Growth and agriculture

in Sub-Saharan Africa

Nationalekonomiska institutionenKandidatuppsats, NEKH01Författare: Måns Hägerdal 850330-3995Handledare: Joakim Gullstrand, Hans FalckVT 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-middle-

income 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

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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.

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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).

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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=KL and shows that

the change in this variable can be expressed as: r=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 r = 0, KL is constant, and the yield of the capital stock must be the same as the growth

of the labor force, that is, n.

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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*, r = 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

Fig 1: Copy of Figure I from Solow 1956, p. 70. Shows the relation between r and r in terms of the functions sF(r,1) and nr. The intersection r* is an equilibrium.

remain under the curve. The expression for r then becomes r=s rα−nr , and the equilibrium

value of r is r*=( sn)1/(1-α). Moreover, real income per head = Y/L = (K/L)α = rα , also tends to

the value (s/n)α/(1-α). It follows that the equilibrium value of K/Y is s/n (or n = sYK ), 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).

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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.

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.

GDP per capitaYear 2010 2005 2000 1995Sub-Saharan Africa 1297,888683 857,3200758 509,348676 554,473881Countries' Mean 2272,387759 1581,105677 896,733205 875,278231Countries' Std deviation 3702,990896 2721,909121 1423,63977 1364,755

F

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Fig 4: Annual GDP growth for SSA for the time period 1995-2010.

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.

For RCAij (i=country, j=sector) I have used the formula:

RCAij = X ij−M ij

X ij+M 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 0 0 1 1 4 4Year 2000 2005 2000 2005 2000 2005Exports 8596042010 13526660182 1543301909 1990779557 224811140 264636376Imports 6536804089 10305556911 666889656 1356217949 763586846 1476503287RCA 0,136077372 0,135157516 0,396532259 0,1895913 -0,545099963 -0,696019358

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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.

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).

1 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).

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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.

1995-2010Observations 82

Coefficients t-quota p-valueConstant 0,479895367 3,261138669 0,001670739RCA (0) y0 0,006316507 0,111216312 0,911741913RCA (1) y0 -0,00514117 -0,11406465 0,909491343RCA (4) y0 0,002362525 0,041885014 0,96670167ln(GDP p.c. y0) -0,010093633 -0,49265961 0,623692358Period 1 dummy -0,486690891 -7,93107478 1,61374E-11Period 2 dummy -0,051448016 -0,94038244 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).

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1995-2000Observations 20

Coefficients t-quota p-valueConstant -0,302968871 -1,1181728 0,281081984RCA (0) y0 0,241218932 1,97013085 0,067572185RCA (1) y0 -0,093979777 -1,12305971 0,27906636RCA (4) y0 -0,155882011 -1,71385586 0,107138285ln(GDP p.c. y0) 0,015805599 0,370881721 0,71591123

2000-2005Observations 31

Coefficients t-quota p-valueConstant 0,305385189 1,355527194 0,1869087RCA (0) y0 -0,151960595 -1,8544003 0,075058294RCA (1) y0 0,0379852 0,519446019 0,607843961RCA (4) y0 0,198707544 2,081219113 0,047403151ln(GDP p.c. y0) 0,032427334 0,979281799 0,336465602

2000-2005: Here RCA (4) (Animal and Vegetable Oils) y0 is significantly larger than 0 (for

significance level = 0.05).

2005-2010Observations 31

Coefficients t-quota p-valueConstant 0,763940337 3,972257348 0,000502561RCA (0) y0 0,102360888 1,237502723 0,226960378RCA (1) y0 -0,031481078 -0,4806231 0,634803959RCA (4) y0 -0,101900082 -1,11685941 0,274269628ln(GDP p.c. y0) -0,064506237 -2,38318895 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.

1995-2005Observations 51

Coefficients t-quota p-valueConstant 0,052399641 0,202929951 0,840084744RCA (0) y0 -0,073358067 -0,71416906 0,47873107RCA (1) y0 -0,004123118 -0,05034891 0,960062343RCA (4) y0 0,038023687 0,386724261 0,700743615ln(GDP p.c. y0) 0,026770123 0,681405624 0,49903119

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2000-2010Observations 62

Coefficients t-quota p-valueConstant 0,518657515 3,304043402 0,001650811RCA (0) y0 -0,051501648 -0,81799742 0,416765111RCA (1) y0 0,022488819 0,429838863 0,66893352RCA (4) y0 0,068908973 0,96138092 0,340421369ln(GDP p.c. y0) -0,012198595 -0,54105937 0,590575054

2000-2010: The constant is significantly positive (for significance level = 0.05).

1995-2010, one period

Observations 20Coefficients t-quota p-value

Constant 1,087882694 2,083787735 0,054696512RCA (0) y0 0,045330007 0,192145387 0,850205395RCA (1) y0 -0,124438048 -0,77175975 0,452244631RCA (4) y0 0,017599361 0,100423575 0,921338013ln(GDP p.c. y0) -0,079211768 -0,96466088 0,350010421

1995-2010 (no RCA)Observations 138

Coefficients t-quota p-valueConstant 0,245244566 1,320482304 0,188892ln(GDP y0) 0,005832086 0,199111101 0,842473

Overall, there are not many significant results, but at least a few (note especially the case of β-

convergence 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.

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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 Sub-

Saharan 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, Addison-

Wesley, USA.

Todaro, Michael P. and Smith, Stephen C. (2011): Economic Development, 11th ed, Addison-

Wesley, USA.

Homepages

The World Bank, ADI: http://databank.worldbank.org/ddp/home.do?Step=2&id=4&DisplayAggregation=N&SdmxSupported=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/

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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 LiberiaBenin MadagascarBotswana MalawiBurkina Faso MaliBurundi MauritaniaCameroon MauritiusCape Verde MozambiqueCentral African Republic NamibiaChad NigerComoros NigeriaCongo RwandaCôte d´Ivoire Sao Tomé and PrincipeDemocratic Republic of the Congo SenegalDjibouti SeychellesEquatorial Guinea Sierra LeoneEritrea SomaliaEthiopia South AfricaGabon SudanGambia SwazilandGhana TogoGuinea UgandaGuinea-Bissau United Republic of TanzaniaKenya ZambiaLesotho Zimbabwe

2 The World Bank, ”Country and Lending groups”, from http://data.worldbank.org/about/country-classifications/country-and-lending-groups#Sub_Saharan_Africa , 2012-08-07.3 The United Nations, ”Composition of macro geographical (continental) regions, geographical sub-regions, 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.

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Appendix 2: SITC Rev.3

(Standard International Trade Classification, Rev.3)

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) 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

4 n.e.s. = not elsewhere specified

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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)

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