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Determinants of Off-Farm Income among Smallholder Rice Farmers in Northern Ghana: Application of a Double-Hurdle Model

Determinants of Off-Farm Income among Smallholder Rice Farmers in Northern Ghana: Application of... Hindawi Advances in Agriculture Volume 2019, Article ID 7246176, 7 pages https://doi.org/10.1155/2019/7246176 Research Article Determinants of Off-Farm Income among Smallholder Rice Farmers in Northern Ghana: Application of a Double-Hurdle Model 1 2 Benjamin Tetteh Anang and Richard W. N. Yeboah Department of Agricultural Economics and Extension, Faculty of Agriculture, University for Development Studies, Tamale, Ghana Department of Agribusiness Management and Finance, Faculty of Agribusiness and Communication Sciences, University for Development Studies, Tamale, Ghana Correspondence should be addressed to Benjamin Tetteh Anang; benjamin.anang@uds.edu.gh Received 16 May 2018; Revised 26 December 2018; Accepted 30 December 2018; Published 13 January 2019 Academic Editor: Innocenzo Muzzalupo Copyright © 2019 Benjamin Tetteh Anang and Richard W. N. Yeboah. iTh s is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Income diversification by farm households has gained the attention of researchers and policy makers due to its commonness especially in developing countries. This study sought to empirically investigate the determinants of off-farm income among smallholder farmers in northern Ghana using a sample of 300 rice farmers. A double-hurdle model was used to determine the factors inu fl encing participation in off-farm work as well as the predictors of actual amounts earned from working outside the farm. eTh results revealed that gender, farming experience, years of education, and access to credit are the factors determining participation in off-farm work while farming experience, years of education, and geographical location are the determinants of income from off-farm work. eTh paper concludes that measures to enhance rural income diversification will spur the rural economy and these measures should seek to address the problem of low level of formal education in rural areas. 1. Introduction The role of the off-farm sector in employment creation, income generation, farm expansion, and poverty reduction Majority of the world’s poor live in rural areas in developing especially in developing countries is well documented (see, countries and depend on agriculture and its related activities for example, [3, 5–7].According to [2],farm households as a source of livelihood. Despite the dependence on agri- diversify their income sources by allocating productive culture in these countries, including Ghana, the agricultural resources among diverse income generating activities includ- sector continues to grapple with challenges that impede its ing farm and off-farm work. Diversicfi ation may be a deliber- growth and contribution to socioeconomic development. ate household strategy or a spontaneous response to crisis as These challenges include dwindling budgetary allocation noted by [8]. It may serve as a safety net for the poor whereas to the agricultural sector, farmers’ inability to acquire and for the rich it may be a means of accumulation. Income replace farm equipment, inadequate credit sources, among diversica fi tion by farm households may also be attributed to others. dwindling and uncertain farm incomes, rising poverty, and In thewakeof the dwindling fortunesand challenges emerging opportunities for off-farm work. Off-farm activities involve participation in remunerative facing agriculture in most developing countries, the rural off- farm sector has emerged as an important source of livelihood work outside the participant’s own farm and have been [1–4]. Income diversicfi ation by farm households has gained recognised to play an increasingly essential role in sustainable development and poverty reduction particularly in rural the attention of governments, policy makers, and researchers because of its commonness and contribution to socioeco- areas [9]. Income from off-farm work supplements on- nomic development especially in developing countries. farm income and helps to expand economic activity and 2 Advances in Agriculture employment opportunities in rural areas. For the agricultural resulting in low level of income. The low income of agricul- sector, income from off-farm activities is an alternative source tural labour as a result of low productivity is a major cause of of income which may be used to finance agricultural pro- rural poverty in many developing countries. A key strategy to enhance agricultural productivity and farm incomes is to duction. Income diversification therefore has the potential to increase farm investment leading to higher productivity. improve human capital, which is embodied in education and Off-farm activities also reduce income uncertainty. As farming experience. Education does not only improve agri- cultural productivity and farm incomes, but also promotes noted by [10], employment diversicfi ation helps households to smooth income through the spread of risks across different off-farm activity participation and the returns from off-farm activities. The reduction in income uncertainty opens up work. Education also facilitates diversification of the rural opportunities to invest in improved production technologies economy away from agriculture. In either way, education to enhance agricultural production. is expected to play a positive role in the rural economy. As indicated by [3], developing the human capital through Income diversification as a livelihood strategy is con- sidered a global phenomenon. According to [11], income training and skill building is important to promote both from nonfarm work contributes 40% to total income in agricultural development and rural off-farm employment. Latin America while, in sub-Saharan Africa, nonfarm income Previous studies on off-farm/nonfarm employment in constitutes between 30% and 42% of total household income. Ghana include [16–21]. Authorssuchas[17, 19] focused on According to [12] the estimated share of nonfarm income in the determinants of participation in rural nonfarm employ- total household income for Asian countries ranges between ment while [18] assessed the relationship between gender, 29% and 32%. poverty, and nonfarm employment in Ghana and Uganda. Agriculture’s contribution to the provision of livelihood Other authors such as [20] explored household-level farm- opportunities in most rural areas cannot be overemphasized. nonfarm linkages and household welfare implications in However, recent structural transformation in the economies Ghana while [21] examined nonfarm work and food security of most countries has seen faster growth in other sec- among farm households in northern Ghana. From these tors of the economy like manufacturing, industry, and the studies, it can be observed that little has been done on service sector, leading to a decline in the contribution of the determinants of total earnings from off-farm income in agriculture to rural livelihoods and gross domestic product Ghana. (GDP). Agriculture therefore cannot be the only source Due to the foregoing, this study was carried out to of livelihood for many rural farm households, hence the investigate the factors influencing participation in off-farm signicfi ance of off-farm enterprises to rural incomes and work as well as the predictors of total amount of off-farm employment creation. For example, [13] highlighted the need income among small-scale farmers in northern Ghana. The for the integration of activities such as nutrition gardening, nfi dings of thestudy areexpected to guidepolicy makers on livestock rearing, product diversicfi ation, and related income measures to improve rural incomes and livelihood security. generation activities as a means of improving household food and nutritional and income security among farmers in 2. Materials and Methods India. An important factor determining employment of rural .. Study Area, Sampling, and Data. The study was carried people in nonagricultural enterprises is level of education. out in the Northern and Upper East Regions of Ghana. The levelof education in most ruralareas of developing These regions and the Upper West Region make up northern countries is lower than what prevails in urban areas, which Ghana where agriculture is the mainstay of the population. makes rural people less likely to be employed in high- Northern Ghana is characterized by savannah vegetation paying jobs in nonagricultural enterprises. Education is also and a unimodal rainfall regime which makes irrigation an an important factor in the decision to participate in off- important requirement for crop production during the dry farm work. Education improves the human capital and the season. likelihood to engage in high-paying nonagricultural jobs. Respondents were selected using multistage stratified Less educated individuals without the requisite skills and random sampling. The Northern and Upper East Regions technical knowhow may find it difficult to participate in many were selected followed by the selection of three major nonagricultural jobs that require specific skills and expertise. irrigation projects for rice production in these areas. These This may impede participation of less educated rural folks included the Botanga Irrigation Scheme in the Northern in the nonfarm labour market. As indicated by [1], better Region, and the Vea and Tono Irrigation Schemes in the education is one of the most important factors aeff cting off- Upper East Region. One hundred (100) rice producers per farm earnings. Similarly, [14] reported that less educated irrigation scheme were sampled to give a total of 300 households in rural Nigeria were limited in their ability to respondents. The total sample was made up of equal number engage in more lucrative off-farm labour activities. As noted of irrigators and nonirrigators. by [15], the nonagricultural sector is vital to employment and income generation in rural areas through the provision of The data collected included individual/household, farm, various economic activities such as petty trading, businesses, and institutional factors. Information on income from cra,ft and services. both farm and off-farm activities was collected. Individ- The marginal productivity of labour in the agricultural ual/household data included gender, age, and educational sector is generally low as a result of disguised employment, attainment of the respondent, household size, among others. Advances in Agriculture 3 Table 1: Data description and summary statistics of the respondents. Variable Definition Mean Std. Dev. Min. Max. Z Participation in off-farm work (1 = yes) 0.427 0.495 0 1 Y Off-farm income (GH¢) per year 1111 1398 0 6300 SEX Sex of respondent (1 = male) 0.78 0.41 0 1 EXP Years of farming experience 20.6 12.2 2 60 EDU Education of respondent (years) 3.85 5.34 0 20 HSZ Household size (number) 9.68 7.20 2 71 DIST Market distance (kilometres) 7.99 4.30 2 18 REG Regional dummy (1 = Northern) 0.33 0.48 0 1 CRED Access to credit (1 = access) 0.40 0.49 0 1 DSPEC Degree of specialization (%) 45.4 25.1 3.6 100 Farm data included farm size, output level, and input and out- zero,𝑥 denotes a vector of explanatory variables,𝛽 represents put prices. Information on access to irrigation and extension a vector of parameters to be estimated, and𝑢 is a random services was also collected. error term. Empirically, the truncated regression model is specified for this study as follows: .. Double-Hurdle Model of Off-Farm Income Determination. The first step in the implementation of the double-hurdle 𝑌 =𝛽 +𝛽 𝑠𝑒𝑥 +𝛽 +𝛽 𝑝 +𝛽 𝑞𝑑 푖 0 1 푖 2 푖 3 푖 4 푖 model relates to the decision or willingness to participate in (4) off-farm work. This binary decision can be modelled as an +𝛽 𝑔 +𝛽 𝑑𝑖𝑠𝑡 +𝑢 5 푖 6 푖 푖 index function using a probit model as follows: where𝑌 is the amount of off-farm income of the 𝑖 th rice ∗ 耠 farmer,𝛽 are coecffi ients of the explanatory variables, and 𝜀 푖 푖 𝑍 =𝑤 𝛼+𝜀 푖 푖 푖 is the random error term. (1) 1, if𝑍 >0 𝑤ℎ𝑒𝑟𝑒𝑍 = 3. Results 0, if𝑍 ≤0 .. Summary Statistics of the Respondents. The summary 𝑍 is a dichotomous variable which takes the value of 1 if the statistics of the respondents are presented in Table 1. respondent is a participant in off-farm work and 0 otherwise, Most of the respondents in the study were male (78%). 𝑤 is a vector of explanatory variables,𝛼 denotes a vector of The average age of the respondents was 41.2 years while parameters, and𝜀 is the error term. household size averaged 10 members. The respondents had The empirical model for rice farmers’ decision to partici- 4 years of formal education and travelled an average of pate in off-farm work is speciefi d for this study as follows: 8 km to the nearest market. Forty-three percent (43%) participated in off-farm work. Total income from off-farm 𝑍 =𝛼 +𝛼 𝑠𝑒𝑥 +𝛼 +𝛼 𝑝 +𝛼 𝑞𝑑 푖 0 1 푖 2 푖 3 푖 4 푖 work averaged 1,111 Ghana Cedis (GH¢) (approximately $232) +𝛼 ℎ𝑠𝑧 +𝛼 𝑔 +𝛼 𝑑𝑐𝑟𝑒+𝛼 +𝛼 𝑑𝑖𝑠𝑡 (2) per annum. On average, respondents had 21 years of farming 5 푖 6 푖 7 8 9 experience and allocated 45% of total land to rice cultivation +𝜀 푖 (a measure of the degree of specialization in rice production). In addition, 33% of the respondents were located in the where 𝑍 measures the choice of the 𝑖 th rice farmer to Northern Region while 40% used credit in farming. participate in off-farm work, 𝛼 is the coefficients of the independent (explanatory) variables, and𝜀 is the error term. .. Amount of Income Earned from Off-Farm Work. Table 2 The second equation in the double-hurdle relates to the presents the amount of income earned from off-farm work amounts of off-farm income earned by the respondents. The by respondents in the study area. Close to 43% of the respon- second hurdle equation can be estimated using a regression dents took part in off-farm work to supplement income truncated at zero (similar to a Tobit model) with the following from on-farm work. Twenty-eight percent of the respondents formulation: earned up to GH¢1,000 which translates into $208. Ten ∗ 耠 𝑌 =𝑥 𝛽+𝑢 푖 푖 푖 percent of the respondents earned between GH¢1,000 and GH¢2,000 from off-farm work. Only 4.3% of the respondents ∗ ∗ (3) 𝑌 , if𝑌 >0 푖 earned between GH¢2,001 and GH¢6,000 from off-farm 𝑤ℎ𝑒𝑟𝑒𝑌 = ∗ work. 0, if𝑌 ≤0 where𝑌 represents the observed income from off-farm work .. Double-Hurdle Regression Estimates of the Determinants which depends on the latent variable𝑌 being greater than of Off-Farm Income. The double-hurdle regression estimates 𝑑𝑠𝑝𝑒𝑐 𝑟𝑒 𝑒𝑥𝑝𝑠 𝑒𝑥 𝑒𝑑𝑢 𝑟𝑒 𝑒𝑥𝑝𝑠 𝑒𝑥 𝑒𝑑𝑢 4 Advances in Agriculture Table 2: Distribution of amount of income earned from off-farm income in total household income for Asia to be 29% to 42%. work by respondents. The amount of income from off-farm work is however very smallwhich may beinadequate to support thehousehold and Off-farm income (GH¢) Number of farmers Percentage its agricultural production. The “one district, one factory” 0 (Nonparticipants) 172 57.3 policy of the government of Ghana which is aimed at creating 1 – 1,000 83 27.7 jobs at the district level is a flagship government policy that could go a long way to enhance rural employment and income 1,001 – 2,000 31 10.3 from off-farm work. 2,001 – 3,000 4 1.3 3,001 – 4,000 1 0.3 .. Factors Influencing Participation in Off-Farm Work. In 4,001 – 5,000 3 1.0 most Ghanaian societies, women play several economic roles 5,001 – 6,000 5 1.7 and are noted for their entrepreneurial abilities. Women More than 6,000 1 0.3 in many rural communities engage in petty trading and other income earning activities to supplement household Total 300 100 income. us, Th women tend to be engaged in multiple off- 1 US dollar equals 4.8 Ghana Cedis. farm activities to supplement household income compared to menand play multiple roles inthe household. The result of this study is consistent with [22] who reported higher of the determinants of off-farm income are presented in participation of women in off-farm work in Malaysia. Other Table 3. The rfi st hurdle related to assessment of the factors previous studies on participation in off-farm work in Ghana influencing the decision to participate in off-farm work using support the n fi dings of this study [17–19]. aprobitmodel (columns 2and 3). The results of the study highlight the important role of The results indicated that gender of the farmer had a education in off-farm work. As noted by [1], education affects significant influence on participation in off-farm work at participation of rural people in off-farm work as well as the 1% significance level. From the results, female respondents amount of income from off-farm work. The result of this had higher participation in off-farm work relative to their study is in consonance with [14] who found that less educated male counterparts. Participation in off-farm work was also households in rural Nigeria were limited in their ability to related to years of formal education. Education had a positive engage in more lucrative off-farm labour activities. and signicfi ant association with off-far work at 1% level. In In addition, the result of the study suggests that experi- addition, farming experience had a positive and signicfi ant enced farmers are more likely to participate in off-farm work relationship with off-farm work at 10% level. Finally, access to compared to less experienced farmers. The result does not credit was negatively related to off-farm work and significant lend itself to easy interpretation but may be indicative of the at 5% level. general lack of job opportunities outside the farm in rural The second hurdle related to the determinants of off- areas. The result agrees with [23] who observed that older farm income (columns 4 and 5). Number of years of farming farmers in Cambodia were more likely to participate in off- experience of the respondents had a positive and significant farm work which is related to their experience. effect on income from off-farm work and was statistically Finally, farmers who had access to credit had a lower significant at 5% level. The quadratic term of the farming propensity to engage in off-farm work, which is consistent experience variable was however positive and significant with apriori expectation. This is because credit tends to at 10% level. In addition, the number of years of formal ease the n fi ancial constraints of farm households and could education of the farmer had a positive and statistically increase on-farm income. Credit-constrained farmers are signicfi ant relationship with off-farm income at 10% level. In therefore more likely to seek employment from off-farm other words, income from off-farm work increased with years sources. The result of this study is contrary to [20], who found of formal education of the respondent. Furthermore, the that access to credit enhanced female participation in off- coefficient of the regional dummy variable was statistically farm employment in Ghana. Owusu et al. [21] also found a signicfi ant at 10% level and indicated that farmers in the positive effect of credit access on participation in nonfarm Upper East Region earned higher income from off-farm work employment in Ghana. compared to those in the Northern Region. The type of off- farm activities included fishing, petty trading, arts and craft, .. Determinants of Off-Farm Income. Education is one businesses, and services. of the key variables in the economic literature influencing participation in off-farm work and total earnings from off- farm work. The result of this study is in consonance with 4. Discussion apriori expectation: education improves the human capital .. Amount of Income from Off-Farm Work. The result and hence the likelihood to engage in high-paying nonagri- cultural jobs. Educated farmers have higher opportunity cost highlights high participation in off-farm work by farmers in northern Ghana. The result is consistent with [11] who of labour and therefore are more likely to trade their labour reported that 30% to 42% of total household income in sub- in the nonfarm labour market. Education is also expected to enhance entrepreneurial abilities and self-employment, Saharan Africa comes from nonfarm work. The study is also consistent with [12] who estimated the share of nonfarm which may enhance the level of income from off-farm Advances in Agriculture 5 Table 3: Maximum likelihood estimates of double-hurdle model for participation in off-farm work and amount of off-farm income. st nd 1 hurdle (Probit model) 2 hurdle (outcome model) Variable Coefficient Std. Error Coefficient Std. Error Sex -0.675∗∗∗ 0.203 0.370 0.173 Education 0.066∗∗∗ 0.015 0.037∗ 0.056 Experience 0.037∗ 0.022 -0.058∗∗ 0.039 Experience squared -0.001 0.000 0.001∗ 0.092 Household size -0.016 0.013 - - Regional dummy 0.330 0.290 -0.416∗ 0.070 Access to credit -0.549∗∗ 0.261 - - Specialization 0.003 0.003 - - Market distance -0.011 0.018 -0.019 0.389 Intercept -0.117 0.367 7.734∗∗∗ 0.000 Sigma 1.151 0.170 Covariance -0.945∗∗∗ 0.289 ∗∗∗ Significant at the 1% level.∗∗ Significant at the 5 % level.∗Significant at 10 % level. work. On the other hand, less educated farmers may not hurdle related to the estimation of amount of income earned have the required skills and technical knowhow which may from off-farm employment. The results of the study indicate hinder their participation in high-paying nonagricultural that small-scale farmers in the study area earn low income wage employment. The n fi ding is consistent with [14] in from off-farm work. This may suggest unavailability of well- their study of off-farm labour market participation in rural paying jobs outside the farm, the lack of requisite skills Nigeria which showed that education enhances employment by farmers to participate in off-farm labour activities, or of rural people in higher-paying nonfarm activities. As inadequate off-farm opportunities. indicated by [24, 25], education creates opportunities for A major conclusion from the study is that human capital farm households to participate in higher-return nonfarm plays an important role in off-farm income determination. activities. Lack of education creates entry barriers to certain Education had a positive inu fl ence on both the decision off-farm employment opportunities [16] leading to a situation to participate in off-farm work and the amount of income referred to as labour market dualism [1] where individuals earned from off-farm employment. u Th s, educated farmers with education get employed in relatively high-paying jobs have higher propensity to participate in off-farm work and while the uneducated end up with relatively low-paying jobs. tend to earn higher incomes from off-farm activities. On Majority of the participants in off-farm work were self- the other hand, farming experience had a negative influence employed as fishermen, petty traders, craftsmen, and small on the amount of off-farm income and a positive inu fl ence business owners. on off-farm employment. This implies that less experienced The study also indicated that experienced farmers had farmers are less likely to participate in off-farm work, but if lower incomes from off-farm work, which is consistent with they participate, they will tend to earn higher income than apriori expectation. Experienced farmers are expected to more experienced farmers. devote more time to farming and less time to off-farm work, Policy recommendations arising from the study include thus earning less income from off-farm activities. the following: To enhance rural employment in off-farm In addition, the study underlined that income from activities, there is the need to promote rural industrialization off-farm work differs across regions. Northern Region has such as the Government of Ghana’s “one district, one factory” greater agricultural potential than the Upper East Region policy initiative. In addition, since most Ghanaian rural in terms of fertile lands for crop production. Land scarcity communities are agrarian in nature, the government of also characterizes agricultural production in the Upper East Ghana must take steps to make agriculture attractive to Region, which may encourage the trading of agricultural keep the youth in particular in farming. Agriculture is the labour for off-farm income. Households facing the challenge largest employment sector in the Ghanaian economy and of land scarcity are therefore more likely to trade agricultural the economic potentials of agriculture to socioeconomic labour for nonagricultural wage employment, which may development have been recognized. Hence, government’s result in higher income from off-farm work. facilitation of measures that promote agriculture such as the provision of credit to farmers will enable producers with large farmlands to increase production. This will boost 5. Conclusion and Policy Recommendations employment in agriculture and reduce rural-urban migration of the youth. Government must also create the enabling The study assessed the determinants of off-farm income environment for value addition to agricultural products of smallholder rice farmers in northern Ghana using a produced in rural areas which will increase opportunities for double-hurdle model. 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Determinants of Off-Farm Income among Smallholder Rice Farmers in Northern Ghana: Application of a Double-Hurdle Model

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Hindawi Publishing Corporation
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Copyright © 2019 Benjamin Tetteh Anang and Richard W. N. Yeboah. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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10.1155/2019/7246176
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Abstract

Hindawi Advances in Agriculture Volume 2019, Article ID 7246176, 7 pages https://doi.org/10.1155/2019/7246176 Research Article Determinants of Off-Farm Income among Smallholder Rice Farmers in Northern Ghana: Application of a Double-Hurdle Model 1 2 Benjamin Tetteh Anang and Richard W. N. Yeboah Department of Agricultural Economics and Extension, Faculty of Agriculture, University for Development Studies, Tamale, Ghana Department of Agribusiness Management and Finance, Faculty of Agribusiness and Communication Sciences, University for Development Studies, Tamale, Ghana Correspondence should be addressed to Benjamin Tetteh Anang; benjamin.anang@uds.edu.gh Received 16 May 2018; Revised 26 December 2018; Accepted 30 December 2018; Published 13 January 2019 Academic Editor: Innocenzo Muzzalupo Copyright © 2019 Benjamin Tetteh Anang and Richard W. N. Yeboah. iTh s is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Income diversification by farm households has gained the attention of researchers and policy makers due to its commonness especially in developing countries. This study sought to empirically investigate the determinants of off-farm income among smallholder farmers in northern Ghana using a sample of 300 rice farmers. A double-hurdle model was used to determine the factors inu fl encing participation in off-farm work as well as the predictors of actual amounts earned from working outside the farm. eTh results revealed that gender, farming experience, years of education, and access to credit are the factors determining participation in off-farm work while farming experience, years of education, and geographical location are the determinants of income from off-farm work. eTh paper concludes that measures to enhance rural income diversification will spur the rural economy and these measures should seek to address the problem of low level of formal education in rural areas. 1. Introduction The role of the off-farm sector in employment creation, income generation, farm expansion, and poverty reduction Majority of the world’s poor live in rural areas in developing especially in developing countries is well documented (see, countries and depend on agriculture and its related activities for example, [3, 5–7].According to [2],farm households as a source of livelihood. Despite the dependence on agri- diversify their income sources by allocating productive culture in these countries, including Ghana, the agricultural resources among diverse income generating activities includ- sector continues to grapple with challenges that impede its ing farm and off-farm work. Diversicfi ation may be a deliber- growth and contribution to socioeconomic development. ate household strategy or a spontaneous response to crisis as These challenges include dwindling budgetary allocation noted by [8]. It may serve as a safety net for the poor whereas to the agricultural sector, farmers’ inability to acquire and for the rich it may be a means of accumulation. Income replace farm equipment, inadequate credit sources, among diversica fi tion by farm households may also be attributed to others. dwindling and uncertain farm incomes, rising poverty, and In thewakeof the dwindling fortunesand challenges emerging opportunities for off-farm work. Off-farm activities involve participation in remunerative facing agriculture in most developing countries, the rural off- farm sector has emerged as an important source of livelihood work outside the participant’s own farm and have been [1–4]. Income diversicfi ation by farm households has gained recognised to play an increasingly essential role in sustainable development and poverty reduction particularly in rural the attention of governments, policy makers, and researchers because of its commonness and contribution to socioeco- areas [9]. Income from off-farm work supplements on- nomic development especially in developing countries. farm income and helps to expand economic activity and 2 Advances in Agriculture employment opportunities in rural areas. For the agricultural resulting in low level of income. The low income of agricul- sector, income from off-farm activities is an alternative source tural labour as a result of low productivity is a major cause of of income which may be used to finance agricultural pro- rural poverty in many developing countries. A key strategy to enhance agricultural productivity and farm incomes is to duction. Income diversification therefore has the potential to increase farm investment leading to higher productivity. improve human capital, which is embodied in education and Off-farm activities also reduce income uncertainty. As farming experience. Education does not only improve agri- cultural productivity and farm incomes, but also promotes noted by [10], employment diversicfi ation helps households to smooth income through the spread of risks across different off-farm activity participation and the returns from off-farm activities. The reduction in income uncertainty opens up work. Education also facilitates diversification of the rural opportunities to invest in improved production technologies economy away from agriculture. In either way, education to enhance agricultural production. is expected to play a positive role in the rural economy. As indicated by [3], developing the human capital through Income diversification as a livelihood strategy is con- sidered a global phenomenon. According to [11], income training and skill building is important to promote both from nonfarm work contributes 40% to total income in agricultural development and rural off-farm employment. Latin America while, in sub-Saharan Africa, nonfarm income Previous studies on off-farm/nonfarm employment in constitutes between 30% and 42% of total household income. Ghana include [16–21]. Authorssuchas[17, 19] focused on According to [12] the estimated share of nonfarm income in the determinants of participation in rural nonfarm employ- total household income for Asian countries ranges between ment while [18] assessed the relationship between gender, 29% and 32%. poverty, and nonfarm employment in Ghana and Uganda. Agriculture’s contribution to the provision of livelihood Other authors such as [20] explored household-level farm- opportunities in most rural areas cannot be overemphasized. nonfarm linkages and household welfare implications in However, recent structural transformation in the economies Ghana while [21] examined nonfarm work and food security of most countries has seen faster growth in other sec- among farm households in northern Ghana. From these tors of the economy like manufacturing, industry, and the studies, it can be observed that little has been done on service sector, leading to a decline in the contribution of the determinants of total earnings from off-farm income in agriculture to rural livelihoods and gross domestic product Ghana. (GDP). Agriculture therefore cannot be the only source Due to the foregoing, this study was carried out to of livelihood for many rural farm households, hence the investigate the factors influencing participation in off-farm signicfi ance of off-farm enterprises to rural incomes and work as well as the predictors of total amount of off-farm employment creation. For example, [13] highlighted the need income among small-scale farmers in northern Ghana. The for the integration of activities such as nutrition gardening, nfi dings of thestudy areexpected to guidepolicy makers on livestock rearing, product diversicfi ation, and related income measures to improve rural incomes and livelihood security. generation activities as a means of improving household food and nutritional and income security among farmers in 2. Materials and Methods India. An important factor determining employment of rural .. Study Area, Sampling, and Data. The study was carried people in nonagricultural enterprises is level of education. out in the Northern and Upper East Regions of Ghana. The levelof education in most ruralareas of developing These regions and the Upper West Region make up northern countries is lower than what prevails in urban areas, which Ghana where agriculture is the mainstay of the population. makes rural people less likely to be employed in high- Northern Ghana is characterized by savannah vegetation paying jobs in nonagricultural enterprises. Education is also and a unimodal rainfall regime which makes irrigation an an important factor in the decision to participate in off- important requirement for crop production during the dry farm work. Education improves the human capital and the season. likelihood to engage in high-paying nonagricultural jobs. Respondents were selected using multistage stratified Less educated individuals without the requisite skills and random sampling. The Northern and Upper East Regions technical knowhow may find it difficult to participate in many were selected followed by the selection of three major nonagricultural jobs that require specific skills and expertise. irrigation projects for rice production in these areas. These This may impede participation of less educated rural folks included the Botanga Irrigation Scheme in the Northern in the nonfarm labour market. As indicated by [1], better Region, and the Vea and Tono Irrigation Schemes in the education is one of the most important factors aeff cting off- Upper East Region. One hundred (100) rice producers per farm earnings. Similarly, [14] reported that less educated irrigation scheme were sampled to give a total of 300 households in rural Nigeria were limited in their ability to respondents. The total sample was made up of equal number engage in more lucrative off-farm labour activities. As noted of irrigators and nonirrigators. by [15], the nonagricultural sector is vital to employment and income generation in rural areas through the provision of The data collected included individual/household, farm, various economic activities such as petty trading, businesses, and institutional factors. Information on income from cra,ft and services. both farm and off-farm activities was collected. Individ- The marginal productivity of labour in the agricultural ual/household data included gender, age, and educational sector is generally low as a result of disguised employment, attainment of the respondent, household size, among others. Advances in Agriculture 3 Table 1: Data description and summary statistics of the respondents. Variable Definition Mean Std. Dev. Min. Max. Z Participation in off-farm work (1 = yes) 0.427 0.495 0 1 Y Off-farm income (GH¢) per year 1111 1398 0 6300 SEX Sex of respondent (1 = male) 0.78 0.41 0 1 EXP Years of farming experience 20.6 12.2 2 60 EDU Education of respondent (years) 3.85 5.34 0 20 HSZ Household size (number) 9.68 7.20 2 71 DIST Market distance (kilometres) 7.99 4.30 2 18 REG Regional dummy (1 = Northern) 0.33 0.48 0 1 CRED Access to credit (1 = access) 0.40 0.49 0 1 DSPEC Degree of specialization (%) 45.4 25.1 3.6 100 Farm data included farm size, output level, and input and out- zero,𝑥 denotes a vector of explanatory variables,𝛽 represents put prices. Information on access to irrigation and extension a vector of parameters to be estimated, and𝑢 is a random services was also collected. error term. Empirically, the truncated regression model is specified for this study as follows: .. Double-Hurdle Model of Off-Farm Income Determination. The first step in the implementation of the double-hurdle 𝑌 =𝛽 +𝛽 𝑠𝑒𝑥 +𝛽 +𝛽 𝑝 +𝛽 𝑞𝑑 푖 0 1 푖 2 푖 3 푖 4 푖 model relates to the decision or willingness to participate in (4) off-farm work. This binary decision can be modelled as an +𝛽 𝑔 +𝛽 𝑑𝑖𝑠𝑡 +𝑢 5 푖 6 푖 푖 index function using a probit model as follows: where𝑌 is the amount of off-farm income of the 𝑖 th rice ∗ 耠 farmer,𝛽 are coecffi ients of the explanatory variables, and 𝜀 푖 푖 𝑍 =𝑤 𝛼+𝜀 푖 푖 푖 is the random error term. (1) 1, if𝑍 >0 𝑤ℎ𝑒𝑟𝑒𝑍 = 3. Results 0, if𝑍 ≤0 .. Summary Statistics of the Respondents. The summary 𝑍 is a dichotomous variable which takes the value of 1 if the statistics of the respondents are presented in Table 1. respondent is a participant in off-farm work and 0 otherwise, Most of the respondents in the study were male (78%). 𝑤 is a vector of explanatory variables,𝛼 denotes a vector of The average age of the respondents was 41.2 years while parameters, and𝜀 is the error term. household size averaged 10 members. The respondents had The empirical model for rice farmers’ decision to partici- 4 years of formal education and travelled an average of pate in off-farm work is speciefi d for this study as follows: 8 km to the nearest market. Forty-three percent (43%) participated in off-farm work. Total income from off-farm 𝑍 =𝛼 +𝛼 𝑠𝑒𝑥 +𝛼 +𝛼 𝑝 +𝛼 𝑞𝑑 푖 0 1 푖 2 푖 3 푖 4 푖 work averaged 1,111 Ghana Cedis (GH¢) (approximately $232) +𝛼 ℎ𝑠𝑧 +𝛼 𝑔 +𝛼 𝑑𝑐𝑟𝑒+𝛼 +𝛼 𝑑𝑖𝑠𝑡 (2) per annum. On average, respondents had 21 years of farming 5 푖 6 푖 7 8 9 experience and allocated 45% of total land to rice cultivation +𝜀 푖 (a measure of the degree of specialization in rice production). In addition, 33% of the respondents were located in the where 𝑍 measures the choice of the 𝑖 th rice farmer to Northern Region while 40% used credit in farming. participate in off-farm work, 𝛼 is the coefficients of the independent (explanatory) variables, and𝜀 is the error term. .. Amount of Income Earned from Off-Farm Work. Table 2 The second equation in the double-hurdle relates to the presents the amount of income earned from off-farm work amounts of off-farm income earned by the respondents. The by respondents in the study area. Close to 43% of the respon- second hurdle equation can be estimated using a regression dents took part in off-farm work to supplement income truncated at zero (similar to a Tobit model) with the following from on-farm work. Twenty-eight percent of the respondents formulation: earned up to GH¢1,000 which translates into $208. Ten ∗ 耠 𝑌 =𝑥 𝛽+𝑢 푖 푖 푖 percent of the respondents earned between GH¢1,000 and GH¢2,000 from off-farm work. Only 4.3% of the respondents ∗ ∗ (3) 𝑌 , if𝑌 >0 푖 earned between GH¢2,001 and GH¢6,000 from off-farm 𝑤ℎ𝑒𝑟𝑒𝑌 = ∗ work. 0, if𝑌 ≤0 where𝑌 represents the observed income from off-farm work .. Double-Hurdle Regression Estimates of the Determinants which depends on the latent variable𝑌 being greater than of Off-Farm Income. The double-hurdle regression estimates 𝑑𝑠𝑝𝑒𝑐 𝑟𝑒 𝑒𝑥𝑝𝑠 𝑒𝑥 𝑒𝑑𝑢 𝑟𝑒 𝑒𝑥𝑝𝑠 𝑒𝑥 𝑒𝑑𝑢 4 Advances in Agriculture Table 2: Distribution of amount of income earned from off-farm income in total household income for Asia to be 29% to 42%. work by respondents. The amount of income from off-farm work is however very smallwhich may beinadequate to support thehousehold and Off-farm income (GH¢) Number of farmers Percentage its agricultural production. The “one district, one factory” 0 (Nonparticipants) 172 57.3 policy of the government of Ghana which is aimed at creating 1 – 1,000 83 27.7 jobs at the district level is a flagship government policy that could go a long way to enhance rural employment and income 1,001 – 2,000 31 10.3 from off-farm work. 2,001 – 3,000 4 1.3 3,001 – 4,000 1 0.3 .. Factors Influencing Participation in Off-Farm Work. In 4,001 – 5,000 3 1.0 most Ghanaian societies, women play several economic roles 5,001 – 6,000 5 1.7 and are noted for their entrepreneurial abilities. Women More than 6,000 1 0.3 in many rural communities engage in petty trading and other income earning activities to supplement household Total 300 100 income. us, Th women tend to be engaged in multiple off- 1 US dollar equals 4.8 Ghana Cedis. farm activities to supplement household income compared to menand play multiple roles inthe household. The result of this study is consistent with [22] who reported higher of the determinants of off-farm income are presented in participation of women in off-farm work in Malaysia. Other Table 3. The rfi st hurdle related to assessment of the factors previous studies on participation in off-farm work in Ghana influencing the decision to participate in off-farm work using support the n fi dings of this study [17–19]. aprobitmodel (columns 2and 3). The results of the study highlight the important role of The results indicated that gender of the farmer had a education in off-farm work. As noted by [1], education affects significant influence on participation in off-farm work at participation of rural people in off-farm work as well as the 1% significance level. From the results, female respondents amount of income from off-farm work. The result of this had higher participation in off-farm work relative to their study is in consonance with [14] who found that less educated male counterparts. Participation in off-farm work was also households in rural Nigeria were limited in their ability to related to years of formal education. Education had a positive engage in more lucrative off-farm labour activities. and signicfi ant association with off-far work at 1% level. In In addition, the result of the study suggests that experi- addition, farming experience had a positive and signicfi ant enced farmers are more likely to participate in off-farm work relationship with off-farm work at 10% level. Finally, access to compared to less experienced farmers. The result does not credit was negatively related to off-farm work and significant lend itself to easy interpretation but may be indicative of the at 5% level. general lack of job opportunities outside the farm in rural The second hurdle related to the determinants of off- areas. The result agrees with [23] who observed that older farm income (columns 4 and 5). Number of years of farming farmers in Cambodia were more likely to participate in off- experience of the respondents had a positive and significant farm work which is related to their experience. effect on income from off-farm work and was statistically Finally, farmers who had access to credit had a lower significant at 5% level. The quadratic term of the farming propensity to engage in off-farm work, which is consistent experience variable was however positive and significant with apriori expectation. This is because credit tends to at 10% level. In addition, the number of years of formal ease the n fi ancial constraints of farm households and could education of the farmer had a positive and statistically increase on-farm income. Credit-constrained farmers are signicfi ant relationship with off-farm income at 10% level. In therefore more likely to seek employment from off-farm other words, income from off-farm work increased with years sources. The result of this study is contrary to [20], who found of formal education of the respondent. Furthermore, the that access to credit enhanced female participation in off- coefficient of the regional dummy variable was statistically farm employment in Ghana. Owusu et al. [21] also found a signicfi ant at 10% level and indicated that farmers in the positive effect of credit access on participation in nonfarm Upper East Region earned higher income from off-farm work employment in Ghana. compared to those in the Northern Region. The type of off- farm activities included fishing, petty trading, arts and craft, .. Determinants of Off-Farm Income. Education is one businesses, and services. of the key variables in the economic literature influencing participation in off-farm work and total earnings from off- farm work. The result of this study is in consonance with 4. Discussion apriori expectation: education improves the human capital .. Amount of Income from Off-Farm Work. The result and hence the likelihood to engage in high-paying nonagri- cultural jobs. Educated farmers have higher opportunity cost highlights high participation in off-farm work by farmers in northern Ghana. The result is consistent with [11] who of labour and therefore are more likely to trade their labour reported that 30% to 42% of total household income in sub- in the nonfarm labour market. Education is also expected to enhance entrepreneurial abilities and self-employment, Saharan Africa comes from nonfarm work. The study is also consistent with [12] who estimated the share of nonfarm which may enhance the level of income from off-farm Advances in Agriculture 5 Table 3: Maximum likelihood estimates of double-hurdle model for participation in off-farm work and amount of off-farm income. st nd 1 hurdle (Probit model) 2 hurdle (outcome model) Variable Coefficient Std. Error Coefficient Std. Error Sex -0.675∗∗∗ 0.203 0.370 0.173 Education 0.066∗∗∗ 0.015 0.037∗ 0.056 Experience 0.037∗ 0.022 -0.058∗∗ 0.039 Experience squared -0.001 0.000 0.001∗ 0.092 Household size -0.016 0.013 - - Regional dummy 0.330 0.290 -0.416∗ 0.070 Access to credit -0.549∗∗ 0.261 - - Specialization 0.003 0.003 - - Market distance -0.011 0.018 -0.019 0.389 Intercept -0.117 0.367 7.734∗∗∗ 0.000 Sigma 1.151 0.170 Covariance -0.945∗∗∗ 0.289 ∗∗∗ Significant at the 1% level.∗∗ Significant at the 5 % level.∗Significant at 10 % level. work. On the other hand, less educated farmers may not hurdle related to the estimation of amount of income earned have the required skills and technical knowhow which may from off-farm employment. The results of the study indicate hinder their participation in high-paying nonagricultural that small-scale farmers in the study area earn low income wage employment. The n fi ding is consistent with [14] in from off-farm work. This may suggest unavailability of well- their study of off-farm labour market participation in rural paying jobs outside the farm, the lack of requisite skills Nigeria which showed that education enhances employment by farmers to participate in off-farm labour activities, or of rural people in higher-paying nonfarm activities. As inadequate off-farm opportunities. indicated by [24, 25], education creates opportunities for A major conclusion from the study is that human capital farm households to participate in higher-return nonfarm plays an important role in off-farm income determination. activities. Lack of education creates entry barriers to certain Education had a positive inu fl ence on both the decision off-farm employment opportunities [16] leading to a situation to participate in off-farm work and the amount of income referred to as labour market dualism [1] where individuals earned from off-farm employment. u Th s, educated farmers with education get employed in relatively high-paying jobs have higher propensity to participate in off-farm work and while the uneducated end up with relatively low-paying jobs. tend to earn higher incomes from off-farm activities. On Majority of the participants in off-farm work were self- the other hand, farming experience had a negative influence employed as fishermen, petty traders, craftsmen, and small on the amount of off-farm income and a positive inu fl ence business owners. on off-farm employment. This implies that less experienced The study also indicated that experienced farmers had farmers are less likely to participate in off-farm work, but if lower incomes from off-farm work, which is consistent with they participate, they will tend to earn higher income than apriori expectation. Experienced farmers are expected to more experienced farmers. devote more time to farming and less time to off-farm work, Policy recommendations arising from the study include thus earning less income from off-farm activities. the following: To enhance rural employment in off-farm In addition, the study underlined that income from activities, there is the need to promote rural industrialization off-farm work differs across regions. Northern Region has such as the Government of Ghana’s “one district, one factory” greater agricultural potential than the Upper East Region policy initiative. In addition, since most Ghanaian rural in terms of fertile lands for crop production. Land scarcity communities are agrarian in nature, the government of also characterizes agricultural production in the Upper East Ghana must take steps to make agriculture attractive to Region, which may encourage the trading of agricultural keep the youth in particular in farming. Agriculture is the labour for off-farm income. Households facing the challenge largest employment sector in the Ghanaian economy and of land scarcity are therefore more likely to trade agricultural the economic potentials of agriculture to socioeconomic labour for nonagricultural wage employment, which may development have been recognized. Hence, government’s result in higher income from off-farm work. facilitation of measures that promote agriculture such as the provision of credit to farmers will enable producers with large farmlands to increase production. This will boost 5. Conclusion and Policy Recommendations employment in agriculture and reduce rural-urban migration of the youth. Government must also create the enabling The study assessed the determinants of off-farm income environment for value addition to agricultural products of smallholder rice farmers in northern Ghana using a produced in rural areas which will increase opportunities for double-hurdle model. 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Barrett, “Smallholder market participation: Concepts and evidence from eastern and southern Africa,” Food Policy,vol.33, at promoting the education of rural people will go a long no. 4, pp. 299–317, 2008. way to enhance their ability to take advantage of available [9] FAO, “Rural non-farm income in developing countries,” e employment opportunities or venture into self-employment State of Food and Agriculture,1998. to improve their income levels and living conditions. Further- more, the provision of skills training to adults without formal [10] A. Gordon, Non-Farm Rural Livelihoods, Natural Resources, education will enable them to take advantage of employment Chatham, UK, 1999. opportunities that do not require formal education and [11] T. Reardon, G. S. Kostas, and P. 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