Increasing Returns to Scale in
Thailand’s Manufacturing
Industry
By Nat Tharnpanich
23/09/09
Thailand’s spatial inequality
Manufacturing establishments tend to be in a large scale
and concentrated in the same area (Arayah 2006).
Population growth rate in Bangkok metropolitan area is
three times as high as those in other mega-cities in
developed countries (World Bank 2009) and has
produced around one-half of the nation’s GDP
(Wisaweisuan 2009).
Evidence of catch-up behaviour is found in the Central
plain and the East with thriving industrial activities while
other regions are still stagnated (Wisaweisuan 2009).
Increasing Returns to Scale (IRS)
Static and internal or firm-level - reduction
in average cost
Dynamic - learning by doing as a function
of cumulative output
External - agglomeration economies which
explains why cities exist
“Backyard Capitalism” (Krugman, 1991)
Objectives of this presentation
To measure the degree of returns to scale
in Thailand’s manufacturing industry
To discuss the role of the degree of returns
to scale in economic growth
History of IRS
Adam Smith (1776)
– the Wealth of Nations
– Division of labour depends on the extent of
the market
Marshall (1920)
– Within-industry
Allyn Young (1928)
– Between-industry
Why were IRS ignored?
Lack of mathematical tools to model IRS
CRS and diminishing returns to factors of
production became increasingly popular in mid
19th century.
Perfect competition and convexity are needed to
prove the uniqueness of Walasian general
equilibrium
It has increasingly received more attention in
economics literature such as New Urban
Economics and New Economic Geography
Neoclassical growth model
Assumptions
– CRS in production
– Perfect competition
– Perfect factor substitutability, K/L is determined solely
by wage-rental ratio.
– Well-behaved aggregate production function
– Diminishing marginal productivity wrt capital and
labour
Growth accounting by Denison (1962) also relies
on these assumptions.
Supply-side model
Predicts convergence
Shortcomings of Neoclassical
growth model
Unsatisfactory empirical support
Ignoring contribution of demand factors
No explanation as to how supply-side
factors are different
Closed economy
Very sectorally neutral
IRS in neoclassical theory
Limited to the context of theory of cost
Firm-level
Static and internal
Production Function Approach
Cobb-Douglas or CES production function
Qt = AeλtLαKβ
qt = c + αlt + βkt
Usually CRS or small IRS are found with a
very good fit.
Weaknesses of the Production
Function Approach
When using time-series monetary data, it
merely captures the income identity
(Felipe et al. 2004).
Qt ≡ wtLt + rtKt
qt ≡ aφwt + (1-a)φrt + alt + (1-a)kt
qt = c + αlt + βkt
Thus, the α and β will always equal factor
shares which add up to unity which
indicates the existence of CRS.
Cumulative Causation Theory
Growth is demand-determined.
Factors of production are endogenous.
Capital accumulation (technique of production or
K/L ratio) is determined primarily by the scale of
production rather than by wage-rental ratio,
causing circular and cumulative growth process.
IRS as a lynchpin of cumulative growth process
Manufacturing as an engine of growth
Surplus labour
Export-led growth model
What determines
growth of demand in
the first place? It is
export demand.
qt = γ(xt)
pt = a + λqt
rdt = wt – pt - τt
xt = η (rdt - rft) + εzt
xt
qt
rdt
pt
Export-led growth model
-pd=f2(r)
This is an equilibrium
model.
r
Verdoorn’s law
r = f1(q)
  wt  rdt   t  p ft    zt 

q  
1  
*
t
Predicts sustained or
widening regional
disparities
q*
q
-pd
Export demand
x = f3(-pd)
Sustained growth
x
q = f4(x)
Verdoorn’s law
pt = a + λqt where λ ≈ 0.5
Measures IRS broadly defined
It is a source of regional disparities when
other parameters are the same between
regions.
Also serves to amplify the existing regional
disparities.
Data
Industrial Census
1997 and 2007 from
NSO
Cross-region within a
country
23,677 and 34,625
establishments
throughout the
kingdom with 10 and
11 persons or more
engaged respectively
Variable
Proxy
Gross output Value added
Labour
Total workers
and total
working hours
Capital
Book value
fixed assets
less value of
land
Verdoorn’s law specifications
Specifications
Condition Degree of returns to scale
et = a1 + b1qt
b1 < 1
1/b1
et = a2 + b2qt+
c2kt
b2 < 1
1/b2
tfit = a3 + b3qt
where tfit=αlt–(1α)kt
b3 < 1
1/b3
Outliers and leverage points are detected by
using DFITS statistics
Results: et = a1 + b1qt
Industry
Degree of returns to scale
Total manufacturing
b1
0.359**
Food and allied
0.154***
6.49
Textiles
0.297***
3.37
Apparel and related
0.297***
3.37
Paper and related
0.253***
3.95
Chemicals and allied
0.040***
25.00
Rubber and plastic
0.706***
1.42
Non-metallic mineral
0.249***
4.02
Metal
0.129***
7.75
Machinery and related
0.275***
3.64
Motor vehicles and allied
0.602***
1.66
Wood and furniture
0.219***
4.57
2.79
Results: et = a2 + b2qt+ c2kt
Industry
b2
c2
Degree of returns to scale
(1/b2)
Total manufacturing
0.377**
0.065
2.65
Food and allied
0.144***
0.261
6.94
Textiles
0.279***
0.191
3.58
Apparel and related
0.248*** 0.293***
Paper and related
4.03
0.057
0.303***
17.54
Chemicals and allied
0.041**
-0.003
24.39
Rubber and plastic
0.476***
0.039
2.10
Non-metallic mineral
0.259*** 0.096***
3.86
Metal
0.097**
0.046
10.31
Machinery and related
0.182***
0.300
5.49
Motor vehicles and allied
0.637***
0.005
1.57
Wood and furniture
0.235***
0.203**
4.26
Results: tfit = a3 + b3qt
Industry
Degree of returns to scale
Total manufacturing
b3
0.379***
Food and allied
0.102**
9.80
Textiles
0.517***
1.93
Apparel and related
0.256**
3.91
Paper and related
0.552***
1.81
Chemicals and allied
0.525***
1.90
Rubber and plastic
0.663***
1.51
Non-metallic mineral
0.383***
2.61
Metal
0.215***
4.65
Machinery and related
0.483***
2.07
Motor vehicles and allied
0.863***
1.16
Wood and furniture
0.478***
2.09
2.84
Augmented Verdoorn’s law
Proxy
Other determinants of productivity
growth
Technological diffusion from high- lnTFP0 where
to low-technology regions
TFP = Qj/LjαKj 1-α
Q 
Density (Ciccone and Hall 1996)

log 
j
H j


Specialisation (Marshall-ArrowRomer)
Diversity (Jacobs 1969)










Q ij Q j 
Q iT QT 
N
Q
Q
k i
j
kj
Q ij H j 
Q iT H T 
 Q kj 


 Qj 


2
N
k i
tfp = β0+β1qt + β2lnTFP0 + β3Density + β4Specialisation, β5Diversity)
Augmented Verdoorn’s law
Industry
qt
lnTFP0
DEN
SPEC
DIV
v ( 1/(1β1)
Total
0.369***
-0.674***
0.228***
na
na
1.59
Food
0.820***
-0.520***
0.172**
-0.015
0.923
5.56
Textiles
0.691***
-4.310**
1.592**
-0.129
-16.644
3.24
Apparel
0.638***
-5.703***
0.981***
-0.009
-1.273
2.76
Paper
0.360***
-1.124***
0.169
-0.0002
-0.292
1.56
Chemicals
0.415***
1.166***
-0.120
0.014
-7.991
1.71
Rubber and
plastic
0.183***
-0.426*
0.058
-0.016
-1.054
1.22
Non-metalic
mineral
0.567***
-0.345**
-0.130
0.008
-0.173
2.31
Metal
0.762***
-0.559**
0.024
0002
-1.015
4.20
Machinery
0.632***
-2.141***
0.380
-0.027
0.374
2.72
Motor vehicles
0.043
-0.974***
0.254***
0.004
3.013
1.04
Wood
0.405***
-1.481***
0.925***
-0.086
3.988
1.68
Verdoorn’s law controversies
Simultaneous equation bias
Static-dynamic Verdoorn law paradox
Simultaneous equation bias
Supply-side specification
qt = a4+b4tfit or tfpt =
a5+b5tfit
b4 > 1 or b5 > 0
CRS or DRS are usually
found
The use of cross-regional
data makes it possible to
assume away the supplyside constraint.
Output
growth
Productivity
growth
Results: tfpt = a4+b4tfit
Industry
tfi
Degree of returns to scale
Total manufacturing
-0.627***
0.373
DRS
Food and allied
-0.667***
0.333
DRS
Textiles
-0.681***
0.319
DRS
Apparel and related
-0.552***
0.448
DRS
-0.144
0.856
CRS
-0.623***
0.377
DRS
Rubber and plastic
-0.038
0.962
CRS
Non-metallic mineral
0.074
1.074
CRS
1.207***
2.207
IRS
Machinery and related
-0.01
0.99
CRS
Motor vehicles and allied
-0.061
0.939
CRS
Wood and furniture
-0.209
0.791
CRS
Paper and related
Chemicals and allied
Metal
The static-dynamic Verdoorn law
paradox
Dynamic version (tfi = a + bq)  strong
IRS are found
Static or log-level version (lnTFI = a +
blnQ)  CRS or small IRS are found
The biased result is caused by Spatial
Aggregation Bias (McCombie and
Roberts, 2007)
Spatial Aggregation Bias
The ideal spatial production unit is
Functional Economic Area (FEA)
At FEA-level, Verdoorn’s law is a power
0.5
relationship. TFI = cQ .
However, values of each FEA are summed
arithmetically.
Spatial Aggregation Bias, TFI = cQ
0.5
Region
No.of
FEAs
TFIj
Qj
lnTFIj
lnQj
1
1
10
100
ln10
2ln10
2
2
20
200
ln2+ln10
ln2+2ln10
3
3
30
300
ln3+ln10
ln3+2ln10
.
.
.
.
.
.
.
.
.
.
TFI0*nj
Q0*nj
lnj+ln10
lnj+2ln10
j
nj(=j)
6
Cross-sectional static VL
5
t=0
4
2
3
lnTFI
t=1
5
6
7
8
lnQ
Cross sectional lnTFIi = α+βlnQi + εi
Cross sectional with time dummy lnTFIi = α+βlnQi +ψDt+ εi
Time fixed effects lnTFIit = αt+ βlnQit + εit
All yield biased Verdoorn relationship
6
Time series static VL
R=1
t=1
4
R=2
2
3
lnTFI
5
t=0
4
6
8
10
12
lnQ
Time-series  lnTFIt = α + βlnQt + εt
Regional fixed effects  lnTFIit = αi+ βlnQit + εit
Both yield unbiased Verdoorn relationship. However, it
merely captures Okun’s law. Nothing is related to IRS.
Data should be cross-regional in dynamic version VL.
Fixed effects static VL
Industry
Time
Region
Two way
lnQ
v
lnQ
v
lnQ
v
Total
0.170***
1.20
0.723***
3.61
0.749***
3.98
Food
0.284***
1.40
0.828***
5.81
0.815***
5.41
Textiles
0.161***
1.19
0.370***
1.59
0.380***
1.61
Apparel
0.148***
1.17
0.401***
1.67
0.508***
2.03
Paper
0.165***
1.20
0.378***
1.61
0.390***
1.64
Chemicals
0.200***
1.25
0.540***
2.17
0.546***
2.20
Rubber and
plastic
0.169***
1.20
0.412***
1.70
0.410***
1.69
Non-metalic
mineral
0.202***
1.25
0.473***
1.90
0.502***
2.01
Metal
0.121***
1.14
0.517***
2.07
0.518***
2.07
Machinery
0.137***
1.16
0.529***
2.12
0.571***
2.33
Motor vehicles
0.177***
1.22
0.271***
1.37
0.270***
1.37
Wood
0.178***
1.22
0.440***
1.79
0.470***
1.89
Conclusion
The existence of IRS, broadly defined, is
indisputable.
With IRS, virtuous circle of growth is possible,
causing regional disparities.
Growth is supply- as well as demanddetermined.
Policies that can effectively manage both
demand- and supply-side factors are needed to
eliminate regional disparities.
Suggested Further Reading
Thirlwall, A. P. (2002). The Nature of Economic Growth: An
Alternative Framework for Understanding the Performance of
Nations. Cheltenham : Edwards Elgar.
McCombie, J. S. L., Pugno, M., and Soro, B. (2002). Productivity
Growth and Economic Performance : Essays on Verdoorn’s Law.
Basingstroke, Palgrave Macmillan.
McCombie, J. S. L. and Thirlwall, A. P. (1994). Economic growth and
Balance-of-Payments Constraint. Basingstroke, Macmillan.