee-Beng Gan, Senior Vice President. Corporate Banking Department, Bank of Commerce, 6 J&n Tun Perak, 50050 Kuala Lumpur, Malaysia.

ing, Singapore Journal

of Asian Economics, Vol. 5, No. 2, 1994, pp. 177-196.

Copyright 0 1994 by JAI Press, Inc.

ISSN: 1049-0078

All rights of reproduction in any form reserved.

177

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(5)2,1994

cation (SSIC). The manufacturing sector has been a major component of the Singapore economy, accounting for 25 percent of real GDP in 1970, 30 percent in 1980 and 29 percent in 1990. As such, the TFP performance of the manufacturing industries has an important bearing on the TFP performance of the overall economy. An earlier study by Tsao (1985) on the TPP growths of 28 manufacturing industries in Singapore during 1970-1979 indicates that the productivity growths were rather poor, with 17 out of the 28 industries experiencing negative TFP growths. The overall TFP growth for the manufacturing sector was 0.1 percent per annum. This study determines whether the low to negative TFP growth found in most manufacturing industries during the 1970’s by Tsao (1985) persisted during the 1980’s. Also, in a recent paper, Young (1992) compared the TPP growths of Hong Kong and Singapore. Young found that, for the Singapore economy as a whole, the TPP growth during the period 1970-1980 was negative and, while productivity growth had improved somewhat during the 1980’s, its contribution to the overall growth of the economy was relatively low. This study can therefore serve to verify whether Young’s finding for the entire economy carries through to the manufacturing sector. This exercise is of some significance since Young’s hypothesis seeking to explain the low TFP growth for the whole economy is actually more relevant to the TPP performance in the manufacturing sector. Young argued that the low TFP growth can be traced to the Singapore government’s “industrial targeting” policy which, by rapidly encouraging new industries, tends to shift the technological frontiers of these industries far beyond the learning capability of the labor force. The insufficient time to “learn-bydoing” the installed technologies had resulted in a low increase in output that could be obtained from the existing capital and labor. This paper is organized as follows. In the next Section, we outline the analytical framework used in this study to compute TFP growths. In Section II, we present our results, and analyze the sources of Singapore’s manufacturing output growth during the 1980’s. The role of structural changes in the overall manufacturing TFP growth in Singapore is next taken up in Section III. In Section IV, we examine whether the observed procyclical fluctuations in TFP growth of the manufacturing industries were due to demand-side or supply-side shocks. Finally, Section V provides our concluding remarks.

I.

ANALYTICAL

FRAMEWORK

FOR GROWTH

DECOMPOSITION

The analytical framework for constructing the Divisia index of total factor productivity growth is premised on the existence of a linear homogenous production function for each industry i:* Q, = F(Ki, Lit E,,

Qi

=

Ki

=

M,,7’)

the gross output of industry i, capital input of the industry

i = 1, 2, . . n

(1)

Total Factor Productivity

L,i Ei Mi 7;:

= = = =

Growth in Singapore

179

labor input of the industry energy input of the industry materials input of the industry time.

In continuous time, the Divisia index of TFP growth can be computed as a residual of the Divisia index of output growth less the weighted sum of the Divisia indices of the growth of the various factor inputs. The discrete time analogue of growth accounting used in this study is based on the transcendental logarithmic (translog) production function as in Jorgenson (1990).3 Assuming conditions of producer equilibrium and constant returns to scale,4 the translog index of TFP growth for industry i is given by: Vr,i = [lnQi(T) - lnQi(T-l)]

- V,i[lnKi(T) - lnKi(T-l)]

- VL,i[lnLi(T)( - ln.&(T-1)] - VE,i[lnEi(T) - lnEi(T-l)] - VM,i[lIlMi(T) - lnMi(T-l)]

i=

1,2, . . . , Iz

(2)

where .VK,i,VL,i,VE,iand VM,iare the value shares of capital, labor, energy and material inputs respectively averaged over periods T and T-l, thus: vK,i = $]VK,i(r, +

V,i(T-l)l; vL,i= +[VL,i(T) + V,t,i(T-l)l

&,I = $V~,i<~+ V~,i(T-l)I; v,t,,i= $[V,tt,i(73 + V~,i(T-l)I i=l,2,...n

(3)

To the extent that there are heterogeneous components in each input, a translog quantit.y index for each input can be computed as the weighted sum of the translog indices of the growth of its individual components, with their average shares in the value of that input as weights, thus:5 lnKi(T) - lnKi(T-1) = C ~,,,,[lnK,,(T)

ldi(T)

- lnLi(T-1) = 2

In&(T) - ln&(T-1)

ltii(T)

V,,i[l~,i(T)

- lnK,(T-l)]

- lnL,i(T-I)]

= C VE,,i[lnE,i(T) - lti,,(T-l)] e

- lnMi(T-1) = C V~~,i[lnM,i(T) - lnMmi(T-l)] m i=l,2,...n

(4)

where k, I, e and m index the various components of the respective inputs. In this study, capital, labor and energy inputs are sub-classified into four asset classes (k = 4), seven

occupational categories (I = 7) and three energy types (e = 3) respectively, while all non-energy intermediate inputs are considered as a single composite material input (m = 1). A more detailed discussion on the actual construction of the translog indices of the various inputs and the data sources is given in the Appendix.

II.

SOURCES

OF MANUFACTURING

OUTPUT

GROWTH

The analytical framework outlined in the previous Section was used to compute the sources of output growths for individual manufacturing industries at the 3-digit SSIC level as well as for the overall manufacturing sector during the 1980’s. We first review the results for the overall manufacturing sector. Table 1 presents the average annual growth rates of total manufacturing output for the whole period 198 l-1990 and for the two sub-periods 1981-1985 and 1986-1990, together with our estimates of the contributions to output growth of capital, labor, energy and material inputs, and of TFP. The contribution of each input is the product of the input growth and the average share of the input in the value of output. Between 198 1 and 1990, the average annual output growth of the manufacturing sector was 6.7 percent. Out of this, TFP growth contributed 1.6 percent (or 24 percent of output growth), with the rest of the increase in output being attributable largely to capital accumulation and increased usage of material inputs. Material input growth contributed 2.7 percent (or 40 percent of output growth) while capital input added another 2.0 percent (or 30 percent of output growth). Among the remaining inputs, labor contributed 0.3 percent, reflecting the limited increase in total employment in the manufacturing sector during the decade, and energy a further 0.1 percent. In the case of capital and labor inputs, changes in the compositions of these inputs contributed insignificantly to output growth.

TABLE 1.

Contribution

to Growth of Overall Manufacturing

Output

1981-90

1981-85

198690

6.68

1.31

12.05

Material Input

2.68

Xl.05

5.41

Energy Input

0.12

0.12

0.12

Capital Input

1.99

2.12

Rate of Growth in Gross Output Contribution

due to Growth in:

Due to changes in composition

of capital

Labor Input Due to changes in composition Total Factor Productivity

-0.03 0.29

of labor

0.01 4.08

1.85 -0.07 0.65

0.09

0.15

0.03

1.60

Xl.80

4.01

Total Factor Productivity

Growth in Singapore

181

During the sub-period 1981-1985, the growth in total manufacturing output was 1.3 percent per annum, and this was accounted for primarily by contribution to growth of the capital input of 2.1 percent per annum. The contributions of other inputs and TFP were largely negative. During the 1986-1990 sub-period, when the growth of output averaged 12.1 percent per annum, TFP growth was the second major source of growth after the growth of material inputs, contributing 4.0 percent to output growth. Hence, contrary to Young’s (1992) findings for the overall economy that TFP growth, which averaged 0.1 percent per annum, accounted for a negligible 1.8 percent of overall GDP growth of 6.7 percent per annum, TFP had been a significant contribution to output growth in the Singapore manufacturing sector during the 1980’s. We next compare, in Table 2, the TFP growth performance of the individual 3-digit SSIC manufacturing industries during the 1980’s to the TFP growth of similar industries during the 1970’~~ The estimates of manufacturing TFP growth for Singapore during the 1970’s provided in Table 2 are taken from Tsao (1985), who also used the same Divisia-Translog index approach based on the same four factor inputs.7 Her results are thus comparable to ours and they enable us to provide a longer time perspective on the TFP growth performance of the Singapore manufacturing industries. Among the comparable 26 industries which appeared in Tsao’s sample and ours, 13 industries experienced increase in TFP growth from the 1970’s to the 1980’s, while another 13 industries experienced deterioration in the TFP growth performance. Examples of industries which registered a decline in TFP performance between the two periods are industrial chemicals, plastic products, iron and steel, and electrical machinery and electronic products. Examples of industries which experienced an improvement in TFP are food, wearing apparel, printing and publishing, petroleum products and industrial machinery. The share in total manufacturing output, averaged over the 1980’s, of the 13 industries which experienced improvement in TFP growth was 54 percent, compared to the 45 percent share of the other 13 industries which experienced deterioration in TFP growth. This helps to explain the improvement in overall manufacturing TFP growth from 0.1 percent per annum in 1970-1979 to 1.6 percent per annum in 1981-1990, even though the average increase in TFP growth between the two periods of the first 13 industries (4.6 percent per annum) was only slightly higher than the average decline in TFP growth of the latter 13 industries (-4.3% per annum). A closer examination of Table 2 indicates that a substantial improvement in TFP growth had occurred in a large number of industries during the latter half of the 1980’s. Among the fifteen industries which experienced positive TFP growth during the sub-period 1986-1990, the average annual rate of increase was 6.4 percent. The relative performance of the individual industries during 1986-1990 can be seen in Figure 1, which plots the scatter of the industries’ TFP growths against total factor input growths. The Figure also displays a 45” line, which delineates equal contributions of both TFP and total factor input growths to industry output growth, and four parallel lines on which output growths are constant at -5,O, 5 and 10 percent respectively.

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TABLE 2.

OF ASIAN ECONOMICS,

Average Annual Total Factor Productivity Manufacturing Industries

Industry

Total Manufacturing *Food

1981-90

1986-90

0.08

1.60

4.01

0.62 1.72

Textiles

Growth of

1970-79

Beverages *Tobacco products

(5)2,1994

3.22 -3.23

1.51 -2.14

7.70 -1.43

11.22

17.04

-5.21

-2.07

*Wearing apparel

-2.11

2.05

4.84

Leather products

-3.06

-4.67

-5.43

*Footwear

-9.91

0.49

*Timber products

A.57

-4.59

-6.08

*Furniture & fixtures

-2.44

-2.01

-1.70

2.18

-3.97

4.39

-1.36

0.35

-0.24

-2.99

Paper & paper products *Printing & publishing Industrial chemical & gases Pharmaceutical

& other chemical products

6.44

1.91 -1.46

4.80

2.48

5.09

products

I .49

#Jelutong processing

8.32

2.64 -

2.94 -

*Petroleum

Rubber products

-1.57

A.65

-11.35

Plastic products

-3.16

-6.07

-8.88 -

5.63

-5.54

5.75

-3.78

4.68

3.52

#Pottery & glass products Structural clay products *Cement Concrete products Nonmetallic

*Fabricated

-10.72

1.51

1.44

4.71

15.14

3.41

-0.77

-2.57

-5.36

mineral product

Iron & steel *Non-ferrous

-3.03

metal metal products

*Industrial machinery Electrical machinery & electronic products

-13.87

Note:

equipment

11.59

-3.59

-3.35

-0.38

-3.28

-2.32

-1.12

Xl.04

-0.54

*Transport equipment #Instrumentation

2.81

1.27 -

5.56 0.39

1.17 7.60 -3.27

* denotes industries experiencing higher TFP growths in 1981-90 than in 1970-79, while #refers to industries not commcm to both sample periods. Estimates for 1970-79 are fromTsao (I 985). Numbers expressed as a percent.

Figure 1 shows, for example, that, during the 1986-1990 sub-period, the tobacco, non-metallic minerals, transport equipment, chemical products, paper products, instrumentation equipment, and electrical machinery and electronic industries experienced more than 10 percent per annum growth in output. Of these industries, the instrumentation equipment industry registered negative TFP growth while the positive TFP

Total Facfor Productivity

Growfh in Singapore

-15%

50 percent the respective output growths. contrast, the leather, rubber, clay, cement concrete industries contracting during 1986-1990 sub-period. these latter the first experienced negative growth, which the low negative total input growth, the last experienced positive growth, which to partially the negative factor input

III.

STRUCTURAL

CHANGE AND TF’F’GROWTH

In this Section, we analyze the implications of individual industry productivity performance and structural change in the manufacturing industries on the overall TFCP growth for the manufacturing sector. Structural change in the present context refers to shifts in the composition of total manufacturing output over time. It necessarily entails the reallocation of resources, such as capital and labor, among the various industries.

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More specifically, we decompose the aggregate TFP growth of the manufacturing sector into two components: (1) a weighted average of the individual industry TFP growths-a within or intra-industry effect, with the weights being the industry shares of total manufacturing output; and (2) an effect known as total reallocation effect (TRE). The first component captures the contribution of individual industry TFP growth to the aggregate manufacturing TFP growth and it indicates the overall importance of changes in productivity within the various industries. The second component, TRE, reflects the productivity consequences of changes in the composition of industrial output among industries with different rates of TFP growth. It has its impact on aggregate manufacturing TFP growth through the reallocation of the various inputs to their most productive uses via either the efficient functioning of factor markets or government policies. The two effects are, however, not entirely independent of each other, nor of TFP growth, as the shift in output shares could result in part from the differential rates of TFP growth between industries. The results of our decomposition of the aggregate manufacturing TFP growth are presented in Table 3. The results indicate that, while productivity changes within the industries themselves had risen during the 1980’s, TRE constituted a more important source of aggregate manufacturing TFP growth. For the whole period 198 l-l 990, TRE contributed two thirds of the aggregate manufacturing TFP growth of 1.6 percent per annum, while changes in TFP growth within the industries contributed the remaining one third. During the second half of the 1980’s, TRE contributed 2.2 percent to the aggregate manufacturing TFP growth of 4.0 percent (or about 54% of the total). Among the various inputs, the effect of reallocation of capital comprised a consistently large component of the TRE. For example, for the whole period, the effect of reallocation of capital amounted to 0.5 percent per annum or about a third of the aggregate manufacturing TFP growth, compared to the negligible effect of the reallocation of labor.8 Thus, structural change, broadly interpreted as the reallocation of resources among industries, had been an essential element in accounting for TFP growth in the manufacturing industries in Singapore during the 1980’s. Another interesting aspect of the relationship between TFP growth and the industry structure of the Singapore manufacturing sector can be gleaned from a comparison of

TABLE 3.

Sources of Aggregate Manufacturing 1981-90

Total Manufacturing

1981X3.5

1986-90

1.60

-0.80

Sectoral TFP Growth (Within Effect)

0.54

-0.76

1.84

Total Reallocation

1.06

-0.04

2.17

Note:

TFP Growth

TFP Growth

Effect (TRE)

Reallocation

of Capital

Reallocation

of Labor

Reallocation

of Material/Energy

Numbers

expressed as a percent

0.51

0.47

-0.04

0.01

0.59

-0.52

4.01

0.55 a.09 1.71

Total Factor Productivity

Growth in Singapore

185

the weighted and unweighted averages of TFP growth for the 27 manufacturing industries. For the whole period 1981-1990, the weighted average of the industry productivity growth was 0.5 percent per annum, while the unweighted average was -1.8 percent per annum. This implies that industries with larger shares of the manufacturing sector output (e.g., petroleum products and transport equipment), tended to experience higher TFP growth than industries with smaller shares on average. This observation holds for both sub-periods within the decade. The main exception is electrical machinery and electronic products-the largest industry category-which recorded relatively weak TFP growth throughout the 1980’s. However, it is the smaller industries (e.g., paper and non-ferrous metal products), that made the greater progress in improving their TFP performance in the 1986-1990 period compared to 1981-1985. This is evident in the fact that the unweighted average TFP growth rose substantially more (by 5.0 percentage points) than the increase in the weighted average TFP growth (by 2.6 percentage points) during the second half decade.

IV.

PROCYCLICAL

TFP GROWTH: SUPPLY VS DEMAND SHOCKS

Table 4 shows that, for most industries, the year to year fluctuations in TFP growth during the 1980’s were positively correlated with the movement in industry output. Correlations with real GDP growth, though largely positive, were however insignificant for almost all industries. This latter observation differs from that found by Shapiro (1987) for US manufacturing. Nevertheless, the procyclicality of TFP growth with industry output growth among the Singapore manufacturing industries is quite significant. Further, the strong, negative correlations between growths in TFP and real prices for all but two of the industries suggest that industry level shocks to productivity are shocks originating from the supply side. This is corroborated by the positive correlations of TFP growths with real wage growths in virtually all the industries, since only true shocks to productivity that increase the marginal product of labor will increase its real wage. In this Section, we evaluate the extent to which the observed procyclical fluctuations in. TFP growth are due to supply or demand shocks, using the methodology proposed by Shapiro (1987). The methodology involves comparing the output-based measure of TFP growth, which we have been focusing all along, with the dual, price-based measure of TPP growth. Under the null hypothesis that measured TFP growths reflect true changes in productivity, the two measures should be identical except for measurement errors or specification errors from incorrect parameterization of the production function. We can derive the dual, price-based measure of TFP growth for the present study as follows. For industry i, its cost function at time Tunder constant returns to scale technology takes the form: Ci = Gi(Qi, R, Wi, Pi, PM, r)

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TABLE 4.

OF ASIAN ECONOMICS,

(5)2,1994

Correlations of Output-Based TFP Growth With Growths in Industry Output, GDP, Real Price and Wage, 1981-90 Correlation Industry output

Industry

of AEi,t with growth in:

GDP

Real Price Real Wage

Total Manufacturing

0.42

-0.18

0.65

-0.66

Food

0.63

-0.07

-0.89

0.87

Beverage

0.62

0.25

-0.83

0.61

Tobacco Products

0.94

0.41

-0.95

0.89

Textile & Textile Manufactures

0.55

0.57

-0.53

0.36

Wearing Apparel

0.67

-0.01

-0.27

0.00

Leather & Leather Products

0.86

0.09

-0.96

0.96

Footwear

0.76

0.60

-0.87

0.74

Timber Products

0.69

0.37

-0.96

0.96

Furniture & Fixtures

0.50

0.02

-0.92

0.88

Paper & Paper Products

0.9 1

0.01

-0.91

0.82

Printing & Publishing

0.71

0.36

-0.26

0.02 0.67

Industrial Chemicals & Gases

0.36

0.05

-0.64

Paints, Pharmaceutical

0.97

-0.01

-0.75

0.70

-0.88

0.90

& Other Chemical Products

Petroleum Products

0.45

-0.35

Rubber Products

0.43

-0.07

-0.52

0.44

Plastic Products

0.28

0.31

-0.77

0.65

Structural Clay Products

0.13

0.34

-0.61

0.56

Cement

0.29

0.13

0.23

0.01

-0.16

-0.12

-0.91

0.89

0.95

0.19

-0.97

0.95

0.80

0.19

-0.85

0.65

0.35

0.26

-0.84

0.86

Fabricated Metal Products

0.71

-0.28

-0.93

0.69

Industrial Machinery

0.58

0.58

-0.80

0.78

Electrical Machinery & Electronic Products

0.38

-0.42

0.03

-0.21

Transport Equipment

0.73

0.13

-0.71

0.62

Instrumentation

0.55

-0.04

-0.68

0.55

Concrete Products Non-metallic

Mineral Products

Iron & Steel Non-ferrous

Note:

Metal

Equipment

The standard errors for the correlation coefficients at the 5% and 10% significance level are 0.59 and 0.50 respectively.

= gi(Ri,

where:

Qi Ri

= =

output; rental

cost

W, Pf

= =

wage

rate

energy

pf”

=

material

of capital

price prices.

Wi, Pi, PM, r).Qi

i = 1,2, . . . ) n

(5)

Total Factor Productivity

Growth in Singapore

187

Taking partial derivative with respect to Qi, the marginal cost function is therefore:

ac; Total logarithmic differentiation yields: dhlMCi p=-.dT

dlngi 3hRi

of the marginal cost function with respect to time

G%lRi+ -.dlngi dT dlnWi

&lgi mllPy +alnP1l.~+-+

dlIl Wi + _._ 3lIlgi dT

alng.

dlnPf

dlIlPf dT (7)

i=l,2,...,n

By definition, the last term on the right-hand-side of the equation with the sign reversed, -6hgi/6T, is the dual, price-based measure of TFP growth. Shepherd’s lemma implies the following conditional factor demand equations:

where I and J refer to factor prices and the corresponding Therefore:

alngi Ii agi -=-.--__ illnZ, gi ilZi

Ii. Ji = w gi . Qi

for

”

quantities

respectively.

I = R, W, PE, PM J=K,L,E,M

where wJ,i is the share of input J in the total cost of output. Under perfect competition, the output price is set equal to the marginal cost, Pi = MC,, and the dual, price-based measure of TFP growth, similarly expressed in discrete time approximation as the primal, output-based measure, is given by A@, where: A@ = ZK,i . AlnRi + WL,i. Aln Wi + wE,i . AlnPF + KM,i. Ally

- AlnPi

= %K,i. [AlnRi - AlnPi] + WL,i. [Aln Wi - AlnPi] + KE,i. [AlnPf - AlnPJ + WM,i. [AlnPy - AlnPiJ

(10)

In Equation 10, A is the first difference operator; and W,,i, WL,i,WE,iand wIM,iare the cost shares of the respective inputs averaged over times T and T-l. We denote the price-based measure of TFlp growth by A&+’to distinguish it from the output-based measure, which will henceforth be represented as A&i. For each industry, we test to see if the output- and price-based measures of TFP growth are the same on average during the period 1981-1990 by regressing the first measure on the second plus a constant term. Under the null hypothesis that the two measures of TFP growths are the same, the slope coefficient and R2 should both equal

188

JOURNAL OF ASIAN ECONOMICS,

TABLE 5.

(5)2,1994

Regression Results Of AEi,ron A&$, 1981-1990 AEii,t=CX+&k~t

Industry

H,, p = 1.0 p-ratio

S.E.E.

D. W.

0.8388

0.0145

2.42

0.9380

0.9878

0.3499

0.0029

1.75

0.9988

0.9588

0.4480

0.0147

1.18

0.9773

Tobacco Products

0.9863

0.03 17*

0.0037

3.50

0.9998

Textile & Textile Manufactures

0.8287

0.1277

0.0192

1.83

0.8942

Wearing Apparel

0.3893

0.1126

0.0504

2.35

0.1389

Leather & Leather Products

1.0327

0.2543

0.0169

1.47

0.9947

Footwear

0.9948

0.8945

0.0178

2.11

0.9884

Timber Products

1.0281

0.1497

0.0075

1.97

0.9977

Furniture & Fixtures

0.9841

0.749 1

0.0112

2.35

0.9813

Paper & Paper Products

0.9502

0.4159

0.0196

1.28

0.9710

s

Total Manufacturing

1.0195

Food Beverage

Printing & Publishing

1.0093

0.9224

0.0111

1.81

0.9366

Industrial Chemicals & Gases

1.0536

0.1910

0.0099

1.06

0.9899

Paints, Pharmaceutical Chemical Products Petroleum Products

0.9756

0.2344

0.0048

1.10

0.9970

& Other

1.OOOl

0.9904

0.0022

2.93

0.9998

Rubber Products

0.9985

0.9542

0.0102

1.41

0.9947

Plastic Products

1.0037

0.9573

0.0135

1.37

0.9650

Structural Clay Products

0.8037

0.2140

0.0735

1.83

0.7923

Cement

0.9984

0.9320

0.0088

2.18

0.9974

Concrete Products

1.0030

0.9436

0.0216

1.82

0.9865

Non-metallic

0.9779

0.345 1

0.0220

1.86

0.9960

0.9989

0.8674

0.0023

1.99

0.9996

Mineral Products

Iron & Steel Non-ferrous

metal

1.0069

0.5775

0.0111

2.21

0.9989

Fabricated Metal Products

0.9504

0.4299

0.0194

1.79

0.9694

Industrial Machinery

1.0090

0.9137

0.0189

1.77

0.9516

Electrical Machinery & Electronic Products

0.9836

0.4785

0.0038

2.65

0.9960

Transport Equipment

1.0837

0.3770

0.0276

1.73

0.9483

Instrumentation

1.0045

0.8193

0.0066

1.10

0.9971

Now.

Equipment

*indicates rejection of the null hypothesis at the 5% level of significance

one. That these are true for almost all the manufacturing industries in Singapore is revealed by the results presented in Table 5. Except for a few cases, the slope coefficients are close to one and quite tightly estimated, i.e. we cannot reject the null hypothesis that they equal one. In addition, the variability in factor prices accounts for a large fraction of the variability in the output-based measure of TFP growth. Hence, the primal, output-based and the dual, price-based measures of TFF growth are almost

Growth in Singapore

Total Factor Productivity

TABLE 6.

189

Regression Results of &i.t on A&, and AlnQi, 1981-1990

&,t = a + PA@,, + rAhQi,t Ho: j3=1.O, Industry

D. W.

0.1404

0.0117

2.45

0.0079

0.6322

0.0031

1.85

0.9988

0.0641

0.4953

0.0147

0.89

0.9800

Y

0.9715

0.1135

Food

0.9844

Beverage

0.9227

Total Manufacturig

FO.0 p-ratio

S.E.E.

P

i? 0.9543

Tobacco Products

0.982 1

0.0037

0.1130

0.0039

3.46

0.9998

Textile & Textile Manufactures

0.7879

0.0302

0.2769

0.0200

2.09

0.9002

Wearing Apparel

0.2192

0.3711

0.0477*

0.0413

2.64

0.4956

Leather & Leather Products

1.0482

-0.0168

0.5194

0.0179

1.39

0.9948

Footwear

1.0020

-0.0071

0.9810

0.0190

2.09

0.9884

Timber Products

1.0517

-0.0318

0.1526

0.0071

1.72

0.9982

Furniture & Fixtures

0.9611

0.0406

0.6537

0.0113

2.12

0.9832

Paper & Paper Products

0.7724

0.1363

0.1575

0.0168

1.75

0.9813

Printing & Publishing

1.1536

-0.1873

0.5150

0.0108

1.82

0.9475

Industrial Chemicals & Gases

1.0652

-0.0119

0.3562

0.0102

1.11

0.9906

Paints, Pharmaceutical Chemical Products Petroleum Products

0.8619

0.1219

0.1548

0.0043

0.79

0.9979

& Other

0.9989

0.0047

0.8984

0.0023

2.84

0.9998

Rubber Products

1.0151

-0.0480

0.4648

0.0097

1.79

0.9957

Plastic Products

1.0074

-0.0073

0.9852

0.0144

1.36

0.9651

Structural Clay Products

0.8968

-0.2119

0.1536

0.0666

2.52

0.8507

Cement

0.9995

- 0.0039

0.9791

0.0094

2.11

0.9974

Concrete Products

0.9967

-0.1013

0.2102

0.0185

1.94

0.9913

Non-metallic

1.0242

-0.0418

0.5368

0.0229

1.75

0.9962

1.0143

-0.0244

0.2051

0.0019

1.43

0.9998

0.9977

0.0681

0.0233*

0.0071

2.42

0.9996

0.8765

0.1221

0.2680

0.0179

1.59

0.9772

Industrial Machinery

1.0300

-0.0191

0.9466

0.0200

1.81

0.9523

Electrical Machinery & Electronic Products

0.9847

-0.0013

0.7865

0.0041

2.64

0.9960

Transport Equipment

0.9478

0.1564

0.0954

0.0222

1.87

0.9707

Instrumentation

0.9836

0.0296

0.1636

0.0055

1.90

0.9983

Mineral Products

Iron & Steel Non-ferrous

Metal

Fabricated Metal Products

Note:

Equipment

*Indicates rejection of the null hypothesis at the 5% level of significance

identical, suggesting that the movements in TFP growth reflect true changes in productivity. We next include an explicit measure of industry demand, AlnQi,,, in the previous regression to test if the Keynesian theory that movements in demand drive TFJP growths were true. In this respect, we differ from Shapiro (1987) in not using an aggregate

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TABLE 7.

OF ASIAN ECONOMICS,

Regression Results of (AE~,~ - A&$ ) on AlnQ,

(5)2,1994

1981-1990

A&i., - A&j’, = a + J3AlnQi.t

.

H,:@O.O Industry

P

Total Manufacturing

0.1085

Food

-0.0064

Beverage

‘0.0170

p-ratio -’ . S.E.E.

r*D.W.

ii2

0.0432* . 0.0111

2.18

0.3457

0.7439

0.003 1

1.87

0.0141

0.7676

0.0151

0.99

0.0115

Tobacco Products

-0.0103

0.0638

0.0040

3.46

0.3661

Textile & Textile Manufactures

-0.0113

0.8061

0.0224

1~58 ’ ’

0.0080

0.2532

0.2718

0.0550’

2.57

0.1483

0.4184

0.0176

1.67

0.0834

0.8412

0.0178

2.09

0.0053

Wearing Apparel Leather & Leather Products Footwear

0.0223 0.0059

Timber Products

0.0026

0.8948

0.0086

2.15

0.0023

Furniture & Fixtures

0.0255

0.5298

0.0110

2.15

0.05 12

Paper & Paper Products

0.0025

0.9530

0.0205

1.19

0.0005

Printing & Publishing

0.060 1

0.5397

0.0109

1.50

0.0488

Industrial Chemicals & Gases

-0.0016

0.9250

0.0111

1.01

00012

Paints, Pharmaceutical Chemical Products Petroleum Products

0).0171

0.4337

0.005 1

1.30

0.0783 0.0246

& Other

0.665 1

0.0022

2.87

Rubber Products

-0.0388

0.2480

0.0093

1.58

0.1625

Plastic Products

-0.0060

0.8829

0.0135

1.37

0.0029

0.0038

Structural Clay Products

0.248

0.0615

0.0646

2.77

0.3712

Cement

-0.0040

0.8325

0.0088

2.10

0.0059

Concrete Products

0).100X

0.0673

0.0173

1.95

0.3588

Non-metallic

-0.0220

0.2685

0.0216

1.81

0.1502

0.0097

0.2484

0.002 1

1.67

0.1623

0.0046*

0.0067

2.42

0.6544

0.6180

0.0199

1.36

0.0325

1.80

0.0048

Mineral Products

Iron & Steel Non-ferrous

Metal

1

0.0663

Fabricated Metal Products

0.0344

Industrial Machinery

-0.0088

0.8495

0.0188

Electrical Machinery & Electronic Products

0.0039

0.7096

0.0039

2.45

0.0183

Transport Equipment

0.1324

0.0296*

0.0212

2.06

0.4659

Instrumentation

0.0232

0.0785

0.0054

1.98

0.3370

Note:

Equipment

-_

*indicates rejection of the null hypothesis at the 5% level of significance

demand measure, such as real GDP growth, as this measure has been shown in Table 4 to be not significantly correlated with industry TFP growth. The test results presented in Table 6 show that the addition of individual industry output growth does not improve the explanatory power of the previous equation. In addition, the null hypothesis that the coefficient on A&yis one and the coefficient on AlnQ, is zero is rejected for only two out of 27 industries at the 5 percent level of significance.

Total Factor Productivity

Growth in Singapore

191

As an alternative to the above test, we impose the restriction that the coefficient on A@’is one by regressing the difference of AEi and A&$’on AlnQ. It can be shown that: AEi - A&+’ = wK,i [(AlnQi - AlnKJ - (AlnRi - AlnPi)] + F,,,i . [(AlnQi - Ah&) - (AlnW; - AlnPJ] + WE,i. [(AlnQ - Altii) - (AlnPF - AlnP,)] + W~,i . [(AlnQi - Al&i)

- (AlnP~ - AlnPJ]

(11)

Thus, in essence, the regression is one in which the weighted average differences in rates of growth of factor productivity and product factor price is regressed on industry output growth. Under the Keynesian alternative, the Solow residual (primal measure of TFP growth) moves independently with aggregate demand: there are cyclical fluctuations in measured productivity because firms hoard labor for example, not because the true productivity of factors of production increases. Consequently, real factor prices are not expected to increase and the deviation between the two measures of TFP growth should be cyclical. The results in Table 7 confirm those in Table 6 that industry output growth does not explain much the temporal variation in AEi after adjusting for the effect of AE{. Moreover, the coefficient on AlnQi remains insignificant for almost every industry, thereby refuting the Keynesian theory in favor of the supply-side story.

V.

SUMMARY

AND CONCLUSIONS

In this study, we provided growth decompositions of 27 manufacturing industries in Singapore during the 1980’s. The average annual TFP growth of the overall manufacturing sector has been estimated to be 1.6 percent, accounting for about a quarter of output growth. It constituted the third largest component of manufacturing output growth after material and capital inputs. Changes in the compositions of the capital stock and labor force, however, played a relatively minor role in output growth. The 1.6 percent per annum TFF growth of the 1980’s was also higher than the 0.1 percent per annum TFP growth estimated by Tsao (1985) for the 1970’s. In fact, there was a significant improvement in manufacturing TFP performance within the period, from -0.8 percent per annum in the first half to 4.0 percent per annum in the second half of the 1980’s. Our results also showed that improvement in TFP growth arising from structural changes, through reallocation of resources, has been a more important component of TFF growth in the Singapore manufacturing sector than productivity changes within the various individual industries. Total reallocation effect constituted two thirds of the annual manufacturing TFP growth during the 1980’s, with the remaining one third

192

JOURNAL OF ASIAN ECONOMICS,

(5)2,1994

being attributable to within industry productivity changes. Among the various inputs, the effect of reallocation of capital had been quite substantial. Hence, contrary to Young’s (1992) findings for the overall economy, TFP growth had been a significant contribution to output growth in the Singapore manufacturing sector during the 1980’s. Reallocation of resources between industries has also been found to be a significant component of manufacturing TFP growth. Although it does not directly refute Young’s conjecture that the Singapore government’s “industrial targeting” policy had led to insufficient learning-by-doing and, hence, the observed low to negative TFP growth for the overall economy, the significant contribution of resource reallocation shows that structural change in the manufacturing sector did not detract from TFP growth. A more affirmative test of Young’s hypothesis, however, would have to come from results of analyses at the establishment-level. Nevertheless, the fact that a large number of industries experienced low or negative average TFP growth during the decade remains an issue of concern. Finally, we found that the observed procyclical fluctuations in TFP growth of the manufacturing industries in Singapore had been more a result of supply-side shocks rather than the effect of demand-side factors working through the Keynesian theory of monopolistic excess capacity and/or labor hoarding.

APPENDIX Data Sources and Methodology for Computing the Translog Indices of the Various Inputs Data Sources: Most of the data used in this study were from the various annual issues of The Report on Census of Industrial Production (CIP) compiled by the Research and Statistics Unit of the Economic Development Board of Singapore. The CIP covered all manufacturing establishments with 10 or more workers in Singapore. The price deflators for computing capital stocks were obtained from various issues of the Yearbook ofStatistics Singapore compiled by the Department of Statistics, Singapore. The sources for other data are described below as they are referred to. Capital Input: Four categories of capital assets were identified in the CIP: (1) non-residential building which includes land, buildings and structures; (2) machinery and equipment; (3) office equipment; and (4) transport equipment. The perpetual inventory method was used to compute the stocks of the different capital assets, with their net book values as at 1 January 1980 used as the initial benchmark levels. Investment expenditures in 1980 constant prices on the different capital assets were computed as the nominal investment expenditures (net of any assets disposed) deflated by the appropriate price deflators for gross domestic capital formation obtained from the Yearbook of Statistics Singapore. The annual depreciation rates used were taken from Table 3.6 of Jorgenson (1990): 0.0361 for non-residential buildings, 0.1048 for

Total Factor Productivity

Growth in Singapore

193

machinery and equipment, 0.2729 for office equipment and 0.2935 for transport equipment. The rental price of capital services for each type of capital asset was computed as in Jorgenson, Gollop and Fraumeni (1987), thus:

. {P[k(T-1) pd7J= l-“.zk(T) (l-u).(l-q

where PKk = pIk

=

8,

= y =

U z

= =

zk

=

“r(r) + Sk ’ P&(T) - [PI,(T) - PIk(T-I)]}

(12)

rental price of capital services of asset type k; the acquisition price of capital; the depreciation rate; the rate of return on capital, assumed to be the same for all asset types, and is the only unknown variable in the equation; the corporate income tax rate; the property tax rate and is only applicable to land, buildings and structures; and the present value of depreciation allowances for capital.

The formula for the rental price of capital services in Equation 12 thus consists of a rate of return, plus a rate of depreciation, less a rate of revaluation, and adjusted for the effects of income tax, property tax and depreciation allowances in force during the period. The revaluation rate was computed as a 3-year moving average of the annual change in the price deflator of the respective capital asset. A corporate income tax rate of 40 percent was used from 1980 to 1986 and 33 percent thereafter. The property tax was set at 23 percent. The calculated depreciation allowances, using a discount rate of 10 percent, for industrial buildings, vehicles and machinery were 0.522, 0.834 and 0.829 respectively. To solve for the rate of return to capital for each industry, y, the sum of the values of capital services over the four asset types was equated to total payments to capital of that industry, thus:

c

pKk(T)

’

h(T) = PC(T)

(13)

k

where A, is the stock of capital type k and PC is total payments to capital derived as a residual of output after all the other inputs have been paid. Once y had been determined, it was substituted back in Equation 12 to solve for the rental prices of the various asset types. The value shares of the capital assets were then computed as: PKk’Ak vKk

=

c

Pirk’

k

Ak

(14)

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JOURNAL OF ASIAN ECONOMICS,

(5)2,1994

and used as weights to aggregate the different capital assets into the overall translog index of capital input for each industry. Following Jorgenson et al. (1987), we also decomposed the growth of the sectoral capital input into growth in the sectoral capital stock and in the quality of capital stock. Labor Input: The construction of the translog quantity index of labor input was based on the number of persons employed in each labor category rather than on the number of hours worked. Seven types of labor inputs based on the following job categories were considered: (1) full time workmen; (2) part time workmen; (3) administrative and managerial workers, including engineers; (4) clerical and other workers; (5) directors; (6) proprietors and partners; and (7) unpaid family workers. Data on remunerations, used to compute the value shares of the different categories of labor, included salaries and wages, pension contributions, value of medical benefits, free and subsidized food and other payments in kind. As for capital input, the growth of labor input was also decomposed into growth in the employment number and in the quality of labor. Material and Energy Inputs: To compute the translog quantity index of material input, a composite material price deflator was required. The composite material price deflator for each industry was constructed by using the input-output and direct import requirement coefficients of the industry as weights for output price indices (for locally sourced inputs) and unit values of imports (for imported inputs) respectively. The input-output and direct import requirement coefficients were obtained from various issues of the Singapore Input-Output Tables compiled by the Department of Statistics, Singapore; while the unit values of imports were computed using data from various issues of Singapore Trade Statistics: Imports and Exports compiled by Singapore Trade Development Board. Industry categories from these other data sources had to be first matched to those of the SSIC from the CIP. The translog quantity index of energy input consisted of mineral fuel, water and electricity, and was similarly computed as those for capital and labor inputs. Acknowledgment:

The authors would like to acknowledge the data support of the Economic Development Board of Singapore and the statistical assistance of our colleague, MS Tok Yoke-Wang. The authors would also like to thank two anonymous referees for their useful comments. The views expressed in this paper are solely the authors’ and should not be attributed to the Monetary Authority of Singapore nor the Bank of Commerce. Any errors in this paper are solely

the authors’.

NOTES 1. TFP growth should be interpreted as measuring not only technological progress in the sense of a shift in the frontier of production function through introduction of new technology, but also improvement in technical efficiency from learning-by-doing process in which workers become more proficient in the use of existing equipment, the use of more efficient management techniques and other measures that enhance the efficiency of existing inputs.

Total Factor Productivity

Growth in Singapore

195

2. We employed the gross output, rather than the value added, approach because the use of the latter tends to bias the measure of TFP growth unless material inputs are weakly separable from non-material inputs in the production function (Bruno, 1978). In addition, as Domar (1961) had shown, the value added measure of TFP growth tends to exceed the gross output measure by a factor equal to the ratio of gross output to value added. Since this ratio varies across industries, not only would the average TFP growth estimated by the value added approach be biased upward, but also inter-industry comparisons of TFP performance would be misleading. 3. Nelson (1973) and Usher (1974) have pointed out the need to define indices appropriate for discrete points of time. As Diewert (1976), Caves, Christensen and Diewert (1982) have shown, the Tomqvist discrete approximation of the Divisia index is exact for the translog production function. Besides, the Tijmqvist index has two additional properties: 11. It is a superlative index in the sense that it is an exact index for any production function which can provide a second order approximation to a linear homogenous production function; Z!. It is a multilateral index in the sense that it satisfies the ‘circular’ property. 4. Recent works have focused on the implications of relaxing the restrictive assumptions of constant returns to scale and perfect competition on the analysis of TFP growth, and measures have been devised to isolate the ‘true’ TFP growth residual from such production characteristics as increasing returns to scale, non-optimizing behaviour and market power (Hall, 1988). An integrated econometric approach that tries to separate these components from the true TFP was pioneered by Morrison (1992). 5. Star (1974) has shown that the disaggregation of input components reduces the bias in the computation of total factor productivity in either cases where: (1) the components are imperfect substitutes for each other or (2) the components change at different rates. 6. The results of growth decomposition for individual industries during the 1981-90 period are available from the authors on request. 7. Tsao’s (1985) labor inputs were expressed in man-hours classified by occupational status (four classes) and sex. The labor inputs used in this study, however, were expressed in number of employees by occupational status (seven classes). Singapore’s Census of Industrial Production (CIP) did not publish labor input in hours nor by sex. Tsao (1985) supplemented her data from other sources which were not consistently compiled as those in the CIP. Hence, the authors did not further classify labor inputs beyond the seven occupational categories referred to in the Appendix. However, given the low value share of labor input in gross output (e.g. about 9.6 percent for overall manufacturing during the 1980’s), the slight difference. in methodology for computing labor inputs should not affect the comparability of the results of the studies. 8. The effect of reallocation of material/energy had also been large. This appears to reflect the shift in outputs among industries with different intensities of use of material/energy, given the limited substitutability of material/energy with other inputs. The estimated effect of material/energy reallocation thereby reflects the shift in output to more efficient industries that is not already captured in the measured contribution of capital and labor reallocation.

REFERENCES Bruno, M. 1978. “Duality, Intermediate Inputs and Value-Added.” Pp. 3-16 in M. Fuss and D. McFadden (eds), Production Economics: A Dual Approach to Theory and Applications. Amsterdam: NorthHolland. Caves, D.W., Christensen, L.R., and Diewert, W.E. 1982. “Multilateral Comparisons of Output, Input and Productivity Using Superlative Index Numbers.” Economic Journal, 92: 73-84.

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(5)2,1994

Diewert, W.E. 1976. “Exact and Superlative Index Numbers.” Journal of Econometrics, 4: 115-145. Domar, E.D. 1961. “On the Measurement of Technological Change.” Economic Journal, w(xI: 709-729. Hall, R.E. 1988. “The Relation Between Price and Marginal Cost in United States Industry.” Journal of Political Economy, 96: 921-947. Jorgenson, D.W. 1990. “Productivity and Economic Growth.” Pp. 19-l 18 in E.R. Berndt and J.E. Triplett (eds), Fifty Years of Economic Measurement. Chicago: University of Chicago Press. Jorgenson, D.W., Gollop, F.M., and Fraumeni, B.M. 1987. Productivity and U.S. Economic Growth. Amsterdam: North-Holland. Morrison, C.J. 1992. “Unravelling the Productivity Growth Slowdown in the U.S., Canada and Japan: The Effects of Subequilibrium, Scale Economies and Markups.” Review of Economics nnd Statistics, LXXIV: 381-393. Nelson, R.R. 1973. “Recent Exercises in Growth Accounting: New Understanding or Dead End.” American Economic Review, 63: 462468. Shapiro, M.D. 1987. “Are Cyclical Fluctuations in Productivity Due More to Supply Shocks or Demand Shocks?’ A.E.A. Papers and Proceedings, 77(2): 118-124. Star, S. 1974. “Accounting for the Growth of Output.” American Economic Review, 64: 123-135. Tsao, Y. 1985. “Growth Without Productivity: Singapore Manufacturing in the 1970s.” Journal of Development Economics, 19: 25-38. Usher, D. 1974. ‘The Suitability of the Divisia Index for the Measurement of Economic Aggregates.” Review of Income and Wealth, 20(3): 273-288. Young, A. 1992. “A Tale of Two Cities: Factor Accumulation and Technical Change in Hong Kong and Singapore.” Pp. 13-54 in NBER Macroeconomics Annual 1992. Cambridge, MA: The MIT Press.

Received May 1993; Revised March 1994