AN ANALYIS OF THE GROWTHHEALTH RELATIONSHIP IN NIGERIA

AN ANALYIS OF THE GROWTH-HEALTH RELATIONSHIP IN NIGERIA By M. Adetunji BABATUNDE Department of Economics, University of Ibadan, Nigeria. tunjiyusuf 19@yahoo. com

INTRODUCTION • There seems to be a broad consensus that economic growth can definitely lead to improvement in health. • For example, economic growth could lead to increased earnings which makes health spending more affordable. • Higher growth could also imply higher public revenue which can translate to higher investment in health infrastructure. • Better health status leads to higher human capital accumulation, technological advancement and enterprise development.

Work longer Good Health 1. Better Learning ability 2. Better Educational outcomes Human Capital Accu. & Tech. Dev. Higher Earnings Higher Productivity Enhanced Tax base & fiscal posture Higher Economic Growth & Par capita GDP Reduced Individual & Household Poverty

• Thus, the question is whether improve health leads to higher growth? • If yes, how important is the contribution of health when one accounts for other potential factors that are empirically known to drive growth? • In addition, most of the studies in Nigeria have related growth to inequality while omitting the role of health in the analysis. • Also, most of these studies are carried out at the micro-level using ‘single point’ survey rather than ‘multiple points’ survey. • These issues lends justification to the empirical analysis that we pursue in this study.

OBJECTIVE OF THE STUDY • Analyze the inter-relationships between economic growth and health status in Nigeria.

METHODOLOGY • Framework – We model income and health within a simultaneous equation framework. – This is to allow for the expected bi-directional causation amongst the variables – Eliminate/Reduce the of problem of endogeniety – This is a significant departure from related studies that have adopted single-equation models to examine the relationship between income and health in Nigeria.

The Growth Equation: • Extension of the basic neoclassical growth model (following Barro (1991) as derived by Bloom and Canning (2004) and also adopted by several author. • yg is growth in per capita real income • inv is measured as (logarithm) of gross fixed capital formation (as a ratio of GDP) • h is measured using (logarithm of) life expectancy at birth, (logarithm of) infant and child mortality, and (logarithm of) overall mortality rate • S is measured using average year of schooling of the adult population and secondary school enrollment rates.

• W is the ratio of working age to total population, Wgt is the rate of growth in W. • In other to examine the impact of public expenditure policy on growth, the X vector contains variables such as § ratio of health expenditure to total expenditure, § per capita health expenditure, § ratio of education expenditure to total expenditure, § per capita education expenditure.

The health equation: • Studies such as Pritchett and Summers (1996), Bhargava et al. (2001) and others have argue that health cannot be treated as an exogenous determinant of growth. • There is evidence to suggest that increased income leads to more investment in health and thus there is strong case for reverse causality. • Therefore following Pritchett and Summers (1996) we postulate that the current level of health status depends upon the initial income per capita and mean years of schooling of the population.

• y is (change in the logarithm of) real per capita income • hexp is total health expenditure as ratio of total expenditure and per capita health expenditure • Doc is the number of doctors per capita, • is a randomly distributed error term with zero mean and constant variance. • Other variables are as defined in the growth equation.

ESTIMATION ISSUES • To be able to examine the results for robustness, we propose to report outputs from 3 SLS. • The 3 SLS is a system method that estimates all of the coefficients of the model, then forms weights and re-estimates the model using the estimated weighting matrix. • Also, given appropriate instrumentation, the 3 SLS is efficient. • A step wise analysis of the relationship is carried out to examine how sensitive the results would be to specification issues.

DATA AND AVAILABILITY • The study made use of annual data between the period 1970 and 2008. • In general, a more robust analysis could have been carried out using state-level data in a panel framework. • However, this is absent for Nigeria, particularly, for a key variable like GDP. • Nevertheless, this study will serve as a basis for such future extensions. • The data used for the analysis are secondary data as published by the National Bureau of Statistics (formerly Federal office of Statistic) over the years. • Complementary sources include publications of the Central Bank of Nigeria and World Bank World Development Indicator.

EMPIRICAL ANALYSIS • The effect of health measured by life expectancy is positive and significant on economic growth even after controlling for initial income levels. • The significant positive relationship between life expectancy implies that a longer life increases the level of growth. • Quantitatively, a 1% change in the life expectancy rate increases the level of growth by 0. 87%. • The significance of life expectancy in all the specification is an indication that high levels of per capita income can be achieved by increasing and improving stock of health human capital, especially when current stocks are at lower end.

• There is evidence of a significant negative effect of death rate on the growth of per capita income. • A 1% increase in the death rate was found to reduce growth by 2. 53%. • Average number years of schooling and school enrollment positively affect economic growth. • In quantitative terms, one extra year increase in the average years of schooling raises the growth rate of income by 0. 068%.

Growth Regression Explanatory Variable INV Dependent variable: Growth rate of per capita income 1 2 3 4 5 6 7 0. 062* (1. 83) 1. 851*** (2. 84) -2. 880* (1. 88) 0. 038** (2. 08) 0. 230*** (4. 45) 0. 066* (1. 75) 0. 963** (1. 96) 0. 019 (1. 29) 0. 003 (0. 91) 0. 001 (0. 140) -16. 836 (2. 65) 0. 784 39 0. 277*** (4. 73) 0. 876* ( 1. 74) 0. 165*** (3. 20) 0. 945*** (3. 28) -2. 535*** (3. 57) 0. 012** (2. 23) 1. 280** (2. 45) -4. 873* (1. 80) 0. 068** (2. 04) 0. 314 (5. 77) 0. 029** (2. 49) 1. 462*** (2. 98) -5. 377* (2. 10) 0. 069** (2. 22) 0. 358*** (6. 66) -0. 018* (1. 62) 0. 021* (1. 67) 1. 597*** (2. 73) -6. 435** (2. 23) 0. 071* (2. 37) 1. 123* (2. 78) -0. 003** (2. 01) 0. 870** (2. 09) 0. 042* (1. 81) 0. 059* (1. 84) 1. 854*** (2. 90) -2. 894* (1. 94) 0. 038*** (2. 22) 0. 231* (4. 45) -0. 065* (1. 54) 0. 948** (1. 95) 0. 018 (1. 30) 0. 023 (0. 93) Constant 0. 771 (0. 94 ) Adjusted R 2 Obs 0. 574 39 14. 625 (4. 00) 0. 597 39 -24. 571 (2. 04) 0. 674 39 -28. 109 (2. 50) 0. 620 39 -30. 308 (2. 63) 0. 663 39 -16. 874 (2. 69) 0. 783 39 LE DR AYS SEC MR WAP GWAP HETE TOTV Note: ****, * significant at 1%, 5% and 10%. Figures in parentheses represent the t-statistics.

• The ratio of the working age of the population emerges as a significant determinant of economic growth in Nigeria. • This implies that an increase in the share of working age population increases the potential labour force which in turn increases the growth rate. • The effects of health expenditure as a ratio of total government expenditure, infant mortality rate and the growth rate of the ratio of working age to total population is not significant on economic growth.

• The health equation result reinforces the estimates of the growth equation. • Increases in income per capita and per capita health expenditure have a positive and statistically significant effect on life expectancy, as expected. • There is a two-way causation between economic growth and health status. • A 1% increase in the growth of income per capita raises life expectancy by 0. 043%. • In addition, a thousand naira increase in per capita health expenditure would lead to 0. 03% increase in life expectancy.

• Years of schooling was found to be a significant determinant of life expectancy. • Also, there is evidence of a significant positive relationship between school enrollment and life expectancy. • The result also showed a positive and statistically significant relationship between availability of doctors and life expectancy in Nigeria. • A 1% increase in the number of physician per capita tends to raise life expectancy by 0. 062%. • The more doctors are available the more the number of lives that are saved.

Health Equation Regression Explanatory Variable Y AYS SEC PCHE DOC Constant R - Square Obs 1 2 3 4 Dependent variable: Life Expectancy at Birth 0. 043** 0. 306*** 0. 316*** 0. 179*** (1. 74) (7. 10) (3. 56) (4. 77) 0. 013 0. 003** 0. 002*** 0. 005* (6. 76) (2. 01) (1. 75) (1. 85) 0. 071*** 0. 079*** 0. 048** (10. 04) (5. 19) (8. 22) 0. 003*** 0. 004*** (1. 85) (1. 63) 0. 062** (1. 69) 1. 732 0. 776 0. 739 1. 121 (11. 64) (6. 74) (3. 06) (11. 34) 0. 609 0. 743 0. 732 0. 922 39 39 Note: ****, * significant at 1%, 5% and 10%. Figures in parentheses represent the t-statistics. The regression is instrumented using lagged values of per capita real income.

• We can therefore posit that reverse causation exists between health and economic growth in Nigeria. • This effect is assumed to work through human capital as measured by years of schooling and school enrollment rate. • However, beyond the econometrics, it is interesting to compare the simple association between the health indicators and per capita income. • The figure highlights the positive relationship between per capita income and life expectancy. • The declining slope of the curve indicates that the effect of life expectancy increases faster at lower than at higher income levels.

• This implies that at low income levels there is a sharp improvement in health as income increases. • There is a strong positive relationship between the two variables up to a threshold per capita income level of about $375 (PPP terms). • The relationship becomes weak as incomes rise beyond that point, although it remains positive. • The relationship between income and crude death rate clearly points to an inverse relationship • At low level of development, crude death rate declines faster, with only a slight improvement in per capita income levels.

• The following policy suggestions are made based on the empirical findings of our study: • Higher investment in health infrastructure and control of diseases that will reduce the death rate is needed to reduce the negative effect of the death rate on growth. • Growth-oriented policies would result in bringing about improvements in the health status of the population. • Policies promoting growth would also have the desirable effect of reducing poverty

• Overall, there is a compelling reason for stepping up both public and private investment in health which would pay off in the long run. • Educational policies that can increase school enrollment should be instituted • Effort should be made to train more doctors based on its importance on life expectancy.

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