Growth and agriculture
in Sub-Saharan Africa
Nationalekonomiska institutionen
Kandidatuppsats, NEKH01
Författare: Måns Hägerdal 850330-3995
Handledare: Joakim Gullstrand, Hans Falck
VT 2013
Contents
Part
Page
1. Introduction
3
2. Background and literature
4
3. Theory
6
4. Empirical method and data
10
5. Results
13
6. Summary
16
7. References
16
Appendix 1 – The countries of SSA
18
Appendix 2 – SITC Rev 3
19
2
1. Introduction
Sub-Saharan Africa is one of the poorest regions of the world. In Economic Development by
Michael P. Todaro and Stephen C. Smith, the region is listed as consisting of 48
countries/regions, whereof 34 are classified as low-income countries and 7 as lower-middleincome countries (Todaro & Smith 2011, pp. 40-41). In a former edition of Todaro & Smith
there is also a special paragraph about “The Crisis in Sub-Saharan Africa”. Here it is
mentioned that the 80s and 90s were harsh periods for the region, even compared to the rest of
the developing world. Economic growth declined and poverty was distinctively increased.
However, these matters seem to have been improved during the first decennium of the 2000s
(Todaro & Smith 2009, pp. 805-807). One can also note, from graphs collected at the World
Bank, that GNI per capita, life expectancy at birth and education levels have all grown
steadily from the 90s and beyond (The World Bank, Data, Sub-Saharan Africa). According to
a recent article by IMF, it is estimated that Sub-Saharan Africa (from here on abbreviated to
SSA) will have an economic growth of 5.25 % in the region during 2013 (IMF African
Department, Sub-Saharan Africa Maintains Growth in an Uncertain World).
One conclusion by Todaro and Smith made in the paragraph mentioned earlier is: “More
emphasis must be put on agricultural and rural development” (Todaro & Smith 2009, p. 807).
Many of the poorest countries (in SSA or elsewhere) are land-abundant and ought to have
comparative advantages in agriculture. This sector could hence function as a way, or part of a
way, out of poverty. One possibility that has come up in recent years is production of crops
for biofuels. Saletan writes in an article on the debate about biofuels: “In a biofuel economy,
the chief asset is open land. Who has open land? Poor countries. Latin America has sugar
cane. Africa and Asia have cassava” (Saletan 2007). Ray means that factor endowments are
indeed a source of comparative advantage. He mentions Bangladesh which was at the time the
largest exporter of jute in the world, largely because of its abundance of labor and land (Ray
1998, p. 631). Perhaps some SSA countries are able to achieve something similar, with
agricultural products. But Ray also mentions that preferences matter; a country might want, or
need, to consume a large part of its production for itself rather than exporting it. This is
typically an issue for developing countries and food (i.e. agricultural products in particular),
not the least because of their low per capita income (Ray 1998, pp. 636-637). The main
purpose of this essay is to examine whether there is a significant correlation between revealed
comparative advantages (RCA) in agricultural products and growth in GDP per capita, in
SSA. I will study 48 countries located in this area (see Appendix 1 to see exactly which
3
countries I am examining, and how they have been chosen). I have used selected data material
from selected years between 1995 and 2010 in order to study whether there is any
convergence between these countries. This is because convergence and growth are closely
connected, at least according to Solow’s theory, and this will be discussed more thoroughly
later on. This can be seen as a study on convergence in SSA with respect to its agriculture.
From here I will first go through some earlier studies on the subject, then discuss the Solow
theory, which I have chosen to base my study on, in detail. Thereafter I will present methods
and data, my results and a summary of the whole paper.
2. Background and literature
Jaques Diouf says in a text about Africa’s agriculture that to achieve growth, (my translation)
“Africa must put more money into agriculture and mobilize more capital, land and work
force” (Diouf 2010, p. 184). But John W. Mellor suggests in an article from 1988 that in many
developing countries, surplus capital from agriculture is wrongly allocated. Most of the work
force is poor and demand for food is low. Those countries need to improve this demand to get
an economic boost and increase incomes. Mellor means that more energy should be put into
agriculture and other labor-intensive sectors, so that in the long run there is potential for
building up an industrial sector. He gives Taiwan as an example: it has grown substantially
over the last decades, and earned 60 % of their foreign currency from agricultural exports in
1960, as opposed to only 10 % in 1980. At the same time, technology matters. If a country is
currently catching up in the technology process, it could happen that agricultural output
growth temporarily exceeds growth in food demand. But it could also happen that food
demand increases so much that increased imports are needed! He calls this “one of the
paradoxes of Third World development” (Mellor 1988, p. 419-436, quote from p. 424). This
is of course relevant for RCA. Lilyan E. Fulginiti, Richard K. Perrin and Bingxin Yu suggest
in a more recent article that agricultural productivity has risen in SSA countries between the
60s and 90s, and especially in 1985-1999. According to them, 2/3 of the labour force, 35 % of
GNP and 40 % of the foreign exchange earnings came from this sector. Overall though, they
look especially on how political institutions, conflicts, wars and degrees of freedom affect
trade and growth (Fulginiti, Perrin & Yu 2004, p. 169-180). This is also relevant but beyond
the scope of this essay.
4
However, Diouf mentions that the yield of cereals in SSA is the lowest in the world (1.2 tons
per hectare) and that increased growth mostly has depended on increased agricultural land
area, following from increasing prices, rather than productivity (Diouf 2010, p. 177). There
are also problems with lack of modern equipment and fertilizers, and dependence on (often
unpredictable) rain (Diouf 2010, p. 178).
Ana I. Sanjuàn-López and P.J. Dawson examine in an article how agricultural exports
contribute to economic growth in developing countries (they have picked 42 countries
whereof 14 are located in SSA). They find that those exports are very important to developing
countries in terms of GDP, not the least for the ones with the lowest incomes per capita.
Otherwise, the relative amount of agricultural exports, both in terms of GDP and of total
exports, varies heavily among the chosen countries. But for the low-income countries, where
most of the SSA countries are located, agriculture has the deepest impact on economic
growth, and the mentioned quotas are the biggest. The authors find support for the so-called
export-led growth hypothesis, and also mention that many low-income countries might have
comparative advantages in agriculture (Sanjuàn-López & Dawson 2010, p. 565-583). In a
similar article, the same Dawson mentions that the share of agricultural exports in terms of
total exports has decreased for all less developed countries, almost by half, between 1975 and
1995 (Dawson 2005, p. 145-152). Donges & Riedel write in 1977: “Many LDCs have shown
that they possess comparative advantage in a wide range of goods”, looking especially on
manufactured goods! (Donges & Riedel 1977, p. 74). Lower exports means lower RCA, and
one question could then be whether a low RCA value in agricultural products is a good or a
bad sign. But this is of course related to my study, which means to see how much RCA affects
growth.
Bano & Scrimgeour have made a study that reminds of what I am trying to do. They examine
the relationship between growth and RCA for New Zealand with respect to their kiwifruit
industry. They find a strong relationship between the export success between 1981 and 2011,
and the correspondingly high RCA value (they use the Balassa index, which can take any
positive value). They also use export data from UN Comtrade (see below) for their
calculations. They mention however that there have been fluctuations on the kiwifruit market,
and that factors like market size and seasonality matters (Bano & Scrimgeour 2012).
5
3. Theory
The Solow (Solow-Swan) model
The theoretical part of this essay is a lot based on SOU (Statens Offentliga Utredningar, SOU
2007:25), which is a study on growth and convergence. Here they use a modified version of
the Solow model, and I have chosen to use this (see part 4 for details).
The original Solow model suggests that GDP (Y) depends on (human or physical) capital (K),
labor (P) and technological progress (A). The last one is mostly often interpreted as a
constant. This is typically written as Y = AKαP1-α, which is a Cobb-Douglas function (Ray
1998, p. 91). This model “implies that economies will conditionally converge to the same
level of income” (Todaro & Smith 2011, p. 146) depending on a number of factors. In this
context, this essay will in some sense study the similarity of the countries of SSA.
The model is an extension of the Harrod-Domar model. The general Solow model is hence
𝑌 = 𝑓(𝐾, 𝐿, 𝐴) or 𝑌 = 𝐴(𝑡) ∗ 𝑓(𝐾, 𝐿) where the A is a time factor. This A(t) is known as
neutral technical change, which simply means that it doesn’t affect the marginal rates of
substitution of the other factors (Solow 1957, p. 312). As implied above, it is often left out.
In an article on long-run growth, Solow starts from the Harrod-Domar model to discover
possible growth patterns of production. He introduces the new variable 𝑟 =
𝐾
𝐿
and shows that
the change in this variable can be expressed as: 𝑟̇ = 𝑠𝐹(𝑟, 1) − 𝑛𝑟, where s is the national
savings rate and n is population growth. “The function F(r,1) […] is easy to interpret. It is the
total product curve as varying amounts r of capital are employed with one unit of labor.
Alternatively it gives output per worker as a function of capital per worker. Thus [this
equation] states that the rate of change of the capital-labor ratio is the difference of two terms,
one representing the increment of capital and one the increment of labor.” (Solow 1956, p. 69)
When 𝑟̇ = 0,
𝐾
𝐿
is constant, and the yield of the capital stock must be the same as the growth of
the labor force, that is, n.
6
Fig 1: Copy of Figure I from Solow 1956, p. 70. Shows the relation between r and 𝑟̇ in
terms of the functions sF(r,1) and nr. The intersection r* is an equilibrium.
Fig 1 shows the relation between the functions nr (with constant slope n) and sF(r,1) which is
assumed to be increasing and concave because of diminishing marginal factor products. When
the lines meet at r*, 𝑟̇ = 0. This is an equilibrium; if one function is larger than the other, then
r will increase/decrease towards this point. This makes it a so-called stable equilibrium. This
type of graph for sF(r,1) is however not the only one possible, but it looks like this for
example for Cobb-Douglas functions. But for other functions, there may be several equilibria,
or none.
In the last case the graphs don’t intersect at all. Either the system is so productive in terms of
capital that per capita income increases forever. Or, the system is so unproductive that per
capita income decreases forever. But Solow states that “under the usual neoclassical
conditions […] no simple opposition between natural and warranted rate of growth is
possible”, where natural and warranted rate refers to growth of the population (and hence
labor force) and growth of capital in terms of saving, respectively. “The system can adjust to
any given rate of growth of [nr], and eventually approach a state of steady proportional
expansion” (Solow 1956, p. 73). This is typically known as a steady state.
As mentioned before, Fig 1 is a typical case for Cobb-Douglas functions. For the function Y =
KαL1-α , the function sF(r,1) must lie above the nr-curve initially, but because α<1 (when
constant returns to scale are assumed), it must decrease eventually, and after some point
7
remain under the curve. The expression for 𝑟̇ then becomes 𝑟̇ = 𝑠𝑟 𝛼 − 𝑛𝑟, and the
𝑠
equilibrium value of r is r*=(𝑛)1/(1-α). Moreover, real income per head = Y/L = (K/L)α = r α ,
also tends to the value (s/n)α/(1-α). It follows that the equilibrium value of K/Y is s/n (or n =
𝑠𝑌
𝐾
),
and this is interpreted as long-run equilibrium growth. That is, warranted rate is equal to the
natural rate (Solow 1956, p. 66-77).
One smooth mathematical implication of the Cobb-Douglas functions is that they can be
expressed log-linearly; in this case, Y = Kα(AL)1-α ↔ ln Y = α* ln K + (1-α)*(ln A + ln L). So
this form can be used to estimate presumptive (log-)linear relationships, and becomes even
easier to use when 1-α is replaced with a new variable β. Modified versions like this are
sometimes used in reports and articles.
Convergence
Ray differs between two types of convergence in growth: unconditional (absolute) and
conditional convergence. Unconditional convergence arises when assuming that all countries
have equal rates or equal changing rates of A, s, n and δ (see above). In this case all countries
converge to a common growth pattern, no matter the initial capital stock (in total or per
capita). This is depicted in Figure 2. That is, all countries have the same steady state; this is
implied by the Solow model. There is however not much reliable evidence of this piece of the
theory. In contrast, the Harrod-Domar model assumes “neutral” growth rates, meaning that
the K/Y-quota is constant; growth generates more growth and vice versa. Both models can be
found to be right and wrong in different aspects (Ray 1998, pp. 74-82). Conditional
convergence is a different hypothesis, assuming that the parameters mentioned earlier are not
equal. This is somewhat related to my study; I want to study the rate of convergence, but I
haven’t made any assumptions about those parameters. Then the Solow model implies that the
countries still converge to their respective steady states. But, these parameters are assumed to
only have level effects; so countries’ growth patterns become parallel but at different levels,
creating convergence in growth. This is depicted in Fig 3. Ray refers to a study which found
that the variation in worldwide GDP p.c. in 1985 could be explained by more than half by
differences in s and n. He points out the advantages of this model, but still notes that there is
no explanation to why these parameters differ between countries (Ray 1998, pp. 82-87).
8
Fig 2: Unconditional convergence. Based on fig 3.7 in Ray 1998, p. 75. Here AB is the steady state
line, and CD and EF represent countries initially placed below and above this line, respectively. In
time they both converge towards the AB line, according to theory.
Fig 3: Conditional convergence. Based on fig 3.11 in Ray 1998, p. 83. The country
displaying the CD line will converge to the AB line, which is a steady state. The country
displaying the EF line will converge to A´B´, which is another steady state, parallel to the
other one. Hence this is convergence in growth.
9
4. Empirical method and data
I will look at the chosen 48 countries with respect to their growth in GDP per capita, and how
it relates to RCA for some agriculturally related sectors. The numbers for GDP per capita
have been downloaded from the World databank, African Development indicators (The
World Bank, ADI, see homepages). These numbers were given in current US dollars. Fig 4
shows a graph based on numbers from this website (GDP growth for SSA, annual %), for the
time period I am studying. Here we can see that there have been some ups and downs in the
GDP growth, with a sharp decline followed by a just as sharp rise during the last years. Table
1 is based on numbers from the same website, and shows GDP p.c. values for the area in
selected years.
Fig 4: Annual GDP growth for SSA for the time period 1995-2010.
GDP per capita
Year
2010
2005
2000
1995
Sub-Saharan Africa
1297,888683 857,3200758 509,348676 554,473881
Countries' Mean
2272,387759 1581,105677 896,733205 875,278231
Countries' Std deviation 3702,990896 2721,909121 1423,63977
1364,755
F
Table 1: GDP p.c. values for SSA countries in selected years; first the whole area, then the
individual countries’ mean and standard deviation. Note that a few countries haven’t reported
these numbers.
10
For RCAij (i=country, j=sector) I have used the formula:
𝑋 −𝑀
RCAij = 𝑋𝑖𝑗+𝑀𝑖𝑗 ,
𝑖𝑗
𝑖𝑗
where X=exports and M=imports (for a given country and sector), RCA>0 means (revealed)
comparative advantage, RCA<0 means (revealed) comparative disadvantage. This formula is
based on Donges & Riedel, who in turn have based it on the more famous Balassa index. This
“RCA concept rests on the assumption that a country’s imports indicate which of the domestic
industries are uncompetitive, whereas the country’s exports point to the industries which
display comparative competitiveness” (Donges & Riedel 1977, pp. 68-69). This is the point of
view I have chosen to take; both exports and imports should be relevant. The examined
sectors are 0 (Food and Live Animals), 1 (Beverages and tobacco) and 4 (Animal and
Vegetable Oils).
The numbers for exports and imports have been found on UN Comtrade (UN Comtrade, see
homepages). I have used the category SITC Rev.3 which is Standard International Trade
Classification, Revision 3, for the chosen sectors. The mentioned sectors have been chosen
simply because they were the ones for that SITC category that had any connection to
agriculture. All those sectors have sub-categories, covering most food and agricultural
products (see Appendix 2, showing all available sectors for SITC Rev.3). Table 2 shows some
example numbers, where RCA has been calculated through the formula above.
All SSA countries
Sector
Year
Exports
Imports
RCA
0
0
1
1
4
4
2000
2005
2000
2005
2000
2005
8596042010 13526660182 1543301909 1990779557
224811140
264636376
6536804089 10305556911 666889656 1356217949
763586846 1476503287
0,136077372 0,135157516 0,396532259 0,1895913 -0,545099963 -0,696019358
Table 2: Exports, imports (in current US dollars) and RCA for all SSA countries for the examined
sectors in 2000 and 2005. Note that some countries haven’t reported these numbers.
11
These numbers have been matched with GDP per capita for some specific years. I am using a
modified version of the Solow growth model1, expressed as:
ln(yi,T/yi,0) = α + βln(yi,0) + (∑𝑗=0,1,4
RCAij,0) + εi ,
where yi,T is GDP per capita for a given country in year T, and 0 depicts the closest examined
year preceding that. RCA is denoted analogously, added together as a sum, and εi is a
residual. This model is based on formula A.14 in SOU (SOU 2007:25, Appendix A, p. 12),
which is identical except for the RCA term, and formula (3.2) (SOU 2007:25, part 3, p. 9)
which has a different summation term, but the idea is more or less the same. This formula can
be used to study conditional convergence (Ibid). Note that
ln(yi,T/yi,0) = α + βln(yi,0) + εi ↔ yi,T/yi,0 = eα + (yi,0)β + eεi,
which is a simplification of the Solow growth model. The examined years are 1995, 2000,
2005 and 2010.
I have looked at criterions described in SOU; here they use (formula A.15):
b = -ln(1+β)/T
as a (unconditional) convergence parameter, where T is a time period and β is the coefficient
for ln(yi,0), as above. If b is positive, or equivalently if β<0, then poor regions (countries)
grow faster than rich ones, and hence they converge to a common level of income (SOU
2007:25, Appendix A, p.13).
The report doesn’t show the mathematical derivations, but claims this is based on the Solow model,
assuming Cobb-Douglas technology (SOU 2007:25, Appendix A, p. 11).
1
12
5. Results
This part suffers a bit from lack of reported numbers. Countries have for some reason often
not reported exports and imports (or at least one of them), and there are huge variations
between specific years. This can in a sense be seen as a finding in itself; many of these
countries might suffer from poor institutions and bureaucracy. I have tried somewhat to
compensate for this by looking at several years and differences between them, but this method
could of course have been extended. So now I will show the regression tables created from
my data material, with the values’ respective t-quotas (t-ratios) and p-values in particular.
Below the years are depicted as shown in section 4, and the first year in the interval is
depicted as y0. For the first regression, which looks at the full time period, I have used period
dummy variables; Period 1 is 1995-2000, Period 2 is 2000-2005 (and Period 3, which has
been excluded to avoid the dummy variable trap, is 2005-2010). Note that y0 changes
between these periods for this first one, as it does for the two 10-year periods further down.
But I have also made one labeled “one period”; this simply looks at the difference between
the first and last year of the interval, treating it as one period. I have also made a regression
without the RCA terms as a comparison. To clarify the sectors again: 0 = Food and Live
Animals, 1 = Beverages and tobacco, 4 = Animal and Vegetable Oils.
1995-2010
Observations
Constant
RCA (0) y0
RCA (1) y0
RCA (4) y0
ln(GDP p.c. y0)
Period 1 dummy
Period 2 dummy
82
Coefficients
0,479895367
0,006316507
-0,00514117
0,002362525
-0,010093633
-0,486690891
-0,051448016
t-quota
3,261138669
0,111216312
-0,11406465
0,041885014
-0,49265961
-7,93107478
-0,94038244
p-value
0,001670739
0,911741913
0,909491343
0,96670167
0,623692358
1,61374E-11
0,350040394
1995-2010: The constant is significantly larger than 0, the Period 1 dummy is significantly
less than 0 (for significance level = 0.05).
13
1995-2000
Observations
Constant
RCA (0) y0
RCA (1) y0
RCA (4) y0
ln(GDP p.c. y0)
20
Coefficients
t-quota
p-value
-0,302968871 -1,1181728 0,281081984
0,241218932 1,97013085 0,067572185
-0,093979777 -1,12305971 0,27906636
-0,155882011 -1,71385586 0,107138285
0,015805599 0,370881721 0,71591123
2000-2005
Observations
Constant
RCA (0) y0
RCA (1) y0
RCA (4) y0
ln(GDP p.c. y0)
31
Coefficients
0,305385189
-0,151960595
0,0379852
0,198707544
0,032427334
t-quota
1,355527194
-1,8544003
0,519446019
2,081219113
0,979281799
p-value
0,1869087
0,075058294
0,607843961
0,047403151
0,336465602
2000-2005: Here RCA (4) (Animal and Vegetable Oils) y0 is significantly larger than 0 (for
significance level = 0.05).
2005-2010
Observations
Constant
RCA (0) y0
RCA (1) y0
RCA (4) y0
ln(GDP p.c. y0)
31
Coefficients
0,763940337
0,102360888
-0,031481078
-0,101900082
-0,064506237
t-quota
3,972257348
1,237502723
-0,4806231
-1,11685941
-2,38318895
p-value
0,000502561
0,226960378
0,634803959
0,274269628
0,024757316
2005-2010: The constant is significantly positive, the coefficient for ln (GDP pc y0) is
significantly negative (for significance level = 0.05). The latter means there is β-convergence.
1995-2005
Observations
Constant
RCA (0) y0
RCA (1) y0
RCA (4) y0
ln(GDP p.c. y0)
51
Coefficients
0,052399641
-0,073358067
-0,004123118
0,038023687
0,026770123
t-quota
p-value
0,202929951 0,840084744
-0,71416906 0,47873107
-0,05034891 0,960062343
0,386724261 0,700743615
0,681405624 0,49903119
14
2000-2010
Observations
Constant
RCA (0) y0
RCA (1) y0
RCA (4) y0
ln(GDP p.c. y0)
62
Coefficients
0,518657515
-0,051501648
0,022488819
0,068908973
-0,012198595
t-quota
3,304043402
-0,81799742
0,429838863
0,96138092
-0,54105937
p-value
0,001650811
0,416765111
0,66893352
0,340421369
0,590575054
2000-2010: The constant is significantly positive (for significance level = 0.05).
1995-2010, one period
Observations
Constant
RCA (0) y0
RCA (1) y0
RCA (4) y0
ln(GDP p.c. y0)
20
Coefficients
1,087882694
0,045330007
-0,124438048
0,017599361
-0,079211768
t-quota
2,083787735
0,192145387
-0,77175975
0,100423575
-0,96466088
p-value
0,054696512
0,850205395
0,452244631
0,921338013
0,350010421
1995-2010 (no RCA)
Observations
Constant
ln(GDP y0)
138
Coefficients
t-quota
p-value
0,245244566 1,320482304 0,188892
0,005832086 0,199111101 0,842473
Overall, there are not many significant results, but at least a few (note especially the case of βconvergence in 2005-2010). This might be because of lack of observations. With more time
and space, this essay and its results could have been extended (e.g. looking at more years
and/or sectors to get more observations). I could also have included more variables, e.g.
agricultural exports as a share of GDP.
15
6. Summary
The main purpose of this essay was to find whether there was a significant correlation
between RCA in agricultural products and growth in GDP per capita, in the countries of SubSaharan Africa. I used a modified version of the Solow model to estimate the presumptive
relationship, and to see whether there is any convergence in growth between the countries.
Due to lack of reported numbers I did not get as much data as I hoped for, which probably is
one explanation to why I didn’t get that many significant results. But according to my
estimated values, the coefficient for RCA(4) (Animal and Vegetable Oils) was significantly
larger than 0 for the period 2000-2005, and there was β-convergence in the period 2005-2010
with respect to GDP per capita. With more time and space, this essay and its results could
have been extended (e.g. looking at more years and/or sectors to get more observations). I
could also have included more variables, e.g. agricultural exports as a share of GDP.
7. References
Articles
Bano, Sayeeda & Scrimgeour, Frank: “The Export Growth and Revealed Comparative
Advantage of the New Zealand Kiwifruit Industry”, International Business Research; Feb
2012, Vol. 5, Issue 2, pp. 73-82.
Dawson, P.J.: ”Agricultural exports and economic growth in less developed countries”,
Agricultural Economics, Vol 33, 2005, p. 145-152.
Donges, Juergen B. & Riedel, James: “The expansion of Manufactured Exports in Developing
Countries: An Empirical Assessment of Supply and Demand Issues”, Weltwirtschaftliches
Archiv, March 1977, 113(1): pp. 58-87.
Fulginiti, Lilyan E., Perrin, Richard K., Yu, Bingxin: ”Institutions and agricultural
productivity in Sub-Saharan Africa”, Agricultural Economics, Vol 31, 2004, p. 169-180.
IMF African Department (Juan Trevino), Sub-Saharan Africa Maintains Growth in an
Uncertain World, from http://www.imf.org/external/pubs/ft/survey/so/2012/car101212b.htm
Mellor, John W.: ”Food demand in developing countries and the transition of world
agriculture”, European Review of Agricultural Economics, Vol 15, 1988, p. 419-436.
16
Saletan, William (2007): “Food Fight. The case for turning crops into fuel”, Slate Magazine,
July 7, from
http://www.slate.com/articles/health_and_science/human_nature/2007/07/food_fight.html .
Sanjuàn-López, Ana I., and Dawson, P.J.: ”Agricultural Exports and Economic Growth in
Developing Countries: A Panel Cointegration Approach”, Journal of Agricultural Economics,
Vol 61, No 3/2010, p. 565-583.
Statens Offentliga Utredningar, ”Plats för tillväxt? Bilaga 2 till Långtidsutredningen 2008”,
SOU 2007:25, printed by Fritzes, Stockholm.
Solow, Robert: ”A contribution to the theory of economic growth”, Quarterly Journal of
Economics, Vol. 70, No 1/1956, pp. 65-94.
Solow, Robert: “Technical change and the aggregate production function”, The Review of
Economics and Statistics, Vol. 39, No 3/1957, pp. 312-320.
Books
Diouf, Jaques (2010), ”Afrikas jordbruk”, from Åke Magnusson (ed.), Afrika – 23 afrikaner
om vägval och utmaningar, p. 176-187, Tre Böcker Förlag AB, Västerås.
Ray, Debraj (1998): Development Economics, Princeton University Press, USA.
Todaro, Michael P. and Smith, Stephen C. (2009): Economic Development, 10th ed, AddisonWesley, USA.
Todaro, Michael P. and Smith, Stephen C. (2011): Economic Development, 11th ed, AddisonWesley, USA.
Homepages
The World Bank, ADI:
http://databank.worldbank.org/ddp/home.do?Step=2&id=4&DisplayAggregation=N&SdmxS
upported=N&CNO=1147&SET_BRANDING=YES
The World Bank, Data, Sub-Saharan Africa:
http://data.worldbank.org/region/SSA?display=map
UN Comtrade: http://comtrade.un.org/db/
17
Appendix 1 – The countries of SSA
Here I will try to define SSA. I have tried to use the simple geographical definition: all
countries that are at least partially located south of the Sahara desert. I have made a
compromise between the countries mentioned at the World Bank (WB)2 and the United
Nations (UN)3. The 48 countries I have looked at are:
Angola
Benin
Botswana
Burkina Faso
Burundi
Cameroon
Cape Verde
Central African Republic
Chad
Comoros
Congo
Côte d´Ivoire
Democratic Republic of the Congo
Djibouti
Equatorial Guinea
Eritrea
Ethiopia
Gabon
Gambia
Ghana
Guinea
Guinea-Bissau
Kenya
Lesotho
Liberia
Madagascar
Malawi
Mali
Mauritania
Mauritius
Mozambique
Namibia
Niger
Nigeria
Rwanda
Sao Tomé and Principe
Senegal
Seychelles
Sierra Leone
Somalia
South Africa
Sudan
Swaziland
Togo
Uganda
United Republic of Tanzania
Zambia
Zimbabwe
The World Bank, ”Country and Lending groups”, from http://data.worldbank.org/about/countryclassifications/country-and-lending-groups#Sub_Saharan_Africa , 2012-08-07.
3
The United Nations, ”Composition of macro geographical (continental) regions, geographical subregions, and selected economic and other groupings”, from
http://unstats.un.org/unsd/methods/m49/m49regin.htm , 2012-08-07. There are small differences
between the two: Djibouti and Equatorial Guinea are included in Eastern and Middle Africa by the UN
respectively, but not in the SSA by the WB. Sudan is included in SSA by the WB, but in North Africa
by the UN. I have chosen to include them all. (I have counted everything that the UN does not include
in North Africa as SSA. They also include a few areas which are not sovereign states, and they will be
excluded for simplicity) I should also mention that South Sudan will be excluded, since it became an
independent state in July 2011, and hence there is not much data to get from that country. It can be
seen as being included in Sudan here.
2
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Appendix 2: SITC Rev.3
(Standard International Trade Classification, Rev.3)







4
0 - Food and live animals
 00 - Live animals other than animals of division 03
 01 - Meat and meat preparations
 02 - Dairy products and birds' eggs
 03 - Fish (not marine mammals), crustaceans, molluscs and aquatic invertebrates, and
preparations thereof
 04 - Cereals and cereal preparations
 05 - Vegetables and fruit
 06 - Sugars, sugar preparations and honey
 07 - Coffee, tea, cocoa, spices, and manufactures thereof
 08 - Feeding stuff for animals (not including unmilled cereals)
 09 - Miscellaneous edible products and preparations
1 - Beverages and tobacco
 11 - Beverages
 12 - Tobacco and tobacco manufactures
2 - Crude materials, inedible, except fuels
 21 - Hides, skins and furskins, raw
 22 - Oil-seeds and oleaginous fruits
 23 - Crude rubber (including synthetic and reclaimed)
 24 - Cork and wood
 25 - Pulp and waste paper
 26 - Textile fibres (other than wool tops and other combed wool) and their wastes (not
manufactured into yarn or fabric)
 27 - Crude fertilizers, other than those of division 56, and crude minerals (excluding
coal, petroleum and precious stones)
 28 - Metalliferous ores and metal scrap
 29 - Crude animal and vegetable materials, n.e.s.4
3 - Mineral fuels, lubricants and related materials
 32 - Coal, coke and briquettes
 33 - Petroleum, petroleum products and related materials
 34 - Gas, natural and manufactured
 35 - Electric current
4 - Animal and vegetable oils, fats and waxes
 41 - Animal oils and fats
 42 - Fixed vegetable fats and oils, crude, refined or fractionated
 43 - Animal or vegetable fats and oils, processed; waxes of animal or vegetable origin;
inedible mixtures or preparations of animal or vegetable fats or oils, n.e.s.
5 - Chemicals and related products, n.e.s.
 51 - Organic chemicals
 52 - Inorganic chemicals
 53 - Dyeing, tanning and colouring materials
 54 - Medicinal and pharmaceutical products
 55 - Essential oils and resinoids and perfume materials; toilet, polishing and cleansing
preparations
 56 - Fertilizers (other than those of group 272)
 57 - Plastics in primary forms
 58 - Plastics in non-primary forms
 59 - Chemical materials and products, n.e.s.
6 - Manufactured goods classified chiefly by material
 61 - Leather, leather manufactures, n.e.s., and dressed furskins
 62 - Rubber manufactures, n.e.s.
 63 - Cork and wood manufactures (excluding furniture)
n.e.s. = not elsewhere specified
19











64 - Paper, paperboard and articles of paper pulp, of paper or of paperboard
65 - Textile yarn, fabrics, made-up articles, n.e.s., and related products
66 - Non-metallic mineral manufactures, n.e.s.
67 - Iron and steel
68 - Non-ferrous metals
69 - Manufactures of metals, n.e.s.
7 - Machinery and transport equipment
 71 - Power-generating machinery and equipment
 72 - Machinery specialized for particular industries
 73 - Metalworking machinery
 74 - General industrial machinery and equipment, n.e.s., and machine parts, n.e.s.
 75 - Office machines and automatic data-processing machines
 76 - Telecommunications and sound-recording and reproducing apparatus and
equipment
 77 - Electrical machinery, apparatus and appliances, n.e.s., and electrical parts thereof
(including non-electrical counterparts, n.e.s., of electrical household-type equipment)
 78 - Road vehicles (including air-cushion vehicles)
 79 - Other transport equipment
8 - Miscellaneous manufactured articles
 81 - Prefabricated buildings; sanitary, plumbing, heating and lighting fixtures and
fittings, n.e.s.
 82 - Furniture, and parts thereof; bedding, mattresses, mattress supports, cushions and
similar stuffed furnishings
 83 - Travel goods, handbags and similar containers
 84 - Articles of apparel and clothing accessories
 85 - Footwear
 87 - Professional, scientific and controlling instruments and apparatus, n.e.s.
 88 - Photographic apparatus, equipment and supplies and optical goods, n.e.s.; watches
and clocks
 89 - Miscellaneous manufactured articles, n.e.s.
9 - Commodities and transactions not classified elsewhere in the SITC
 91 - Postal packages not classified according to kind
 93 - Special transactions and commodities not classified according to kind
 96 - Coin (other than gold coin), not being legal tender
 97 - Gold, non-monetary (excluding gold ores and concentrates)
I - Gold, monetary
II - Gold coin and current coin
(Source: UN Statistical Division, from http://unstats.un.org/unsd/cr/registry/regcst.asp?cl=14
2013-04-04)
20