Structural Transformation to Manufacturing
and Services: What Role for Trade?
Kym Anderson and Sundar Ponnusamy∗
Understanding how and why economies structurally transform as they grow
is crucial for making sound national policy decisions. Typically, analysts
who study this issue focus on sectoral shares of gross domestic product and
employment. This paper extends those studies to include exports, including
exports of services. It also considers mining, in addition to agriculture and
manufacturing, and recognizes that some of the products of these four sectors
are nontradable. The section on theory presents a general equilibrium model
that provides hypotheses about structural change in different types of economies
as they grow. These are then tested econometrically with annual data for the
period 1991–2014 for a sample of 117 countries. The results point to the
futility of adopting protective policies aimed at slowing deagriculturalization
and subsequent deindustrialization in terms of sectoral shares, since those trends
inevitably will accompany economic growth. Fortuitously, governments now
have more efficient and equitable ways of supporting adjustments needed by
people who choose or are forced to leave declining industries.
Keywords: comparative advantage, declining sectors, patterns of structural
change, productivity growth
JEL codes: F11, F43, F63, N50, O14
IO. introduzione
Most countries begin the process of economic growth with the vast majority
of people engaged in producing staple food. As labor productivity improves with
industrial capital accumulation or importation, an increasing number of workers
are attracted to manufacturing and service activities—what Lewis (1954) simply
called the modern sector. Lewis assumed that labor was more productive in the
modern sector than in the traditional (mainly subsistence agriculture) sector (Gollin
2014), which leads one to expect the share of the population employed in agriculture
and eventually the absolute number employed on farms to decrease. Later in
∗Kym Anderson (corresponding author): Emeritus Professor, University of Adelaide and Honorary Professor
of Economics, Australian National University. E-mail: kym.anderson@adelaide.edu.au; Sundar Ponnusamy: PhD
candidate, University of Adelaide. E-mail: sundar.ponnusamy@adelaide.edu.au. This is a revision of a paper prepared
for an ADB conference on New Perspectives on Asian Development, Bangkok, Thailand on 19–20 July 2018; E
for the Agricultural and Applied Economics Association Annual Meetings in Washington, DC on 5–7 August 2018.
The authors thank the conference participants, the managing editor, and the journal referees for helpful comments
and suggestions. ADB recognizes “Korea” as the Republic of Korea. The usual ADB disclaimer applies.
Asian Development Review, vol. 36, NO. 2, pag. 32–71
https://doi.org/10.1162/adev_a_00131
© 2019 Asian Development Bank and
Asian Development Bank Institute.
Pubblicato sotto Creative Commons
Attribuzione 3.0 Internazionale (CC BY 3.0) licenza.
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Structural Transformation to Manufacturing and Services 33
the development process, the manufacturing sector’s share of employment and
eventually the number of workers in manufacturing decline as well (Herrendorf,
Rogerson, and Valentinyi 2014; Fort, Pierce, and Schott 2018). Those economies
fortunate enough to be well endowed per capita in minerals and energy raw
materials or in natural forests find that mining (including of native forests by felling
trees) employs some workers, but that its share of total employment tends to be
quite small and also declines in the course of a nation’s economic development.
Gross domestic product (GDP) shares follow a similar pattern to employment
shares. Tuttavia, agriculture’s GDP share often declines faster than its employment
condividere. By contrast, GDP shares of mining and manufacturing often decline slower
than their employment shares, implying that labor productivity in those two
sectors grows faster than the national average. Such labor productivity differences
the margin, migration of labor from traditional agriculture to
mean that, at
manufacturing is likely to speed up economic growth. The GDP share of services
has tended to grow slower than its employment share because (like traditional
agriculture) it is relatively labor intensive, and it has had relatively slow productivity
growth—although that is beginning to change for some services thanks in part
to the information and communication technology (ICT) revolution (Duernecker,
Herrendorf, and Valentinyi 2017).
This pattern of structural transformation in the course of national economic
growth has been going on for many decades (Clark 1957; Kuznets 1966; Syrquin
1988; Syrquin and Chenery 1989; Timmer, de Vries, and de Vries 2015). IL
pace of these sectoral changes varies widely across countries, Tuttavia, and not
only because of their different rates of economic growth (Nickell, Redding, E
Swaffield 2008).1 Also, over time, peak shares of manufacturing in total GDP
and employment have gradually fallen, and this has been occurring at earlier real
per capita income levels. Inoltre, in some developing countries, urbanization
is occurring without much industrialization (Rodrik 2016; Gollin, Jedwab, E
Vollrath 2016; Felipe, Mehta, and Rhee 2018; Nayyar, Vargas Da Cruz, and Zhu
2018).
Far more varied across countries are developments in the sectoral shares of
national exports—a feature that is often ignored in comparative studies of structural
transformation. Some of the world’s high-income countries have managed to retain
a comparative advantage in a small number of primary products, while some
low-income countries have already built a comparative advantage in one or more
services (Tavolo 1). Inoltre, as part of the current wave of globalization, ulteriore
lowering of trade costs and government restrictions on trade is accelerating the
1There is also a vast literature on structural transformation within sectors as growth proceeds and its
consequences in terms of inequality, poverty alleviation, and other indicators of inclusiveness. Vedere, Per esempio,
Laborde et al. (2018) on agricultural transformation patterns. in questo documento, we treat economic growth as exogenous,
and we leave in the background its impact on factor markets, factor shares of GDP, and income distribution across
occupations, regions, households, and individuals.
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34 Asian Development Review
Tavolo 1. Top 30 Economies by ‘Revealed’ Comparative Advantagea in Agriculture,
Mining, Manufacturing, and Services, 2014
Agriculture
Mining
Manufacturing
Services
Malawi
Guyana
8.35 Angola
7.90 Algeria
6.07 Bangladeshb
5.76 People’s
1.65 Bermuda
1.65 Macau, China
Benin
Paraguay
Burkina Faso
7.81 Kuwait
7.49 Nigeria
7.18 Brunei
Cote d’Ivoire
New Zealand
Darussalam
7.02 Saudi Arabia
6.65 Oman
Uruguay
Ethiopia
Argentina
Burundi
Moldova
Zimbabwe
Nicaragua
Honduras
Fiji
Uganda
Ecuador
6.14 Mongolia
5.98 Azerbaijan
5.87 Qatar
5.73 Kazakhstan
5.38 Sierra Leone
5.29 Guinea
5.25 Zambia
5.22 Bolivia
4.55 Russian
Federation
4.49 Niger
4.48 Colombia
Republic of
China
5.70 Botswana
5.67 Slovak Republic
5.58 Czech Republic
1.55 Grenadab
1.53 Palau
1.50 Maldives
5.24 Mexico
5.20 Republic of
Korea
1.44 Antigua and Barbuda
1.40 St. Kitts and Nevis
Japan
5.17
5.13 Viet Nam
5.06 Germany
4.99 Slovenia
4.84
4.83 Hungary
4.62 Svizzera
4.62 Poland
4.18 Thailand
Italy
Israel
3.87
3.84 Pakistan
1.37 Sint Maarten
1.37 Cabo Verde
1.34 Aruba
1.33 Dominicab
1.32 French Polynesia
1.31 Vanuatub
1.24 Luxembourg
1.23 St. Lucia
1.18 Timor-Lesteb
1.17 Malta
1.17 St. Vincent and the
Grenadinesb
1.15 Sao Tome and
Principe
Guatemala
4.44 Democratic
3.67 Tunisia
Republic of
Congo
Tanzania
4.38 Republic of
3.66 Austria
1.14 Samoa
Belize
Brasile
Kiribati
Mauritania
Senegal
Ukraine
Chile
Yemen
4.38 Bahrain
4.13 Mozambique
4.09 Mauritania
4.01 Trinidad and
Tobago
3.54 Australia
3.15 Peru
3.13 Norway
Iceland
Costa Rica
Myanmar
2.98 Ecuador
2.97 Chile
2.91 Cameroon
3.64 Romania
3.51 Macedonia
3.48 Turkey
3.47 Cambodia
3.36 Belgium
3.34 Philippines
3.33 Hong Kong,
China
3.15 France
3.14 El Salvador
2.62 Malaysia
1.14 Cyprus
1.14 Bahamas
1.14 Lebanon
1.12 Montenegrob
1.10 Tonga
1.08 Djiboutib
1.06 Afghanistan
1.02 Curacaob
1.01
Jamaica
1.01 Nepal
4.60
4.55
4.36
4.32
4.25
4.23
4.19
4.18
4.15
4.15
4.05
4.05
3.94
3.93
3.91
3.87
3.84
3.77
3.75
3.72
3.62
3.58
3.58
3.54
3.52
3.49
3.29
3.28
3.14
2.85
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Notes:
aIndex of “revealed” comparative advantage (RCA) is the share of a sector in an economy’s total goods and service
exports divided by that sector’s share in global international trade in goods and services (Balassa 1965). The export
shares range from 62% A 24% for agriculture, 96% A 41% for mining, 86% A 52% for manufacturing, E 98% A
61% for services. (There are well over 30 more economies whose services share of exports exceeds twice the global
average of 21%.)
bDue to insufficient data for some other variables, these economies are not included in Figures 4, 6, E 7 and in the
regressions reported in subsequent tables.
Fonte: Authors’ compilation based on United Nations (2018) export value data for goods and International
Monetary Fund balance of payments data for services as presented in World Bank (2018).
Structural Transformation to Manufacturing and Services 35
fragmentation of production processes. This is making an ever-higher proportion of
goods and services internationally tradable and changes in comparative advantage
less predictable (Baldwin 2016, 2019; Constantinescu, Mattoo, and Ruta 2018;
Rodrik 2018).
Economies that are well endowed with natural resources per worker and
per unit of produced capital, and thus have a comparative advantage in farming
or mining, often fret that specializing in primary production and exports slows
their economic growth. That concern stems from two facts. Primo, the international
terms of trade for such countries have faced a long-term decline and are more
volatile than those for other countries.2 Second, the tradable sectors of high-income
countries typically have been dominated by manufactures. Spurred by Prebisch
(1950, 1959) and Singer (1950), pessimism about primary products caused many
newly independent developing countries to provide import protection for their
manufacturing sectors from the 1960s to at
Quello
protectionist policy choice, far from boosting their long-run economic growth, led
resource-rich developing economies—as well as Australia and New Zealand—to
grow slower than others until they belatedly opened their economies (Anderson
1998). Even during the present decade, that pessimism has led governments of some
resource-rich emerging economies to seek ways to diversify away from their main
export activities when prices of those primary exports slumped. It stems in part
from not realizing that growth in, Dire, the mining sector creates jobs not only in that
sector but also in the industries producing nontradables, as that boost in the nation’s
income translates to more consumption of all normal products, including those that
have to be produced domestically.
the 1980s. Ironically,
least
for
technological
the differences
There are numerous explanations
in structural
transformation patterns across countries. Commonly included in these explanations
are differences in rates of
improvements (since multifactor
productivity growth rates differ across sectors and in their factor-saving bias), rates
of change in relative factor endowments (since factor intensities of production vary
across sectors), and international terms of trade (since countries differ in their
comparative advantages). Demand considerations are less commonly considered,
yet per capita incomes matter because income and price elasticities of demand for
products differ across sectors, including nontradables. Also important are policies
that distort relative domestic producer and consumer prices of products in each
sector.
Recent empirical attempts to explain observed structural changes have
tended to focus on one or a subset of countries, sectors (normally ignoring mining),
or contributing factors (particularly labor productivity), and they have tended to
2Vedere, Per esempio, Spraos (1980); Pfaffenzeller, Newbold, and Rayner (2007) and the references therein on
price trends; and Williamson (2012) on historical evidence of volatile terms of trade leading to slower growth rates
for commodity exporters than rates enjoyed by exporters of predominantly manufactured goods.
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36 Asian Development Review
focus on employment or GDP shares and ignore the trade dimension (as pointed out
by Matsuyama 2009). Yet changes in sectoral export shares may reflect changes in a
country’s comparative advantages or in policies affecting their trade specialization
and may help explain differences in changes in sectoral shares of not just exports
but also GDP and employment.
The purpose of this paper is to explore, for each of the four key sectors
(agriculture, mining, manufacturing, and services), the contributions of changes in
per capita incomes, relative factor endowments, and sectoral productivity growth on
sectoral shares of GDP, employment, and exports since 1990. We chose this limited
time period so as to have a large sample of countries covering the full spectrum of
per capita incomes.
The paper begins by summarizing standard theory that can explain the above
trends and stylized facts regarding structural changes in a closed economy as it
grows and thus also in the global economy. It then examines how that theory differs
when one considers a small open economy that is able to trade with the rest of the
world given that country’s terms of trade. The differences between closed and open
economies are small for sectoral shares of GDP and employment, but can be large
for sectoral shares of exports. The paper then takes that theory to a panel of annual
data for 117 countries over the 25 years until 2014, to show the extent to which
declines in the relative importance of primary and manufacturing sectors in GDP,
employment, and exports are explained by changes in per capita income, relative
factor endowments, and sectoral productivity growth.
The results are unsurprising for GDP and employment shares, whose decline
in primary production and then manufacturing can be viewed as symptoms of
successful economic growth. Tuttavia, sectoral export shares, and thus indexes of
“revealed” comparative advantage, are far more varied across the spectrum of per
capita incomes: there are numerous developing countries with export specialization
in services even at low per capita income levels, while high-income countries that
are relatively well endowed in agricultural land or mineral reserves per worker
have retained export specialization in a few primary products. This makes clear
that it is not inevitable that a growing economy will pass from production and
export specialization in primary products to manufactures and then services: some
will skip the manufacturing phase while others will grow rich (and have a large
nontradables sector) and remain specialized in exports of primary products (Gill
et al. 2014).
The structure of this paper is as follows. The first section summarizes
what trade and development theory would lead one to expect about structural
transformation as economies grow. Sources of the data to be used to test a set of
hypotheses are then described. As a prelude to the econometrics, scatter diagrams
are presented to show the spread and mean of sectoral shares at different levels of
real per capita income. Regression results are then presented to show the extent
to which sectoral share changes are explained by changes in per capita income,
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Structural Transformation to Manufacturing and Services 37
relative factor endowments, E, in the case of agriculture, productivity growth in
that sector. The final section draws out several important implications for policies of
both high-income and emerging economies, including those with extreme relative
factor endowments.
II. Theory
It
is helpful
to begin by first considering a closed economy,
then an
open two-sector economy, and then one that also includes a sector producing
nontradable products. To keep the analysis as simple as possible, we assume that
there are no intermediate inputs and all markets are perfectly competitive and
free of government interventions so that there is full employment of all factors of
production.3 Growth is assumed initially to come exogenously from improvements
in total factor productivity (TFP) with no changes in aggregate factor endowments.4
The influence of factor endowment changes is considered later in this section.
UN.
Gross Domestic Product Shares of a Closed Economy
Consider first a closed economy with only two sectors: agriculture and
nonagriculture. If its economic growth was due to productivity growth occurring
equally rapidly in both sectors, their supply curves would shift out at the same rate.
This is illustrated in Figure 1, where it is assumed that the two sectors’ supply curves
coincide initially and hence also subsequent to productivity growth, which lowers
marginal costs equally in the two sectors. In this closed economy, the demand curves
for the two sectors’ outputs are shown to cross on that common supply curve and
hence each sector has a 50% share of GDP at point X, given the assumed absence of
intermediate inputs. Because people spend a declining proportion of their incomes
on food as their incomes rise, the demand curve shifts out less for agricultural goods
than for other products after productivity-improving income growth. Così, outputs
of both sectors rise but less so for agriculture, and the price of farm products falls
relative to the price of nonfarm products—and more so the more price inelastic the
demand for food. The GDP share of agriculture (nonagriculture) is below (above)
50% at the new equilibrium points Y and Z. It would fall even more over time in that
growing economy as income and price elasticities of demand for food fall further
below 1 as per capita income rises (Engel 1857). And a faster rate of reduction in
marginal costs in agriculture than in the rest of the economy (as suggested by the
3Changes in taxes, subsidies, or quantitative restrictions on production, consumption, or trade in products or
factors used to produce them also affect the structural transformation of an economy but are ignored here.
4The emphasis on technical change as the key source of economic growth that is inducing structural
transformation is consistent with recent empirical literature (Herrendorf, Rogerson, and Valentinyi 2013, 2014;
Herrendorf, Herrington, and Valentinyi 2015).
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38 Asian Development Review
Figura 1. Shifts in Demand and Supply Curves for Agricultural and Nonagricultural
Products in a Closed Growing Economy
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Sag = agricultural supply, Sna = nonagricultural supply, Dag = agricultural demand, Dna = nonagricultural demand.
Fonte: Authors’ adaptation from Figure 5.2 of Johnson, D. Gale. 1991. World Agriculture in Disarray, revised
edition. London: St. Martin’s Press.
empirical work of Martin and Mitra 2001; and Gollin, Parente, and Rogerson 2002)
would reinforce that tendency.
This model is appropriate not only for a closed economy but also for the
world economy as a whole: it suggests that the ratio of the international prices
of agricultural products to other products will decline over time as global per
capita income grows. This is consistent with what happened over the 20th century
(Pfaffenzeller, Newbold, and Rayner 2007).
The effects of these tendencies in a closed economy can also be seen in Figure
2, where AB represents the initial production possibility curve and U captures the
community’s preferences (questo è, society would be indifferent about consuming any
bundle of farm and nonfarm products indicated by that curve). The tangency point
E is the initial equilibrium outcome where supply equals demand for each of the two
products in this closed economy. The initial equilibrium price of all other products
in terms of farm goods is given by the (negative) slope of price line 1, and the
two sectors are shown again to have a 50% share of GDP initially. Then economic
growth, whether due to productivity growth or an increase in factor endowments,
Structural Transformation to Manufacturing and Services 39
Figura 2. Effects of Productivity Growth in Agriculture and Nonagriculture Sectors in a
Closed Growing Economy
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Fonte: Authors’ adaptation from Anderson, Kym. 1987. “On Why Agriculture Declines with Economic Growth.”
Agricultural Economics 1 (3): 195–207.
would shift AB to the northeast to A’B’ if the shift is equiproportionate. IL
associated growth in per capita income would lead to a new equilibrium at E’, Dove
the share of income spent on farm products would be lower than at E (because the
income and price elasticities of demand for food are less than 1). Even though the
quantity of food consumed may have risen (from F to F’), the consumed quantity
of other products has risen more (from N to N’); and the relative price of farm
products is lower (price line 2 is steeper than price line 1). In this simple model
with no intermediate inputs, so that price times quantity summed over all products
is equal to GDP, the share of agriculture in GDP falls. It would fall even more if
productivity growth in agriculture exceeded that of the rest of the economy, come
that E moves to E” where price line 3 is even steeper than price line 2.
B.
Gross Domestic Product Shares of a Small Open Economy
What about a small open economy that can export any share of its production
or import any share of its consumption of both farm and nonfarm products at the
40 Asian Development Review
Figura 3. Effects of Productivity Growth in Agriculture and Nonagriculture Sectors in a
Small Open Growing Economy
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Fonte: Authors’ adaptation from Anderson, Kym. 1987. “On Why Agriculture Declines with Economic Growth.”
Agricultural Economics 1 (3): 195–207.
prevailing international terms of trade? Then instead of its initial equilibrium at
point E in Figures 2 E 3, this economy would produce at point Eo and consume
at point Co in Figure 3, where the international terms of trade are given by (IL
negative of) the slope of EoCo. In that case, this economy’s farm sector would have
a larger share of GDP at Eo than it had at E when it was closed.
IL
If productivity growth occurred in this small open economy but
international terms of trade remained unchanged, agriculture’s share of GDP would
rise or fall depending only on whether that growth is biased toward farm or nonfarm
production. If productivity growth is sectorally unbiased, agriculture’s share would
remain unchanged at Eo’ in Figure 3. If economic growth abroad is similarly
unbiased, it would lower the relative price of farm products for reasons mentioned
above, in which case this small economy’s international terms of trade would
deteriorate and its new equilibrium would be at point Eo”.
To generalize, if productivity growth is occurring abroad and is not heavily
biased against agriculture, the farm’s share of GDP in this small open economy will
decline unless its own productivity growth is sufficiently biased toward agriculture
Structural Transformation to Manufacturing and Services 41
(contrary to the rest of the world) for the change in quantity to more than offset its
terms of trade deterioration.5 The agricultural growth bias would have to be even
stronger in a large farm-exporting economy since its growth would further depress
the country’s international terms of trade.
C.
Adding a Nontradables Sector
In reality, a large part of each economy involves the production and
consumption of nontradable goods and services because of these products’
prohibitively high trade costs. The prices of nontradables are determined solely by
domestic demand and supply conditions and related policies because the quantity
demanded has to equal the quantity produced domestically.
If one were to combine the two tradable sectors into one “super sector” of
tradables, then the above closed economy conclusion that agriculture’s share of
GDP is likely to decline over time will be stronger if the share of tradables in GDP
declines in growing economies.
Available evidence suggests that
the income elasticity of demand for
services—which make up the vast majority of nontradables—is well above unity
in developing countries and tends to converge toward unity as incomes grow
(Lluch, Powell, and Williams 1977; Kravis, Heston, and Summers 1983; Theil and
Clements 1987). If productivity growth is equally rapid for nontradables as for
tradables, while demand grows faster for nontradables than for tradables, both the
price and quantity and hence the value of nontradables will increase relative to that
of tradables. This is illustrated in Figures 2 E 3 if the axes are relabeled “tradables”
and “nontradables” in place of “agricultural goods” and “nonagricultural products”,
rispettivamente. If productivity growth is faster in tradables than in nontradables, it is
even more likely that the share of nontradables in GDP would rise and the real
exchange rate (the price of nontradables relative to tradables) would appreciate. In
that case, the share of tradables in GDP would fall.
At the global level, the income elasticity of demand for manufactured
consumer goods also matters, as Figures 2 E 3 showed for agriculture. While
that elasticity may be above 1 in low-income countries, it falls increasingly below
1 as countries become more affluent.6 Hence, the manufacturing sector is also
likely—thanks to the nature of demand for services—to come under pressure to
decline eventually even in small open economies as they become affluent, following
the pattern for agriculture. Again, the exceptions would be in those small open
economies where manufacturing TFP growth is exceptionally rapid.
5If the source of growth was entirely learning-by-doing in the manufacturing sector, it is even more certain
that agriculture will decline in this small open economy, as shown formally by Matsuyama (1992).
6Empirical estimates for the United Kingdom and the United States support a declining income elasticity of
demand for manufactured goods as per capita income rises (Herrendorf, Rogerson, and Valentinyi 2014, Figura 6.7).
See also Matsuyama (2009), Boppart (2014), and Lawrence (2018).
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42 Asian Development Review
D.
Allowing for Mining
To also be relevant to resource-rich economies, we assume now that the
natural resource-based tradables sector involves mining as well as agriculture.
Domestic demand for ores, minerals, and energy raw materials rise as a
country begins to industrialize, build more infrastructure, and become more
affluent. But then, such demand tends to fall as high-tech manufacturing and
services increasingly dominate nonprimary production, although improvements in
technology can at times alter this inverted U-shaped relationship with real GDP per
capita (Radetzki and Tilton 1990, Crowson 2018). Mining differs from other sectors
in that it can expand not only because of sectoral TFP growth but also following the
discovery of new reserves, which is commonly exploited with the help of mining-
specific foreign capital inflows.
E.
Allowing for Some Services to Be Tradable and Some Goods to Be
Nontradable
As trade costs fall, an increasing range of goods and services are becoming
internationally tradable (Liu et al. 2018). By 2014, services accounted for at
least 40% of national export earnings in about one-third of all countries (IL
global average was 21%). Some of these tradable services are based on natural
resources (per esempio., tourism in conservation parks, beaches, and ski resorts; and gas
pipelines or transport corridors), while others take advantage of low wages (call
centers) or sophisticated financial sectors (international banking and insurance). A
accommodate these activities, we include resource-based services in agriculture and
mining in the natural resources sector and the rest in manufactures in the “other
tradables” sector.
The sectoral GDP and employment shares data for each economy do
not indicate the proportion of each sector’s jobs or output that are producing
nontradables. One can think of the service shares as being “nontradables” if it were
the case that the number of service jobs or GDP value related to tradables were
equivalent to those for goods that are nontradables.
F.
Employment Shares
Given our initial assumption of no changes in aggregate factor endowments,
the above reasoning is close to sufficient for understanding changes in sectoral
shares of labor employment: agriculture (services) shares decline (rise) as per
capita income grows, while manufacturing shares follow an inverted U-shaped
sentiero. Complications arise, Tuttavia, Quando (io) there are lags in labor migrating out
of declining sectors or (ii) labor productivity growth differs substantially between
sectors.
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Structural Transformation to Manufacturing and Services 43
Historically, out-migration from agriculture has been sluggish because it
typically requires a physical, social, and cultural move from living on or near a
farm to a town or city—something that is far less likely to be necessary for an urban
worker moving to a new manufacturing or service sector job. Così, the decline in
the share of employment in agriculture may lag the decline in agriculture’s share
of GDP. It is also possible that the employment share statistics are biased because
they do not take into account the full extent to which off-farm activities provide
farm households with some of their income (often a substantial share—see Otsuka,
Estudillo, and Sawada 2009). Because those data refer simply to main occupations
rather than hours worked, they also understate the productivity of farm workers per
hour, since they do not account for the degree of underemployment in farming given
its seasonality (McCullough 2017).
The share of mining in employment, by contrast, is typically less than its
share of GDP in settings where mining is highly capital intensive. Infatti, questo è
the norm, not only in high-income countries but also in numerous resource-rich
developing countries that are open to mining-specific (including human) capital
inflows from abroad. Such capital inflows, and the (often associated) discovery of
new subsoil or subseabed reserves, can be a significant source of both mining sector
GDP growth and structural transformation—but not necessarily of more local jobs
if local workers lack the skills required for those tasks. This contrasts with mining
booms before World War I that attracted immigrants for such labor-intensive tasks
as panning for gold.
Productivity impacts on sectoral employment can be positive or negative.7
On the one hand, the adoption by one sector of labor-saving technologies can
raise its output and perhaps exports but reduce its employment, thereby pushing
labor to other sectors (Gollin, Parente, and Rogerson 2002, 2007). On the other
hand, labor could be pulled out of a sector due to new job prospects in another
sector that is enjoying faster TFP growth and/or faster demand growth associated
with spending higher incomes (Lucas 2004; Gollin, Parente, and Rogerson 2007).
The push element has always been present for farmers and, more recently, for
factory workers where robotics and digitalization are the latest influences. Artificial
intelligence will replace some workers, but the income growth it generates will
lead to the creation of new jobs (Acemoglu and Restrepo 2018, Baldwin 2019).
The net effect of the latter pull factor on sectoral employment is uncertain, but if
it favors nontradable services, that would be a further reason to expect declines in
employment in the various tradable goods sectors.
7According to the induced innovation hypothesis, productivity growth will be biased in favor of saving
the scarcest factor of production (Hicks 1963, Hayami and Ruttan 1985). That hypothesis is more likely to
be supported in countries at the technological frontier, while producers in emerging economies will choose
whatever is most profitable from among the full spectrum of available technologies as their relative factor prices
change.
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44 Asian Development Review
G.
Allowing for Factor Endowment Changes
The assumption at the outset of this theory section has been that national
income growth comes from exogenous technological change. Productivity also
changes as climates change, affecting various sectors unevenly. Growth also results
from investments in innovation or importation and adaptation of technologies
from more advanced economies. Income growth can also result from net factor
accumulation over and above depreciation.8 Natural resource capital, Per esempio,
can be discovered through mining exploration or improved through investment (per esempio.,
clearing and fencing farmable land). Produced capital can also be enhanced through
domestic investment or by importing capital from abroad; and the stock of labor
can change through births exceeding deaths, changes in labor force participation
(per esempio., more women choosing paid work), population aging, and immigration net of
emigration.
Any of these changes alters the per worker endowments of natural resources
and produced capital and hence the country’s comparative advantages. According
to Rybczynski (1955), growth in the aggregate stock of capital per worker can have
the effect, at constant relative product prices, of expanding the output of the most
capital-intensive industries and shrinking that of the most labor-intensive industries.
In developing countries where agriculture is among the most labor-intensive
industries, along with such industries as clothing and footwear, the growth in the
stock of capital per worker can be another source of relative decline in those sectors
of growing economies. Martin and Warr (1993, 1994) found that this has been the
case for agriculture in Indonesia and Thailand.
H.
Export Shares: Less Clear-Cut
What about sectoral export shares? These shares depend on the country’s
comparative advantage and on how rapidly the tradability of each sector’s output
increases as trade costs are lowered. Per esempio, if investments in transport-related
infrastructure cause a small economy’s trade costs to fall relative to those of
the rest of the world, this will alter its comparative advantages and cause it to
be internationally competitive in a larger number of products (Venables 2004).
Should its farm products gain more from the decline of trade costs than its
nonfarm products, Per esempio, the country would see its comparative advantage
in agriculture strengthen.
The two key workhorse theories of comparative advantage developed in the
20th century are the Heckscher–Ohlin model, in which all factors of production
are intersectorally mobile, and the specific-factors model, in which one factor is
8Infatti, Jorgenson and Griliches (1967) argue that if all investments in capital were fully taken into account,
they would fully explain economic growth, leaving no residual to be labeled as “technological change.”
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Structural Transformation to Manufacturing and Services 45
specific to each sector. These two models have been blended to account for primary
sectors that use specific natural resource capital (farmland and mineral deposits)
in addition to intersectorally mobile labor and produced capital (Krueger 1977,
Deardorff 1984). This blended model suggests we should expect primary product
exports from relatively lightly populated economies that are well endowed with
agricultural land and/or mineral resources to those economies that are densely
populated with few natural resources per worker.
Leamer (1987) developed this Krueger–Deardorff blended model further
and related it to paths of economic development. If the stock of natural resource
capital is unchanged, rapid growth of produced capital (physical capital plus human
skills and technological knowledge) per hour of available labor tends to strengthen
comparative advantage in nonprimary products. By contrast, a discovery of minerals
or energy raw materials would strengthen that country’s comparative advantage in
mining and weaken its comparative advantage in agricultural and other tradable
prodotti, other things being equal.9 Such a mineral discovery would also boost the
country’s income and hence the demand for nontradables, which would cause its
sectorally mobile resources to move into the production of nontradable goods and
services, further reducing farm and industrial production.
At early stages of economic development, a country with high trade costs is
typically agrarian, with most GDP and employment in the agriculture sector (Quando
home-produced food is included in the national accounts). If such a country has a
relatively small stock of agricultural land and other natural resources per worker,
labor rewards will be low. It may be autarkic initially, but as its trade costs fall or
government trade restrictions are removed, it will develop a comparative advantage
in unskilled labor-intensive, standard-technology manufactures. Then as the stocks
of industrial and human capital per worker grow, there will be a gradual move
toward exporting more of those manufactures that are relatively intensive in their
use of physical capital, skills, and knowledge.10
In the standard Heckscher–Ohlin model of international trade, in which
factors of production are perfectly intersectorally mobile, international trade in
9Columns 3–5 of Table 2 are close to the relative factor endowment ratios in the trade theory developed by
Leamer (1987). They require imagining Leamer’s triangle in which countries are points and each of the three sides
represents one of the factor endowment ratios (natural resources per worker, produced capital per worker, and natural
resource per unit of produced capital). The closer a point is to the natural resource apex of the triangle, the stronger
that country’s comparative advantage in resource-based products.
10The above theory of sectoral changes and evolving comparative advantages has been used successfully
to explain the 20th century “flying geese” pattern of comparative advantage and then disadvantage in unskilled
labor-intensive manufactures, as some rapidly growing economies expand their endowments of industrial capital per
worker relative to the rest of the world (Ozawa 2009). It has also been used to explain the evolving patterns and
project future patterns of trade between Asia’s resource-poor first- and second-generation industrializing economies
and their resource-rich trading partners (Anderson and Smith 1981, Anderson and Strutt 2016). It is less likely
to explain bilateral trade patterns in the current century due to fragmenting production processes and lengthening
regional and global value chains (Baldwin 2016, 2019; Constantinescu, Mattoo, and Ruta 2018; Liu et al. 2018;
Rodrik 2018).
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46 Asian Development Review
Tavolo 2. Gross Domestic Product, Agricultural Land, Mineral Resources, and Other
Capital Endowments in Asia and Other Economies Relative to the World (per capita),
2000–2004 and 2014
Agricultural Agricultural Mineral
Other
Land Value Resources Capital per
per Capitab per Capitab Capitab,c Capitab
GDP
per
Total
Land per
Workera
2000–2004
Bangladesh
Taipei,China
Republic of Korea
Japan
India
Viet Nam
Philippines
People’s Republic of
China
Thailand
Indonesia
Myanmar
Cambodia
Malaysia
Lao People’s
Democratic
Republic
Asia
stati Uniti
Sub-Saharan Africa
Latin America
Middle East and
North Africa
New Zealand
Australia
World
4
8
9
12
15
18
21
28
32
40
59
65
74
151
24
144
165
207
280
326
1,799
100
Land per
Capitaa
2000–2004
8
5
5
5
22
14
19
54
39
27
42
49
41
42
34
178
148
171
91
550
2,856
100
2014
2014
2014
2014
36
low
48
25
59
104
65
156
131
78
n/a
82
143
135
102
117
78
139
83
366
202
100
1
1
1
1
15
35
8
63
9
43
low
0
109
51
63
119
39
122
2,287
n/a
1,584
100
6
high
273
355
9
13
17
60
35
25
low
6
136
12
33
640
11
73
19
high
500
100
10
208
256
350
14
18
26
71
54
32
12
10
103
20
37
503
17
91
108
409
571
100
GDP = gross domestic product.
Notes:
aA percentage of the world average, based on hectares.
bA percentage of the world average, based on United States dollars at the market exchange rate.
cOther capital refers to non-natural produced (including human) capital.
Fonte: Authors’ compilation drawing on 2000–2004 World Development Indicators data assembled in Sandri,
Valenzuela, and Anderson (2006) E 2014 World Bank data in Lange, Wodon, and Carey (2018).
products is a perfect substitute for trade in factors in that product price equalization
across countries due to product trade would generate factor price equalization
(Mundell 1957). This is not so in the specific-factor or blended-trade models,
Tuttavia, where rewards to intersectorally mobile labor will tend to be above (below)
the global average in countries that are lightly (densely) populated. This wage
difference may be sufficient to induce international labor movements.
Specifically, natural resource-abundant economies may attract, from more
densely populated countries, migrants who seek to become farmers or miners in
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Structural Transformation to Manufacturing and Services 47
frontier regions. That would raise the settler economy’s total, if not per capita GDP,
and cause its primary sector’s share of GDP to fall more slowly than in economies
that are growing equally rapidly but are less abundant in natural resources. Also, if
resource-rich economies direct some of their capital investment to forms of capital
(including new technologies) that are specific to primary production, they would not
develop a comparative advantage in manufacturing or services until a later stage of
development, at which time their exports from those nonprimary sectors would be
relatively capital intensive. This is all the more likely if new technologies developed
for the primary sector become increasingly labor saving as real wages rise—leading
potentially to what are known as factor intensity reversals. This happens when a
primary industry in a high-wage country retains competitiveness against low-wage
countries by that industry becoming more capital intensive. The primary sector’s
share of GDP would decline more slowly the faster its productivity growth
compared to the average global rate, both relative to that of other sectors.
International prices of some commodities typically have cycles around their
long-run trends. Inoltre, new discoveries of raw materials are made from time
to time. A boom in one of the main tradable sectors of a country that is not
matched in (many) other countries has the effect of strengthening that country’s
real exchange rate. Questo, in turn, draws resources to that sector and to the
sectors producing nontradables, such as services, and thus away from other sectors
producing tradables, other things being equal. It also raises national income and
thus boosts the domestic demand for both locally produced and imported products.
Together, these forces reduce the volume of exports from nonbooming sectors
and the domestic currency price of those exports and hence their aggregate value
(Corden 1984).
Such a boom in a key export sector could be supply driven (per esempio., the discovery
of a mineral or energy raw material deposit) or demand driven (per esempio., a rise in the
international price of that sector’s output). In entrambi i casi, the boom may attract
immigrants and capital inflows and thus expand the domestic economy. In the latter
case, it will show up as an improvement in the country’s international terms of trade.
The more capital funding for new investment coming in from abroad, the earlier and
larger will be the initial appreciation in the real exchange rate. Later, the exchange
rate appreciation will reverse as the boom moves from its investment phase to
its export phase and starts to return dividends, and possibly repatriate capital, A
foreign investors (Freebairn 2015). Even so, if a newly discovered mineral deposit
takes many decades to deplete, the economy will continue to have a higher per
capita income, and shares of mining and nontradables in GDP and employment
will continue to be higher than prior to the mineral discovery, as will the share of
exports from mining. This is another way in which trade can alter one’s expectations
about structural transformation of a particular economy to manufacturing and
services.
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48 Asian Development Review
Sectoral shares of exports (and imports) are also affected by preferences
if (contrary to the assumptions of most trade theories) consumer preferences are
nonhomothetic (Markusen 2013). As already noted, many foods (services) have an
income elasticity of demand below (above) 1, and that elasticity declines toward
0 (1) as incomes grow. Within the food bundle, demand elasticities for staples
fall much earlier than for nonstaples such as horticultural and livestock products
(Bennett 1936, 1941). Producer demands for minerals and energy raw materials
rise as countries begin to industrialize and become more affluent, but then fall as
services increasingly dominate GDP. Nel frattempo, the income elasticity of demand
for mainstream manufactured consumer goods, while it may be above 1 in low-
income countries, falls increasingly below 1 as countries become affluent. Because
production of income-elastic goods tends to use skilled labor relatively intensively
(Caron, Fally, and Markusen 2014), this alters the skill premium in wages and hence
also affects the competitiveness of different sectors.
Three further examples of how trade can affect structural transformation
relate to tradable services. The first is tourism: as international passenger transport
costs fall or real incomes grow rapidly in populous countries, the comparative
advantage in tourist-related services strengthens for countries with natural beauty
and a pleasant climate located near high-income countries with fewer such assets.
Another example relates to transit services. Landlocked countries, particolarmente
smaller ones with large neighbors, have a comparative advantage in providing
transit services, such as underground pipelines or access to roads, rail, and rivers.
Yet another example are call centers and information technology services requiring
English-language capability: the ICT revolution has strengthened the comparative
advantage in such labor-intensive services for those low-wage countries where
English is widely spoken. Tuttavia, these specific factors contributing to trade
specialization of certain developing countries (natural beauty, transport or pipeline
corridors, English-language skills) are not included in the regressions below.
IO.
Impact of Market-Distorting Policies
Changes in taxes, subsidies, or quantitative restrictions on the production,
consumption, or trade of products, or the factors or intermediate inputs used to
produce them, can affect the structural transformation of an economy.
The large differences in relative factor endowments and hence comparative
advantages among growing economies ensure that concerns vary regarding the
consequences of uninhibited structural transformation for rural–urban income
disparities, food and energy security, food safety, and environmental degradation.
This has contributed to systematic differences in the use of trade and other
price-distorting policies in responding to those concerns. Differing perceptions of
risk have also led to different policies toward new technologies.
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Structural Transformation to Manufacturing and Services 49
Specifically, developing country governments tend to depress agricultural
relative to manufacturing incentives facing producers, but they gradually change to
the opposite sectoral bias as the country passes through the upper-middle-income
stage. This has the effect of artificially boosting initial shares of manufacturing in
GDP and employment but slowing the relative decline of agriculture as the economy
becomes affluent (Anderson 2009), for reasons explained in Anderson, Rausser,
and Swinnen (2013). Since these sectoral support policies typically have a strong
antitrade bias, they reduce the ratio of trade to GDP and reduce the number of
products in which the country is internationally competitive. How they alter sectoral
shares of exports is less certain: they may raise or lower agriculture’s share of that
shrunken volume of exports, Per esempio.
In addition to keeping food prices artificially low, developing country
governments also commonly subsidize fuel consumption. As countries become
more affluent, Tuttavia, emerging economies will begin to worry more about
pollution and the rapidly rising fiscal cost of fuel subsidies, and so those subsidies
are phased out and eventually replaced by taxes on at least hydrocarbon sources
of fuel (OECD 2015, Coady et al. 2017). This means that mining’s share of
exports initially goes down but less so as income growth proceeds, and it may
eventually be inflated if fuel consumption by firms and households is discouraged
less domestically than in the rest of the world as the country becomes more affluent.
That pattern will be accentuated if national carbon emission taxes are adopted and
more effectively enforced in countries with high per capita incomes, particolarmente
if border tax adjustments are not used to discourage the relocation of fossil fuel-
intensive industries to less regulated poorer countries.
Apart from these long-run trends in sectoral policies, governments in some
natural resource-rich countries assist tradable sectors that lag behind when there is a
boom in, Per esempio, the mining sector. This may offset the burden of adjustment
to real exchange rate movements for some tradable industries, but it exacerbates
that burden on other tradable industries. Inoltre, adjustment needs change as
the mining sector transitions from its investment phase to its export phase and
eventually to the end of the boom (Corden 1984, Freebairn 2015), making it difficult
for such interventions to target particular groups in a timely and temporary manner.
An alternative source of sectoral boom can result from new technologies. IL
Green Revolution that resulted from investments in agricultural research provided
a boom to wheat, rice, and maize production from the 1960s in countries for
which it was most suited. That lowered prices of staples in those adopting countries
and in international markets, which reduced the competitiveness of grain farmers
elsewhere. Likewise, the adoption of genetically modified (GM) varieties of corn,
soybean, and cotton since the mid-1990s has boosted agriculture in countries that
have approved their production, but again this has depressed the output and net
exports of GM-free substitutes in countries that have chosen to not allow the
production or use of GM crops.
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50 Asian Development Review
J.
Summary of Structural Transformation Hypotheses
The following hypotheses are among those suggested by the above theory:
1. The shares of agriculture (services) in GDP and employment will fall
(rise) as per capita income rises, while the manufacturing sector’s shares
will initially rise and then eventually fall after countries reach a high per
capita income. Tuttavia,
(UN)
(B)
(C)
(D)
in lightly populated settler economies, the agriculture (or mining)
sector’s decline may be postponed if large numbers of immigrants
are allowed to expand the farming (mining) frontier, and more so
if productivity growth in this economy is especially fast in that
primary sector;
the share of exports of labor-intensive manufactures in total exports
will decline as the stock of capital and hence per capita income
grows, while the share of exports of capital-intensive manufactures
in total exports will rise;
the decline in the share of employment in agriculture will lag
the decline in agriculture’s share of GDP to the extent
Quello
out-migration of farm workers is sluggish, implying farm labor
productivity will become relatively low;
the share of agriculture (services) in global employment will
eventually decline (rise), Ma
is not clear whether global
Esso
employment in manufacturing will rise or fall as the share transfers
from high-income to developing countries; E
(e)
the shares of services may be high, especially in exports, for
developing countries with a strong comparative advantage in
tourism, transit, call centers, or information technology services.
2. The share of employment in mining will be below mining’s share of
GDP, particularly in developing countries that encourage the inflow
the sector’s labor
of foreign mining-specific capital,
productivity will be high.
implying that
3. Countries with a relatively large endowment of natural resources per
worker will have a relatively large share of nontradables (hence possibly
of services) in GDP as well as a relatively high share of exports from
one or both primary sectors.
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Structural Transformation to Manufacturing and Services 51
4. Manufacturing shares of GDP, employment, and especially exports will
be relatively large in countries with a relatively small endowment of
natural resources per worker except in those developing countries with
a strong comparative advantage in such services as tourism, transit, call
centers, or information technology.
5. Exports of manufactures will be less capital intensive the smaller a
country’s per worker endowment of capital (both natural resources and
produced capital).
6. Agriculture’s shares of GDP and exports (if not also employment) will
be higher the higher the rate of TFP growth in that sector relative to
the rest of the economy. In particular, those shares will be higher for
countries that have adopted high-yielding green revolution or GM crop
varieties.
III. Data for Pertinent Variables
In order to test the above hypotheses, we have assembled annual data from
1990 A 2016 for more than 160 countries. An earlier start year is not possible
without having to shrink the sample size and thereby reduce the spectrum of
countries in terms of income per capita. Even then, we had to draw on several
sources to get all the desired variables. In definitiva, we were constrained to 117
countries and the years 1991–2014 for a full set of data for all the variables listed
below.
Specifically, the three sets of national variables whose trends we seek to
explain for each of the four sectors (agriculture, mining, manufacturing, E
services) are
(io)
sectoral shares of GDP (value added), Sv;
(ii)
sectoral shares of employment, Se; E
(iii)
sectoral shares of exports of goods and services, Sx.
Data sources are as follows: Sv are from World Bank (2018); Se are from
World Bank (2018), except for manufacturing shares which are from International
Labour Organization (2018); and export value data in current United States dollars
to generate Sx are from World Bank (2018), which draws from United Nations
(2018) trade data for goods and from the International Monetary Fund balance of
payments data for services.11
11The Standard International Trade Classification (SITC) codes for agriculture are SITC 0, 1, E 2, except
for 27, 28, E 4. For mining they are SITC 27, 28, 3, E 68; and all other merchandise items are classified as
manufactures. Within the latter are labor-intensive manufactures such as textiles, clothing, and footwear (SITC 65,
84, E 85).
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52 Asian Development Review
The explanatory variables used to explain shares and indexes are:
(io) Real income per capita. This is defined as the natural log of GDP
per capita, measured at purchasing power parity (constant 2011
international dollars). The data are from World Bank (2018).
(ii)
Factor endowments. The data are from Lange, Wodon, and Carey
(2018) expressed in 2014 US dollars for the years 1995, 2000,
2005, 2010, E 2014. We have expressed them per worker using
employment data from World Bank (2018), interpolating linearly for
the years in between, extrapolating back to 1990 using the same rate
of change between 1995 E 2000, and extrapolating forward to 2016
using the same rate of change between 2010 E 2014. Three factor
endowment per worker ratios are used:
(UN) agricultural land, defined as the discounted sum of the future value
of crop and pasture land rents;
(B) mineral and energy raw material
reserves, defined as the
discounted sum of the value of rents generated over the lifetime
of the reserves; E
(C) produced capital (physical and human), where physical capital
includes machinery, equipment, edifici, and urban land
measured at market prices, and human capital is defined as
the discounted value of earnings over each person’s lifetime
(disaggregated by gender and employment status).
(iii) National TFP growth rate estimates for agriculture. These are available
up to 2012 from Fuglie, Ball, and Wang (2012).
IV. Evidence of Structural Transformation as Per Capita Incomes Grow
Before turning to the regression results in the next section, this section looks
at just the relationship between per capita income and sectoral shares. In Figures
4a–4d, the four sectors’ shares of GDP, employment, and exports are plotted against
the natural log of per capita real GDP (our indicator of real income). Each dot is a
country–year pair, and the bold local polynomial line is the best fit of the data. These
figures provide support for hypothesis 1, questo è, shares of agriculture (services) In
GDP and employment are lower (higher) the higher is per capita income, while the
manufacturing sector’s shares initially rise and then eventually fall after countries
reach a high per capita income.
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Structural Transformation to Manufacturing and Services 53
Figura 4. Sectoral Shares of GDP (value added), Employment, and Exports as Real Per
Capita Incomes Rise, 1990–2016
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54 Asian Development Review
Figura 4. Continued.
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Exceptions to this hypothesis can also be found in the results. A particularly
striking one is agriculture’s GDP share in Australia: in the 10 decades to 1950,
that share remained within the 20%–30% range (Figure 5a) even though real
per capita income more than doubled over that period. The reason was very
rapid farm productivity growth: this lightly populated settler economy’s high real
wages encouraged the development and widespread adoption of labor-saving farm
technologies as well as rapid immigration (Anderson 2017). This is consistent
with hypothesis 1a. Also clear from Figure 5a, and supporting hypothesis 1, È
the rise and fall in the manufacturing sector’s share of Australia’s GDP. That share
peaked in 1960 at 30%, similar to the peak for other high-income countries. Ma
as Australia’s protection to manufacturing declined after removing import quotas
in the 1960s and lowering tariffs from 1972, that sector’s share fell very rapidly.
Structural Transformation to Manufacturing and Services 55
Figura 4. Continued.
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56 Asian Development Review
Figura 4. Continued.
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GDP = gross domestic product.
Fonte: Authors’ compilation (see text).
Structural Transformation to Manufacturing and Services 57
Figura 5. Sectoral Shares of Gross Domestic Product and Exports in Australia, 1840–2017
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GDP = gross domestic product.
Fonte: Anderson (2017), updated and backdated by the authors.
58 Asian Development Review
Figura 6. Shares of Exports of Labor-Intensive Manufactures in Total Exports as Real Per
Capita Incomes Rise, 1990–2016
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GDP = gross domestic product.
Fonte: Authors’ compilation (see text).
By 2016 it was just 6%, compared with an average of 14% in other high-income
countries (World Bank 2018). Figura 5 also strongly supports hypothesis 3: having
a relatively large endowment of natural resources per worker, Australia’s goods
exports are dominated throughout by primary products, either mining or agricultural
depending on relative prices and timing of mineral discoveries, and services (mostly
nontradables) are a large share of its GDP.
To explore hypothesis 1b, we separated exports of
labor-intensive
manufactures (defined simply as textiles, clothing, and footwear, which are SITC
65, 84, E 85, rispettivamente) from other manufactures and plotted the share of this
subsector of exports against real per capita income. Figura 6 shows strong support
for that hypothesis: the share of exports of labor-intensive manufactures in total
exports initially rises but then declines as per capita incomes rise.
To explore hypothesis 1c, we can examine labor productivity for each sector
by comparing the sector’s shares of GDP and employment. A GDP share above
(below) the employment share suggests that the sector’s labor productivity is above
(below) the national average. These shares are jointly plotted in Figure 7. IL
images are indeed consistent with the hypothesis that farm labor productivity is
relatively low. Figura 7 also reveals that it is manufacturing rather than services that
tends to have above-average labor productivity. Unfortunately, data on mining value
added are not separately available for many countries and so it is not possible to
explore hypothesis 2 to confirm if mining also tends to have above-average labor
Structural Transformation to Manufacturing and Services 59
Figura 7. Sectoral Proportions of Gross Domestic Product and Employment as Real Per
Capita Incomes Rise, 1990–2016
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GDP = gross domestic product.
Fonte: Authors’ compilation (see text).
60 Asian Development Review
Figura 8. Share of Global Employment by Sector, 1991–2017
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Fonte: Compiled by the authors from data in World Bank. 2018. World Development Indicators. Washington, DC.
https://data.worldbank.org/products/wdi (avuto accesso 10 novembre 2018).
productivity (although it often does because of the very high capital intensity of
mining even in low-income countries).
Hypothesis 1d concerns shares of global employment. Figura 8 shows that
the share of agriculture (services) in global employment has indeed been declining
(rising), while employment in industry has maintained its share at 22%–23%,
consistent with Felipe and Mehta’s (2016) finding that there is little trend in the
estimated global share of manufacturing.12 With slower growth and greater capital
intensity of industry in high-income countries than in developing countries, IL
share of industry jobs that are in the high-income countries has dropped by one-third
between 1991 E 2014, from 27% A 18%. The share of global exports of
manufactures originating from developing countries is rapidly converging to the
share from high-income countries, which has fallen from above 90% prior to the
mid-1980s to less than 70% since 2012 (Figura 9).
As for hypotheses 1e and 3, Tavolo 1 reveals that the 30 countries with
the highest shares of services in their exports are mostly small developing
countries (often tropical tourist islands), and there is only one high-income country
12Industry includes manufacturing, mining, construction, electricity, water, and gas (ISIC divisions 10–45).
Unfortunately, more disaggregated global employment data are not available in World Bank (2018).
Structural Transformation to Manufacturing and Services 61
Figura 9. Share of Global Exports of Manufactured Goods in High-Income and
Developing Countries, 1986–2017
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Fonte: Compiled by the authors from data in World Bank. 2018. World Development Indicators. Washington, DC.
https://data.worldbank.org/products/wdi (avuto accesso 10 novembre 2018).
in that list (Luxembourg, although data were unavailable for some rich, tiny
tax-haven countries). Tavolo 1 also reveals that the 30 countries with the highest
shares of primary products in their exports include some high-income countries
(Australia, New Zealand, and oil-rich countries of the Middle East) and numerous
middle-income countries, not just low-income countries. Also clear from Table 1 È
that countries specializing relatively heavily in manufactures cover the full spectrum
of national per capita incomes. Questo è, specializing in primary production and
exports is not inconsistent with an economy growing to high-income status, just as
being internationally competitive in manufactures or services is not confined only
to high-income countries.
V. Regression Results
We now turn to the results of a fixed effects panel regression. Since the
hypothesized relationships between sectoral shares and per capita income are not
linear, we use the natural log of per capita income and the square of that term.
The other key variables are the three factor endowment ratios, since trade theory
suggests they should influence production specialization of open economies. These
ratios are the value per worker of the stock of agricultural land, mineral and energy
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62 Asian Development Review
Tavolo 3. Determinants of Sectoral Shares of Valued Added,
1991–2014 (% of GDP)
Agriculture Manufacturing
−41.909***
(−4.46)
ln YPC
ln YPC squared
Agricultural endowment
Capital endowment
R-squared (adjusted)
Observations
No. of countries
Country fixed effects
Year fixed effects
2.014***
(3.99)
2.071*
(1.82)
0.39
2,504
117
Yes
Yes
8.828
(1.44)
−0.415
(−1.24)
−1.858
(−1.10)
0.14
2,409
116
Yes
Yes
Services
10.126
(1.01)
−0.485
(−0.84)
4.064
(1.64)
0.33
2,500
117
Yes
Yes
GDP = gross domestic product, ln = natural logarithm, YPC = income per capita.
Notes: t statistics in parentheses. *P < 0.1, ***p < 0.01.
Source: Authors’ computations.
resources, and produced capital (physical and human). In addition, we test whether
agriculture’s sectoral shares are impacted by farm productivity growth.
Table 3 presents the results aimed at explaining the sectoral shares of GDP
(value added).13 Consistent with the convex line in Figure 4a for the agriculture
sector, both the log of income per capita and its square have significant coefficients.
The endowment of agricultural land per worker also has a significant coefficient and
its sign is positive, which is consistent with trade theory. The income coefficients
for manufacturing also have the expected signs and are consistent with the inverted
U-shaped line in Figure 4c. The coefficient for produced capital per worker is
negative but not significant for manufacturing. For services, the coefficient on the
income terms are not significant, but their values suggest that the sector’s share
of GDP rises almost linearly with income, which is consistent with Figure 4d.
The services’ coefficient on produced capital per worker is positive but again not
significant. The adjusted R-squared values range from 0.14 to 0.39.
The results aimed at explaining the sectoral shares of employment are in
Table 4. In this case, the income terms are all very significant. Agriculture and
manufacturing have the same signs as in the value added equations. For mining,
the signs of the coefficients are consistent with the inverted U-shape in Figure 4b,
while for services they again imply close to a linear upward trend. Agricultural and
mineral endowments contribute positively to employment in those primary sectors,
but the coefficients are not quite significant at the 10% level. Capital endowments
per worker again make insignificant contributions to aggregate employment in
manufacturing and services. The adjusted R-squared value for mining is low
13Mining is missing because we had an insufficient number of countries with data on mining’s share of GDP.
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Structural Transformation to Manufacturing and Services 63
Table 4. Determinants of Sectoral Shares of Employment, 1991–2014
(% of total employment)
ln YPC
ln YPC squared
Agricultural endowment
Mineral endowment
Capital endowment
R-squared (adjusted)
Observations
No. of countries
Country fixed effects
Year fixed effects
Agriculture
−46.42***
(−4.46)
1.934***
(2.95)
1.189
(1.26)
0.39
2,599
113
Yes
Yes
Mining
Manufacturing
1.994***
(2.68)
−0.129***
(−2.89)
32.846***
(6.34)
−1.901***
(−6.63)
Services
1.614+++
(0.25)
0.453+++
(1.16)
0.025
(1.42)
0.10
2,303
104
Yes
Yes
−0.285
(−0.37)
0.40
2,598
113
Yes
Yes
0.045
(0.03)
0.59
2,599
113
Yes
Yes
ln = natural logarithm, YPC = income per capita.
Notes: t statistics in parentheses. ***p < 0.01. For services, F statistics in parentheses. +++p(F) < 0.01.
Source: Authors’ computations.
Table 5. Determinants of Sectoral Shares of Exports, 1991–2014
(% of all merchandise and service exports)
Agriculture Mining Manufacturing
−51.343***
(−2.42)
−10.631
(−0.71)
0.560
(0.64)
64.43***
(2.82)
−3.443***
(−2.76)
LIM
17.49++
(1.28)
−1.232++
(−1.63)
Services
15.661
(0.60)
−0.872
(−0.63)
ln YPC
ln YPC squared
Agricultural endowment
Mineral endowment
Capital endowment
R-squared (adjusted)
Observations
No. of countries
Country fixed effects
Year fixed effects
3.241***
(2.71)
2.779
(1.44)
0.21
2,063
109
Yes
Yes
0.258
(0.76)
0.16
1,837
100
Yes
Yes
−0.980
(0.25)
0.06
2,061
109
Yes
Yes
−1.523
(−0.82)
0.11
2,049
108
Yes
Yes
4.042
(1.21)
0.03
2,369
112
Yes
Yes
LIM = labor-intensive manufacturing, ln = natural logarithm, YPC = income per capita.
Notes: t statistics in parentheses. ***p < 0.01. For labor-intensive manufacturing, F statistics in parentheses.
++p(F) < 0.05. Labor-intensive manufacturing includes textiles, clothing, and footwear.
Source: Authors’ computations.
(consistent with the wide range of incomes between countries with a comparative
advantage in mining), but for other sectors they range from 0.39 to 0.59.
The results for sectoral shares of exports are in Table 5. The income terms
are somewhat less significant than in the employment equations but still have
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64 Asian Development Review
Table 6. Determinants of Agriculture’s Shares of Value Added,
Employment, and Exports, 1991–2014 (%)
ln YPC
ln YPC squared
Agricultural endowment
Agricultural TFP growth
R-squared (adjusted)
Observations
No. of countries
Country fixed effects
Year fixed effects
Value Added
−46.855***
(−3.77)
Employment
−38.806***
(−3.56)
Exports
−48.918*
(−1.87)
2.201**
(3.23)
1.539
(1.48)
2.811
(0.99)
0.40
1,995
99
Yes
Yes
1.522**
(2.39)
2.218**
(2.07)
−0.225
(−0.18)
0.45
2,088
98
Yes
Yes
3.123**
(2.18)
3.159
(1.52)
8.071**
(2.33)
0.22
1,669
95
Yes
Yes
ln = natural logarithm, TFP = total factor productivity, YPC = income per capita.
Notes: t statistics in parentheses. *p < 0.1, **p < 0.05, ***p < 0.01.
Source: Authors’ computations.
the expected signs. This is also true for endowments per worker. The adjusted
R-squared values are lower for the export equations than for the value added and
employment equations. This is expected, given the wide range of comparative
advantages between countries at each income level.
The agricultural equations are repeated in Table 6 but with an additional
explanatory variable: TFP growth rate in agriculture. The coefficients for this
variable are not very significant, but their signs suggest that faster farm TFP growth
adds to the sector’s shares of GDP and exports but reduces its employment share
(perhaps because of its labor-saving bias). Ideally, this variable should measure
agriculture’s TFP growth relative to that of other sectors, but unfortunately there
are no estimates available for nonagricultural TFP growth during 1991–2014 for
the more than 95 countries in our sample.
In short, these results are at least somewhat supportive of the following
structural transformation hypotheses:
1. The shares of agriculture (services) in GDP and employment are
lower (higher) the higher a country’s per capita income, while the
manufacturing sector’s shares initially rise and then eventually fall as
countries approach high-income status.
2. The share of exports of labor-intensive manufactures in total exports
declines as per capita income expands.
3. The decline in the share of employment in agriculture lags the decline
in agriculture’s share of GDP.
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Structural Transformation to Manufacturing and Services 65
4. Countries well endowed with farm land (mineral or energy resources)
per worker have a larger share of their exports from the farm (mining)
sector.
5. Exports of manufactures are more labor intensive the smaller a country’s
per worker endowment of capital.
6. Agriculture’s shares of GDP and exports are higher, and its share of
employment is lower the higher the rate of TFP growth in that sector.
Even though the statistical significance of relative factor endowments is not
strong in the above equations for our sample of 117 countries, openness to trade
is important to the structure of economies with extreme endowments, including
affluent resource-rich countries still specialized in primary products and developing
countries already heavily specialized in exporting services.
VI. Policy Implications
The theory outlined earlier, and the above empirical results provide clear
lessons for governments. The most fundamental lesson is that the agriculture
sector inevitably will eventually decline in the course of economic growth. Hence,
intervening to prevent that decline with price-supportive policies will require those
supports to continue to rise over time, at ever-greater cost to consumers and/or
taxpayers per farm job retained or farm business saved.
Second and equally well known, the activities of producing and exporting
manufactured products that use unskilled labor intensively are likely to expand
initially in densely populated, natural resource-poor countries, but, as national
real wages rise, such industries will also inevitably decline as a share of growing
economies. Hence, protecting jobs and factories in such industries from import
competition will also become ever more expensive over time.
Third and less well known, manufacturing as a whole as a share of GDP
will inevitably decline, and in high-income countries its share of employment has
been declining even faster than its GDP share (Figure 4c). Hence, policies aimed at
slowing deindustrialization, like those aimed at slowing deagriculturalization, will
become ever more expensive over time per job or factory saved.
Abandoning protectionist trade policies aimed at slowing the relative decline
of such sectors, and thereby accelerating economic growth via dynamic gains
from trade, does not of course prevent governments from assisting those exiting
and declining industries. Indeed, the economy will be more able to afford to
do so by being more open. Moreover, there are now far cheaper and easier
ways for governments to target income supplements to needy households. Such
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66 Asian Development Review
Figure 10. Share of Adults with a Bank Account, Mobile-Money Account, or an
Equivalent in Developing Economy Regions and High-Income Countries, 2011 and 2017
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Source: Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar, and Jake Hess. 2018. The Global
Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution. Washington, DC: World Bank.
https://datacatalog.worldbank.org/dataset/global-financial-inclusion-global-findex-database (accessed 10 November
2018).
payments were unaffordable in developing countries in the past because of the
fiscal outlay involved and the high cost of administering small handouts. However,
the ICT revolution has brought financial inclusion to developing countries at an
astonishingly fast pace in recent years: the share of adults with a bank or mobile
money account rose from 42% to 63% in developing countries between 2011 and
2017 (Demirgüç-Kunt et al. 2018), and it rose substantially in all regions in those
6 years (Figure 10). This phenomenal advance in access to electronic banking
is making it possible for conditional cash transfers to be provided electronically
as direct government assistance to even remote rural households and females of
low-income countries.
If open countries are still unsatisfied with the contribution of their farmers
to national food security, as reflected in food self-sufficiency ratios, an alternative
to protectionism would be to subsidize investments in agricultural research and
development, rural education and health, rural roads, and other rural infrastructure
improvements. If countries currently underinvest in such activities, extra support
could also boost economic growth.
Finally, a comparative advantage in mining is not confined to low- and
middle-income countries (Table 1). This is not consistent with the resource curse
theory (van der Ploeg 2011, Frankel 2012). In fact, the very long-term growth
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Structural Transformation to Manufacturing and Services 67
rates of some oil-abundant economies have been exceptionally high (Michaels
2011). This finding, together with general evidence that opening up contributes
to economic growth (e.g., Lucas 2009), calls into question the efficacy in
emerging economies of governments contemplating policies designed to diversify
the economy away from primary production—which they often consider when
commodity prices slump. Rather than distortive sectoral policies that discourage
mining (or cash cropping), a better response to concerns over volatile terms of trade
involves macroeconomic and generic social protection policies that can help ease
adjustments to the nation’s real exchange rate changes as international commodity
prices go through their inevitable cycles.
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