Rural Non-Farm Enterprises for Empowerment of Women in Anambra State, Nigeria

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1 Scientia Agriculturae E-ISSN: X / P-ISSN: DOI: /PSCP.SA Sci. Agri. 11 (1), 2015: PSCI Publications Rural Non-Farm Enterprises for Empowerment of Women in Anambra State, Nigeria Mbah E.N 1, Igbokwe E.M 2 1. Department of Agricultural Extension and Communication, University of Agriculture Makurdi, Nigeria* 2. Department of Agricultural Extension, Faculty of Agriculture, University of Nigeria, Nsukka, Nigeria** Corresponding Author evanmbah@gmail.com Paper Information Received: 17 March, 2015 Accepted: 27 June, 2015 Published: 20 July, 2015 Citation Mbah EN, Igbokwe EM Ecology and Physiology of Plant growth in relation to soil salinity. Scientia Agriculturae, 11 (1), Retrieved from (DOI: /PSCP.SA ) Key words: Rural, non-farm, occupation, women, empowerment, Nigeria A B S T R A C T The survey examined rural non-farm enterprises for empowerment of women in Anambra State, Nigeria. Interview schedule was used to collect data from a sample of 462 respondents. Data was analyzed using percentage, mean scores, standard deviation and multiple linear regression. Majority (60.2%) of the respondents were engaged in petty trading, about 12% were involved in tailoring, 7.8% engaged in teaching, 7.1% were involved in making of confectioneries, 6.4% were involved in hair dressing/weaving of hairs, 6.0% were public servants, among others. The respondents got into various non-farm enterprises through formal training in school, on-the-job training, apprenticeship and family mentoring. Sources of funds were mostly from self-help efforts (45.5%) and husbands (42.0%). They were highly constrained by lack of women empowerment training programmes in rural areas (M= 3.5), poor skill training (M= 3.5), inadequate training opportunities (M= 3.4), poor educational attainment (M= 3.3), high cost of transportation (M= 3.3), inadequate finance/credit facilities (M= 3.3), among others. Results of the multiple linear regression analysis on relationship between socio-economic factors and non-farm enterprises show that the overall regression model was significant (F= 6.263; p 0.05), accounting for 13.3% (R-squared) of the variance. The study recommends the need to improve the efficiency and performance of rural women in non-farm enterprises by ensuring that adequate rural infrastructure such as roads, electricity and pipe-borne water are put in place by state and federal governments. It highlights that policy makers should also formulate and implement policies that will meet economic empowerment needs of rural women PSCI Publisher All rights reserved. Introduction Rural dwellers are principally occupied in farming, fishing, hunting and food processing (Ekong, 2010). However, occupations in rural areas are not all farm-oriented. Women who form majority of the rural dwellers are involved in non-farm occupations such as weaving, dress making, petty-trading, hair dressing, teaching, midwifery, making of confectioneries, among others. This is because farming is a seasonal occupation in Nigeria except in areas where some forms of irrigation are practiced thereby enabling the production of crops off season. Most rural women therefore endeavor to supplement their incomes with petty jobs outside the farm. According to Onchan (2001), the non-farm sector (NFS) plays a crucial role in employment generation. The establishment of rural-based industries, in particular, has often been very effective in creating new job opportunities and providing supplemental income. Diversified production and trade activities have also offered rural communities better employment prospects and accordingly more stable growth of their economies. The rural non-farm economy is a very important part of rural Nigeria. It accounts for a large proportion of total rural employment and the rural income. A study carried out by Ajani (2012) indicated that rural women derived higher incomes mostly from occupations such as petty trading, teaching, catering services, tailoring, public services and making of confectioneries. Ajani (2012) reiterated that non-farm occupations play major role in generating high income for the rural women and efforts should be geared towards improving the activities of rural women in non-farm occupations through provision of rural industries, effective poverty reduction programmes and establishment of vocational skill acquisition centres in order to increase their incomes. From the

2 point of view of rural development, rural non-farm enterprises provide important sources of employment and income to women as well as to the poor small and landless farmers. Promoting this type of enterprises will therefore help raise employment and income, especially for rural women. RNF enterprises refer to wage-earning activities, when employed, and self employed activities in manufacturing, commerce, and other services. Davis (2003) defined rural non-farm enterprise (RNFE) as non-agricultural activities which generate income to rural households (including income in-kind and remittances), either through waged work or in selfemployment. RNFE includes both employment and self-employment. Rural non-farm enterprises cover many types of activities such as manufacturing, trade and services. They are located in the villages and towns where villagers can work parttime and/or set up small or micro type of enterprises that are labor-intensive. They usually use local raw materials, relatively simple and traditional technologies, some hired labor, and mostly owned funds from personal savings (Onchan, 2001). Rural small-scale industries form an important part of the rural non-farm sector and are getting increasing attention from policymakers to generate employment and income for rural development. In the past few decades, non-farm enterprises or non-farm employment has received increasing attention among policymakers and planners in many developing countries. This interest reflects, in part, the inability of the agriculture sector to absorb the rapidly growing rural labor force. The nonfarm sector has been expanding at a high rate as rapid economic transformation has been occurring. This is expected to increase rural employment, contribute to economic growth, improve income distribution, and alleviate poverty. In this regard, available evidence shows that non-farm income is important to farm households in developing countries (Maja and Dil, 2007). Its average share in total rural household income was 42 percent in Africa, 40 percent in Latin America, and 32 percent in Asia. Moreover, the importance of non-farm income has also been increasing over the past few decades (Reardon, Taylor, Stamoulis, Lanjouw, et al., 2000). The roles of infrastructure and credit are important for the promotion of rural non-farm enterprises. Without good rural roads, transport and communications, it will be difficult to promote the development of trade, marketing and distribution network. Other social infrastructures like education and health services are also essential in many ways and must be adequately provided. Regarding credit or finance, this is generally essential for all kinds of development activities especially non-farm enterprises. Specialized credit institutions may be established to provide special services to rural non-farm enterprises, including small-scale and micro enterprises (Onchan, 2001). The majority of the population in Nigeria still live in rural areas and in most cases, the primary occupation of people in the rural areas is farming. The job-generating capacity of the sector, however, has been hindered by a number of factors such as the size of landholdings which are usually small, limited adoption of modern technology, lack of adequate rural infrastructure and instability of farm prices. The significant decline of the sector s contribution to the economy in terms of GDP in many countries, as well as the impact of trade liberalization are also limiting the ability of agriculture to create more employment opportunities in the rural areas. Accordingly, governments at all levels in the country have been prompted to promote the development of the RNF sector as a complementary measure for rural development. Such promotion has been pursued specifically to generate employment opportunities and hence improve household incomes in rural areas (Davis, 2003). The common problems facing RNFE includes: inadequate rural infrastructure, particularly, roads, electricity and communications facilities; lack of adequate skilled labor in rural areas; limited access to credit; lack of adequate access to information/lack of awareness about non-farm employment opportunities; inadequate supply of raw materials for sustaining rural industries; lack of training facilities in rural areas; inadequate incentives and government support; and lack of effective and efficient strategies that will enable rural industries to compete in or penetrate the global market. Government programs that promote RNFE are not enough to promote RNFE and do not really address its concerns. Thus, the government will need to pay more attention to the policies that will encourage more RNF activity. Funding and investment opportunities available for non-farm employment is visibly lacking despite efforts by government and nongovernment agencies. This lack of funding and investment opportunities hampers the growth of small and medium enterprises which are the major creators of non-farm employment. This raises the following pertinent questions. What are the non-farm enterprises engaged by rural women? What are the modes of occupational entry for each enterprise? And what are the problems encountered by rural women in non-farm enterprises? Specifically, the study was designed to: identify various non-farm enterprises carried out by rural women; ascertain modes of occupational entry for each enterprise; and identify problems faced by rural women in rural non-farm enterprises. Hypothesis of the study There is no significant relationship between socio-economic factors and rural non-farm enterprises among rural women in the study area. 43

3 Methodology The study was carried out in Anambra State, Nigeria. The state which is located on latitude and north and longitude and east shares boundary with Enugu State on the north, Delta State on the south, Kogi State on the west and Imo State on the east. It has an estimated population of 4.18 million and land area of approximately 5,025 sq.km (National Population Commission (NPC), 2006). The estimated population of rural women in Anambra State is 1.44 million (NPC, 2006). The population of the study comprised rural women in the four agricultural zones. Map of the study area is shown in figure 1 below. Majority of rural women in the State are involved in the production of arable crops such as yam, cassava, cocoyam, maize, vegetables and raising of farm animals like sheep, goat, and poultry. The state also produces perennial crops such as oil palm, mango, oil bean, pear, breadfruit, among others. The primary occupation of people in the area is farming, though there is diversification into non-farm enterprises such as petty trading, handicraft, among others. There are four agricultural zones in the state, namely; Aguata, Anambra, Awka and Onitsha. All the four agricultural zones were purposively used for the study. Anambra zone is made up of four (4) extension blocks comprising 45 circles, Awka zone comprises five (5) blocks and 35 circles, while Aguata zone is made up of six (6) extension blocks, comprising 45 circles. There are also six (6) extension blocks comprising 30 circles in Onitsha zone. Two (2) rural blocks were selected from each of the zones, while three (3) circles were selected from each of the blocks using simple random sampling. In each of the circles, 20 rural women were selected using simple random sampling. Eight (8) blocks and 24 circles, comprising 480 respondents were supposed to be used for the study. Data for the study were collected using interview schedule/questionnaire. Interview schedule was used for illiterate rural women while copies of questionnaire were administered to literate rural women by the researcher and trained assistants. The interview schedule/questionnaire was divided into three sections (A to C) based on the specific objectives of the study. Eighteen copies of the questionnaire were not filled properly and were dropped leaving 462 used for analysis. Percentage, mean scores, standard deviation, factor analysis and multiple linear regression were used for data analysis. Figure 1. Map of Anambra State showing the study area Results and Discussion Socio-economic characteristics of respondents Results in Table 1 indicate that majority (60.5%) of the respondents were within the age range of years. The mean age of the respondents was 50.0 years. This implies that majority of the respondents were young and in productive years, hence greater involvement in non-farm activities. The standard deviation was 11.7 (Table 1). This shows that ages of the respondents vary so much. As rural women grow older, there is a possibility of less involvement in the number of occupations 44

4 carried out by such individuals and vice versa. The findings is supported by Ranjan (2006) who notes that human capital attributes such as age broadens employment and entrepreneurial options for individuals participating in rural non-farm enterprises. Majority (69.3%) of the respondents was married, while 25.3% and 5.4% of the respondents were widowed and single, respectively. This indicates that most of the respondents have husbands who may be providing financial support for them in their activities. Oberhauser and Pratt (2004) note that married people have responsibility for provision of household needs of their families hence greater involvement in non-farm activities for economic empowerment. Data on level of education of the respondents reveal that majority (93.3%) were literate with about 16% having completed higher education (Table 1). The mean year of formal education was 9 years. The standard deviation was 4.5. It shows that the level of education of the respondents vary so much, indicating that there were those with higher education as well as those without formal education. It therefore implies that with a majority of the respondents having formal education they are better equipped to enter into various non-farm enterprises. The findings agree with Ranjan (2006) who asserts that level of education increases participation rate in occupations for rural women. Educated rural women are likely to possess skills which facilitate successful involvement in non-farm activities. This includes the ability to manage a business, process relevant information and adapt to changing demand patterns. They also have greater aspirations with regard to working outside agriculture. Majority (62.8%) of the respondents had a household size of 1-5 persons and 36.8% had a household size of 6-10 persons. The mean household size was 5 persons. Size of household can be a key variable in determining whether the respondents should be engaged in non-farm enterprises. Large household size could serve as source of labour used in non-farm activities. This is in agreement with Economic and Social Commission for Asia and the Pacific (ESCAP), (1999) which reports that rural families are characterized by large family size, demanding for greater involvement in non-farm activities in order to meet up with household responsibilities during off season farming periods. Emodi (2009) reiterates that rural households in Nigeria are characterized by high number of members with high dependency ratio. About 45% of the respondents had over 19 years working experience with a majority (34.9%) having years of experience. The standard deviation was This shows that working experience of the respondents vary much from the mean. The mean working experience was 20.1 years. This shows that most of the respondents have been involved in non-farm activities for quite a period of time thus making them to be economically empowered. Table 1. Percentage distribution of socio-economic characteristics of the respondents (n= 462) Variable Percentage Mean (M) Standard deviation (SD) Age (years) and above 4.2 Marital status Single 5.4 Married 69.3 Widowed 25.3 Educational qualification (years) No formal education Primary school attempted 12.5 Primary school completed 28.8 Secondary school attempted 11.2 Secondary school completed 25.3 OND/NCE holders 8.9 HND/first degree 6.6 Household size (persons) and above 0.4 Working experience (years) and above 3.5 *Multiple responses 45

5 Types of non-farm enterprises carried out by the respondents A greater proportion (60.2%) of the respondents were involved in petty trading, 11.5% were involved in tailoring, 7.8% were teachers, 7.1% were involved in making of confectioneries, while 6.4% of them were involved in hair dressing/weaving of hairs (Table 2). Other non-farm enterprises include: public service (6.0%), frying of beans balls, yams and potatoes (4.2%), handicrafts such as making of brooms (3.9%), catering service (3.9%), making of baskets (3.2%), making of soap and pomade (2.9%), among others. This implies that the respondents were involved in non-farm enterprises to enable them obtain additional income to empower themselves financially. The findings are in line with Haggblade (1999) who reports that women dominate many of the non-farm activities such as petty trading, tailoring and many services that will grow most rapidly during structural transformation. Continuing, he notes that they also hold a major interest in many of the declining rural non-farm occupations such as basket making. Consequently, women will be key actors in the economic transition of Africa s rural economy. Table 2. Percentage distribution of respondents according to their involvement in non- farm enterprises (n= 462) Types of enterprise Percentage Handicrafts such as making of brooms 3.9 Making of baskets 3.2 Making of hand fans 1.0 Making of beads 0.5 Petty trading on food items such rice, beans, gari and palm oil 60.2 Tailoring/making of dresses 11.5 Making of confectioneries such as cake, chin-chin, meat pie and buns 7.1 Making of soap and pomade 2.9 Frying of beans balls, yams and potatoes 4.2 Hair dressing/weaving of hair 6.4 Teaching 7.8 Traditional birth attendance 1.0 Catering service 3.9 Wage labour 0.7 Public service 6.0 *Multiple responses Estimated annual income ( ) from non-farm enterprises in 2010 Results in Table 3 show that 37.5% of the respondents earned from sale of brooms, while 25.0% earned 6,001-8,000, among others. The mean income earned from broom production was about 4,969. This implies that the respondents made low income from the sale of brooms considering the small amounts they realized from sales. Majority (53.8%) of the respondents earned 5,000 or less from basket production, while 46.2% earned 10,001-15,000.The mean amount earned was 7, About 99% of the respondents earned less than or equals to 4,000 from sale of hand fans, while 0.8% earned between 4,001 and 8,000. The mean income from sale of hand fans was 8,000. This implies that the respondents did not obtain much income from sales of hand fans. Entries in Table 3 show that 99.6% of the respondents earned less than 10,000 from sale of beads, while 0.4 of them earned 20,001 and above. The mean income realized from sale of beads was 25,000. This shows that the respondents had high income from sale of beads. Distribution of respondents according to estimated income from petty trading is also shown in Table 3. This indicates that 77.6% of the respondents earned 100,000 or less than that from petty trading, 9.8% earned 100, ,000, about 6% earned 200, ,000, among others. The mean income from petty trading was about 103,019. This implies that the respondents got higher incomes from petty trading. This could make the respondents to diversify more into different forms of petty trades in order to obtain high incomes which enable them to be economically strong to take care of family responsibilities such as payment of children s school fees and other basic needs. A greater proportion (54.0%) of the respondents earned 50,000 or less from tailoring, 40.0% earned 50, ,000, while about 4% and 2.0% earned 100, ,000 and 150, ,000, respectively. The mean income from tailoring was 55,736. Data in Table 3 reveals that about 50% of the respondents involved in making of confectioneries earned 80, ,000 while 35.7% and 10.8% earned 40,001-80,000 and over 120,000, respectively. The mean income from making of confectioneries was about 85,129. A greater proportion (58.3%) of the respondents involved in making of soap and pomade earned 40,001-80,000 while 25.0% and about 17% earned 40,000 or less and over 80,000, respectively. The mean income was 53,500. The respondents obtained high income from sale of soap and pomade, probably because they produced on a large scale. A greater proportion (69.6%) of the respondents earned 50,000 or less from frying of beans balls, yams and potatoes, about 17% earned between 50,001 to 100,000, while 4.3% and 8.7% earned over 150,000 and 100, ,000, respectively. The mean income was about 67,504. The income realized from sale of beans balls, yams and potatoes could be seen as a manageable amount which could enable them to fend for members of the households. 46

6 Results in Table 3 indicate that 63.0% of the respondents earned 40,001-80,000 from hair dressing and weaving of hair, while 22.2% and about 15% earned 20,001-40,000 and over 80,000, respectively. The mean income was about 59,422. About 70% of the respondents involved in teaching earned above 400,000, while 18.2% and about 9% earned 200, ,000 and 300, ,000, respectively. The mean income from teaching was about 456,758. The income earned by the respondents from teaching is quite high, this could be attributed to the fact that the respondents involved in teaching were placed on monthly salary which was mostly paid by the government. Majority (67.7%) of the respondents involved in traditional birth attendance earned 300, ,000, while 33.3% earned 100, ,000. The mean income was about 286,667. A greater proportion (58.3%) of the respondents who were public servants earned above 300,000, while 37.5% and 4.2% earned 100, ,000 and 200, ,000, respectively. The mean income was about 186,472. About 31% of the respondents earned above 300,000 from catering services, 30.7% earned 100, ,000, among others. The mean income was about 284,615. Most (60.0%) of the respondents earned 40,001-80,000 from wage labour while 20% earned 20,001-40,000, among others. The mean income from wage labour was 60,000. Table 3 also reveals total non-farm income and mean income of 67,494,900 and 125,364, respectively. This implies that the respondents recorded high incomes from non-farm enterprises. The findings of this study contradicts Lanjouw and Lanjouw (2001) who note that rural non-farm enterprises have been viewed as a low productivity sector which generates inferior goods expected to wither away as a country develops and incomes rise. This is also in line with Ranjan (2006) who notes that lack of adequate knowledge about the potential role of the rural non-farm sector, an integral component of the rural economy had resulted in a relatively scant cognizance of its role in the overall development process. This gap in knowledge is attributed to rural non-farm sector s great heterogeneity, coupled with inadequate attention at both the empirical and theoretical level. Table 3. Percentage distribution of respondents based on estimated income from non-farm enterprises in 2010 Estimated income ( ) Percentage Mean (M) Standard deviation (SD ) Non-farm enterprises Handicraft Broom (n= 16) 2, ,001-4, ,001-6, ,001-8, ,001-10, Baskets (n= 13) 5, ,001-10, ,001-15, Hand fans (n= 4) 4, ,001-8, Making of beads(n= 2) 10, ,001-20, ,001 and above 0.4 Petty trading (n= 277) 100, , , , , , , ,001 and above 4.2 Tailoring (n= 50) 50, , , , , , , Making of confectioneries (n= 28) 40, ,001-80, , , ,001 and above 10.8 Making of soap and pomade (n= 12) 40, ,001-80, ,001 and above

7 Frying of beans balls, yams and potatoes (n= 23) 50, , , , , ,001 and above 8.7 Hair dressing/weaving of hair (n= 27) 20,000-20,001-40, ,001-80, ,001 and above 14.8 Teaching (n= 33) 200, , , , , ,001 and above 69.7 Traditional birth attendance (n= 6) 100, , , , , , , Public service (n= 24) 100, , , , , ,001 and above Catering service (n= 13) 100, , , , , ,001 and above 30.8 Wage labour (n= 5) 20,000-20,001-40, ,001-80, ,001 and above 20.0 Overall mean Modes of occupational entry for each non-farm enterprise All (100%) the respondents involved in teaching occupation got formal training in school, 84.6% entered into public service through formal training in school, 99.6% of the respondents got into basket making through on-the-job training, 90.4% started making beads through apprenticeship, among others. Most of the non-farm enterprises engaged in were either very simple or the skills needed could be acquired outside the formal school system through family mentoring, friends and on-thejob training. Formal training was not adequately represented as modes of entry in most of the non-farm occupations in spite of many poverty alleviation programmes and skill acquisition training programmes introduced by the government. May be the respondents were not beneficiaries of such programmes. In as much as government had established such programmes, some of them have failed to achieve the desired goals since most of them were politicised. When the regime of any government which introduced a programme is over and another government takes over, that will be the end of such programme. The new government introduces a different programme which can be for self interest. Another factor for poor representation of formal training could be the desire for people to make quick money, they will not have the patience to learn a skill instead they prefer to be involved in petty trading for quick returns. Efforts should be made to encourage rural women to acquire formal training for skill acquisition. This is to enable them acquire skills that will necessitate entry into various occupations for better returns. This is in agreement with Fafchamps and Minten (1998) who report that certain employment opportunities in non-farm enterprises may not require a great deal of capital, experience or skill, but a friendship or kinship relationship might be an important determinant of access or entry. 48

8 Table 4. Percentage distribution of respondents according to modes of occupational entry for each occupational areas (n= 462) Types of No training Apprenticeship Family On-the-job Vocational Formal training in school enterprises mentoring training Handicrafts such - - as making of brooms Making of baskets Making of hand fans Making of beads Petty trading on food items such rice, beans, gari and palm oil Tailoring/making of dresses Making of confectioneries such as cake, chin-chin, meat pie and buns Making of soap and pomade Frying of beans balls, yams and potatoes Hair dressing/weaving of hair Teaching Traditional birth attendance Public service Catering service Wage labour Major sources of funds Data in Table 5 reveal that the major sources of funds for the respondents were self generation, husband, children, parents, thrift (isusu) and government. The Table indicates that majority (45.5%) of the respondents generate funds by themselves, 42.0% got from their husbands, while about 8% got from their children, among others. This shows that husbands of the respondents assist in provision of funds for non-farm enterprises since majority of them were married women as shown in Table 1. Table 5. Percentage distribution of respondents according to major sources of funds (n= 462) Major sources of funds Percentage Self generation 45.5 Husband 42.0 Children 8.0 Parents 0.4 Isusu 0.9 Government 3.2 Problems faced by rural women in non-farm enterprises The major problems faced by rural women in occupational diversification were lack of women empowerment training programmes in rural areas (M= 3.5), poor skill training (M= 3.5), inadequate training opportunities (M= 3.4), poor educational attainment (M= 3.3), high cost of transportation (M= 3.3) and inadequate finance/credit facilities (M= 3.3). Other problems include: increase in workload of domestic chores alongside with occupations (M= 3.2), inadequate provision of loan (M= 3.2), high cost of labour (M= 3.1), inadequate labour saving technology (M= 3.1), high health risks (M= 3.1), lack of access to modern technology/capital (M= 3.1), poor market networks (M= 3.1), among others (Table 6). Standard deviation was also presented in Table 6. It was observed that standard deviation for most of the problems were less than one, while others were more than one. This shows that there was no uniformity as regards the responses of the respondents which gave rise to disparities in the various problems indicated by the respondents. 49

9 The respondents were highly constrained by training-related problems as indicated in Table 6. This poses a lot of challenges among the rural women. The findings of this study are in agreement with Singh and Kumar (1995) which point out that numerous socio-economic factors such as family responsibilities like child care and food preparation, poor health, limited access to education and lack of skills constrain the ability of women to devote considerable time to economic activities. Vyas and Bhargava (1995) reiterate that social disapproval and family pressures faced by many women discourage them from entering into economic activities outside the household. In the past, women were regarded as people who should not have a job outside their communities. Presently, there exists family approval for them to be gainfully employed wherever job opportunity exists. Table 6. Mean score of problems faced by rural women in occupational diversification (n= 462) Problems Mean scores (M) Standard deviation (SD) Poor educational attainment Social norms restricting female mobility and ability to work outside household Government policy due to taxes, licenses, roadblocks, residence permits (multiple taxation) Increase in workload of domestic chores alongside with occupations Inadequate provision of loan Inadequate training opportunities Poor road networks Unavailability of labour High cost of labour Domestic chores not leaving enough time to pursue other activities Inadequate labour saving technology Working longer hours Inadequate finance/credit facilities Low wages/poor conditions of work Absence of social security benefits High health risks Lack of enabling policy environment to promote women s entrepreneurship Lack of access to modern technology/capital Lack of personal security and risk of sexual harassment Low level of self-confidence Socio-cultural barriers such as exclusive responsibility for household work Poor market information on prices of goods and services High cost of production leading to less competitive prices Lack of women empowerment training programmes in rural areas. Poor market networks High cost of transportation Poor skill training Test of hypothesis Relationship between socio-economic factors and non-farm enterprises Results of the multiple linear regression analysis on relationship between socio-economic factors and non-farm enterprises are presented in Table 7. The overall regression model was significant (F= 6.263; p 0.05), accounting for 13.3% (R-squared) of the variance. Variables that had a significant relationship with non-farm enterprises were age (t= 4.402; p= 0.000), marital status (t= ; p= 0.017), working experience (t= ; p= 0.000), and household size (t= 2.173; p= 0.030). Years spent in school (t= ; p= 0.618), membership of organization (t= ; p= 0.382), number of agricultural extension contacts (t= ; p=0.230), number of non-farm extension contact (t= ; p= 0.575), cosmopoliteness (t= 1.752; p= 0.080) and distance to nearest urban market (t= 1.220; p= 0.223) were not significant with non-farm enterprises. Age of the respondents had a significant influence on non-farm enterprises. This could be as a result of the fact that the respondents were in their productive years, and this could help them to become involved in multiple occupations. Marital status had a significant influence on non-farm enterprises. Married rural women have the tendency to diversify in occupations. This is to enable them sustain their families economically. The husbands and children could assist 50

10 them financially in their economic activities. There exists a significant relationship between working experience and non-farm enterprises of the respondents. The more experienced an individual is, the more she tends to specialize on such enterprise. Household size had significant influence on non-farm enterprises. The larger the household size, the more the respondents are bound to be involved in non-farm enterprises. Table 7 also reveals that there was no significant relationship between years spent in school and non-farm enterprises. Better educated rural women are more likely to take up lucrative jobs for better pay and settle for it while uneducated ones are likely to be involved in less remunerative jobs with less pay thereby leading to multiple occupations in order to meet up with family responsibilities. There was also no significant relationship between membership of organization and non-farm enterprises. This implies that membership of organizations is not a guarantee for the respondents to be involved in various nonfarm enterprises. Most rural women who are members of many organizations battle with time and financial commitments. There was also no significant relationship between non-farm extension contacts and non-farm enterprises. Rural women may not want to diversify into various occupations when they have non-farm extension contacts which may come from either governmental or non-governmental organizations. Cosmopoliteness had no significant relationship with non-farm enterprises as seen in Table 7. Visits to urban towns may not necessitate involvement in non-farm enterprises, even when ideas or interactions were gotten by the respondents. They still need to consider other factors such as finance. Distance to nearest urban market also had no significant relationship with non-farm enterprises. The ability of the respondents to be involved in non-farm enterprises may not be dependent on the proximity to nearest urban market. The tendency to be involved may not be there, especially when it is an agrarian community. Based on the regression results, the null hypothesis was rejected. Table 7. socio-economic factors influencing non-farm enterprises Variables Unstandardized coefficients Standardized coefficients B Std. Error Beta T Sig Constant Age Marital status Years spent in school Working experience Household size Membership of organization Number of non-farm extension contact Cosmopoliteness Distance to nearest urban market Dependent variable : Occupational diversification R 2 = 0.133; Adjusted R 2 = 0.112; F-value = 6.263; p 0.05 Conclusion and Recommendations The economic planning assumption is that all rural people are involved in agricultural production, but the study show that rural women are involved in non-farm enterprises such as petty-trading, tailoring, teaching, hair dressing, making of confectioneries, public service, among others. This is to enable them obtain income to take care of their families financially. The respondents indicated that they were highly constrained by training-related problems such as lack of women empowerment training programmes in rural areas, poor skill training, inadequate training opportunities, poor educational attainment, among others. Strategies for promoting rural non-farm enterprises for employment generation among rural women involve the development of small-scale businesses or SMEs in rural areas, target marketing, micro-financing schemes and development of women entrepreneurs. The study recommends that the role of government is crucial in providing necessary infrastructure and other support services needed by rural women for economic empowerment. Human resources development, availability of financial/credit facilities and women s participation in making policies on rural non-farm small-scale enterprises remain paramount. References Ajani EN Occupational diversification among rural women in Anambra State, Nigeria. PhD Thesis, Department of Agricultural Extension, University of Nigeria, Nsukka, p. 84. Davis R.2003.The rural non-farm economy, livelihoods and their diversification: Issues and options. Department for International Development, p

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