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Growth Accounting in ECOWAS Countries: A Panel Unit Root and Cointegration Approach

Mohamed Ben Omar Ndiaye and Robert Dauda Korsu

Abstract Long term economic growth is necessary for poverty reduction and it can be enhanced by increasing the productivity of factors of production. There have been various policy efforts to strengthen economic growth in the ECOWAS region but sustainable economic growth coupled with accelerated poverty reduction remains a challenge. The paper therefore investigates the sources of economic growth in the ECOWAS region with a view to unearthing whether growth of the region during the period 1980–2012 was driven more by factor accumulation or factor productivity. The methodology involves the estimation of a production function with real capital stock and labour as inputs while real GDP is the output, over the period 1980–2012 for the ECOWAS countries. Panel unit root and panel cointegration tests including the Levin-Lin-Chu, Maddala-Wu and Im-PesaranShin tests for unit root and the Pedroni, Kao and Westerlund tests for cointegration are applied. Fixed and random effect models of production function are estimated. The growth accounting technique is then applied to the estimated shares of capital and labour in production. The results show that during the period 1980–2012, with the exception of Nigeria and Cote d'Ivoire productivity growth was not the hardcore of the growth observed in the ECOWAS countries but the growth was driven by factor accumulation. In addition, the contribution of labour to growth was positive but low in all the countries, the contribution of capital was negative in Cote d'Ivoire and Nigeria but positive in the other countries and that of total factor productivity was negative in Burkina Faso, Cape Verde, Ghana, Guinea, Mali, Niger and Senegal. The policy implication of this result is that in order to enhance long run economic growth in ECOWAS countries there is need to exert more efforts at raising productivity of factors of production. This requires more efforts at building human capacity for labour to be more effective and more investment in infrastructure, especially energy, in order to make capital more productive.

Keywords Growth accounting • Panel unit root • Panel cointegration • ECOWAS

JEL Classification O47 • O55

1 Introduction

The causes of differences in growth among countries and variations in growth over time is the centerpiece of the growth literature. The Solow growth model (Solow 1957) for example maintains that in the short run, economic growth is driven by savings while long run growth is driven by a mystery variable, representing the effectiveness of labour. This is discussed in Lucas, 1990 and Romer 2012. The effectiveness of labour is represented by knowledge or technology but the dynamics of labour effectiveness or technology is unexplained in the Solow model and the Neoclassical model in general. On this note, the Solow model is considered as an exogenous model. Later developments led to the endogenous growth model though other forms of exogenous models had been in existence (the infinite horizon model-Ramsy-Cass-Koopmans model and the overlapping generations model-the Diamond model). The endogenous model (Romer 1986; Lucas 1988) posit that investment in research and development (R&D) sector determines technology and the stock of ideas. Thus making workers more production determine long run growth. Hence it is productivity that determines long run growth.

Sustainable economic growth is a concern to policymakers as it is necessary though not sufficient for economic development. This has been long documented by academics and policymakers in both developed and developing countries. It is also emphasized in Todaro and Smith (2012). Knowledge of the contribution of factors of production to the growth process relative to their productivity is therefore necessary in an effort to have direction about sustainable growth that is inclusive and pro-poor.

The average growth of the ECOWAS countries was 3.5 % in 2000, which was lower than the Sub-Sahara African average of the same year, 5.5 %. In 2005 it increased to 5.3 % in ECOWAS and 6.2 % in Sub-Sahara Africa. In 2012, ECOWAS average growth was 6.4 % with sub-Saharan Africa average being

5.4 %. Taking country by country case from 1980 to 2012, some countries observed negative growth in some years while in the same years some others had high growth rates. In addition, in a given country, growth was negative in some countries but high in some years. Table 1 presents some growth trend for the ECOWAS region.

There is dearth of empirical studies on the sources of growth in Sub-Sahara Africa in general and ECOWAS Countries in particular. We are not aware of a study on the ECOWAS Countries as a group even though there are numerous common agenda courses discussed by the various ECOWAS Member States and the countries face challenge on poverty reduction and sustainable growth, though some countries have recently recorded extremely high growth rates—for example, Ghana grew by 15.0 % in 2011 driven by rebasing and Sierra Leone grew by 15.2 % in 2012 driven by discovery of iron ore. These rates were more than 100 % of the average growth rates of sub-Sahara Africa. The objective of the paper is therefore to investigate the contributions of capital, labour and their productivity (total factor productivity) to the growth of the region since the 1980s. Such investigation is imperative as it is informative in terms where emphasis has to be placed by policymakers on their drive towards sustainable growth that is inclusive.

Table 1 Growth rates of ECOWAS countries

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

SUb-Sahara Africa

5.7

5.2

5.6

5.9

6.2

6.4

5.6

2.5

4.5

5.2

5.4

ECOWAS

3.9

7.7

5.6

5.9

5.3

5.7

5.6

5.6

6.8

6.5

6.4

UEMOA

1.3

3.4

2.7

4.2

2.9

3.2

3.9

3.1

3.9

4.4

5.4

• Benin

4.4

3.9

3.1

2.9

3.8

4.6

5.0

2.7

2.8

3.5

5.4

• Burkina Faso

4.6

8.0

4.6

8.6

5.5

3.6

5.2

3.2

5.2

5.5

9.0

• Cote d'Ivoire

-1.7

-1.4

1.2

1.7

0.7

1.6

2.3

3.8

3.0

4.0

9.8

• Guinea Bissau

-7.2

0.3

2.8

4.3

2.1

3.2

3.2

3.0

3.5

4.3

-1.5

• Mali

4.3

7.6

2.3

6.1

5.3

4.3

5.0

4.5

4.5

6.0

-1.2

• Niger

5.8

3.8

-0.8

7.4

5.8

3.4

9.6

-0.9

7.5

3.2

11.4

• Senegal

1.2

6.7

5.8

5.7

2.5

4.9

3.2

2.2

4.0

4.4

3.5

• Togo

-1.3

4.8

2.5

1.2

3.9

2.1

2.4

3.2

3.4

3.9

5.9

WAMZ

4.6

9.1

6.4

6.4

5.9

6.4

6.1

6.4

7.6

7.1

6.7

• Gambia

1.3

7.4

6.6

0.3

3.4

6.0

6.1

4.6

5.5

5.1

3.9

• Ghana

4.5

5.2

5.6

5.9

6.4

6.5

8.4

4.7

6.6

6.5

7.9

• Guinea

4.2

1.2

2.3

3

2.4

1.8

4.9

-0.3

1.9

4.0

3.9

• Liberia

7.8

-1.9

-2.8

1.4

3.1

3.2

3.5

3.6

3.7

3.9

8.3

• Nigeria

4.6

9.6

6.6

6.5

6

6.5

6.0

6.7

7.9

7.3

6.6

• Sierra Leone

6.5

10.7

9.6

7.6

6

6

4.0

3.2

5.0

4.9

15.2

Cape Verde

5.3

4.7

4.3

5.6

10.1

8.6

6.1

4.0

5.6

4.5

1.0

There are studies at country specific levels on the issue but a holistic study on ECOWAS Countries is not a common place in the literature. For example, Dike (1995) and Kallon (2013) where on Nigeria and Sierra Leone respectively. There are also studies on group of countries, for example, Zelleke and Sraiheen (2012) for 31 sub-Sahara African countries and Shaaeldin (1989) on Tanzanian, Zambia and Zimbabwe. The dearth of studies on growth accounting in the region is explained by the fact that data on the stock of capital is not readily available for many Sub-Sahara African Countries. However, data on gross capita formation which is essentially the change in the stock of capital is available in most of the statistical institutions in the ECOWAS region as in the case of data on output and labour—though unemployment data generation remains a challenge to most of the countries. Thus, in an effort to decompose the growth of output into total factor productivity growth and factor accumulation, we also construct a series for capital stock for each of the countries over the period 1980–2012.

The rest of the paper is organized as follows. Section 2 discusses the methodology. Section 3 is the empirical results and Sect. 4 is conclusion and policy implications.

 
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