Decomposing Total Factor Productivity Growth
in Manufacturing and Services
∗
NEIL FOSTER-MCGREGOR AND BART VERSPAGEN

Using the World Input–Output Database, this paper calculates total factor
productivity (TFP) growth for a sample of 40 economies during the period
1995–2009 to show that TFP growth in Asian economies has been relatively
strong. In a number of Asian economies, TFP growth in services has outpaced
that in manufacturing. This paper presents a novel structural decomposition of
TFP growth and shows that the main drivers of aggregate productivity growth, como
well as differences in productivity growth between services and manufacturing,
have been changing factor requirements. These effects tend to offset the negative
productivity effect of a declining ratio of value added to gross output.

Palabras clave: manufacturing and services, structural decomposition, total factor
productivity
JEL codes: O40, O57

I. Introducción

A great deal of effort has been expended in trying to understand why
differences in the dynamics of productivity persist across both economies and time
(ver, Por ejemplo, Temple 1999). The reason for such an interest is clear: relatively
minor differences in productivity growth between economies, when sustained over
tiempo, can lead to large differences in standards of living. One particular strand of
this literature highlights and attempts to explain the relatively strong performance
of Asian economies in terms of productivity growth in the recent past (ver, para
ejemplo, Joven 1992, Krugman 1994, Felipe 1997).

en este documento, we update the discussion of the relative performance of Asian
economies vis-`a-vis the rest of the world. Using data from the World Input–Output
Database (WIOD), the paper confirms the relatively strong performance of Asian
economies in terms of total factor productivity (TFP) growth over the period
1995–2009. The paper further shows that while for most economies in the sample,
TFP growth in manufacturing has outpaced that of TFP growth in services—which

∗Neil Foster-McGregor (Autor correspondiente): Research Fellow, United Nations University–Maastricht Economic
and Social Research Institute on Innovation and Technology. Correo electrónico: foster@merit.unu.edu. Bart Verspagen: Director,
United Nations University–Maastricht Economic and Social Research Institute on Innovation and Technology. Correo electrónico:
verspagen@merit.unu.edu. The authors would like to thank the participants at the Asian Development Outlook–Asian
Development Review Conference held in Seoul in November 2015, the managing editor, and an anonymous referee
for helpful comments. The usual disclaimer applies.

Asian Development Review, volumen. 34, No. 1, páginas. 88–115

C(cid:3) 2017 Asian Development Bank
and Asian Development Bank Institute

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DECOMPOSING TOTAL FACTOR PRODUCTIVITY GROWTH 89

is consistent with the view that productivity in services is in general lower than
in manufacturing (ver, Por ejemplo, Baumol 1967)—in a number of economies,
particularly Asian economies, TFP growth in services has been faster than in
manufacturing, lending some support to the concept of an “Asian services model”
(Park and Noland 2013).

In search of an explanation for the relatively strong performance of Asian
economies and of the different dynamics of productivity in manufacturing and
services, this paper presents a novel structural decomposition of TFP growth by
building upon the work of Dietzenbacher, Hoen, and Los (2000). Our approach
decomposes the growth of TFP into changes in factor requirements, changes in
the value-added content of output, and changes in the structure and composition of
intermediate and final demand.

The approach adopted is related to recent contributions, such as McMillan,
Rodrik, and Verduzco-Gallo (2014) and Timmer, de Vries, and de Vries (2015), OMS
use sectoral-level productivity data to decompose aggregate productivity changes
into effects of within-industry changes in productivity and effects of sectoral labor
reallocations, with the results tending to suggest that within-sector productivity
changes often drive aggregate productivity changes. This paper is also interested
in decomposing productivity changes but moves away from the traditional shift-
share analysis of McMillan, Rodrik, and Verduzco-Gallo (2014) and Timmer, de
Vries, and de Vries (2015). En cambio, the current paper builds upon the approach
of Chenery, Shishido, and Watanabe (1962); Feldman, McClain, and Palmer
(1987); Wolff (1985); and Dietzenbacher, Hoen, and Los (2000) who use structural
decomposition methods to decompose productivity growth into the growth of its
constituent parts (p.ej., value added and labor requirements).1 Adopting a structural
decomposition approach to decompose productivity has a number of advantages,
most notably by acknowledging that industries are interdependent (both within and
across economies) and through input–output linkages allowing one to capture the
productivity effects of these interactions. With the rise of global value chains (GVCs)
(see Amador and Cabral [2016] for a recent survey), understanding and identifying
the impacts of these input–output relations on productivity growth is a timely and
worthwhile exercise.

Using the developed structural decomposition of TFP growth, this paper
decomposes overall TFP growth rates as well as differences in TFP growth between
the manufacturing and service sectors. The results suggest that declining factor
requirements are the main determinant of TFP growth in the sample of WIOD
economías, with a declining domestic value-added content of gross output serving to
reduce TFP growth in most economies. The role of input–output linkages tends to be
limitado, though some evidence of a role for the changing structure and composition of
intermediate and final goods demand is found in some economies. When considering

1See chapter 13 in Miller and Blair (2009) for more details on structural decomposition analysis.

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90 ASIAN DEVELOPMENT REVIEW

differences in the relative performance of manufacturing and services, declining
factor requirements again tend to dominate, though a role for input–output linkages
is also evident for a number of economies. En general, the services productivity
advantage that is witnessed in many Asian economies has no simple or single
explicación, with changing factor requirements and changing input–output structure
and composition being either more or less important in different economies.

The remainder of the paper is organized as follows. Section II discusses and
describes the data. Section III describes the decomposition methodology. Sección
IV presents the main results and section V concludes.

II. Data and Descriptive Analysis

Data are drawn from the WIOD (Timmer 2015).2 The WIOD reports data
on socioeconomic accounts, international input–output tables, and bilateral trade
across 35 industries and 40 economías (plus the rest of the world) over the
period 1995–2009.3 Data on value added, gross output, and intermediate purchases
needed for the decomposition described in the following section are taken from the
world input–output tables and are expressed in millions of United States dollars.
Two sets of tables are given, one reporting values in current prices and a second
reporting values in previous year prices.

We construct TFP growth and undertake the structural decomposition on a

year-on-year basis, thus allowing us to consider growth in real TFP ( ˆg

Fi
t ) como

Fi
ˆg
t

= ln

φt−1
t
φt−1
t−1

= ln

vt−1
t
vt−1
t−1

− ¯αt ln

lt
lt−1

− ¯βt ln

kt
kt−1

t

where the superscript refers to the year in which prices are measured; eso es, vt−1
is the value added in period t using previous year (t − 1) prices. The factor inputs
labor (yo) and capital (k) are taken from the socioeconomic accounts and expressed
in real terms (hours worked in the case of labor; en 1995 prices and domestic
currencies in the case of capital stocks).4 The labor share (a) is calculated as the
share of labor compensation in value added, with the capital share being calculated
as the residual (β = 1 − α). We use a Tornqvist approximation for the labor and
capital shares, thus allowing for these shares to be time-varying ( ¯αt = 1
2 (αt−1 + αt )
and ¯βt = 1
2 (βt−1 + βt )). Some existing evidence suggests that these shares are not
constant over time, with a declining labor share often observed (ver, Por ejemplo,
Elsby, Hobijn, and Sahin 2013).

2See www.wiod.org for more details.
3See Table A.1 in the Appendix for a list of economies and sectors.
4We converted the capital stocks from 1995 domestic currencies to United States dollars using the 1995

nominal exchange rates provided in the WIOD.

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DECOMPOSING TOTAL FACTOR PRODUCTIVITY GROWTH 91

Our main interest is in considering longer-term changes in TFP (the growth
rate between 1995 y 2009), with the growth of real TFP between 1995 y 2009
calculated as

Fi
ˆg
1995:2009

=

2009(cid:2)

t=1996

ln

φt−1
t
φt−1
t−1

Mesa 1 reports for each of the 40 WIOD economies the initial (1995) level and the
cumulative growth rate of TFP over the period 1995–2009, along with unweighted
averages for four economy groups: Asia, non-Asian developed, European Union
(EU) new member states (NMS), and non-Asian developing. Results are reported
for an economy’s total TFP and for manufacturing and services TFP separately.5
The data confirm previous studies and our expectations that TFP growth has been
stronger in Asia than in other regions, with cumulative TFP growth of 35.5% en
Asia over the period 1995–2009. The TFP growth rate during the review period was
also strong in EU NMS at 26.8% y (en un grado menor) in non-Asian developing
economies at 22%, while TFP growth in developed economies was relatively low
en 8.8%. These averages hide a great deal of heterogeneity within each group, con
TFP growth in the People’s Republic of China (PRC) as high as 89%, comparado
with growth rates of 17.5% for Japan; 15.4% for Taipei,Porcelana; y (perhaps most
surprisingly) 15.2% for Indonesia.

When considering manufacturing and services separately, we find that TFP
growth in manufacturing outpaced TFP growth in services in EU NMS and
non-Asian developed economies, with the difference being more than 15 porcentaje
points in the case of non-Asian developed economies and more than 20 porcentaje
points in the case of EU NMS. Such results are consistent with the view of Baumol
(1967) that productivity growth in services tends to be lower than in manufacturing.
In the cases of Asia and non-Asian developing economies, sin embargo, we observe
that TFP growth is higher in services than in manufacturing. De nuevo, there is a great
deal of heterogeneity within economy groups. Por ejemplo, in Asia, services TFP
growth outstrips manufacturing TFP growth by more than 40 percentage points in
India, while TFP growth in manufacturing is more than 55 percentage points higher
than services TFP growth in the Republic of Korea.

Even in the PRC and the Republic of Korea, where TFP growth in
manufacturing exceeds that in services, the growth rate of TFP in services was
still higher than the average rate for the full sample of economies. In all six
Asian economies (and three of the four non-Asian developing economies), services
TFP growth over the period 1995–2009 was above 15%, with growth of TFP in
manufacturing exceeding 15% in just three Asian economies (and two non-Asian

5See Table A.2 in the Appendix for details of which individual industries are considered to comprise

manufacturing and services.

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92 ASIAN DEVELOPMENT REVIEW

Mesa 1. Descriptive Statistics for Total Factor Productivity Growth

All Sectors

Services

Manufacturing
φ1995

φ1995

φ1995

0.467
0.913
0.388
5.383
4.802
4.021

Fi
Fi
Fi
ˆg
ˆg
ˆg
1995:2009
1995:2009
1995:2009
37.33%
29.16%
35.46%
84.65%
88.85% 0.409
0.682
89.03%
31.33%
19.72% 0.832
1.564
15.24%
43.77%
2.84% 0.524
0.433
32.24%
18.63%
1.08% 5.480
6.616
17.45%
22.95%
3.726
78.57% 5.075
43.40%
22.66%
3.660 −16.11% 4.190
15.41%
5.11%
20.73%
8.83%
9.93%
6.048 −12.56% 4.291
5.31%
4.162
14.74% 10.588
7.829
7.49%
28.68% 6.972
0.75%
20.50% 8.555
3.96% 11.721
8.914
15.79%
33.70% 4.019
16.52%
5.492
3.817
5.52%
23.32% 6.179
9.19% 17.909
8.105
0.40%
8.82% 6.492
1.89% 11.812
6.758
0.66%
1.40% 4.979
6.492
2.82%
4.875
3.96%
55.12% 6.493
19.60%
7.608
6.493
12.02%
48.84% 5.483
17.94% 10.218
5.926
6.061
14.73%
30.78% 5.877
13.16% 10.007
0.78%
7.03% 2.128
3.928
3.67%
2.216
4.451
4.869
27.15% 6.215 −3.58%
7.00%
7.411 −11.24% 5.105 −6.05%
5.473 −5.40%
9.334 −18.54% 4.597
5.122
8.41%
6.01%
12.42%
34.57% 7.455
9.124
13.95%
7.150
15.55% 3.513 −9.88%
3.442
3.487 −1.59%
8.94%
41.09% 6.868
7.872
16.98%
6.929
9.66%
39.00% 5.373
13.13%
7.139
5.396
16.55%
36.73%
26.80%
14.19%
1.331 −16.40% 0.519
7.69%
25.34%
10.55% 3.252
4.213
24.49%
10.44%
51.57% 0.724
1.168
23.99%
23.30%
58.32% 1.090
1.370
34.14%
14.57%
39.05% 1.297
1.865
30.20%
1.102
29.36%
22.56%
41.32% 0.705
24.27%
31.72% 0.942
1.319
31.52%
15.06%
11.03% 2.309
4.157
12.25%
21.83%
87.05% 1.738
2.203
52.30%
6.73%
22.42% 0.700
1.127
19.32%
13.97%
50.60% 0.663
1.120
27.10%
6.35%
29.24%
53.48% 4.589
5.766
21.03%
8.38%
22.02%
6.16%
1.149 −0.22%
28.13%
28.91%
0.630
21.80%
26.62%
1.118
28.02%
32.78%
0.924

Asia
People’s Republic of China
Indonesia
India
Japón
Republic of Korea
Taipéi,Porcelana
Non-Asian Developed
Australia
Austria
Bélgica
Canada
Alemania
Dinamarca
España
Finland
Francia
Reino Unido
Greece
Irlanda
Italia
Luxembourg
Los países bajos
Portugal
Suecia
United States
EU New Member States
Bulgaria
Cyprus
Czech Republic
Estonia
Hungary
Lithuania
Latvia
Malta
Poland
Romania
Eslovaquia
Slovenia
Non-Asian Developing
Brasil
México
Russian Federation
Pavo
EU = European Union.
Notas: This table reports the initial (1995) level of total factor productivity (TFP) by economy for (i) todo
World Input–Output Database sectors, (ii) the manufacturing sector only, y (iii) the service sector only,
así como el (cumulative) growth rate of TFP over the period 1995–2009. TFP growth rates for the four
economy groups are unweighted averages.
Fuente: Authors’ calculations using the World Input–Output Database. www.wiod.org

1.798 −36.72% 1.228
15.01% 0.630
1.085
42.35% 1.346
1.049
12.88% 0.781
1.064

0.715
3.155
0.810
1.129
1.405
0.738
1.039
2.504
4.265
0.899
0.757
6.183

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DECOMPOSING TOTAL FACTOR PRODUCTIVITY GROWTH 93

Cifra 1. Scatterplot of Manufacturing and Services Total Factor Productivity
Growth, 1995–2009

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AUS = Australia; AUT = Austria; BEL = Belgium; BGR = Bulgaria; BRA = Brazil; CAN = Canada; CYP
= Cyprus; CZE = Czech Republic; DEN = Denmark; EST = Estonia; FIN = Finland; FRA = France; GER =
Alemania; GRC = Greece; HUN = Hungary; IND = India; INO = Indonesia; IRE = Ireland; ITA = Italy; JPN =
Japón; KOR = Republic of Korea; LTU = Lithuania; LUX = Luxembourg; LVA = Latvia; MEX = Mexico; MLT
= Malta; NET = The Netherlands; POL = Poland; POR = Portugal; PRC = People’s Republic of China; ROM =
Romania; RUS = Russian Federation; SVK = Slovakia; SVN = Slovenia; SPA = Spain; SWE = Sweden; TAP =
Taipéi,Porcelana; TFP = total factor productivity; TUR = Turkey; UKG = United Kingdom; USA = United States.
Fuente: Authors’ calculations using World Input–Output Database. www.wiod.org

developing economies). This outcome suggests that services production need not
imply low overall TFP growth and may further point to the possibility of an “Asian
services model” (Park and Noland 2013).

These differences between TFP growth in manufacturing and services can be
further observed in Figure 1, which plots TFP growth in manufacturing against that in
services for the period 1995–2009. This figure further shows that there is only a weak
correlation between services and manufacturing TFP growth. When considering all
observaciones, the correlation coefficient is 0.35. It falls to 0.14 when the major outlier,
the PRC, is excluded from the calculation.6 There are also numerous individual
cases where services TFP growth outperforms that of manufacturing. In a number
of these cases, the difference partly reflects poor—and often negative—TFP growth
in manufacturing (p.ej., Australia; Bulgaria; Brasil; India; Italia; Luxembourg; y

6A simple regression of manufacturing TFP growth on a constant and services TFP growth results in a
coefficient of 0.64 (significant at the 5% nivel) when the PRC is included and 0.36 (not significant) when the PRC is
excluded.

94 ASIAN DEVELOPMENT REVIEW

Taipéi,Porcelana). In other cases—most notably Indonesia and Japan in Asia as well
as Cyprus, Malta, México, and Turkey—higher TFP growth rates for services arise
despite positive TFP growth rates for manufacturing.

To understand further these differences in TFP growth, both across economies
and between manufacturing and services, we now proceed to decompose TFP growth
using structural decomposition methods in the following section.

III. Metodología

The decomposition method employed in this paper builds upon that developed
by Dietzenbacher, Hoen, and Los (2000) for labor productivity, with the current
paper decomposing TFP growth rather than the growth of labor productivity. El
decomposition of labor productivity changes undertaken by Dietzenbacher, Hoen,
and Los (2000) results in six components: two reflect changing labor productivity
levels for each industry in each economy, two reflect changing industry output
shares across economies, and two reflect changing trade relationships between
economías. In their analysis, Dietzenbacher, Hoen, and Los (2000) show that changes
in labor requirements per unit of gross output are the biggest determinant of labor
productivity changes for six European economies, with part of this positive impact
being offset by the productivity-decreasing effect of a smaller share of value added
in gross output.

We begin by defining a number of variables used by Dietzenbacher, Hoen,
and Los (2000), where N represents the number of industries per economy (35) y
C the number of economies (40 plus the rest of the world):7

v: aggregate value added (escalar);
yo: aggregate labor inputs (escalar);
Pi: aggregate labor productivity, v/l (escalar);
A: matrix of input coefficients (N C × N C), with typical element ar s

i j denoting
the input of product i from economy r per unit of output in industry j in
economy s;

l: Leontief inverse (N C × N C), L ≡ (I − A)−1;
F: matrix of final demands (N C × C), with typical element

f r s
i
demand for product i produced in economy r by economy s;

giving the final

7WIOD reports for the rest of the world aggregate all variables that we need for our analysis other than data on
labor and capital use and compensation. We therefore include the rest of the world as a 41st economy in our analysis,
setting the labor and capital variables to some arbitrary values. Doing this allows us to easily include intermediate
and final demand from the rest of the world in our calculations while not affecting the measured values of labor
productivity and TFP for our 40 economies of interest.

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DECOMPOSING TOTAL FACTOR PRODUCTIVITY GROWTH 95

F: vector with element f r

i giving the final demand for output of industry i in economy
r (N C × 1); f = Fe where e is the C × 1 summation vector consisting of
unos;

λ: vector with elements λr

i giving the use of labor per unit of gross output in industry

i in economy r (N C × 1); y

μ: vector with elements μr

i in economy r (N C × 1).

i giving the value added per unit of gross output in industry

In order to extend the analysis to a decomposition of TFP growth, we further define
the following additional variables:

k: aggregate capital inputs (escalar),8
t : vector with elements τ r

industry i in economy r (N C × 1),

i giving the use of capital per unit of gross output in

a: labor share in total compensation of capital and labor (escalar), y
b: capital share in total compensation of capital and labor (escalar).

Given the above definitions we can further define

v = μ(cid:5)X
l = λ(cid:5)Lf and
k = τ (cid:5)Lf

where x is the N C × 1 vector of gross output levels x r

i of industry i in economy r :

x = Ax + f = (I − A)−1 f = Lf

To decompose TFP growth, we start with a general form of the production function:

vt = F (φt , lt , kt )

with φ being TFP. Taking logs and derivatives with respect to time we get

˙v
v

= Fφφ
v

˙φ
Fi

+ Fll
v

˙l
yo

+ Fkk
v

˙k
k

8We assume that capital is a primary factor of production rather than a produced input to production. En
his analysis, Wolff (1985) assumes the latter by introducing an additional sector capturing the production of capital
goods.

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96 ASIAN DEVELOPMENT REVIEW

˙φ
Assuming that technology is Hicks neutral, the growth rate of TFP gφ = Fφ
v
Fi
becomes gφ = ˙φ
Fi , while assuming competitive markets implies that factors are
paid their social marginal products; eso es, Fk = r and Fl = w. We can then
write

gφ = ˙v
v

−

wl
v

˙l
yo

− r k
v

˙k
k

The capital and labor shares are written as β = r k
assumption of constant returns to scale we have β + un = 1.
Using the discrete time approximation, we then have

v and α = wl

v , and under the

ˆgφ = ln

vt
vt−1

− ¯αt ln

lt
lt−1

− ¯βt ln

kt
kt−1

con

ˆgφ = ln

φt
φt−1

Using v = μ(cid:5)Lf, l = λ(cid:5)Lf, and k = τ (cid:5)Lf, we can write aggregate TFP growth
como

ˆgφ = ln

(cid:3)

μ(cid:5)
μ(cid:5)

1L1f1
0L0f0

(cid:4)

(cid:3)

− ¯αt ln

λ(cid:5)
1L1f1
λ(cid:5)
0L0f0

(cid:4)

(cid:3)

− ¯βt ln

(cid:4)

t (cid:5)
1L1f1
t (cid:5)
0L0f0

(1)

The first two terms on the right-hand side of equation (1) can be written as

ln

μ(cid:5)
μ(cid:5)

1L1f1
0L1f1

+ ln

μ(cid:5)
μ(cid:5)

0L1f1
0L0f1

+ ln

μ(cid:5)
μ(cid:5)

0L0f1
0L0f0

y

¯αt ln

λ(cid:5)
1L1f1
λ(cid:5)
0L1f1

+ ¯αt ln

λ(cid:5)
0L1f1
λ(cid:5)
0L0f1

+ ¯αt ln

λ(cid:5)
0L0f1
λ(cid:5)
0L0f0

The third term can be written as

¯βt ln

t (cid:5)
1L1f1
t (cid:5)
0L1f1

+ ¯βt ln

t (cid:5)
0L1f1
t (cid:5)
0L0f1

+ ¯βt ln

t (cid:5)
0L0f1
t (cid:5)
0L0f0

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DECOMPOSING TOTAL FACTOR PRODUCTIVITY GROWTH 97

Combining and rearranging these terms gives

ˆgφ = ln

μ(cid:5)
μ(cid:5)

− ¯αt ln
(cid:4)

1L1f1
0L1f1
t (cid:5)
0L1f1
t (cid:5)
0L0f1

λ(cid:5)
1L1f1
λ(cid:5)
0L1f1
(cid:3)
μ(cid:5)
μ(cid:5)

ln

+

− ¯βt ln

− ¯βt ln

(cid:3)

+

ln

t (cid:5)
1L1f1
t (cid:5)
0L1f1

0L0f1
0L0f0

− ¯αt ln

λ(cid:5)
0L0f1
λ(cid:5)
0L0f0

λ(cid:5)
0L1f1
λ(cid:5)
0L0f1

μ(cid:5)
μ(cid:5)

− ¯αt ln
(cid:4)

0L1f1
0L0f1
t (cid:5)
0L0f1
t (cid:5)
0L0f0

− ¯βt ln

(2)

Dietzenbacher, Hoen, and Los (2000) note that equation (2) can be further
decomposed to incorporate the distinction between the effects of aggregate
production structure changes and aggregate final demand changes, and the effects
of changing international trade (with respect to both intermediate inputs and final
demand deliveries). To achieve this, the following matrices are defined:

A∗: a matrix constructed by stacking C identical N × N C matrices of aggregate
intermediate inputs per unit of gross output by industry and economy (N C ×
N C matrix), ∀r. [a∗]r s
i j

(cid:5)
C
r =1 ar s
i j ;

=

TA: a matrix of intermediate trade coefficients, representing the shares of each
economy in aggregate inputs by input, industria, y economía (N C × N C
r [t A]r s
matrix), [t A]r s
i j
i j

/ [a∗]r s

i j , y

= ar s
i j

= 1;

(cid:5)

F∗: a matrix constructed by stacking C identical N × C matrices of final demand

for product i in economy s (N C × C matrix). ∀r. [ f ∗]r s
i

=

(cid:5)
C
r =1 f r s
i

yo; y

(cid:7)
r s
i

= f r s
i

/ [ f ∗]r s
i

TF: a matrix of final demand trade coefficients, representing the shares of economy
r in aggregate final demand for product i in economy s (N C × C matrix).
(cid:6)
= 1.
t F
(cid:6)
I − A∗ ◦ TA

We can then write the Leontief inverse as L =
mi,
where ◦ denotes the Hadamard product (of elementwise multiplication). Using these,
we can decompose TFP growth further as

(cid:7)−1 and f =

F∗ ◦ TF

(cid:5)
r

(cid:7)
r s
i

, y

t F

(cid:6)

(cid:6)

(cid:7)

ˆgφ = [θ1] − [i2] − [i3] + [i4] + [i5] + [i6] + [i7]

(3)

con

θ1 = ln

μ(cid:5)
μ(cid:5)

1L1f1
0L1f1

representing the productivity effects of changes in the value added per unit of gross
output by industry;

θ2 = ¯αt ln

λ(cid:5)
1L1f1
λ(cid:5)
0L1f1

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98 ASIAN DEVELOPMENT REVIEW

representing the productivity effects of changes in labor requirements per unit of
gross output by industry;

θ3 = ¯βt ln

t (cid:5)
1L1f1
t (cid:5)
0L1f1

representing the productivity effects of changes in capital requirements per unit of
gross output by industry;
(cid:6)

(cid:8)

(cid:9)

(cid:6)

θ4 =

ln

μ(cid:5)
0
μ(cid:5)
0

(cid:6)

I −
I −
(cid:6)

(cid:9)
A∗
1
(cid:9)
A∗
0
(cid:9)

− ¯βt ln

t (cid:5)
0
t (cid:5)
0

(cid:6)

I −
I −

(cid:9)

A∗
1
A∗
0

◦ TA
1
◦ TA
1

(cid:10)(cid:7)−1 f1
(cid:10)(cid:7)−1 f1
◦ TA
1
◦ TA
1

(cid:10)(cid:7)−1 f1
(cid:10)(cid:7)−1 f1

− ¯αt ln

(cid:11)

λ(cid:5)
0
λ(cid:5)
0

(cid:6)

I −
I −

(cid:9)

A∗
1
A∗
0

◦ TA
1
◦ TA
1

(cid:10)(cid:7)−1 f1
(cid:10)(cid:7)−1 f1

representing the productivity effects of changes in the interindustry structure (p.ej.,
due to technological change, factor substitution, and changing output compositions
within industries);
(cid:9)
A∗
0
(cid:9)
A∗
0
(cid:9)

(cid:10)(cid:7)−1 f1
(cid:10)(cid:7)−1 f1

◦ TA
1
◦ TA
0

◦ TA
1
◦ TA
0

I −
I −
(cid:6)

I −
I −

− ¯αt ln

A∗
0
A∗
0

μ(cid:5)
0
μ(cid:5)
0

λ(cid:5)
0
λ(cid:5)
0

θ5 =

ln

(cid:11)

(cid:8)

(cid:9)

(cid:9)

(cid:6)

(cid:6)

(cid:6)

(cid:6)

(cid:10)(cid:7)−1 f1
(cid:10)(cid:7)−1 f1
◦ TA
1
◦ TA
0

(cid:10)(cid:7)−1 f1
(cid:10)(cid:7)−1 f1

− ¯βt ln

t (cid:5)
0
t (cid:5)
0

(cid:6)

I −
I −

(cid:9)

A∗
0
A∗
0

representing the productivity effects of changes in trade structure with respect to
commodities and services used as intermediate inputs (p.ej., due to changes in
sourcing patterns associated with GVCs);

(cid:8)

θ6 =

ln

μ(cid:5)
μ(cid:5)

0L0
0L0

(cid:9)
F∗
(cid:9)
1
F∗
0

(cid:10)

(cid:10)

mi

mi

◦ TF
1
◦ TF
1

− ¯αt ln

(cid:9)
F∗
(cid:9)
1
F∗
0

(cid:10)

(cid:10)

mi

mi

◦ TF
1
◦ TF
1

λ(cid:5)
0L0
λ(cid:5)
0L0

− ¯βt ln

(cid:11)

(cid:9)
F∗
(cid:9)
1
F∗
0

(cid:10)

(cid:10)

mi

mi

◦ TF
1
◦ TF
1

t (cid:5)
0L0
t (cid:5)
0L0

representing the productivity effects of changes in final demand composition (p.ej.,
due to substitution by consumers, investors, or third economies following relative
price changes or changing preference structures); y

(cid:8)

θ7 =

ln

μ(cid:5)
μ(cid:5)

0L0
0L0

(cid:9)
F∗
(cid:9)
0
F∗
0

(cid:10)

(cid:10)

mi

mi

◦ TF
1
◦ TF
0

− ¯αt ln

(cid:9)
F∗
(cid:9)
0
F∗
0

(cid:10)

(cid:10)

mi

mi

◦ TF
1
◦ TF
0

λ(cid:5)
0L0
λ(cid:5)
0L0

− ¯βt ln

(cid:11)

(cid:9)
F∗
(cid:9)
0
F∗
1

(cid:10)

(cid:10)

mi

mi

◦ TF
1
◦ TF
0

t (cid:5)
0L0
t (cid:5)
0L0

representing the productivity effects of changes in the trade structure as regards
commodities and services used for final demand purposes.

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DECOMPOSING TOTAL FACTOR PRODUCTIVITY GROWTH 99

(2000) note that

Dietzenbacher, Hoen, and Los

structural change
decompositions are not unique and that the sensitivity of decomposition results
can be very large. In the additive case, Dietzenbacher and Los (1998) find that
reversing the weights and taking the average of the two types of decompositions
generates results that are generally close to the average of all decomposition forms,
with the variance of the results being much smaller. We follow Dietzenbacher, Hoen,
and Los (2000) and undertake both decompositions, reporting the average of the
two decompositions in the analysis below.

The above equations provide estimates of the various partial effects on
TFP growth for the entire sample of 41 WIOD economies (including the rest of
el mundo) aggregated across economies and industries. To obtain estimates for
single economies (across industries) or single industries (across economies), nosotros
replace the vectors μ, λ, and τ with diagonal matrices with the same elements along
the main diagonal and zeroes elsewhere, further premultiplying all numerators and
denominators with (1 × N C) aggregation vectors.

IV. Decomposition of Aggregate Total Factor Productivity Growth

This section reports results for the decomposition of TFP growth using the
method described above. We begin by undertaking the decomposition of TFP growth
for the aggregate (todo 35 WIOD sectors) of each of our economies. Adopting the
same approach as discussed in section II, we decompose aggregate real TFP growth
by summing up year-on-year real TFP growth and year-on-year real changes in the
components of TFP growth, calculated using previous year price data. Tal como,
the Leontief inverse and the final demand vector are calculated in both current and
previous year prices. After undertaking the decomposition of aggregate TFP growth,
we then undertake the decomposition for manufacturing and services separately,
calculating the contributions of the different components to the difference in TFP
growth between manufacturing and services. When presenting the results, nosotros reportamos
results for the full sample of 40 economies in the Appendix, with results for the
six Asian economies and a comparison to (unweighted) average values for the
other economy groups (EU NMS, non-Asian developing economies, and non-Asian
developed economies) reported in the main text.

Cifra 2 reports the results of the TFP decomposition for the six Asian
economies and the three economy aggregates, with economies and regions listed in
ascending order of initial TFP levels. Table A.3 in the Appendix reports results for
the full sample of economies. The line in Figure 2 represents the growth rate of TFP
entre 1995 y 2009, while the bars decompose TFP growth into its constituent
parts.9 As we have already seen in section II, the growth rate of TFP between 1995

9Since some elements of the decompositions are negative (they work against the direction of the change in

TFP), only the absolute value of the sum of the different terms equals 100%.

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100 ASIAN DEVELOPMENT REVIEW

Cifra 2. Structural Decomposition of Total Factor Productivity Growth, 1995–2009

EU NMS = European Union new member states, F struc. = final demand structure, F trd. = final demand composition,
Int. ind. = intermediate input structure, Int. trd. = intermediate input composition, K req. = capital requirements, l
req. = labor requirements, PRC = People’s Republic of China, TFP = total factor productivity, VA ratio = value-added
ratio.
Fuente: Authors’ calculations.

y 2009 was found to be highest for the PRC at about 89%. The TFP growth
rate was also above the sample average in India at about 32% and it exceeded the
average in Indonesia and the Republic of Korea as well. In Japan and Taipei,Porcelana,
TFP growth was lower than the sample average.

In terms of the decomposition, we observe positive values for the contribution
of the growth of labor requirements for all economies, with the values being relatively
large for all Asian economies except Japan and (en un grado menor) Indonesia.
These values were particularly large for the PRC. The values for this component
tend to be large relative to the contributions of most other components, incluido
capital requirements, which suggest that labor-saving process innovation and the
substitution of direct labor played an important role in enhancing TFP in most
economías, particularly in Asia. In the case of Asian economies, we find that the
decline in labor input per unit of gross output would have increased TFP by between
a low of 9 percentage points in Japan to a high of 47 percentage points in the PRC,
assuming that no other factors changed. Relatively large effects of changes in labor
requirements were also found for the Republic of Korea (43 puntos de porcentaje) y
Taipéi,Porcelana (29 puntos de porcentaje), which is perhaps surprising given its relatively
poor TFP growth during the review period. Such outcomes are consistent with the
results of Dietzenbacher, Hoen, and Los (2000) for European economies, who also
found in their decomposition of labor productivity that the factor with the largest
positive impact was the change in labor input per unit of gross output.

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DECOMPOSING TOTAL FACTOR PRODUCTIVITY GROWTH 101

Also consistent with the results of Dietzenbacher, Hoen, and Los (2000) es el
result that a smaller share of value added in gross output tends to have a productivity-
decreasing effect. A potential explanation for such a development relates to the
increasing role of GVCs in production that have led to more intermediate deliveries
across borders, raising the intermediate content (and lowering the value added) de
local gross production. Sin embargo, there are a number of exceptions to this general
conclusion as 11 economies in the full sample reported positive contributions from
the change in value added to gross output, including a number of EU transition
economías (p.ej., Estonia, Latvia, Lithuania, Eslovaquia, Slovenia) as well as both
Japan and India. In the case of Asian economies, the results suggest that the
decline in value added to gross output would have decreased TFP by about 14
percentage points in the PRC had no other factors changed, with declines of about
15 percentage points observed for Taipei,China and about 7 percentage points for
both Indonesia and the Republic of Korea. Even these smaller numbers for Indonesia
and the Republic of Korea tend to be large relative to the other economy groups:
declines of 5.4 puntos de porcentaje, 3.1 puntos de porcentaje, y 7.8 puntos de porcentaje,
respectivamente, were observed for non-Asian developed economies, EU NMS, y
non-Asian developing economies.

The effects of changes in capital inputs per unit of gross output are mixed
across economies, with declines in capital inputs per unit of gross output found to
have lowered TFP in 22 economies and increased it in 18 economías. Among all
economías, positive effects were the largest in the PRC (42 puntos de porcentaje) by a
wide margin. In Asia, the effects of declining capital usage per unit of gross output
were also positive in Indonesia (13 puntos de porcentaje), Japón (4 puntos de porcentaje),
and the Republic of Korea (1 percentage point), but had a negative impact in India
(6 puntos de porcentaje) and Taipei,Porcelana (5 puntos de porcentaje).

In terms of the remaining four factors, our findings are again consistent
with Dietzenbacher, Hoen, and Los (2000) in that there is little evidence of a large
productivity growth effect in most economies. Sin embargo, intermediate composition
and intermediate trade structure play an important role in enhancing TFP growth
in a number of economies, most notably in non-Asian developed economies, EU
NMS, and India. Such results suggest that by changing their sourcing patterns and
intermediate trade structure, these economies were able to increase TFP growth,
a finding that may be related to the expanding role of GVCs and the increased
fragmentation of production. In the case of India, the composition of intermediates
is the stronger of the two effects, while for EU NMS and non-Asian developed
economies the intermediate trade structure plays the more dominant role. Este
would suggest that among these two groups a realignment of economy sourcing
patterns rather than shifts in intermediate composition due to technological change
is the more important source of TFP growth.

Final demand composition and trade structure are also found to make a
relatively large contribution to TFP growth for India, EU NMS, and non-Asian

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102 ASIAN DEVELOPMENT REVIEW

developed economies, with the final demand trade structure dominating the two
efectos. Final demand composition and trade structure together account for more
than 10% of overall TFP growth in all other economies except the PRC, Indonesia,
y japon. In the case of Indonesia, the effects of final demand trade structure as
well as the trade structure of intermediate demand are found to be negative.

En general, the results suggest that declining labor and capital requirements per
unit of gross output are the main contributors to TFP growth, more than offsetting
the negative effect of a smaller share of value added in gross output. The more
successful Asian economies during the period tended to minimize their decline in
the share of value added in gross output while significantly reducing the labor and
capital requirements per unit of gross output. Al mismo tiempo, there appears to
be no single recipe for success, with the PRC benefiting significantly from a drop
in capital requirements per unit of gross output, the Republic of Korea benefiting
almost exclusively from a drop in labor requirements per unit of gross output, y
India benefitting significantly from changes in the structure of intermediate and final
demand.

We now turn to the discussion of the structural decomposition of TFP growth
for manufacturing and services, examining whether the decomposition can shed any
light on the differences in the evolution of TFP in manufacturing and services across
economías. In Tables A.4 and A.5 in the Appendix, we report the full decomposition
for all 40 economies for both manufacturing and services. In the main text, nosotros
concentrate on the comparison between the sample of Asian economies and the other
three economy groups, reporting the decomposition of manufacturing and services
TFP growth in Figures 3 y 4, respectivamente, and the results of the decomposition of
the difference in the (cumulative) growth rate of TFP for manufacturing and services
En figura 5.10

Figures 3 y 4 reveal that declines in the ratio of labor and (to a lesser
extent) capital requirements tend to explain the largest part of TFP growth in
both manufacturing and services. While the importance of labor requirements is
fairly consistent across economies and economy groups, the results for capital
requirements are mixed. A declining ratio of capital to gross output spurred TFP
growth in both manufacturing and services in the PRC; in manufacturing in the
Republic of Korea; and in services in Indonesia, Japón, and non-Asian developing
economías. In the case of manufacturing, sin embargo, an increasing ratio of capital
to gross output negatively impacted TFP growth in many economies, most notably
India; Indonesia; Japón; and Taipei,Porcelana. Reductions in TFP growth in services
due to increasing ratios of capital to gross output are observed for Taipei,China and
non-Asian developed economies.

10These contributions are calculated simply as the difference in the values of the contributions to

manufacturing and services TFP growth.

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DECOMPOSING TOTAL FACTOR PRODUCTIVITY GROWTH 103

Cifra 3. Structural Decomposition of Manufacturing Total Factor Productivity Growth,
1995–2009

EU NMS = European Union new member states, F struc. = final demand structure, F trd. = final demand composition,
Int. ind. = intermediate input structure, Int. trd. = intermediate input composition, K ratio = capital ratio, L ratio =
labor ratio, PRC = People’s Republic of China, TFP = total factor productivity, VA ratio = value-added ratio.
Fuente: Authors’ calculations.

Cifra 4. Structural Decomposition of Services Total Factor Productivity Growth,
1995–2009

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EU NMS = European Union new member states, F struc. = final demand structure, F trd. = final demand composition,
Int. ind. = intermediate input structure, Int. trd. = intermediate input composition, K ratio = capital ratio, L ratio =
labor ratio, PRC = People’s Republic of China, TFP = total factor productivity, VA ratio = value-added ratio.
Fuente: Authors’ calculations.

The results in Figures 3 y 4 further show that changes in intermediate
and final demand structure and trade play an important role in some economies.
Changes in intermediate and final demand structure account for a relatively large

104 ASIAN DEVELOPMENT REVIEW

Cifra 5. Structural Decomposition of Differences in Manufacturing and Services Total
Factor Productivity Growth, 1995–2009

EU NMS = European Union new member states, F struc. = final demand structure, F trd. = final demand composition,
Int. ind. = intermediate input structure, Int. trd. = intermediate input composition, K req. = capital requirements, l
req. = labor requirements, PRC = People’s Republic of China, TFP = total factor productivity, VA ratio = value-added
ratio.
Fuente: Authors’ calculations.

proportion of the TFP growth in manufacturing in Indonesia. These two terms are
also relatively important for services TFP growth in India; the Republic of Korea;
and Taipei,Porcelana; as well as in non-Asian developed economies and non-Asian
developing economies.

Given the discussion in section II, an explanation is desired for the varying
performance of manufacturing and services TFP growth across economies, incluido
whether there is a single explanation for the relatively faster growth of TFP
for services in many Asian economies. Cifra 5 plots the difference in growth
between manufacturing and services (solid line) for select economies and economy
grupos, with a negative value indicating that TFP grew faster in services than
in manufacturing. While in many cases, the difference in TFP growth between
manufacturing and services during the review period was relatively small, in other
casos, the differences were large. Por ejemplo, TFP growth in manufacturing
exceeded that in services by more than 50 percentage points in the Republic of
Korea, while TFP growth in services exceeded that in manufacturing by about 40
percentage points in India and Taipei,Porcelana.

Cifra 5 reports the contributions of the different decomposition terms to the
difference in TFP growth between manufacturing and services. For most economies,
the majority of the difference in TFP growth between manufacturing and services is
due to differences in the ratios of labor and capital to gross output, highlighting the
role of capital requirements. There are some exceptions, sin embargo, with Japan being

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DECOMPOSING TOTAL FACTOR PRODUCTIVITY GROWTH 105

an interesting example. The decline in capital requirements in Japan was strong in
services, explaining all of the difference in TFP growth between manufacturing and
services; but the decline in labor requirements favored the manufacturing sector, de este modo
dampening the difference between TFP growth in services and manufacturing. A
similar outcome was found for non-Asian developing economies, while TFP growth
was higher in manufacturing in EU NMS. Declines in the ratio of value added
to gross output tended to be larger in the sector that performed relatively poorly,
which can also help explain differences in TFP growth between manufacturing and
services. There are exceptions, sin embargo, with the changes in value added to gross
output dampening the productivity advantage of manufacturing in the PRC and the
productivity advantage of services in Indonesia and Japan. While smaller, hay
also a significant effect from structural change (changing structure of intermediate
and final demand) for many economies, with changes in intermediate trade patterns
also being relevant for a number of economies, most notably India; Indonesia; y
Taipéi,Porcelana.

Considering the economies in which we observe a higher TFP growth rate in
services, there is no pattern that clearly stands out in terms of the factors driving the
services advantage. Among Asian economies, India stands out in terms of its high
contribution of the structure of intermediates to the services advantage, sugerencia
that structural change has been relatively important there. This term also plays a
relatively important role in the case of non-Asian developing economies. En el
cases of Taipei,China and Indonesia, the structure of intermediates also plays an
important role by dampening the differences in TFP growth between services and
manufacturing. In Indonesia, final demand trade is an important contributor to the
TFP growth advantage of services relative to manufacturing, with the structure of
intermediates and the structure of final demand and intermediate trade dampening
this advantage. Taipéi,China represents another interesting example, with relatively
strong declines in the ratio of capital to gross output in services and in the ratio of
value added to gross output in manufacturing explaining the TFP growth advantage
for services. The relatively strong decline in value added in gross output for
manufacturing in Taipei,China can be contrasted with the relatively strong decline
in value added to gross output for services in the PRC. In Japan, changes in all
factors other than the ratio of capital to gross output favor the manufacturing sector,
emphasizing the relatively strong decline in the ratio of capital to gross output in
services that enabled services TFP growth to be higher than manufacturing TFP
growth during the review period.

V. Conclusión

This paper examined differences in TFP growth among a sample of 40
economías, including six Asian economies, and further distinguished between TFP
growth in the manufacturing and service sectors. Over the period 1995–2009, asiático

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106 ASIAN DEVELOPMENT REVIEW

economies tended to perform relatively well in terms of TFP growth, parcialmente
reflecting a convergence in TFP levels. Consistent with existing evidence, TFP
growth in manufacturing tended to outpace that in services for most economies.
There are exceptions, sin embargo, particularly among Asian economies, sugerencia
en
that productivity growth in services need not always be lower than that
manufacturing.

To shed light on these productivity growth differentials across economies
and between manufacturing and services, this paper introduced a novel structural
decomposition of TFP growth into effects due to changes in factor requirements per
unit of gross output, changes in value added per unit of gross output, and changes in
the structure and composition of intermediate and final goods. The results suggest
eso, for most economies, declines in factor requirements—labor in particular—per
unit of gross output can explain a large proportion of TFP growth over the period
1995–2009. Además, declines in factor usage offset the negative contribution
to TFP growth of a declining ratio of value added to gross output. Changes in the
structure and composition of intermediate and final goods tended to contribute less
to TFP growth, though they remain important for some economies, particularly
changes in the structure of intermediate and final goods, which may partly reflect
the role of GVCs in changing sourcing patterns.

The relatively strong performance of services in Asian economies during
the review period does not appear to have a single explanation in terms of
our decomposition calculations, which show interesting differences among Asian
economías. While factor requirements, particularly capital requirements, por unidad
of gross output remain important for most economies, changes in the structure of
intermediates and in final demand composition are also important factors for some
economies in explaining the services advantage.

Our findings suggest that although factors such as trade, structural change,
and demand dynamics can play a significant role in some economies, they are not
the factors that have driven the rise of the service sector in Asia. Bastante, changing
labor requirements have driven productivity growth in services in Asia. De este modo, el
idea of services as a traditional sector in which (labor) productivity cannot grow at
high rates is subject to revision, particularly with regard to Asia.

References∗

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∗ADB recognizes “Hong Kong” as Hong Kong, Porcelana.

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DECOMPOSING TOTAL FACTOR PRODUCTIVITY GROWTH 107

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Dietzenbacher, Erik, and Bart Los. 1998. “Structural Decomposition Techniques: Sense and

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Molinero, Ronald, and Peter Blair. 2009. Input–Output Analysis: Foundations and Extensions (Segundo

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108 ASIAN DEVELOPMENT REVIEW

Apéndice

Cuadro A.1. List of Economies

Economy

Region

People’s Republic of China Asia
Taipéi,Porcelana
India
Indonesia
Japón
Republic of Korea

Code

PRC
TAP
IND
INO
JPN
KOR

AUT
BEL
DEN
FIN
FRA
GER
GRC
IRE
ITA
LUX
NET
POR
SPA
SWE
UKG

Austria
Bélgica
Dinamarca
Finland
Francia
Alemania
Greece
Irlanda
Italia
Luxembourg
Los países bajos
Portugal
España
Suecia
Reino Unido

Bulgaria
Cyprus
Czech Republic
Estonia
Hungary
Latvia
Lithuania

BGR
CYP
CZE
EST
HUN
LVA
LTU
MLT Malta
Poland
POL
Romania
ROU
Eslovaquia
SVK
Slovenia
SVN

Brasil
BRA
CAN
Canada
MEX Mexico
EE.UU

United States

EU15

EU12

Americas

Australia
Russian Federation
Pavo

AUS
RUS
TUR
EU = European Union.
Fuente: World Input–Output Database. www.wiod.org

Otro

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DECOMPOSING TOTAL FACTOR PRODUCTIVITY GROWTH 109

Tabla A.2. Industries and Industry Classification

Code

AtB
C

Industria

Agriculture, Hunting, Forestry, and Fishing
Mining and Quarrying

15t16
17t18
19
20
21t22
23
24
25
26
27t28
29
30t33
34t35
36t37 Manufacturing, not elsewhere classified; Recycling

Food, Beverages, and Tobacco
Textiles and Textile Products
Leather, Leather and Footwear
Wood and Products of Wood and Cork
Pulp, Paper, and Printing and Publishing
Coke, Refined Petroleum, and Nuclear Fuel
Chemicals and Chemical Products
Rubber and Plastics
Other Non-Metallic Mineral
Basic Metals and Fabricated Metal
Machinery, not elsewhere classified
Electrical and Optical Equipment
Transport Equipment

Sector

Primary

Manufacturing

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51

52

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60
61
62
63

Electricity, Gas, and Water Supply
Construction
Sale, Maintenance, and Repair of Motor Vehicles and Motorcycles; Retail

Services

Sale of Fuel

Wholesale Trade and Commission Trade, Except of Motor Vehicles and

Motorcycles

Retail Trade, Except of Motor Vehicles and Motorcycles; Repair of

Household Goods
Hotels and Restaurants
Inland Transport
Water Transport
Air Transport
Other Supporting and Auxiliary Transport Activities; Activities of Travel

Agencies

64
j
70
71t74
l
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Post and Telecommunications
Financial Intermediation
Real Estate Activities
Renting of Machinery and Equipment and Other Business Activities
Public Administration and Defense; Compulsory Social Security
Educación
Health and Social Work
Other Community, Social and Personal Services
Private Households with Employed Persons

Fuente: World Input–Output Database. www.wiod.org

/

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110 ASIAN DEVELOPMENT REVIEW

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