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African J. Economic and Sustainable Development, Vol. 1, No. 1, 2012 27

Copyright 2012 Inderscience Enterprises Ltd.

Exploring sectoral elasticity vis--vis per workerincome with a focus to agriculture: a study ofSub-Saharan Africa

Ronald Ravinesh Kumar*

School of Government, Development and International Affairs,

Faculty of Business and Economics,

FBE Green House, Laucala Campus,

University of the South Pacific,

Suva, Fiji

E-mail: [email protected]*Corresponding author

Radika Kumar

Ministry of Foreign Affairs and International Cooperation,

Economics Division, Level 2, South Wing BLV Complex,

87 Queen Elizabeth Drive, Nasese,

Suva, Fiji

E-mail: [email protected]

Abstract: In this study, we explore the contribution from sectoral sharetowards per worker income in the Sub-Saharan Africa (SSA) region over the

years, 1980 to 2009. Within the last three decades, agriculture and services didnot contribute positively towards growth in per worker income. Manufacturingsector on the other hand, had a transcending effect with a positive contributorypower (0.31%) towards the long-run growth and sustainability of theregion. Therefore, targeted sub-sectoral policy reforms to strengthenbackward-forward integration of agriculture and services sectors withmanufacturing operations whilst taking into account the historical diversity ofthe region are vital matters for development and growth discourse in the region.

Keywords: agriculture; manufacturing; services; economic growth; ARDLbounds approach; Sub-Saharan Africa; SSA.

Reference to this paper should be made as follows: Kumar, R.R. andKumar, R. (2012) Exploring sectoral elasticity vis--vis per worker incomewith a focus to agriculture: a study of Sub-Saharan Africa, African J.Economic and Sustainable Development, Vol. 1, No. 1, pp.2748.

Biographical notes: Ronald R. Kumar is affiliated with the School ofEconomics, Faculty of Business and Economics at the University of the SouthPacific, where he works as a Teaching Assistant. He was a recipient of theSasakawa Young Leaders Fellowship Foundation (SYLFF) award in20092010 by the School of Government, Development and InternationalAffairs under the auspices of Tokyo Foundation. He holds a MCom inEconomics and currently, he is pursuing MA in Development Studies. Hisspecific area of research includes migration, development and growtheconomics in developing countries.


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28 R.R. Kumar and R. Kumar

Radika Kumar is affiliated with Ministry of Foreign Affairs and International

Cooperation in the Government of Fiji Islands, where she works as anEconomic Planning Officer and she is currently pursuing her MCom inEconomics from the School of Economics at the University of the SouthPacific. Her area of research is on trade in services with particular focus toPacific Island countries.

1 Introduction

The African region is a composition of fifty-three individual countries of which 48

countries comprise the Sub-Saharan Africa (SSA) region. The countries within the

African region have experienced varied level of progress which is characteristicallydistinguished by the pace of industrialisation, oil exports, and fragility due to

socio-political events. Nevertheless, the challenges facing many of them are complex,

ranging from low productivity to high unemployment and from undeveloped middle class

to poor health and abject poverty.

Although about a quarter of the regions economies are performing well, the

majority of SSA countries are still lagging behind in growth, voice and democratic

accountability, control of corruption, political stability, rule of law and lack of

government effectiveness towards addressing environmental diversities and promoting

social choices which are critical to social and economic progress (Sen and Scanlon, 2004;

Sen, 1999, 1997). Further, as noted by many researchers and scholars (Nevile,

2007; Alesina, 2003; Sen, 2000; Stiglitz and Squire, 1998; Rostow, 1985; Amin, 1972,

among others), the exclusion from the credit market, gender related-related inequalitiesand unfavourable labour markets marred by elements and presence of colonialism and

ethnic conflicts impinge on sectoral development and critical economic activities in the

region.

Subsequently, in the height of increasing food prices and the need to expedite

productivity and economies of scale in agriculture viz. manufacturing and services

sectors within the SSA region, the paper explores the contributory powers from these

three key sectors within the augmented Solow model (Solow, 1956) framework with

insights from the some of the prominent pioneers of growth theory (Domar, 1961, 1952;

Harrod, 1959; Schumpeter, 1933). The study is of interest in at least three ways. First, the

contribution of sectors towards the overall economic growth is underscored. Secondly,

the study provides a sectoral approach to policy discourse which can be targeted for

long-term development of the region. Thirdly, the study outcomes relooks, if not reminds

the poignant history and development in agriculture vis--vis other sectors in the African

region.

The rest of the paper is set out as follows: a brief literature survey is provided

exploring the development patterns in Africa with focus to agriculture development,

followed by a short analysis on the trends and developments in sector performance. The

fourth section discusses the data, methods and results. Finally, the paper is concluded

with some policy directions for the SSA region.


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Exploring sectoral elasticity vis--vis per worker income with a focus to agriculture 29

2 A brief literature survey

Agriculture development has been considered as the engine of growth for many low

income and developing countries in terms of its contribution in providing cheap food,

raw materials, labour, savings, and consequent spill over effect on demand for

non-agricultural commodities (Staatz and Dembele, 2008; Diao et al., 2006; Tiffin and

Irz, 2006; Bravo-Ortega and Lederman, 2005; Doward et al., 2004; Dorosh and

Haggblade, 2003; Gollin et al., 2002; Timmer, 2002; Delgado et al., 1998; Lipton, 1977).

Further, agriculture has the export generating capacity, particularly for economies that

are in their early stages of development and heavily reliant on primary resources (Dethier

and Effenberger, 2011; Johnston and Mellor, 1961; Lewis, 1954). It is also argued that

the relationship between agriculture and overall economic growth is dependent on the

degree of openness, productivity in agriculture sector versus non-agriculture, and the

pace and effectiveness of industrialisation strategy (Gollin, 2010; Dercon, 2009).To emphasise the impact of agriculture on economic progress, various researchers

(Bezemer and Heady, 2008; Diao et al., 2006; Mosley, 2002; Lipton, 1987) compare and

differentiate the African agriculture development with the East Asian. They

comparatively assess the agricultural expenditure with the price regimes, macroeconomic

policies, political stability, health, education, and infrastructure in these regions. While

few recent empirical analysis have shown contrary views (Gardner, 2005), quite a lot of

studies have found agriculture as a catalyst to long run growth and development for many

developing countries (De Janvry and Sadoulet, 2009; Self and Grabowski, 2007).

However, balancing agriculture and industry (presumably manufacturing and

services) is an important yet a difficult dimension of development policy given the fact

that multi-dimensional causality between agriculture, manufacturing and services exists,

which are largely influenced by pricing and elasticity of resources and products, relative

differences in farm size, (incomplete) missing markets for insurance and credit or links to

financial markets, limited market access and (imperfect) market information, insecure

property and usage rights, unequal income distribution, access and availability of public

goods, pace of research and innovation, labour market distortions, degree of rural-urban

investment and climatic conditions (Dethier and Effenberger, 2011; Christiaensen et al.,

2010; Restuccia et al., 2008; Binswanger and Deininger, 1997; Stiglitz, 1987; Bauer,

1956, 1955).

Within the African context, Amin (2004, 2001, 1984, 1983) in his various studies

provides the historical diversity of Africa highlighting a gamut of factors and issues

explaining the (under)development of African region viz. agriculture development, many

of which by and large, are tied to the colonial and (unfair) trade systems. Furthermore,

looking at the historical diversity of Africa, Amin points out that African society cannot

be reduced to a single mode of production and therefore, for better understanding, need tobe segmented as:

1 primitive community

2 tributary

3 slave-based

4 small-scale trade


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5 the capitalist all of which overlapping across different times, to vividly characterise

the African development, resource (gold, diamonds and copper) depletion, andexploitation of (forced) cheap labour which not only made modern Africa a

dependent peripheral societies excluded at a global level but further exacerbated the

polarisation of wealth at the centre.

Moreover, the individual countries in the SSA region are too small, heterogeneous and

many of them lack the necessary resources to achieve economies of scale in the

production and marketing of their products and therefore need to work cooperatively,

through economic and monetary integration, as a region if they are to achieve significant

levels of economic growth and compete in the international markets with the super

powers and other emerging regional blocs (Staatz and Dembele, 2008; Nnanna, 2006).

Notably, a stable and credible policy environment, an open and competitive economy,

and a focused public sector are instrumental to development but high levels of corruption,

volatility in real exchange rate distortions, and a lack of rule of law are most detrimentalto investment and productivity (Stiglitz and Squire, 1998). Nnanna (2006) highlights that

the demand management strategies paired with keeping an over-valued exchange rate

regime have been equally detrimental to the development of agriculture in African

countries.

Despite the evidence of growth in aid inflows to SSA region (c.f., USAID, 2011), the

share of aid for agriculture development has fallen dramatically since 1980s, much of

which have been channelled in the areas of political reforms (Shleifer, 2009; Goldsmith,

2001). Evidently, all SSA countries spend less than 10% of their aid budget on

agriculture, thus further threatening the sustainability of total factor productivity (the

amount of output per unit of input of total factors used in the production process) growth

in the coming years (Fan et al., 2009).

Noting the International Monetary Funds (IMFs) failure to support recovery of manydeveloping economies, Kapur (1998) points out that most of the IMF programs in Africa

at the end of the 1970s were not only ill-advised but also overlooked the burgeoning

problems which emerged from the adverse socio-political activities. Similarly, Plank

(1993) highlights that the structural and sectoral policy reforms of IMF and World Bank

have discredited traditional notions of sovereignty in many parts of Africa which had

adverse effects on agriculture and other productions. Stiglitz (1999) also puts some blame

on the World Banks failure to recognise that most low-income countries, particularly in

Africa, did not have access to international capital markets, which resulted in funds

flowing disproportionately to a few selected countries, sectors (excluding health and

education) and farmers particularly to finance infrastructure from which cost (of lending)

can be recouped. Moreover, Stiglitz (2002), using examples from SSA region among

other developing countries, condemns the IMFs structural reform policies which further

exacerbated the existing income inequalities and wealth distribution, poverty and slow

progress towards growth. Thompson (2004) on the other hand, in the context of World

Trade Organizations (WTO) promotion for free-trade with respect to African Growth

and Opportunity Act (AGOA), highlights that the latter had no positive impact on the

macroeconomic performance and economic conditions of workers in many of the of

African countries.

However, the criticisms and failures of multinational corporations highlighted by

Stiglitz (2002) are challenged as well as explicated by Ghosh (2002). Ghosh, although

acknowledging the negative effects some developing countries experienced in their effort


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to recover, counter argues that the sources of these failures were mainly due to the

inherent weak internal governance structure of the countries from the past, and thereforepropose that developing regions need to:

a have an institutional framework for global macroeconomic management

b have a new and better international financial architecture to deal with the problems

of crisis management and prevention

c integrate with the world economy with the aim to promoting development (c.f.,

Lawrence, 2007) whilst disciplining the Trans National Corporations (TNCs)

d have in place international rules and institutions that govern cross-border movement

of people exacerbated by globalisation.

In regards to the manufacturing sector development and growth, Rodrik (2008) highlights

that the former has enabled skill upgrading, capital deepening and enhancement ofworker productivity and therefore has a positive contributory power to increase income

and employment. Further, Rajan and Subramanian (2011) underscore the use of aid in

developing the manufacturing sector and characterise manufacturing exports as a vehicle

for growth take-off besides having a transcending effect on economic activities.

Moreover, many researchers have identified services sector to have a critical role in

economic development with greater benefits evident in cases where the services are

relatively cheaper due to low wage cost and speedy development of key sectors in the

economy (Banga and Goldar, 2004; Kongsamut et al., 2001; Francois and Reinert, 1996;

Bhagwati, 1984; Chenery, 1960). However splintering effect (that is, when indirect

production activities are outsourced thus raising the demand for producer services as

intermediate input) has influenced the growth of services sector (Bhagwati, 1984).

Further, in some small and low income economies where sectors such as banking andfinance are not fully developed or is affected by massive capital outflows due to large

foreign ownership, the effect on growth is either negative or nil (Kumar, 2011a, 2011b).

On the other hand, in some relatively well developed and developing and small

economies, services like electricity, information and communications technology (ICT),

tourism, financial development and remittance inflows (trade in services) are critical

drivers of growth (Jayaraman et al., 2011, 2010, 2009; Jalava and Pohjola, 2008).

3 Trends and patterns in sectoral trade

Agriculture is critical for both human welfare and economic growth in Africa. In SSA,

roughly two-thirds of the population live in rural areas and are dependent on agriculture

for their livelihoods. However, agriculture sector overall had suffered poor performance

over the last three decades despite having momentary trajectory of good performance.

The growth in population and consequent demand for food has resulted in expansion of

food, however at the cost of deforestation and land degradation. Low farm productivity in

Africa also has been attributed to the use of traditional crop varieties, shrinking plots of

land, scarce and unreliable water supply, crop losses from pests, diseases and

natural disasters, inequitable land-distribution patterns besides high proportion of

population being landlocked, inefficient and unfair markets and poor agricultural and


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transportation infrastructures (Pratt and Yu, 2009; Toenniessen et al., 2008; Collier and

Gunnings, 1999).Table 1 shows the key indicators of the SSA region. In Table 2 to Table 4, the top ten

sectoral shares (agriculture, manufacturing and services) are listed with the SSA region as

a whole.

Table 1 Selected key indicators for SSA

Land area (sq. km. 000) 23,613.4

Agricultural land (% of land area) (20032007) 44.2

Population (2009: millions) 840.3

Rural population (% of total population) (20052009) 64.0

Per capita GDP (US$) current prices (20052009) 1,062.0

Annual growth rate in percent (20062010) 5.4

Annual inflation in percent (GDP deflator) (20052009) 8.7

Fiscal balance of Central Government as percent of GDP (20052009) 13.5

Current account balance as percent of GDP (20072010) 0.9

External debt as a percent of GDP 23.4

Private financial flows (net in billions USD) (20062010) 22.4

Reserves (in billions of USD) (20062010) 146.9

Ratio of reserves to imports of goods and services (20062010) 46.1

Source: World Bank (2010) and IMF (2011)

Table 2 Agriculture share (% GDP)

Year 19851994 19952004 2005 2006 2007 2008* 2009

Central African Republic 46.9 53.1 54.4 55.0 53.9 52.9 55.5

Sierra Leone 42.6 51.8 51.6 51.1 49.9 50.2 51.4

Comoros 38.9 45.5 51.0 45.2 45.3 45.8 45.2

Ethiopia 58.6 50.1 46.7 47.9 46.2 43.8 50.7

Congo, Dem. Rep. 38.4 49.8 45.5 45.7 42.5 40.2 42.9

Rwanda 37.8 41.2 38.7 38.6 35.6 37.4 38.7

Malawi 43.4 36.2 32.9 34.2 34.3 34.3 35.9

Ghana 46.5 40.7 40.9 30.4 29.0 31.0 31.7

Mozambique 40.5 29.4 27.0 27.9 27.7 30.5 31.5

Gambia 29.8 31.6 32.1 30.3 28.7 28.5 27.5

Sub-Saharan Africa 18.6 17.9 16.9 16.2 14.9 11.9 12.3

Note: *Ranked by 2008 figures. Figures in range of years are averages calculated by theauthors.

Source: World Bank (2010)


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Table 3 Manufacturing share (% GDP)

Year 19851994 19952004 2005 2006 2007 2008* 2009

Swaziland 31.8 39.8 40.0 43.4 44.4 44.4 44.4

Mauritius 23.8 23.0 19.8 20.0 19.8 20.1 19.4

Lesotho 15.5 19.3 20.5 21.8 20.3 19.1 17.0

Cote dIvoire 17.9 19.3 19.3 17.8 17.5 18.0 18.0

South Africa 22.3 19.5 18.5 17.5 16.9 16.5 15.1

Madagascar 10.7 11.7 14.0 14.2 15.5 15.3 15.4

Malawi 17.2 13.4 13.9 13.6 14.1 14.2 14.5

Mozambique 10.0 12.3 15.5 16.0 15.4 14.0 13.6

Namibia 12.7 12.4 13.6 15.6 17.0 13.7 14.7

Senegal 14.9 16.3 15.2 14.2 14.3 12.7 12.7


Notes: *Ranked by 2008 figures. Figures in range of years are averages calculated by theauthors.


Table 4 Services share (% GDP)

Year 19851994 19952004 2005 2006 2007 2008* 2009

Seychelles 77.7 70.1 75.6 77.1 77.7 77.7 78.3

Cape Verde 65.2 71.8 74.0 73.2 73.0 72.1 70.7

Mauritius 54.3 61.2 66.4 66.9 67.1 66.4 66.6South Africa 55.4 63.9 66.2 66.0 65.5 64.3 65.8

Kenya 50.5 52.0 53.7 54.8 65.0 63.9 62.1

Eritrea 60.2 59.3 56.9 57.2 56.5 63.3 63.4

Senegal 56.6 57.3 59.5 62.2 63.0 62.8 61.7

Madagascar 55.8 57.1 55.9 56.4 56.4 57.5 58.5

Gambia 56.7 54.8 54.6 55.5 56.5 56.4 57.1

Lesotho 42.7 46.0 56.3 53.5 52.8 54.3 57.5


Notes: *Ranked by 2008 figures. Figures in range of years are averages calculated by the

authors.Source: World Bank (2010)

The Table 2 to Table 4 shows the countries within the SSA region with different rankings

for the sectoral shares to GDP. For instances, while agriculture (as a percent of GDP) is

highest in countries like Central Republic Africa, Sierra Leone, Comoros and Ethiopia

(Table 2), manufacturing (as a percent of GDP) is topped by Swaziland, Mauritius,

Lesotho and Cote dIvoire among other countries (Table 3). Similarly services (as a

percent of GDP) are highest in countries like Seychelles, Cape Verde, Mauritius and

South Africa among others (Table 4). Overall, comparatively, manufacturing (as a


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percent of GDP) has caught-up and even exceeded the agriculture (as a share of GDP)

between 20072009 periods.The growth in GDP in the SSA region has been positive since 1995 and remained

stable despite the current global economic downturn (Table 5). Similar trend is noticed in

the growth of per capita GDP, except for 2009. In line with the positive trends in GDP

growth, manufacturing growth remained positive and relatively higher than agriculture

and services between the 1970 to 2006 periods. On the other hand, FDI inflows growth

rate have been largely negative, supported by declining trends in investment. Urban

population and overall population growth has been on the rise while growth in energy use

indicates mixed trends.

Table 5 Annual percentage growth of selected indicators for SSA region

Year 19651974 19751984 19851994 19952004 2005 2006 2007 2008 2009

Agriculture(%GDP)

n.d. 1.3 2.4 3.5 4.4 3.1 3.9 3.6 3.5

Domesticsavings(%GDP)

1.3 2.1 1.9 0.0 0.9 0.7 5.3 3.5 3.8

energy use percapita

0.5 0.7 0.8 0.3 1.0 0.5 2.1 0.1 n.d.

Exports(%GDP)

1.0 1.0 1.0 1.1 4.5 2.2 1.4 6.2 18.8

FDI, netinflows

n.d. 9.9 9.8 5.1 35.7 8.1 23.1 4.8 10.3

GDP 5.0 2.1 1.6 3.9 5.7 6.3 6.5 5.1 1.7

GDP per capita 2.2 0.8 1.1 1.2 3.2 3.7 3.9 2.5 0.8

Imports(%GDP)

1.3 0.9 1.3 0.9 4.4 3.9 2.6 9.5 17.1

Industry(%GDP)

4.3 2.8 0.3 3.9 6.2 7.0 7.9 3.9 2.7

Investment(%GDP)

n.d. 2.7 1.5 0.1 4.4 3.7 7.3 7.0 2.4

Manufacturing(%GDP)

7.0 3.6 0.6 3.4 5.7 5.7 6.2 3.6 5.2

Population (%) 2.6 2.9 2.8 2.6 2.5 2.5 2.5 2.5 2.5

Services(%GDP) 4.7 3.0 1.3 4.3 5.8 2.2 6.9 6.1 1.8

UrbanPopulation

5.1 4.8 4.5 4.1 3.8 3.9 3.9 3.9 3.8


Moreover, disaggregating public expenditure on various sub-sectors of the SSA region, it

is evident that the largest share (as a percent) is attributed to fuel and energy production,

manufacturing, construction and general administration, while the least is in transport and

communication (services) and agriculture between 2000-2005 periods (Table 6).


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Table 6 Composition of public expenditure (%)

Year Agriculture Education HealthTransport andcommunication

Socialsecurity

Defence Other*

1980 7.1 14.4 4.9 11.0 2.9 19.7 40.1

1990 5.5 14.5 4.5 4.5 2.5 17.1 51.5

2000 3.8 14.1 6.7 4.7 5.0 8.8 56.9

SubSaharanAfrica

2005 6.3 15.4 8.1 5.8 2.8 6.5 55.1

Note: *Others include fuel and energy, manufacturing, and construction, and generaladministration.

Source: Taken from World Bank as reported in Fan et al. (2009, p.4).

4 Data, method and results

For the purpose of this study, all three sectors are assumed to be critical for the economic

growth of SSA region. However, at the outset, the impact of each sector on per worker

income cannot be overly speculated and therefore largely depend on the overall

performance from the sectors. For instances, the development in agriculture is largely

influenced by myriad of factors constraining its growth as discussed in the literature

earlier on. Similarly, low levels of investment and decline in exports and foreign direct

investment are likely to have adverse impact on service sector performance.

The analysis uses 30 years annual data for the period 19802009. The capital stock

utilised for the study has been built up by a perpetual inventory method.1 Population is

used as a proxy for labour stock since there is no consistent time series data on

employment. All data including agriculture (AGRt)

2

, manufacturing (MANt)

3

and services(SERt)

4 as a percent of GDP respectively, are sourced from the World Development

Indicators and Global Development Finance, World Bank (2010) database.

Subsequently, AGRt, MANt, and SERt are used in the conventional Cobb-Douglas

production function, with the Hicksneutral technical progress. The per worker output

(yt) equation is defined as:

, where 0 1t t ty A k = < < (1)

whereA = stock of technology and k= capital per worker, and is the profit share. The

Solow model assumes that the evolution of technology is given by

0T

tA A e= (2)

whereA0 is the initial stock of knowledge and Tis time.To capture the effect of,AGRt,MANt, and SERton total factor productivity (TFP),At

is defined as:

( ), , ,t t t t A f T AGR MAN SER= (3)

which is expanded as:

0gT

t t t t A A e AGR MAN SER = (4)

and therefore:


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( )0gT

t t t t t

y A e AGR MAN SER k = (5)

and finally, the above can be formulated as:

,t t t t t Ly g LAGR LMAN LSER = + + + (6)

where, L denotes difference in logs of respective variables, and the intercept term gt isthe steady state growth rate of output (TFP) due to other likely growth factors not

included in the analyses.

Since the number of observations is small, the bounds testing approach under ARDL

procedure developed by Pesaran et al. (2001) is deployed. In bounds testing approach,

pre-testing of unit roots is not required and it is possible to investigate cointegration of

the levels of the variables, irrespective of their order. However, with a view to meeting

the criticism that it is difficult to accept that variables of different orders are cointegrated,the unit root tests first conducted to ensure they are of the same order before entering

them into the analyses.

In computing unit root tests to examine the time series properties of the variables, the

ADF and Phillips-Perron test statistics are used. From the test results, all variables are

non-stationary in their levels however they are stationary in their first differences

(Table 7).

Table 7 Results of unit root tests

ADF Phillips and PerronVariable

Level First difference Level First difference

Ly 1.854 3.321 * 2.929 2.66 *Lk 1.606 3.869 *** 3.545 3.47 **

LAGR 0.760 4.604 *** 0.746 4.55 ***

LMAN 2.725 3.195 ** 2.653 5.39 ***

LSER 1.853 4.907 *** 2.091 4.93 ***

Notes: The ADF critical values are based on Mckinnon. The optimal lag is chosen on thebasis of Akaike Information Criterion (AIC). The null hypothesis for both ADFand Phillips-Perron tests is a series has a unit root (non-stationary). ***, **, and*denotes the rejection of the null hypothesis of unit root at 1%, 5%, and 10% levelof significance respectively.

The next step is to examine the existence of a long run relationship between yt, kt,AGRt,

MANtand SERtby using bounds test.The ARDL equations are specified as follows:

10 11 1 12 1 13 1 14 1

15 1 16 11 12

1 0

13 14 15 1

0 0 0

t t t t t

p p

t i t i i t i

i i

p p p

i t i i t i i t i t

i i i

Ly Ly Lk LAGR LMAN

LSER TREND Ly Lk

LAGR LMAN LSER

= =

= = =

= + + + +

+ + + +

+ + + +

(7)


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20 21 1 22 1 23 1 24 1

25 1 26 21 22

1 0

23 24 25 2

0 0 0

t t t t t

p p

t i t i i t i

i i

p p p

t i t i i t i t

i i i

Lk Ly Lk LAGR LMAN

LSER TREND Ly Lk

LAGR LMAN LSER

= =

= = =

= + + + +

+ + + +

+ + + +

(8)

30 31 1 32 1 33 1 34 1

35 1 36 31 32

0 0

33 34 35 31 0 0

t t t t t

p p

t i t i i t i

i i

p p p

i t i i t i i t i t i i i

LAGR Ly Lk LAGR LMAN

LSER TREND Ly Lk

LAGR LMAN LSER

= =

= = =

= + + + +

+ + + +

+ + + +

(9)

40 41 1 42 1 43 1 44 1

45 1 46 41 42

0 0

43 1 44 1 45 1 4

0 1 1

t t t t t

p p

t i t i i t i

i i

p p p

i t i t i t t

i i i

LMAN Ly Lk LAGR LMAN

LSER TREND Ly Lk

LAGR LMAN LSER

= =

= = =

= + + + +

+ + + +

+ + + +

(10)

50 51 1 52 1 53 1 54 1

55 1 56 51 520 0

53 54 55 5

0 1 1

t t t t t

p p

t i t i i t ii i

p p p

i t i i t i i t i t

i i i

LSER Ly Lk LAGR LMAN

LSER TREND Ly Lk

LAGR LMAN LSER

= =

= = =

= + + + +

+ + + +

+ + + +

(11)

Table 8 Results of bound tests

Dependent variable Computed F-statistic

Lyt 7.17*

Lkt 3.24

LAGRt 3.62

LMANt 1.63

LSERt 1.41

Pesaran, Shin and Smith. (2001)a

Critical value Lower bound value Upper bound value

1% 4.40 5.72

5% 3.47 4.57

Notes: Table CI. v: Case V with unrestricted intercept and unrestricted trend, p.300;*indicates significance at 1% level.

Source: aCritical values are obtained from Pesaran et al. (2001)


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There are two steps in examining the relationship between Lyt, Lkt, LAGRt, LMANt and

LSERt. First, equations (7) to (11) are estimated by ordinary least squares techniques.Second, the existence of a long-run relationship can be traced by imposing a restriction

on all estimated coefficients of lagged level variables equating to zero. Hence, bounds

test is based on the F-statistics (or Wald statistics) with the null hypothesis of no

cointegration (H0: = i1 = i2 = i3 = i4 = i5 = 0) against its alternative hypothesis of a

long-run cointegration relationship (H1: i1i2i3i4i5 0). The results of thebounds test are reported in Table 8, confirming the presence of a long run relationship

amongst the variables when only real output per worker (Lyt) is set as the dependent

variable. The computed F-statistics forLyt is 7.17, which is significant at 1% level.

Table 9 Autoregressive distributed lag estimates

ARDL (1, 1, 0, 0, 0) selected based on Akaike information criterion

Dependent variable is LY

30 observations used for estimation from 1980 to 2009

Regressor Coefficient Standard error t-Ratio

LY(1) 0.3543 *** 0.0956 3.706

LK 1.1594 *** 0.2613 4.437

LK(1) 0.9740 *** 0.1984 4.909

LAGR 0.1722 *** 0.0364 4.728

LMAN 0.2033 *** 0.0489 4.156

LSER 0.7129 *** 0.0825 8.638

C 5.4576 *** 0.6643 8.215

T 0.0060 *** 0.0008 7.289

R2 = 0.99182 2R

= 0.98220

S.E. of regression = 0.00722 F-statistics [F( 7, 22)] = 381.085

Mean of dependent variable = 6.28910 S.D. of dependent variable = 0.06952

Residual sum of squares = 0.00115 Equation Log-likelihood = 109.998

Akaike info. criterion = 101.998 Schwarz Bayesian criterion = 96.3931

DW-statistic = 1.74340 Durbins h-statistic = 0.82480

Diagnostic tests

Test statistics LM version F version

p-value p-value

(A): Serial correlation 2(1) = 0.5891 0. 443 F(1, 21) = 0.4206 0.524

(B): Functional form 2(1) = 0.5853 0. 444 F(1, 21) = 0.4179 0.525

(C): Normality 2(2) = 0.2997 0. 861 Not applicable

(D): Heteroscedasticity 2(1) = 0.0195 0.889 F( 1, 28) = 0.01820 0.894

Notes: ***Acceptance of coefficient at 1% level of significance;Rejection of null hypothesis of (A)(D) biasness at 1% level of significance.


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Having confirmed the existence of a long-run relationship between output (yt) with

capital stock (kt), AGRt, MANt, and SERt, a number of diagnostic test results (see thelower panel of Table 9), such as Lagrange multiplier test of residual serial correlation,

Ramseys RESET test using the square of the fitted values for correct functional form,

normality test based on a test of skewness and kurtosis of residuals, and

heteroscedasticity test based on the regression of squared residuals on squared fitted

values, showed that the equation performed well as the disturbance terms are normally

distributed and serially uncorrelated with homoscedasticity of residuals, duly confirming

the model has a correct functional form. Besides, the CUSUM and CUSUM of Squares

plot (Figure 1 and Figure 2) shows the parameters of the model are relatively stable over

time.

Figure 1 Plot of cumulative sum of recursive residuals

-5

-10

-15

0

5

10

15

1980 1985 1990 1995 2000 2005

Note: The straight lines represent critical bounds at 5% significance level

Figure 2 Plot of cumulative sum of squares of recursive residuals

-0.5

0.0

0.5

1.0

1.5

1980 1985 1990 1995 2000 2005

Note: The straight lines represent critical bounds at 5% significance level

The ARDL estimate (Table 9) indicates a positive lagged effect of income to long run

growth (0.35), which is plausibly due to effective overall growth policies in the SSA

region. The high capital share in the equation is offset by the negative share in the

lag-one period, which is plausibly due to poor use of technology and capital stock

depletion in later periods. Further, agriculture and services have negative income


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elasticity, which implies that as income increased, the contribution from agriculture and

services declined.

Table 10 Dependent variable:RGDP/Labour(Ly) ARDL(1, 1, 0, 0, 0)

Long-run coefficients Short-run coefficients

Regressor CoefficientStandard

erort-ratio Regressor Coefficient

Standarderror

t-ratio

Lk 0.2870 *** 0.07409 3.8734 Lk 1.1594 *** 0.26127 4.4374

LAGR 0.2668 *** 0.06355 4.1990 LAGR 0.1723 *** 0.03644 4.7278

LMAN 0.3149 *** 0.07789 4.0433 LMAN 0.2033 *** 0.04893 4.1555

LSER 1.1042 *** 0.16972 6.5064 LSER 0.7130 *** 0.08254 8.6382

C 8.4527 *** 1.15820 7.2980 C 5.4576 *** 0.66431 8.2155

T 0.0093 *** 0.00186 5.0109 T 0.0060 *** 0.00827 7.2894ECT(1) 0.6457 *** 0.09561 6.7532

Short-run equation statistics

R2 = 0.90608 2R

= 0.87619

S.E. of regression = 0.00722 F-statistics [F(6, 23)] = 35.3723

Mean of dependentvariable

= 0.00213 S.D. of dependent variable = 0.02053

Residual sum of squares = 0.00114 Equation Log-likelihood = 109.998

Akaike Info. Criterion = 101.998 Schwarz Bayesiancriterion

= 96.3931

DW-statistics = 1.74340

ECT = Ly 0.2870Lk + 0.2668LAGR 0.3149LMAN + 1.1042LSER 0.0093T 8.4527

Note: ***Significant at 1% level

The long-run estimation (Table 10) shows similar outcomes as Table 9. The capital stock

share is about 0.29 which is close to the stylised value of one-third (c.f., Rao, 2010; Ertur

and Koch, 2007). Furthermore, the elasticity for agriculture is 0.27 and services is 1.10

while the manufacturing sector has the elasticity of 0.31, all significant at 1% level. Time

trend5, is positive and significant, both in the long run (0.009) and the short-run (0.006),

however having a marginal contributory power towards per worker income, indicating

weak response to supply side shocks. The trend captures (positively) the supply side

responses such as investment decisions, labour market distortions and credit expansions

in the short and long run respectively.

Further, in the short-run, the manufacturing sector elasticity coefficient is 0.20 whichis positive and significant at 1% level. On the other hand, the agriculture and services

have a negative elasticity of 0.17 and 0.71 accordingly. These results underscore the

production shift towards manufacturing as the economy grows relative to agriculture and

services. Evidently, the long-run elasticity is larger than the short-run because prices are

not completely flexible and hence (labour) markets are slow to clear in the short run.

Further, the Granger causality test was conducted to identify the causation effect

(Table 11). The results show unidirectional relationship running from output to capital,

from manufacturing to output, and from manufacturing to capital stock. Therefore, it is


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evident that growth in output Granger-cause growth in capital; growth in manufacturing

base Granger-cause growth in output and growth in capital stock accordingly.

Table 11 Granger causality tests with ECT from bounds test

F-statistics

Ly Lk LAGR LMAN LSER

ECT(t-statistics)

Ly 2.33458* 1.78771 0.5001 0.7734 0.6457

(6.7532)

Lk 1.0100 1.7635 0.1206 1.1776 0.20208

(15.6453)

LAGR 0.5171 0.7046 0.7266 1.5920 1.4445

(7.9702)

LMAN 3.4005** 2.5609* 1.8715 0.8065 0.62023

(3.1603)

LSER 1.3591 1.6301 1.2196 0.5643 1.2832

(6.0297)

Notes: *, **, and *** indicates rejection of the null hypothesis of no causality at 10% and5% respectively;and indicates significance level at 1% and 5% respectively based ont-statistics and correct sign.

5 Conclusions and policy discussion

We have shown that agriculture and services sectors had negative elasticity coefficients,

whilst manufacturing sector had positively contributed towards improving per worker

income in the SSA region. The study resonates, if not underscores, some of the

concerning and long-standing poignant issues in agricultural and other production sectors

in the African region highlighted by some of the prominent scholars (Sen, 2000; Stiglitz

and Squire, 1998; Rostow, 1985; Amin, 1972; among others).

In light of the results, it is clear that SSA countries need to increase their budgetary

allocations to agriculture. A big challenge looming ahead for the countries is the recent

surge in world food prices, the recent financial crisis and the series of disasters affecting

the horn of Africa which threatens to reverse any past gains in poverty reduction. Higher

food prices will severely affect rural and urban consumers, especially among the poor. By

increasing spending in agriculture and rural development now, the region can avert this

threat by rapidly increasing agricultural productivity and ensuring a long term and stable

supply of affordable food to millions (Fan et al., 2009).

Our results also coincides with the notion that agriculture-led path out of poverty does

not occur automatically simply if the agricultural sector grows. It will require that

agricultural growth be spread among a broad class of smallholder entrepreneurs to

broaden the demand for labour-intensive goods and that some of that growth be tapped

for investment in education, health, infrastructure and better-functioning labour markets

(Ulimwengu and Sanyal, 2011; Staatz and Dembele, 2008).


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Subsequently, each country within the SSA region should focus on key sector reforms

that are most likely to have the largest impact at every stage of economic development.For example, the agriculture and land use policy can be reviewed to promote clear flow

of benefits from the ownership of land. Fostering sustained growth through domestic and

foreign investment, private sector development, improved public sector management and

sectoral development within the overarching framework of good governance are vital.

Additionally, voice and accountability, political stability, corruption control and rule

of law are critical for equitable growth. Countries in North Africa that experienced high

growth such as Tunisia and Egypt have also experienced social unrest and undergone

regime change, stemmed by highly inequitable growth and unproductive or non-existent

job prospects and corrupt institutions, making some of the SSA countries highly

susceptible to unrest. Further, such social unrests and prolonged civil wars divert

resources, including human capital, away from productive income generating activities.

Poor infrastructure and high cost of transportation impose substantial barriers to entry,thus allowing transport companies to maintain a monopoly and charge prohibitively high

prices. Gender equality and female empowerment and participation in all sectors, backed

by increased health expenditure towards improving well being and reducing child and

maternal mortality are required so as to freeing up and strengthening resources for

productive activities which can be deployed to critical sectors of the economy.

Natural disasters quite often hit hard the horn of Africa creating a grave concern on

the staple food security and sustainable livelihood of many besides population

displacement. Therefore, disaster and crisis management need to be organised and aid

from donors need to focus more on investing in technologies to anticipate and formulate

strategies to manage these crisis and support agriculture. Moreover, noting that

development aid flows to Africa from multinational corporations (IMF and World Bank)

have resulted in controversial, if not contrary outcomes, the donor and recipient (SSA)

countries need to explore better mechanism to channel aid which is linked to solid

growth initiatives and projects to have a welfare enhancing effect.

Further, projects related to agro-marketing need to be promoted and monitored so that

benefits reach the small holder farmers. The drive need to come from relevant

government bodies such and non-government organisation with an inclusive approach to

dialogue. Ensuring the availability of investment fund to buy land and other capital inputs

for agricultural production, effective coordination of agri-business with financial services

and integrating private sector participation including manufacturing and services sector

operations with agriculture development initiatives will create the environment for

overall growth and development. For instance, linking tourism (service) and food

processing (manufacturing) with locally produced commodities will boost domestic

agriculture. Further, encouraging private sector and non-government organisation

partnership in sub-sectors like fisheries, forestry, cash-crop industries among others aresome important areas to look at. Among other things, the focus need to be more on

branding, promoting and diversifying domestically produced commodities both in local

and international markets to reduce dependency on imported items, and tax incentives

and subsidies need to be defined and engineered to support growth in sectoral

(agriculture, manufacturing and services) productivity.

In addition, specific sub-sector based (scientific and non-scientific) research, training

and capacity building in areas of agriculture development, financial literacy and the use

of technology and media to access and penetrate local and international markets for new

and existing commodities need to be promoted.


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The legal aspects governing the sectoral investment need to be scrutinised so that

(new) investors are incentivised to invest in without compromising the law and order.Therefore, the right institutional environment and infrastructure need to be in place so

that various key sector performances can be enhanced at operational and managerial

levels.

Finally, the effective participation of the SSA countries in the multilateral rules of

trade via WTO and other regional trade bodies are vital. In order for the agricultural

sector to expand and gain efficiency, bringing competition in the sectors is imperative.

Active negotiations by the countries for its offensive and defensive tariff are vital, whist

striving to suss out ways in which trade can promote their development optimally. Sub

sectors within the agricultural sector which are inefficient need to be liberalised (if not

reformed) as well as Green box subsidies can be provided to ensure that the sector

improves over time. In contrast to this, the more productive sectors such as

manufacturing which are duly competitive can offset the effects of preference erosionthrough tariff reductions in agriculture. It is imperative that the countries also identify

precisely their special and sensitive products in the agricultural sector for the agricultural

modality in the recent Doha rounds and also make targeted cuts in non-agricultural

market access (NAMA) commodities or choose the best mix of tariff cuts, whilst

providing the countries more room for policy space on one hand (c.f., Stiglitz and

Charlton, 2005) and having a binding mechanisms which ensures monetary compensation

in cases where poor countries lose out as a result of subsidies and preference erosion, on

the other. Moreover, the need for solidifying North-South cooperation, particularly in

relation to investment in agriculture, energy, raw materials, reforestation, and disaster and

crisis management, in order to have resource foundations for growth in both regions and

to assure resources are priced reasonably to permit growth in both regions are critical for

long-run growth and sustainable development.

Acknowledgements

The corresponding author would like to thank the SYLFF for the opportunity to pursue

this research. Further, the authors would like to sincerely thank the anonymous reviewers

and referees for their invaluable comments and suggestions.

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Notes

1 The capital stock is computed for the period of 19602008, using perpetual inventory method,where investment is proxied by gross fixed capital formation, initial capital stock is estimatedto be 1.5 times the real GDP of 1960 and the depreciation rate of 10.5% is used. Dataset from1980 onwards was used in the analysis to maintain consistency.

2 Agriculture value added corresponds to ISIC divisions 15 and includes forestry, hunting, andfishing, as well as cultivation of crops and livestock production (see World Bank, 2010) forfull definition).

3 Manufacturing value added refers to industries belonging to ISIC divisions 1537 [see WorldBank (2010) for full definition].


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4 Services value added correspond to ISIC divisions 5099 and they include value added in

wholesale and retail trade (including hotels and restaurants), transport, and government,financial, professional, and personal services such as education, healthcare, and real estateservices. Also included are imputed bank service charges, import duties, and any statisticaldiscrepancies noted by national compilers as well as discrepancies arising from rescaling [seeWorld Bank (2010) for full definition].

5 The trend captures the supply side responses such as investment decisions, labour, marketdistortions and credit expansion in the short and long run, accordingly.

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