Obs. |
||||||
LRGDP |
924 |
23.33 |
1.52 |
19.30 |
26.86 |
|
LEXP |
924 |
56.45 |
8.06 |
35.71 |
76.08 |
|
MORT |
924 |
11.92 |
4.33 |
4.74 |
27.54 |
|
FERT |
924 |
5.14 |
1.36 |
1.99 |
7.77 |
|
QOG |
924 |
0.41 |
0.14 |
0.04 |
0.90 |
|
SOCIOECO |
924 |
3.86 |
1.61 |
0.50 |
8.00 |
|
Regions with Minimum/Maximum Values |
||||||
Minimum |
Maximum |
|||||
LRGDP |
Western Africa |
Western Africa |
||||
LEXP |
Western Africa |
Northern Africa |
||||
MORT |
Northern Africa |
Western Africa |
||||
FERT |
Northern Africa |
Western Africa |
||||
QOG |
Western Africa |
Southern Africa |
||||
SOCIOECO |
Eastern Africa |
Western Africa |
Source: World Bank’s World Development Indicators (2017); International Country Risk Guide, (2017). Computed using STATA version 14.
The results further revealed that the average quality of government across the regions was about 0.41 with a standard deviation of about 0.14. While the highest quality of government was recorded in Southern Africa given by a scale of about 0.90 (precisely in Namibia), the lowest quality of government was recorded in Western Africa (in Liberia) which was scaled about 0.04. This shows that corruption was rampant in Western Africa which may be due to lack of adherence to law and order as well as poor bureaucracy quality and vice versa in the case of Southern Africa. Finally, the average socioeconomic status across the African regions was about 3.85 with a standard deviation of about 1.61, and the minimum and maximum values of about 0.50 and 8.0 were respectively found in Eastern Africa (in Zimbabwe) and Western Africa (in Cote D’ivoire).
Inferential analysis
The econometric results of equations (1) and (2) are presented in table 1 based on the estimation of fixed effect and random effect panel models. However, the interpretations of the results were conducted based on the dictates of the Hausman test which identify
Table 2: Results of Fixed Effect and Random Effect Models
Dependent Variable: Log of Real Gross Domestic Product (RGDP) |
||||
Model 1 |
Model 2 |
|||
Independent Variables |
Fixed Effect |
Random Effect |
Fixed Effect |
Random Effect |
LEXP |
0.04 |
0.04 |
0.07 |
0.07 |
(0.01)*** |
(0.01)*** |
(0.02)*** |
(0.02)*** |
|
MORT |
-0.01 |
-0.01 |
0.11 |
0.11 |
(0.01) |
(0.01) |
(0.04)*** |
(0.04)*** |
|
FERT |
-0.34 |
-0.34 |
-0.51 |
-0.52 |
(0.02)*** |
(0.02)*** |
(0.04)*** |
(0.04)*** |
|
QOG |
-0.40 |
-0.40 |
-0.31 |
-0.31 |
(0.09)*** |
(0.09)*** |
(0.08)*** |
(0.08)*** |
|
SOCIOECO |
0.02 |
0.02 |
0.02 |
0.02 |
(0.01)** |
(0.01)*** |
(0.01)** |
(0.01)** |
|
Central Africa |
– |
– |
– |
4.22 |
(2.51)* |
||||
Eastern Africa |
– |
– |
– |
9.32 |
(2.08)*** |
||||
Northern Africa |
– |
– |
– |
-2.93 |
(2.11) |
||||
Western Africa |
– |
– |
– |
-3.10 |
(2.13)* |
||||
LEXP*Central Africa |
– |
– |
-0.05 |
-0.06 |
(0.03)* |
(0.03)* |
|||
LEXP*Eastern Africa |
– |
– |
-0.13 |
-0.13 |
(0.02)*** |
(0.02)*** |
|||
LEXP*Northern Africa |
– |
– |
0.04 |
0.04 |
(0.02)* |
(0.02)* |
|||
LEXP*Western Africa |
– |
– |
0.05 |
0.05 |
(0.02)** |
(0.02)** |
|||
MORT*Central Africa |
– |
– |
-0.17 |
-0.17 |
(0.05)*** |
(0.05)*** |
|||
MORT*Eastern Africa |
– |
– |
-0.29 |
-0.29 |
(0.04)*** |
(0.04)*** |
|||
MORT*Northern Africa |
– |
– |
-0.15 |
-0.15 |
(0.05)*** |
(0.05)*** |
|||
MORT*Western Africa |
– |
– |
-0.03 |
-0.03 |
(0.04) |
(0.04) |
|||
FERT*Central Africa |
– |
– |
0.29 |
0.29 |
(0.07)*** |
(0.07)*** |
|||
FERT*Eastern Africa |
– |
– |
0.31 |
0.31 |
(0.05)*** |
(0.05)*** |
|||
FERT*Northern Africa |
– |
– |
0.54 |
0.54 |
(0.06)*** |
(0.06)*** |
|||
FERT*Western Africa |
– |
– |
0.24 |
0.24 |
(0.05)*** |
(0.05)*** |
|||
Constant |
23.06 |
23.01 |
21.64 |
20.65 |
(0.40)*** |
(0.47)*** |
(0.50)*** |
(1.83)*** |
|
No. of Observations |
924 |
924 |
924 |
924 |
No. of Countries |
33 |
33 |
33 |
33 |
R-Squared |
0.78 |
0.78 |
0.85 |
0.85 |
F-Statistics |
628.78 |
– |
282.16 |
– |
[0.00] |
[0.00] |
|||
Wald Test (χ2) |
– |
3165.54 |
– |
4847.94 |
[0.00] |
[0.00] |
|||
Hausman Test (χ2) |
0.98[0.97] |
8.28 [0.94] |
Source: Author’s computation using STATA version 14.
Note: Figures in ‘()’ and ‘[]’ are standard errors and probability values respectively.
***, ** and *indicate significance at the 1%, 5% and 10% levels respectively.
Dummy variables for the region take the value of 1; 0 otherwise.
the most preferred model between fixed effect and random effect estimations.
Table 2 shows the estimated effects of health indices on economic growth in Africa as well as their regional effects on the economic growth of African sub-regions. As can be seen, model 1 measures the main effects of life expectancy, mortality rates and fertility rates on the economic growth regardless of any regional variation among African sub-regions. Model 2, has an additional variables of regional dummy, and their interactions with health indices in order to examine the regional differences in the effects of life expectancy, mortality rates, and fertility rates on economic growth among African sub-regions. It should be noted that interpretation of results is based on the chosen model by the Hausman test between fixed and random effect estimates.
For model 1, the Hausman test revealed that random effect estimates are more appropriate with a p-value of greater than 0.05. Also, the Wald test p-value of 0.00 indicates that the null hypothesis of no joint significance is rejected. However, the results represent the impact of health indices on economic growth in Africa as a whole and Southern Africa in particular which the reference point region. From the findings, the estimated coefficient of life expectancy is positive and significant which conformed to the a priori expectation. The result indicated that an increase in life expectancy by 1 year would increase economic growth in Africa by about 0.04%. On the other hand, the coefficient of mortality rate is negative as expected but statistically insignificant. Also, the result shows that fertility rate has negative and statistically significant impact on economic growth. Specifically, a 1% increase in fertility rates would bring about 0.34% decrease in African economic growth. For control variables, the coefficient of quality of government (which implies lower level of corruption, increase in bureaucracy quality and adherence to low and order) is negative and statistically significant. The result revealed that a 1% increase in the quality of government index would reduce economic growth by about 0.40%. This is contrary to the a priori expectation of the study. Finally, the estimated coefficient of socioeconomic status is found to have a significant and correct sign in the model, indicating that a 1% increase in socioeconomic status in Africa would increase economic growth by about 0.02%, though the magnitude is very small similar to that of life expectancy.
However, model 2 repeated the estimation but included the regional dummy variables and their interaction terms with life expectancy, mortality rates and fertility rates. This is to investigate whether the impact of these health indices on economic growth differs across African sub-regions. Thus, the life expectancy, mortality rates and fertility rates were interacted with four dummy variables for Central Africa, Eastern Africa, Northern Africa and Western Africa respectively. Southern Africa dummy was omitted not only to avoid dummy variable trap but also to serve as a reference point. Similarly, the Hausman test shows that random effect estimates are more appropriate.
From the overall results, it can be observed that all the coefficients, including the interaction terms coefficients, are statistically significant except coefficient of Northern Africa dummy and that of interaction between mortality rates and Western Africa dummy. This suggests that there is an interactive relationship between the variables of health indices (life expectancy, mortality rates and fertility rates) and regional characteristics in at least some, if not all regions. This is further buttressed by comparing the two models (additive and the interaction models, that is, model 1 and model 2) in terms of their diagnostic tests. For instance, although the Wald test p-value of 0.00 in both models indicates that the null hypothesis of no joint significance is rejected, the value of χ2 in the interaction model (4847.94) is higher than that of the additive model (3165.54). Additionally, the R-square (R2) value of the interaction model is about 85% compared to only 78% for the additive model. These results suggest that the model with the interaction term is better than the model that contains only main effects.
From the respective results, it can be seen that there is significant difference in the values of the coefficients of health indices in model 2 (interaction model) compared to model 1 (additive model). For instance, the coefficient of life expectancy is still positive and significant with relatively higher magnitude. Specifically, the results showed that a 1 year increase in life expectancy increases economic growth by about 0.07% in Africa. On the contrary, the coefficient of mortality rates is now positive and statistically significant. The result showed that a 1% increase in mortality rate would increase economic growth in Africa by about 0.11% which is against the a priori expectation of this study. For fertility rates, the coefficient is still negative and significant, showing that a 1% increase in fertility rates reduces economic growth in Africa by about 0.51%. Furthermore, all coefficients of regional dummy variables as well as the coefficients of their interaction with health indices exhibited varying signs and are significant except coefficients of Northern Africa dummy and that of interaction between mortality rates and Western Africa dummy. This indicates the existence of interactive relationship between regional characteristics and the variables of health indices. Last but not the least, the coefficients of the control variables still maintained their signs and significantly influence economic growth in Africa as whole and across regions in particular.
However, the fact that coefficient of interaction terms are statistically significant except coefficient of interaction between mortality rates and Western Africa dummy, this suggests that regional characteristics significantly influence the effectiveness of life expectancy, mortality rates and fertility rates on economic growth in nearly all regions. Therefore, the task now is to examine the efficacy of regional dummy variables in influencing the impact of health indices on economic growth among African sub-regions. This is simply done by adding the coefficient of the interaction term in each region with the main effect. The coefficient of the interaction term captures the additional effect of a given variable of health indices on economic growth in a particular region when regional dummy is 1, while the main effect is when regional dummy is 0. This is presented in table 3.
From table 3, the coefficients of LEXP, MORT, and FERT were taken from model 1 in table 2 which represent their respective impacts on economic growth in Africa as a whole and Southern Africa in particular which is the reference point region as earlier interpreted. Therefore, the first part of table 3 presents the results of regional differences in
the impact of life expectancy on economic growth in Africa.
Table 3: Regional Differences in the Impact of Health Indices on Economic Growth
Interaction: Regional Dummy*Life Expectancy |
||||
Coefficients |
||||
Variables |
CA |
EA |
NA |
WA |
LEXP |
0.07 |
0.07 |
0.07 |
0.07 |
Regional Dummy*LEXP |
-0.06 |
-0.13 |
0.04 |
0.07 |
Total Effect |
0.02 |
-0.06 |
0.11 |
0.12 |
Interaction: Regional Dummy*Mortality Rates |
||||
MORT |
0.11 |
0.11 |
0.11 |
0.11 |
Regional Dummy*MORT |
-0.17 |
-0.29 |
-0.15 |
-0.03a |
Total Effect |
-0.07 |
-0.19 |
-0.04 |
0.08 |
Interaction: Regional Dummy*fertility Rates |
||||
FERT |
-0.52 |
-0.52 |
-0.52 |
-0.52 |
Regional Dummy*FERT |
0.29 |
0.31 |
0.54 |
0.24 |
Total Effect |
-0.22 |
-0.21 |
0.02 |
-0.27 |
Source: Author’s computation, extracted from table 1 above.
Note: Computations are based on Random effect estimates in Model 2.
Dummy variables for the region take the value of 1; 0 otherwise.
- Interaction term is NOT significant.
The coefficient of life expectancy (that is 0.07) shows its impact on economic growth in Southern Africa and Africa in general. However, when the respective regional coefficients of interaction terms are added which are significantly different from zero, it can be seen that the total effect of life expectancy on economic growth significantly differs across the regions. The positive coefficient means that a variable of health indices has larger effect (more positive or less negative) in a given region and vice versa. For instance, the results revealed that for every increase in life expectancy, Northern and Western Africa would expect an additional increase in economic growth by about 0.04% and 0.0.05% respectively on top of the main effect (that is, 0.07+0.04 for Northern Africa and 0.07+0.05 for Western Africa). On the other hand, Central Africa and Eastern Africa would have less economic growth from the main effect by about 0.06% and 0.13% respectively (that is, 0.07+ (-0.06) for Central Africa and 0.07+ (-0.13) for Eastern Africa).
Therefore, cumulatively, a 1 year increase in life expectancy leads to increase in economic growth in Western Africa by about 0.12%, followed by Northern Africa (0.11%), Southern Africa (0.07%), and Central Africa (0.02%). For Eastern Africa, the result shows a negative impact on economic growth, indicating that a 1 year increase in life expectancy reduces economic growth in Eastern Africa by about 0.06%. On the whole, the results show that life expectancy contributes positively and most effectively on the economic growth of Western Africa when compared to Southern and Central African region, while the reverse is the case in Eastern African region.
The second part of the table presents the results of regional differences in the impact of mortality rates on economic growth in Africa. In a similar way, the results revealed that for every 1% increase in mortality rates, Central Africa, Eastern Africa, and Northern Africa would cumulatively expect less economic growth by about 0.07%, 0.18%, and 0.04% respectively. This clearly shows that mortality rate has more negative effect on the economic growth of Eastern Africa followed by Central Africa and then Northern Africa. On the other hand, the effect of mortality rates in Southern and Western regions shows unexpected positive sign, indicating that for every 1% increase in mortality rate, economic growth increases by about 0.11% and 0.08% respectively.
The third and also last part of the table presents the results of regional differences in the impact of fertility rate on economic growth in African regions. Again, it can be seen from the overall results that for every 1% increase in fertility rate, economic growth decreases in Central Africa by about 0.22%, in Eastern Africa by 0.21%, in Western Africa by 0.27%, and Southern Africa by 0.52%. Comparatively, fertility rate is said to have more negative impact on economic growth in Southern Africa, followed by Western Africa, Central Africa and then Eastern African region. However, the effect of fertility rate in Northern Africa though very small in magnitude, is found to be positive which indicate that for every 1% increase in fertility rate, economic growth in Northern Africa rises by about 0.02%.
Discussion
The results of Random Effect estimates in model 2 confirmed the existence of significant regional differences in the impact of health indices (proxied by life expectancy, mortality rate and fertility rate) on economic growth across African regions as evidenced by the coefficients of interaction terms between respective regional dummy variable and the variables of health indices which were mostly found to be statistically significant. This suggests that there is an interactive relationship between the variables of health indices and regional characteristics in at least some, if not all regions which further indicates that the impact of life expectancy, mortality rate and fertility rate on economic growth differs across African sub-regions as presented in table 2. This has buttressed the work of (15) who also found regional variations in the impact health expenditure on child mortality rate in Africa.
Specifically, compared to the reference point, that is, Southern Africa in which the coefficient was 0.07%, the results have shown that life expectancy has larger positive and statistically significant impact on economic growth in Western Africa (0.12%) and Northern Africa (0.11%) and less positive impact in Central Africa (0.02%). The positive and significant impact of life expectancy on economic growth in these regions have supported the work of [2] that productivity growth should be positively correlated with the level of health particularly the average level of life expectancy in a country. On the contrary, the results revealed that life expectancy has negative and statistically significant impact on economic growth in Eastern Africa by about -0.06%. This has not only contradicted the works of [1] and [2] but also the a priori expectation of this study. However, the positive results are not surprising for the case of Western Africa and Northern Africa being the second and third regions with largest population respectively in Africa. Hence, all things being equal, increase in life expectancy in these two regions may imply increase in human resource availability and cheap labour which consequently attracts more investment and output at lower cost. Being the region with lowest population, the relatively high positive impact of life expectancy on economic growth in Southern Africa may be due to its high quality of government compared to other regions which in turn, provides high quality labour force in terms of education, health and skills and consequently increased productivity and economic growth. For the case of Central Africa where the impact of life expectancy on economic growth is of smaller magnitude compared to other regions, may be due to the low per capita health expenditure in the region. As rightly posited by [1], investment in health, raises efficiency in human capital which in turn boosts individuals’ productivity and consequently economic growth. Thus on the contrary, lower spending on health may be detrimental to quality of life of the labour force and its productivity thereby retarding the speed of economic growth. However, the negative impact of life expectancy on economic growth in Eastern Africa, despite being the region with largest population, may not be unconnected with poor socioeconomic conditions which may result to high number of uneducated, unskilled and low productivity of the labour force in the region.
Similarly, the results revealed that mortality rate has negative and statistically significant impact on economic growth in Central Africa, Eastern Africa and Northern Africa. However, the impact is more devastating on the economic growth of Eastern Africa (by about 0.19%) followed by Central Africa (0.07%) and then Northern Africa (0.04%). This conformed to the a priori expectation of this study and also attested the reports of [7] and [8] that increasing mortality from infectious diseases such as HIV/AIDS, Ebola, Malaria and tuberculosis have not only swept away improvements in life expectancy over the years but also contributed significantly to the slow growth of African countries. Contrarily, the results showed that mortality rate has positive impact on economic growth in Southern Africa (by about 0.11%) and Western Africa (0.08%) but the impact is insignificant in the case of the later region because of the insignificant coefficient of the interaction term. This positive impact was highly unexpected. However, this could be buttressed by the theoretical argument of [20] who stated that increase in mortality has been an important incentive to increase investment in education and health which consequently promote economic growth, saying that the post war experience of India is consistent with this incentive.
Finally, the results further reported the impact of fertility rate on economic growth across African sub-regions. The results showed that fertility rate has negative and statistically significant impact on the economic growth of all regions except Northern Africa where the impact was reported positive and equally significant by about 0.02%. In terms of magnitude, fertility rate has more negative impact on economic growth in Southern Africa (by about -0.52%) followed by Western Africa (-0.27%), Central Africa (-0.22%) and then Eastern Africa (-0.21%). This has corroborated the findings [9] that increase in fertility rate raises population growth in the long-run; and where there are no job opportunities to absorb the increased population, it may result to increase in unemployment, dependency ratio, social vices and insecurity which cumulatively hamper the process of growth and development of African economies.
Based on the above discussions, the major findings obtained from the study are summarized as follows:
- The study confirmed the existence of significant differences in the impact of life expectancy, mortality rate and fertility rate on economic growth across African regions based on the results of Random Effects estimates presented in model 2 of table 2.
- Life expectancy has more positive impact on economic growth in Western Africa and less positive in Central Africa; while in Eastern Africa, the impact of life expectancy on economic growth is negative.
- Mortality rate has more negative impact on economic growth in Eastern Africa and less negative in Northern Africa; while in Southern Africa, mortality rate has positive impact on economic growth.
- Fertility rate has more negative impact on economic growth in Southern Africa and less negative in Eastern Africa; while in Northern Africa, fertility rate has positive impact on economic growth.
Policy implication and recommendations
The overall results of the study have shown the existence of significant regional differences in the impact of health indices (life expectancy, mortality rate and fertility rate) on economic growth in Africa. Thus, it becomes apparent that each region has its own peculiar characteristics such as population size, socioeconomic conditions, political stability, quality of institutions, etc. which will undoubtedly influence the impact of health indices on economic growth compared to other regions. The implication here is that, each region may require distinct policies, programs and interventions necessary to improve the level of its health indices for the purpose of achieving economic growth and development. Therefore, the study recommended that policy makers at various levels of government, international donor agencies; regional and continental organizations should take note of these regional differences in the levels of health indices and degree of economic growth in each region when designing health-related development policies and programs for Africa in order to bridge the regional differences. Also, governments and health related donor agencies should specifically increase the level their spending on community health and medical services as this may improve the efficiency and effectiveness in relation to prevention, diagnosis and treatment of diseases that bedeviled some of the studied countries like Sierra Leon, Nigeria, and Niger which cause higher mortality and hampered human capital development.
Above all, since improvement in health indices is believed to increase labour participation rate, productivity and income which in turn, leads to greater economic growth, the study recommends that governments of the selected regional countries should pursue strong health policies and programs in order to create preconditions for boosting the levels of health indices for increasing economic growth and development. This can be in form of increasing access to quality education, health services, and other social amenities by adopting “Free-Service-For-All” approach particularly on women and children who are more vulnerable. Equally, there should be rigorous commitment to create more job opportunities particularly through Public-Private-Partnership (PPP). Otherwise, improvements in health status accompanied by high unemployment will render the former to be ineffective in determining economic growth.
References:
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