POLICY RESEARCH WORKING PAPER 2702
Productivity versus Productivity and factor
endowments both play an
Endowments important role in growth in
Singapore's manufacturing
A Study of Singapore's Sectoral industries. But productivity is
more important as a source of
Growth, 1974-92 growth in the electronics
industry, while factor
Hiau Looi Kee endowments make a larger
contribution in other
industries.
The World Bank
Development Research Group
Trade H
November 2001
POLICY RESEARCH WORKING PAPER 2702
Summary findings
Productivity and the Rybczynski effects of factor endowments to sectoral growth. The results show that
endowments have been highlighted as the two main both are important. But productivity is more important
reasons behind the growth of newly industrializing as a source of growth in the electronics industry, while
economies in East Asia. However, empirical studies at factor endowments make a larger contribution in other
the aggregate level do not find support for these claims. industries.
Focusing on Singapore's manufacturing industries, Kee
estimates the contributions of productivity and factor
This paper-a product of Trade, Development Research Group-is part of a larger effort in the group to study the
relationship between trade, productivity, and economic growth. Copies of the paper are available free from the World Bank,
1818 H Street NW, Washington, DC 20433. Please contact Lili Tabada, room MC3-333, telephone 202-473-6896, fax
202-522-1159, email address Itabada(worldbank.org. Policy Research Working Papers are also posted on the Web at
http://econ.worldbank.org. The author may be contacted at hlkee@aworldbank.org. November 2001. (35 pages)
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about
development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The
papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this
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countries they represent.
Produced by the Policy Research Dissemination Center
Productivity versus Endowments: A Study of Singapore's Sectoral
Growth, 1974-92
Hiau Looi Kee*
*Developrnent Research Group, The World Bank, MSN: MC8-810, 1818 H Street, N.W., Washington, DC 20433.
Tel: (202) 473 4155, Fax: (202) 522 1557, E-mail: hlkcc@worldbank.org. I would like to give special thanks to
Robert Feenstra for his ilsightful guidance and commenits. Discussions with Lee Branstetter, Deborah Swensonl, and
Gary Hunt are gratefully acknowledged. I am also indebted to all seminar participants in the Western Economic
Association International Conference 2000, University of Alberta, University of Colorado-Denver, Franklin and
Marshall College, University of Georgia, University of Maine, Mount Holyoke College, University of Notre Dame,
National University of Singapore, University of Western Michigan, University of Virginia and the World Bank.
1 Introduction
The renewed interest in growth theory since the second half of the 1980s has stirred a huge volume
of theoretical and empirical research on economic growth. As a result, being the fastest growing
economies for the past three decades, the economic "miracles" of the four East Asian Newly
Industrializing Economies (NIEs) have drawn a lot of attention.' However, the reasons for their
extraordinary growth rates are still far from being settled.
There are two main theories that attempt to explain the growth of the East Asian NIEs. Both
schools center around the growth effects of international trade, but differ in the channel by which
trade influences growth. The first school originated from the new growth theory2 emphasizes the
role of productivity growth. One of the papers in this school, Lucas (1988) introduces the effect
of trade on productivity growth through a learning-by-doing mechanism. He advocates that the
growth of the East Asian NIEs is a result of productivity growth, which in turn is due to the
production experience accumulated in the export markets. Subsequent papers by Young (1991)
and Lucas (1993) also explore the growth effects of trade in a similar way. Thus, this school
postulates that the growth of the four East Asian NIEs is a result of productivity growth that is
associated with trade.
However, the controversial findings of Young (1992, 1995) appear to cast doubt on the produc-
tivity growth hypothesis of this school. Using growth accounting techniques, Young shows that
there is in fact no sign of productivity growth in Singapore. The average annual growth rate of
primal total factor productivity (TFP) of Singapore is almost zero for the period 1974 to 1992.
The growth rates of primal TFP of the other three economies are also far from impressive. Based
on Young's finding, Krugman (1994) claims that the growth of the East Asian NIEs is purely
input driven, and is comparable to the miraculous growth experience of the Soviet Union in the
1 The East Asiaii NIEs consist of Sinigapore, Honig Kong, Taiwan (Chinia), anid the Republic of Korea. Their
average annual growth rates of GDP for the past three decades are around 8 percent.
2 The iiew growth theory is also knownj as the endogenous growth theory.
1
1950s - an economic legacy that was not sustainable due to the inherent nature of diminishing
returns of capital accumulation.
The recognition of this input driven growth pattern gave rise to the second school led by
Findlay (1996) and Ventura (1997). Ventura shows that in a general equilibrium setting a siall
open economy can sustain high growth through the Rybczynski effects of factor accumulat.on.
Given that factor prices are equalized through the trading of goods, when an economy experiences
growth in a factor, say capital, the capital intensive industries in the economy will grow at the
expense of the non-capital intensive industries. Diminishing returns to factor accumulation do not
set in due to factor price equalization of international trade. Thus for this school, the East Asian
miracle is driven by the rapid growth of factor endowments under the influence of international
trade.
Empirical research at this area has been mainly focusing on the aggregate statistics of t;hese
economies, which overlooks the sectoral relocation of resources within the economy. Even in a
recent work, when the dual approach is used to challenge Young's primal approach, Hsieh (1999)
finds no evidence of diminishing returns to capital investment in Singapore. In order to capture the
growth effect of international trade through the Rybczynski effects of factor accumulation, sec-,oral
study in a general equilibrium setting is essential, which so far has been rare in the literature.
Using industry level data of Singapore's manufacturing sector, this paper sets out to test the
two theories directly by comparing the relative contributions of productivity and factor accuimu-
lation to the growth of the industries in this sector. The methodology of this paper closely follows
Harrigan (1997) with a twist in the empirical specification, which adopts a general equilibrium
framework based on a translog revenue function.3
The estimation results show that for the electronics industry in the Singapore manufacturing
sector, the growth effect of productivity clearly dominates that of factor accumulation. In conLt cast,
3 Harrigani uises the translog reveniue function to study the relationship between the patterns of international
trade, factor endowmernts and productivity differences of the OECD countries.
2
factor accumulation plays a much bigger role for the rest of the industries in the sector, with the
exception of the primary products industry. For the primary products industry, productivity and
factor endowments are found to be equally important.
Thus, the results of this paper suggest that while the Rybczynski effects of factor accumu-
lation are more relevant for the non-electronics industries, the new growth theory is supported
by the electronics industry. In addition, given that nearly 60 percent of the value added of the
manufacturing sector is generated in the electronics industry and the primary products industry,
we can conclude that for the manufacturing sector as a whole, the role of productivity is at least
as important as that of factor endowments.
This paper is organized as follows. A theoretical model utilizing a translog revenue function
is developed in Section 2. Section 3 presents the data used and is followed by a description of the
empirical strategy in Section 4. The regression results are shown in Section 5. Section 6 presents
a direct comparison between the growth contribution of productivity and factor endowments, and
Section 7 concludes this paper.
2 Theoretical Model
2.1 A General Equilibrium Set Up
Consider a neoclassical small open economy with fixed aggregate factor supplies, constant returns
to scale production technology, and perfectly competitive good and factor markets.
Let Rt be the total value added, or the GDP, of the economy in period t. There are M factors
and N industries in this economy, with each industry producing only one good.4 The general
equilibrium of this economy is obtained by maximizing the total value added subject to all the
production and resources constraints:
max Rt = Ptyt
s.t. ynt = Antfn (v.t), n=1,...,N
4 To be precise, each industry produces one composite good.
3
N
E Vnt = Vt, V, ER , (R)
n=1
where pt and Yt are the value added price and output vectors,5 A,t is the Hicks neutral technology
level of industry n, and vt is the endowment vector of the economy.
The above program is equivalent to
N
max Rt = (pntAAt) Ynt
n=l
s.t. nt= fn (VrLt)
IV
E V,t = Vt (2)
nl=l
which shows that productivity and prices enter the program multiplicatively 6
The assumption of constant returns to scale in production functions ensures that the second
order sufficient conditions for maximization hold. Hence the solution to the first order conditicns
will result in the optimal value function
Rt* = R* (ptAtn ,vt) , 1 3)
where * denotes the optimum, and At = diag {Alt, A2t, ..., ANtI is a N x N diagonal matrix tiat
defines the level of Hicks neutral technology of the economy. The second order sufficient conditions
also imply that RA is convex in Pt, and At.
By the envelope theorem, the output of industry n is equal to the partial derivative of R* with
respect to the price of n:
9*= tR* (ptAt, vt) 4
y> Yt = Yn (ptAt vt), Vn = , .N. (5)
Thus the output of industry n depends on the value added prices and productivity of all industries.
It also depends on the total factor endowments in the economy.7
9 Throughout this paper, the term 'output" refers to the real value added of the industry.
I This multiplicative property of productivity and prices is highlighted by Harrigan (1997). who suggests .hat
empirically we can model productivity in a similar way as we model prices.
Please notice that sirice we focus oni the total value added of the economy, intermediate inputs and materials
4
If we multiply both sides of Equation (4) by P., we will have an expression that defines the
share of industry n in total value added R*,
nt PntIJnt = 8 Pnt (6)
S* _ Iln R*(ptAt,vt)
t t = SF(P*tAt, vt) , Vn = I,, N (7)
alnp,t
In other words, the share of industry n in total value added equals the elasticity of total value
added with respect to the price of n. In addition, given the multiplicative nature of prices and
productivity, for every industry n, the elasticities of total value added with respect to Pnt and Ant
equalize:
a lnR* (ptAt,v,) _ IlnR* (ptAt,v,)
9 lnPnt - 9 In Ant
In other words, the share of industry n also equals the elasticity of total value added with respect
to productivity of n.
Hence in a general equilibrium framework, the share of industry n in total value added of an
economy depends not only on its own value added price and own technology, but also depends on
the prices of all other goods, their technology and the total endowments of the economy.
With a similar method, we can also show that the share of factor m in total value added equals
the elasticity of total value added with respect to the quantity of m :8
nt - a In' (8)
Our ultimate objective is to estimate the contributions of productivity and factor endowments
to output growth of the industries. One method would be to estimate the elasticities of output
with respect to productivity and factor endowments, and use the estimated elasticities to construct
the corresponding contributions.
do not enter the output function explicitly. However, intermediate inputs and materials would still affect output
indirectly via their influence on the value added prices. In other words, the value added price of a good reflects not
only its market price, it also reflects the prices of intermediate inputs and materials.
8 By the zero profit conditioii, or the natioiial inicoine identity, total value added equals total cost of primiiary
factors at the optimum, Rt = wtvt. Thus, the share of factor m in total value added is
= -V t W t- _____ ____ i9Ct vmt _ 9Rt; vmt a In R VM
Smt- VmRt CVmt => Smt = ma t C = avmt Rt 8lfmt vt
5
Specifically, for every industry n and k, Ynt equals sR , and snt equals 81nR (p,A,,v,) Given
the shares of n and k, the elasticity of n's output with respect to the productivity of k , :s a
linear function of the partial effect, aai:
A a Iny t
nkt Dc ln Akt
1 aS*n
= s#, nAk s4t, Vn,k=1, ...,N. (9)
Similarly, for every industry n and factor m, the factor elasticity of n with respect to m, Efnt
is also linear in the partial effect a<:
, = (9 In Ynt
' mt- ' In v,t
= sS atIn*v + S, Vn = 1, ..., N, Vm = 1, .., M. (10)
The factor elasticity is known as the Rybczynski elasticity in the literature.
Thus, our empirical strategy would be first to estimate the partial effects of productivrity
and factor endowments on the output shares, namely aa and a"' . Subsequently, we will
construct the elasticities using the corresponding estimated partial effects and shares, as according
to Equations (9) and (10). Finally, for every industry n, we can then measure its portion of growth
that is due to the growth of productivity in industry k, or the growth of factor mn, as the procuct
of the corresponding elasticity and growth rate:
ltnkt = CnktAkt, Vn, k = 1, ...,N, and 11)
7nmt = Envmtmt, Vn = 1, ..., N, Vm = 1, ..., M. 12)
The convexity of R* in prices, which requires that all the own price elasticities be non-negative,
can serve as a specification test of the model. The elasticity of the output of industry n with respect
to the price of industry k is
EP a 2In y,t
tnkt - 1lnk
6
1 as. ant + s-1, Vn = k, n, k = 1, ...,N
= , n k t(13)
{ *talfnpkl + ks Vn# k, n,k=1,...,N
Moreover, the multiplicative property of productivity and prices in s* (ptAt, vt) implies that
aau = aInAk l Hence, for every pair of industries n and k, the cross price elasticity equals the
cross productivity elasticity, while the own price elasticity equals the own productivity elasticity
minus one. In other words, to make sure that all the own price elasticities are non-negative, all
the own productivity elasticities have to be not less than one:
en1t > 1, Vn. (14)
This property can be best represented by Figure 1, which shows that a 10 percent increase in the
productivity of industry X will result in a more than 10 percent increase in the output of X, given
the relative price of X remains the same.
2.2 The Translog Revenue Function
To implement the model empirically, let us assume that R* is a translog function of productivity,
value added prices and factor endowments, with productivity and value added prices of goods
entering multiplicatively.
N N N
In R* (ptAt, vt) = aoo + E ao. In (A.tp.t) + EE a,k In (Antpat) In (Aktpkt)
n=1 n=l k=1
+ E bor In vrnt + - bZ b In v ..mt In vlt
m=1 m=l 1=1
N M
+ cnm In (Antp,nt) In vmt (15)
n=1 m=l
This translog revenue function approach follows Harrigan (1997), which originated from the
GNP function developed by Kohli (1991). Kohli's GNP function depends on prices of goods,
the factor endowments of the economy as well a time index, t. The inclusion of time index
into the GNP function is due to the assumption that technology or productivity level shifts over
time. In other words, productivity does not enter the GNP function explicitly. Recognizing the
7
multiplicative property of productivity and prices in theory, Harrigan (1997) explicitly introducetd
productivity into the translog GNP function, as shown in Equation (15) in order to study 1l;e
effects of productivity and endowments differences on the trade patterns of the OECD countrie-;.9
Without lost of generality, let R* be symmetric such that
ank = akn,, Vn, k = 1, ...,N,
bi1 = bl V, b'm, I = ,..., M. (16)
In addition, to ensure that R* is homogenous in degree one with respect to ptAt and vt, we
impose the following restrictions:
N N 'A
Y: ao. = 1, ank = 0, Cnm =0, Vln = 1, .. N,
n=1 k=1 m-1
Mi M N
E bom = 1, b,, = 0, E Cnm V= m = 1, ,.M. (17)
m=l 1=1 n=1
Thus, the share of industry n in total value added can be derived as the elasticity of R* with
respect to p,t based on Equations (15), (16), and (17):
N M
sn (ptAt.vt) = ao0 + Zankln(Aktpkt) + , c In v.t, Vn = 1, ...,N, l 18)
k=1 m=1
with ank and crn representing the partial effects of productivity and factor endowments on output
shares, aln k and a I respectively.
In other words, for every industry n, k, and factor m, we can estimate the partial effects, Ink
and cm,, by regressing output share of n on the levels of productivity, price indices, and fa(tor
endowments, as according to Equation (18).
Equation (18) involves the levels of productivity and price indices, which are known to be
highly nonstationary according to Keller and Pedroni (1999). This causes the ordinary least
squares estimates of ank and c,, to be inefficient. Nevertheless, given that the partial effects,
9 Subsequent work on production characteristics of US firms by Feenstra, Halnson and Swenson [1998] also
employs a similar framework.
8
ank, and cnin, are invariant over time, we can get around the nonstationarity problem by taking
the first difference of Equation (18).
Equation (19) presents the first difference of Equation (18) with the variable St denotes the
growth rate of 1.11 It shows that for every industry n, k and factor m, the change in share of
industry n, ds,, depends on the growth rates of productivity, Akt, value added prices, Pkt, and
factor endowments, 0nt,
N M
ds, (ptAt,,v) = ank (Akt +Pkt) + V n Vn = 1, ..., N. (19)
k=l m=1
Good measurements of the growth rate of productivity and value added price are quite difficult
to obtain. Nevertheless, given that only the sum of the two growth rates, Akt + Pkt, matters in
Equation (19), we can avoid the potential measurement errors by utilizing the dual definition of
total factor productivity (TFP),
Akt -Wkt - Pt,t (20)
where Wikt = >,M=1 O.ktilV.kt denotes the weighted average of the growth rates of input prices,
w,. Here the cost shares of input in total value added, Bin, is used as the weights to construct
Wkt. We can therefore rewrite Equation (19) as
N M
ds4 (ptAt, vt) = S an.kwkt + 5, C..ODt, Vn = 1,..., N. (21)
k=l m=1
Thus the change in share of industry n, depends on the weighted averages of the growth rates
of input prices of all industries, and the growth rates of factor endowments. Equation (21) will
form the basis of our estimation for ank and Cni, Vn, k, m.
Finally, for every industry n, k, and factor m, the estimated productivity elasticity and the
in To justify the first difference, Dickey-Fuller unit root tests have been performed on the levels of productivity,
price index, and output share of each industry. The results indicate that for most of the series, the unit root
hypothesis cannot be rejected at the 95% confidence level. On the other hand, the nonstationarity problem is less
severe when the unit root test is applied to the first difference of the series. The detailed results on the tests are
available upon request.
Specifically, it _ inzt -int- .
9
factor elastcity are respectively
A = ark + s, and 22)
tnkt k 2
8nt
onmt = + sm 23)
3 Data
The data set focuses on Singapore's manufacturing sector, which consists of a panel of 7 industries
for a period of 19 years, from 1974 to 1992.12 Table 1 presents a description of the data set.
All the data is published in the Report of the Census of Industrial Production, Singapore and. the
Yearbook of Statistics of Singapore.
There are two types of factor endowments: capital and labor. Capital inputs consist of land
and buildings, and machinery capital.13 Each type of capital is individually constructed by
the standard perpetual inventory method with a different depreciation rate, as listed in Table 1.
Labor inputs consist of workers, which represents the unskilled labor, and other employees, which
represents the skilled labor.'4
For each industry, the market price of each type of labor is measured as the respective type's
unit cost of labor.15 On the other hand, the market price of each type of capital is captured by
the corresponding rental price of capital. It is constructed according to the internal nominal rate
of return specification model developed by Jorgenson, Gollop and Fraumeni (1987). For a detailed
12 Accordirng to the Yearbook of Statistics of Singapore, the share of foreign net investment commitments iii the
manufacturing sector was 84 percent in 1980. In 1992, the number decreased slightly to 80 percent. Thus it is
appropriate to conclude that while most of the capital investment in the manufacturing sector is of foreign origin
and there was niot much of the reallocation of capital input between manufacturing sector and other sectors of the
economy. On the other hand, the share of the Singapore's labor force working in the manufacturing sector WAS 30
percent in 1980. It dropped only slightly to 27 percent in 1992. This again demonstrates that there was limited
reallocation of factors between the manufacturing and the non-manufacturing sectors. This justifies us focusing
only on the manufacturing sector and the reallocationr of fa(tors between the industries within the manufacturing
sector.
3 Machinery capital includes machinery equipment, transport equipment, and office equipment.
L According the literatture, the eduietion level attained is a better measure for the skill level of a worker. However
since there is nou detailed published data oni the educatiorn level of the labor force of Sinigapore, the skill level of a
worker is thus classified according to their occupations in this paper.
15 Somiie complications arise due to the reclassification of data for the later years, please refer to the append.ux for
the details.
10
description of the construction of the rental prices, please refer to the appendix.
The growth of the productivity of the industries obtained using Equation (20) is known as
the growth rate of dual TFP. Under the assumptions of constant returns to scale and perfect
competition, growth rate of dual TFP equals to the actual productivity growth:
- dual -
TFPt_ Wnt - Pnt = Ant (24)
Table 2 presents a summary of the data set. According to Table 2, for the period 1974 to 1992,
the average annual growth rate of value added of the industries varies between -1.5 percent and
16.1 percent. Thus there is a wide range of growth patterns in the manufacturing sector.
The largest industry in the manufacturing sector is the electronics industry. It produces nearly
50 percent of the total value added of the sector. In contrast, with a value added share of less
than 2 percent, the rubber & wood industry is the smallest industry in the sector.
Data on the change in value added share of the industries shows that three of the seven
industries have become relatively larger. Overall, the average annual change in the shares of the
industries ranges from -0.4 percent to 0.9 percent.
The fastest growing industry in the sector is the electronics industry. It also has the highest
average annual growth rate of productivity. Thus, if productivity is important in explaining output
growth, as hypothesized by the new growth theory, we would expect to find some evidence in this
industry.'6
On the other hand, the average annual growth rate for prices in this industry is -1.9 percent,
which means that the own price of goods produced in this industry have been declining. Intuition
tells us that if own price has any effect on output growth, the effect would at best be modest in
this industry.
The bottom half of Table 2 presents data on the endowments of the Singapore manufacturing
sector. It is clear that both capital inputs and labor inputs are growing for the sector, and
16 Note that the electronics industry is also the largest exporting industry in Singapore. If we expect trade to
have an effect on growth, it should be the most evident in this industry.
11
since capital inputs as a whole grows nearly twice as fast as labor inputs, we will expect capir i1
endowments to play a bigger role in explaining the sectoral growth patterns.
4 Empirical Strategy
In order to estimate the growth contributions of productivity and factor endowments of Singapor 's
manufacturing sector, the empirical model that consists of 7 equations, as described in Equation
(21), will be fitted. Moreover, given that, for each equation, the dependent variable is the chan,e
in share of output of one of the seven industries in the sector, the error terms of the regressions vw ill
be correlated across equations by construction. Hence the proper way to implement the empirical
model will be to estimate it as a system of equations using seemingly unrelated regressions.
Specifically, the following model will be fitted:
7 4
dsnt = a. + 43P4 + >3 ckkt + >Y Cnmmt + u7nt, Vn = 1, . 7 (2 5)
k=1 m=1
7 4 7
a0k = a~, >an1k =0 >3c,0, ,>c,. = 0, Vn, k,m. (6)
ank = ak., 2_aE =° E Cnm = °, E Y O n , .
k=1 m=1 n=1
Equation (25) shows the seven equations to be estimated, and Equation (26) presents the thirty
five restrictions. For each equation, the dependent variable is the change in share of output, wi:h
u,, being the industry specific error term.
Independent variables for each equation include the weighted averages of the growth rates
of input prices of all the seven industries, and the growth rates of the endowments of the fcur
factors. These variables are derived directly from the theoretical model. Besides these variabls,
an industry specific effect, a., is introduced into each equation to control for the unobserv-ed
variation of the error terms that is specific to the industry."7 In addition, in order to test the
hypothesis that the effects of value added prices on output embrace the effects of intermediate
inputs and materials, the growth rate of industry specific import prices, P7, is also introduced
'7 An example on the inidustry fixed effect would be the industry specific tax policy. For a detailed expositioi of
the theoretical model with the inclusion of the industry fixed effect, a,,, please refer to the Appeoidix.
12
into each equation.'8
We will first estimate all the seven unrestricted equations presented in Equation (25) indi-
vidually using OLS regressions. All the cross-equation restrictions in Equation (26) will then be
tested. The results of the tests will form the basis of the estimation when the seven equations are
fitted as a system of equations using seemingly unrelated regression.
Finally, since all the dependent variables add up to zero,
N
Zdsnt.= O, Vt = 1, ..., T,
k=1
the system of equations is singular. When dealing with a singular system of equations, the standard
treatment in the literature is to exclude one of the equations from the system. Barten (1969) shows
that the likelihood function of the system is completely irrelevant to which equation is dropped.
Thus, we shall follow the standard treatment to drop one of the equations from Equation (25),
and employ the maximum likelihood estimation, or equivalently the iterative seemingly unrelated
regression, to fit the system.
5 Results
5.1 The Ordinary Least Squares Regressions
The results of the unrestricted OLS estimations are shown in Table 3. There are a total of seven
columns in the table, each column represents the regression result of one industry. The dependent
variable of each regression is the change in share of the industry in the column, and there are
thirteen explanatory variables for each regression. These explanatory variables are categorized
into two groups. The first consists of the weighted averages of the growth rates of input prices
of the various industries, and the second includes the growth rates of the four factors and import
prices. The industry fixed effects are presented as the constant terms in the table.
18 Kohli (1991) shows that irnports could be an important input of production for the GNP function. However,
since we only focus on the value added of the industries in the theoretical model, import prices are not explicitly
included earlier. Nevertheless, given that imports are parts of the intermediate inputs of production, any changes
in import prices would still affect output through the changes of value added prices. In other words, movements of
valie added prices einbrace the movements of import prices.
13
As shown in bold in Table 3, all of the estimated own productivity partial effects, a, are
positive. The 35 restrictions listed in Equation (26) are tested, and only the following 6 restrictic ns
are rejected at the 95% confidence level:
4 7 7 7
a27 = a72, a57 = a75, Z CSm = 0, Zalk = 0. Za2k = 0, EaSk = 0.
m=1 k=1 k=i k=1
In other words, the symmetry property of the value added function is violated between the
rubber & wood industry and the miscellaneous manufactures industry. It also fails to hold be-
tween the primary products industry and the miscellaneous manufactures industry. The constant
returns to scale assumption is rejected by the primary products industry, while the homogeneity
assumption of prices is rejected by the food industry, the rubber & wood industry, and the prirnary
products industry.
Table 3 also shows that the growth rate of own import price is only significant in the ftod
industry. Thus for the vast majority of the manufacturing sector, the hypothesis that imports do
not enter the value added function cannot be rejected.
5.2 The Iterative Seemingly Unrelated Regressions
When the equations are estimated as a system using iterative SUR, those restrictions that w-ere
rejected in the previous OLS regressions are dropped. In addition, since the food industry is the
only industry that has a significant estimate on the partial effect of import price , it is chosen
to be dropped from the system to avoid singularity. The result of the estimation is presentecd in
Table 4.
The set up of Table 4 is similar to that of Table 3, with the only difference being the exclusion
of the growth rate of import price as an explanatory variable. All the partial effects of own
productivity, which are shown in bold in Table 4, are positive and significant. This satisfies the
theoretical restriction of the model that the partial effect of own productivity cannot be negative.
Moreover, majority of the partial effects of cross productivity are also significant, which indicate
14
the existence of the spillover effects of productivity across industries.19
The effects of factor endowments on the changes in shares of the industries are mixed. Skilled
labor has a positive and significant effect on the growth of the primary products industry, while
unskilled labor significantly contributes to the rubber & wood industry. Land and buildings are
important in explaining the growth of the chemicals industry and the miscellaneous manufactures
industry, while machinery capital is vital for the petroleum industry.
Before we move on to convert the estimated partial effects of productivity and factors into the
corresponding elasticities, a close comparison can be drawn against Harrigan (1997).2 First,
unlike Harrigan (1997), all of the own productivity partial effects are estimated to be significantly
positive in Table 4. This makes the regression results of this paper more conformable with the
theory.
In addition, Harrigan finds that while highly educated workers and non-residential construction
are associated with lower output shares, producer durables and moderately educated workers
are associated with larger output shares. If we take highly educated workers as skilled labor,
non-residential construction as land and buildings, producer durables as machinery capital, and
moderately educated workers as unskilled labor, then the regression results shown in Table 4
actually present an interesting contrast. In our case, there is no factor that is only associated
with either higher or lower output shares. We find positive and significant effects of the growth of
skilled labor in the share of primary products industry. It also has positive effects on the share of
the electronics industry and the miscellaneous manufactures industry even though the estimates
are not significant. On the other hand, positive significant effects of land and buildings are found
19 It may be concerned that non-negative own price elasticity is a necessary but not sufficient condition for the
maximization program. Sufficient condition would requires the Hessian matrix to be negative definite. However,
giveii that all the poinit estimates of the regression result are subject to inidividual stanldard errors, cLeckinlg the
property of the Hessian matrix using point estimates may not be too informative. Same problem applies to the
attempt to generate the eigen values of the Hessian matrix from the estimated coefficients. In other words, the
theoretical sufficient condition rmay not be emiipirically applicable.
2n In order to study the effects of productivity and factor endowments on the trade pattern of the OECD countries,
Harrigan estinmated a system of equations similar to Equation (18). In other words, our current model is the first
difference version of Harrigan (1997).
15
in the chemicals industry and the miscellaneous manufactures industry which again did not show.v
up in Harrigan (1997).
5.3 The Estimated Growth Effects and Contributions
Since we are interested in the effects of productivity and factor endowments on the output on
industries, we need to transform the estimated partial effects from Table 4 into the corresponding
elasticities as according to Equations (22) and (23).
5.3.1 Productivity
Table 5 shows the estimated productivity elasticities of the six industries. Each cell shows the
percentage change in output of the industry in the column due to a 1 percent change in productivity
of the industry in the row.21
As shown in bold in Table 5, all of the estimated own productivity elasticities are positive
and significant. The range of the estimated own productivity elasticities is between 0.9 and 1.3.
In addition, none of the estimated own productivity elasticities is statistically significantly les;
than unity. In other words, for each of the six industries in the manufacturing sector, a 1 percen.
increase in the own productivity will induce at least 1 percent increase in the output of the industry.
Given that own price elasticity equals own productivity elasticity minus one, the regression resul.
satisfies the specification of the theoretical model that the own price elasticities should not be
negative.22
All the figures in Table 5 that are not in bold are the cross productivity elasticities. Nearly half
of the cross productivity elasticities are significant, which suggest the existence of the interindustr r
spillover effects of productivity growth. Note that, the estimated cross productivity elasticitie3
are always less than the own productivity elasticities, which makes intuitive sense.
21 For example. a 1 perceiit increase in productivity in the food inldustry causes the output of the rubber 1.;
wood iirltdstry to decrease by 0.35 percent. It also leads to a 0.02 percent incre-ase irn the output of the petroleumr
i2[(lustry.
22 Please rcfcr to Equatioii (14).
16
Table 6 details the effects of productivity growth on output growth of the industries. With
the exception of the last row, each cell shows the percentage change in output of the industry
in the column solely due to the actual productivity growth of the industry in the row. As it is
specified in Equation (11), the value of each cell equals to the value of the corresponding cell in
Table 5 multiplied by the average annual growth rate of productivity of the industry in the row.
The total changes in output of each industry due to the productivity growth of all the industries
are presented in the last row, which sums up all the statistically significant effects in each column.
Overall productivity growth has significant and positive growth effects in the industries. Indus-
try that benefits the most from the productivity growth in the industry is the electronics industry.
The 5 percent average annual productivity growth in the industry causes its output to growth by
4.6 percent annually. In contrast, the industry that benefits the least from its own productivity
growth is the petroleum industry. Its output only increases by 0.4 percent due to its productivity
growth.
While the largest positive spillover effect of productivity is found between the miscellaneous
manufactures industry and the primary products industry, the largest negative spillover effect is
found between the miscellaneous manufactures industry and the rubber & wood industry. Produc-
tivity growth in the miscellaneous manufactures industry causes output of the primary products
industry to increase by 2.6 percent annually. It also causes the output of the rubber & wood
industry to decrease by 4 percent annually.
As shown in the last row of Table 6, when all the significant interindustry spillover effects
on productivity are taken into consideration, the electronics industry remains the industry that
benefits the most from the overall productivity growth of the sector. In contrast, the strong adverse
spillover effect from the miscellaneous manufactures industry to the rubber & wood industry causes
the total effect of productivity growth in the latter to be slightly negative. Overall productivity
growth of the sector is also important in the primary products industry and the miscellaneous
manufactures industry.
17
5.3.2 Factor Endowments
Table 7 presents the estimated factor elasticities. These elasticities are also known as the Ry-
bczynski elasticities, which measure growth of output due to the growth of the factor endowmenr:s
in an economy. Similar to Table 5, each cell shows the percentage change in output of the industry
in the column due to a 1 percent growth of the factor in the row.
First let us look at the labor inputs. Output of the primary products industry, and the miscel-
laneous manufactures industry are responsive to the growth of skilled labor of the manufacturilLg
sector. The estimated skilled labor elasticities of both industries are positive and significant. OIn
the other hand, growth of the unskilled labor significantly benefits the rubber & wood industry,
and significant hurts the primary products industry. Thus, by the definition of the Rybczynski
elasticity, we can conclude that the primary products industry and the miscellaneous manufac-
tures industry are relatively skilled labor intensive, while the rubber & wood industry is relatively
unskilled labor intensive. This result seems reasonable given the nature of goods produced in tae
industries.
For the case of capital inputs, industries that respond positively to the growth of land a:id
buildings are the chemicals industry, the electronics industry, and the miscellaneous manufactures
industry. In other words, these industries use land and buildings intensively in their production.
On the other hand, machinery capital has significant and positive impact on the petroleum indusr ry
and the electronics industry. Thus machinery capital is the intensive factor for these industrie3.
The estimated effects of factor endowments on output growth of the industries are presentred
in Table 8. Similar to Table 6, the value of each cell is constructed as shown in Equation (].12).
It shows the percentage change in output of the industry in the column solely due to the ac.tial
growth of the factor in the row. The total significant effects on output of each industry due to .he
growth of all factors are again presented in the last row.
Focusing only on the statistically significant estimates, it is apparent that the effects of factor
18
endowments are generally greater than that of productivity for all the industries with the sole
exception of the electronics industry. As shown in the last row, the total of the significant effects
range from 3.5 percent in the primary products industry to 15.1 percent in the petroleum industry.
The growth of skilled labor on average increases the output of the primary products industry
by nearly 5.8 percent annually. It also rises the output of the miscellaneous manufactures industry
by 2.7 percent. On the other hand, the growth of unskilled labor on average increases the output of
the rubber & wood industry by 4.7 percent, while it decreases the output of the primary products
industry by 2.3 percent.
Relative to labor input, capital input generally plays a bigger role in the growth of the sector.
The growth of land and buildings increases the output of the chemicals industry by 8.7 percent.
It also rises the output of the miscellaneous manufactures industry and the electronics industry
by 6.1 percent and 1.5 percent respectively. Similarly, machinery capital increases the output of
the petroleum industry and the electronics industry by 15.1 percent and 2.2 percent respectively.
6 The Growth Decomposition
Table 9 presents the contributions of productivity and factor endowments on output growth of the
manufacturing sector. In order to stay focused, we break down the contribution of productivity on
output growth into the contribution of the own productivity and the cross productivity. Similarly,
factor endowments are categorized into labor input and capital input. Labor input consists of
skilled and unskilled labor, while capital input consists of land and buildings, and machinery
capital.23
The value of each cell is the sum of the statistically significant contributions of the variable in
the row on the output of the industry in the column.24 In addition, beside productivity and factor
endowments, the contributions of prices and industry fixed effects are also included in the table
23 A detailed version of this table is included in the Appendix.
24 The incluision of those contributions which are not statistically significant into the calculation of total contri-
bution does not change the qualitative result of the table. Please refer to the appendix for details.
19
for completeness5 25 For each industry, the contribution of productivity is constructed as ratio
of the estimated effect of productivity from Table 6 to the total estimated effects of productivi. y,
factor endowments, prices, and the fixed effect. The contributions of factor endowment, prices,
and fixed effect are also constructed in a similar way.26 In other words, the contributions of
productivity, factor endowmnents, prices, and the fixed effect are normalized such that the sum of
the contributions equals to 100 percent.
When we compare the contributions of productivity and factor endowments at a disaggregat. ed
level, labor input is fouind to be most important for the growth of the rubber and wood indust-y,
and the primnary products industry. Together these two industries produce 15 percent of the val ie
addedl of the sector. On the other hand, capital input plays tbe largest role in the petroleum
industry. the chemicals industry and the miscellaneous manufactures industry. These industr.es
account for 33 percent of the total value added. Finally, own productivity growth is the mcst
prominent source of grow-th for the electronics industry which produces 46 percent of the valae
added of the sector. The effects of prices and fixed effects on all industries are negligible.
When focusing on the total contributions of productivity and factor endowments, Table 9 sho-vs
the contribution of factor endowrnents are generally greater than that of productivity, with r oe
exception in the electronics industry. For the electronics industry, the contribution of productivity
is greater tlhan that of factor endowment by nearly 23 percent. With a large contribution from crc ss
productivity, the role of productivity is also considerable high in the primary products indust:v.
The contribution of productivity in this industry is only 8 percent smaller than that of factor
endowments.
Finally, what can we conclude regarding the relative importance of productivity and factor
2, As inertio ied in the theoretica. imiodel, given the iniultiplicative property of prodictivitv and prices in The
value added funrlction. RA (ptA, v,) , prices have a sinmilar growth effect on oiitput as productivity. However, sii ce
-he growtth rates of prices are small in the ranulfacturing sector, as shown in Table 2, the actoial impact of pr.,:es
oil Ol.tpilt 'growth iS expected l i he verv iii orlest. This is why we did not disetiss ahosit the growth effect of pr., cs
inl the earlier se. triotI For a detraled exposition oln the growtth effect of prices, please refer to the appendix.
Please refei to the; ippeiidix for tile details oil thLe cioastruriction oii trhe growtlh effect of the indlustry fixed effe -t.
20
endowments in the manufacturing sector as a whole ? As shown in Figure 2, the above industry
evidence suggests that 46 percent of the value added of the manufacturing sector derives from
an industry that relies most heavily on productivity as the source of growth. In contrast, ap-
proximately 35 percent of the total value added of the sector is originated from industries that
are driven by the growth of factor endowments. The result also shows for 13 percent of the to-
tal value added of the sector, the role of productivity and factor endowments are almost equally
important.27
7 Conclusion
What contributes most to the remarkable growth of Singapore's manufacturing sector? Pro-
ductivity growth or factor accumulation? At an industry level, regression results indicate that
productivity and factor endowments are both important in explaining the growth of the sector,
from 1974 to 1992.
The role of productivity is most prominent in the electronics industry, which is also the largest
and fastest growing industry in the sector. Productivity is almost as important as factor endow-
ments in the primary products industry. As for the rest of the sector, the role of factor endowments
is clearly dominant.
Thus this paper suggests that, for the period of 1974 to 1992, the Rybczynski effects of factor
accumulation, as advocated by Ventura (1997) and Findlay (1996), play a more relevant role in
explaining the growth of the non-electronics part of the sector. In contrast, the growth of the
electronics industry is best explained by the productivity driven hypothesis of the new growth
theory, as advocated by Lucas (1988, 1993). Finally, given the strong growth prospect of the
electronics industry, productivity growth could play a even more important role in the Singapore's
manufacturing sector in the future.
27 The shares only add up to 96 percent because food industry in dropped from the regressiorn.
21
A Appendix
A.1 Translog Revenue Function with Fixed Effects
To introduce fixed effects into the model, let consider the following specification:
N N N
In R' (ptAt, v,) = a .o + E (aon + ant) In (Antpnt) + 2 E E ank In (Antpnt) In (Aktpkt)
n=1 n=1 k=1
M M Ml
+ E born in vmt + - bZ m ln vmt ln vit
m=1 m=1 1=1
N M
+E E cTm In (Antpnt) In vmt (A.27)
n=1 m=1
Equation (27) is identical to our original translog revenue function, Equation (15), except t:tat
ant is added to the first summation of the function. Differentiate Equation (27) with respect to
lnpnt gives us the share equation:
N M
sn* (ptAt, v,) = ao. + ant + a ank ln (Aktpkt) + Z Cnm lnvm-t, Vn = 1, ..., N. (A.28)
k=1 Tn=1
By first difference sn (ptAt, vt) and substituting the dual definition of TFP, we arrive at the
following equation,
7 4
dSnt = an + Z a,.kzkt + E Cnmrmt + Unt, Vfn (A.29)
k=1 m=1
which shows that the change in share of each industry depends on an industry fixed effect, an.
Notice that an fixed effect in the change in share equation is equivalent to a trend effect in ;he
share equation. The effect of fixed effect on output growth of industry n, is the growth of output
that results from the change in time trend, t:
alny* - 1 asnt + alnR;
at S n t + at
N
a, + a anln (Antpnt) (A.30)
nt n=1
Thus, with the appropriate normalization such that the average annual levels of producti' ity
and prices of the industries are unity, the average annual growth rate of output in industry n 1r iat
is specific to the industry is
alny;, - a,. Vn. (A.31)
at S2
22
A.2 Detailed Growth Decomposition
Table 10 is the detailed version of Table 9, which breaks down productivity, prices and factor
endowments into smaller categories. Notice that the total contributions (figures in bold) in this
table is not directly comparable to that of Table 9 as the latter only shows the statistically
significant contributions.
A.3 Rental Price of Capital
Assume that rate of return of capital, p, is the same for all assets, and q. is the price of investment
good m, then rental price of capital good type m in year t, rmt, is
(1-u) (1- mI [qmt-lpt + 45mqmt - (qmt -qmt-i)] (A.32)
-r, = 0, for m f land and buildings
where u is the corporate income tax rate, z is the present value of depreciation allowances for
capital (for tax purposes), and Tm is the property tax rate and is only applicable to land and
buildings. Thus rental price of capital good m, consists of the returns to capital investment,
qmt-ipt, plus the depreciation of capital, 6mqmt, less the possible capital asset appreciation, qmt-
qmt-1, and adjusted for the taxes.
The sum of the payments of each type of capital good, rmtKmt, equals value added less the
payment to other input:
M
E rmtKmt = PtYt - wtLt (A.33)
m=l
Nominal rate of returns to capital, Pt, can be solved by substituting Equation (32) into Equation
(33), for all capital goods. To get rmt, substitute the generated Pt back to Equation (32).
A.4 Data on Skilled and Unskilled Workers
The Report on the Census of Industrial Production (CIP) of Singapore publishes annual data on
most of the variables needed in this study. However, since 1991, CIP of Singapore stop publishing
detailed data on the breakdown of the employment structure of the industries. Only the total
23
number of workers and the total remunerations are available. In order to maintain the size of the
sample in this paper, data on workmen, other employees, and their respective wage bills need to
be constructed.
First. the shares of workmen and other employees in total workers are calculated for periDd
prior to 1991. A simple time series plot sbIoWs that the share of workmen has been declining while
the share of other employee has been rising. Thus, as a conservative measure, for 1991 and 1992,
I assume that the growth rates of the twvo shares stay at the 1990 level. Using the fixed grow th
rates. I constructed the corresponding shares of workmen and other employee in total worker, in
1991 and 1992. A similar method is also applied to the construction of the corresponding of wage
bills of the two types of workers.
24
References
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25
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26
Table 1: Data Description
Years.:1974- 1992
Product classification system: There are 7 industries which briefly correspond to the nine categories of
the one-digit SITC (Rev.3). The categories, and their three-digit SIC constituent parts are listed below.
Industry Description SITC SIC Description
Ind. 1 Food 0 311/312 Food
1 313 Beverage
4 314 Tobacco Products
Ind. 2 Rubber & Wood 2 331 Wood
355 Rubber
Ind. 3 Petroleum 3 353/354 Petroleum
Ind. 4 Chemicals 5 351 Chemicals
352 Paints & Pharmaceuticals
Ind. 5 Primary Products 6 321 Textiles
323 Leather
341 Paper
356 Rubber Products
361/362 Pottery & Glass
363 Bricks, Tiles, and Clay
364 Cement
365 Concrete
369 Non-Metallic Mineral
371 Iron & Steel
372 Non-Ferrous Metal
381 Fabricated Metal
Ind. 6 Electronics 7 382 Machinery
383 Electrical
384 Electronic
385 Transport Equipment
Ind. 7 Miscellaneous Manufactures 8 322 Wearing Apparel
324 Footwear
332 Fumiture
342 Printing & Publishing
357 Plastic Products
386 Instrumental Equipment
390 Other Manufacturing
Share of each industry in total value added of manufacturing sector
Source: Report of the Census of Industrial Production, Singapore (CIP)
Prices of good: Singapore manufactured products price index
Source: Yearbook of Statistics, Singapore
Growth rate ofproductivity
Measured by the growth rate of dual TFP, which equals to the weighted average of the growth rates
of input prices minus the growth rate of output price. Source: CIP
Factor endowments of manufacturing sector
Capital Two types of capital input, generated by the perpentual inventory method:
1. Land and building, depreciation rate is 0.0361.
2. Machinery Capital: i) Machinery Equipment, depreciation rate is 0.1048.
ii) Transport equipment, depreciation rate is 0.2935.
iii) Office equipment, depreciation rate is 0.2729.
Labor Two types of labor input:
1. Workers, this refers to persons employed directly in the process of production.
2. Other employees, includes working directors, managers, supervisors, engineers, technicians,
and clerical staff.
Source: CIP
27
Table 2: Data in a Glance. 1974 - 1992
Rubber & Primary Miscellaneous
Variables Years All Food Wood Petroleum Chemicals Products Electrori:cs Manufactures
Growth rate of 1975 -64279 -2.3624 -2.8749 -40.1142 13.9405 6.8579 14.190') 19.0940
output 1992 13.0743 4.1541 7.3156 2.7736 1.2536 12.8418 15.8022 8.5858
mean 10.7210 7.0722 -1.4918 3.1165 13.8727 9.4302 16.135!i 10.8208
Share of i975 100 7.0824 2.4430 17.7349 5.3430 14.6039 40.591 i 12.2018
value added i992 100 4.3733 0.3209 7.0254 9.3366 12.0677 53.870(3 13.0053
mean 100 5.6170 1.6510 12.4458 7.6192 13.2308 46.1436 13.2926
Change in value 1975 0 0.4301 -0.3751 -6.7709 0.6861 -0.1960 3.776. 2.4496
addedshare 1992 0 0.0328 -0.0296 -1.6005 -1.1009 0.3300 2.165: 0.2030
mean 0 -0.1804 -0.2782 -0.3808 0.2024 -0.3036 0.9175 0.0233
Growth rate of 1975 3.0529 2.3417 -13.5820 4.4017 -5.4456 -6.4005 -8.6643 2.3717
price ofgoods 1992 -7.0381 0.5696 -10.2140 -17.2613 -8.7011 -4.1414 -5.636) -2.8838
mean 0.1370 1.4804 0.3940 0.8004 1.6731 1.1893 -1.92111 1.8056
Growth rate of 1975 -13.5900 -6.6125 7.8259 -36.3972 2.8993 -3.6083 4.7604 0.3299
productivity* 1992 8.0000 -2.7550 11.5253 -2.2404 -9.7166 8.6015 10 1351 5.0511
,=,_______ mean 3.7700 0.2510 3.2130 0.4322 2.3935 1.8564 4.9854 3.5765
Skilled Unskilled Land & Machinery Machinery Transport Office
Factor Endowments Labor** Labor** Building Capital*** Equipment Equipment Equiprnnt
Growth 1975 0 ''093 -9.5296 15.2818 5.8056 5.7261 4.9535 9.65-:
rates 1992 3.7816 -1i0756 5 6359 8.4065 7.5986 8.2988 21.48'9
mean 4.9275 2.5903 8.6840 8.8582 8.7074 7.0689 13.57(f6
Sharcin 19,5 14.3932 20.2161 30.2780 35.1140 32.9967 1.1755 0.94) 3
value added 1992 16.4942 17.1537 24.7725 41.5565 38.1369 1.0553 2.3643
mean 14.3094 18.3434 26.7979 40.4978 37.8519 1.2583 1.38'15
Notes: A11 values are in percentage terms. Mean values are the annual averages for the period 1974-1992.
*productivity is measured as the dual total factor productivity.
*Thcre is no published data on Skilled Labor and Unskilled Labor for 1991 and 1992. For these years, the glowth rates and the
shares are constructed according to the descriptions in appendix.
*"Machinery Capital consists of Machinery, Transport, and Office Equipment.
28
Table 3: Dependent Variables - Changes in Shares
Estimation method: OLS - unrestricted regression
Total system observations: 108
Eq (I) Eq(2) Eq(3) Eq(4) Eq(5) Eq(6) Eq(7)
Independent Rubber & Pr-imary
Variables: Food Wood Petroleum Chemicals Products Electronics Misc.
Food 0.0442*** -0.0069 0.0338 -0.018 -0.0115 0.0016 -0.0374***
(0.0025) (0.0049) (0.0334) (0.0153) (0.0072) (0.0361) (0.011)
,. Rubber & -0.0044*** 0.0213*** -0.0437* 0.0045 0.0093** 0.0052 0.0017
Wood (0.0013) (0.003) (0.0233) (0.0083) (0.0042) (0.0213) (0.0064)
c
' Petroleum -0.0065*** -0.0039 0.0818*** 0.013 -0.0294*** -0.0357* -0.0257***
o (0.0012) (0.0025) (0.017) (0.0111) (0.0041) (0.0188) (0.0086)
s Chemicals -0.0091*** -0.006* -0.044 0.0816*** 0.0092* -0.0461 -0.0063
(0.0015) (0.0032) (0.0273) (0.0147) (0.0053) (0.0338) (0.0096)
0
.=Primary -0.004 -0.0021 -0.0042 0.0109 0.1106*** -0.1379*** 0.0041
o Products (0.0028) (0.0062) (0.0419) (0.0211) (0.0092) (0.0532) (0.0149)
5
> Electronics -0.0178*** -0.0006 -0.0443 -0.018 -0.0393*** 0.1887*** -0.0441***
(0.0029) (0.006) (0.04) (0.0173) (0.0088) (0.0473) (0.0138)
.0
'Z Miscellaneous -0.0193*** -0.0273** 0.0829 -0.034 -0.1112*** 0.0124 0.0246
Manufactures (0.0064) (0.0128) (0.1121) (0.0404) (0.0214) (0.1106) (0.0443)
Skilled -0.0039 -0.0191 -0.2228 -0.0204 0.0155 0.1903 0.0512
Labor (0.0109) (0.0193) (0.1411) (0.0763) (0.0306) (0.1578) (0.0489)
Unskilled -0.0035 0.0314* 0.1305 -0.0772 0.0318 -0.033 0.0061
Labor (0.0084) (0.0168) (0.123) (0.0735) (0.0265) (0.1578) (0.0544)
C
2 Land& 0.0203*** -0.0008 -0.0918 0.0502 0.1171*** -0.1644* 0.0826***
z Building (0.0067) (0.0139) (0.0945) (0.0405) (0.0202) (0.0999) (0.0321)
C Machinery -0.0173*** -0.015 * 0.1488*** -0.0375 0.0413*** -0.0582 -0.0365
Capital (0.0045) (0.0079) (0.0545) (0.0302) (0.0118) (0.062) (0.0252)
Own Import -0.0079** 0.0007 -0.0019 -0.0261 -0.0181 0.0908 0.0821
Price (0.004) (0.0039) (0.014) (0.0316) (0.0232) (0.107) (0.0556)
Constant 0.0004 0.0013 -0.0085 0.0036 -0.013*** 0.0185** -0.0049*
(0.0005) (0.001) (0.0081) (0.003) (0.0015) (0.0073) (0.0026)
Sample size 18 18 18 18 18 18 18
R-squared 0.9861 0.9375 0.8956 0.8937 0.9599 0.8326 0.9015
Note: All figures in bold are the own partial effects of productivity. Standard errors are in parentheses.
*, **, and *** indicate significance at 90%, 95%, and 99% confidence levels respectively.
29
Table 4: Dependent Variables - Changes in Shares
Estimation method: MLE - iterative restricted seemingly unrelated regression
Total system observations: 108
Eq (1) Eq(2) Eq(3) Eq(4) Eq(5) Eq(5)
Independent Rubber & Primary
Variables: Wood Petroleum Chemicals Products Electronics Misc.
Food -0.0067** -0.0042 -0.0037 0.0072 -0.003 -0.(235***
(0.0033) (0.0028) (0.0029) (0.0087) (0.0052) (0.( 057)
X Rubber & 0.0219*** -0.0052*** -0.0054** (.001 -0.0027 -0.( 106***
e Wood (0.0018) (0.0019) (0.0024) (0.0039) (0.0038) (0.(035)
C.
; Petroleum -0.0052*** 0.0949*** -0.0084 -0.03*** -0.0288** -0.(183***
(0.0019) (0.0125) (0.006) (0.0093) (0.0123) (0.(05)
, Chemicals -0.0054** -0.0084 0.0633*** -0.0011 -0.0358*** -0.(088*
(i (0.0024) (0.006) (0.0088) (0.0093) (0.0114) (0.(,046)
Primary 0.001 -0.03*** -0.0011 0.1118*** -0.0921*** 0.0 145*
" Products (0.0039) (0.0093) (0.0093) (0.0199) (0.0161) (0.(076)
X Electronics -0.0027 -0.0288** -0.0358*** -0.0921*** 0.2167*** -0.0543***
X- (0.0038) (0.0123) (0.0114) (0.0161) (0.021) (0.(077)
Miscellaneous -0.0207*** -0.0183*** -0.0088* 0.0771*** -0.0543*** 0.1D)11U***
Manufactures (0.0061) (0.005) (0.0046) (0.0123) (0.0077) (0.0128)
___________________--____________________________________________________________.._________
Skilled -0.0144 -0.1492 -0.0836 0.1361* 0.0986 0.(1528
Labor (0.0171) (0.1235) (0.0569) (0.0751) (0.1248) (0(1436)
4 Unskilled 0.027** 0.0777 0.0222 -0.1403** 0.0168 -0.1\359
R Labor (0.011) (0.0696) (0.0339) (0.0444) (0.072) (0.0259)
$ Land & 0.0002 -0.0908* 0.0559** -0.0389 -0.0421 0.(157***
U Building (0.0071) (0.0503) (0.0223) (0.0304) (0.0499) (0 (119)
Machinery -0.0128* 0.1623*** 0.0054 -0.0064 -0.0733 -O.0(739***
Capital (0.0066) (0.0526) (0.0231) (0.0306) (0.0523) (0 1)182)
Constant 0.0005 -0.0086*** 0.0002 -0.0051*** 0.0154*** -04011
(0.0005) (0.0026) (0.0012) (0.0018) (0.0027) (0( (009)
Sample size 18 18 18 18 18 18
R-squared 0.9317 0.8555 0.8464 0.5968 0.7978 0.8568
Note: All figures in bold are the own partial effects of productivity. Standard errors are in parentheses.
Food Industry is dropped out of the system to avoid singularity.
*, *, and *** indicate significance at 90%, 95%, and 99% confidence levels respectively.
30
Table 5: The productivity elasticity
Effect in terms of percentage change in output in:
Rubber & Miscellaneous
Wood Petroleum Chemicals Primary Products Electronics Manufactures
Food -0.3483* 0.0223 0.0079 0.1107* 0.0498** -0.1207***
(0.2015) (0.0223) (0.0386) (0.0655) (0.0195) (0.0427)
Rubber & 1.3425*** -0.0253* -0.055* 0.0243 0.0107 -0.0634**
Wood (0.1112) (0.0154) (0.0318) (0.0291) (0.0094) (0.0266)
' Petroleum -0.1904* 0.8873*** 0.0145 -0.1025 0.0621** -0.0134
5 (0.1164) (0.1004) (0.0784) (0.0706) (0.0279) (0.0377)
C6 Chemicals -0.2538* 0.0089 0.9066*** 0.0677 -0.0015 0.0101
(0.1468) (0.048) (0.1157) (0.0704) (0.0263) (0.0349)
' Primary 0.1947 -0.109 0.1175 0.9773*** -0.0673** 0.2416***
Products (0.2335) (0.075) (0.1222) (0.1506) (0.0301) (0.0572)
- Electronics 0.2986 0.2301** -0.0091 -0.2347* 0.9311*** 0.0526
(0.23) (0.0989) (0.1502) (0.1217) (0.0455) (0.0581)
Miscellaneous -1.1236*** -0.0143 0.0176 0.7158*** 0.0152 0.8932***
Manufactures (0.3717) (0.0403) (0.0608) (0.0929) (0.0198) (0.0962)
Note: Figures in bold are the own productivity elasticities. Standard errors are in parentheses.
The productivity elasticity of industry n with respect to industry k equals the share of industry k plus
the ratio of the corresponding estimated cross partial effect (from Table IV) to the share of industry n.
, **, and *** indicate significance at 90%, 95%, and 99% confidence levels respectively.
Figure 1: The Effect of a 10 Percent Increase in Productivity of Industry X
Good Y
I 0 '- AS < t\; ~~~~~~Px/py
XO >\ XI Good X
> 10% increase]
31
Table 6: The effects of productivity on ou:put growth
Effect in terms of percentage change in output in:
Rubber & Primary MisLellaneous
Wood Petroleum Chemicals Products Electronics Man ufactures
Food -0.0874* 0.0056 0.002 0.0278* 0.0]25** -0.)303***
(0.0506) (0.0056) (0.0097) (0.0165) (0.0049) (0.0 107)
Rubber & 4.3133*** -0.0812* -0.i767* 0.0781 0.0343 -0.2336**
W o ood (0.3574? (0.0496) (0.1022) (0.0936) (0.0301) (0.0 )56)
Petroleum -0.0823* 0.3835*** 0.0063 -0.0443 0.0268** -0.0358
(0.05031 (0.0434) (0.0339) (0.0305) (0.0121) (0.0163)
Chemicals -0.6076* 0.0212 2.17** 0.1619 -0.0036 0.0 41
_(0.3513) (0.1149) (0.277) (0.1684) (0.0629) (0.0 834)
Pintarn; 0.3615 -0.2023 0.2181 1.8142*** -0.1249** 0.4.-85***
79 Products (0.4335) (0.1392) (0.2268) (0.2796) (0.0559) (0.1062)
Electronics 1.4884 1.1473** -0.0452 -1.1699* 4.6419*** 0.2t)23
(1.1468) (0.493) (0.7488) (0.6066) (0.227) (0.-:894)
Mliscellaneous -4.0186*** -0.0512 0.0629 2.5599*** 0.0542 3.1 (045***
Manufactures (1.3294) (0.1442) (0.2175) (0.3324) (0.0707) (0.-3441)
Total Effect -0.4826 1.4516 1.9933 3.2320 4.5563 3.41191
Note: Standard errors are in parentheses. Total effect refers to the sum of all significant estimates in each col imn.
The effect of prodlootivity growth in industry k on output in industry n equals the productivity elasticith of
industry n with respect to industry k multiplied by the average annual productivity growth of industry 1:.
* *. and *** indicate signiificance at 90%. 95%, and 99% confidence levels respectively.
Tabie 7: The factor elasticity
Effect in terins of percentage change in output in:
Rubber & Primary discellaneous
Wood Petroleum Chemicals Products Electronics 'vlanufactures
Skilled -0.7314 -1.0554 -0.9542 1.1718** 0.3569 0.5406*
I.abor (1.0344) 0.992) (0.7473) (0.5673) (0.2705) 0.328)
Unskilled t.8199*** 0.8076 0.4753 -0.8767*** 0.2198 0.0868
Labor (0.6686) (0.5592) (0.4451) (0.3352) (0.156) 0.1946)
Land & 0.283 -0.4617 1.002*** -0.0258 0.1768* ).6969***
L Bu'iding (0.4307) (0.4043) (0.2922) (0.2296) (0.1081) 0.1429)
Machinery -0.372 1.7089* 0.4763 0.3566 0.2461** 0.1513
Capital (0.4026i (0.4224) (0.303) (0.2316) (0.1133) 0.1368)
Note: Standard errors are in parentheses. TIhe factor elasticity of industry n with respect to factor m equals tle share of
flactor tn plus the ratio of the corresponding estimated partial effect (from Table IV) and the share of industry n.
* and *** indicate sigmficance at 90%, 95%° and 99% confidence levels respectively.
32
Table 8: The effects of factor endowments on output growth
Effect in terms of percentage change in output in:
Rubber & Primary Miscellaneous
Wood Petroleum Chemicals Products Electronics Manufactures
Skilled -3.6041 -5.2004 -4.7018 5.7742** 1.7585 2.664*
Labor (5.0969) (4.888) (3.6823) (2.7955) (1.3327) (1.6162)
Unskilled 4.7141*** 2.092 1.2313 -2.2709*** 0.5692 -0.2248
. Labor (1.732) (1.4486) (1.153) (0.8683) (0.4041) (0.5042)
: Land & 2.458 -4.009 8.7015*** -0.2245 1.535* 6.0523***
; Building (3.7399) (3.5109) (2.5376) (1.9939) (0.939) (1.2413)
Machinery -3.2955 15.1375*** 4.2194 3.1591 2.18** -1.3404
Equipment (3.566) (3.742) (2.684) (2.0518) (1.0038) (1.2114)
Total Effect 4.7141 15.1375 8.7015 3.5033 3.7150 8.7163
Note: Standard errors are in parentheses. Total effect refers to the sum of all significant estimates in each column.
The effect of factor m growth on output in industry n equals the factor elasticity of industry n with respect
to factor m multiplied by the average annual growth of factor m.
*, * and *** indicate significance at 90%, 95%, and 99% confidence levels respectively.
Table 9: The contributions of productivity and factor endowments on output growth
Rubber & Primary Miscellaneous
Wood Petroleum Chemicals Products Electronics Manufactures
Productivity -38.77 15.80 18.68 70.22 39.12 27.92
OwnProductivity 346.48 4.17 20.33 39.42 39.85 26.16
Cross Productivity -385.24 11.63 -1.66 30.80 -0.73 1.76
Factor Endowments 378.67 164.76 81.53 76.12 31.90 71.39
Laborlnput 378.67 - - 76.12 - 21.82
Capitallnput - 164.76 81.53 - 31.90 49.57
Prices of Goods -239.91 -4.92 -0.20 37.87 0.37 0.69
Fixed Effect - -75.64 - -84.21 28.61 -
TOTAL 100.00 100.00 100.00 100.00 100.00 100.00
Note: All values are in percentage terms. The value of each cell refers to the total significant contributions of the
variable in the row on the output of the industry in the column, and are normalized such that all the figures in
bold add up to 100. Please refer to the text for the construction of the values. For a detailed version of this table
please refer to the Appendix.
refers to value is not statistically significant.
33
Table 10: The detailed contributions of productivity and factor endowments on output growth
Rubber & Primary Miscellaneous
Wood Petroleum Chemicals Products Electronics Manufactures
Total
Productivity 94.3377 74.4029 18.6658 43.3309 32.6354 37.6455
Food -6.0327 0.3407* 0.0165 0.3514** 0.0879*** -0.3092***
(3.3912) (0.1799) (0.0699) (0.1745) (0.0303) (0.0991)
Rubber& 297.593*** -4.9379*** -1.4744** 0.9868 0.2413 -2.0771***
Wood (23.9573) (1.5923) (0.7367) (0.9927) (0.1864) (0.7906)
Petroleum -5.6781* 23.3297*** 0.0523 -0.5602* 0.1886'* -0.0591
(3.3731) (1.3925) (0.2442) (0.3233) (0.0747) (0.1507)
Chemicals -41.919* 1.2926 18.104*** 2.0472 -0.0252 0.2463
(23.5506) (3.6852) (1.9969) (1.7856) (0.3897) (0.7711)
Primary 24.9385 -12.3109*** 1.8197 22.9339*** -0.8783** 4.5762***
Products (29.0554) (4.4676) (1.6348) (2.9652) (0.3465) (0.9814)
Electronics 102.6941 69.8048*** -0.3774 -14.7885** 32.6399*** 2.6758
(76.8691) (15.8195) (5.3977) (6.4327) (1.4071) (2.6748)
Miscellaneous -277.2583*0* -3.1161 0.5251 32.3603*** 0.3811 32.5926***
Manufactures (89.109) (4.6261) (1.5679) (3.5249) (0.4382) (3.1797)
Total Factor
Endowments 18.7979 487.9476 78.8428 81.3833 42.4897 72.9605
Skilled -248.6645 -316.398** -39.2265 72.9926** 12.3648 27.1798*
Labor (341.6495) (156.8401) (26.5437) (29.6443) (8.2594) (14.9357)
Unskilled 325.2456*** 127.2797*** 10.2724 -28.7063*** 4.0026 -2.2934
Labor (116.0986) (46.4809) (8.3112) (9.2079) (2.5044) (4.6593)
Land& 169.5857 -243.9113** 72.595*** -2.8377 10.7934* 61.7498***
Building (250.6922) (112.6533) (18.2924) (21.1438) (5.8197) (11.471)
Machinery -227.3689 920.9772*** 35.2019* 39.9346* 15.3289** -13.6757
Capital (239.0331) (120.0698) (19.3474) (21.7583) (6.221) (11.1949)
Total
Price -229.652 -39.5419 0.2861 24.2813 1.4398 -2.0823
Food -35.5756* 2.0094* 0.0972 2.0721*' 0.5181*0* -1,8236***
(19.9984) (1.0609) (0.4121) (1.0288) (0.1785) (0.5847)
Rubber& 9.3107*** -0.6056** -0.01808** 0.121 0.0296 -0.2547***
Wood (2.9382) (0.1953) (0.0903) (0.1217) (0.0229) (0.097)
Petroleum -10.5159* -5.4901** 0.0968 -1.0374* 0.3493** -0.1095
(6.247) (2.579) (0.4522) (0.5988) (0.1383) (0.2792)
Chemicals -29.3014* 0.9035 -1.3034 1.431 -0.0176 0.1722
(16.4619) (2.576) (1.3959) (1.2482) (0.2724) (0.539)
Primary 15.9775 -7.8873*** 1.1658 -0.3412 -0.5627** 2.9319***
Products (18.6151) (2.8623) (1.0474) (1.8998) (0.222) (0.6288)
Electronics -39.5723 -26.8987*** 0.1454 5.6986** 0.9307* -1.0311
(29.6209) (6.0959) (2.08) (2.4788) (0.5422) (1.0307)
Miscellaneous -139.975*** -1.5732 0.2651 16.3372*-* 0.1924 -1.9675
Manufactures (44.987) (2.3355) (0.7915) (1.7796) (0.2212) (1.6053)
Fixed Effect 2.1652 4.2281*.* 0.0221 -0.49*** 0.23440*. -0.0852
TOTAL I I 1 1 1 1
Note: All values are in percentage tertns. The value of each cell refers to the total contributions of the variable
in the row on the output of the industry in the column, and are normalized such that all the figures in
botd ardd up to tO0. Please refer to the text for the construction of the values.
34
Figure 2: The Contributions of Productivity and Factor Endowments
Miscellaneous
Manufactures Rubber & Wood Petroleum
14% 2% 13% Chemicals
8%~~~~~~~~8
Primary Products
14%
Electronics
49%
Notes: The size of pie represent the share of each industry in the total value added of the manufacturing sector.
Industry that is mainly factor endowments driven
Industry that is mainly productivity driven
Industry that is driven equally by factor endowments and productivity
35
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