Employment Diversification Towards Non Farm Sector

Araujo (2004) conducted a study to assessed the impact of non-agricultural rural employment on poverty and to explored in which environment policy interventions in education and roads can be more effective in reducing poverty through non agriculture rural employment. He found that manufacturing employment is more poverty reducing than services in semi urban municipalities. He suggested that poverty is higher in municipalities with higher income inequality and with lower government expenditure and these two effects are stronger in semi-urban than in rural municipalities. He also observed that interventions in education and roads are poverty reducing through manufacturing employment in semi-urban municipalities and through services employment in all municipalities.

Schwarze and Zeller (2005) described the income activities of rural households and examined the determinants of non-farm diversification by using Tobit model. He found that agricultural activities are the most important source of income for rural households in the vicinity of the Lore Lindu National Park in Indonesia which contribute 68% to total household income with the remaining 32% originating from non agricultural activities. He also found that the poverty index and the access to formal financial market both have a positive impact on the share of non-agricultural income.

Zhu et al. (2005) observed the role of non-farm income in reducing rural poverty and inequality in China. He found that without non-farm employment, rural poverty would be much higher and deeper and that income inequality would be higher as well. He concluded that participation in non-farm activities provides rural households with an additional source of income, improving their living standards and narrowing income gaps as well.

Fabusoro et al. (2010) conducted a study to examine the forms and determinants of livelihood diversification among rural farm families in Ogun state, Nigeria. The study revealed the importance of diversification as it accounted for 69.1% of household income. Education, Household size and income were significant predictors of diversification. The non-farm activities identified in the study have the potential for enhancing the capability of individuals and households to construct positive livelihoods. The successes of these activities depend largely or partly on success achieved in agriculture which is a significant determinant of diversification.

Asmah (2011) found that level of welfare is higher for those farm households who work in non-farm sector than farm sector. The non-farm diversification activities and household welfare are mostly driven by household assets and compositions including household age structure, education level and gender. Livelihood diversification is an important mean of enhancing welfare and deserves attention.

Darry and Kuunike (2012) identified the types and determinants of probability of participation in non-farm employment areas in the upper West Region of Ghana. He observed five groups of NFEAs such as extractive, manufacturing/processing, constructive, commercial and direct services. Overall, commercial services dominate the non-farm economic activities (32%) followed by constructive industry (21%); manufacturing (20%), extractive industry (18%) with personal services recording the least (9%). The predominant non-farm economic activities found include trading charcoal and fuel wood production, casual employment in building and construction pito (local beer) brewing, stone mining, food vending and retail shop operation. The factors such as sex, age, marital status, education, vocational training, belongingness to a group and location also found to be significantly associated with the probability of participation in NFEAs.

Akaahol (2014) examined the determinants of diversification using Logit model and also the impact of diversification on household welfare by using OLS regression model in Benue state. He found that probability of diversification increases with male-headed household, education, credit and market and decreases with farming experience. He also found that diversification, age, education and credit have a positive effect on household welfare while household size has a negative effect.

Katega and Lifuliro (2014) investigated the extent to which rural non-farm activities contribute to alleviating poverty in participating households. He also examined the factors affecting the performance of rural non-farm activities and the mechanisms through which rural non-farm and farm activities are interlinked. He found a number of factors affected the performance of non- farm activities, including inadequate capital, lack of business education, poor business premises, inefficient transport to and from markets, and women’s gender roles. He observed that rural farm and non-farm activities are interlinked because in most households farm activities provided the capital for starting and running non-farm activities and non-farm activities provided the income to purchase farm inputs. He also found that rural non-farm activities contributed a significant share of total income in participating households and enabled these households to purchase food and consumer goods, pay for medicine and health care, pay for the education of children, as well as invest in farm inputs to enhance the productivity of agricultural activities such as crop farming and livestock keeping. He concluded that rural non-farm activities play an important role in alleviating both income and non-income poverty.

Zeray et al. (2015) investigated the push and pull factors that influence the participation decision of rural households in non-farm activities by using Multinomial Logistic Regression and the income obtained from this sector by using Tobit model. He observed that only 21% of the total household income was derived from different non-farm activities with a participation rate of 46%. He found that better education, land holding, access to irrigation and number of adult members positively influenced the likelihood of involvement in non-farm activities and also access to credit, better land size, livestock and number of adults in the household significantly and positively influenced the share of income from RNFE. He concluded that the household with better economic condition are pulled to the non-farm sector by the better return from the non-agricultural sector.

Sultana et al. (2015) conducted a study to present an empirical evidence of the state of income diversification and its impact on household’s well-being in the rural areas of Rajshahi district of Bangladesh. A multistage random sampling technique is used for collecting the data. To measure the level of income diversification Simpson Index of Diversity is calculated and to measure the level of well-being, household consumption expenditure is used. He observed that the extent of income diversification is comparatively low in the study area and it has positive and significant effect on household’s well-being.

Abdulsalam et al. (2015) examined the factors that determine non-farm occupations among rural farming households and to what livelihood strategies has improved the well-being of their households. It has been observed that the factors that influence the rural farming household decision to participate in non-farm activities showed slight variation from those influencing level of decision (livelihood strategies) taken to engage in non-farm activities and where it does, not by the same magnitude and direction. Distance travelled and adjusted household size was found to significantly influence the farmer’s decision and education, poverty status and per-capita income did influence the level of participation significantly.

Guatam and Anderson (2016) assessed the role of livelihood diversification in household well-being in Humla, a remote mountain district in West Nepal. He found a uniform pattern of diversification in terms of the no. of activities undertaken for livelihood but a highly varying degree of resultant well-being across households. He analyzed that well-being was associated with household’s involvement in high return sectors which is dependent on antecedent level of resources and assets. The resource rich households diversify into high return sectors and substantially improve their well-being while the resource poor households, on the other hand, are forced to continue their low return diversification. He concluded that livelihood diversification have a highly skewed effect leading to inequality of income and well-being.

Kessle et al. (2017) investigated the determinants of diversification using Logit model. He found that the institutional factors such as secured land, right perception and cooperative membership have the positive effect on farm household’s decision to participate in non-agricultural activity while age, education and distance to the proximate market have a negative effect.

Ogunsipe et al. (2017) analyzed the contributions of non-farm activities in addressing rural unemployment and determined factors influencing small holder framer’s participation in non-farm activities in Ondo-East local Govt. areas of Ondo State, Nigeria by using Tobit model. He found that age, education, wage earned access to credit and distance were the significant variables influencing participation in non-farm activities. He concluded that non-farm activities help to reduce unemployment supplement farm income, provide a safety net and alleviate poverty among households.

Odoh et al. (2017) determined the effects of socio-economic characteristics of rural households on non-farm income diversification and analyzed the factors that influence farm household’s participation in different non-farm activities. He observed that socio-economic characteristics of the farm households have significant effect on their non-farm income. He found that 82.5% of the farm households diversified their income, 17.5% solely dependent on income from farming activities. He factors that influenced rural farm household’s participation in different non-farm activities were found to be poor returns to agricultural activities, small farm size, risk and uncertainties in agriculture, membership of social organization, poor household earnings from farming, limited access to credit facilities and profit motive. 

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