**4.2 Income from farming activities**

206 Health Management – Different Approaches and Solutions

The findings of this study corroborate the results of the Core Welfare Indicator Survey (NBS, 2006). The indicators of health access for Ekiti State obtained from the Core Welfare Indicator Survey (NBS, 2006) are presented in Table 6. The table shows that access to health facility in the State was 68.9%. Access to health facility in the urban areas was 72.8%, while in the rural areas, it was 64.6%. Access to prenatal care in Ekiti State was 99.9%. Delivery by health professionals was 92.1% while fully vaccinated children was 86.4%. In the urban areas, the percentage for fully vaccinated was 88.6, while the percentage for the rural areas was 84.3. The need for medical services was defined for those who were sick or injured in the four weeks preceding the survey. About 6.1% of households in the state indicated need for medical services. In the urban areas the percentage was 6.0, while in the rural areas it was 6.1. About 8.0% of households in Ekiti State used medical services within the four weeks preceding the survey. Lower number of households (7.5%) used medical services in the urban areas than in the rural areas (8.6%) within the four weeks preceding the survey. It appears there were more health challenges in the rural areas of the state. The results of this survey clearly indicate that access to health facility was higher in the urban areas than in the rural areas. However, the need for and the use of medical services were higher in the rural

Indicator Urban (%) Rural (%) Whole State (%)

This section discusses the results of the primary data analyzed on the use of TM and OM by farming households in Ekiti State. The focus of this section is the assessment of rural-urban utilization of TM and OM as against the assessment of access to OM facilities in the previous

Table 4 presents the test of significance of difference of means of rural and urban socioeconomic and demographic variables. The mean age of the farmers in Ado LGA was 51 years, while that of farmers in Irepodun/Ifelodun LGA was 59 years. Thus, farmers in Irepodun/Ifelodun LGA were older than those in Ado LGA. For both locations, however, it can be seen that most of the people engaging in farming activities were above 50 years old. Therefore, it could be concluded that farmers are aging in the study area and the need for sound health to remain productive will increasingly become important in the nearest future. Also, there is a need for young and more agile people, with interest in farming, to be

Access to health facility 72.8 64.6 68.9 Prenatal care N.A. N.A. 99.9

professional N.A. N.A. 92.1 Need medical services 6.0 6.1 6.1 Use medical services 7.5 8.6 8.0 Fully vaccinated children 88.6 84.3 86.4

areas than in the urban areas.

Delivery by health

N.A. – Not Available Source: NBS (2006)

section.

Table 6. Health Access Indicators for Ekiti State

**4.1 Socio-economic characteristics of households** 

encouraged to take over from these aging farmers.

**4. Assessment of rural-urban utilization of TM and OM** 

The mean income from farming activities per household per annum was N76,748.56 for Irepodun/Ifelodun LGA and N124,822.94 for Ado LGA. There was statistically significant difference between the average incomes at the 1% level (Table 7). This is understandable because the rural areas are usually at a disadvantage compared with the urban areas in market prices (World Bank, 1993; Mafimisebi, 2010). Most rural dwellers are into farming as their main economic activity. The rural areas lack storage facilities and most farm products become perishable within few days of harvesting (Lancaster & Coursey, 1984). Thus, there is a glut of agricultural products in the rural markets where farmers witness low patronage and have to dispose of their products at lower prices. They can only sell at better and more remunerative prices obtainable in the urban markets if they own or can afford payment for transport facilities to convey their products to the urban centres. This easier, cheaper and timely access to urban markets in Ado and surrounding towns by farmers in Ado LGA may have been responsible for the significant difference in farm incomes between the two sets of farmers.

### **4.3 Expenditure on TM and OM by urban and rural farming households**

The empirical results in Table 7 show that the average amounts of money expended per annum on OM for treatment of common ailments by farmers in urban and rural areas were N10,160 (\$67.7) and N4,530 (\$30.2), respectively. The corresponding amounts of

Health Infrastructure Inequality and Rural-Urban Utilization of

areas (NBS, 2006).

**4.4 Factors determining use of TM** 

the greatest impact on use of TM.

Age of household

Sex of household

Number of elderly

Religion of

Orthodox and Traditional Medicines in Farming Households: A Case Study of Ekiti State, Nigeria 209

the households the rural areas consulted traditional healer compared with 4.6% in the urban

The estimates of the binary logistic regression for both rural and urban farmers are shown on Table 8. Generally, the binary logit model showed a commendably good fit to the data for both sets of farmers. The value of the Chi-square test was significant at 1% for rural and urban farmers. This indicates a rejection of the hypothesis that the model lacks explanatory power. The model correctly predicted 88.5% and 74.4% of the observations for rural and urban farmers, respectively. From Table 8, it could be seen that household size, education and income (significant at 1%) and the number of elderly people in a household (significant at 5%) had the greatest influence on use of TM by rural farmers. For farmers in the urban areas, age and education (significant at 5%) and household size (significant at 1%) exerted

**Variable Rural Households Urban Households** 

Constant -3.2992 ---------- -4.0066 ---------

head 0.3266 0.0254 1.4287\* 0.0052 Size of household 1.2368\*\* 0.0047 0.8896\*\* 0.0122

head 0.1084 0.0288 0.1175 0.0147 Education -1.7347\*\* 0.0193 -1.6264\* 0.0246 Household income -1.5489\*\* 0.0176 0.0775 0.0064

people 0.9266\* 0.0137 0.0636 0.0045

household head 0.0594 0.0094 0.0396 0.0066 Observation number 60 60 LR statistic (χ2) 118.245\*\* 136.844\*\* Degree of freedom 7.000 7.000 Log likelihood -244.616 -219.927 McFadden R2 0.522 0.473 % Predicted right 88.514% 74.447%

Additional insights can be obtained using the marginal effects calculated as the partial derivatives of the non-linear probability function, evaluated at each variable's sample mean. For instance, for the rural farmers, a unit increase in years of formal education and income, after the mean values, reduced the probability of use of TM by 0.0193 and 0.0176, respectively. This could be due to the fact that educated people have greater tendencies to accept western influence and regard TM as unhygienic, demonic, occultic and sinful

Note: The marginal effects are calculated at the mean of the predictor variables

\*Significant at 5% level and \*\* significant at 1% level

Table 8. Logistic Model of Determinants of Use of TM

**Estimated Marginal Coefficient Effects** 

**Estimated Marginal Coefficient Effects** 

money spent on TM were N2,118 (\$14.1) and N730 (\$4.9) per annum, respectively. There were significant difference in the expenditures on TM in Ado and Irepodun/Ifelodun LGAs at 5% level. The expenditures on OM in the two LGAs were also significantly different at 1% level. This means urban farmers spend more on both TM and OM than rural farmers.

The results show that expenditures on TM and OM in the urban LGA were higher than the corresponding expenditures in the rural LGA. This might be due to the higher level of income in the urban LGA. It also worth noting that expenditures on TM is expected to be lower than those on OM because TM resources are locally available compared with OM resources which are mostly imported. This might therefore account for the lower expenditures on TM in both LGAs. Similarly, TM resources are cheaper or almost free in the rural LGA (Mafimisebi & Oguntade, 2010) thus making TM expenditure in the rural LGA lower than in the urban LGA. The implication of this is that TM is more affordable and hence more accessible in the rural LGA (Mafimisebi & Oguntade, 2010).

The responses on the preferences of households in the use of OM and TM revealed that about 91.7% of the household heads in the rural LGA and 60.8% of the household heads in the urban LGA preferred the use of TM for common ailments that are not life-threatening and therefore would not require surgical interventions. For life-threatening ailments, 88.3% and 41.7.0% of the farming households in the rural and urban LGAs, respectively, preferred combining OM with treatment from TM.


\*Significant at 5%, \*\* significant at 1% Source: Data analysis

Table 7. Test of Significance of Difference of Mean Values of Rural and Urban of Socioeconomic and Demographic Variables

Results from the FGDs showed that 100% and 50.0% of farmers groups in the rural LGA and urban LGA, respectively, indicated preference for TM when and if an ailment is capable of been treated by both methods. Also, 83.3% of farmer's groups in the rural LGA reported preferring to complement OM with TM in both cases of simple and complicated medical conditions. These findings tend to show that the rural dwellers have developed some preference for TM. This higher level of preference for TM in the rural LGA is in consonance with the findings of the Nigeria Core Welfare Indicator Study which revealed that 9.1% of the households the rural areas consulted traditional healer compared with 4.6% in the urban areas (NBS, 2006).

#### **4.4 Factors determining use of TM**

208 Health Management – Different Approaches and Solutions

money spent on TM were N2,118 (\$14.1) and N730 (\$4.9) per annum, respectively. There were significant difference in the expenditures on TM in Ado and Irepodun/Ifelodun LGAs at 5% level. The expenditures on OM in the two LGAs were also significantly different at 1% level. This means urban farmers spend more on both TM and OM than

The results show that expenditures on TM and OM in the urban LGA were higher than the corresponding expenditures in the rural LGA. This might be due to the higher level of income in the urban LGA. It also worth noting that expenditures on TM is expected to be lower than those on OM because TM resources are locally available compared with OM resources which are mostly imported. This might therefore account for the lower expenditures on TM in both LGAs. Similarly, TM resources are cheaper or almost free in the rural LGA (Mafimisebi & Oguntade, 2010) thus making TM expenditure in the rural LGA lower than in the urban LGA. The implication of this is that TM is more affordable

The responses on the preferences of households in the use of OM and TM revealed that about 91.7% of the household heads in the rural LGA and 60.8% of the household heads in the urban LGA preferred the use of TM for common ailments that are not life-threatening and therefore would not require surgical interventions. For life-threatening ailments, 88.3% and 41.7.0% of the farming households in the rural and urban LGAs, respectively, preferred

Z-value P-value Irepodun/

Ado LGA (Urban)

and hence more accessible in the rural LGA (Mafimisebi & Oguntade, 2010).

Mean Value

Ifelodun LGA (Rural)

Age (yrs) 59 51 22.86 0.0342\* Household size 6.7 5.9 4.24 0.6643 Farm size hectares 2.26 1.49 16.112 0.0402\*

experience 35 28 12.108 0.0019\*\* Years of formal education 8.3 11.6 11.747 0.0474\* Household size (N) 76,748.56 124,822.94 27.449 0.016\*\* Expenditure on OM 4,530 10,160 27.986 0.0023\*\* Expenditure on TM 730 2,118 11.625 0.0441\*

Table 7. Test of Significance of Difference of Mean Values of Rural and Urban of Socio-

Results from the FGDs showed that 100% and 50.0% of farmers groups in the rural LGA and urban LGA, respectively, indicated preference for TM when and if an ailment is capable of been treated by both methods. Also, 83.3% of farmer's groups in the rural LGA reported preferring to complement OM with TM in both cases of simple and complicated medical conditions. These findings tend to show that the rural dwellers have developed some preference for TM. This higher level of preference for TM in the rural LGA is in consonance with the findings of the Nigeria Core Welfare Indicator Study which revealed that 9.1% of

rural farmers.

Variables

Years of farming

Source: Data analysis

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

economic and Demographic Variables

combining OM with treatment from TM.

The estimates of the binary logistic regression for both rural and urban farmers are shown on Table 8. Generally, the binary logit model showed a commendably good fit to the data for both sets of farmers. The value of the Chi-square test was significant at 1% for rural and urban farmers. This indicates a rejection of the hypothesis that the model lacks explanatory power. The model correctly predicted 88.5% and 74.4% of the observations for rural and urban farmers, respectively. From Table 8, it could be seen that household size, education and income (significant at 1%) and the number of elderly people in a household (significant at 5%) had the greatest influence on use of TM by rural farmers. For farmers in the urban areas, age and education (significant at 5%) and household size (significant at 1%) exerted the greatest impact on use of TM.


Note: The marginal effects are calculated at the mean of the predictor variables \*Significant at 5% level and \*\* significant at 1% level

Table 8. Logistic Model of Determinants of Use of TM

Additional insights can be obtained using the marginal effects calculated as the partial derivatives of the non-linear probability function, evaluated at each variable's sample mean. For instance, for the rural farmers, a unit increase in years of formal education and income, after the mean values, reduced the probability of use of TM by 0.0193 and 0.0176, respectively. This could be due to the fact that educated people have greater tendencies to accept western influence and regard TM as unhygienic, demonic, occultic and sinful

Health Infrastructure Inequality and Rural-Urban Utilization of

Asian Development Bank, Manila.

*Status Report,* (Atlanta, GA: CDC

is at the moment largely unregulated.

**6. References** 

Kenya

22:3–4

of Churches

Strategy, Pp111.

Health, Abuja, September

Report 2008- 09

Orthodox and Traditional Medicines in Farming Households: A Case Study of Ekiti State, Nigeria 211

properly and safely use TM, should be given important considerations in Nigeria's national health policy. Overall, the findings of the study clearly indicate the need for government in Nigeria to continue to play active role in the provision of health services in a sector that is increasingly being dominated by private entrepreneurs who are driven by the profit motive. In the current circumstances, farming households that are unable to access OM either because of the cost or distance to such facilities are being compelled to patronize TM; which

ADB (2006). Key Indicators 2006: Measuring Policy Effectiveness in Health and Education.

Aigbokhan, B.E. (2000). Poverty, Growth and Inequality in Nigeria: A Case Study. *AERC* 

Aigbokhan, B.E. (2008). Growth, Income and Inequality in Nigeria. Economic Commission

Anderson, E. (2010). Growth Incidence Analysis for Non-Income Welfare Indicators:

Anyanwu, J.C., Erhijakpor, A.E.O. (2007). Health Expenditures and Health Outcomes in

Castillo-Salgado C., Schneider C., Loyola E., Mujica O., Roca A. & Yerg T (2001). Measuring

Centers for Disease Control and Prevention (CDC) (2001) *Public Health's Infrastructure: A* 

Chavunduka, G (2009). Christianity, African Religion and African Medicine. World Council

Dixon, P. M., Weiner J., Mitchell-Olds T., & Woodley R (1987). Boot-Strapping the Gini

Ekiti State Planning Commission (2004). State Economic Empowerment and Development

Fasola, T.R.(2006). The Impact of Traditional Medicine on the People and Environment of

Federal Republic of Nigeria (2004). Revised National Health Policy, Federal Ministry of

Handler, A., Issel, M. & Turnock, B. (2001). A Conceptual Framework to Measure

Nigeria. In: *Sustainable Environmental Management in Nigeria* Ivbijaro M.F.A.,

Performance of the Public Health System. *American Journal of Public Health* 91, No. 8

for Africa, ACGS/MPAMS Discussion Paper No.3, 33pp. http://www.uneca.org/acgd/mdgs/GrowthInequalityPoverty.pdf

Africa, Economic Research Working Paper No 91, December

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*Research Paper* 102. Nairobi. African Economic Research Consortium, 63 pp.,

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Health Inequalities: Gini Coefficient and Concentration Index. *Epidemiol Bull* 2001,

(Fasola, 2006, Chavunduka, 2009; Mafimisebi & Oguntade, 2010). In the same vein, higher incomes may tend to give a household access to the more expensive OM which is regarded as faster in action and status enhancing. On the contrary, an increase in household size and the number of elderly people in the household beyond the mean value will increase the probability of use of TM by 0.0047 and 0.0137, respectively. This is understandable because if household size increases in a scenario of constant or slowly rising income, per capital expenditure reduces making the household to prefer the cheaper TM to OM in the case of a health problem. In the same way, with increase in the number of elderly people that are usually repositories of TM knowledge, there is a higher probability of use of TM.

Surprisingly, age of household head that was statistically insignificant in the model for rural farmers was significant at 5% in the model for urban farmers. For urban farmers, a unit increase in the age of household head and household size will lead to 0.0054 and 0.0122 increases in the probability of using TM. This may be a result of the fact that higher age confers higher and better information on and knowledge of TM in Africa where such knowledge is most willingly shared among the elderly. On the other hand, a unit increase in income will translate to a 0.0064 fall in the probability of using TM.
