1.

Comparative Analysis of Poverty and Income Inequality Among Food Crop and Livestock Farmers in Ilesa Metropolis, Osun State

The study focus on the living conditions of respondents in the study area, determine their poverty level and examine the existence of income inequality among farmers in the study area. A multistage random sampling was used to select the 150 respondents from the two Local Governments Areas; a well structured questionnaire was used to collect primary data from the farmers. Data collected was analysed using descriptive statistics, Gini coefficient and FGT poverty ratio. Majority of the farmers are male and the mean age for food crop, livestock and mixed farmers are 51years, 50years and 49 years respectively. Household size of 5 members or less is generally above average in the study area. Livestock farmers accounted for the highest value respondents without formal education i.e. 32.5 percent; most of the farmers are educated. While personal savings and cooperative sociteis are the major source of fund for agricultural production in the study area, commercial banks are least patronized for funding in the 3 categories of farm practices. Result of the living condition of the respondents reveals that 58 3 percent of the mixed farmers lived in family house, while 20 percent of livestock farmers lived in their personal apartment. While 62 percent of crop farmers uses pit latrine for faecal disposal, 13.3 percent of mixed farmers used water closet. Majority of the respondents sourced their water from dug well, only 1.7 percent of those engaged in mixed farming sourced water from government provision of pipe borne water. The result showed that the moderate poverty line are 1,222.86, 1566.45 and 1381.26 for food crop farmers, livestock farmers and farmers that engage in mixed farming respectively while the core poor are 611.43, 783.22 and 690.63 for the three group of farmers. Poverty is most pervasive among mixed farmers with a value of 21.7 compared to other categories. Farmers growing food crops recorded the least poverty incidence, depth and severity. Income is most unequally distributed among livestock farmers as represented by a value of 0.04 and also they have the highest social welfare (7145.24) because of the higher mean income of N17, 863.10. Integrated community development is recommended because rural community problems are multifaceted and need several approaches of various institutions to meet these problems to improve levels of living.

overty is the inability to adequately meet the basic human necessities, such as food, shelter, clothing and medicare. It is also a state of deprivation of human needs to which a person, household, community or nation can be subjected. It is a broadly multidimensional, partly subjective phenomenon, often viewed as both the cause and symptoms of Author : Department of Agric. Economics, Ladoke Akintola University of Tech, Ogbomoso. E-mail : [email protected] underdevelopment. It is manifested in many ways including the lack of capability by individual or group to function and feed well in the society (Sen,1996). The incidence of poverty among farmers and farm labourers is related to the broader society in which they live. Poverty is a result of low level of assets, coupled with low returns. The poor have very few assets beyond their own labour, which is inevitably spent in tedious, backbreaking, low paid work. Poverty breeds poverty. A poor individual or family has a high probability of staying poor. Low incomes carry with them high risks of illnesses, limitations on mobility, limited access to education deprivation but also in terms of an individual's inability to access basic social amenities (CBN/World Bank, 1999). It can also be said that poverty as a way of live is characterized by low calorie intake, inaccessibility to adequate health facilities low quality education system, low income, unemployment, and under employment and inaccessibility to various housing and societal facilities (Onibokun and Kumuyi, 1996).

In Nigeria, studies reveal that income inequality is increasing in rural and urban areas and this can be linked to the growing dimension of poverty. Aigbokhan (2000), argued that inequality adversely affects growth through a number of channels. Firstly inequality may generate social conflict over dimensional issues that diminish the security of property rights thereby lowering investment and economic growth, Secondly, he said that the need to reduce poverty through lowering inequality in the face of social conflict might encourage higher taxes. These higher taxes lower the rate of return to private access and thereby affect accumulations; such imperfections would mean that people cannot really borrow to finance education from their own resources and this affect the rate of growth as it affect the rate of growth of labour force. And thirdly, inequality through its effects on investment in human capital may increase fertility. Meludu and Adekoya (2005) reported that poverty is also strongly influenced by education and location, in Nigeria poverty is seen as a rural problem where majority of the inhabitants engage in agricultural productions as a means of livelihood. The agricultural sector employed about 65 percent of the population for

-Poverty manifests itself not economic in only the production of food and livestock for consumption, raw materials for agro allied industries as well as pharmaceutical industries etc. the low performance of the agricultural sector can be attributed to subsistence level of production which consequently result in reduced income thereby causing disparity in income compared to their counterparts. To make up for this low income, farmers engage in other income earning activities which further widens the gap created by income inequality (Ipinnaiye, 2001) .

According to Caninada and Goudsward (2001), agricultural activities are common in rural areas while non-farming activities dominate the urban area. Intrasectorally, income inequality is lower within the agricultural sector than the non-agricultural sector. Kuznet (1963) observed that average income from nonagricultural sector were higher than those from agricultural activities and were associated with differences in organization, technology and productivity. He also deduced that, income inequality in agricultural sector was still higher than income inequality in nonagricultural sector for the undeveloped than the developed countries. Jacobs (2000) in his study stated that total income inequality accounted for, by differences between age groups is very low (less or equal to 5 percent) , therefore age does not explain much of the observed income inequality. Inequality was observed to be more prevalent between individual for the same groups.

economics factors to increasing income inequality. They reported that changes in returns to household characteristics in particular, changes to education are responsible for about 50 percent change in income inequality, this was also supported by Alayande (2003). Omonona (2001) also reported the varying levels of inequalities in the sources of per capita income of the households, according to the study, the higher the years of education of the household head the higher the incentive to get income from non farm wage employment.

It is also worthy to note that reduction in rural infrastructure on the part of the government can also contribute to poverty hence, income inequality. A good road network especially in rural areas, efficient and cost effective irrigation system, rural electrification and improvement in per capita energy consumption apart from upgrading of marketing infrastructure the need to improve health delivery system, improve quality of education and provide access to safe drinking water and sanitation to all can reduce income disparity and alleviate poverty. In view of this, the study examines the socio economic characteristics of the respondents, the level of income as well as the living conditions that determine poverty levels of households in the study of area.

The study was carried out in Ilesa metropolis, Osun state. Ancient Ilesa city used to be a single Local Government Area, until November 1988, when it was divided into two LGAs i.e. Ilesa West and Ilesa East local Government Areas accordingly which makes up the metropolis. Ilesa East local Governmet is about 113km 2 in land area and size and it is located on 17° 30 1 North of the equator and West local Government is about 114km in land area and size and it is located on 19° 30 1 South of the equator and 5° -75 1 A multistage random sampling technique was used to select the respondents. The first stage involves the random selection of two wards each from the 2

LGAs. Two villages each was also selected at random from each of the ward to make a total of eight villages, this forms the second stage. The last stage involves random selection of 20 farmers from each village to give a sample population of 160 respondents. However, data from 150 respondents was finally used for the analysis. The remaining 10 questionnaire were discarded due to incomplete information.

Descriptive statistics, poverty indices and Gini coefficient was used to analyse data collected. The estimation of Gini coefficient involves ranking the units of observation on the basis of some quality of interest and then estimating cumulative proportions. It shows the distribution of expenditure above the poverty line. The closer the distribution, the better the people while the more dispersed the distribution, the more pronounced poverty is in the area of study. Gini coefficient is estimated according to Sen (1996).

The -coefficient involves ranking the units of observation on the basis of some quality of interest and then estimating cumulative proportions. It shows the distribution of expenditure above the poverty line. The closer the distribution, the better the people while the more dispersed the distribution, the more pronounced poverty is in the area of study.

Gini coefficient is estimated as:

The Foster, Greer and Thorbecke (FGT) poverty measures are additive. This means that the poverty measures of the population as well as a whole is equal to the weighted sum of the poverty measures for the population subgroups, with the weights defined by the population shares of the subgroups. It is written as: This is the proportion of people below the poverty line. Mean per capita income is calculated and the poverty line is drawn to separate the poor from the non poor. Head count ratio is used to calculate the number of households whose members have per capital income below the poverty line. When there is no aversion to poverty, it is expressed as:

Where H = Headcount ratio. This index measures the incidence of poverty.

Poverty gap is the aggregate short fall of income of all the poor from the specified poverty line. It measures the difference between actual income and minimum non-poverty income. It is denoted as ?? 1 and is expressed as:

The severity of poverty indices, denoted by P?, is the sum of the square of poverty depth divided by the number of poor households. It allows for concern about the poorest of the poor by attaching greater weight to the poorest of the poor than of those just below the The distribution of respondents according to their socio-economic characteristics is presented in table 1. The result reveals that 86 percent food crops farmers are male, 66.7 percent of farmers engaging in mixed farming are also male, while only 14 percent of the food crops farmers are female. This is an indication that most of the food crops and livestock farmers, as well as respondents engaged in mixed farming are male. The reason for this is not far fetched as agricultural production is tedious in nature especially growing food crops relative others. About 50 percent of the respondents are between the age range of 40 -59 with an exception of live stock farmers. The mean age is 51.34 years, 50.30 years and 49.37 years respectively for food crops, livestock and farmers engaged in mixed farming respectively. This implies that most of these farmers are in their productive age and therefore they can participate actively in various agricultural productions. Majority of the respondents (88% of food crops, 77.5% of livestock and 81.7 of mixed farming) are married while others are single, divorced or widowed. This indicates that married people were more involved in agricultural production in the study area. The higher percentage of married respondents agrees with Jibowo (1992) who reported that the higher percentage of farming populace is made up of married people.

The table further reveals that 46 percent of food crop farmers have about 5 household members while other categories have above average i.e. 70 and 68 percent for livestock and mixed farming respectively. this implies that majority of the farming households in the study area do not have large household size, hence income earned from farming activities will be expended on these members which will consequently improve their welfare. Most of the farmers in the three categories of farmers have one form of formal education or the other ranging from primary education to tertiary education, about 22 percent accounted for respondents who grow food poverty line. It is expressed as: August ?? percent represent respondents that engage in livestock and mixed farming that have no formal education respectively. Literacy level among the respondents is high which may affect their productivity in various enterprises.

Furthermore, the distribution of respondents based on sources of funding for their agricultural activities shows that personal saving (76.0%, 55.0%, and 73.3%) and cooperative (46.0%, 32.5%, and 41.7%) are the predominant sources of funding for food crop and livestock farmers and those who are involved in mixed farming respectively. Source of funding percentage i.e. 6.0, 5.0 and 3.3 percents for the three (3) categories of farmers. The result reveals that commercial banks are less patronized for financial support for farming in the study area. This may be due to avoidance high interest rate on collected loan.

While 90.0 and 98.0 percent of food crop farmers cultivate cassava and maize respectively, about 70.0, 76.0, and 31.0 percent of respondents that engage in mixed farming produce cassava maize and yam along with livestock production respectively. Also, the distribution of respondents according to types of livestock raised shows that 52.5 and 32.5 percent engage in goat and poultry production, 63.3% and 33.3% are mixed farmers raising goat and poultry along side with food crop farming. Few of the farmers rear pig (12.5%) or rear it along side crop farming (6.7%). This therefore implies that mixed farming of goat, poultry and crops are predominant. Majority of the respondents in the study area earn less than N40,000 from their farming in the study area. Only 6.0, 7.5 and 15.0 percent earn as much as N80,000 in food crop and livestock production and mixed farming respectively. This is an indication that earning from farming activities is generally low in the area.

Volume XI Issue V Version I 2 discusses the distribution of the respondents according to the standard of living in the study area. Majority of the farmers are indigene in all the categories of farming under consideration. While 76.0 percent of food crop farmers and 55.0 percent of livestock farmers are indigenes about 56.7 percent of *Multiple choices respondents that engage in mixed farming are indigenes but there is a wider variation in those that are indigenes under the mixed farming category, which is 43.3 percent of those in this category are non indigenes against the 24.0 and 15.0 percent in others that are also non indigenes. The distribution of August respondents according to ownership of house of residence shows that about 58.3% and 52.5% of farmers that engage in mixed farming and livestock farmers respectively live in family houses while 40.0% of food crop farmers reside in rented apartment. However, 42.0, 20 and 23percent of food crop farmers, livestock producers and those who engage in mixed farming live in their personal place of abode. It therefore implies that the farmers live in varying categories of houses with family house and rented house having a larger proportion.

Based on type of apartment that the farmers reside, majority of the food crop farmers (66.0%), livestock farmers (57.5%) and mixed farming respondents (46.7%) dwell in face to face houses. While, 35.0% of farmers that engage in mixed farming live in flat, only about 20.0 percent of food crop and livestock farmers live in flats. This implies that majority of the respondents dwell in face to face apartment. Sixty-two, 57.5 and 46.7 percent of food crop farmers, livestock farmers and mixed farming respondents live in apartments that uses pit latrine to dispose faecal waste. Apartments that uses water closet in the study area accounted for only 20.0, 22.5 and 13.3 percent of food crop and livestock farmers and mixed farmers respectively. it is observed that use of conventional toilet is predominant in the study area.

The major source of water in the study area is dug well, 64.0%, 60.0%, and 48.3% of food crop farmers, livestock farmers and those who engage in both get their water from the well, while 43.3% of both food crop and livestock farmers get their water from stream, 26.0% of food crop farmers source from pipe borne water provided by government. About 50.0%, 47.5% and 31.7% of food crop farmers, livestock farmers and both livestock and food crop farmers respectively affirmed the presence of tarred but damaged road in their locality, while, 35.0%, 30.0% and 26.7% of livestock, food crop and mixed farming farmers claimed that the roads in their communities are not tarred but they are motorable. Only about 5 percent livestock and food crop farmers have roads that are not motorable.

This can consequently affect easy transportation of their produce from their farm gate. On the means of transportation in the study area 52.0 and 20.0 percent of food crop farmers travel by public transportation respectively while 35.0 percent of the livestock farmers uses motorbike as a means of transportation, while 48.3% of those farmers who cultivate both food crop and rear livestock trek to their farmland. This implies that the farmers employ various means of transportation. The farmers that cultivate crops and rear animals have highest level of poverty incidence, poverty gap and poverty severity of 21.7%, 18.0% and 14.9% respectively. This implies that farmers who engage in mixed farming are the poorest compared with their counterpart. However, it is worthy to note that the category that has the lowest incidence of poverty (Po) was for food crop farmers (14.0%), with poverty gap (8.6%) and poverty severity (5.3%). The result shows that poverty is more pervasive among farmers that engage in mixed farming compared with food crop farmers and livestock farmers. The values of 14.0%, 17.5% and 21.7% poverty head count are lower when compared with 38%, 35% and 37% reported for urban areas in Nigeria in 1985, 1992 and 1996 (Aigbokan, 2000, FOS, 1997. The low values of poverty severity index that is, 5.3%, 12.0% and 14.0% for food crop farmers, livestock farmers and both food crop and livestock farmers respectively, indicate that poverty is not too severe in all the cases. For instance, the poverty severity index of food crop farmers of 5.3% reveals that approximately three (3) farmers out of fifty (50) sampled food crop farmers are extremely poor, the poverty severity index of livestock farmers of 12.0% means that approximately five (5) farmers out of forty (40) sampled livestock farmers are extremely poor. Lastly, the poverty severity index of mixed farming is 15%, indicating that approximately nine (9) farmers out of sixty (60) sampled food crop and livestock farmers are extremely poor.

The Gini coefficient of income distribution among f ood crop farmers as shown in Table 3 is 0.33 for food cr op farmers, livestock farmers is 0.40 while that of crop and livestock farmers is 0.39. The Gini coefficient of income distribution of livestock farmers is the highest. This mean that income is most unequally distributed among liv estock farmers while it is more unequally distributed a mong respondents that engage in mixed farming and least among food crop farmers. The higher the value of August s ocial welfare, the higher the general welfare of the fa rmers group. Social welfare value is derived from the me an income and the Gini coefficient of a particular gro up of people. The higher the mean income and the lo wer the Gini coefficient, the higher is the social welfare of t he group (Salimonu et al., 2006). Livestock farmers have t he highest social welfare (7145.24) because of higher mean income (17863.10) and highest Gini coefficient (0 .40) while the social welfare of crop and livestock farmers is higher (6351.64), due to higher Gini coefficient (0.39) and high mean income (16286.25). This study had found out that poverty and income inequality exist among farmers in the study area especially among the farmers practicing mixed farming. Poverty alleviation strategy can be effective only if measures are simultaneously taken on several fronts with a view to increase the income of the poor families. In view of this, it is recommended that, integrated community development should be adopted in providing rural infrastructures to improve the living standard of the rural community. Also, government will also need to step up investment in rural infrastructure. Private sector / Voluntary sector can play a very effective role in dissemination of knowledge and providing backward and forward linkages necessary for making any economic activity of the poor viable.

IV.