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Ritsumeikan International Affairs
Vol.7 (2009)
The Impacts of Vertical and Horizontal
Export Diversification on Growth:
An Empirical Study on Factors Explaining the Gap between
Sub-Saharan Africa and East Asia’s Performances
YOKOYAMA, Kenji & ALEMU, Aye Mengistu
© Institute of International Relations and Area Studies, Ritsumeikan University
The Impacts of Vertical and Horizontal
Export Diversification on Growth:
An Empirical Study on Factors Explaining the Gap
between Sub-Saharan Africa and East Asia’s
Performances
YOKOYAMA, Kenji* & ALEMU, Aye Mengistu**
Abstract
This paper empirically investigates the separates effects of
vertical and horizontal export diversification on economic growth
based on a panel data of 41 countries from Sub-Saharan Africa
(SSA) and East Asia. First, the study revealed that East Asian
countries have been successful to diversify their exports both vertically and horizontally; whereas SSA’s diversification attempt was
too minimal and its contribution for growth and structural change
on the economy was insignificant. Secondly, the study confirms
that, though horizontal diversification is positively correlated with
growth, its contribution was found to be not impressive compared
to vertical diversification for growth. The possible explanation is
that, unlike vertical diversification which is mainly growth-oriented and having a dynamic spillover effects on the economy, horizontal diversification is mainly stability-oriented and less-growth oriented. This paper, therefore, calls into question the policy advices
of some researchers that proposed Africa’s emphasis should be on
horizontal diversification through increasing the number of priRITSUMEIKAN INTERNATIONAL AFFAIRS Vol.7, pp.49-90 (2009).
*Yokoyama Kenji is a Professor at Ritsumeikan Asia Pacific University, 1-1, Jumonjibaru,
Beppu-shi, Oita-ken 874-8577, Tel: 977-78-1000 Fax: 977-78-1001,
E-mail: [email protected]
**Alemu Aye Mengistu is a PhD Candidate, Graduate School of Asia Pacific Studies,
Ritsumeikan Asia Pacific University, Beppu-shi, Oita-ken, Japan Postal
Address:Kannawa-Miyuki, 5 Kumi, AP Matsumi (Room 106), Beppu-shi, Oita-ken, 8740045 Phone number: +81 8033601404, E-mail: [email protected]
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【Vol. 7
mary export products. This paper argues that SSA should give
more emphasize for vertical diversification by means of valueadded ventures and forward and backward production linkages.
In doing so, countries in SSA must create sufficient levels of
human and physical capital, infrastructure, appropriate policies
and strong institutions.
Keywords:
Export Diversification, Vertical Diversification, Horizontal
Diversification, Economic Growth, Sub-Saharan Africa, and East
Asia
I. INTRODUCTION
“When the sun began to set on Europe’s foreign empires, and former
colonies across the globe began in the 1960s to prepare themselves for independence, no body was that worried about Africa. The anxiety was all for
Asia. After all, Africa was a place of great mineral riches and vast agricultural fecundity. Asia, by contrast, seemed to have only problems and population” (Commission for Africa, 2005:16).
It was not with out reason that, we started the itroduction with the
above remark. At the time of their independence in the 1960s, income per
capita in most SSA was fairly comparable with that of East Asia. In fact,
in the 1960s, much of the expectation was from SSA to perform more economic achievement than East Asia because of its large endowments of
natural resources. However, starting from quite similar per capita income
in the early 1960s, Sub-Saharan Africa (SSA) and East Asia experienced a
divergent development path and outcome for the last three/ four decades.
The annual growth in Real GDP per capita of SSA averaged about 0.44%
over the period 1975-2004, compared to about 4.1% for East Asian
economies during the same period.
Table 1 shows income comparisons between the years 1965 and 2000
for selected East Asian and SSA countries. In 1965, for instance, Korea
Republic and Thailand had income per capita of $130 and $140 respective-
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The Impacts of Vertical and Horizontal Export Diversification on Growth（YOKOYAMA & ALEMU） 51
ly; which had been lower than some SSA Countries such as Congo DR,
Ghana, Congo Republic, Cote d’Ivoire and Niger. By year 2000, Korea and
Thailand have registered a per capita income of $ 9,010 and $2,020 respectively. In the same time span, however, Ghana, Congo Republic, Cote
d’Ivoire have managed to increase their per capita income from $230, $170
and $200 only to $330, $590 and $680, respectively. In the same token,
South Africa which is regarded as the best economy in Africa and
Singapore in East Asia had exactly equal income per capita of $ 540 each
in 1965. In year 2000, however, South Africa registered a per capita
income of $3,060 where as Singapore achieved a per capita income of $
23,350 which was almost 7.6 times the performance of South Africa.
Figure 1 also indicates how SSA and East Asia diverged in income per
capita since the 1970s after having more or less the same beginning in the
1960s.
Table 1: Per Capita Income in 1965 and 2000 (in current US$)
Country
Sub-Saharan Africa
Cameroon
Congo, Rep.
Congo, DR
Cote D’Ivoire
Ghana
Niger
Nigeria
Sierra Leone
South Africa
East Asia
Japan
Korea, Rep.
Hong Kong, China
Singapore
China
Malaysia
Thailand
1965
2000
2000/1965
140
170
330
200
230
180
120
160
540
580
590
90
680
330
180
260
130
3,060
4.1
1.7
0.3
3.4
1.4
1.0
2.2
0.8
5.7
910
130
690
540
100
330
140
35,420
9.010
26,410
23,350
840
3,250
2,020
38.9
69.3
38.3
43.2
8.4
9.8
14.4
Data Source: WDI Database
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【Vol. 7
Figure 1 : Income per capita in Sub-Saharan Africa and East Asia, 1975 and 2004
Income per capita of SSA and East Asia
35000
Income per capita (in PPP)
30000
25000
20000
2004
1975
15000
10000
5000
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Country
Unlike East Asia, Africa hardly benefited from the boom in manufactured
exports. According to UNCTAD (2003), Africa’s share in world merchandise exports fell from 6.3 per cent in 1980 to 2.5 per cent in 2000 in value
terms. Similarly, Africa’s share of total developing-country merchandise
exports fell to almost 8 per cent in 2000 from its value of 1980. In contrast,
East Asia’s share of global merchandise exports increased from 18 per cent
in 1980 to 22 per cent in 2000, while its share of total developing-country
merchandise exports increased from almost 60 to 72 per cent over the
same period. Similarly, its share in global manufactures trade increased
threefold, reaching 21.5 per cent in 2000. The value of East Asia’s total
exports recorded 7 per cent average annual growth over the period under
review, compared to a mere 1 per cent for Africa (table 2).
Similarly, SSA’s share in world imports fell from 3.1 % in 1980 to 1.4%
in 2002, while East Asia again increased its world import share from
13.1% to 20.8% in the same time span (UNCTAD, 2003).
Table 2 : Export Structure of Africa and East Asia, 1980 and 2000
Region
1980
2000
a
All Merchandise
Africa share in:
Global exports
Developing Countries
East Asia share in:
Global exports
Developing Countries
b
Manufactures All Merchandise Manufactures
6.3
20.3
0.8
7.8
2.5
7.9
0.8
3.0
18.1
58.5
7.1
66.9
22.4
72.0
21.5
79.0
Source: UNCTAD (2003)
‘a’ refers Standard International Trade Classification (SITC) 0–9 & ‘b’ refers SITC 5–8,
less 68.
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The Impacts of Vertical and Horizontal Export Diversification on Growth（YOKOYAMA & ALEMU） 53
SSA countries are heavily dependent on a narrow base of few agricultural
and mineral exports for foreign exchange earnings (Table 3) and have had
to endure the consequences of all problems resulting from the fluctuation
of commodity prices in world markets. About 17 of the 20 most important
export items of Africa are primary commodities and resource-based semimanufactures. On average, world trade in these products has been growing much less rapidly than manufactures. In fact, world trade in other primary commodities that account for an important proportion of total
exports of Africa such as coffee, cocoa, cotton and sugar, has been sluggish,
with the average growth of trade in such products in the past two decades
barely reaching one-third of the growth rate of world trade in all products
(UNCTAD, 2003). For instance, world prices for many of the commodities
that Africa exports declined between 1990 and 2000: Cocoa, Cotton, sugar
and copper by over 25%, coffee by 9% and minerals overall declined by
14% (WTO, 2001). As noted in Ng and Yeats (2002), one-half of traditional
products in SSA experience average price changes of 50 % or more during
the 1990’s.
Table 3 Main Exports of Selected Sub Saharan Africa Countries
COUNTRY
Benin
Burkina Faso
Burundi
Chad
Congo, Dem. Rep
Ethiopia
Gabon
Kenya
Mali
Mauritius
Niger
Nigeria
Rwanda
South Africa
Sudan
Zambia
EXPORTS
Cotton, Palm oil
Cotton, Animal Products, Gold
Coffee, Tea, Sugar, Cotton, Hides
Cotton, Oil, Livestock, Textiles
Diamonds, Copper, Coffee, Cobalt, Crude oil
Coffee, Hides, Oil seeds, Beeswax, Sugarcane
Crude Oil, Timber, Manganese, Uranium
Tea, Coffee, Horticultural products, Petroleum products
Cotton, Gold, Livestock
Sugar, Clothing, Tea, Jewellery
Uranium, Livestock products
Petroleum, Petroleum products, Cocoa, Rubber
Coffee, Tea, Hides, Tin ore
Gold, Diamonds, Metals & Minerals, Cars, Machinery
Oil, Cotton, Sesame, Livestock & Hides, Gum Arabic
Copper, Minerals, Tobacco
Source: Osakwe (2007)
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Theoretical analysis suggests that agricultural commodity prices fall relative to manufacturing products because of relatively inelastic demand and
because of the lack of differentiation among producers. On the demand
side, the development of synthetic substitutes further displaces agricultural commodities as intermediate inputs, reducing at least the growth in
demand. This drastic decrease of the SSA presence in world trade was not
only the result of the deterioration of the terms of trades (due to low
income elasticity of demand) in primary commodities, but also of the loss
of competitiveness in manufactures. Ironically, SSA’s manufacturing sector grew significantly in the 1960’s; more than 8% annual average growth
rate of manufacturing value added was substantially higher than the corresponding GDP growth rate, although the base of the manufacturing sector from which this growth derived was quite small. This growth performance, however, could not be sustained. It decelerated sharply in the
1970’s and was virtually stagnant in the 1980’s. By the mid 1980’s, the
capacity utilization rates in manufacturing was well below 35% in many
SSA countries (World Bank, 1993).
The question is, therefore, why countries in East Asia achieved breakthrough in economic development in the past 3-4 decades while countries
in Sub-Saharan Africa did not? Sachs et al. (2004) identified the main
characteristics of SSA weak economic performance and poverty traps as:
(i) the level of capital (human & physical) so small that it falls below the
threshold needed to start modern production processes; (ii) very low levels
of savings, which make the capital accumulation impossible; (iii) high
rates of population growth from the rural poor who see young children as
an economic asset and (iv) a very slow diffusion of technology from abroad.
On the other hand, the major characteristics of economic growth in East
Asian countries are: (1) rapid accumulation of savings and high rates of
investment, (2) high level of foreign direct investment (FDI) and it was
appropriately used as an alternative source of foreign currency and a big
factor in capital formation, (3) high investment on basic infrastructure
including marketing information system, (4) rapid increases in levels of
schooling, (5) rapid growth of manufactured exports, and (6) stable macroeconomic and institutional environment have been instrumentals in creating confidence in policy makers.
Moreover, one of the most remarkable features of growth in East
Asian countries is that, it was accompanied by rising economic equality
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The Impacts of Vertical and Horizontal Export Diversification on Growth（YOKOYAMA & ALEMU） 55
(Gerber, 2005). Hence, the East Asian experience has called into question
the idea that economic growth in developing countries follows a “Kuznet’s
curve,” in which equality first declines and then rises. Although the conditions that led to greater income equality were rooted in the unique historical experiences of each country, it is also evident that each of the East
Asian countries had a similar set of highly visible wealth-sharing mechanisms such as land reform, free public education, free basic health care,
and significant investments in rural infrastructure. These policies didn’t
equalize income by themselves, but they provided people with the tools
they needed to raise their individual incomes and gave hope for the future.
Table 4 shows that for all regions, it is Africa where income is most unequal as shown by the Gini coefficient of 0.51.
Table 4 : Income Inequality Measures by World Regions
Region
Africa
East Asia and Pacific
South Asia
Latin America
Industrialized Countries
Gini Coefficient Share of top 20 % Share of Middle Class Bottom 20 %
0.51
0.38
0.32
0.49
0.34
50.6
44.3
39.9
52.9
39.8
34.4
37.5
38.4
33.8
41.8
5.2
6.8
8.8
4.5
6.3
Source: Deininger and Squire (1996)
The success of East Asian countries to shift from producing a low productive primary commodities to producing a more productive manufactured
products reflects even latecomers are able to specialize in high growth
areas if some of the pre-conditions are fulfilled. Thus, as Masuyama and
Vandenbrink (2001) pointed it out, “unless a country diversifies critical
supporting industries, the development of few industries alone will not
produce growth in the economy.”
At this point it is necessary to make a distinction between vertical
and horizontal diversification. While horizontal diversification entails
broadening of the primary export mix in order to stabilize the volatility of
global commodity prices, vertical diversification involves a radical change
in export structure and further uses of existing and new innovative export
products by means of value-added ventures such as processing and marketing. While both horizontal and vertical diversifications are expected to
positively induce economic growth, the requirements for the two could
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vary considerably in terms of technological, managerial and marketing
skills. Accordingly, it is vertical integration that may require more
advanced technology, skills and initial capital than horizontal diversification. Vertical diversification can also be more linked with higher learning
possibilities that, in turn, may produce greater dynamic externalities than
that of horizontal diversification. For-example, vertical diversification
takes place by moving up the value chain to produce manufactured products as in Korea, China, and Malaysia. Horizontal diversification is
achieved by producing non traditional dynamic exports such as cut flowers
as it has been started recently in Kenya, Uganda and Ethiopia to supplement or partially replace the traditional exports like coffee and tea.
It should be stressed that diversification is not a phenomena that contradicts the notion of comparative advantage especially in the case of
developing countries. Rather, it a process of broadening comparative
advantages into new sectors. Moreover, diversification should be seen as a
dynamic process, not as a static one.
A country’s export pattern is a good predicator of future growth
(Hausmann and Klinger, 2007). Volume of total exports in turn is determined by the three main factors: the world demand for exports of the
given commodity, competitiveness of the given product and the degree of
export diversification of that country (Athukorala, 1991). For instance, in
the 1960s, agricultural export performance was similar among Indonesia,
the Philippines and Thailand, both in nominal and real value terms. But
in the decades since then, the three countries have shown different performances in agricultural exports. The main important factor resulting in the
differences is the ability of diversification and adjustment of agricultural
exports when the market conditions changed (Hirohisa, 2003). As Pinaud
and Wegner (2004) put it, African economies still lack proper “shockabsorbers” to withstand internal (e.g. drought, floods, and political instability) and external (e.g. volatility of commodity prices and exchange
rates) shocks alike. Thus, the capacity of smoothing shocks highly depends
on the ability of African policy makers to diversify their economies. The
case in point in this paper is, therefore, to assess the separate effects of
vertical and horizontal export diversification for economic growth.
Although diversification can’t be expected to become the only panacea for
SSA economic problems, it is one of the key measures for structural solutions (De Ferranti et al, 2002). That is why the economic report on Africa
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The Impacts of Vertical and Horizontal Export Diversification on Growth（YOKOYAMA & ALEMU） 57
(2007) presents the theme of diversification as a new paradigm for Africa’s
development and the report argues that diversification is a prerequisite to
achieving positive development in the continent.
II. OBJECTIVE OF THE STUDY, RESEARCH QUESTIONS AND
PROPOSITIONS
Objective of the Study
The overall objective of this study is to identify and measure the
effects of vertical and horizontal export diversification on economic
growth, both in SSA and East Asia. The overall assumption is that the
East Asian experiences could provide valuable lessons that policy-makers
in sub-Saharan Africa could adapt to their own contexts. Meanwhile, the
study is expected to fill the gaps by closely looking diversification from its
vertical and horizontal dimensions and thus recommend which one may
contribute more to growth. Therefore, an attempt has been made to
answer the following key policy questions.
Research Questions
1. If diversification can have a positive impact on a country’s growth
and development prospects, what are the policy options available to
support that process?
2. Which areas of export diversification (vertical or horizontal) may be
considered priorities in Africa and why?
3. What other factors cause the differences in income per capita
growth over time and across countries?
4. What would be the lessons from East Asia to SSA with regard to
diversification in particular and economic growth in general?
Propositions
(a) Although vertical and horizontal diversifications are expected to
stimulate economic growth, it is vertical integration that has a
dynamic spillover effect on the economy and contribute more to
economic growth;
(b) Higher value of investment (domestic and foreign) raises the
steady-state level of output per effective worker; the growth rate;
(c) Human capital is expected to be one of the key determinant for eco-
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nomic growth based on endogenous growth theory and the Solow
neo-classical exogenous theory;
(d) Since the levels of human capital and FDI are below the threshold
level in SSA, thus their contribution to economic growth may not
be satisfactory in SSA compared to East Asia;
(e) Since the theory of ‘absolute convergence’ predicts poorer countries
typically grow faster in income per capita, ‘initial GDP/capita’ is
inversely related with growth- rate;
(f) Flexible exchange-rate system and openness in a given country
may create favorable conditions for export growth and thereby to
economic growth;
(g) Political Instability is inversely related with export diversification
and growth due to the fact that rate of saving and investment
tends to be low in countries with frequent wars.
III. Conceptual Framework for Export Diversification
Three quarters of developing country exports in early 1980s were primary commodities, but now around 80% of developing countries are manufactures. However, Africa has not been part of this transformation.
Processed products in Africa account only for less than 10% of the total
exports from the continent. There are different views why Africa concentrated only on the export of primary products. On one hand, as Wood and
Mayer (2001) noted, the concentration of Africa’s export on unprocessed
primary products is largely caused by the region’s combination of low levels of education and abundant natural resources. On the other hand,
Collier (2002) argues that Africa’s current comparative advantage in primary commodities is often due, not to its intrinsic endowments or location,
but to a poor investment climate that is policy oriented.
It is a common knowledge that the most dynamic developing area in
the world economy in terms of growth and transformation during the period under review was East Asia. While at the beginning of this period the
industrial base of the economies in this region was small, they transformed into industrialized countries within a relatively short period of
time. What accounts, then, for the growth miracles in East Asia? Some
economists argue that their rapid growth is explained by their ability to
2009】
The Impacts of Vertical and Horizontal Export Diversification on Growth（YOKOYAMA & ALEMU） 59
imitate foreign technologies. By adopting technology developed abroad,
these countries managed to improve their production function substantially in a short period of time. In other words, these countries achieved a
very rapid growth in total factor productivity (TFP). On the other hand,
recent studies revealed that East Asia’s exceptional growth can be traced
to large increases in measured factor inputs: increases in labor-force participation, increases in the capital stock, and increases in educational
attainment (Mankiw, 2003).
Moreover, Japan’s role in the development of the East Asian production structure over the past thirty or so years should be also taken into
account. This has occurred through provision of Japanese war reparations,
aid, and investment leading to high levels of intra and inter-regional
trade, through the creation of a web of production networks based around
Japanese firms. For instance, in the 1991-1993 periods, Japanese FDI in
East Asia totaled 16.6 percent, while FDI from USA and European countries had been 10 percent and 3.6 percent, respectively (Kelly, 2002). By
the same token, in 1993, Japan was the largest trading partner of China,
Thailand, Malaysia and Indonesia, and the second largest trading partner
(after the US) of South Korea, Taiwan, Singapore and the Philippines.
Of the total $11.2 billion Japanese ODA in 1993, Asia’s share was
about 59.5 %, and within Asia itself, China was the major recipient.
Japan’s financial aids to East Asian countries in comparison with other
regions are shown in table 5.
Table 5 : Regional Distribution of Japan’s Bilateral ODA (%)
Region
Asia
(northeast)
(southeast)
Middle east
Africa
Latin America
Europe
Oceania
Unclassifiable
1970
98.3
Source: Kelly (2002)
3.3
2.3
-4
-0.2
0
0.3
1980
70.6
4.2
43.9
2.5
18.9
6.0
0.2
0.5
1.2
1985
67.7
15.3
37.6
1.7
15.0
8.8
1.1
0.9
4.8
1990
59.3
12.0
34.3
1.5
15.4
8.1
6.9
1.6
7.1
1993
59.5
17.7
29.9
2.2
15.9
9.0
1.7
1.7
10.0
1995
54.4
15.2
24.6
6.8
12.6
10.8
1.5
1.5
12.3
1997
46.5
8.0
21.4
7.8
12.1
10.8
2.0
2.4
18.3
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East Asian countries have benefited from Japan not only in development
aid, but also more importantly they benefited from technological transfer
through FDI and exports. Furuoka (2005) used Kaname Akamatsu’s
‘Flying Geese’ model to analyze Japan’s role in East Asian economic development. In the initial stage of the formation of the second gaggle of flying
geese, Japan (the leading goose) exported manufactured goods to the second-tier geese, i.e., South Korea, Taiwan, Hong Kong and Singapore. All
those countries later came to be known as Asian Newly Industrializing
Economies (NIEs). Once local demand for imported goods in NIEs had
reached a certain threshold, Japan proceeded to establish production
bases there. In the next stage, Asian NIEs became able to produce internationally competitive products, while Japan assisted the NIEs’ efforts of
industrialization by providing them foreign aid. Eventually, Asian NIEs
themselves became exporters of manufactured goods to other countries
(third-tier geese), such as China and three ASEAN countries, namely
Thailand, Malaysia and Indonesia. Besides exports, NIEs, following a pattern that had been established by Japan, were bringing investments to
those countries. All the while, as had been the case with Asian NIEs,
Japan assisted the third-tier geese’s efforts to industrialize and modernize
their economies by supplying them vast amounts of money as foreign aid.
Unfortunately, Africa lacks a ‘leading goose’ similar to Japan that
may lead the flocks in the continent. Perhaps, South Africa can emerge as
a leading economic power and contribute to Africa at large and the
Southern African Development Community (SADC) region in particular in
terms of FDI and technological transfer. In the same token, North African
countries have a geographical proximity advantage with Europe and if
they properly utilized it, the wide European market can be used as a stimulant for export growth, production diversification and technological
transfer.
However, it should be noted that the most enduring cause of the poor
performance of African economy was not only due to lack of ‘economic
power’ in the continent, but also the nature of African institutions and the
quality of political regimes and that of the leadership are also partially
responsible for the low performance in economic development at large, and
insignificant export diversification and structural changes in particular.
As Mkandawire and Soludo (1999) noted, the state in Africa has failed signally in its developmental mission because of various interrelated factors:
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The Impacts of Vertical and Horizontal Export Diversification on Growth（YOKOYAMA & ALEMU） 61
(i) its “excessive” and “counterproductive” intervention in domestic economic process; (ii) its over bureaucratization and bloated size; (iii) its over
centralization of development, which discourages local (private) initiatives
and the rural (productive) sector.
On the other hand, there are also cases to associate the economic
development performance of Africa since early 1980s with its experience
related to the most criticized ‘structural adjustment program that was
sponsored by the World Bank and IMF. According to Collier and Gunning
(1997), GDP per capita declined by 1.3 % per annum during the 1980s, a
full 5 percentage points below the average for all low-income developing
countries. This deterioration was even worse during 1990-94, at 1.8% per
annum, further widening the gap with other developing countries to 6.2
percentage points. Likewise, the external debt of SSA has more than doubled over the adjustment period, without any increase in economic growth
to sustain its servicing in the future. For instance, the indebtedness of
Nigeria has increased over the adjustment period. The debt stock, which
was only $18.9 billion before the SAP period, had risen to $33.2 billion by
1991. Inflation which averaged 18 percent between 1980 and 1985, rose to
an average of 24 percent between 1986 and 1991; by the end of 1992 it
was over 46 percent and remained in that high range through 1993. (Ake,
1996).
Africa’s infrastructural base and human capital formation, which
were deemed to be fragile at the beginning of adjustment, have deteriorated even further. Africa’s capacity to manage the crisis has been further
eroded through massive brain-drain and demoralization of the civil service, caused by sharply declining real wages and massive retrenchments
(Mkandawire and Soludo, 1999). Human development indicators-life
expectancy, infant mortality, and school enrollment-have worsened. In a
study by Ali (1998), which used data from IFAD and classified countries
according to the WB categories, 10 SSA countries were classified as “
intensively adjusting” (Ghana, Kenya, Malawi, Tanzania, and Zambia),
“other adjusting” (Gabon, Gambia and Mali), or “ non-adjusting” (Ethiopia
and Lesotho). Accordingly, for the intensively adjusting countries, it was
found that the index of rural poverty had increased from 56.6% in 1965 to
62.4% in 1988. Similarly, for the other adjusting countries, the index of
rural poverty increased from 45.1% in 1965 to 60.7% in 1988. In contrast,
the head-count ratio for the non-adjusting group decreased from 65.8% in
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1965 to 43.6% in 1988 (Ali, 1998; Mkandawire and Soludo, 1999).
Generally table 6 shows how poverty in Africa was intensified, since the
implementation of SAP in many of the African countries. Thus, SAP
aggravated the already existing problems in SSA..
Table 6 : SAPs and poverty in SSA, 1985 and 1990
Country
Cote d’ Ivoire
Ghana
Kenya
Mauritania
Rwanda
Senegal
Tanzania
Uganda
Zambia
Zimbabwe
Head-count ratio (%)
Change in poverty
Change in macroeconomic
1985
1990
(% points)
policy (score)
40.31
29.00
53.17
32.17
31.59
49.65
53.53
37.10
48.53
56.71
45.93
33.49
58.83
35.52
37.94
54.75
59.79
44.69
52.54
67.26
5.62
4.49
5.66
3.35
6.35
5.10
6.26
7.59
4.01
10.55
-1.3
2.2
0.5
0.5
-0.2
0.5
1.5
0.2
-0.3
1.0
Source: Ali (1998)
While African policy makers felt that a fundamental cause of Africa’s
structural problem was precisely its over specialization in primary production; on the contrary, the World Bank report entitled “ Accelerated
Development in Sub-Saharan Africa: An agenda for Action ” (1981) which
became the classic perspective of the Breton Woods institutions was recommending Africa to concentrate on primary production, particularly agricultural products. As a result, except some diversification attempts by few
countries, many of SSA economies remain un-diversified. According to
Collier (2004), commodity-dependent SSA countries have two options:
diversifying away from primary commodities, or learning how to live with
commodity dependency better. For many SSA countries diversification is
quite feasible, but in doing so, they have to develop the required pooled
skilled labor and attract the necessary capital investments in order to
move first into agro processing and then into light and capital intensive
manufacturing,
One of the most influential theories that explain why the comparative
advantage of many developing countries has shifted from the export of pri-
2009】
The Impacts of Vertical and Horizontal Export Diversification on Growth（YOKOYAMA & ALEMU） 63
mary products to manufactured products is that of the “product cycle” theory. The product life cycle theory by Raymond Vernon (1966) argued that,
for manufactured goods, comparative advantage may shift over time from
one country to another. This happens because these goods go through a
product life cycle. Once the product is invented, then overt time becomes
more standardized as consumers and producers gain familiarity with its
features. Standardized manufacturing routines are increasingly common,
using low-skilled and semi-skilled labor in assembly type operations.
Accordingly, an increasing share of the world’s output is moving to developing countries where abundant unskilled and semi-skilled labors keep
labor costs low. Secondly, the product cycle theory emphasizes that commercial successes of consumer durables depend on product development
mainly based on cost-cutting mass production and the use of known technology, rather than on technological breakthroughs.
The neoclassical economic theory predicts that, when a relatively poor
country starts accumulating capital and enters the cone of diversification,
the Rybczynski effect occurs and the share of the capital-intensive aggregate should go up. This in turn would reduce industrial concentration and
increase diversification. On the other hand, the endogenous growth model
states that greater diversifications of exports occur through learning-bydoing and learning-by-exporting and through imitation of developed countries (Pineres and Ferrantino, 1997:376). In the same token, what appears
to be crucial is also creating an environment that creates competition and
thus to acquire new skills and this can be performed through exports.
Without the pressure from outside competitive forces, acquisition of
human capital, and thus overall economic growth, may be slow (Husted
and Melvin, 2007).
So far we have seen the importance of export diversification mainly on
the supply side. However, diversification may also result more endogenously from a growing demand for a variety of goods as a country’s income
increases (Imbs and Wacziarg, 2003). In other words, production patterns
respond to changes in the structure of demand and then generate increasing sectoral diversification through the “Engel effect”. The most influential
research on diversification by Imbs and Wacziarg (2003) has identified two
stages of diversification in the process of economic development. First,
poor countries tend to diversify as their incomes rise; then, the level of
diversification will reach to a turning point and later begin to become
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more specialized. In this case, the diversification of an economy could be
related to its development level, measured by GDP per capita, through an
inverted-U shaped relation. Therefore, a country ought to undertake
investment in such a way that this turning point occurs as a result of
attaining deep diversification. Because, it is only after the attainment of
deep diversification that countries can shift to the second stage that tends
towards specialization. Hence, almost all developed countries and some
high income developing countries today are in the stage of re-concentration after they successfully passed the diversification stage in the past.
Concentration
Diversification
Re-Concentration
Thus, the stages of diversification will follow the following steps:
Thus, the question is that if countries get back to re-concentration, why
should they need to diversify? The point is that, there is a fundamental difference between countries that are in the first stage of concentration and
countries that come-back to re-concentration. The main difference is that
the former specializes largely in primary products whose relative prices
are falling from time to time; where as the latter specializes in high value
added and knowledge intensive products whose relative prices are on a
rise from time to time.
Both standard neoclassical growth theory and the more recent
endogenous growth theories pointed out those technological differences
across nations were the primary explanation of long-term growth differences as well as of wealth and income inequality around the world (Romer,
1990). Neoclassical theory, however, considers technology as both universally available and applicable, and explains technological differences as
variations in the endowments of production factors and infrastructure
(Stokke, 2004). In contrast, endogenous growth theory drops two central
assumptions of the Solow model, (i) that technological change is exogenous, and (ii) that the same technological opportunities are available in all
countries. It considers that technology differences and the limited capability of developing countries to absorb new knowledge are the main reasons
for persistent low productivity, and therefore for poverty (Lucas, 1990).
Combining the findings of new trade and endogenous growth theory suggests that the interplay of economies of scale, externalities and national or
2009】
The Impacts of Vertical and Horizontal Export Diversification on Growth（YOKOYAMA & ALEMU） 65
international spillovers of knowledge and technology can be crucial for the
diversification experience of “late-comers.” Generally, there are about five
important channels of how diversification may influence growth or
income:
1. Diversification may be considered as an input (a production factor)
that increases the productivity of the other factors of production
(Romer, 1990) ;
2. The second route is that diversification may increase income by
expanding the possibilities to spread investment risks over a wider
portfolio of economic sectors (Acemoglu and Zilibotti 1997) ;
3. Diversification is expected to have a positive contribution to Total
Factor Productivity (TFP) growth, and by extension, to economic
growth;
4. Diversification may also have a positive effect to growth because of
the existence of economies of scope in production. Economies of
scope exist when the same inputs generate greater per unit profits
when spread across multiple outputs than dedicated to any one output; and
5. Through forward and backward linkages, production of a diversified export structure is also likely to provide stimulus for the creation of new industries and expansion of existing industries elsewhere in the economy (Hirschman, 1958).
Similarly, Gylfason (2002) has clearly explained the interaction of diversification and economic growth along other variables as shown in the diagram below.
Six Determinants of Growth
Investment
Trade
Education
Growth
Stability
Diversification
Democracy
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The above diagram explains that there are about six different kinds of
producible capital that are needed to sustain economic growth. First, saving and investment are obviously necessary to build up physical capital.
Second, education is needed to build up human capital. Third, macroeconomic stability encourages the accumulation of financial capital, i.e. financial depth, which helps lubricate the wheels of production and thus
increases economic efficiency and growth. Fourth, increased trade with the
rest of the world helps to technology transfer as well as to strengthen the
capital base of domestic activity. Fifth, increased democracy can be viewed
as an investment in social capital by which is meant the infrastructural
glue that hold society together and keeps it working harmoniously. Sixth,
diversification is expected to increase income by expanding the possibilities to spread investment risks over a wider portfolio of economic sectors.
As a whole, the model indicates that factors that are good for growth also
encourage economic diversification.
Therefore, it is important to investigate in details how vertical and
horizontal exportdiversification, along other institutional and policy variables may affect a country’s growth in income per capita. The following
section is dealing with this issues.
IV. Determinants of Economic Growth
Based on economic theories and previous empirical studies, the following independent variables have been indentified:
(a) Vertical and Horizontal Diversification
There is a positive relationship between export diversification (vertical
and horizontal) and economic growth due to the roles it contributes in
increasing returns to scale and dynamic spillover effects and (De Ferranti
et al., 2002; Al-Marhubi, 2000; Hausmann, et al., 2006; Matthee and
Naude, 2007; Funke and Ruhwedel, 2005).
(b) Domestic Investment
According to Solow model’s prediction, the investment rate is a key determinant of whether a country is rich or poor. Investment, in turn, depended
on high saving rates. The neoclassical growth models of Solow-Swan and
Ramsey also predict that, an exogenously higher value of I/Y raises the
steady-state level of output per effective worker; the growth rate.
2009】
The Impacts of Vertical and Horizontal Export Diversification on Growth（YOKOYAMA & ALEMU） 67
(c) FDI Inflow
FDI can increase competition, making domestic companies more efficient
and stimulates sectoral and product diversification. In line with this, FDI
would have a positive impact on industrialization as well as economic
growth at large, since it adds to the domestic accumulation of resources.
(d) Human Capital
“If you plan for a year, plant a seed. If for ten years, plant a tree. If for a
hundred years, teach the people. When you sow a seed once, you will reap a
single harvest. When you teach the people, you will reap a hundred harvests” (K’uan-tzu, 551-470BC).
It is also widely accepted that growth theory has considered human
capital as the major determinant of economic growth. The stock of human
capital has been proxied by the level of secondary school educational
attainment. However, it should be noted that developing domestic human
resources through education and training is along-term process to
increase the innovative capacity of an economy.
(e) Initial GDP per Capita
The Solow-Swan and Ramsey hypothesis of ‘absolute convergence’ predicts
that, poorer countries typically grow faster in income per capita in order to
catch up to the richer countries. The Solow model, however, emphasized
that economies may converge depending on why they differed in the first
place. If two economies with the same steady state happened by historical
accident to start off with different capital stocks, then we should expect
them to converge. The economy with the smaller capital stock will naturally grow more quickly like exhibited in the case of Japan and Germany
after the World War II. On the other hand, if two economies have different
rates of saving, then we should not expect convergence. Instead each economy will approach its own steady state.
(f) Population Growth
The Solow model predicts that economies with higher rates of population
growth will have lower levels of capital per worker and therefore lower
incomes. The population variable is mainly used to see the scale effects.
On the other hand, endogenous growth theory hypothesized that, larger
economies would perform better. Thus, the effect of population growth on
economic growth is ambiguous.
(g) Exchange Rate
In theory, the depreciation of the local currency exchange rate against the
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US$ should represent a window of opportunity for exports to be boosted.
But, sometimes agricultural exports do not respond as expected, mainly
due to lack of trade finance caused by the inefficient domestic banking
system, the excessive dependence on imported inputs, which may be more
expensive in local currency terms, and a decline in terms of trade during
economic crises. When trade volumes do not respond to exchange-rate
changes, the trade balance moves in the “wrong” direction: depreciation
makes the country’s trade deficits rise in the short run, but it becomes rising to surpluses again; and this phenomenon is called the J-curve effect
(Ito, 2001). Generally, it has been hypothesized that exchange rate has a
positive link with economic growth.
(h) Openness
The degree of openness of a given country has been assumed to be one of
the necessary stimulants to export and economic growth. For instance,
until 1858, Japan was almost entirely isolated from world trade. In that
year, the Japanese government ended self-imposed trade restrictions and
began trading with the rest of the world. Once trade began, Japan specialized in three commodities: silk, silkworm eggs, and tea. Within 12 years
after markets were opened, foreign trade had increased by 7,000 percent.
Generally, the combination of improved terms of trade as well as the gains
from adopting improved technologies from abroad may have accounted for
as much as 65% rise in real national income (Huber, 1971; Husted and
Melvin, 2007).
(i) Political Instability
Political Instability is inversely related with export diversification and
economic growth due to the fact that rate of saving and investment tends
to be low in countries with frequent wars, revolutions, and coups (Mankiw,
2003).
(j) Rule of Law
The links between rule of law and economic growth has been given due
attention in recent days. It has been assumed that rule of law in a given
country is positively and significantly related to growth.
Thus, the independent variables and their expected relationships with
economic growth have been summarized in table 7.
2009】
The Impacts of Vertical and Horizontal Export Diversification on Growth（YOKOYAMA & ALEMU） 69
Table 7 :- Independent variables, their expected signs and data sources
Variable
Indicator
Vertical
The Ratio of Manufactured
Diversification
Exports to Total Exports
Horizontal
Number of Export Products.
Diversification
+/- Data Sources
+
Own calculation based on data from
World Bank and UNCTAD databases.
+
UNCTAD Database
based on SITCs three-digit
products classification
Domestic Capital
Ratio of GFCF to GDP
+
WDI Database
FDI
Ratio of net FDI to GDP
+
WDI Database
Secondary School
+
Human Capital
Initial GDP/C
Barro-Lee (2000) and supplemented
Enrollment Ratio to total
by data from WDI for some countries
population with age 15 and
and for years beyond 2ooo for all
above.
countries in the sample.
Real per capita GDP (initial) +
IMF Database
Population Growth Annual growth in population +
Summer et al (2006) Penn World Table
Exchange Rate
Summer et al (2006) Penn World
Exchange rate of local
+
currency with that of US$
Openness
Degree of openness based on
Table Version 6.2
+
Sachs and Warner (1995)
Political Instability Collier and Hoeffler’s (2004)
Summer et al (2006) Penn World
Table Version 6.2
-
‘War dummies’ for countries
Collier and Hoeffler (2004) war index
tables
suffered from war.
Rule of Law
Civil Liberty Index
+
World Freedom House
V. The Data and stylized Facts
This study has made an intensive empirical analysis for a panel of 41
countries (32 from SSA and 9 from East Asia) 1975 to 2004. Panel data
are better able to identify and measure effects that are simply not
detectable in pure-cross sections or pure time series data. Inline with this,
annual data should be avoided in growth studies since the results might
be affected by short-run business cycle effects (Folster and Henrekson,
2001). Thus, the averages over five-year periods are used instead of annual observations resulting in a six- five year periods covering the 1975 to
2004 time span. Hence, except initial GDP per capita and initial human
capitals which were only measured at the beginning of each five-year peri-
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ods, the remaining explanatory variables are measured in their averages
over a five-year period.
Although ‘school enrollment’ has been taken as a proxy for education
(human capital), ‘education-square’ has also been included to capture
diminishing or increasing effects on the dependent variable (GDP/Capita
growth). It is widely known that quadratic functions are used quite often
in applied economics to capture decreasing or increasing marginal effects.
Accordingly, when the coefficient on education is positive and the coefficient on education-square is negative, the quadratic has a parabolic shape.
On the other hand, when the coefficient on education is negative and the
coefficient on education-square is positive, the quadratic has a U-shape
with the implication of increasing marginal effects (Wooldridge, 2006:201).
This implies that, at lower value of education (school enrollment ratio),
there is no as such significant effect on GDP/C growth. At some point,
however, the effect becomes positive and very significant. In practice, it is
important to know where this turning point is. Accordingly, the turning
point (minimum or maximum) of the function is always achieved at the
coefficient on education (school enrollment ratio) over the absolute value
of the coefficient on education-square:
X* = | b1 / (2 b2 ) | , where, b1 and b2 are the coefficients of the education and education-square variables, respectively.
VI. RESEARCH METHODOLOGY AND ESTIMATION
Measuring Diversification
An increasing export orientation of the manufacturing sector, accompanied by a rising share of manufactures in total exports, is part of the
“normal” pattern of structural change in the growth process of developing
countries. Since vertical diversification (VDIV) mainly implies out of primary into manufactured exports, it can be measured by the share of manufactured exports to total exports [Elbadawi, 1999; Hood and Mayer, 2001;
Munemo, 2007; and others]
VDIV = (TMX) / (TX)
(1)
Where, VDIV is the index of vertical diversification, TMX is value of total
manufactured exports, and TX is value of total exports.
Similar to the works of Herzer and Nowak-Lehmann (2006), horizon-
The Impacts of Vertical and Horizontal Export Diversification on Growth（YOKOYAMA & ALEMU） 71
2009】
tal diversification (HDIV) in this study has been proxied by the number of
export sectors classified by the Standard International Trade Classification
(SITC) at the three-digit level. Thus, the maximum value of the index is
239 (the number of individual three-digit products in SITC revision 2),
and its minimum (theoretical) value is zero, for a country with no exports.
UNCTAD annually present the number of products exported (or imported)
at the three-digit SITC, with those products that are greater than
$100,000 or more than 0.3 per cent of the country total exports (or
imports). Hence, the vertical and horizontal diversification of SSA and
East Asia countries have been estimated and presented in fig 2 & 3 below.
Figure 2 : Vertical Export Diversification in SSA and East Asia, 1975 and 2004
Vertical Diversification in SSA and East Asia
200
180
160
VDIV Index
140
120
2004
100
1975
80
60
40
20
C
B
ur
ki
na
fa
am so
er
oo
C
on n
go
D
C
R
on
go
R
.
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th
io
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a
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a
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ig
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ia
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.A
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hi
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s
in
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Th e
ai
la
nd
0
Country
Figure 3 : Horizontal Export Diversification in SSA and East Asia, 1975 and 2004
Horizontal Diversification in SSA and East Asia
500
450
400
350
HDIV
300
2004
250
1975
200
150
100
50
ki
ur
B
C
na
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am so
er
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go
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on
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Country
72
RITSUMEIKAN INTERNATIONAL AFFAIRS
【Vol. 7
The Growth Model
The augmented Solow-Swan Model based on Mankiw, Romer, Weil (1992)
for growth has assumed the following augmented Cobb-Douglas production function with three inputs:
Y = K a Hl (AL) 1-a-l
(2)
Where Y is output, K is physical capital, H is human capital, A is the level
of technology, and L is labor. The parameters a and l are positive, and
a+l< 1.
Yit=b0+b1HUMCAPit +b2 DOMINVit+b3 FDIit+b4 VDIVit+b5 HDIVit b6
Zit+eit
(3)
Where i indexes the countries under study, t denotes the year, and
Zitrepresents the set of additional control variables including macroeconomic policy variables as mentioned in Table 9 of section 3 of this paper,
and eit is the idiosyncratic errors. Thus, the model takes both the crosssection dimension and the time-series dimensions into consideration.
There is a reverse causation from diversification to growth, and again
from growth to diversification as noted by Imbs and Wacziarg (2003).
Similarly, there is a reverse causation from growth to investment, in addition to the causation from investment to growth like noted by Blomstrom,
Lipsey, and Zejan (1993) ; Barro and Sala-i-Martin (1998). In other words,
not only investment causes economic growth, but also economic growth
influences the propensity to invest. In doing so, one should decide whether
it is necessary to use an instrumental variable, i.e., whether a set of estimates are consistent or not. Davidson and Mackinnon (1993) suggest an
augmented regression test called Durbin-Wu-Hausman (DWH test) test for
endogeneity, which can easily be carried out for the residuals of each
endogenous right-hand side variable, as a function of all exogenous variables. Accordingly, the Durbin-Wu-Hausman test for the suspected
endogenous variables and the test-statistic results are displayed in table 8
below.
2009】
The Impacts of Vertical and Horizontal Export Diversification on Growth（YOKOYAMA & ALEMU） 73
Table 8 : DWH test for Endogeneity and Results
Variables
Vertical Diversification
Horizontal Diversification
Domestic Investment
Foreign Direct Investment
Openness
Exchange Rate
GDP Initial
Test for Residuals
vdiv_res =0
hdiv_res =0
gfcf_res =0
fdigdp_res =0
openness_res =0
exchange_res =0
GDP initial_res =0
P-Value
0.0061***
0.0001***
0.0986*
0.0001***
0.0078***
0.0005***
0.0000***
Hence, from table 8 we can note that, except the domestic investment
variable which has become significant at 10% level, all suspected endogenous variables have become statistically significant at 1% level. In other
words, the above variables are indeed endogenous and should be instrumented with appropriate instrumental variables. Therefore, if the
assumption of strict exogeneity of the explanatory variables with the idiosyncratic errors fails, then, we can’t apply the common panel data analysis
approaches such as random effects, fixed effects, and first differencing
methods. As a result, a 3SLS with instrumental variables will be
employed in addition to SUR techniques. The instruments comprise some
of the original variables and lags of the other variables; lag variables are
reasonable candidates as instruments because the correlation of the residuals in the growth regressions between the two periods is never substantial (Barro and Sala-i-Martin (2001). Accordingly, the average value of
Vertical Diversification, Horizontal Diversification, Domestic Investment,
FDI, Exchange Rare, and Degree of Openness for the preceding five years
have been considered as instruments for the above variables respectively.
On the other hand, School Enrollment Ratio, Population Growth, Political
Instability and Rule of Law are considered as pre-determined, and they
enter as their own instruments in the regressions.
In line with this, test for heteroscedasticity was conducted using
Breusch-Pagan test, White test, and Cook-Weisberg (Score) test and as a
result the null-hypothesis of homoskedasticity was rejected at 1%, 1%, and
10% significance levels, respectively. This implies that there is evidence of
heteroskedasticity in which the error variance is not constant. Hence, corrective measures were taken and the standard errors have been adjusted
74
RITSUMEIKAN INTERNATIONAL AFFAIRS
【Vol. 7
accordingly. Likewise, test for serial correlation for the error terms was
conducted using Wooldridge test for autocorrelation in panel data and the
result yields a p-value of 0.2641, that implies there is no evidence of serial
correlation (first order autocorrelation) and hence the error terms are not
correlated. Moreover, though stationary test for panel data is a recent phenomenon, this study employed the Levin-Lin-Chu for stationarity test and
as a result the null-hypothesis of non-stationarity was rejected at 1% significance level; i.e. the growth dependent variable is stable with constant
mean, variance and standard error.
VII. EMPIRICAL RESULTS AND MAIN FINDINGS
Descriptive Statistics
The average growth in income per capita for the full sample (table 9)
was about 1.2 % with a minimum growth rate of -12 percent and a maximum growth rate of 11 percent. Looking the same statistics from the subsamples of SSA and East Asia will confirm that the average growth in
income per capita in SSA was only 0.21 percent, while that of East Asia
was 4.64 percent in the same period of time. What is astonishing most is
also the wide variation recorded in income per capita growth that ranges
from -12 percent (minimum) to about 11 percent (maximum). This implies
that SSA countries themselves are characterized by wide variation in economic performance due to various institutional and country specific
macroeconomic policy constraints. On the other hand growth in income
per capita in East Asia also varies from -1.2 percent (minimum) to 9.6 percent (maximum).
2009】
The Impacts of Vertical and Horizontal Export Diversification on Growth（YOKOYAMA & ALEMU） 75
Table 9 : Descriptive Statistics for the Full-Sample
Variable
Observation
Mean
Std. Dev. Minimum
maximum
GDP/Capita Growth
246
1.180
3.648
-12
11
Vertical Diversification
246
24.914
28.962
0.001
96.4
Horizontal Diversification
246
94.001
76.024
4.8
233
Domestic Investment
246
19.999
8.236
3.6
48
Foreign Direct Investment
246
1.913
3.325
-5.4
26.36
Initial Human Capital
246
17.953
15.217
0.9
61.9
Human Capital-Square
246
552.931
804.038
0.81
3831.61
Population Growth
246
2.386
1.034
-5
6
Initial GDP (in log)
246
3.136
0.483
2.292
4.469
Exchange Rate (in log )
246
1.58
1.802
-11.539
4.321
Openness
246
13.993
5.864
5.944
54.376
Political Instability
246
0.293
0.456
0
1
Rule of Law
246
4.675
1.447
1
7
In the same token, though the average vertical diversification index in the
full-sample was only 24.9 percent, a close look at the sub-samples again
confirms that SSA has recorded only 12 percent compared to East Asia’s
66 percent outstanding performance. The divergent performance of East
Asia and SSA in economic activities is not only bounded with vertical
diversification; but also in horizontal diversification as well. The evidences
from table 10 and table 11 again imply that the average number of export
goods from SSA was estimated only at 64, while it was about 202 for East
Asia. Generally, the evidences show that East Asia and SSA have become
more and more divergent in their diversification performances both vertically and horizontally.
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Table 10 : Descriptive Statistics for SSA Sub-Sample
Variable
Observation
Mean
Std. Dev. Minimum
Maximum
GDP/Capita Growth
192
0.208
3.336
-12
11
Vertical Diversification
192
13.394
15.359
0.001
74.426
Horizontal Diversification
192
63.728
53.373
4.8
233
Domestic Investment
192
17.491
7.034
3.6
48
FDI
192
1.529
2.864
-5.4
26.36
Initial Human Capital
192
13.215
11.792
0.9
57
Human Capital-Square
192
312.948
562.548
0.81
3249
Population Growth
192
2.609
0.984
-5
6
Initial GDP (in log)
192
2.995
0.373
2.292
4.180
Exchange Rate (in log )
192
1.614
1.963
-11.539
4.321
Openness
192
14.758
6.272
5.944
54.376
Political Instability
192
0.313
0.465
0
1
Rule of Law
192
4.919
1.281
1.6
7
Table 11 : Descriptive Statistics for East Asia Sub-Sample
Variable
Observation
Mean
GDP/Capita Growth
54
4.636
Std. Dev. Minimum
2.415
-1.2
Maximum
9.6
Vertical Diversification
54
65.877
28.930
1.775
96.4
Horizontal Diversification
54
201.638
36.432
91
232.8
Domestic Investment
54
28.919
5.605
17.6
45.8
FDI
54
3.280
4.372
-0.82
17.84
Initial Human Capital
54
34.8
14.101
8.3
61.9
Human Capital-Square
54
1406.20
947.383
68.89
3831.61
Population Growth
54
1.593
0.793
0
3.2
Initial GDP (in log)
54
3.638
0.501
2.362
4.469
Exchange Rate (in log )
54
1.474
1.056
0.185
3.958
Openness
54
11.271
2.749
6.420
17.674
Political Instability (War Dummy)
54
0.222
0.420
0
1
Rule of Law
54
3.807
1.667
1
7
Similar statistics on the other important variables in the two regions confirm similar scenarios as we exhibited it in the case of diversification performances. For instance, the average fixed capital formation as a ratio of
GDP in East Asia was about 29 percent, whilst it was only 18 percent in
SSA. Similarly, the average FDI ratio to GDP in East Asia was recorded at
2009】
The Impacts of Vertical and Horizontal Export Diversification on Growth（YOKOYAMA & ALEMU） 77
3.3 percent, while it was only 1.5 percent in SSA. Similar comparison of
SSA and East Asia in terms of their secondary schooling attainment
varies from an average of 13 percent for SSA to 35 percent for East Asia.
In line with other previous studies, the descriptive statistics from the subsamples show that the average population growth in SSA was about 2.61
percent, while it was only about 1.6 percent for East Asia. Since the
majority of SSA’s populations are under working age, it has been believed
that the higher and fast population growth rate has also contributed for
the sluggish economic growth in the continent.
Regression Results and Main Findings
The empirical results for the full-sample has been provided by table
12 in which the first three columns are based on SURE estimation techniques under different scenarios, whereas, columns 4, 5, and 6 display the
results based on instrumental variables estimation using simultaneous
equations. Accordingly, ‘vertical export diversification’ has been found to
be one of the major determinants of economic growth which is statistically
significant at 1 percent level using SURE estimation techniques.
Likewise, vertical diversification has become again the most important
positive determinant of growth even when the variables are instrumented
for endogeneity concerns.
For instance, the coefficient for vertical diversification in column 1 of
table 12 implies that a one percentage increase in the share of manufactured products in total exports results in a 4.4 percent increase in GDP
per capita. The coefficient of this same variable has become 0.0336 when it
has become instrumented, and this again implies that a one percentage
increase in the share of manufactured products in total exports results in
3.4 percent increase in GDP per capita.
It is however interesting to note that the importance of vertical diversification to GDP per capita growth is not similar between East Asia and
SSA. For instance, column 1 of table 13 shows that a one percentage
increase in the share of manufactured products in total exports may contribute in more than 5 percent increase in GDP per capita growth in East
Asia.
78
RITSUMEIKAN INTERNATIONAL AFFAIRS
【Vol. 7
Table 12 : SURE & 3SLS Estimations with Instrumental Variables for the full
sample
GDP/C growth
Vertical
Diversification
Horizontal
Diversification
Domestic
SURE-I
.0444***
(.0105)
.0017
(.0040)
.2169***
SURE-II
.0415***
(.0107)
.0022
(.0039)
.2201***
SURE-III
.0394***
(.0108)
.0004
(.004)
.221***
INST-I
.0336**
(.0132)
.0084*
(.005)
.1157***
INST-II
.0291**
(.0134)
.0094*
(.0051)
.1254***
INST-III
.0285**
(.0139)
.0072
(.0053)
.1221***
Investment
FDI
(.0277)
.1812***
(.0594)
-2.5357***
(.6531)
-.0086
(.0476)
.001
(.0008)
.1035
(.2042)
.7557*
(.4129)
.2621
(.0979)
(.0276)
.1819***
(.0592)
-2.782***
(.6744)
-.0103
(.0474)
.001
(.001)
.0857
(.2038)
.8532**
(.4173)
.2743***
(.0993)
-.2104
(.1517)
(.0276)
.1742***
(.0591)
-2.532***
(.6488)
-.003
(.0473)
.0007
(.0008)
.0946
(.2029)
.4986
(.434)
.2920***
(.0974)
(.0444)
.3353***
(.1001)
-2.918***
(.7495)
-.0124
(.0512)
.0011
(.001)
.0215
(.2157)
.4517
(.4382)
.3859***
(.1060)
(.0443)
.3449***
(.0997)
-3.286***
(.7721)
-.0183
(.051)
.001
(.001)
-.0103
(.2155)
.5708
(.4423)
.3541***
(.1074)
-.2413
(.1591)
(.0442)
.3321***
(.1014)
-2.865***
(.7455)
-.0092
(.0511)
.0011
(.0009)
.0141
(.2140)
.2621
(.484)
.3806***
(.1053)
-.0616*
(.0339)
3.0591
(2.0116)
246
.4548
-.0485
(.0524)
4.6567** 6.8977*** 5.2681***
(2.2641) (2.6569) (2.3529)
246
246
246
.4063
.4113
.4155
Initial GDP/C
Education
(Initial)
EducationSquare
Population
Growth
Political
Instability
Foreign
Exchangex
Rule of Law
Openness
Constant
No.of obs.
R Square
2.0659)
(1.9485
246
.4475
3.8736*
(2.3378)
246
.4518
2009】
The Impacts of Vertical and Horizontal Export Diversification on Growth（YOKOYAMA & ALEMU） 79
Table 13 : SURE and 3SLS Estimations with Instrumental Variables for East
Asia
GDP/C growth
Vertical
Diversification
Horizontal
Diversification
Domestic
SURE-I
0513***
(.0163)
.0075
(.0086)
.1055**
SURE-II
.0581***
(.0159)
-.0002
(.009)
.0598
SURE-III
.0545***
(.0171)
.0069
(.0087)
.1051**
INST-I
.0506**
(.0243)
.0213
(.0151)
-.0479
INST-II
.0554**
(.0221)
.0078
(.016)
-.0944
INST-III
.0765***
(.0253)
.0112
(.0146)
-.030
Investment
FDI
(.0512)
.2581***
(.0781)
-5.052***
(.7879)
-.1537
(.097)
.0022
(.0015)
.3537
(.4025)
-1.4317
(.9375)
3623
(.3793)
(.0534)
.1906**
(.0812)
-3.758***
(.9644)
-.2013
(.0956)
.0029**
(.0014)
.3296
(.3864)
-1.4754
(.8996)
.1963
(.3719)
.4871**
(.2254)
(.0510)
.275***
(.0836)
-5.169***
(.8134)
-.1612
(.0977)
.0023
(.0015)
.3851
(.4054)
-1.2945
(.9668)
.3869
(.3808)
(.1422)
.4736***
(.1201)
-6.726***
(1.0796)
-.194
(.1156)
.0029*
(.0017)
.7017
(.5726)
-2.7712
(1.6988)
.9399*
(.5185)
(.159)
.3435***
(.1456)
-4.861***
(1.777)
-.2229
(.1105)
.0035**
(.0017)
.6337
(.5222)
-2.8844*
(1.6411)
.6424
(1.6411)
.5151
(.4249)
(.1419)
.5824***
(.1516)
-7.473***
(1.2636)
-.2301
(.1212)
.0032*
(.0018)
.7507
(.6001)
-1.810
(1.6031)
1.0295*
(.5537)
Initial GDP/C
Education
(Initial)
EducationSquare
Population
Growth
Political
Instability
Exchange
Rate
Rule of Law
Openness
Constant
No.of obs..
R Square
.0533
(.0956)
15.714*** 12.668*** 15.44***
93.0867) (3.2795) (3.117)
54
54
54
.5669
.6013
.5693
.2876*
(.1632)
22.05*** 18.316*** 21.476***
(4.7559) (4.2591) (4.8131)
54
54
54
.4407
.5077
.4045
Where as in SSA, except column 2 of table 14, there was no evidence that
vertical diversification contributed for growth in income per capita. This
is mainly because of the share of manufactured products in total exports
in SSA has still been under the threshold level to make any impact on economic growth. This calls for SSA to have a strong effort to vertically diver-
80
RITSUMEIKAN INTERNATIONAL AFFAIRS
【Vol. 7
sify its exports from the existing few primary products to a large mix of
value-added manufactured products; as witnessed in East Asia.
On the other hand, the study confirms that, though horizontal diversification is still positively correlated with growth in income per capita, its
contribution to growth was not found to be so impressive compared to vertical diversification. The empirical evidence from the regression analysis of
the full-sample (table12) highlighted that it is only in some cases (column
4 &5 of table 12) that horizontal diversification is statistically significant
at 10 percent level. Accordingly, the coefficients of horizontal diversification in columns 4 & 5 from the full sample imply that a one percentage
increase in numbers in export sectors will increase GDP/Capita by only
0.8% and 0.9%, respectively. In fact, the analyses from the sub-samples
have not confirmed any significant relationship between horizontal export
diversification and growth in GDP per capita.
2009】
The Impacts of Vertical and Horizontal Export Diversification on Growth（YOKOYAMA & ALEMU） 81
Table 14 : SURE and 3SLS Estimations with Instrumental Variables for SSA
GDP/C growth
Vertical
Diversification
Horizontal
Diversification
Domestic
Investment
FDI
Initial GDP/C
Education
(Initial)
EducationSquare
Population
Growth
Political
Instability
Exchange
Rate
Rule of Law
SURE-I
.0293
(.0227)
-.0027
(.0065)
.0012
(.0675)
.5289**
SURE-II
.0385**
(.0157)
-.0035
(.0048)
.1739***
(.0345)
.1802**
SURE-III
.0268
(.0229)
-.0033
(.0067)
.01
(.069)
.5004*
INST-I
.0293
(.0226)
-.0026
(.0065)
.0012
(.0675)
.529**
INST-II
.01919
(.0229)
-.0014
(.0062)
.0301
(.0648)
.4356*
INST-III
.0268
(.0229)
-.0033
(.0068)
.01
(.0694)
.5004*
(.2481)
-.0903
-1.123
-.0659
(.0685)
.0015
(.0013)
.3687
(.2633)
.4296
(.5729)
.3933***
(.1284)
(.0832)
-1.4078
(.9129)
-.0747
(.0594)
.0019*
(.0011)
.3297
(.2324)
-1.288***
(.4977)
.2991***
(.108)
-.4865**
(.1889)
(.2599)
-.1664
-1.1295
-.064
(.0682)
.0016
(.0013)
.3687
(.2607)
.3128
(.604)
.3939
(.1273)
(.248)
-.0903
-1.123
-.0659
(.0685)
.0015
(.0013)
.3687
(.2633)
.4296
(.5729)
.3933***
(.1284)
(.2492)
-.7299
-1.0798
-.0769
(9.0658)
.0017
(.0013)
.3143
(.2535)
.8377
(.5788)
.355***
(.1233)
-.5021**
(.2146)
(.2598)
-.1664
-1.1295
-.064
(.0682)
.0016
(.0013)
.3687
(.2607)
.3128
(.604)
.3939***
(.1273)
Openness
Constant
No.of obs..
R Square
-1.8964
(2.9557)
192
.1666
1.861
(2.901)
192
.3078
-.0332
(.0632)
-1.2504
(3.2251)
192
.1829
-1.8965
(2.9557)
192
.1666
2.3266
(3.2338)
192
.2267
-.0332
(.0632)
-1.25
(.0632)
192
.1829
Thus, although we can’t rule out the positive impact of horizontal diversification to economic growth, it is more evident that vertical diversification
is the main engine of growth, and this was exactly what East Asians’ did
in the past. Eventually, what SSA has to learn from East Asia is that, policy makers in the region should give more focus on vertical diversification
towards value-added products than the horizontal one.
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RITSUMEIKAN INTERNATIONAL AFFAIRS
【Vol. 7
The study also asserts that domestic investment and FDI are strong key
factors for growth in income per capita (table 12), inline with our proposition at the outset. For instance, the coefficient of ‘domestic investment’
from column 1 of table 12 indicates that a one percentage increase in the
ratio of fixed capital formation to total GDP will increase GDP/capita by
21.7 percent. Similarly, the results from the instrumental variable estimation in the same table confirms that, a one percent increase in the ratio of
gross fixed capital formation to GDP yields about 11.5%, 12.5%, and 12.2%
increases in growth in GDP per capita as displayed in column 4, 5, and 6
of table 12, respectively.
Similar to domestic investment, FDI has been found to be a very
strong factor to induce growth in income per capita as shown in table 12.
For instance, the instrumental variable estimations from columns 4-6 of
table13 show that a one percent increase in the ratio of FDI to GDP would
result in 33.5%, 34.5%, and 33.2% increase in GDP/C growth, respectively.
Interestingly, the results from the full-sample analysis are more or less
consistent with the results from the SSA and East Asia sub-samples (table
14 &13). Particularly, the results from East Asia sample confirmed that
FDI has been one of the major factors for the regions fast growth in
income per capita for the last three decades. If we single out column 6 of
table 14, we can find that a one percent increase in the ratio of FDI to
GDP in East Asia may bring a 58.2 percent increase in GDP/C growth.
The effect of FDI to growth in GDP/C has been less significant in SSA
compared to East Asia as expected. This is mainly because of FDI in SSA
is too low and concentrated only in few oil producing and mineral-rich
countries like Nigeria, Angola, and South Africa.
The empirical evidences from the sub-samples confirm that, it was
FDI more than domestic capital, which contributed to economic growth in
East Asia. However, this doesn’t mean that East Asia’s high saving tradition didn’t contribute for economic growth in the each country of the
region. Yes, it did; but not as much as the contribution of FDI to growth.
What lessons can be learned from East Asia is that, SSA countries should
do their utmost efforts to attract FDI by creating a conducive atmosphere
including political stability, macroeconomic stability and favorable institutions so as to minimize investment risks.
The ‘Education’ variable which was proxied by the level of educational
enrollment has been recognized as one of the main determinants for eco-
2009】
The Impacts of Vertical and Horizontal Export Diversification on Growth（YOKOYAMA & ALEMU） 83
nomic growth. In this study, the marginal effects of education has been
analyzed by including the ‘square’ of the education variable as it has been
commonly practiced by various researchers, since there is strong justification that school enrollment/attainment would have a delayed effect after a
certain point. In other words, including square of ‘school enrollment ratio’
can capture whether or not education has a diminishing or increasing
effects on growth in GDP/C. Accordingly, it has been found that the coefficient on school enrollment ratio is negative and the coefficient on education-square is positive, which implies that the growth model will exhibit a
U-shape with the implication of increasing marginal effects. The interpretation for such kind of results is that, at lower value of school enrollment,
an additional school enrollment will not have a significant effect on
growth. At some point, however, the effect becomes positive and very significant. In this case, the empirical evidence from our analysis shows that
the marginal effect of education has become statistically significant in the
case of East Asia; rather than SSA. For instance, the marginal effect of
school enrollment to growth in East Asia from table 13 was found to be 0.3
percent using instrumental variable estimations. The significance of the
education variable in SSA sub-sample is even too minimal and this is
mainly due to the low level of educated labor which is under the threshold
level with no significant effect on growth.
In practice, it is important to know where this turning point of the
marginal effect of education to be positive will occur. According to
Wooldridge (2002), the turning point (minimum or maximum) of the function is always achieved at the coefficient on education over the absolute
value of the coefficient on education-square and roughly estimated as follows:
Education* = | b1 / (2 b2 ) |
Where, b 1 and b 2 are the coefficients of education and educationsquare, respectively. For instance, the coefficients of education and education-square in column 4 of table 13 of the East Asian sample have been 0.194 and 0.0029, respectively. Thus, the turning point (minimum) value
of school enrolment ratio can be calculated as:
Education* = | -0.194/ 2 (0.0029) | = | -0.194/0.0058| = 33.5
This implies that it is only after a school enrollment ratio of 33.5 %
that the role of education will be significant to growth in GDP/C. Where
as, the average school enrollment ratio estimated for SSA in the descrip-
84
RITSUMEIKAN INTERNATIONAL AFFAIRS
【Vol. 7
tive statistics (table 10) was only 13.2 % which is too low in stimulating
economic growth in the region and that is why the education variable has
become less significant in SSA’s sub-sample. However, it is obvious that
the availability of a large pool of skilled labor force is a pre-requisite for
FDI inflow as well as for a country to diversify its economy and exports.
One of the most notable results from the full sample analysis is that,
initial GDP per capita is found to be a strong negative determinant to
growth in income per capita, which confirms the Solow-Swan- Ramsey
hypothesis of absolute convergence such that poorer countries grow faster
than the richer ones and thereby catch up to the richer countries. For
instance, the coefficient for initial GDP/C in column 1 of table 12 for the
full-sample analysis is estimated at -2.5357. This implies that convergence
occurs at the rate of 2.5% per year. The results of the full-sample analysis
on the effect of initial GDP/C on growth is quite consistent with the
results in the East Asia’s sub-sample analysis (table 13). In fact, the rate
of convergence in East Asia has been recorded much higher than the rate
of growth estimated for the full samples. For instance, the coefficients of
initial GDP/C in columns 4, 5, and 6 of table 13 show that convergence
occurs at the rate of 6.7 %, 4.95, and 7.5%, respectively. These results
appear to be much close to the realty in the ground that East Asia has
started from a very low level of income per capita, and achieved a tremendous success in it, which is now more close to the developed world.
However, this same variable (initial GDP/Capita) in the SSA sub-sample (table 14) was not found to be significant; regardless of its negative
sign as theoretically proposed. This implies that there was no significant
evidence of convergence in SSA to catch up with rich countries in the past
three decades. This again confirms that SSA which was once in equal level
of income per capita with East Asia had experienced a stagnated and
sometimes a retarded economic growth for the past three decades.
However, though it was not significant, the negative sign of the coefficient
points out some indications of convergence in the region.
Theoretically, the effect of population growth to economic growth is
disputable. On one hand, the Solow model predicts that economies with
higher rates of population growth will have lower levels of capital per
worker and therefore lower incomes. On the other hand, the endogenous
growth theory predicts that population has scale effects and hence larger
population and larger economies perform better. Coming to the empirical
2009】
The Impacts of Vertical and Horizontal Export Diversification on Growth（YOKOYAMA & ALEMU） 85
results in our analysis both for the full-sample and the sub-samples, no
evidence has been found for population growth to be a significant factor
for growth in income per capita.
Foreign exchange has been found to be positively and statistically significant at 1 percent level and implies that a depreciating local currency
would result for stimulating production and boosting exports. For
instance, column 4, 5, and 6 of the instrumental variable estimation in
table 12 implies that a one percentage decease in the value of the local
currency against US$ will result a 0.4% increase in GDP per capita
growth.
Finally, other control variables such as degree of openness, property
rights have been found to be significant determinants of economic growth
in the case of East Asia. Where as, political instability has been found to
be a negative factor for economic growth as it is mainly noted in the case
of Sub-Saharan Africa. It is evident that in countries with frequent wars
and political turmoil, the rate of saving and investment tends to be low.
Generally, East Asia’s success was highly attributed by their huge
investment on human capital through education and the high rate of
physical capital accumulation mainly driven by foreign direct investment
(FDI). Quite the opposite, the level of human capital (skilled labor) and
physical capital formation including the level of FDI in SSA has been
under the threshold level in playing a positive role to materialize a structural change in the economy. In line with this, countries in East Asia
have managed to diversify their economies and exports mainly vertically
and transformed their economies from being exporters of few primary
commodities in the late 1960s and early 1970s to exporter of high valued
manufacturing and service products after the mid-1980s. Unfortunately,
SSA’s export diversification attempt was too minimal and most of the
countries in the continent are still dependent on the export of few agricultural and mineral products which are highly vulnerable for price shocks.
The paper then argues that the inference that SSA can replicate the East
Asian experience is largely relevant as long as countries in SSA create
favorable conditions as discussed above.
VIII. CONCLUSION AND POLICY RECOMMENDATIONS
First of all, the empirical results confirmed that export diversification;
86
RITSUMEIKAN INTERNATIONAL AFFAIRS
【Vol. 7
especially vertical diversification played a vital role to induce economic
growth in the case of East Asia. East Asia’s success was highly attributed
by their huge investment on human capital through education and the
high rate of physical capital accumulation mainly driven by foreign direct
investment (FDI). Conversely, the level of human capital (skilled labor)
and physical capital including FDI in SSA has been under the threshold
level in playing a positive role to materialize significant export diversification and structural change in the economy.
Secondly, the empirical results from this research have revealed that
it is ‘vertical diversification’ which is more important than the horizontal
one to stimulate economic growth. This, therefore, calls into question the
policy advice of some researchers that proposed Africa’s emphasis should
be on horizontal diversification through increasing the number of primary
export products.
Thirdly, diversification in general and vertical diversification in particular in SSA would not be an easy task: countries seeking to diversify
must have sufficient levels of human and physical capital as well as an
adequate infrastructure to support the export diversification strategies.
Moreover, stronger production linkages between the particular country
and the world’s leading multinational companies should be created mainly
through foreign direct investment.
Fourthly, the success of East Asian countries to shift from producing a
low productive primary commodities to producing a more productive manufactured products reflects even latecomers are able to specialize in high
growth areas if some of the pre-conditions are fulfilled. Therefore, the
paper argues that the inference that SSA can replicate the East Asian
experience is largely relevant if not all in all.
Fifthly, the quality of government institutions, policies and political
atmosphere may determine the overall direction of economic strategies
including diversification, and hence ‘good governance’ and political stability are pre-requisites for any economic strategy to be materialized in SubSaharan Africa.
Finally, it should be noted that export diversification is just one of the
key policy measures to be undertaken for structural change and economic
growth. Thus, it would not be considered as a panacea for SSA’s deep rooted economic problems.
2009】
The Impacts of Vertical and Horizontal Export Diversification on Growth（YOKOYAMA & ALEMU） 87
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