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Structural Change, Globalization and Economic Growth in China and India

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In their period of rapid economic growth China and India have experienced profound structural transformations. The aim of the paper is to analyze the relation between structural change, the process of globalization and economic growth in the two great Asian countries, using a highly disaggregated dataset for the 1987-2009 period. While China had a longer and more intensive productivity growth than India, the latter had a somewhat more balanced growth. Both countries registered higher within-sectors gains in productivity than between-sectors ones. Our analysis also shows that there exist important feedbacks between structural change, globalization and economic growth over time. When the reallocation of labor is large, it may positively impact on the future rates of economic growth. At the same time, however, it seems that a too rapid economic growth may hinder a smooth reallocation of labor. In both countries, new policies should be designed to favor labor movement across sectors and areas, to reduce the wage-productivity differentials and to integrate the informal sector in formal markets in India, in order to foster structural changes and enhance economic growth. If a too unbalanced economic growth has somewhat limited the extent of structural change, globalization has on the contrary promoted it. High level of export, import and FDI not only has been related to higher rates of economic growth, but also to a deeper reallocation of resources across sectors, modifying the comparative advantage and reorganizing the production.
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The European Journal of Comparative Economics
Vol. 12, n. 2, pp. 133-163
ISSN 1722-4667
Available online at http://eaces.liuc.it
Structural Change, Globalization and Economic
Growth in China and India
Vittorio Valli, Donatella Saccone
1
2
Abstract
In their period of rapid economic growth China and India have experienced profound structural
transformations. The aim of the paper is to analyze the relation between structural change, the process
of globalization and economic growth in the two great Asian countries, using a highly disaggregated
dataset for the 1987-2009 period. While China had a longer and more intensive productivity growth
than India, the latter had a somewhat more balanced growth. Both countries registered higher within-
sectors gains in productivity than between-sectors ones. Our analysis also shows that there exist
important feedbacks between structural change, globalization and economic growth over time. When
the reallocation of labor is large, it may positively impact on the future rates of economic growth. At
the same time, however, it seems that a too rapid economic growth may hinder a smooth reallocation
of labor. In both countries, new policies should be designed to favor labor movement across sectors
and areas, to reduce the wage-productivity differentials and to integrate the informal sector in formal
markets in India, in order to foster structural changes and enhance economic growth. If a too
unbalanced economic growth has somewhat limited the extent of structural change, globalization has
on the contrary promoted it. High level of export, import and FDI not only has been related to higher
rates of economic growth, but also to a deeper reallocation of resources across sectors, modifying the
comparative advantage and reorganizing the production.
JEL codes: O11, O53, O57, P51
Key words: Structural change, globalization, economic growth, China’s economy, India’s
economy.
1. Introduction
The aim of this essay is to analyze, in a comparative perspective, the
relation between structural change, the process of globalization and economic
growth in two great emerging economies, China and India, in their period of
rapid development.
The studies on structural change and the patterns of economic
development were introduced in Japan by Akamatsu (1935 and 1962) in an
original way, the "wild-geese-flying approach" then generalized by Kiyoshi
Kojima (2000) and Ozawa (2001 and 2010). The structural view was also
independently furthered in a different way by other great authors such as Colin
Clark (1940), who inaugurated the three-sectors approach, Simon Kuznets (1957)
and Alexander Gerschenkron (1962).
1
Paragraphs 1-5 are mainly due to Vittorio Valli, paragraphs 6-8 to Donatella Saccone. A preliminary
version of the paper was presented in the first workshop of OEET (Turin Center on Emerging
Economies) held in Turin, on 12-13 March 2015.
2
Department of Economics and Statistics “Cognetti de Martiis”, University of Torino, and OEET-Turin
Centre on Emerging Economies
Contact information: vittorio.valli@unito.it, donatella.saccone@unito.it
EJCE, vol.12, n.2 (2015)
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Between the 1960s and the late 1980s the main contributions were due to
Chenery (1960), Chenery and Taylor (1968 ), Taylor (1969), Keesing and Sherk
(1971), Chenery and Syrquin (1975), Chenery et al. (1979), Kader (1985),
Chenery, Robinson and Syrquin (1986), Syrquin (1988), Chenery and Syrquin
(1989).
Recent contributions have mainly focused on cross-country analyses, more
disaggregated approaches and country studies. For example, Haraguchi and
Rezonja (UNIDO, 2010), De Vries et al. (2012), McMillan and Rodrik (2011), Lin
(2011), Lin and Rosenblutt (2012) have carried out important cross-country
analyses; Li, Menginstae et al. (2011) have focused on China and India; Kochhar
et al. (2006) on India; Wang et al. (2007) on China. However, most of the cross-
country studies have jointly studied market or mixed economies, often
overlooking the particular structural features of Communist central planned
economies like China up to 1978 as well as of heavily regulated mixed economies
like India up to 1992. These features have greatly influenced the period of
transition and rapid growth in the two great Asian economies. Countries in
profound transition between different systemic mechanisms of regulation and
control in the economy may have very different structural transformations with
respect to countries that in the same period have fully maintained their systemic
characteristics.
Moreover, few contributions have tried to analyze the relations between
structural change, globalization and economic growth. The initial conditions, the
pace of economic growth and the way in which a country has entered the
globalization process are fundamental factors in order to understand the different
economic structures of China and India and their changes over time.
The paper is structured as follows. Paragraph 2 introduces the initial
structural conditions in China and India, while paragraphs 3 and 4 trace the path
of economic development and the process of globalization in the two countries.
Social problems related to structural change and globalization are briefly
discussed in paragraph 5. To better typify structural changes, a shift-share
decomposition analysis on aggregate labor productivity is presented in paragraph
6, followed by an econometric investigation on the relation between economic
growth, structural change and globalization in paragraph 7. Paragraph 8
concludes.
2. China and India: the initial structural conditions
As table 1 shows, some important structural differences already existed
between China's and India's economies in 1978, when China’s radical economic
reforms began.
In 1978, the percentage share of agriculture on total employment was about
the same in the two countries, while the percentage on value added was much
higher in India. Although in 1978 India had a level of per capita GDP in PPPs
V. Valli, D. Saccone, Structural Change, Globalization and Economic Growth in China and India
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135
somewhat higher than China, the share of industry was already higher in China
than in India, in terms both of value added and employment, while the share of
services was much lower in China than in India. This is largely a consequence of
a systemic difference, namely the fact that China was a Communist centrally
planned economy, where services were usually overlooked and heavy industry
was strongly privileged over light industry. Moreover, China’s industry was
essentially constituted by large state companies with an average productivity
higher than that of India’s industry, sharply divided between a relatively small
number of large state or private companies and many firms of the informal
economy exhibiting a very low productivity.
Interestingly, as Kochhar et al. (2006) pointed out, also in India in 1980,
controlling for the level of development and the size of the economy, the
percentage of services was somewhat lower than in the other developing and
emerging countries, though larger than in China, while manufacturing industry’s
share was a little higher than in the other developing countries, though much
lower than in China. However, in a few years China surpassed India in terms of
per capita GDP and increased its industrial and service sectors much faster than
India.
Table 1. Percentage sectorial shares in China and India in 1978
Employment Value added
Sectors China India China India
Agriculture, forestry, animal husbandry, fishing 71 71 28 44
Industry, mining, quarrying, construction 17 13 48 24
Services 12 16 24 32
Total economy 100 100 100 100
Sources: National Bureau of Statistics of China (2008) for China. For India, see Bosworth and Collins (2008), p. 49.
It is also important to notice that in 1978 China’s percentage of value added
in agriculture on total value added was much lower than the percentage of
employment on total employment. Moreover, the percentage in agriculture’s
value added was much lower than in India, while the percentage in industry was
already double than in India. This implies that the ratio between industrial
productivity and agricultural productivity was particularly high in China, and this
was largely dependent on systemic differences and on the strategic choices of
China’s planners and India's policy makers.
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3. Economic development and structural change in China and India: an
overview
By comparing the trends of some of the most important macroeconomic
indicators, it is possible to see that economic development was very different in
terms both of the pace of economic growth and its duration in the two great
Asian countries. Table 2 and 3 show that real GDP, real labor productivity,
exports in volume and, above all, real investment have grown more rapidly in
China than in India, while employment has grown faster in India than in China.
The phase of rapid growth began in 1978 in China and in the second half of the
1980s or in 1992 in India
3
, and the average rate of growth was considerably
higher in China than in India especially in the 1978-92 years.
Table 2. China and India: some macroeconomic indicators.
China India
Indicators 1978
1992 2012 1978
1992 2012
Total GDP in billions 2012 EKS
US $ 869.7
2310.1
14012.9
751.4
1.409.4
5.374.7
Per capita GDP in 2012 EKS
US $ 831 1983 10371 1160
1622 4431
Labor productivity per employed
person in 2012 EKS US $ 1872
3510 18325 3306
4324 11048
Total employment (millions) 464.5
658.2 764.6 227.3
325.9 486.5
Gross capital formation (a) 71.6 192.2 1910.4 39.1 84.0 452.5
Exports index in volumes (b) 100.0
474.0 9482.0 100.0
187.6 1468.6
(a) In billions US dollars at 2005 constant prices and 2005 constant exchange rates, 2011 instead of 2012.
(b) 1980 instead of 1978, volume index of merchandise exports 1980=100.
Sources: for the first four rows, Conference Board (2013), Total Dataset; for rows 5 and 6, UNCTAD (2014).
3
An acceleration of economic growth occurred in India in the second half of the 1980s and was
strengthened after the economic reforms of 1992.
V. Valli, D. Saccone, Structural Change, Globalization and Economic Growth in China and India
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137
Table 3: China and India: annual average rates of change (1978-2012)
China India
1978-1992
1992-2012 1978-1992 1992-2012
Real total GDP in EKS 7.2 9.4 4.6 7.0
Real per capita GDP in EKS 6.4 8.7 2.4 5.2
Real labor productivity in EKS
4.7 8.6 2.0 4.9
Total employment 2.5 0.8 2.6 2.1
Real gross capital formation (a)
7.4 12.8 6.5 9.3
Exports in volumes (b) 13.7 18.1 5.4 10.8
In billions US dollars at 2005 constant prices and 2005 constant exchange rates, 2011 instead of 2012.
1980 instead of 1978, volume index of merchandise exports 1980=100.
Sources: for the first four rows, Conference Board (2013), Total Dataset; for rows 5 and 6, UNCTAD (2014).
In the second period (1992-2012), although China continued to have a
higher rate of growth than India, there was a marked acceleration of economic
growth in both the economies. China and India rapidly increased their exports,
capital accumulation and attraction of FDI. However, after the global financial
crisis begun in the US in 2007-8, in both the countries there was a consistent
reduction in the rate of growth.
If we concentrate the analysis on the structural changes occurred in the two
countries in the period covered by our disaggregated dataset (1987-2009), we can
see that China reduced the percentage of agriculture both in employment and in
valued added, and increased the absolute and relative size of its industrial sector
much more than India. The exceptionally rapid rise in investment, value added
and productivity in China has mainly regarded the industrial sector, while
agriculture and services have contributed less. However, the share of services in
employment constantly grew also in China surpassing in the 1990s the share of
industry (see Table 4).
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Table 4: Employment and value added by sector in China and India (1987-2009)
China India
Employment (%) 1987 1992 2004 2009 1987 1992 2004 2009
Agriculture 58 58 47 38 65 63 56 54
Industry 23 21 22 28 16 16 19 20
Services 19 20 31 34 19 21 25 26
Total economy 100 100 100 100 100 100 100 100
China India
Value added (%) 1987 1992 2004 2009 1987 1992 2004 2009
Agriculture 30 27 13 9 30 29 19 14
Industry 36 38 52 53 27 27 27 26
Services 34 35 35 38 43 44 54 60
Total economy 100 100 100 100 100 100 100 100
Sources: See our database for 1987-2009 (paragraph 6).
Indeed, if compared to India (but also to other developing and emerging
economies), China’s process of industrialization has been much more rapid and
extensive, while the service sector, starting from a very low level, has grown
substantially. However, it has remained less extensive than in India. India has
reached, and then surpassed, the average percentage level of the tertiary sector of
other several developing and emerging countries, improving in particular the
specialization in the production and export of software and other ICT services.
If we consider the internal composition of industry and services in the two
countries, we discover other important differences, that we will discuss in detail
in paragraph 6. Here we can anticipate that China has progressively built an
industry much larger and stronger than India especially for office machines and
ITC equipment, but also for steel, textile, automobiles and clothes, while India
has reached a good position in the pharmaceutical and steel industries and in
software services.
Some of the main determinants of the different patterns of development in
the two countries may be so summarized:
A) In 1978 China had already a larger industrial base than India,
although China had then a lower per capita GDP.
B) Since 1978 China has introduced radical economic reforms that have
strongly favored industrialization much earlier than India (about
14 years in advance).
V. Valli, D. Saccone, Structural Change, Globalization and Economic Growth in China and India
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139
C) China’s rate of saving and investment has been much larger than in
India, and investment went mainly to manufacturing industry,
constructions and a part of the services sector.
D) China favored industrialization more than India maintaining
relatively low prices for agricultural goods and for some basic
inputs provided by state corporations.
E) China had an extensive use of the fordist- toyotist model of growth
4
,
while India limited it almost exclusively to the formal sector,
which employs only about one tenth of the total labor force.
F) China opened its economy to external trade and foreign investment
earlier and much more extensively than India, as we will see in
next paragraph.
G) In the 1978-2012 period in China there was a vast increase in income
and wealth inequalities, while absolute poverty diminished. In
India the rise in inequalities was less severe, but there remained a
large level of absolute and relative poverty.
H) In China the extraordinarily rapid process of industrialization and
urbanization and the absence of adequate environmental policies
led to a great rise of pollution. In India the rise of pollution was
substantial, but lower than in China.
As regards India, it is interesting to quote a passage from Kochhar et al.
(2006) “[…] we argue that the nature of the policies India followed after
independence in 1947 created unique specializations prior to the economic
reforms that started in the 1980s. Relative to other comparable poor countries,
India’s emphasis on tertiary education, combined with a variety of policy
distortions, may have channeled the manufacturing sector into more skill-
intensive industries. Furthermore, the government’s desire to create capital goods
production capability, especially through public-sector involvement, implied that
India had a greater presence in industries that required scale (and capital) than
other developing countries. Regulatory penalties and constraints on large private
enterprise implied, however, that within most industries, the average scale of
enterprise was relatively small. Finally, rigid labor laws as well as constraints on
the scale of private enterprises may well have limited India’s presence in labor-
intensive manufacture, the usual specialization in a populous developing
country.”
4. The globalization process
The period of very rapid economic growth in China (from 1978 up to now)
and in India (from the late 1980s or 1992 up to now) fully occurred during the
4
See, for a more detailed analysis, Valli and Saccone (2009), pp. 102-105; Valli (2015).
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second wave of economic globalization. This wave started at the beginning of the
1970s and was greatly extended and strengthened in the 1990s and 2000s, after
the collapse of the Soviet empire and the growing trade and FDI liberalization in
China and India. Since the 1990s there was a sort of feedback between rapid
economic growth and the progressive insertion of China and India in the
globalization process
5
.
The main steps of the globalization process in both countries are
summarized in tables 5 and 6.
Table 5. China and India: some globalization indicators.
China India
Years 1990
2000
2010
2012
1990
2000
2010
2012
Degree of openness (%) (a) 12.8 22.2 27.5 26.4
8.0 14.2 23.5 26.8
Inward FDI stock as % of
GDP (b) 5.1 16.2 9.9 10.3
0.5 3.5 12.3 12.2
Outward FDI stock as % of
GDP (b) 1.1 2.3 5.3 6.3 0.1 0.4 5.8 6.4
Current account balance, % of
GDP (b) 3.0 1.7 4.0 2.4 - 2.2
- 1.0
- 3.1
-4.9
Simple average tariff rates
(manufactured goods, ores and
metals) (b), (c)
42.5 15.9 9.0 8.9 81.3 31.4 9.0 n.a.
Merchandise exports (in % of
world exports) (b) 1.8 3.9 10.3 11.1
0.5 0.7 1.5 1.6
(a) (Exports + Imports of goods and services) / 2 in % of GDP at current prices and current rates of exchange
(source: UNCTAD, 2014).
(b) Source: UNCTAD (2014).
(c) For China: 1992 instead of 1990 and 2011 instead of 2012; for India: 2009 instead of 2010.
In the 1980s, 1990s and 2000s China rapidly transformed its economy from
a closed and heavily protected to an open and interconnected economy. Its
degree of openness went up from 12.8 % in 1990 to 26.4 % in 2012 (see table 5),
while its share in world merchandise exports rapidly rose from 1.8 % in 1990 to
11.1 % in 2012, almost seven times India’s level.
5
On the globalization process and the two great Asian countries see, for example, Srinivasan (2006);
Winters and Yusuf (2007); Bensidoun, Lemoine and Unal (2009), and Marelli and Signorelli (2011).
Beretta and Targetti Lenti (2012) have in particular analysed the trade relations between China and India
in the globalization period.
V. Valli, D. Saccone, Structural Change, Globalization and Economic Growth in China and India
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141
Table 6: Main steps towards globalization in China and India.
China India
Major economic reforms since 1978:
A) Responsability system in agriculture.
B) Institution of SEZs (Special Economic Zones):
Shenzhen, chosen in 1978, officially declared SEZ
in 1980. Other SEZs introduced, at first mainly on
the coast, then also in some internal zones. They
have attracted many FDI in the form of joint
ventures with Chinese firms, mainly aimed at
increasing exports.
C) Institution and expansion of TVEs (township and
village enterprises).
1980s: First timid economic
reforms, especially in the second
half of the decade, but maintenance
of high protectionism, import
substitution policies and heavy
internal economic regulations.
D) 1990s: Gradual recognition of private ownership
and expansion of private enterprises and joint
ventures with foreign corporations.
Increasing liberalization and rapid expansion of
foreign trade and FDI inflows.
1991-92: Major economic reforms:
progressive internal and external
liberalization and sharp reduction of
economic regulations.
1995: Entry in WTO
E) 2001: Entry in WTO. Strong expansion in external
trade, accumulation of huge surpluses in the balance
of current accounts and of large international
reserves. Some problems for the expansion of
exports after the great US-EU 2008-2013 financial
crisis.
Maintenance of capital movements controls which
contributed to lessen the adverse effects of 2008-
2013 financial crisis, but some limited steps towards
a reduction of capital controls, such as QFII =
qualified foreign institutional investment and QDII
= qualified domestic institutional investment, in
2002 and 2006.
2000s: Rapid rise in FDI inflows
and then of FDI outflows.
Maintenance of strategic forms of
international capital movements
controls, which contributed to
lessen the adverse effects of 2008-
2013 financial crisis.
Thanks to SEZs (Special Economic Zones) and a gradual FDI
liberalization, China attracted a massive inflow of FDI, usually consisting in joint
ventures of foreign multinationals with Chinese firms. In this way, China could
briskly increase its capital accumulation, its exports and its technical knowledge.
In the 2000s China also began to rapidly increase its outward FDI
6
.
Up to 1991, India had severely constrained both international trade and
FDI inflows. It gradually opened FDI inflows in the second half of the 1990s and
especially in the 2000s, also creating its own SEZs. In recent years, India has
registered a rapid rise in inward and outward FDI. As regards trade, India has
lagged about a decade after China in the opening process, but has increased very
6
On the theoretical determinants of this trend see, for example, Andreff and Balcet (2013).
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rapidly its openness degree in the 2000s. However, India has exhibited a lower
international competitiveness than China in several industrial sectors. This was
partly due to higher unit labor cost in the formal sector of India’ economy, but
also to the less extensive and less diversified industrial base, the small size, poor
productivity and low propensity to export of a large number of enterprises in the
informal sector, the lower exploitation of scale economies, and the inadequate
system of infrastructures. This resulted in less export opportunities in
manufacturing than in China, although for some sectors, such as refined
petroleum, jewels, pharmaceutical products, cars, India’s exports are very large,
while for software and other ICT services India has become the second largest
international net exporter. However, in the field of raw materials, the imports of
oil, coal and gold are huge, while India mainly exports raw cotton and iron ore.
In sum, while China has maintained a structurally positive balance of current
accounts since 1994, India has often registered deficits. However, both countries
preserved different degrees of control on capital movements and thus suffered
less than Western countries from the impact of the 2008-2013 great financial
crisis.
5. Structural change, globalization and social problems
The timing and the particular way in which China and India structurally
transformed their economy and entered into the globalization process had
important consequences on various social problems.
First of all there was the rise in economic inequalities. The rise was dramatic
in China both between households and regions. It was more limited in India
between households, but substantial between regions.
China gradually passed from very low levels of economic inequalities
between household in 1978 to high levels in 2012, superior to the United States
and to most European countries. The period of rapid growth and the expansion
of industrial and services activities and of exports favored in particular the urban-
industrial areas on the coast, the ZES and the rural villages near them, which
could easily sell a large part of their agricultural goods on rich urban markets.
These rural villages could invest in industrial and tertiary activities part of their
agricultural profits building new TVEs (Township and Village Enterprises) and
thus creating more wealth for their citizens. On the contrary poor rural villages in
the internal part of the country had much less growth opportunities. So structural
change and export possibilities contributed to increase inequalities between both
households and provinces.
In India the great gap between rural and urban incomes and between
workers in the formal and informal sector increased when there was, especially
since 1992, an acceleration in the expansion of exports, of modern services and
the formal sector of industry. This contributed to enlarge regional income
inequalities and wages disparities between workers in the formal and the informal
sectors.
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143
A second deep social problem was poverty. Notwithstanding the phase of
rapid growth, in India poverty remained important, especially in rural areas and in
urban slums. In China absolute poverty decreased considerably over time, but it
persisted significantly especially in some internal rural areas. Moreover, the large
mass of internal immigrants without hokou (the permit to move from rural to
urban areas) had in general worse job opportunities, lower wages and scarce
welfare benefits if compared with the citizens of the towns where they migrated.
Rapid structural change had led to more migration opportunities and very rapid
urbanization, but also to a steep rise in the price of housing in the big cities,
forcing internal immigrants and the poorest part of the population to move to
cheaper, but more desolated, suburban areas.
A third important social problem was the environment. In China rapid
growth, industrialization and urbanization led to a huge rise of pollution and of
consumption of the soil as well as a large destruction of precious old buildings
and beautiful traditional landscapes. In the 1990s and 2000s there was the
enforcement of some anti-pollution policies and in 1998 the institution of SEPA
(State Environmental Protection Agency) and in 2008 of a Ministry for
environmental protection, but the results were meager. The policies were, in fact,
utterly inadequate to cope with the effects of the dramatic rise in energy
consumption and electricity, mainly provided with heavily polluting coal, of the
massive rise in automobiles and trucks circulation, of urban expansion and
congestion, etc
7
.
In India the growth of pollution was slower than in China partly because of
the less rapid growth and industrialization process. The environmental policies
were a little more effective than in China, and in several cases they were spurred
by the judiciary system and by local authorities. However, also in India the level
of air and water pollution has considerably increased over time, contributing,
together with the modest hygienic conditions and poor wastes disposal treatment,
to several health problems.
Finally, the two Asian countries had experienced several socio-political
problems associated to the high level of corruption, the deficit of democracy in
the Chinese political system and the great ethnic, religious, and caste divisions in
India.
6. A disaggregated analysis on labor productivity
A more disaggregated analysis on the economic effects of structural change
is based on decomposing the changes in aggregate labor productivity for China
and India. Our database consists in time series data, from 1987 to 2009, on the
value added at 1995 constant price, employment and productivity at a detailed 33
7
For a comparison between China’s and India’s environmental policies see. for example, Garrone, Tecco
and Vecchione (2012), pp. 215-264.
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sector level for China and 31 for India (see the list of sectors in Appendix 1). In
order to construct our database and obtain consistent time series from 1987 to
2009, we matched two different sources of data both elaborated as projects of
the Groningen Growth and Development Centre (GGDC).
The first is the BRICs sector database introduced by de Vries et al. (2012),
that provides a harmonized annual time series on the variables of our interest -
value added, price deflators and employment by 35 sectors- for India and China
(along with Brazil and Russia). The BRICs database covers the period 1987-2008
for China and 1981-2008 for India. The second database is the Social Economic
Account (SEA) as a part of the World Input-Output Database project (WIOD)
covering 40 countries from 1995 to 2009 (Timmer, 2012), including China and
India. The SEA offers a wide set of variables, among which value added, price
deflators and employment by 35 sectors. Both the databases use 1995 as the base
year to deflate the value added, so that 1995 can be used as the joining link
between the two sources of data. However, if we compare employment and value
added data in 1995, we notice some inconsistencies between the absolute values
of the two databases, due to the fact that the SEA uses updated data, while the
BRICs sector database is based on early data. Because the SEA represents the last
vintage of data, we selected it as the data source of reference
8
.
Since we wanted to cover a time span as large as possible and equal for
China and India, we exploited the fact that from the BRICs database we can
obtain a series starting from 1987 (while the SEA data start from 1995). To solve
the problem, we matched the two databases by applying the following
methodology. In order to cover the period 1987-2009, we calculated the growth
rates of value added at 1995 constant price and of employment from the BRICs
database for the period 1987-1995 and, then, we applied backward the resulting
rates of growth to the 1995 SEA data to get consistent absolute values. In this
way, data from 1987 to 1994 are estimates that use 1995 as the joining link
between the two databases to match them, while data from 1995 to 2009 are the
original data provided by the SEA database.
In this way, we constructed a new database providing the value added at
1995 constant price, employment and productivity per employed person for the
period 1987-2009. For China, we have data for 33 sectors, while for India for 31
sectors (see Appendix 1 for further explanations).
The database is then used to analyze the changes in the aggregate
productivity level. Indeed, this latter can originate from both changes in
productivity within each sector and the movement of labor across sectors
presenting different levels of productivity. To take into account these two
8
We want to thank Gaaitzen de Vries for his support in selecting the most reliable source of data and for
his precious suggestions on how to match the two databases.
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145
different effects, the first called ‘within effect’ and the second called ‘reallocation
effect’, we use the methodology originally proposed by Fabricant (1942) and
adopted in recent studies on structural change (see de Vries et al., 2012; McMillan
and Rodrik, 2011).
At first, we just consider the three main economic sectors, i.e. agriculture,
industry and services. The change in the aggregate productivity level can be
written as a sum of the two effects:
i
iii
ii
YYY
+=
ϑϑ
[1]
where ∆Y is the aggregate productivity change between two periods of
time,
i
Y
and
i
ϑ
respectively represent the productivity change and the change
of the employment share in sector i, while
i
Y
and
i
ϑ
the average productivity and
the average employment share of sector i in the two periods of time. The first
addend represents the ‘within effect’ and the second addend the ‘reallocation
effect’. The ‘reallocation effect’ can also be considered as a residual given by the
difference between the aggregate productivity change and the ‘within effect’ (de
Vries et al., 2012), and it can be seen as an index of structural change.
However, as suggested by de Vries et al. (2012), we could lose important
information on structural change if we limit the analysis to the disaggregation by
the three main economic sectors. Then, we carry out the decomposition of the
aggregate productivity changes by considering both the changes across the three
main economic sectors i and the changes across the subsectors j within each of
the three sector i. Specifically, we consider 33 subsectors for China and 31 for
India. By adopting the methodology suggested by de Vries et al. (2012), we write
the change in aggregate productivity between two periods of time as follows:
∑ ∑
++=
ji
iijj
RRYY
ϑϑ
[2]
The first addend represents the ‘within effect’, i.e. the sum of the
productivity changes in each subsector j (
j
Y
), weighted by its average share on
overall employment (
j
ϑ
). The second addend in brackets, representing the total
‘reallocation effect’, is composed by two parts. The first part is the sum of the
reallocation effect within each sector i (R
i
), weighted by its average employment
share (
i
ϑ
). We will call it reallocation effect 1. The second part, R, is given by the
reallocation effect calculated for the 3-sector equation:
i
ii
Y
ϑ
(see equation 1).
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146
It represents the ‘reallocation effect’ across the sectors i. We will call it
reallocation effect 2. The above equation can then be re-written as:
∑ ∑
++=
ji
iii
iijj
YRYY
ϑϑϑ
[3]
In sum, using this method, the overall reallocation effect is given by both
the movement of labor across sectors i and the movement of labor across
subsectors j within each sector i.
If we divide equations 1 and 3 by Y, we can obtain the contribution of both
the within effect and the reallocation effects to the productivity growth. The
productivity growth rate is then decomposed by three factors: the changes in
weighted productivity within subsectors j, the movement of workers across the
three main sectors (agriculture, industry, services), and the movement of workers
across the subsectors within each of the three sectors .
6.1 China
The decomposition results for China are reported in table 7. Apart from a
slowdown from 1988 to 1990, the productivity per employed person always
increased at high rates over the analyzed period and, especially, from 1991 to
1995 and from 2004 to 2007, with growth rates over the 10%. If we look at the
growth decomposition, we can notice that the within effect was always greater
than the total reallocation effect (reallocation effect 1 plus reallocation effect 2).
However, if we disjointedly look at the two components of the total reallocation
effect, it emerges that the reallocation effect 2, i.e. the reallocation of workers
across the three sectors (agriculture, industry and services), played an important
role in determining the total productivity growth, as common for countries
starting from the early stages of development. In particular, we can divide the
path of structural change in four sub-periods.
The first sub-period, 1987-1991, was characterized by negative or relatively
low growth rates of total productivity, determined by a low weighted productivity
growth within subsectors and a misdirected reallocation of workers across
sectors, partially counterbalanced by a movement of workers, within each sector,
from subsectors with lower to higher productivity. Indeed, from 1988 to 1990,
there was a temporary increase in the employment share of agriculture. In the
second sub-period, 1991-1997, total productivity grew at rates sometimes over
the 12%, supported by productivity gains within subsectors and a suitable
reallocation of labor across the three main sectors, notwithstanding a
misallocation of workers across subsectors. In particular, in this period there was
a movement of workers from agriculture to industry and, especially, services.
From 1991 to 1997, the employment share moved from 58.9% to 49.9% in
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147
agriculture, from 21.6% to 23.7% in industry, and from 19.5% to 26.4% in
services. In 1994, for the first time after the economic reforms, the employment
share of services exceeded the employment share of industry. As can be noticed
from table 8, the reallocation of workers towards industry and, above all, the
tertiary sectors was an important factor behind the total productivity growth,
even if a too high share of workers remained employed in agriculture.
Table 7: Productivity growth decomposition – China.
Years Productivity
growth Within % Reallocation 1 % Reallocation 2 %
1987-1988
6.9
5.6
80.6
0.9
13.0
0.4
6.5
1988-1989
-1.9
-1.5
80.0
0.8
-40.6
-1.2
60.6
1989-1990
-1.5
-0.8
53.1
0.3
-17.4
-1.0
64.3
1990-1991
4.2
3.2
76.9
0.2
4.6
0.8
18.5
1991-1992
12.4
11.7
94.5
-0.4
-3.6
1.1
9.1
1992-1993
12.6
11.9
94.4
-1.6
-12.5
2.3
18.0
1993-1994
13.6
12.0
88.1
-1.1
-8.3
2.8
20.3
1994-1995
15.4
12.9
83.9
0.0
0.2
2.4
15.9
1995-1996
8.3
6.5
78.5
-0.2
-2.7
2.0
24.2
1996-1997
7.7
6.9
89.0
0.1
1.7
0.7
9.3
1997-1998
6.6
9.2
139.9
-2.6
-38.8
-0.1
-1.1
1998-1999
6.5
7.2
110.8
0.0
0.4
-0.7
-11.2
1999-2000
7.3
9.0
123.5
-1.3
-18.2
-0.4
-5.3
2000-2001
6.8
7.6
111.2
-0.6
-8.3
-0.2
-2.9
2001-2002
7.9
9.4
118.1
-0.5
-5.9
-1.0
-12.3
2002-2003
9.2
7.3
79.4
0.8
8.3
1.1
12.4
2003-2004
9.0
5.5
60.8
0.3
3.8
3.2
35.4
2004-2005
10.3
6.6
64.4
0.2
1.7
3.5
33.9
2005-2006
12.0
8.8
72.9
-0.1
-1.2
3.4
28.3
2006-2007
13.6
10.7
78.6
-0.3
-2.1
3.2
23.6
2007-2008
9.6
8.0
83.4
0.0
0.1
1.6
16.5
2008-2009
8.5
6.6
78.5
0.0
0.0
1.8
21.5
Source: Our calculations based on de Vries et al. (2012) and Timmer (2012). The sum of column 3, 5 and 7 gives the
productivity growth rate. The sum of column 4, 6 and 8 gives 100%.
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Table 8: Productivity growth decomposition with sectoral structural change- China
Years Productivity
growth Within
Reallocation
1
Reallocation
2 agriculture
industry
services
1987-1988
6.9
5.6
0.9
0.4
-0.16
-0.23
0.83
1988-1989
-1.9
-1.5
0.8
-1.2
0.49
-1.52
-0.12
1989-1990
-1.5
-0.8
0.3
-1.0
0.39
-0.70
-0.67
1990-1991
4.2
3.2
0.2
0.8
-0.30
0.28
0.80
1991-1992
12.4
11.7
-0.4
1.1
-0.41
0.26
1.27
1992-1993
12.6
11.9
-1.6
2.3
-0.79
0.93
2.13
1993-1994
13.6
12.0
-1.1
2.8
-0.96
0.97
2.75
1994-1995
15.4
12.9
0.0
2.4
-0.89
0.55
2.79
1995-1996
8.3
6.5
-0.2
2.0
-0.67
1.07
1.61
1996-1997
7.7
6.9
0.1
0.7
-0.23
0.43
0.52
1997-1998
6.6
9.2
-2.6
-0.1
-0.04
-0.43
0.39
1998-1999
6.5
7.2
0.0
-0.7
0.11
-1.09
0.26
1999-2000
7.3
9.0
-1.3
-0.4
-0.03
-1.13
0.78
2000-2001
6.8
7.6
-0.6
-0.2
0.00
-0.46
0.26
2001-2002
7.9
9.4
-0.5
-1.0
0.00
-2.15
1.18
2002-2003
9.2
7.3
0.8
1.1
-0.26
0.50
0.90
2003-2004
9.0
5.5
0.3
3.2
-0.62
2.21
1.61
2004-2005
10.3
6.6
0.2
3.5
-0.59
3.19
0.91
2005-2006
12.0
8.8
-0.1
3.4
-0.61
2.97
1.04
2006-2007
13.6
10.7
-0.3
3.2
-0.48
3.52
0.18
2007-2008
9.6
8.0
0.0
1.6
-0.33
0.92
0.99
2008-2009
8.5
6.6
0.0
1.8
-0.37
1.11
1.07
Source: Our calculations are based on de Vries et al. (2012) and Timmer (2012).
Although high productivity gains within subsectors, over the third sub-
period 1997-2002 the potential growth of total productivity was partially hindered
by a misallocation of labor both across and within sectors, subtracting on average
1.5 percentage points to the total productivity growth. With regard to the
reallocation of workers across sectors, we can see that while the employment
share of services continued to increase, the employment share as well as the
absolute number of workers in industry decreased until 2002, with the industrial
employment share reaching the level presented in 1990 (21.4%). It seems, then,
that the reallocation of workers towards the tertiary sector had a positive effect
on total productivity growth, but only until the point it started to subtract an
excessive number of workers from the industry sector. In table 8, indeed, we can
observe that in this period the contribution of the reallocation of labor in
industry hindered the total productivity growth, probably due to the decrease in
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its employment share. Finally, in the sub-period 2003-2009, the productivity gains
within subsectors and a new well-directed reallocation of workers across sectors
determined high growth rates of total productivity, with a moderate slowdown at
the end of the period, probably caused by the world financial crisis. In this
period, the employment share and the absolute number of workers in industry
turned to increase and, as can be seen in table 7, this contributed by around 3
percentage points to the total productivity growth before its moderate slowdown.
Given the predominant contribution of the within effect to the total
productivity growth, it is useful to individuate which sectors and subsectors have
been more dynamic in terms of productivity. From 1987 to 2009, productivity
grew by 481% in the whole economy, while at a detailed level it increased by
170% in agriculture, 611% in industry and 258% in services. It is evident that the
major productivity gains occurred in industry. In particular, some industrial
subsectors presented an outstanding performance: transport equipment (+
1630%), other non-metallic mineral (+ 1618%), manufacturing not elsewhere
classified and recycling (+ 1377%), machinery not elsewhere classified (+
1162%), basic metals (+ 1029%), electrical and optical equipment (+ 1001%),
followed by mining and quarrying (+814% ), wood and cork (+ 722%), chemicals
and chemical products (+ 624%), food , beverages and tobacco (+ 598%), rubber
and plastics (+ 569%), and leather and footwear (+ 526%). In the remaining
industrial subsectors (textiles; pulp, paper, printing and publishing; coke, refined
petroleum and nuclear fuel; electricity, gas and water supply; construction)
productivity increased but at a slower pace than total productivity. The service
sector was, on the contrary, more polarized between subsectors with remarkable
productivity gains and subsectors with a performance below or in line with the
total productivity growth. Among the first: post and telecommunications (+
2660%), water transport (+ 2195%), renting of machinery and equipment, and
other business activities (+ 1071%), health and social work (+ 801%), and public
administration, defence and compulsory social security (781%).
6.2 India
The decomposition results for India are shown in table 9. From the table, it
is evident that the total productivity growth has been much lower in India than in
China, even after the economic reforms. Moreover, if in China the within effect
always represented the most important component of the total productivity
growth, in India it seems that the total reallocation effect played a predominant
role in some years. In particular, we can notice that, while in China the major
reallocation effects occurred across sectors, in India the movement of workers
both across and within sectors gave a great contribution to the total productivity
changes, sometimes hindering and sometimes fostering them.
By analyzing the Indian path of structural change, we can underline some
key-years. First, in 1990-91 there was a decrease in total productivity given by a
huge fall of weighted productivity within subsectors, in part counterbalanced by a
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better reallocation of labor across and within sectors that avoided a further
decrease of 6.3 percentage points in total productivity. In particular, as we can see
from table 10, 5 percentage points were recovered by the movement of workers
towards the secondary and, especially, the tertiary sector. Second, in the period
1994-1999, i.e. immediately after the economic reforms, there were notable
increases in total productivity determined above all by important productivity
gains within subsectors and, partially, by the movement of labor from sectors
with lower to higher productivity, although a still persistent misallocation of
workers within each sector.
Table 9: Productivity growth decomposition - India
Years Productivity
growth Within % Reallocation
1 % Reallocation
2 %
1987-1988
8.2
6.9
83.8
0.2
2.8
1.1
13.4
1988-1989
5.7
6.1
105.8
0.7
12.9
-1.1
-18.7
1989-1990
4.7
5.4
113.8
0.5
10.4
-1.1
-24.1
1990-1991
-2.1
-8.4
406.8
2.4
-114.9
3.9
-191.9
1991-1992
3.1
2.3
75.6
0.8
24.3
0.0
0.0
1992-1993
3.3
3.0
90.0
0.5
16.1
-0.2
-6.1
1993-1994
1.6
0.8
50.7
0.4
22.5
0.4
26.8
1994-1995
6.5
6.1
93.7
-0.3
-4.7
0.7
10.9
1995-1996
4.7
5.2
109.0
-0.7
-15.4
0.3
6.4
1996-1997
3.4
3.1
91.3
-0.7
-20.1
1.0
28.8
1997-1998
5.6
5.9
105.4
-0.9
-16.0
0.6
10.6
1998-1999
7.9
7.2
91.2
-0.9
-11.1
1.6
19.9
1999-2000
-0.5
-2.2
433.0
0.9
-178.6
0.8
-154.5
2000-2001
1.9
0.2
11.5
0.6
30.3
1.1
58.2
2001-2002
6.2
2.4
38.7
0.6
10.3
3.2
51.0
2002-2003
2.5
1.0
40.7
1.2
48.1
0.3
11.2
2003-2004
5.5
4.4
80.4
1.1
20.1
0.0
-0.6
2004-2005
7.4
8.5
115.4
0.0
0.0
-1.1
-15.4
2005-2006
10.0
9.5
95.1
0.5
5.2
0.0
-0.3
2006-2007
12.6
8.7
69.0
0.8
6.5
3.1
24.5
2007-2008
6.8
5.0
72.7
0.3
4.9
1.5
22.5
2008-2009
8.8
7.2
81.8
0.4
4.4
1.2
13.9
Source: Our calculations are based on de Vries et al. (2012) and Timmer (2012). The sum of column 3, 5 and 7 gives the
productivity growth rate. The sum of column 4, 6 and 8 gives 100%.
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Third, in 1999-2000, total productivity slightly fell, once again due to a
negative weighted productivity growth within subsectors, partially
counterbalanced by positive reallocation effects. This temporary slowdown in
productivity growth can be probably ascribed to the deceleration of the reform
process after almost a decade of extraordinary transformations. After that, total
productivity turned to grow, especially driven first by an appropriate reallocation
of workers across and within sectors (2000-2003), with an important role of
industry and services in 2000-2002, and then by huge productivity gains within
subsectors (2004-2009). The highest rates of total productivity growth were
registered between 2005 and 2007, with values over 10%.
With regard to the productivity performance of sectors and subsectors over
the whole analyzed period, we can observe a more equilibrated but slower path
with respect to the Chinese case. From 1987 to 2009, total productivity grew by
200%, with an increase of 67%, 135% and 209% in agriculture, industry and
services respectively. The variance of productivity across subsectors was lower
than in China, since only few subsectors showed a performance notably higher
than the average trend. In industry: coke, refined petroleum and nuclear fuel (+
849%), chemicals and chemical products (+ 324%), electrical and optical
equipment (+ 277%), and electricity, gas and water supply (+ 226%). In service:
post and telecommunications (+ 931%), public administration, defence and
compulsory social security (+ 303%), and financial intermediation (300%).
If compared to the Chinese path of structural change, it can be noticed that
India followed a more balanced, but slower and less definite path. In China, the
increase in the productivity within sectors and subsectors, in particular in
industry, was the driving force of total productivity growth. After an initial
slowdown, total productivity growth constantly increased over the analyzed
period, with a moderate deceleration from 1997 to 2002 due to a too pronounced
decrease in the employment share in industry rather than to a reduction of
productivity growth. On the contrary, in India the increases in total productivity
were lower and not constant, with important effects deriving from insufficient
productivity gains within sectors and subsectors, misallocation of labor across
subsectors and a still high share of workers employed in agriculture (54% in
2009). Only in recent years, and in particular since 2005, it seems that in India the
rates of productivity growth reached levels analogous to the Chinese
performance. Of course, among many differences between the two countries, we
have to take into particular account two of these. First, the economic reforms in
China began around 15 years before than in India and, then, it is possible that a
clearer path of structural change will occur in India in next years. Second, India is
characterized by a huge presence of the informal sector, that could have
decelerated the possibility of high productivity gains and hindered a stable path
of structural change.
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Table 10: Productivity growth decomposition with sectoral structural change- India
Years Productivity
growth Within
Reallocation
1
Reallocation
2 Agriculture
Industry
Services
1987-1988
8.2
6.9
0.2
1.1
-0.39
0.79
0.70
1988-1989
5.7
6.1
0.7
-1.1
0.38
-0.87
-0.58
1989-1990
4.7
5.4
0.5
-1.1
0.36
-0.88
-0.61
1990-1991
-2.1
-8.4
2.4
3.9
-1.12
0.95
4.11
1991-1992
3.1
2.3
0.8
0.0
0.02
-0.40
0.38
1992-1993
3.3
3.0
0.5
-0.2
0.08
-0.45
0.17
1993-1994
1.6
0.8
0.4
0.4
-0.11
-0.19
0.73
1994-1995
6.5
6.1
-0.3
0.7
-0.20
0.27
0.64
1995-1996
4.7
5.2
-0.7
0.3
-0.10
0.57
-0.18
1996-1997
3.4
3.1
-0.7
1.0
-0.25
0.39
0.83
1997-1998
5.6
5.9
-0.9
0.6
-0.14
0.06
0.68
1998-1999
7.9
7.2
-0.9
1.6
-0.38
0.41
1.54
1999-2000
-0.5
-2.2
0.9
0.8
-0.25
1.13
-0.10
2000-2001
1.9
0.2
0.6
1.1
-0.33
1.05
0.40
2001-2002
6.2
2.4
0.6
3.2
-0.83
1.88
2.13
2002-2003
2.5
1.0
1.2
0.3
0.01
-0.67
0.95
2003-2004
5.5
4.4
1.1
0.0
-0.04
0.51
-0.50
2004-2005
7.4
8.5
0.0
-1.1
0.26
-0.82
-0.58
2005-2006
10.0
9.5
0.5
0.0
-0.01
0.19
-0.21
2006-2007
12.6
8.7
0.8
3.1
-0.58
1.46
2.22
2007-2008
6.8
5.0
0.3
1.5
-0.29
0.74
1.08
2008-2009
8.8
7.2
0.4
1.2
-0.19
0.30
1.10
Source: Our calculations are based on de Vries et al. (2012) and Timmer (2012).
7. An econometric investigation: growth, structural change and
globalization
As McMillan and Rodrik (2011) pointed out, along with the productivity
growth, the extent of structural change is a key driver of development. However,
there may exist complex feedbacks between structural change and economic
growth. On the one hand, structural change may either enhance economic
growth, if it is well-directed, or hinder it if the reallocation of labor mismatches
the productivity changes across sectors. On the other hand, the pace of economic
growth may influence the size of structural change. Indeed, a too fast economic
growth, mainly led by high productivity gains within sectors rather than by a well-
directed reallocation of labor, may further result in small contribution of
structural change to overall labor productivity. As occurred in both China and
India, a rapid economic growth is more likely to be geographically unbalanced,
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with the consequence that the movement of workers across sectors, regions and
areas may be hindered. This may be further aggravated by the presence of
barriers to internal migration as well as of restrictions to the transfer of labor
across sectors. Moreover, when economic growth derives from impressive
increase in productivity, the wage adjustment to labor productivity levels may be
not immediate, creating important wage-productivity differentials and misleading
labor reallocation. Finally, fast rises in economic growth and in productivity
within sectors are also due to technical progress, with the adoption of capital
intensive technologies, reducing the demand of labor and the absorption of
workers in the higher-productivity sectors. These issues have been partially
discussed by Ding and Knight (2009).
At the same time, both the size of structural change and economic growth
are expected to be interrelated to the higher exposure to globalization and the
progressive openness to international markets. High levels of export, import and
FDI not only can impact on economic growth but also on the reallocation of
resources across sectors, modifying the comparative advantage and reorganizing
the production. As a consequence, it is reasonable to expect not only that there
are feedbacks between economic growth and structural change over time, but
also that both depend, in part, on the degree of economic openness. Starting
from our assumption, we will consider economic growth and structural change as
endogenous, given their potential reciprocal feedbacks, and will try to understand
how they have been related to globalization. To this purpose, the best candidate
is a vector autoregressive model (VAR), allowing to verify and catch the
feedbacks between economic growth and structural change over time, as well as
to estimate how globalization has been jointly related to both of them.
In VAR models, the endogenous variables are explained by their past values
along with the past values of all the other endogenous variables. To satisfy the
stability condition, the VAR requires that the variables in the model result to be
absolute stationary. In our case, we consider two endogenous variables: structural
change (i.e. the ‘reallocation effect’ in equation 2, calculated at the highest
possible disaggregated level) expressed in percentage terms, and the rate of
growth of per-capita GDP. To test the hypothesis of endogeneity, a Granger
causality test is performed after the VAR. The Granger causality test is based on
the fact that time does not run backward. The event X is said to “Granger
causes” the event Y if past values of event X can help to explain the event Y
(Koop, 2005). Even if it has been often pointed out that caution should be used
to interpret the results of the Granger causality test as a strict relation of causality,
the test turns to be useful to test for endogeneity. Indeed, when past values of
event X help to explain the event Y and simultaneously past values of event Y
help to explain the event X, it can be deduced that there are continuous
feedbacks between the two events and that they have to be considered as
endogenous in the model.
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Another advantage of VAR models is that they can be extended to add
exogenous regressors jointly related to all the endogenous variables. In our case,
this is useful to account for the relation of both structural change and economic
growth (endogenous variables) with economic openness (exogenous term). We
use VAR to regress the structural change and the growth rate of per-capita GDP
on alternative variables of economic openness: the KOF index of economic
globalization (Dreher et al., 2008)
9
, and export, import and FDI as a percentage
of GDP
10
.
Our data suffer from a restricted time-span, from 1987 to 2009, that can
limit the explanatory capacity of our estimations. To cope with this problem, we
make some adjustments. First, in order to smooth and de-trend data, we
transform all the variables in five-years moving averages. Second, we estimate the
VAR adjusting with small-sample degrees-of-freedom
11
. Furthermore, we use
small-sample adjustments by adopting t and F statistics instead of the large-
sample normal and chi-squared statistics. Finally, an important issue in VAR is
the selection of the optimal lag order, based on minimizing the values of FPE
(final predictor error), AIC (Akaike information criterion), HQIC (Hannan-
Quinn information criterion) and SBIC (Schwarz-Bayesian information criterion).
Fortunately, we notice that the difference in AIC, HQIC and SBIC is not so large
with different lag orders (from 1 to 4) in all the specifications of the model. As a
consequence, we choose to use a first-lag order avoiding to lose further
observations: even if it does not turn to be the optimal specification according to
all the criteria, the first-lag order keeps low the values of AIC, HQIC and SBIC.
Results for China and India are reported respectively in tables 11 and 12.
All the VARs satisfy the stability condition. In general, the Granger causality tests
reveal that there is a continuous interaction between structural change and
economic growth. The null hypotheses that structural change "does not Granger
cause" the growth of per-capita GDP and that the growth of per-capita GDP
"does not Granger cause" structural change are both rejected. This confirms that
both the variables must be considered endogenous and that the VAR is the
appropriate model to our analysis
12
. Results for China reveal that both structural
9
The KOF index of economic globalization is a weighted index accounting for trade as a percentage of
GDP, FDI stocks as a percentage of GDP, portfolio investment as a percentage of GDP, income
payments to foreign nationals as a percentage of GDP, hidden import barriers, mean tariff rate, taxes on
international trade, and capital account restrictions. The advantage to adopt it relies on the possibility of
taking into account the various dimensions of economic globalization.
10
Since the variable FDI/GDP caused some instability problems in the VAR for China, we expressed it
in terms of growth rates.
11
Specifically, in STATA, 1/(T-mparms) is used instead of the large-sample divisor 1/T, where mparms is
the average number of parameters in the functional form for y_t over the K equations.
12
To check the robustness of our results, we re-estimated the model using the reallocation effect across
the three main economic sectors as a proxy for structural change. Also in this case, VARs satisfy the
stability condition, while the coefficients and the statistical significance do not considerably vary.
V. Valli, D. Saccone, Structural Change, Globalization and Economic Growth in China and India
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155
change and economic growth are cumulative process, since the coefficients of
their lagged values are positive and highly significant. For India, only structural
change appears to be a cumulative process, even if at a lower level of significance.
These difference are probably due to the fact that economic reforms as well as
the impressive economic growth in India began around 15 years later than in
China, so that a cumulative path of growth and structural change in India is not
well typified by our data.
In the equation for structural change, the coefficients of the lagged per-
capita GDP growth are negative and significant both for China and India (except
in specification 4 for China). It seems that high past values of the rate of growth
are related with a lower contribution of structural change to overall labor
productivity. This can be explained by what we discussed above: in China, as well
as in India, the rapid economic growth has been associated with geographically
unbalances, barriers to internal migration, difficulties in the transfer of labor
between sectors, slow wage adjustment to labor productivity, and the adoption of
capital intensive technologies. Moreover, in India the huge presence of the
informal sector may have further distorted the reallocation of labor.
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Table 11: China. VAR estimation. Endogenous variables: structural change and per-capita GDP growth.
dep. variable: structural change
(1) (2) (3) (4)
structural change L1. 0.9932*** 0.9212*** 0.9289*** 0.9091***
pc GDP growth L1. -1.9388** -1.9393** -2.0810***
-0.0315
globalization index 0.5313***
exp/GDP 0.6184***
imp/GDP 0.9639***
FDI/GDP -0.0155
cons -6.3825 2.4471 -1.9446 1.1513
R-sq 0.91 0.91 0.93 0.85
Prob>F 0.00 0.00 0.00 0.00
dep. variable: pc GDP growth
structural change L1. 0.0218** 0.0177* 0.0184** 0.0066
pc GDP growth L1. 0.6858*** 0.6820*** 0.6578*** 0.8600***
globalization index 0.0305
exp/GDP 0.0366
imp/GDP 0.0642**
FDI/GDP 0.0124*
cons 1.3953* 1.9052*** 1.6273** 1.2310
R-sq 0.86 0.86 0.88 0.87
Prob>F 0.00 0.00 0.00 0.00
H
0
: pc GDP growth "does not
Granger cause" structural change:
Prob>F
0.04 0.03 0.01 0.97
H
0
: structural change "does not
Granger cause" pc GDP growth:
Prob>F
0.04 0.07 0.05 0.53
***, ** and * mean coefficients are significant respectively at 98% or more, 95% and 90%. Since the variable FDI/GDP
does not satisfy the stability condition, it has been transformed in rates of growth. Analogous results are obtained if variables
are first differenced. All the VAR estimations satisfy the stability condition. The variable FDI/GDP is expressed in terms
of growth rates to avoid instability problems.
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157
Table 12. India. VAR estimation. Endogenous variables: structural change and per-capita GDP growth.
dep. variable: structural change
(1) (2) (3) (4)
structural change L1. 0.4299* 0.4651** 0.5204** 0.5738**
pc GDP growth L1. -18.172** -22.391***
-26.706***
-22.262**
globalization index 4.8037***
exp/GDP 7.9287***
imp/GDP 6.9006***
FDI/GDP 43.607***
cons -78.990*** -10.328 11.568 55.249
R-sq 0.71 0.72 0.71 0.63
Prob>F 0.00 0.00 0.00 0.00
dep. variable: pc GDP growth
structural change L1. 0.0081* 0.0088** 0.0098** 0.0102*
pc GDP growth L1. 0.2509 0.1712 0.1154 0.3377
globalization index 0.0976***
exp/GDP 0.1595***
imp/GDP 0.1323***
FDI/GDP 0.6282**
cons 0.4157 1.8056*** 2.2016*** 2.6040***
R-sq 0.92 0.92 0.91 0.87
Prob>F 0.00 0.00 0.00 0.00
H
0
: pc GDP growth "does not
Granger cause" structural change:
Prob>F
0.04 0.02 0.01 0.05
H
0
: structural change "does not
Granger cause" pc GDP growth:
Prob>F
0.07 0.05 0.04 0.09
***, ** and * mean coefficients are significant respectively at 98% or more, 95% and 90%. All the VAR estimations
satisfy the stability condition.
All these factors have reduced the size of structural change, in a process of
productivity growth mainly due to within-sector productivity gains rather that to
labor reallocation. If the unbalanced economic growth has created cumulative
obstacles to a well-directed reallocation of labor, it seems on the contrary that the
process of economic openness has favored it in both countries. The coefficients
of the globalization indicators are all positive and significant, except for
FDI/GDP in China. The reorganization of production and the change in the
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economic structure as a consequence of a higher openness to international
markets have impacted on the reallocation of workers towards the higher-
productivity sectors.
As expected, in the equation for the per-capita GDP growth, it emerges
that past values of structural change have a positive and significant relation with
economic growth in both countries (except in specification 4 for China). When
the reallocation of labor is productive and large, this positively impact on the
future rates of economic growth, while the contrary happens when the
contribution of structural change to overall labor productivity is low or negative.
It seems then that a too rapid economic growth has hindered an efficient
reallocation of labor, that however may in turn contribute to economic growth.
Policies favoring labor movement across sectors and areas, reducing the wage-
productivity differentials and integrating the informal sector in formal markets in
India, may foster structural change and further enhance economic growth. As
expected, the indicators of globalization are positively related to economic
growth, even if they are not always significant for China. This may be due to the
fact that the equation for the per-capita GDP growth does not take into account
other important factors helping to explain it. However, this is beyond our specific
interest. The focus of our analysis was not to explain what has determined
economic growth in the two countries, but rather to do a first step to study
structural change and its relation with economic growth and globalization. The
separate equation in the VAR for the per-capita GDP growth is functional to
explain structural change, since the continuous feedbacks between the two
variables confirmed by the Granger causality test imposed to consider both the
variables as endogenous.
8. Conclusions
The aim of our paper was to investigate, in a comparative perspective, the
relation between structural change, globalization and economic growth in China
and India. After a general introduction on the patterns of structural changes,
growth and globalization in the two countries and their effect on social problems,
we deeply studied the path of structural change and its contribution to the overall
labor productivity, by using a database reporting data at a highly detailed sector
level. The two countries, and in particular China, have experienced impressive
increases in overall labor productivity. Even if India has shown a less rapid but
more balanced path, the gains in labor productivity have been especially
concentrated in some specific industrial and tertiary subsectors. However, the
main contribution to these gains has derived by within-sector increase in
productivity, while the reallocation of labor has not always been directed to those
sectors presenting the highest productivity levels.
We further investigated the relation of structural change with both
economic growth and globalization by adopting VAR models. Three main results
emerged from our analysis. First of all, there exist important feedbacks between
V. Valli, D. Saccone, Structural Change, Globalization and Economic Growth in China and India
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159
structural change and economic growth over time. Present values of the index of
structural change and of per-capita GDP growth are related to past values of
each other. Second, when the reallocation of labor is large, it may positively
impact on the future rates of economic growth. At the same time, however, it
seems that a too rapid economic growth may hinder a suitable reallocation of
labor. On both countries, then, new policies should be designed to favor the
voluntary labor movement across sectors and areas and to reduce the wage-
productivity differentials. In particular, India needs to overcome the dualism
between formal and informal sector, that misleads the reallocation of labor and
entraps a huge amount of workers in low-productivity activities. On the contrary,
China should start to shift the emphasis to a more harmonized reallocation of
labor, so far neglected in favor of impressive productivity gains. Probably, the
recent course undertaken by the Chinese economy toward a more balanced
pattern of growth is moving in this direction. Third, if a too unbalanced
economic growth has limited the extent of structural change, globalization has
promoted it. High levels of export, import and FDI not only have been related to
higher rates of economic growth, but also to a better reallocation of resources
across sectors, modifying the comparative advantage and reorganizing the
production.
References
Akamatsu K. (1935), ‘Waga kuni yomo kogyohin no susei’ [The Trend of Foreign Trade in
Manufactured Woolen Goods in Japan], Shogyo Keizai Ronso (Higher Commercial School of
Nagoya) 13, July, 129–212.
Akamatsu K. (1962), ‘A Historical Pattern of Economic Growth in Developing Countries’,
Developing Economies, Preliminary Issue, 1, 3–25.
Andreff W., Balcet G. (2013), ‘Emerging Countries' Multinational Companies Investing in
Developed Countries: at Odds with the HOS Paradigm?, European Journal of Comparative
Economics, 10(1), 3-26.
Bensidoun I., Lemoine F., Unal D. (2009), ‘The Integration of China and India into the World
Economy: A Comparison, European Journal of Comparative Economics, 6(1), 131-155.
Beretta S., Targetti Lenti R. (2012), ‘India and China. Trading with the World and Each Other’,
Economic and Political Weekly, November 13, xlvii, 44.
Bosworth B., Collins S.M. (2008), Accounting for Growth: Comparing China and India, Journal
of Economic Perspectives, 22(1), 45-65.
Chenery H.B. (1960), ‘Patterns of Industrial Growth’ , American Economic Review, 50(4), 624-654.
Chenery H. B, Taylor L. (1968), ‘Development Patterns: Among Countries and Over Time”,
Review of Economics and Statistics, 50(4), 391-416.
Chenery H.B. (1979), Structural Change and Development Policy, Oxford University Press, New
York.
Chenery H. B., Robinson S., Syrquin M. (eds.) (1986), Industrialization and Growth: A Comparative
Study, Oxford University Press, New York.
Chenery H. B., Syrquin M. (1975), Patterns of development 1950–1970, Oxford University Press,
London.
EJCE, vol.12, n.2 (2015)
Available online at http://eaces.liuc.it
160
Chenery H. B., Syrquin M. (1989), ‘Patterns of Development, 1950 to 1983’, World Bank
Discussion Paper, WBP41, World Bank, Washington D.C.
Clark C. (1940), The Conditions of Economic Progress, Macmillan, London.
De Vries G.J. et al. (2012), ‘Deconstructing the BRICs: Structural Transformation And
Aggregate Productivity Growth’, Journal of Comparative Economics, 40, 221-227.
Ding S., Knight J. (2009), ‘Why has China Grown so Fast? The Role of Structural Change’,
Proceedings of the German Development Economics Conference, Frankfurt, Verein für Socialpolitik,
Research Committee Development Economics.
Dreher A., Noel G., Pim M. (2008), Measuring Globalisation Gauging its Consequences, Springer,
New York.
Fabricant S. (1942), Employment in Manufacturing, 1899-1939: An Analysis of Its Relation to the
Volume of Production, NBER, New York.
Garrone G., Tecco N., Vecchione E. (2012), Tra crescita e sostenibilità: quale governance ambientale?, in
Balcet G., Valli V. (2012), Potenze economiche emergenti: Cina e India a confronto, Il Mulino, Bologna.
Gerschenkron A. (1962), Economic Backwardness in Historical Perspective, Belknap Press of Harvard
University Press, Cambridge, Mass.
Haraguchi N., Rezonja G., (UNIDO) (2010), ‘In Search of General Patterns of Manufacturing
Development’, UNIDO Working Paper, 02/2010, Wien.
Kader A. (1985), ‘Development Patterns among Countries Reexamined’, Developing Economies,
23(3), 199-220.
Keesing D. B., Sherk D. R. (1971), ‘Population Density in Patterns of Trade and Development’,
American Economic Review, 61(5), 956-961.
Kochhar K. et al. (2006), ‘India’s Pattern of Development: What Happened, What Follows?’,
IMF Working Paper, WP/06/22, IMF, Washington D.C.
Kojima K. (2000), ‘The ‘Flying-Geese’ Model of Asian Economic Development: Origin,
Theoretical Extensions, and Regional Policy Implications’, Journal of Asian Economics, 11, 375-
401.
Koop G. (2005), Analysis of Economic Data, John Wiley & Sons, Chichester (UK).
Kuznets S. (1957), ‘Quantitative Aspects of the Economic Growth of Nations: II. Industrial
Distribution of National Product and Labor Force’, Economic Development and Cultural Change,
5(4) Supplement, 1-111.
Li W., Menginstae T., Xu C. (2011), Diagnosing Development Bottlenecks: China and India, Policy
Research Working paper, World Bank,
http://elibrary.worldbank.org/doi/book/10.1596/1813-9450-5641
Lin J.Y. (2011), ‘From Flying Geese to Leading Dragons’, Policy Research Working paper, 5702,
World Bank, Washington.
Lin J.Y., Rosenblutt D. (2012), ‘Shifting pattern of Economic Growth and Rethinking
Development’, Policy Research Working paper, 6040, World Bank, Washington.
Maizels A. (1968), Exports and economic growth of developing countries, Cambridge University Press,
London.
Marelli E., Signorelli M. ( 2011), ‘China and India: Openness, Trade and Effects on Economic
Growth’, European Journal of Comparative Economics, 8(1), 129-54.
McMillan M., Rodrik D. (2011), ‘Globalization, Structural Change and Productivity Growth’,
NBER Working Paper, 17143.
National Bureau of Statistics of China (2008), China Statistical Yearbook.
Ozawa T. (2001), ‘The ‘Hidden’ Side of the ‘Flying-Geese’ Catch-Up Model: Japan’s Dirigiste
Institutional Setup and A Deepening Financial Morass’, Journal of Asian Economics, 12, 471-491.
V. Valli, D. Saccone, Structural Change, Globalization and Economic Growth in China and India
Available online at http://eaces.liuc.it
161
Ozawa T. (2010)
,
The (Japan born) “Flying-Geeese” theory of economic development
revisited-and reformulated from a structuralist perspective, Working paper series, 291, October’,
Columbia University, www.gsb,columbiaedu/cjeb/research
Srinivasan T.N. (2006), China, India and the World economy, Background Paper for Winters Y.
(eds.), Dancing with China, World Bank, Washington D.C.
Syrquin M. (1988), ‘Structural Change and Economic Development: the Role of the Service
Sector’, Journal of Development Economics, 28(1), 151-154.
Taylor L. (1969), ‘Development Patterns: A Simulation Study’, The Quarterly Journal of Economics,
83(2), 220-241.
Timmer M. (ed.) (2012), The World Input-Output Database (WIOD): Contents, Sources and Methods,
wired at
http://www.wiod.org
UNCTAD (2014), http://unctadstat.unctad.org/ReportFolders/reportFolders.aspx
Valli V. (2015), The Economic Rise of China and India, Accademia University Press, Torino
Valli V., Saccone D. (2009), ‘Structural Change and Economic Development in China and
India’, European Journal of Comparative Economics, 6(1), 101-29.
Wang X., Fan G., Liu P. (2007), ‘Pattern and Sustainability of China’s Economic Growth
Towards 2020’, NERI-China Working Paper, NERI, Shanghai .
Winters Y. (eds.) (2007), Dancing with Giants, World Bank, Washington D.C.
EJCE, vol.12, n.2 (2015)
Available online at http://eaces.liuc.it
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APPENDIX 1: SECTOR CLASSIFICATION AND CODES
SECTOR SUBSECTORS CODES
AGRICULTURE
agriculture, hunting, forestry and fishing AtB
INDUSTRY mining and quarrying C
INDUSTRY food , beverages and tobacco 15t16
INDUSTRY textiles and textile 17t18
INDUSTRY leather, leather and footwear 19
INDUSTRY wood and of wood and cork 20
INDUSTRY pulp, paper, paper , printing and publishing 21t22
INDUSTRY coke, refined petroleum and nuclear fuel 23
INDUSTRY chemicals and chemical 24
INDUSTRY rubber and plastics 25
INDUSTRY other non-metallic mineral 26
INDUSTRY basic metals and fabricated metal 27t28
INDUSTRY machinery, nec 29
INDUSTRY electrical and optical equipment 30t33
INDUSTRY transport equipment 34t35
INDUSTRY manufacturing nec; recycling 36t37
INDUSTRY electricity, gas and water supply E
INDUSTRY construction F
SERVICES sale, maintenance and repair of motor vehicles and
motorcycles; retail sale of fuel 50
SERVICES wholesale trade and commission trade, except of motor
vehicles and motorcycles 51
SERVICES retail trade, except of motor vehicles and motorcycles;
repair of household goods 52
SERVICES hotels and restaurants H
SERVICES other inland transport 60
SERVICES other water transport 61
SERVICES other air transport 62
SERVICES other supporting and auxiliary transport activities;
activities of travel agencies 63
SERVICES post and telecommunications 64
SERVICES financial intermediation J
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SERVICES real estate activities 70
SERVICES renting of m&eq and other business activities 71t74
SERVICES public admin and defence; compulsory social security L
SERVICES education M
SERVICES health and social work N
SERVICES other community, social and personal services O
SERVICES private households with employed persons P
For China data for subsector 50 are not available since this subsector is
included partly in subsector 51 and partly in subsector 52. Moreover, subsector P
is included in O. Therefore, the database distinguishes 33 subsectors for China.
For India, the subsector 19 (leather and footwear) is included in subsector 17t18
(textile and textile products). Moreover, transport services (60, 61, 62, 63) are all
accounted in subsector 60. Therefore, the database distinguishes 31 subsectors
for India.
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