Peak Journal of Agricultural Science Vol. 2 (3), pp 36-50, July, 2014 www.peakjournals.org/sub-journals-PJAS.html ISSN 2331-5784 ©2014 Peak Journals

Full Length Research Paper

Income inequality and self rated health in rural Nigeria Alawode O. O.* and Lawal A. M. Accepted 14 July, 2014

This research identified the key health problems, determined the pattern of inequality, and assessed the effect of income inequality on self rated health of people in Akinyele Local Government Area of Oyo State. Structured questionnaire was used to collect data from a random sample of 200 households from five rural communities in the area. Data were analysed using descriptive statistics, Lorenz curve and multinomial logistic regression. Results showed that the commonest disease in the area was malaria (79.5%) and large percentage (91%) of respondents spent ₦1,000 or less on healthcare with mean health expenditure of ₦449.00 (±811.78). The Lorenz curve showed that there is an unequal distribution in income with Gini coefficient of 0.2448 which was significant at 1%. Increase in income and higher level of education increases the likelihood of having good health status, while increase in age increases the likelihood of having poor health status. Attainable policies which aim at improving the health status of rural dwellers will enable them to attain certain level of health care that will allow them to live a socially and economically productive life. Key words: Income inequality, self rated health, rural Nigeria, health facilities, determinants of health.

Department of Agricultural Economics, University of Ibadan, Nigeria. *Corresponding author. E-mail: [email protected], [email protected]

INTRODUCTION According to the United Nations University/World Institute for Development Economic Research (UNU/WIDER, 2000), poverty and income inequality are closely related and it has been argued that income inequality is a manifestation, as well as a strong cause of poverty. When economic growth increases, poverty rate decreases, but as income inequality increases, the incidence of poverty also increases. Because of the linkage between income inequality and poverty, reducing income inequality has become a major public policy challenge among development agencies and povertyreduction experts. Yet, in most developing countries, discussions about poverty reduction strategies often focus almost exclusively on income growth, neglecting the potential roles of income redistribution and inequality. Most of the discussions often fail to recognize that, to achieve reduction in poverty, income growth has to be equitably distributed. Self rated health is the self judgment of the level of standard of the general condition of the body or mind in

terms of the presence or absence of illness, disease or injury. Self rated health also refers to a survey technique commonly used in medical research in which participants are invited to assess aspects of their own health conditions by answering a series of questions which are typically structured using a likert scale (Fayers, 2005). At the individual level, it has been established that richer people have better health because they can afford goods and services, medical care, better nutrition, sanitation and housing that promote health. At low income levels, people are more likely to fall sick due to malnutrition, inability to attend schools and therefore will be less able to work. National Bureau of Statistics (NBS, 2010) reported on the rising level of poverty in Nigeria. The report has provided some insight into the growing frustration and the attendant rising wave of violence in some parts of the country in recent times. The report showed that 60.9% of the Nigerian population or approximately 100 million people lived in abject poverty in 2010. It went further to

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state that these classes of people live on less than $1 or ₦160 a day. Economists observed that income inequalities were not in lockstep with the nation's growth rate of 7.87% in 2010. In other words, the rich are getting richer and the poor are sinking deeper into poverty. The NBS data showed that the percentage of Nigerians living in abject poverty has increased from 54.4% in 2005 to 61% in 2010. Income inequality could damage health through two pathways. Firstly, a highly unequal society implies that a substantial segment of the population is impoverished, and poverty has bad implications for health. Secondly and more contentiously, income inequality is thought to affect the health of not just the poor, but the better off in society as well. The spill over effects of inequality have in turn been attributed to the psychosocial stress resulting from invidious social comparisons, as well as the erosion of social cohesion (Kawachi et al., 2004). The importance of public health and burden of income inequality are obviously broader under the second scenario. At the millennium summit held at the United Nations headquarters in 2000, world leaders agreed to a global agenda of among other things, reducing poverty, disease, hunger, illiteracy and environmental degradation by 2015. This agenda has been referred to as Millennium Development Goals (MDGs). Also in the world summit on sustainable development held in Johannesburg in 2002, poverty and disease were again at the forefront. One important factor in the creation of inequality is variation in individuals' access to education. Education, especially in an area where there is a high demand for workers, creates high wages for those with this education (Ali and Ahmad, 2013). However, increases in education first increase, and then decrease growth as well as income inequality. As a result, those who are unable to afford education, or choose not to pursue optional education, generally receive much lower wages. Most of the dwellings in a typical Nigerian town are deficient in meeting the basic requirements of living and therefore remain unfit for habitation, according to public health standard (Bamgbose et al., 2005). Economic viability and level of education are the two major determinants of poor living environment which is detrimental to the health of dwellers. Small ailments in most cases get complicated because of lack of money to seek immediate necessary medical care. Some people resorted into selfmedication or natural medicine as means of haleness. However, herbal medicines, though natural, cause serious illness from allergy to liver or kidney malfunction, to cancer and even death (WHO, 2001). Generally, the context in which an individual lives is of great importance for his health status and quality of life. It is increasingly recognized that health is maintained and improved not only through the advancement and application of health science, but also through the efforts and intelligent lifestyle choices of the individual and society. According to WHO (2001), the main

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determinants of health include the social and economic environment, the physical environment, and the person's individual characteristics and behaviours. More specifically, key factors that have been found to influence whether people are healthy or unhealthy include; income and social status, education and literacy, unemployment, biology and genetics, food, healthcare services, gender and culture, etc (WHO, 2003). In view of the foregoing, the general objective of this study was to assess the relationship between income inequality and self rated health among rural households using Akinyele local Government Area of Oyo State as a case study. The specific objectives of the study were to: i. Identify the key health problems and health facilities present in the rural area. ii. Describe the socio-economic characteristics of respondents in relation to income and self rated health. iii. Determine the pattern of income inequality. iv. Examine the relationship between income inequality and self rated health.

Hypothesis tested The tested hypothesis is stated in the null form: H0: There is no significant relationship between income inequality and self rated health.

Literature review Numerous studies have reported an association between the level of income inequality in a population and aggregate health outcomes; average health among people living in high-inequality areas appears to be lower than among people living in low-inequality areas. A statistically significant relationship has been reported in studies using aggregate data both across countries (Rodgers, 1979; Wilkinson, 1992) and across regions within countries (Kawachi and Kennedy, 1997; Kaplan et al., 1998). This observation has led researchers to argue that increasing income dispersion directly translates into poor health, thereby suggesting additional welfare gains from more progressive income redistribution policies. This argument is embodied in Wilkinson‟s (1992) controversial income inequality hypothesis (IIH), which posits that the primary determinant of differences in health performance among developed countries is the extent of differences in the disparity between the incomes of the rich and the poor within countries rather than differences in income levels. Authors have typically conjectured that inequality has an effect on health either because it is a source of psychosocial stress, which eventually leads to stress related afflictions, or because it fosters the development of environments hazardous to public health (Daly et al.,

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1998). However, providing a fully convincing theory characterizing the actual direct (or indirect) pathways by which inequality affects health remains a contentious issue. Subramanian and Kawachi (2004) identified three potential pathways by which greater income inequality may translate into poorer health. First, according to a structural pathways argument, increased inequality leads to spatial concentrations (poverty, race, and ethnic enclaves) and residential segregation is potentially detrimental to individual health (Wen et al., 2003). Second, building on the argument that individual health is influenced by social relations, the social cohesion and collective social pathway suggests that inequality affects health by weakening social cohesion and holding back the formation of social capital beneficial to health (Kawachi and Kennedy, 1997). Third, policy pathways hypothesis posits that income inequality could affect health via influence on the implementation of particular social and health related policies (Neckerman and Torche, 2007; Schwabish et al., 2006). Furthermore, studies have cast doubt on the robustness to model specification of the empirical association found in macro level analyses and have questioned the comparability of data sources both across countries (Gravelle et al., 2002; Judge et al., 1998) and across U.S. states (Mellor and Milyo, 2001, 2002). Rodgers (1979), Gravelle (1998) and Gravelle et al. (2002) cautioned that this apparent causal relationship may just be a statistical artefact if individual health is a nonlinear function of income. In order to identify the effect of income inequality on health, one needs to turn to individual level data and to control for relevant confounders, particularly individual income. Recent studies have taken this approach, and the new evidence about an association between health and income inequality is mixed at best. Few comparable micro level studies have examined the robustness of this association outside the United States. Results from these studies generally corroborate U.S. findings. For instance, Shibuya et al. (2002) found no significant evidence that income inequality measured at the prefecture level has a detrimental effect on self rated health status in Japan. Likewise, Gerdtham and Johannesson (2004) found no significant effect of community level income inequality on mortality in Sweden. Weich et al. (2001, 2002) found a significant association between the Gini coefficient in Britain‟s regions and mental disorders and self reported health status, but the results were highly sensitive to the choice of inequality measure. Lorgelly and Lindley (2008) confirmed the absence of a clear association within Britain. Distinguishing the effect by gender has been largely overlooked so far (Macintyre and Hunt, 1997). This is surprising given the often cited gender paradox in health; that is, women experience higher rates of various measures of morbidity despite living longer than men (Rieker and Bird, 2005).

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There are evidences in the literature that point to the increasing level of income inequality in developing countries over the last two decades. For instance, Canagarajah et al. (1997) reported increasing level of income inequality between 1980s and 1990s as shown by an increase in the Gini-coefficient from 38.1% in 1985 to 44.9% in 1992. World Bank (2003) found that in 1997, the Gini index of income inequality was 0.506. Using the 2004 National Living Standard Survey (NLSS) data, Oyekale et al. (2006) found that the overall Gini index for Nigeria was 0.580. In sectoral sense, the study found income inequality to be higher in rural areas (Gini – 0.5808) as compared to urban areas (Gini – 0.5278), and that employment income increases income inequality while agricultural income decreases it. On the contrary, however, Awoyemi and Adeoti (2004) found that agricultural income is inequality increasing while wage and self-employed income are inequality decreasing. Oluwatayo (2008), in a micro-survey of some households in Ekiti State found the Gini-coefficient to be 0.3570. World Bank (2003) showed that in 1996/1997, Gini index was 0.506, 0.477 and 0.407 for Nigeria, Cameroon and Ghana respectively. Also, using 1998 data, World Bank (1996) estimated Gini-indices of 0.613 for Zambia and 0.613 for Central Africa. Ferreira (1996), in rural Tanzania found that during the period of structural adjustment, while income inequality increased between 1983 and 1991, there was a reduction in poverty. From all these studies, it can be deduced that income inequality is high in many African nations especially Nigeria.

METHODOLOGY Area of study The study was conducted in Akinyele Local Government Area of Oyo state. Akinyele is one of the eleven local governments that make up Ibadan metropolis. Its headquarters is at Moniya. The local government was created in 1976 and it shares boundaries with Afijio Local Government Area to the North, Lagelu Local Government Area to the East, Ido Local Government Area to the West and Ibadan North Local Government Area to the South. Using a 3.2% growth rate from 2006 census figures, the 2010 estimated population for the Local Government is 239,745. The Local Government has 12 wards and covers an area of 54,147,745 ha (54,148sq kms). The wards are as follows: Ikereku, Olode/Talontan, Idioro, Ojoo/Ajibode, Laniba, Ijaye, Ajibade, Alabata, Elekuru, OlorisaOkoo, Okegbemi and Iroko wards (en.wikipedia.org/Akinyele). The major occupations of people residing in the area are farming, carpentry, trading, marketing, as well as food processing. Agriculture provides employment for over 90% of the people through the production of food crops

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such as yam, maize, cassava, soybeans, plantain and melon. Also, tree crops such as cocoa, citrus, and oil palm are produced in the area.

Sources of data and sampling procedure Simple random sampling technique was employed in the study to allow for good degree of representativeness. Out of the twelve rural wards, five communities were randomly selected and forty households were randomly selected from each rural community giving a sample size of two hundred households. Structured questionnaire was then administered among the selected households in each community. The five communities that were randomly selected are; Olorisa-oko, Alabata, Ajibade, Ijaye, and Elekuru. Information was sought from the individual household on both quantifiable and nonquantifiable factors such as income and expenditure pattern, household size, age, sex of household heads, accessibility to improved health care of households, and self rated health status.

Analytical techniques Descriptive statistical analysis was used to analyze the data and describe the respondents in relation to level of income and self rated health. This comprises frequency distribution tables, measures of central tendencies and measures of dispersion. The frequency distribution was used to classify raw data into categories. Gini coefficient was used for the measurement of income inequality and multinomial logistic regression was used for estimating the variables associated with health status of the respondents.

Gini coefficient This is used to show the degree of income inequality between different households in a population. The Gini coefficient is a precise way of measuring the position of the Lorenz curve. It has a value between 0 and 1 and it is worked out by measuring the ratio of the area between the Lorenz curve and the 45° line to the whole area below the 45° line. If the Lorenz curve is the 45° line, then the value of the Gini coefficient would be zero. In general, the closer the Lorenz curve is to the line of perfect equality, the less the inequality and the smaller the Gini coefficient. The Gini coefficient is computed as:

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is the income Gini. A Gini coefficient of 0 expresses perfect equality, where all values are the same (that is, everyone has an exactly equal income). A Gini coefficient of 1 expresses maximal inequality among values (where only one person has all the income).

Multinomial logistic regression Regression has been defined as the amount of change in (the value of) one variable associated with a unit change in (the value of) another variable. Multinomial logistic regression is a logistic model having a dependent variable with more than two levels (Lawal, 2003). Outside the difference in the number of levels of the dependent variable, the multinomial logistic is very similar to the binary logistic and most of the standard tools of interpretation analysis, and model selection can be applied. The binary logistic dependent variable is normally coded 0 or 1, whereas the multinomial dependent can be coded 1, 2, M (that is, it starts at 1 rather than 0) or 0, 1, 2, M-1), where M = number of categories of dependent variables. The model was fitted and estimated using multinomial logistic regression. The choice of this method was based on the fact that health perception (dependent variable) is a categorical variable which can take five (5) levels (0, 1, 2, 3 and 4). In this study, 0 is the excellent health group; 1 is the very good health group, 2 is the good health group, 3 is the fair health group and 4 is poor health group. The good health group was taken as the base outcome or the reference group. The model was utilized to identify the socio-economic characteristics responsible for respondents‟ perception of their health. Multinomial logistic regression allows each category of an unordered response variable to be compared to a reference category, providing a number of logistic regression models. The model can therefore be stated as follows: Prob (Yi) = f (X1, X2, X3, X4, X5, X6) Where; Yi = Health perception; Yi = 0, 1, 2, 3 or 4; X1 = Income; X2 = Household size; X3 = Education; X4 = Age; X5 = Household distance to health centre; X6 = Cost of treatment of ailments. The determination of income inequality by Gini coefficient was done using DAD Software on Stata.

Limitations to the study These include:

Where n is the number of observation, μ is the mean of the distribution, yi is the income of the household and Igini

1. Data collected from majority of the respondents were based on memory recall because some of them did not keep adequate records of their expenditure. 2. Majority of those interviewed considered some

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information too personal to be revealed, especially their income level and age. 3. Some respondents were illiterate, so this might lead to over estimation or under estimation of some information in some instances. 4. Some respondents were not willing to give their personal information seeing that there was no monetary gain in it for them. And they were used to the fact that government officials usually come to ask about their situation but nothing has been done to improve it.

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the highest percentage of 49.5, followed by respondents with household sizes of 7-9 members (30%), while 12.5% of the respondents had household sizes of between 10 and 15 members. The least household size (1-3 members) was found among 7.5% of the households probably because their children were already settled or they had just started raising children. Large household sizes will likely result because the households were in their productivity stage and thus kept on increasing the population size, thus, increasing the pressure on the available health facilities and reducing the efficiency of such facilities.

RESULTS AND DISCUSSION Socio–economic characteristics of respondents

Health expenditure group

The socio-economic characteristics of the respondents are presented in Tables 1 and 2.

The categorization of households based on their average monthly expenditure on health shows that 91% of households spent less than ₦1,000 in a month, 5.5% of households spent between ₦1,000 and ₦2,000 in a month, and 2.0% spent between ₦2,000 and ₦3,000, while 1.5% of households spent between ₦4,000 and ₦5,000 in a month. Many households do not go for medical treatment; they rather go for herbs that are very much cheaper to buy. The mean health expenditure was ₦449.00 (±811.78).

Age distribution The results show that 21.0% of the respondents were 35 years and below; 28.5% were in the age bracket 36-45 years; and 33.5% were in the age bracket 46-55 years. This means that 83.0% of the respondents were 55 years and below with mean age of 46 years (±10.5); majority of the respondents were in their active productive age.

Monthly income Educational status The highest percentage (40.0%) of the respondents had secondary education; 26.0% were primary school leavers; 13.0% had tertiary education and 21.0% had no formal education. With high percentage (79.0%) of respondents with formal education, awareness and adoption of innovation in health related programmes is expected to be relatively easy.

Occupation Most (30.5%) of the respondents were engaged in farming while the least (10.0%) percentage were transporters. Other forms of occupation were trading (27.5%), artisan (20.0%), and civil service (12.0%).

Respondents‟ mean income was ₦24.178.3 (±10,703.66) with a range of ₦64,500. Also, 4.5% of respondents earned less than ₦10,000 while 6.0% of respondents earned more than ₦40,000 as their income. In general, from the grouping of respondents into income groups, those that earned less than ₦10,000 fell into low income group, those that earned between ₦10,001 and ₦30,000 fell into middle income group while those respondents that earned above ₦30,000 fell into the high income group. It can therefore be deduced that majority of the respondents fell into the category of middle income group (67.0%).

Access to sanitation

health

facilities,

infrastructure

and

Access to health and educational facilities Marital status The highest percentage of the respondents (89.0%) were married, 9% were widowed while 2.0% were divorced.

Household sizes Respondents with household size of 4-6 members had

From Table 3, it is shown that Ijaye had the highest number of health facilities, educational facilities and infrastructural facilities while Laleye had the least number. Also, it was found that the five communities have the following social amenities; electricity, motorable road, dispensaries and health centres. From qualitative data, it was confirmed that though electricity was available, the service was very poor and this might hinder

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Table 1. Socio-economic characteristics of respondents.

Variable Age (years) ≤ 35 36 – 45 46 – 55 56 – 65 >65 Total Education Primary Secondary Tertiary Non formal Total Occupation Artisan Civil Service Farming Trading Transporter Total Marital status Divorced Married Widowed Total Household size ≥3 4–6 7–9 10 – 12 > 13 Total Health expenditure (₦) ≤ 1,000 1,001 – 2,000 2,001 – 3,000 3,001 – 4,000 ≥ 4,000 Total Income category ≤ 10,000 10,001 – 20,000 20,001 – 30,000 30,001 – 40,000 > 40,000 Total

Number of respondents

Percentage

Cumulative (%)

42 57 67 26 8 200

21.0 28.5 33.5 13.0 4.0 100.0

21.0 49.5 83.0 96.0 100.0

52 80 26 42 200

26.0 40.0 13.0 21.0 100.0

26.0 66.0 79.0 100.0

40 24 61 55 20 200

20.0 12.0 30.0 27.5 10.0 100.0

20.0 32.0 62.5 90.0 100.0

4 178 18 200

2.0 89.0 9.0 100.0

2.0 91.0 100.0

15 99 60 21 5 200

7.5 49.5 30.0 10.5 2.5 100.0

7.5 57.0 87.0 97.5 100.0

182 11 4 0 3 200

91.0 5.5 2.0 0.0 1.5 100.0

91.0 96.5 98.5 0.0 100.0

9 79 55 45 12 200

4.5 39.5 27.5 22.5 6.0 100.0

4.5 44.0 71.5 94.0 100.0

Source: Field study, 2013.

some of their activities. Also, due to this, some health

problems had to be referred to hospitals in the city.

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Table 2. Statistics of socio-economic variables.

Variable Age(years) Household size (number) Income (₦) Health Expenditure (₦)

Min. 22 2 5,700 0

Max. 73 18 70,200 5,000

Range 51 16 64,500 5,000

Mean 45.61 (±10.5) 6.64 (±2.49) 24,178.3 (±10,703.66) 449.00 (±811.79)

Median 46.0 6.0 22,900 200.00

Mode 35.0 6 14,000 0

Source: Field study, 2013. Note: Figures in parentheses are the standard deviations of the mean.

Table 3. Access to health and educational facilities.

Facilities Private Hospitals Number of Doctors Number of Nurses Number of Bed Space Govt. Primary Schools Govt. Secondary Schools

Alabata 1 4 7 2 1

Ijaye 1 1 4 4 3 1

OlorisaOko 1 3 2 1 -

Ajibade 3 6 1 1

Laleye 1 -

Source: Field Study, 2013.

Table 4. Access to water source.

Water source Well Water Stream Borehole Total

Number of respondents 165 13 22 200

% 82.5 6.5 11.0 100.0

Cumulative (%) 82.5 89.0 100.0

Source: Field Study, 2013.

Table 5. Access to toilet facilities.

Toilet facilities Pit toilet Open ground Water closet Total

Number of respondents 49 135 16 200

% 24.5 67.5 8.0 100.0

Cumulative (%) 24.5 92.0 100.0

Source: Field Study, 2013.

Access to water source

Access to toilet facilities

Table 4 shows that 11.0% of respondents had access to borehole; 6.5% got water from streams while 82.5% of respondents used well water. Pipe borne water was not available in the communities. Meanwhile, consumption of untreated water from streams and wells could be dangerous to health.

It is shown in Table 5 that 8.0% of respondents made use of water closet; 24.9% made use of pit toilet while 67.5% of respondents made use of open ground. It can be seen that the largest percentage of respondents did not have toilets in their homes; rather, they go into bushes near their homes to defecate. This could lead to increasing

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Table 6. Household distance to the nearest health centre.

Distance (m) ≤ 100 m >100 m – 200 m >200 m – 300 m >300 m – 400 m >400 m – 500 m >500 m Total

Number of respondents 59 59 13 16 41 12 200

% 29.5 29.5 6.5 8.0 20.5 6.0 100.0

Cumulative (%) 29.5 59.0 65.5 73.5 94.0 100.0

Source: Field Study, 2013.

Table 7. Statistics of distance from the nearest health centre.

Variable Distance (m)

Minimum 50

Maximum 2000

Range 1,950

Mean 234.3 (±200)

Median 200

Mode 200

Source: Field Study, 2013.

Table 8. Experience of illness in the last one month.

Response Yes No Total

Number of respondents 120 80 200

% 60.0 40.0 100.0

Cumulative (%) 60.0 100.0

Source: Field Study, 2013.

health problems. Also, the use of unkempt pit latrines and water closets are serious sources of cholera, diarrhoea and dysentery.

40.0% experienced no ailment in the last one month. The high percentage of respondents that experienced one sickness or the other might be as a result of poor personal hygiene, malnutrition and mosquito bites.

Household distance to the nearest health centre Common diseases in the area Table 6 shows that 94% of households were within 500 m of health centres, while 6% were more than 500 m. The fact that a good number of households were close to the health centre with mean distance of 234.3 m (±200) (Table 7) does not mean that the demand of the rural people is adequately met. Also, medical personnel and health facilities that will improve the healthy standard of living were not sufficient.

Table 9 shows the common diseases in the study area as enumerated by the respondents; 79.5% mentioned malaria, followed by typhoid (10.5%), dysentery (6.5%) and tuberculosis (3.5%). The commonest of all is malaria which may be as a result of factors such as poor/unclean water source, poor sewage disposal, unhygienic environment, bushes surrounding the house which may harbor mosquitoes, and so on.

Self perception of health status Places/Methods of treatment of diseases Experience of illness by respondents in the last one month From Table 8, 60.0% of the respondents experienced one ailment or the other in the last one month, while

Table 10 indicates that the higher percentage (69.5%) used the orthodox method of treatment while 30.5% used the traditional method of treatment (local herbs). In most cases, abuse of local herbs can lead to complications

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Table 9. Common diseases in the area.

Diseases Malaria Dysentery Typhoid Tuberculosis Total

Number of respondents 159 13 21 7 200

% 79.5 6.5 10.5 3.5 100.0

Cumulative (%) 79.5 86.0 96.5 100.0

Source: Field Study, 2013.

Table 10. Places/Methods of treatment of diseases.

Methods of treatment Dispensary Hospital/Health centre Local Herbs Total

Number of respondents 58 81 61 200

% 29.0 40.5 30.5 100.0

Cumulative (%) 29.0 69.5 100.0

Source: Field Study, 2013.

Table 11. Respondents‟ self rated health.

Self rated health Excellent Very good Good Fair Poor Total

Number of respondents 19 65 67 45 4 200

% 9.5 32.5 33.5 22.5 2.0 100.0

Cumulative (%) 9.5 42.0 75.5 98.0 100.0

Source: Field Study, 2013.

and such people are eventually rushed to the hospital.

Income group and health status

Respondents’ self rated health Table 11 shows respondents‟ self rated health; 9.5% rated their health as excellent, 32.5% as very good, 33.5% as good, 22.5% as fair, while just 2% rated their health as poor. However, some respondents considered it as a curse against themselves if they rate their health status as “poor” so they prefer to say “good” even when it is actually “poor”.

Income group respondents

and

socio–economic

factors

of

Table 12 shows the results on income groups of respondents with respect to their health status, age, household size, occupation and educational status.

The largest percentage (2.5%) of respondents earning less than ₦10,000 had fair health status; respondents (16.0%) earning between ₦10,001 and ₦20,000 had good health; and respondents (9.5%) earning between ₦20,001 and ₦30,000 had very good health status. For respondents earning between ₦30,001 and ₦40,000, the highest percentage (8.5%) was in the category of very good health status and for respondents earning >₦40,000, the highest percentage (2.5%) was in very good health. Also, the highest number (3.5%) of respondents with excellent health was in the income category of ₦30,001₦40,000; the highest number of respondents (11.5%) with very good health status can be seen in the income category of ₦10,001-₦20,000, and the highest number of respondents (16.0% and 8.0% respectively) with good and fair health status were in the income category ₦10,001-₦20,000. It can be seen that no respondent in

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Table 12. Income group and socio-economic factors of respondents.

Income group/ socio-economic factors Health Status Excellent Very good Good Fair Poor Total Age ≤ 35 36 – 45 46 – 55 56 – 65 > 66 Total HH Size ≤3 4–6 7–9 10 – 12 > 12 Total Occupation Farming Trading Artisanship Transportation Civil Service Total Education Primary Secondary Tertiary Non formal Total

≤ ₦10,000

₦10,001-₦20,000

₦20,001-₦30,000

₦30,001-₦40,000

>₦40,000

Total

- (0%) 1 (0.5%) 3 (1.5%) 5 (2.5%) - (0%) 9 (4.5%)

5 (2.5%) 23 (11.5%) 32 (16.0%) 16 (8.0%) 3 (1.5%) 79 (39.5%)

5 (2.5%) 19 (9.5%) 15 (7.5%) 15 (7.5%) 1 (0.5%) 55 (27.59%)

7 (3.5%) 17 (8.5%) 13 (6.5%) 8 (4.0%) - (0%) 45 (22.5%)

2 (1.0%) 5 (2.5%) 4 (2.0%) 1 (0.5%) - (0%) 12 (6.0%)

19 (9.5%) 65 (32.5%) 67 (33.5%) 45 (22.5%) 4 (2.0%) 200 (100%)

1 (0.5%) 1 (0.5%) 5 (2.5%) 2 (1.0%) - (0%) 9 (4.5%)

22 (11.0%) 21 (10.5%) 16 (8.0%) 15 (7.5%) 5 (2.5%) 79 (39.5%)

13 (6.5%) 11 (5.5%) 22 (11.0%) 6 (3.0%) 3 (1.5%) 55 (27.5%)

5 (2.5%) 16 (8.0%) 21 (10.5%) 3 (1.5%) - (0%) 45 (22.5%)

1 (0.5%) 8 (4.0%) 3 (1.5%) - (0%) - (0%) 12 (6.0)

42 (21.0%) 57 (28.5%) 67 (33.5%) 26 (13%) 8 (4.0%) 200 (100%)

- (0%) 6 (3.0%) 3 (1.5%) - (0%) - (0%) 9 (4.5%)

7 (3.5%) 45 (22.5%) 22 (11.0%) 4 (2%) 1 (0.5%) 79 (39.5%)

2 (1.0%) 30 (15.0%) 16 (8.0%) 7 (3.5%) - (0%) 55 (27.5%)

- (0%) 24 (12.0%) 12 (6.0%) 7 (3.5%) 2 (1%) 45 (22.5%)

- (0%) 1 (0.5%) 6 (3.0%) 3 (1.5%) 2 (1%) 12 (6.0%)

9 (4.5%) 106 (53.0%) 59 (29.5%) 21 (10.5%) 5 (2.5%) 200 (100%)

6 (3.0%) 2 (1.0%) 1 (0.5%) - (0%) - (0%) 9 (4.5%)

41 (20.5%) 22 (11.0%) 8 (4.0%) 6 (3.0%) 2 (1.0%) 79 (39.5%)

6 (3.0%) 19 (9.5%) 19 (9.5%) 5 (2.5%) 6 (3.0%) 55 (27.5%)

8 (4.0%) 7 (3.5%) 8 (4.0%) 9 (4.5%) 13 (6.5%) 45 (22.5%)

- (0%) 5 (2.5%) 4 (2.0%) - (0%) 3 (1.5%) 12 (6.0%)

61 (30.5%) 55 (27.5%) 40 (20.0%) 20 (10.0%) 24 (12.0%) 200 (100%)

3 (1.5%) 4 (2.0%) - (0%) 2 (1.0%) 9 (4.5%)

29 (14.5%) 26 (13.0%) 2 (1.0%) 22 (11.0%) 79 (39.5%)

12 (6.0%) 23 (11.5%) 9 (4.5%) 11 (5.5%) 55 (27.5%)

6 (3.0%) 20 (10.0%) 13 (6.5%) 6 (3.0%) 45 (22.5%)

2 (1.0%) 7 (3.5%) 2 (1.0%) 1 (0.5%) 12 (6.0%)

52 (26.0%) 80 (40.0%) 26 (13.0%) 42 (21.0%) 200 (100%)

Source: Field Study, 2013.

income group of ≤₦10,000 perceived his health status as excellent and no respondent in income group of more than ₦40,000 perceived his health as poor. It can therefore be said that the higher the income of respondents, the higher the chances of better health status. Also, some respondents believed that it is a taboo to say that he or she is sick; they therefore perceived their health as good even though it was fair or poor.

among respondents between the ages of 46 and 55 years; the percentage of respondents earning between ₦30,001 and ₦40,000 was highest (10.5%) among respondents between the ages of 46 and 55 years, while that of those earning above ₦40,000 was highest (4%) among respondents between the ages of 36 and 45 years. It can be said here that these respondents were in their productive ages and were still active to work in order to earn a living.

Income group and age Income group and household size It can be said that the percentage of respondents earning between ₦20,001 and ₦30,000 was highest (11.0%)

Results show that those earning between ₦10,001 and

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₦20,000 were highest (22.5%) among respondents with household size of 4-6, and those earning between ₦20,001 and ₦30,000 were highest (15.0%) among respondents with household size of 4-6; those earning between ₦30,001 and ₦40,000 were highest (12.0%) among respondents with household size of 4-6, while respondents earning above ₦40,000 were highest (3.0%) among respondents with household size of 7-9. It can therefore be seen that the low and middle income earners had larger household sizes and may lead to inability of household heads to cater for the needs of the household members. The results are in agreement with the results in Ali and Ahmad (2013) that larger household exacerbate poverty levels.

Income group and occupation Among the respondents earning less than ₦10,000, the highest percentage (3.0%) was in farming and those earning between ₦10,001 and ₦20,000 were highest (20.5%) in farming; those earning between ₦30,001 and ₦40,000 were highest (6.5%) in civil service. Respondents earning greater than ₦40,000 were highest in trading (2.5%). It can be seen that farming had the highest percentage in all income groups even though most of them were middle income earners. This may be as a result of farming being the major occupation in the study area.

Income group and educational status The highest percentage (2.0%) of respondents earning less than ₦10,000 were those with secondary education while respondents earning between ₦10,001 and ₦20,000 was highest (14.5%) among respondents with primary education. Respondents earning between ₦30,001 and ₦40,000 (10.0%) and those earning above ₦40,000 (3.5%) were highest among those with secondary education.

Health status respondents

and

socio-economic

factors

of

Table 13 shows the results on health status of respondents with respect to their age, household size, occupation and educational status.

46

health, while the age category 56-65 years was high (7.0%) in fair health status, and for age category 66 years and above, the highest number of respondents (2.0%) was in fair health status. It can therefore be said that age affects health status.

Health status and household size The highest number of respondents in household size of less than 3, and 7-9 were in good health (2.5 and 10.0% respectively), for 4-6 and greater than 12 household sizes, the highest percentages had very good health (21.5 and 1.5% respectively), and for 10-12 household size, the highest can be seen in good and fair health status (7.0% each). Also, the highest percentages of respondents with excellent, very good, good, fair health status categories can be seen in household size of 4-6 with 5.0, 21.5, 17.0, and 10.0% respectively, while respondents that were highest (1.5%) in poor health status were found with household size of 7-9.

Health status and occupation The status with the highest number of respondents was good health status with 12.0% of farmers, 8.0% of traders, 6.5% of artisans, 4.0% of transporters, and 3.0% of civil servants. The status with the lowest number of respondents was poor health status with 1.5% of farmers and 0.5% of artisans, while traders, transporters and civil servants did not have poor health status. Majority of transporters claimed to have good health status because they do not fall sick as the dry gin and concoctions they take make them healthy. Majority of civil servants had very good health status as a result of literacy and awareness on what ailments they have, where and how to seek for solutions to such ailments.

Health status and educational status It can be seen that the highest number of respondents with primary education had good health status (10.5%); for secondary education, the highest percentage (15%) can be seen among respondents with very good health status. Also, for tertiary education, it can be seen among respondents with very good health status (5.0%); while for non-formal education, the highest number of respondents (6.0%) can be seen among respondents with good health status.

Health status and age The highest number of respondents in the age category of less than 35 years (9.0%) and 36-45 years (14.0%) can be seen in very good health status; for age category 46-55 years, the highest percentage (14.5%) had good

Determination coefficient

of

income

inequality

by

Gini

From the Lorenz curve in Figure 1, „total 2‟ was used to

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Table 13. Health status and socio economic factors of respondents.

Health status/ socio-economic factors Age ≤ 35 36 – 45 46 – 55 56 – 65 > 65 Total HH Size ≤3 4–6 7–9 10 – 12 > 12 Total Occupation Farming Trading Artisan Transportation Civil service Total Education Primary Secondary Tertiary Non formal Total

Excellent

Very good

Good

Fair

poor

Total

14 (7.0%) 4 (2.0%) 1 (0.5%) - (0%) - (0%) 19 (9.5%)

18 (9.0%) 28 (14%) 16 (8.0%) 3 (1.5%) - (0%) 65 (32.5%)

9 (4.5%) 19 (9.5%) 29 (14.5%) 7 (3.5%) 3 (1.5%) 67 (33.5%)

1 (0.5%) 6 (3.0%) 20 (10.0%) 14 (7.0%) 4 (2.0%) 45 (22.5%)

- (0%) - (0%) 1 (0.5%) 2 (1.0%) 1 (0.5%) 4 (2.0%)

42 (21.0%) 57 (28.5%) 67 (33.5%) 26 (13%) 8 (4.0%) 200 (100%)

4 (2.0%) 10 (5.0%) 3 (1.5%) 1 (0.5%) 1 (0.5%) 19 (9.5%)

- (0%) 43 (21.5%) 14 (7.0%) 5 (2.5%) 3 (1.5%) 65 (32.5%)

5 (2.5%) 34 (17.0%) 20 (10.0%) 7 (3.5%) 1 (0.5%) 67 (33.5%)

- (0%) 20 (10.0%) 18 (9.0%) 7 (3.5%) - (0%) 45 (22.5%)

- (0%) - (0%) 3 (1.5%) 1 (0.5%) - (0%) 4 (2.0%)

9 (4.5%) 107 (53.5%) 58 (29.0%) 21 (10.5%) 5 (2.5%) 200 (100%)

1 (0.5%) 9 (4.5%) 5 (2.5%) 1 (0.5%) 3 (1.5%) 19 (9.5%)

16 (8.0%) 17 (8.5%) 16 (8.0%) 5 (2.5%) 11 (5.0%) 65 (32.5%)

24 (12.0%) 16 (8.0%) 13 (6.5%) 8 (4.0%) 6 (3.0%) 67 (33.5%)

17 (8.5%) 13 (6.5%) 5 (2.5%) 6 (3.0%) 4 (2.0%) 45 (22.5%)

3 (1.5%) - (0%) 1 (0%) - (0%) - (0%) 4 (2.0%)

61 (30.5%) 55 (27.5%) 40 (20.0%) 20 (10.0%) 24 (12.0%) 200 (100%)

2 (1.0%) 12 (6.0%) 5 (2.5%) - (0%)

16 (8.0%) 30 (15.0%) 10 (5.0%) 9 (4.5%)

21 (10.5%) 27 (13.5%) 7 (3.5%) 12 (6.0%)

11 (10.5%) 11 (5.5%) 4 (2.0%) 2 (1.0%)

2 (1.0%) - (0.5%) - (0%) 4 (2.0%)

52 (26.0%) 80 (40.0%) 26 (13.0%) 42 (21.0%)

19 (9.5%)

65 (32.5%)

67 (33.5%)

45 (22.5%)

4 (2.0%)

200 (100%)

Source: Field Study, 2013.

0

.2

.4

L(p)

.6

.8

1

Lorenz Curve

0

.2

.4

.6

.8

Percentiles (p) 45° line

total_2

Figure 1. Determination of income inequality by Gini coefficient.

1

47

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48

Table 14. Estimation of income inequality and self rated health.

Log likelihood Health perception 0 Age Education Income Distance Cost of treatment -cons 1 Age Education Income Distance Cost of treatment -cons 2 3 Age Education Income Distance Cost of treatment -cons 4 Age Education Income Distance Cost of treatment -cons Health perception = 0 Health perception = 1 Health perception = 2 Health perception = 3 Health perception = 4

Coeff.

Std. Err.

Number of obs = LR 2 chi (36) = 129.0 Prob>chi2 = 0.0000 Pseudo R2= 0.2356 Z

P >/z/

200

-2.242835*** -.0000288 .0000866** .002176 .0001227 5.688865

.0583887 .0001317 .000041 .0019769 .0000785 2.308627

-3.84 -0.22 2.11 1.10 1.56 2.46

0.000 0.827 0.035 0.271 0.118 0.014

-1.0229*** .0000209 .0000142 .0018866 .000053 2.69022 (Base outcome)

.0274279 .0001003 .0000256 .0012179 .0000718 1.264551

-3.73 0.21 0.56 1.55 0.74 2.30

0.000 0.835 0.578 0.121 0.460 0.022

.8666528*** -.0002696** .0000191 .0023812** .0001465** -5.232324

.0288618 .000157 .0000332 .0013483 .0000748 1.538927

3.00 -1.72 0.58 1.77 1.96 -3.40

0.003 0.046 0.565 0.047 0.050 0.001

.1458465** -.0008652 .0000135 .0017736 -.0008535 -9.973213

.087627 .000626 .0001371 .0044157 .0010667 4.529426 Excellent health Very good health Good health Fair health Poor health

1.66 -1.38 0.10 0.40 -0.80 -2.20

0.046 0.167 0.922 0.688 0.424 0.028

Source; Data Analysis 2013; ***, and**represents levels of significance at 1 and 5% respectively.

represent the total income of all respondents in Akinyele Local Government Area. The Gini coefficient was determined and the value is 0.2448, which shows that there is actually income inequality. In other to know its level of significance, the Gini coefficient was divided by its standard error. Estimate Level of significance = Standard error

0.2448 Level of significance =

= 22.64 0.010813

If the level of significance is less than 1.8, it is not significant; If it is between 1.8 and 2.39, it is significant at 10%; If it is between 2.4 and 2.6, it is significant at 5%; If it is greater than 2.6, it is significant at 1%. Therefore the result is significant at 1%. The Gini coefficient (0.2448) is not very high probably due to low range of income

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because most of the respondents were farmers doing almost same activities.

Interpretation of analysis

49

Therefore, it is concluded that the difference between income inequality and self-rated health has been found to be statistically significant.

multinomial logistic regression

Health perception is the dependent variable with good health status as the base outcome or the reference category in the multinomial logistic regression.

Relationship among variables Age: For every unit increase in age, the log-odds of having excellent health status decreases by 0.243, the log odds of having very good health status decreases by 1.023, the log odds of having fair health status increases by 0.867, while the log odds of having poor health status increases by 0.146. This implies that the older the respondent is, the lesser the likelihood of having excellent health status, or very good health status, and the greater the likelihood of having fair health status or poor health status. Educational status: If there is a unit change in educational level, the log odds of having fair health status decreases by 0.000027. This means that the higher the educational level of respondents, the lesser the likelihood of having fair health status. Distance to the nearest health centre: If there is a change in household distance to the nearest health centre, the log odds of having fair health status increases by 0.0023. This implies that the farther the health centre is to where the respondents reside, the greater the likelihood of having fair health status. Cost of treatment: If there is a unit change in cost of treatment, the log odds of having fair health status increases by 0.0001465. This means that the more the respondents need to spend on the treatment of their health, the greater the likelihood of respondents having fair health status. Income: For every unit increase in the income of respondents, the log odds of having excellent status increases by 0.0000866, this implies that if the income of respondents increases, the greater the likelihood of having excellent health status. Hypothesis testing: For income inequality, the z test statistics for the predictor income inequality is 2.11 (Table 14) with an associated p-value of 0.035 which is less than 0.05. Having set the alpha level to 0.05 confidence level, the null hypothesis which states that “There is no significant relationship between income inequality and self-rated health” is not true and is hereby rejected.

Conclusion and Recommendations It was concluded from this study that there is inequality in the distribution of income as shown by the Lorenz curve (Figure 1) even though the level of inequality indicated by the Gini ratio (0.2448) is low due to the homogeneity of the study area; that is, the people are predominantly farmers and will therefore not have much variation in their income. The main factors affecting poor health status are inadequate health facilities, inadequate infrastructural facilities, age of respondents, income of respondents, lack of formal education, and distance of household to the health centre. Health problems in the rural area could be described in terms of lack of safe water, unkempt environment and poor health care service. As a result from the findings of this study, there is need for improvement in the existing health facilities as well as creating more. There is also a need for health education. Government needs to provide basic necessities such as electricity, good environmental management strategies, more schools, pipe borne water and housing at low cost to the rural dwellers. Execution of policies which aim at improving the health status of the rural dwellers will enable them to attain a certain level of health care that will allow them to live a socially and economically productive life. Since other occupations apart from farming account for the inequality in income, with farming being the predominant source of income for respondents in the study area, there is a need to upgrade technologies for agricultural production in order to further improve equity in the distribution of income.

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