Explaining Inequality and Welfare Status of Households in Rural Nigeria: Evidence from Ekiti State

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1 Humanity & Social Sciences Journal 3 (1): 70-80, 008 ISSN IDOSI Publications, 008 Explaining Inequality and Welfare Status of Households in Rural Nigeria: Evidence from Ekiti State I.B. Oluwatayo Department of Agricultural Economics and Extension Services, University of Ado-Ekiti, P.M.B. 5363, Ado-Ekiti, Ekiti State, Nigeria Abstract: A number of studies have shown that inequality is increasing in the rural and urban areas of Nigeria and this can be linked to the growing dimension of poverty. The study focused on analysing inequality and welfare status of rural households in Ekiti State, Nigeria. The result presented was based on primary data collected from a random sample of 40 households selected from four rural communities in the state. Descriptive statistics, Lorenz curve, Gini coefficient and regression analysis were employed as analytical techniques. Both the Lorenz curve and Gini coefficient were used to examine the distribution of households income and estimate the level of inequality respectively. However, the regression analysis was used to examine the determinants of households expenditure on food and non-food items (proxy for welfare). The Lorenz curve analysis showed that there s an unequal distribution in the income and other indicators of welfare with a Gini coefficient of The regression analysis revealed that income (8.518E-05**), household size (1.336E-0*) and size of farmland cultivated (1.084E-04*) were positively related to the welfare status of the households while marital status (-.51E-0) and primary occupation (-.95E-0) were negatively related to it. The recommendations arising from the study were that there is a need to upgrade technologies for increased agricultural production in order to further improve income levels of households and equity in the distribution of income arising from farming activities since this remains the major source of income of households in the study area. More so, there should be provision of infrastructural facilities in order to improve the welfare status of the households. Key words: Income inequality % Poverty % Rural households % Welfare % Ekiti State INTRODUCTION Income distribution pattern is a major concern in the determination of the level of economic growth and development. For instance, many different measures have been used to estimate the economic and social health status of a community or country at large: unemployment rates, median property values and sales tax collection measure, the strength of local economies, population growth and employment in a nation indicates how a region is changing demographically and economically. One measure that is infrequently used is that of income inequality. In 1980, the top fifth of the American household took home 44.1 percent of all income and the bottom fifth took home just 4. percent. By 1994, the top fifth captured 49.1 percent of all income and the share of the bottom fifth had fallen to 3.6 percent. Over this same period, the share of the top 5 percent of the households went from 16.5 percent to 1. percent of all income. Also in Nigeria, accompanying the rapid economic growth between 1965 and 1975 was a serious income disparity which widened substantially [1-3]. This is to show that though the economy may be performing strongly, the gap between the lower income households and the upper income households is growing, which is an indication that the rapid economic growth experienced has only resulted in further concentration of national income in the hands of few proportion of the population [1,5]. This national trend is also reflected at the community or city level, which makes income inequality a useful metric in understanding the state of the community. Corresponding Author: Dr. I.B. Oluwatayo, Department of Agricultural Economics and Extension Services, University of Ado-Ekiti, P.M.B. 5363, Ado-Ekiti, Ekiti State, Nigeria 70

2 Previous studies have shown that income inequality has risen in many developing countries over the last two decades [6,7]. When citizens of a country or community have such drastically diverging fortunes, the result can be social and political fracturing. It has therefore become evident that the policy environment required for rapid economic growth cannot be provided by policies which result in further concentration of national income in the hands of few proportion of the population. Many empirical studies have assessed the impact of some macro-economic policies on economic development based on the level of income inequality during the periods of their implementation. A common model proposed has explained this secular trend based on inter-sectoral income differentials and changes in the income of the citizenry resulting from the growth process [1,8-10]. Variations in the level of income obtained by people in the rural areas is on the increase which could very much be linked to the growing dimension of poverty even among the rural households, as a high level of income inequality produces an unfavourable environment for economic growth and development.poverty which is substantially a rural phenomenon as is the case in most low income countries like Nigeria, like an elephant is more easily recognized or acknowledged than explained or defined, but in the real sense can be defined as existing when resources of individuals or families are insufficient to provide a socially acceptable standard of living. With this, narrowing down poverty to the household level which is the focus of this study could be invariably defined in terms of inadequacy of income to support a minimum standard of decent living. The components that make up the acceptable standard of living can be represented as a composite whole by the real income expressed in Naira. It therefore can be seen that poverty, can in every sense be linked to the income level of individuals of households since their standard of living is a measure of the income obtained or received by them.the study is based on understanding poverty as a function of the factor income inequality. By analyzing income inequality, the extent of poverty in a country could be predicted. Meanwhile, a high level of income inequality exists in many low income countries of sub-saharan Africa (SSA) of which Nigeria is inclusive. It is also widely believed that majority of the people in sub-saharan Africa live in the rural areas. These rural communities are majorly agrarian with majority of them owning just a small piece of land on which they grow crops hardly sufficient to feed themselves let alone to sell in order to generate income. They therefore live on small and meagre income as compared to urban dwellers who earn more than rural dwellers due to their higher literacy level, education etc. Usually people in the urban areas invest their time and money to acquire skills and hence earn higher income [11]. The rural dwellers are thus more vulnerable to poverty. Income inequality has many social and economic implications. It endangers the ability of the people to think of themselves as one nation or people which may in turn lead to increase in violence, corruption and make people see themselves as an object of poverty. This study therefore tries to examine income inequality and the extent of poverty among residents of rural areas in Nigeria. Theoretical Framework and Literature Review The Concept of Inequality: Inequality is a property of the distribution in a population of some (presumably valued) resource such as income, wealth e.t.c. It could also be viewed or conceptualised as the dispersion of a distribution either in terms of consumption, income or any other quality or attribute that shows or tells the welfare status of a population. In another case, inequality refers to the difference that exists across groups (for example countries, individuals, races, gender etc.) which could be in terms of income, opportunity, employment, wealth etc. Income inequality is some measure of the extent of differences in income received by individuals in the population from the lowest to the highest disparity in the levels of income among individuals in the economy. Income distribution is a description of the fractions of population that are at various levels of income. The larger the difference in income, the worse the income distribution, the smaller the better. Conceptually distinct as they may be, income inequality is often studied as part of broader analysis covering poverty and welfare. Inequality is a broader concept than poverty in that it is defined over the whole distribution, not only the censored distribution of individuals or households below a certain poverty line [1,13]. Kuznets [14], based on long run time series data for three developed countries (US, England, Germany) hypothesized a time path of inequality for nations undergoing economic development with an increase in inequality in the early stages, followed by a decreased in later stages. This has come to be known as the Kuznet U-shaped curved hypothesis on relationship between inequality and development. According to World Bank [1], decomposition of income inequality is required for both authentic and analytical reasons as policy analysts and economists may wish to access the contribution to overall inequality within and between different sub-groups of the population, for example within and between workers in agricultural and industrial sectors, or urban and rural sectors. Decomposition of inequality measures can shed light on 71

3 both its structure and dynamics. Inequality decomposition is a standard technique for examining the contribution to inequality of particular characteristics and income package influences. These analyses were pioneered by Bourguignon [15], Cowell [16] and Shorrocks [17,18]. Ipinnaiye [3] found that decomposition analysis of income shows that non-farm income contributes the most to overall income inequality in both the peri-urban areas of Ibadan. Also, in 000 income inequality was higher in peri-urban areas than urban areas. Adebayo [19] found that in the rural areas in Ibadan metropolis, agricultural income contributes most to the overall income inequality accounting for 91% while rental income makes the least contributing to overall rural income inequality accounting for just 0.17%. In the urban areas, non-farm income makes the largest contribution to overall income inequality accounting for 80% while transfer income reduces urban overall income inequality by 0.13%. Elbers et al. [0] estimated income inequality for Ecuador, Mozambique and Madagascar. Based on a statistical procedure that combines households survey data with population census data, their analyses showed that the share of within-community inequality in overall inequality is high. Specifically, Gini-coefficients computed were between and in Madagascar and Mozambique respectively. Oyekale [1] in a micro-survey of some households in Ibadan revealed Gini-coefficient to be , while Adejare [] estimated World Bank [3] shows that in 1996/1997, Gini index for Nigeria was 0.506, and for Cameroon and Ghana respectively. Also, using 1998 data, World Bank [3] estimated Gini-indices of for Zambia and Central Africa respectively. Ferreira [4] in rural Tanzania discovered 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. Research Methodology Study Area: The study area is Ekiti State. The state is one of the six states constituting the south-western region of Nigeria. The state has 16 Local Government Areas. According to the 006 Population Census, the State has a population of,384,1 (1,1, 609 males and 1,171,603 females) and a land area of 5, 435sq km. The study area is chosen particularly because it ranks high among the poor states in Nigeria and because it is predominantly agrarian since agriculture is the primary occupation of the dwellers in the state. The men are predominantly farmers while the women are farmers and also engage in trading. Even for the many educated indigenes in the formal sector employment, farming remains a major secondary occupation. Sources, Types of Data and Sampling Technique: The data used or employed for this study were of the primary type and were collected through a general household survey with the administration of well-structured questionnaires. Information gathered includes the following: a) Socio-Economic Data: This includes information on age of household head, sex of the household head, years of formal education, marital status, types of occupation, household income, household size, membership of any association (e.g. group or cooperative societies). b) Household Expenditure Data: data collected under this include expenditure on food and non-food items such as health, communication, education, transportation, recreation, housing e.t.c. The sampled households were selected using simple random sampling technique. This was achieved by getting the list of villages in the study area. The villages were numbered out of which four were randomly selected. Samples were then randomly drawn from households in each of the villages. In all, 70 questionnaires were administered and 40 were used in the analysis. The remaining ones were rejected because of incomplete information. Analytical Technique: Analytical techniques employed include descriptive statistics (frequencies and the mean to analyse the pattern of income distribution). Also used, are the Lorenz curve and the Gini coefficient for the measurement of income inequality and regression model for estimating the variables associated with welfare status of the respondents. Hypothetical Concentration Curve of Income Distribution: This curve is widely used to show income or expenditure inequality. The Lorenz curve illustrates income distribution of a country. The main feature of the Lorenz curve includes 7

4 100 % Cumulative Percentage of Income Line of perfect equality Lorenz Curve Fig. 1: Hypothetical Concentration Curve of Income Distribution the curve and the line of perfect equality. Figure 1 shows the horizontal axis, which measures the proportion of the population while the vertical axis shows the proportion of the national income that they receive. The farther away the Lorenz curve is from the line of perfect equality, the more unequal the distribution of income in that country. 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: n i = 1 n + 1 Igin ( Y) = i Yi n µ Where; n = number of observation µ = mean of the distribution y i = income of the with household I = Income Gini gini Multiple Regression Analysis: 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. The Multiple Regression Analysis therefore helps to determine the effect of changes in the explanatory variables on the dependent variable. Model Specification The implicit function is given as: Q = f (X..X, µ) 1 n Q = expenditure on food and non-food items X X = explanatory variables 1 n µ = error term Four functional forms were tried on the regression model analysis in order to get the one that best fits the data. These functional forms are linear, exponential, semi-logarithmic and double-logarithmic functions. The general forms of this function are specified below: Linear function: Exponential function: Q = b 0 + b1x 1 + bx + b3x 3 + b4x 4 + b5x 5 + ìt Ln Q = b 0 + b1x 1 + bx + b3x 3 + b4x 4 + b5x 5 + øt 73

5 Table 1: Distribution of Respondents by Primary Occupation Primary occupation Number of Respondents % Distribution Farming Government employment Trading Private firm Craft and artisan Self employment Total Table : Distribution of Respondents by their Other Source of Income Other source of income Number of Respondents % Distribution Farming Government employment Trading Private firm Craft and artisan Self employment Total Table 3: Distribution of Respondents by Income from Primary Occupation Income from primary occupation (N) Number of Respondents % Distribution Less than 5, ,100 10, ,100 15, ,100 0, Greater than 0, Total Semi-log function: Double-log function: Q = Inb 0 + b1inx 1 + binx + b3inx 3 + b4inx 4 + b5inx 5 + øt Ln Q = Inb 0 + b1inx 1 + binx + b3inx 3 + b4inx 4 + b5inx 5 + ìt X 1 = Marital status (Dummy: unmarried = 1, married = 0) X = Household size X 3 = primary occupation (Dummy: farming = 1, non-farm work = 0) X 4 = Farm size (in hectares) X 5 = income (Naira) The equation selected was based the signs of the coefficients, number of significant variables and size of the coefficient of multiple determinations (R ). This is the equation that best fits the data and it is the lead equation. Data Description, Results and Interpretation Socioeconomic Characteristics of Respondents Primary Occupation of Respondents: Household s distribution by primary occupation as shown in Table 1 reveals that 51.7 percent of the sampled households have farming as their primary occupation while 15 percent are engaged in crafts and work as artisans. Breakdown of other trades include 10 percent in government employment, 11.7 percent in trading, 6.7 percent in self employments while 5 percent work in private firms as their primary occupation. Secondary Occupation of Respondents: Also from Table, it can be seen that 46.7 percent of the sampled households indicate farming as their secondary occupation while 0.8 percent are engaged in crafts and work as artisans. This distribution generally reveals the relative importance of farming to other occupations in the study area. 74

6 Table 4: Distribution of Respondents by Size of Household Household Size Number of Respondents % Distribution Total meals Average meals per day Total Table 5: Distribution of Respondents by Size of Farmland Farm size (ha) Number of Respondents % Distribution Non farmers ha Greater than 1.5ha Total Table 6: Households Poverty Indicator by Healthcare Healthcare Number of Respondents % Distribution General hospital Private hospital Herbal hospital Total Income from Primary Occupation of Respondents: From Table 3, it is observed that majority earn income between the range of N5, 100 to N10, 000. While only 8.3 percent fall into the high income group with income above x 0, 000. Household Size of Respondents: As indicated in Table 4, a greater share of the sampled households have household size of between 9 to 1, 90 respondents representing 37.5 per cent are in this group. It can also be observed that the larger the size of the household, the lesser the number of times they eat per day. While those with household size of between 1 to 4 and 5 to 8 eat 3 times daily, those with household size between 9 to 1 and 13 to 16, eat times daily while those with household size between 17 and 0 eat once daily. Since respondents are low income earners and large family size implies more expenditure on food items, households with larger household size tends to eat lesser than those with smaller household size. Farm Size of the Respondents: The breakdown by size of farmland in Table 5 shows that majority of the respondents; about 1.7 percent have farm size of between 0.51 ha to 0.1 ha. This is an indication that the farmers operate at subsistence level which may be due to the fact that majority of the farmers are tenant farmers and do not have sufficient money to obtain a large piece of land for farming. It may also be due to the fact that the farmers do not have access to credit facilities and as such lack capital or resources to expand their scale of production. Social Indicators of Poverty in the Study Area: A number of social indicators of poverty in the study area were considered in order to examine the welfare status of the respondents. They are presented in the tables below: Healthcare: As shown in Table 6, the result of the analysis show that more than half of the sampled households (138 respondents representing 57.5%) make use of herbal treatment about 36.7% patronise the general hospital while 5.8% patronise the private hospital. Majority of the household make use of herbal treatment because of lack of money to pay hospital bills, government hospitals not being close by and as such conditions might have worsened before getting to the 75

7 Table 7: Distribution of Respondents by Eating Habits Eating Habit Number of Respondents % Distribution Total Table 8: Household Poverty Indicator by Means of Transportation Means of transportation Number of Respondents % Distribution Trekking Car Public transport Bicycle Total Table 9: Regression Result of Correlates of Welfare Status Variables Coefficients Constant 7.78(0.41) Marital status (x ) Household size (x ) Primary occupation (x ) Size of farmland (x ) Income (x ) R = 30.% * = Significant at 5% ** = Significant at 1% Figures in parentheses are standard errors. Source: Computation from Survey Data, E-0(0.067) 1.336E-0*(0.015) -.95E-0(0.037) 1.084E-04**(.000) 8.518E-05**(.000) hospital. Some also have the belief that herbs are more effective than orthodox medicine and since they have it around them, why then should they expend money on orthodox medicine. This is in line with the World Bank [5] description of poverty as lack of access to health services, because the poor cannot afford good healthcare which are often non-available in the rural areas. Eating Habit: Table 7 shows that a greater share of the respondents (51.7 percent) eats twice a day, little over one-third (34. per cent) of the households eat three times a day while 10 percent of the respondents eat once a day. This could be due to inadequate funds or insufficient income to secure a 3-square meal and large family size. Also the households feed majorly on starchy foods with very little supplements from proteinous foods and vitamins. This also is an indication of poverty in the study area. Means of Transportation: Table 8 shows that 33.3% of the respondents rely on public transport for their means of transportation while 4.5% rely on bicycle and 17.5 percent on trekking. Those who have bicycles are majorly farmers and still go on foot especially in times of harvest, this is because most of the roads that lead to their farms are just footpaths and are thus not pliable by cars. Explaining Correlates of Welfare Status among Households in the Study Area: By considering those criteria for selecting the lead equation, the exponential function was the most suitable for the data and hence becomes the lead equation and the result of the regression analysis is presented in Table 9. As revealed in Table 9, it is very clear that martial status, household size, primary occupation, size of farmland and income explained about 30.% of the variation in the expenditure of households on food and non-food items (proxy for welfare). The low value of the R is not very uncommon due to the measurement error usually associated with using crosssectional data. These variables are of two parts namely: 76

8 Table 10: Computation of the Lorenz Curve of Income Distribution of Respondents Number of HH % households Cumulative % Cumulative % holding income holding income of households Total income in % Total income of income within Income category (x) in each category in each category in each category each category within each category each category < ,000 10, ,000 15, ,000 0, >0, Total Note: in Column (5) 5.16 = N51, = N 58, = N 77, = N 305, = N = N 1,111,100 Variables that have positive relationship with the expenditure of households i.e. expenditure increases as these variables increase. These variables include the following: Income: the coefficient of income is positive and significant at 1 percent level. This implies that increase in income results in increase in expenditure. This is so because an increase in income signifies an increase in the purchase of both food and non food items. Size of Farmland: The coefficient of size of farmland is positive and significant at 1 percent level. This implies that as size of farmland increases, more income is generated from it and as such expenditure increases. Household Size: The coefficient of household size is positive and significant at 5 percent. The implication is that if household size increases by one individual, expenditure also increases, in other words expenditure of household increases as household size increases. The second parts are variables having a negative relationship with expenditure i.e. as these variables increase the expenditure of households decreases. These include the following: Primary Occupation: The result reveals that the coefficient of primary occupation (of which more than half of the respondents are farmers) is negative. This implies that as the number of people engaged in farming increase, expenditure decreases as the farmers get the greater part of their food items from their farms and by so doing, overall expenditure on both food and non-food items decreases. Marital Status: This reveals that the coefficient of marital status is negative. The implication of this is that as unmarried respondents increases, the lesser their expenditure while the more the married respondents, the higher the expenditure. Thus, unmarried respondents spend lesser on than their married counterparts. Lorenz Curve and Gini Coefficient Analysis: This analysis is meant to examine the pattern of income distribution among the households in the study area and also to show the income inequality among households in the study area. The Lorenz Curve of income distribution which is a product of the graphical representation of the data in Table 10. Table 11 shows a deviation from the line of perfect equality DB (Figure ). This shows that income is unequally distributed in the study area. This is further confirmed by the Gini ratio, which is a measure of inequality in the distribution of income. A Gini ratio of was obtained for the study area. This indicates that a greater proportion of the respondents in the study area are in the low income group without about 60.8 percent of them earning income below x10, 000 while a few proportion are in the middle income group. The value of inequality (0.3570) is low because of the homogenous nature of the study area and majorly, respondents engaged in the same occupation both primarily and secondarily. By so doing, there will not be much variation in their income. 77

9 A 100 B 90 CUMULATIVE PERCENTAGE OF INCOME A 3 B 4 5 LINE OF PERFECT EQUALITY LORENZ CURVE 10 D 1 C CUMULATIVE PERCENTAGE OF HOUSEHOLD Fig. : Lorenz Curve of Income Distribution of Household Table 11: Cumulative Distribution of Income Cumulative percentage of households Cumulative percentage of income Calculation of Gini Coefficient Area A + Area B 100 X 100/ 5000 Area X 4.6/ Area 47.5 X ( )/ Area 3 0 X ( )/ 874 Area X ( )/ Area X ( )/ 735 Total Area B Area A Gini coefficient /5000 = Area A/(Area A + Area B) or 35.7 % or 36% SUMMARY OF MAJOR FINDINGS This study focused on analysing income inequity and welfare status among rural households in Ekiti State, Nigeria. A total of 40 respondents were selected through simple random sampling techniques. On an overall, the findings of this study reveal that: C The respondents in the study area are predominantly farmers representing about 51.7 percent of the total respondents for primary occupation. 78

10 C Also it was revealed that respondents are majorly low-income earners with about 60.8 percent of them earning income below x10, 000 monthly. This is due to the seasonality of agriculture, uncertainty inherent in agricultural production and also lack of credit facilities to increase their scale of production. C Household size is also seen to influence the expenditure of the respondents. While those with household size of between 9 and 16 eat on the average two meals per day, those with household size between 17 and 0 eat once daily and those between 1 and 8 eat three times daily. C The regression result showed that the welfare status of the respondents is directly related to the household size (X ), size of farmland (X 4 ) and income (X 5 ), while marital status (X 1 ) and primary occupation (X ), are known to be negatively related to their welfare status of the households. Also, the (R ) coefficient of multiple determination is 30. percent indicating that about 30 percent of the variation in the expenditure pattern of households is explained by the explanatory or independent variables. C Meanwhile the Lorenz curve analysis showed an unequal distribution of income among the respondents in the study area with a Gini coefficient of CONCLUSION AND RECOMMENDATIONS From the foregoing, income inequality and welfare status of respondents in the study area is influenced by their socioeconomic characteristics. Although there is inequality in the distribution of income as shown by the Lorenz curve (Figure ), the level of inequality indicated by the Gini ratio (0.3570) is low due to the homogeneity of the study area being that the respondents are predominantly farmers and will therefore not have much variation in their income. Poverty in the study area was described in terms of lack of access to safe water, lack of access to health services, lack of access to inputs needed, hunger, malnutrition, erratic supply of electricity, toilet facility, bad housing, means of transportation (non-motorable roads to farms) etc. Resulting from the findings of this study, the following recommendations are made to improve income distribution, income generation, consumption pattern and general welfare of the respondents. C C C Since other occupation 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 the need to upgrade technologies for agricultural production in order to further improve equity in the distribution of income. Provision of social infrastructural facilities such as portable water, health services, electricity, good roads, etc. Large family size should be discouraged through education and measures like birth control or family planning. REFERENCES 1. Matlon, P., Income Distribution among Farmers in Northern Nigeria: Empirical Result and Policy Implications. African Rural Economy Paper No. 18 East Lansing, Mich, USA: Michigan State University.. Aigbokhan, M.S., Poverty Alleviation in Nigeria; Some Macroeconomic Issues NES Annual Conference Proceedings, pp: Ipinnaiye, A.O., 001. A Decomposition Analysis of the Sources of Income Inequality in Ibadan Metropolis. Unpublished B. Sc. Project Dept. of Agric. Economics, U.I. 4. Aigbokhan, M.S., The Impact of Adjustment Policies and Income Distribution in Nigeria: An Empirical Study Research Report, No. 5. Development Policy Centre (DPC), Ibadan, Nigeria. 5. Clarke, G.L. X. Colin and H. Zou, 003. Finance and Income Inequality: Test of Alternative Theories. World Bank Policy Research Working Paper 984, Washington D.C.: World Bank. 6. Addison, T. and G.A. Cornia, 001. Income Distribution Policies for Faster Poverty Reduction. WIDER Discussion Paper No. 001/93, World Institute for Development Economic Research. 7. Kanbur, R. and N. Lustig, Why is Inequality Back on the Agenda Paper Prepared for the Annual Bank Washington DC. April 8-30: Ahluwalia, M.S., Income Distribution and Development Some Stylised facts. American Econs (May)

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