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7 Empirical Results

7.1 The Statistical Results of Convergence: Sigma Convergence

Figure 6 shows the evolution of the standard deviation of the logarithm of per capita income for the WAEMU zone. A trend towards convergence in the WAEMU is clearly drawn on this graph with the decline over time of the gap in per capita income growth (see Table 3). This convergence trend is partly explained by the slow-down of economic growth in Coˆte d'Ivoire which was initially the most advanced country of the Union.

The obvious impact of exogenous factors on growth, other than the initial GDP, calls for an analysis of the beta convergence.

Fig. 6 Sigma convergence: dynamics of the standard deviation of log (PIB/INHABITANT).

Source: Author from World Development Indicators (WDI 2014)

Table 3 The dynamics of standard deviation of GDP per capita in WAEMU (sigma convergence)

Year

1980

1981

1982

1983

1984

1985

1986

Stand-dev log(pib/hbt) in %

53.66

53.02

52.01

49.73

50.34

50.50

48.44

Year

1987

1988

1989

1990

1991

1992

1993

Stand-dev log(pib/hbt) in %

48.71

46.59

45.87

45.67

44.08

44.44

45.26

Year

1994

1995

1996

1997

1998

1999

2000

Stand-dev log(pib/hbt) in %

43.68

44.29

44.57

44.66

43.21

43.58

43.26

Year

2001

2002

2003

2004

2005

2006

2007

Stand-dev log(pib/hbt) in %

41.78

40.92

40.06

41.48

41.24

40.49

40.75

Year

2008

2009

2010

2011

2012

Stand-dev log(pib/hbt) in %

39.32

40.61

39.64

37.94

38.55

Source: Author from World Development Indicators (WDI 2014)

7.2 The Econometric Results

Table 4 reports the results of the growth and beta or conditional convergence model in the WAEMU. Stationarity of the variables of the model were first examined. This common approach in the analysis of time series is relatively new in panel data analysis.

The Im and Shin Pesaran test which is one of the most common tests, has been used for the analysis of stationarity. The test results show that the series of the model are not affected by a unit root (See Table 5).

Table 4 confirms the conditional convergence among countries of the WAEMU. The coefficient of the initial level of GDP per capita is negative and significant at 1 %. The value of the coefficient of this traditional convergence factor is -0.0601, which corresponds to an average convergence rate of 0.24 % point.

The past growth of GDP per capita has a positive and significant impact on the current growth rate. In fact, an improvement in the per capita GDP of 1 % leads to an increase in the average growth rate for the next year of 0.34 % point.

The results of the Union were also examined in relation to factors other than the growth of GDP per capita. These factors included infrastructure services, health, education, private investment, and the population growth.

Table 4 Conditional convergence and determinants of the economic growth in the WAEMUa

Dependent variable: GDP per capita growth

Coef.

Std. Err.

P > |z|

Log (gdpi0) initial gdp/capita

-0.0601351***

0.0067859

0.000

Lag gdp per capita growth

0.3413356***

0.0174823

0.000

Log (life expectancy)

0.0637631***

0.0098955

0.000

Log (roads)

0.0027284***

0.0003998

0.000

Log (electricity)

-0.0004631

0.000549

0.399

Log (ict)

0.0000446**

0.0000206

0.031

Education (primary)

0.000024

0.0000465

0.605

Education (secondary)

0.0002336***

0.0000734

0.001

Population growth

-0.0093897***

0.0012232

0.000

Log (investment)

0.0073644***

0.0010867

0.000

Notes: Number of observations ¼ 248; Prob > χ2 ¼ 0.0000

***Significant at 1 %, **significant at 5 %

aInstruments for differenced equation: GMM-type: L(2/.). GDP per capita growth; first differenced explanatory variables are standard instruments for differenced equation

Source: Author

Table 5 Unit root test: IPS test

Source: Author

The results obtained in terms of infrastructure services shows that better access to road infrastructure and ICT has a significant effect on the growth of the GDP per capita. Indeed, an additional investment in road infrastructure and ICT leads to a significant increase in the long-run trend rate of economic growth.

These results confirm the fact that infrastructure acts as a catalyst for the longrun economic growth. In particular, recent studies indicate that the road infrastructure is crucial both for agriculture, trade, and poverty reduction (Anyanwu and Erhijakpor 2009). The work by Ben Youssef and M'henni (2004) also showed that ICT is essential to stimulate entrepreneurship, innovation, and to accelerate growth in developing countries.

The strong contribution of electricity to manufacturing competitiveness and economic growth is undeniable. However, the results obtained indicate non-significant impact of consumption of electric energy on growth. This result could be explained by the low quality of energy infrastructure in the WAEMU zone. Indeed, interruptions and irregularity in the provision of electricity is considered a major obstacle to private sector development.

The determining role of training and education in the growth and development process is confirmed by the results. The additional growth points associated with education are significant from secondary education. This result is mainly due to the positive externalities generated by the investment in human capital (education and training).

The estimates also show the significant impact of health measured by life expectancy on growth. Thus, a 1 % improvement in life expectancy at birth results in increased growth of GDP per capita of 0.06 % point. Improved health significantly enhances economic and productivity growth in the Union.

Three main channels explain this result. First, greater life expectancy results in increased savings that enhance the growth of the capital stock and thus that of GDP (Zhang et al. 2003). Higher life expectancy encourages increasing investments in education, causing a positive effect on growth. Finally, healthy people are more productive, better able to adapt to new technologies and to sustainably increase GDP (Aghion et al. 2010).

Population growth would have a negative impact on the growth rate of the GDP per capita. This negative effect can be explained by the rapid population growth that puts pressure on the ability of states to meet universal access to education, health, and infrastructure services.

The positive and significant coefficient of investment confirms the accumulation of physical capital as a growth factor. The impact of the accumulation of physical capital is estimated at 0.007 additional point of growth. This result implies that investments in the WAEMU zone have a long run effect on the economic growth.

 
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