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2 Sub-regional Characteristics and Some Stylized Facts

The WAMZ like other African sub-regions is home to flourishing agriculture and trade characterized by very diverse ethnic and tribal make up. The WAMZ countries are faced with a number of growth impediments such as corruption, high fiscal deficits and inflation, among others. Most of them became independent in the early 1960s with the exception of Liberia established by the American Colonization Society in 1847 and they have had their own share of different types of governance.

At the early stage of their independence, the phase of growth was initiated by the planned industrialization-based strategy of import-substitution to reduce dependence on manufactured imports and widespread protection. Agriculture was ascribed a secondary role of supplying raw materials and providing tax revenues to finance developments in other sectors (Acemoglu et al. 2001). Like South-Asia, the import-substitution based strategy was marred by inefficiency and stagnation; and a series of economic reforms in the form of trade liberalisation, industrial and financial sector deregulation were undertaken as a growth revival approach backed and prescribed by the International Monetary Fund (IMF) and the World Bank in the 1980s (Rao and Cooray 2012). The introduction of this agenda (which are classical/neoliberal in features) in the name of Structural Adjustment Programmes (SAP) albeit marginally reversed the declining and non-satisfactory performance of these economies (Ekpo 2014) especially during the 1990–2000.

The study considers the six countries of the West African Monetary Zone—The Gambia, Ghana, Guinea, Liberia, Nigeria and Sierra Leone in its general discussion and specifically four countries (The Gambia, Ghana, Nigeria and Sierra Leone) in its empirical analysis.

The WAMZ may be perceived to be homogenous and hence, striving towards a monetary and economic union. In order to show whether these countries are very similar or heterogeneous requires an empirical investigation through a crosssectional dependence test (Cooray et al. 2013). However, it is equally necessary to display some basic preliminary evidence in terms of their economic performance (GDP growth, inflation rates, GDP per capita and fiscal deficit), economic characteristics (capital accumulation, structure of GDP or output and skills measured by average years of schooling), demographic structure (population growth rate, life expectancy at birth and adult literacy rate), external operation (trade openness and foreign direct investment, net inflows) and political profile (corruption indices, political independence and successful military coups). The panels (A, B and C) of Table 1 present these characteristics.

Table 1 Economic and socio-political characteristics of the West African monetary zone

Panel B

Trade opennessa

Population growth rate (%)

Life expectancy at birth, total (years)

Literacy rate, adult female (% of females ages 15 and above)

Foreign direct investment, net inflows (BoP, current US$)

Trade (% of GDP)

1980–

1989

1990–

1999

2000–2012

2012

1980

2011

Gender gap

2003

2012

%

Growth

The Gambia

80.63

4.09

3.03

3.13

3.19

46

58

25 (2000)

42 (2011)

18,272,720

33,524,674

83

Ghana

54.00

2.99

2.56

2.48

2.17

52

61

50 (2000)

65 (2010)

136,751,000

3,294,520,000

2,309

Guinea

59.34d

2.58

4.03

2.20

2.56

41

56

18 (2003)

12 (2010)

78,966,000

605,400,000

667

Liberia

101.84

1.55

2.49

3.26

2.79

46

60

32 (1994)

27 (2007)

372,220,000

1,354,100,000

264

Nigeria

50.64

2.63

2.52

2.64

2.68

46

52

44 (1991)

41 (2008)

2,005,390,033

7,101,031,884

254

Sierra Leone

47.52

2.52

0.09

3.03

1.91

41

45

37 (2004)

52 (2011)

8,615,050

548,073,515

6,262


Panel C

Average years of schooling

Fiscal deficit

Political independence

Number of military coups

1970

1975

1980

1985

1990

1995

2000

2005

2010

Average 2008–

2012

Ghana

3.58

4.27

4.94

5.52

5.89

6.06

6.57

6.80

7.26

-2.26

1957

5 (1981)

Gambia

0.51

0.73

0.97

1.27

1.81

2.45

2.64

3.08

3.58

-8.44

1965

1 (1994)

Guinea

NA

NA

NA

NA

NA

NA

2.4

2.8

3.3

-8.34

1958

2 (2008)

Liberia

1.14

1.66

2.14

2.59

2.91

3.01

3.43

4.16

5.11

-0.64

1847

1 (1980)

Nigeria

1.6

2.1

2.70

3.30

3.90

4.60

5.50

6.10

6.80

-2.6

1960

6 (1993)

Sierra Leone

0.87

1.12

1.40

1.73

2.05

2.38

2.67

3.07

3.42

-4.74

1961

5 (1997)

Panel D

Country

Growth of output 2006–2011

2006

2007

2008

2009

2010

2011

The Gambia

6.5

6.3

5.9

4.6

6.1

3.3

Ghana

6.4

6.1

7.2

3.5

6.4

13.6

Guinea

2.5

1.8

4

0.3

2.4

3.6

Liberia

7.8

9.5

7.1

4.6

6.8

6.4

Nigeria

6.1

6.4

5.3

5.6

6.4

7.2

Sierra Leone

7.4

6.4

5.5

4

6

5.3

Sources for Panel C: (a) World Development Report (2013),

(b) Transparency International (2013) and (c) Barro-Lee (2013)

1Average values over the period 1970–2012;

^ average values (1986-2012)

2Average values over the period 2008–2012

3Average values over the period 2000–2012

4Average values (1986–2012)

The growth rates of the WAMZ member economies have increased especially when the 1970–2012 average values are compared with those of later years from 2006 to 2012. In 2012 Sierra Leone recorded the highest growth rate of 15.22 % followed by Liberia and Ghana. Along the same trajectory, the growth rates of the other WAMZ economies except Guinea were above 6 %. This impressive growth rates including that of Guinea interestingly were achieved during the period of the global economic downturn and principally driven by mineral and commodity exports (Ekpo 2014) and above the population growth rates.

Gross capital formation as a percentage of GDP in the WAMZ has been very impressive as the data in Table 1 also show. In count, the economies have been experiencing relative macroeconomic stability using inflation as a proxy. Inflation rates though are mostly still double-digits, they have been substantially subdued. During the period 1970–2012, average inflation rates were 11.43 % for The Gambia, 31.59 % Ghana, while Liberia, Nigeria and Sierra Leone recorded 93.52, 20.29 and 29.41 percent, respectively. The average inflation rates for the countries during the period 2008–2012 plummeted to 4.02 % for The Gambia, 15.9 % for Ghana and 11.25 % for Liberia. Nigeria, nonetheless recorded a single digit of 7.63 % during the same period. The implication is that WAMZ countries macroeconomic management is improving.

In addition, female literacy rate is improving as gender gaps are declining except in countries just emerging out of political instability. Skill acquisition, proxied by average years of schooling has been maintaining increased trajectory. On corruption ranking by the Transparency International indices in Table 1 shows that except for Ghana which recorded 63rd position in ranking of 177 countries in 2013, other WAMZ member countries' position was abysmally poor. The Gambia was ranked 127th most corrupt country out of 177 counties, while Liberia and Nigeria ranked 150th and 144th, respectively. This implies that corruption and rent-seeking constitute a challenge in the WAMZ member economies (Ekpo 2014).

From the foregoing, it is crystal clear that a conclusion as to whether the WAMZ economies are similar and hence are good candidates for economic union or are characterized by heterogeneous growth path is beyond mere discussion of the descriptive data and hence requires some modest empirical investigation for example, through a cross-sectional dependence test in panel analysis. The cross-sectional dependence test describes the interaction between cross-sectional units

(i.e. households, firms and countries) and has been widely discussed and applied in the spatial literature [see for example, Cooray et al. (2013), Bailey et al. (2012), Sarafidis and Wansbeek (2010), Hoyos and Sarafidis (2006), Pesaran (2004), Cerrato (2001)].

Since the last decade and a half, there has been a plethora of evidence in the literature that substantial panel data models exhibit cross-sectional dependence in the errors. The errors may arise from the presence of common shocks and unobserved components which eventually become part of the error term, spatial dependence, and idiosyncratic pairwise dependence in the disturbances (Hoyos and Sarafidis 2006). Some sources of cross-sectional dependence so far identified are; first, the interdependencies between cross-sectional units as a result of increasing countries integration of economic and financial entities; second, microeconomic applications as a result of individual propensity to respond similarly to common unobserved factors, or common shocks. This could result from neighborhood effects, interdependent preferences, group behavior, social norms, etc. The estimation of cross-section dependence according to Hoyos and Sarafidis (2006) depends on factors such as the magnitude of the correlations across cross-sections and crosssection dependence. In the estimation process, different possibilities arise. If crosssectional dependence is caused by unobserved presence of common factors which are uncorrelated with the controlled variables, the standard fixed-effects and random effects models could be consistent but they are biased and not efficient.

The issues considered in cross-sectional dependence include modelling crosssectional dependence, measuring it, testing for its presence, and carrying out counterfactual exercises under alternative network formations (Bailey et al. 2012). This study, however, partly focuses on testing for the presence of cross-sectional dependence. Cross-sectional dependence arises from unobservable common factors or common shocks akin to serial correlation in time series analysis. Cooray et al. (2013) are of the view that if cross-correlation exists from the test results in the case of countries test, then the countries move together, i.e., they are driven by common factors and hence, they have some similarities. The following section discusses some of the various tests and statistics used in determining the presence of cross-sectional dependence.

 
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