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Does Off-Farm Work Improve Farm Income? Empirical Evidence from Tolon District in Northern Ghana

Does Off-Farm Work Improve Farm Income? Empirical Evidence from Tolon District in Northern Ghana Hindawi Advances in Agriculture Volume 2020, Article ID 1406594, 8 pages https://doi.org/10.1155/2020/1406594 Research Article Does Off-Farm Work Improve Farm Income? Empirical Evidence from Tolon District in Northern Ghana Benjamin Tetteh Anang , Kwame Nkrumah-Ennin, and Joshua Anamsigiya Nyaaba Department of Agricultural Economics and Extension, Faculty of Agriculture, University for Development Studies, Tamale, Ghana Correspondence should be addressed to Benjamin Tetteh Anang; benjamin.anang@uds.edu.gh Received 3 October 2019; Revised 30 June 2020; Accepted 25 August 2020; Published 7 September 2020 Academic Editor: Othmane Merah Copyright © 2020 Benjamin Tetteh Anang et al. )is 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. Participation of farm households in off-farm work has gained prominence in recent times as an income diversification strategy. )e effect of off-farm work on farm income is however unclear. )is paper therefore sought to provide empirical evidence of the income effect of off-farm activity participation using a cross section of maize farmers in Tolon District of Ghana as a case study. In order to account for sample selection bias, the Heckman selection model was used to estimate the factors influencing participation in off-farm work and the determinants of farm income. Furthermore, the study employed propensity score matching to evaluate the impact of off-farm work on farm income. )e results indicate that participation in off-farm work is influenced by sex, age, and years of formal education of the respondent, farm size, and number of dependents while farm income is influenced by age of the respondent, farm size, and access to credit. In addition, the result of the propensity score matching revealed that participants in off-farm work increased their farm income by at least GH¢ 1702 as a result of income diversification. )e rural economy therefore provides off-farm and on-farm linkages that enhance farmers’ income from agriculture. )e creation of employment oppor- tunities outside the farm will therefore complement on-farm work and enhance income from farming. According to the existing literature, there is increasing 1. Introduction recognition of the role that off-farm work plays particularly Most developing countries including Ghana depend on in smallholder agriculture in developing countries [4]. For agriculture as an importance source of livelihood. It is most agrarian communities, farming is considered as the estimated that in Ghana, more than 60 percent of the main occupation. Off-farm work is thus any activity un- population are engaged in agriculture as a source of dertaken by the farmer or farm household outside farming as livelihood [1]. Majority of the farmers are smallholders an additional source of income. )is is opposed to nonfarm who cultivate less than 2 hectares of farm land [2] and work which relates to all activities that are not related to account for about 80 percent of the food produced locally farming (such as dressmaking and commerce). Hence, off- [3]. Yields of most crops are generally below achievable farm work includes farm-related activities carried out by levels due to reliance on natural rainfall for production, farm households for income such as exchange of labour for low adoption of improved production technologies, and cash on another farmer’s farm. )e major sources of off- lack of access to services such as agricultural extension farm income in Ghana include commerce, agroprocessing, and farm credit. In response to liquidity constraints and charcoal production, seasonal migration, brewing of local declining farm incomes, many smallholders diversify gin, basketry, and collection and sale of firewood [4, 5]. production and have multiple sources of income apart According to Chang and Wen [6], participation in off-farm from farming, which have implications for agricultural work by farm households is a persistent phenomenon productivity and farm income. globally, with a steady increase in the dependence of farm 2 Advances in Agriculture does income from off-farm work ease the liquidity con- families on income from off-farm work. Income from off- farm work is regarded as an important source of livelihood straints of farm households enabling greater use of farm resources in production? To the extent that farmers are able for farm households and a means to diversify household income source. A study in rural Ghana by Jolliffe [7] in- to invest earnings from off-farm activity into their farm dicated that about 74% of Ghanaian farm households par- business, farm output and productivity are expected to grow ticipated in off-farm work. Also, research showed that, on and exert a positive influence on farm income. average, 65% of American farm households were engaged in )is study is motivated by the lack of empirical evidence off-farm work [8]. Chang and Wen [6] also reported that of the effect of off-farm work on farm income of smallholder about 75% of Taiwanese farm households earned off-farm farmers in Ghana. )e objective of this paper is therefore to assess the contribution of off-farm work to farm income in salaries. )e increasing importance of off-farm work to farm Tolon District of Ghana. )e paper contributes to the lit- erature on income diversification and its effects on house- households’ economic well-being has generated a lot of discussion among researchers regarding the role off-farm hold welfare by empirically estimating the magnitude and direction of impact of off-farm work on farm income of work plays in household welfare, especially in terms of food security, agricultural productivity, and household income. It smallholder farmers. is a commonly held view that participation in off-farm work )e rest of the paper is structured as follows. Section 2 is expected to reduce on-farm labour availability and its describes the methods used in the study, which provides a allocation and thus constrain agricultural productivity. On background of the study area, sampling and data collection, the contrary, it is also believed that off-farm work enables and method of data analysis and data description. Section 3 farm households to stabilize household income and reduce presents the results and discussion of the major findings of the study. )e conclusion and recommendations from the vulnerability and uncertainties associated with agricultural production. As indicated by the extant literature, partici- study are provided in Section 4. pation in off-farm work has two effects on production: a negative lost-labour effect and a positive income or liquidity- 2. Materials and Methods relaxing effect [9]. )e lost-labour effect occurs when the household loses farm labour to off-farm activities, while the 2.1. Study Area and Sampling. )e study was conducted in income effect occurs when the household earns income from the Tolon District of the Northern Region of Ghana. )e area off-farm activities which it can invest into farming. )e effect is part of the northern savannah zone of Ghana and is well of off-farm work on farm income will however depend on known for its agricultural production. )e area experiences a which of the two effects is stronger. single rainfall regime per annum and is known for the Off-farm work as a risk management tool that reduces cultivation of crops such as rice, maize, and groundnut, income variability of farm households has been reported by which are important staples. )e population of the district some authors [10, 11]. As demonstrated by Mishra and stood at 72,990 according to the 2010 Population and Goodwin [12], farm households may depend on off-farm Housing Census (PHC). An estimated 92% of the population work to stabilize household income because farm com- are engaged in agriculture. modity prices are more variable than off-farm wages. It is )ree communities, namely, Nyankpala, Dundo, and expected from the theory of production that a risk-averse Gbushalagu, were selected for the study due to their agri- farmer will choose to allocate labour and other resources to cultural potential. Fifty farmers were randomly selected activities that are less risky to the point where the expected from each community to give a total sample of 150 farmers marginal returns are equal for the different activities. )e who were interviewed face-to-face using a semistructured higher variability in farm commodity prices is therefore questionnaire. Cochran’s formula for sample size determi- expected to drive participation in off-farm work. nation indicated that the number of farmers selected for the An earlier study by Lanjouw [13] noted that the rise in study is a representative sample. In the absence of a well- off-farm activity by farm households is as a result of de- defined sample frame, households were selected at random clining farm incomes and the need to safeguard against in each community by taking into account the distribution production risks. )is finding is corroborated by a recent of households. Information solicited from farmers included study by Akinrinde et al. [14] which indicated that declining individual, household and farm characteristics, production farm income is the main reason for income diversification activities, production costs and returns, and access to among Nigerian farmers. Alasia et al. [15] on the other hand production resources and services. view participation in off-farm work as a form of self-in- surance which enables farm households to stabilize household income. 2.2. Empirical Models. Participation in off-farm work and Several studies allude to a positive effect of off-farm work the factors determining farm income were analysed em- on agricultural productivity, food security, and household pirically using the Heckman Selection Model, while a income of farm households [16–18]. For most farm nonparametric treatment effect model was used to estimate households, farm income constitutes a significant part of the the effect of off-farm work on farm income. )e Heckman total household income. A policy question which is relevant selection model can be estimated using either maximum to most rural farm households is whether or not off-farm likelihood or a two-step approach. )is study adopted the work contributes directly to farm income. In other words, maximum likelihood approach because it overcomes some Advances in Agriculture 3 of the limitations associated with the two-step approach φ α 􏼁 φ −α 􏼁 φ c x /α 􏼁 u u i u λ α 􏼁 � � � , (4) such as the possibility of the correlation factor to lie outside i u 1 − ϕ α 􏼁 ϕ α 􏼁 ϕ c x /α 􏼁 u u i u the prescribed range. Methodologically, endogenous switching regression is an alternative approach for esti- where φ represents the normal density function while ϕ mating the income effect of off-farm work. However, the indicates the normal distribution function. Heckman selection model was preferred because it provided As indicated by Heckman [19], when there is sample a better fit of the data. selection bias, an ordinary least squares (OLS) regression without the inverse Mill’s ratio will lead to inconsistent parameter estimates. )us, for this study, OLS regression of 2.2.1. Heckman Selection Model. Estimation of the Heckman Y on w without the correction factor or inverse Mill’s ratio i i selection model entails the estimation of a selection equation (λ (α )) will lead to inconsistent β estimates. Consequently, i u (off-farm participation model) using either probit or logit the inverse Mill’s ratio was included as an additional ex- model followed by estimation of an outcome equation (farm planatory variable in the outcome equation (equation (2)). income model) using least squares regression while ac- )e identification criterion requires that at least one counting for sample selection bias. )e assignment of variable which influences participation in off-farm work but households into participants and nonparticipants in off- not farm income is included in the model. )e number of farm work is nonrandom. Hence, OLS regression will not dependents was chosen as an exclusion variable. )is is provide consistent parameter estimates of the outcome because the number of dependents has a direct influence on equation. To address this problem, there is the need to participation in off-farm work, particularly on the number of construct a correction factor, otherwise known as the inverse hours worked but does not directly influence farm income. Mill’s ratio, which is appended to the outcome equation as )e choice of number of dependents as an exclusion variable an additional explanatory variable. is supported by Anang [20] who used the dependency ratio )e choice to participate in off-farm work can be esti- as an exclusion variable in a study on the effect of off-farm mated using a probit model which is specified as follows: work on agricultural productivity in northern Ghana. Z � c x + u , (1) i i i 2.2.2. Propensity Score Matching: Estimating the Effect of Off- where Z is a latent variable which measures the probability th Farm Work on Farm Income. Impact evaluation studies in that the i household participates in off-farm work, such the extant literature have relied on estimation of average that the observed variable Z � 1 if the household partici- treatment effects as direct measures of the impact of in- pates in off-farm work and Z � 0 if otherwise, x indicates a i i ∗ terventions in the agricultural and other sectors. )e effect of vector of exogenous factors influencing Z , and c represents an intervention or exposure on those who received the a vector of parameters to be estimated. treatment is an important measure in impact evaluation In the second stage analysis, the amount of farm income studies. )us, to quantify the effect of off-farm work on farm (Y ) is regressed on a set of exogenous factors, w , for all i i income, we estimated the average treatment effect on the situations where the selection equation equals one (Z � 1), treated (ATT). )e average treatment effect (ATE) given the using the estimated inverse Mill’s ratio as an additional observable data is denoted by explanatory variable. 􏼌 􏼌 Hence, given that Z � 1, we have the following: 􏼌 􏼌 i 1 0 􏼌 􏼌 ATE � E Y T � 1 − E Y T � 0 , (5) 􏼐 􏼌 􏼑 􏼐 􏼌 􏼑 Y � β w + v , (2) i i i where Y is the farm income of individuals who participated where Y indicates the amount of farm income, w is a vector in off-farm work and Y is the farm income of nonpartic- i i of variables influencing farm income, and β is a vector of ipants in off-farm work, T � 1 represents the farmers who parameters to be estimated. participated in off-farm work (referred to as the treated), and )e error terms u and v have bivariate normal distri- T � 0 represents nonparticipants in off-farm work (referred i i butions with zero means, standard deviation δ and δ , and to as the untreated or control). According to [21], E(Y | T � u v correlation coefficient ρ. While Z and x are both observable 1) − E(Y | T � 0) is equal to zero for the case of a rando- i i for a random sample, Y is observed only when the mised design (i.e., in the absence of selection bias). However, household participates in off-farm work (Z � 1). )e in the presence of selection bias, the ATE result from Heckman selection model is specified as follows [19]: equation (5) provides a biased estimate of the impact of off- 􏼌 􏼌 􏼌 􏼌 ∗ farm work on farm income. Hence to overcome this bias, we 􏼌 􏼌 E 􏼐Y Z � 1􏼑 � E 􏼐Y Z > 0􏼑 􏼌 􏼌 i i i i need to estimate the average treatment effect on the treated ′ (ATT), using the observational data, and conditioning on a � E 􏼐Y u > c x 􏼑 i i i (3) 􏼌 vector of farm and household characteristics X as follows: ′ ′ � β w + E 􏼐v u > c x 􏼑 􏼌 􏼌 i i i i 1 0 ATT � E(Δ | X, T � 1) � E 􏼐Y − Y 􏼌 X, T � 1􏼑 � E � β w + βσ λ α , 􏼌 􏼌 i v i u 􏼌 􏼌 1 0 􏼌 􏼌 􏼐Y 􏼌 X, T � 1􏼑 − E 􏼐Y 􏼌 X, T � 1􏼑. where λ (α ) is the inverse Mill’s ratio, which has the fol- i u (6) lowing specification: 4 Advances in Agriculture However, the counterfactual E(Y | X, T � 1) is unob- respondents however have low level of education, a situation servable, hence assumptions are made to estimate it as which can negatively affect uptake of innovation and ability 0 0 follows: E(Y | X, T � 1) � E(Y | X, T � 0). )us, the ATT to access and use information for agricultural production. equation becomes Also, the respondents have an average household size of 9 and 3 dependent members and possess 2.7 hectares of land 1 0 􏼌 ATT � E(Δ | X, T � 1) � E 􏼐Y − Y X, T � 1􏼑 for agricultural production out of which 2 hectares is al- (7) 􏼌 􏼌 1 􏼌 0 􏼌 located to maize production. )is shows that maize pro- 􏼌 􏼌 � E􏼐Y X, T � 1􏼑 − E 􏼐Y X, T � 0􏼑. 􏼌 􏼌 duction is an important economic activity among rural dwellers. In Ghana, farm households depend on maize for food and income. Close to 43 percent of the respondents 2.3. Sampling Procedure and Sources of Data. )e data for the participated in off-farm work while 46 percent used credit in study were collected from smallholder maize farmers in 3 farming. In addition, 58 percent of the respondents are male communities in the Tolon District of Northern Ghana. Fifty indicating lower female participation in maize cultivation. farmers were selected from each community to give a total Maize is a food security crop and household heads, most of sample of 150. )e communities and respondents were whom are male, are anticipated to engage in its cultivation randomly selected. Face-to-face interviews were carried out for home consumption and cash sales. On average, farmers’ with each respondent using semistructured questionnaire. gross income from maize cultivation was GH¢ 2599 (ap- )e questionnaire contained both open- and close-ended proximately US$ 490). questions. )e questions covered farm, household and in- Table 3 presents the distribution of farmers’ gross in- stitutional factors, and off-farm and production decisions, come from maize cultivation. Majority (76%) of the farmers among others. obtained up to GH¢ 2500 as gross income from farming. )e figure is very low, considering that maize is the most dominant crop grown by nearly every household in northern 2.4. Description and Expected Signs of the Explanatory Ghana due to its important as a staple crop and also as cash Variables. )e description and expected signs of the vari- crop for large scale producers. Participants in off-farm work ables included in the analysis are provided in Table 1. Male had higher farm income compared with nonparticipants. farmers are expected to have higher farm income, but sex is hypothesized to have an indeterminate effect on participa- tion in off-farm work. Also, older farmers are expected to be 3.2. Results of the Heckman Selection Model. )e results of more experienced in farming and more endowed with the Heckman selection model are presented in Table 4. )e production resources than younger farmers, which is ex- log-likelihood ratio (LR) test is significant at 1% indicating pected to lead to a higher farm income. Older farmers, by the presence of selection bias, which justifies the use of the virtue of family obligations and dependents, are expected to Heckman model to correct for the bias. In other words, have higher participation in off-farm work. Education en- estimating separate equations for the selection and outcome hances employability and is therefore hypothesized to in- models would result in biased estimates. crease participation in off-farm work. At the same time, education enhances the human capital which is expected to 3.2.1. Factors Influencing Participation in Off-Farm improve farm income. Farmers with access to credit are Activities. )e estimates of the factors influencing par- expected to have lower participation in off-farm work since ticipation in off-farm work using the Heckman selection credit eases the financial burden of farm households. Credit model are presented in Table 4. )e study indicates that the also enhances farm production through acquisition of farm likelihood of maize farmers to participate in off-farm work inputs and financing of farm operations, which is expected is affected by age, sex, education, and total agricultural to enhance farm profits. Farmers with large farms are ex- land. pected to be progressive farmers and better-off compared to Participation in off-farm work is higher for women. those with small farm holdings, leading to less participation Women play several roles in the household apart from in off-farm work and higher farm income. In addition, a farming. )ey are involved in petty trading, arts, and craft higher number of dependents implies greater financial and are more disposed to take up extra income earning burden on households hence higher likelihood to take part activity to support the household’s income. )e marginal in off-farm work. However, the number of dependents is not effect of sex indicates that the probability of women en- expected to have any direct effect on farm income. gaging in off-farm work is 0.214 higher than men. Ahmed and Melesse [22] as well as Man and Sadiya [23] obtained 3. Results and Discussion similar results in their studies in eastern Ethiopia and 3.1. Characteristics of the Respondents. We first describe the Malaysia, respectively. As observed by Ahmed and Melesse characteristics of the sample as shown in Table 2. )e re- [22], female-headed households were more likely to engage spondents have an average age of 38 years and 6.5 years of in off-farm work compared with male-headed households formal education. )e respondents are therefore in their because female-headed families take part in off-farm work to youthful age, a situation which is conducive for agricultural offset their relative lower farm income compared with male- headed families. )e result however disagrees with the production. )is is because agriculture in most developing countries, including Ghana, involves a lot of drudgery. )e findings of Pramanik et al. [24] in their study on the rural Advances in Agriculture 5 Table 1: Description and expected signs of the explanatory variables included in the analysis. Expected sign Variable Description Model 1 Model 2 Sex Dummy � 1 if farmer is male; 0 otherwise ± + Age Age of farmer in years + + Education Years of formal education + + Credit access Dummy � 1 for credit access; 0 otherwise − + Maize farm size Maize farm size in hectares − + Dependents Number of dependents + NA 1 US$ � 5.4 Ghana Cedis (GH¢). Model 1 is the probit participation model. Model 2 is the farm income (outcome) equation. Table 2: Descriptive statistics of the variables used for the analysis. Full sample Participants Nonparticipants Variable Mean diff. Mean S. D. Mean S. D. Mean S. D. ∗∗∗ Farm income 2599 3490 3457 4313 1962 2570 1495 Sex 0.58 0.50 0.52 0.50 0.63 0.49 −0.11 Age 38.3 10.5 37.4 9.43 39.0 11.3 −1.64 ∗∗∗ Education 6.47 6.91 8.48 7.73 4.98 5.84 3.50 ∗∗∗ Maize farm size 2.02 2.12 2.63 2.80 1.56 1.26 1.07 Credit access 0.46 0.50 0.47 0.50 0.45 0.50 0.02 ∗∗∗ Dependents 2.80 2.31 2.17 2.11 3.27 2.36 −1.10 ∗ ∗∗ )e t value of the difference in means between participants and nonparticipants. Statistical significance at 10% level; statistical significance at 5% level; ∗∗∗ statistical significance at 1% level. Table 3: Distribution of gross farm income of the respondents. Full sample Participants Nonparticipants Variable Freq. (%) Freq. (%) Freq. (%) Up to 2,500 114 76.0 43 67.2 71 82.5 2,501–5,000 23 15.3 13 20.3 10 11.6 5,001–7,500 2 1.3 1 1.56 1 1.2 7,501–10,000 3 2.0 1 1.56 2 2.3 10,001–12,500 2 1.3 2 3.12 0 0 12,501–15,000 2 1.3 1 1.56 1 1.2 15,001–17,500 1 0.7 0 0 1 1.2 17,501–20,000 2 1.3 2 3.13 0 0 Above 20,000 1 0.7 1 1.56 0 0 Total 150 100 64 100 86 100 Mean 2,599 4,204 2,511 Minimum 140 480 140 Maximum 21,120 21,120 15,600 nonfarm economy in Bangladesh as well as Beyene [25] in a contribution of off-farm activities to farm income in Borno study to assess the determinants of off-farm participation State, Nigeria. decision of farm households in Ethiopia. Consistent with a priori expectation, the results indicate Younger farmers are more likely to engage in off-farm that educated farmers are more likely to work off-farm. work as shown by the coefficient of the age variable. As Education enhances the human capital and opens up op- farmers become older, they become less disposed to engage portunities for employment off the farm. An additional year in off-farm work. Younger people are more likely to find jobs of education increases the likelihood of participation in off- because they are more energetic. Due to the low poverty level farm work by 0.023. )e results are consistent with Matshe in most rural communities, off-farm employment for rural and Young [27] in their study on off-farm labour allocation decisions in Zimbabwe, as well as Abdulai and CroleRees people involves labour-intensive activities which are better suited to younger people. )e quadratic term for respon- [28] in their study on income diversification among dent’s age shows that participation in work off-farm in- households in Southern Mali. )e result also agrees with creases at a decreasing rate with age of the farmer. )e result Seng [29] in a study on the effect of nonfarm work on is consistent with Pramanik et al. [24] in their study in household food consumption in rural Cambodia, Pramanik Bangladesh as well as Bila et al. [26] in their study on the et al. [24] in their study on the rural nonfarm economy in 6 Advances in Agriculture Table 4: Results of the Heckman selection model of off-farm work and farm income. Probit model Outcome equation Independent variables Coefficient Std. error Coefficient Std. error ∗∗ Sex −0.523 0.241 437.2 552.0 ∗∗∗ ∗∗∗ Age 0.290 0.096 −814.0 223.9 ∗∗∗ ∗∗∗ Age squared −0.004 0.001 12.26 2.992 ∗∗∗ Education 0.054 0.018 −18.41 37.86 ∗∗∗ ∗∗∗ Farm size 0.590 0.127 849.6 150.3 ∗∗ Access to credit −0.116 0.236 1281 541.3 ∗∗ Number of dependents −0.121 0.050 ∗∗∗ ∗∗∗ Constant −5.610 1.901 14202 4283 Inverse Mill’s ratio (λ) −81.68 0.095 Rho −0.881 0.079 Sigma 2342.7 303.5 Lambda −2064.7 423.4 Selected observations 64 Unselected observations 86 Wald chi (6) 187 Prob> chi 0.00 2 2 ∗ ∗∗ LR test of independent equations (rho � 0): chi (1) � 8.94, prob> chi � 0.003. Statistical significance at 10% level. Statistical significance at 5% level. ∗∗∗ Statistical significance at 1% level. (6) and (1) indicate the degrees of freedom for the chi-squared estimation. Bangladesh, and Owusu et al. [5] in their study on nonfarm experience in farming, their production and income in- work and food security in northern Ghana. McCarthy and crease. )is is shown by the positive sign of the quadratic term of the age variable. As farmers gain experience in Sun [30] estimated separate models for men and women and observed that household education levels had positive effect farming, this is expected to translate into more efficient ways on both female and male participation in off-farm em- of production and informed decision-making to maximise ployment in rural northern Ghana. farm profits and income. Participation in off-farm work was found to be positively )e results further indicate that the farm size variable is related to farm size and significant at 1% level. )is indicates significant at 1% level and positively associated with farm that farmers with larger agricultural lands are more likely to income, which is consistent with a priori expectation. )is take up employment off the farm. )e result is contrary to a implies that an increase in farm size results in an increase in priori expectation and hard to explain. A unit increase in farm income. Larger farm operators are therefore able to total agricultural land increases the likelihood of working off increase their income from farming. In addition, the study the farm by 0.041. )e result agrees with Pramanik et al. [24] showed that the effect of credit on farm income was positive who found that farmers with larger plots were more likely to and significant at 10% level. Hence, access to credit enhanced participate in nonfarm activities in Bangladesh. McCarthy the farm income of maize farmers in the study area. )e and Sun [30] also observed that the size of owned land had a result is consistent with a priori expectation as credit eases positive influence on participation in off-farm employment farm liquidity constraints and acquisition of farm inputs in rural northern Ghana. while enabling timeliness in carrying out farm operations to )e number of dependents had a negative and significant maximise output and profits. effect on participation in off-farm work, which is contrary to expectation. Households with many dependents are ex- pected to have a higher propensity to participate in off-farm 3.3. Income Effect of Off-Farm Work. In order to quantify the income effect of off-farm work, we proceeded to estimate the work, but the result suggests otherwise. )e result is at variance with the findings of Anang [20] which indicated average treatment effect on the treated (ATT) using the that an increase in the number of dependents increased the nearest neighbour and kernel matching methods (Table 5). We used the two matching methods in order to compare and propensity of smallholder rice farmers to engage in off-farm work in northern Ghana. check for robustness of the estimates. )e results indicated that participation in off-farm work increases the farm in- come of participants by GH¢ 1702 using the nearest 3.2.2. Results of the Outcome (Farm Income) Model. )e neighbour method and GH¢ 1776 in the case of the kernel- estimates of the parameters of the farm income equation based method. )e result indicates a positive and significant th using the Heckman selection model are presented in the 4 effect of off-farm work on farm income. What the result th and 5 columns of Table 4. )e results indicate that farm implies is that participants in off-farm work are able to income initially decreases with age of the farmer as shown by increase their farm income by at least GH¢ 1702 as a result of income diversification. )e result is supported by Osarfo the negative coefficient of the age variable. In other words, the younger the farmer, the higher the income from maize et al. [31] who showed that participation in nonfarm work had a positive impact on the income of rural farm cultivation. However, as farmers become older and gain Advances in Agriculture 7 Table 5: Estimates of the average treatment effect of off-farm work on farm income. Estimation method Treated Control ATT Robust S.E. t statistic ∗∗ Nearest neighbour matching 64 33 1701.7 737.1 2.309 ∗∗∗ Kernel matching 64 79 1776.3 526.3 3.375 ∗∗ ∗∗∗ Note: the outcome variable is farm income. Statistical significance at 5% level. Statistical significance at 1% level. Agriculture—Statistics, Research and Information Director- households in the Upper East and Upper West Regions of Ghana. )e important role that off-farm income plays in ate (SRID), Accra, Ghana, 2013. [3] R. E. Namara, H. Horowitz, B. Nyamadi, and B. Barry, Ir- household income has been elaborated by other authors rigation Development in Ghana: Past Experiences, Emerging such as Ogbanje et al. [32] who estimated the off-farm in- Opportunities, and Future Directions, GSSP Working Paper come share of household income in North Central Nigeria at No. 0027, Ghana Strategy Support Program (GSSP), Accra, 50.28%. Ghana, 2011, https://www.agriskmanagementforum.org/ sites/agriskmanagementforum.org/files/Documents/IFPRI% 4. Conclusion 20-%20Irrigation%20in%20Ghana.pdf. [4] B. T. Anang, “Effect of off-farm work on agricultural pro- )e study assessed the effect of participation in off-farm ductivity: empirical evidence from Northern Ghana,” Agri- activities on farm income of maize farmers in Tolon District cultural Science and Technology, vol. 11, no. 1, pp. 49–58, 2019. of Northern Ghana. Due to the problem of sample selection [5] V. Owusu, A. Abdulai, and S. Abdul-Rahman, “Non-farm bias, the study employed Heckman selection model to in- work and food security among farm households in Northern vestigate the factors influencing participation in off-farm Ghana,” Food Policy, vol. 36, no. 2, pp. 108–118, 2011. work and the determinants of farm income, while propensity [6] H.-H. Chang and F.-I. Wen, “Off-farm work, technical effi- score matching was used to estimate the impact of off-farm ciency, and rice production risk in Taiwan,” Agricultural work on farm income. )e results indicated that partici- Economics, vol. 42, no. 2, pp. 269–278, 2011. pation in off-farm work is influenced by sex, age, and years [7] D. Jolliffe, “)e impact of education in rural Ghana: exam- of formal education of the respondent, farm size, and ining household labor allocation and returns on and off the number of dependents, while farm income is influenced by farm,” Journal of Development Economics, vol. 73, no. 1, age of the respondent, farm size, and access to credit. As- pp. 287–314, 2004. sessment of the impact of off-farm work on farm income [8] J. Fernandez-Cornejo, Off-Farm Income, Technology Adop- indicated that participants in off-farm work increased their tion, and Farm Economic Performance, Economic Research farm income by at least GH¢ 1702 as a result of income Report No. ERR-36, Economic Research Service, U.S. De- diversification. )e rural economy therefore provides off- partment of Agriculture, Washington, DC, USA, 2007. farm and on-farm linkages that enhance farmers’ income [9] R. O. Babatunde, “On-farm and off-farm works: complement from agriculture. )e result also suggests that the negative or substitute? Evidence from Nigeria,” University of Ilorin, lost-labour effect of off-farm activity participation is less Department of Agricultural Economics and Farm Manage- than the positive liquidity (income) effect, resulting in an ment, Ilorin, Nigeria, Maastricht School of Management Working Paper 2015/02, 2015. income gain for the farm. )e study therefore concludes that [10] H. S. El-Osta and M. J. Morehart, “Determinants of poverty participation in off-farm work enables maize farmers to among U.S. farm households,” Journal of Agricultural and improve their farm incomes, thereby improving household Applied Economics, vol. 40, no. 1, pp. 1–20, 2008. welfare. )e creation of employment opportunities outside [11] H. S. El-Osta, A. K. Mishra, and M. J. Morehart, “Determi- the farm will therefore complement on-farm work and nants of economic well-being among U.S. farm operator enhance income from farming. households,” Agricultural Economics, vol. 36, no. 3, pp. 291–304, 2007. Data Availability [12] A. K. Mishra and B. K. Goodwin, “Farm income variability and the supply of off-farm labor,” American Journal of Ag- )e data supporting the findings of the study are available ricultural Economics, vol. 79, no. 3, pp. 880–887, 1997. upon request from the corresponding author. [13] P. Lanjouw, =e Rural Non-farm Sector in Ecuador and Its Contribution to Poverty Reduction and Inequality, World Conflicts of Interest Bank, Policy Research Department, Washington, DC, USA, )e authors declare no conflicts of interest. [14] A. F. Akinrinde, K. F. Omotesho, and I. Ogunlade, “)e issue of income diversification among rural farming households: References empirical evidence from Kwara State, Nigeria,” Journal of Agribusiness and Rural Development, vol. 3, no. 49, pp. 231– [1] Government of Ghana, =e Budget Statement and Economic 238, 2018. Policy of the Government of Ghana for the 2017 Financial Year, [15] A. Alasia, A. Weersink, R. D. Bollman, and J. Cranfield, “Off- Ministry of Finance, Accra, Ghana, 2017, http://www.mofep. farm labour decision of Canadian farm operators: urbani- gov.gh/, 1st edition. zation effects and rural labour market linkages,” Journal of [2] MoFA (Ministry of Food and Agriculture), Agriculture in Ghana: Facts and Figures (2012), Ministry of Food and Rural Studies, vol. 25, no. 1, pp. 12–24, 2009. 8 Advances in Agriculture [16] F. Ellis and H. A. Freeman, “Rural livelihoods and poverty reduction strategies in four african countries,” Journal of Development Studies, vol. 40, no. 4, pp. 1–30, 2004. [17] K. Gebregziabher, E. 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Beyene, “Determinants of off-farm participation deci- sion of farm households in Ethiopia,” Agrekon, vol. 47, no. 1, pp. 140–161, 2008. [26] Y. Bila, B. S. Mshelia, and J. H. Landi, “Off farm activities and its contribution to household income in Hawul Local Gov- ernment Area, Borno State, Nigeria,” Journal of Agriculture and Veterinary Science, vol. 8, pp. 9–13, 2015. [27] I. Matshe and T. Young, “Off-farm labour allocation decisions in small-scale rural households in Zimbabwe,” Agricultural Economics, vol. 30, no. 3, pp. 175–186, 2004. [28] A. Abdulai and A. CroleRees, “Determinants of income di- versification amongst rural households in Southern Mali,” Food Policy, vol. 26, no. 4, pp. 437–452, 2001. [29] K. Seng, “)e Effects of nonfarm activities on farm house- holds’ food consumption in rural Cambodia,” Development Studies Research, vol. 2, no. 1, pp. 77–89, 2015. [30] N. McCarthy and Y. 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Does Off-Farm Work Improve Farm Income? Empirical Evidence from Tolon District in Northern Ghana

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Hindawi Advances in Agriculture Volume 2020, Article ID 1406594, 8 pages https://doi.org/10.1155/2020/1406594 Research Article Does Off-Farm Work Improve Farm Income? Empirical Evidence from Tolon District in Northern Ghana Benjamin Tetteh Anang , Kwame Nkrumah-Ennin, and Joshua Anamsigiya Nyaaba Department of Agricultural Economics and Extension, Faculty of Agriculture, University for Development Studies, Tamale, Ghana Correspondence should be addressed to Benjamin Tetteh Anang; benjamin.anang@uds.edu.gh Received 3 October 2019; Revised 30 June 2020; Accepted 25 August 2020; Published 7 September 2020 Academic Editor: Othmane Merah Copyright © 2020 Benjamin Tetteh Anang et al. )is 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. Participation of farm households in off-farm work has gained prominence in recent times as an income diversification strategy. )e effect of off-farm work on farm income is however unclear. )is paper therefore sought to provide empirical evidence of the income effect of off-farm activity participation using a cross section of maize farmers in Tolon District of Ghana as a case study. In order to account for sample selection bias, the Heckman selection model was used to estimate the factors influencing participation in off-farm work and the determinants of farm income. Furthermore, the study employed propensity score matching to evaluate the impact of off-farm work on farm income. )e results indicate that participation in off-farm work is influenced by sex, age, and years of formal education of the respondent, farm size, and number of dependents while farm income is influenced by age of the respondent, farm size, and access to credit. In addition, the result of the propensity score matching revealed that participants in off-farm work increased their farm income by at least GH¢ 1702 as a result of income diversification. )e rural economy therefore provides off-farm and on-farm linkages that enhance farmers’ income from agriculture. )e creation of employment oppor- tunities outside the farm will therefore complement on-farm work and enhance income from farming. According to the existing literature, there is increasing 1. Introduction recognition of the role that off-farm work plays particularly Most developing countries including Ghana depend on in smallholder agriculture in developing countries [4]. For agriculture as an importance source of livelihood. It is most agrarian communities, farming is considered as the estimated that in Ghana, more than 60 percent of the main occupation. Off-farm work is thus any activity un- population are engaged in agriculture as a source of dertaken by the farmer or farm household outside farming as livelihood [1]. Majority of the farmers are smallholders an additional source of income. )is is opposed to nonfarm who cultivate less than 2 hectares of farm land [2] and work which relates to all activities that are not related to account for about 80 percent of the food produced locally farming (such as dressmaking and commerce). Hence, off- [3]. Yields of most crops are generally below achievable farm work includes farm-related activities carried out by levels due to reliance on natural rainfall for production, farm households for income such as exchange of labour for low adoption of improved production technologies, and cash on another farmer’s farm. )e major sources of off- lack of access to services such as agricultural extension farm income in Ghana include commerce, agroprocessing, and farm credit. In response to liquidity constraints and charcoal production, seasonal migration, brewing of local declining farm incomes, many smallholders diversify gin, basketry, and collection and sale of firewood [4, 5]. production and have multiple sources of income apart According to Chang and Wen [6], participation in off-farm from farming, which have implications for agricultural work by farm households is a persistent phenomenon productivity and farm income. globally, with a steady increase in the dependence of farm 2 Advances in Agriculture does income from off-farm work ease the liquidity con- families on income from off-farm work. Income from off- farm work is regarded as an important source of livelihood straints of farm households enabling greater use of farm resources in production? To the extent that farmers are able for farm households and a means to diversify household income source. A study in rural Ghana by Jolliffe [7] in- to invest earnings from off-farm activity into their farm dicated that about 74% of Ghanaian farm households par- business, farm output and productivity are expected to grow ticipated in off-farm work. Also, research showed that, on and exert a positive influence on farm income. average, 65% of American farm households were engaged in )is study is motivated by the lack of empirical evidence off-farm work [8]. Chang and Wen [6] also reported that of the effect of off-farm work on farm income of smallholder about 75% of Taiwanese farm households earned off-farm farmers in Ghana. )e objective of this paper is therefore to assess the contribution of off-farm work to farm income in salaries. )e increasing importance of off-farm work to farm Tolon District of Ghana. )e paper contributes to the lit- erature on income diversification and its effects on house- households’ economic well-being has generated a lot of discussion among researchers regarding the role off-farm hold welfare by empirically estimating the magnitude and direction of impact of off-farm work on farm income of work plays in household welfare, especially in terms of food security, agricultural productivity, and household income. It smallholder farmers. is a commonly held view that participation in off-farm work )e rest of the paper is structured as follows. Section 2 is expected to reduce on-farm labour availability and its describes the methods used in the study, which provides a allocation and thus constrain agricultural productivity. On background of the study area, sampling and data collection, the contrary, it is also believed that off-farm work enables and method of data analysis and data description. Section 3 farm households to stabilize household income and reduce presents the results and discussion of the major findings of the study. )e conclusion and recommendations from the vulnerability and uncertainties associated with agricultural production. As indicated by the extant literature, partici- study are provided in Section 4. pation in off-farm work has two effects on production: a negative lost-labour effect and a positive income or liquidity- 2. Materials and Methods relaxing effect [9]. )e lost-labour effect occurs when the household loses farm labour to off-farm activities, while the 2.1. Study Area and Sampling. )e study was conducted in income effect occurs when the household earns income from the Tolon District of the Northern Region of Ghana. )e area off-farm activities which it can invest into farming. )e effect is part of the northern savannah zone of Ghana and is well of off-farm work on farm income will however depend on known for its agricultural production. )e area experiences a which of the two effects is stronger. single rainfall regime per annum and is known for the Off-farm work as a risk management tool that reduces cultivation of crops such as rice, maize, and groundnut, income variability of farm households has been reported by which are important staples. )e population of the district some authors [10, 11]. As demonstrated by Mishra and stood at 72,990 according to the 2010 Population and Goodwin [12], farm households may depend on off-farm Housing Census (PHC). An estimated 92% of the population work to stabilize household income because farm com- are engaged in agriculture. modity prices are more variable than off-farm wages. It is )ree communities, namely, Nyankpala, Dundo, and expected from the theory of production that a risk-averse Gbushalagu, were selected for the study due to their agri- farmer will choose to allocate labour and other resources to cultural potential. Fifty farmers were randomly selected activities that are less risky to the point where the expected from each community to give a total sample of 150 farmers marginal returns are equal for the different activities. )e who were interviewed face-to-face using a semistructured higher variability in farm commodity prices is therefore questionnaire. Cochran’s formula for sample size determi- expected to drive participation in off-farm work. nation indicated that the number of farmers selected for the An earlier study by Lanjouw [13] noted that the rise in study is a representative sample. In the absence of a well- off-farm activity by farm households is as a result of de- defined sample frame, households were selected at random clining farm incomes and the need to safeguard against in each community by taking into account the distribution production risks. )is finding is corroborated by a recent of households. Information solicited from farmers included study by Akinrinde et al. [14] which indicated that declining individual, household and farm characteristics, production farm income is the main reason for income diversification activities, production costs and returns, and access to among Nigerian farmers. Alasia et al. [15] on the other hand production resources and services. view participation in off-farm work as a form of self-in- surance which enables farm households to stabilize household income. 2.2. Empirical Models. Participation in off-farm work and Several studies allude to a positive effect of off-farm work the factors determining farm income were analysed em- on agricultural productivity, food security, and household pirically using the Heckman Selection Model, while a income of farm households [16–18]. For most farm nonparametric treatment effect model was used to estimate households, farm income constitutes a significant part of the the effect of off-farm work on farm income. )e Heckman total household income. A policy question which is relevant selection model can be estimated using either maximum to most rural farm households is whether or not off-farm likelihood or a two-step approach. )is study adopted the work contributes directly to farm income. In other words, maximum likelihood approach because it overcomes some Advances in Agriculture 3 of the limitations associated with the two-step approach φ α 􏼁 φ −α 􏼁 φ c x /α 􏼁 u u i u λ α 􏼁 � � � , (4) such as the possibility of the correlation factor to lie outside i u 1 − ϕ α 􏼁 ϕ α 􏼁 ϕ c x /α 􏼁 u u i u the prescribed range. Methodologically, endogenous switching regression is an alternative approach for esti- where φ represents the normal density function while ϕ mating the income effect of off-farm work. However, the indicates the normal distribution function. Heckman selection model was preferred because it provided As indicated by Heckman [19], when there is sample a better fit of the data. selection bias, an ordinary least squares (OLS) regression without the inverse Mill’s ratio will lead to inconsistent parameter estimates. )us, for this study, OLS regression of 2.2.1. Heckman Selection Model. Estimation of the Heckman Y on w without the correction factor or inverse Mill’s ratio i i selection model entails the estimation of a selection equation (λ (α )) will lead to inconsistent β estimates. Consequently, i u (off-farm participation model) using either probit or logit the inverse Mill’s ratio was included as an additional ex- model followed by estimation of an outcome equation (farm planatory variable in the outcome equation (equation (2)). income model) using least squares regression while ac- )e identification criterion requires that at least one counting for sample selection bias. )e assignment of variable which influences participation in off-farm work but households into participants and nonparticipants in off- not farm income is included in the model. )e number of farm work is nonrandom. Hence, OLS regression will not dependents was chosen as an exclusion variable. )is is provide consistent parameter estimates of the outcome because the number of dependents has a direct influence on equation. To address this problem, there is the need to participation in off-farm work, particularly on the number of construct a correction factor, otherwise known as the inverse hours worked but does not directly influence farm income. Mill’s ratio, which is appended to the outcome equation as )e choice of number of dependents as an exclusion variable an additional explanatory variable. is supported by Anang [20] who used the dependency ratio )e choice to participate in off-farm work can be esti- as an exclusion variable in a study on the effect of off-farm mated using a probit model which is specified as follows: work on agricultural productivity in northern Ghana. Z � c x + u , (1) i i i 2.2.2. Propensity Score Matching: Estimating the Effect of Off- where Z is a latent variable which measures the probability th Farm Work on Farm Income. Impact evaluation studies in that the i household participates in off-farm work, such the extant literature have relied on estimation of average that the observed variable Z � 1 if the household partici- treatment effects as direct measures of the impact of in- pates in off-farm work and Z � 0 if otherwise, x indicates a i i ∗ terventions in the agricultural and other sectors. )e effect of vector of exogenous factors influencing Z , and c represents an intervention or exposure on those who received the a vector of parameters to be estimated. treatment is an important measure in impact evaluation In the second stage analysis, the amount of farm income studies. )us, to quantify the effect of off-farm work on farm (Y ) is regressed on a set of exogenous factors, w , for all i i income, we estimated the average treatment effect on the situations where the selection equation equals one (Z � 1), treated (ATT). )e average treatment effect (ATE) given the using the estimated inverse Mill’s ratio as an additional observable data is denoted by explanatory variable. 􏼌 􏼌 Hence, given that Z � 1, we have the following: 􏼌 􏼌 i 1 0 􏼌 􏼌 ATE � E Y T � 1 − E Y T � 0 , (5) 􏼐 􏼌 􏼑 􏼐 􏼌 􏼑 Y � β w + v , (2) i i i where Y is the farm income of individuals who participated where Y indicates the amount of farm income, w is a vector in off-farm work and Y is the farm income of nonpartic- i i of variables influencing farm income, and β is a vector of ipants in off-farm work, T � 1 represents the farmers who parameters to be estimated. participated in off-farm work (referred to as the treated), and )e error terms u and v have bivariate normal distri- T � 0 represents nonparticipants in off-farm work (referred i i butions with zero means, standard deviation δ and δ , and to as the untreated or control). According to [21], E(Y | T � u v correlation coefficient ρ. While Z and x are both observable 1) − E(Y | T � 0) is equal to zero for the case of a rando- i i for a random sample, Y is observed only when the mised design (i.e., in the absence of selection bias). However, household participates in off-farm work (Z � 1). )e in the presence of selection bias, the ATE result from Heckman selection model is specified as follows [19]: equation (5) provides a biased estimate of the impact of off- 􏼌 􏼌 􏼌 􏼌 ∗ farm work on farm income. Hence to overcome this bias, we 􏼌 􏼌 E 􏼐Y Z � 1􏼑 � E 􏼐Y Z > 0􏼑 􏼌 􏼌 i i i i need to estimate the average treatment effect on the treated ′ (ATT), using the observational data, and conditioning on a � E 􏼐Y u > c x 􏼑 i i i (3) 􏼌 vector of farm and household characteristics X as follows: ′ ′ � β w + E 􏼐v u > c x 􏼑 􏼌 􏼌 i i i i 1 0 ATT � E(Δ | X, T � 1) � E 􏼐Y − Y 􏼌 X, T � 1􏼑 � E � β w + βσ λ α , 􏼌 􏼌 i v i u 􏼌 􏼌 1 0 􏼌 􏼌 􏼐Y 􏼌 X, T � 1􏼑 − E 􏼐Y 􏼌 X, T � 1􏼑. where λ (α ) is the inverse Mill’s ratio, which has the fol- i u (6) lowing specification: 4 Advances in Agriculture However, the counterfactual E(Y | X, T � 1) is unob- respondents however have low level of education, a situation servable, hence assumptions are made to estimate it as which can negatively affect uptake of innovation and ability 0 0 follows: E(Y | X, T � 1) � E(Y | X, T � 0). )us, the ATT to access and use information for agricultural production. equation becomes Also, the respondents have an average household size of 9 and 3 dependent members and possess 2.7 hectares of land 1 0 􏼌 ATT � E(Δ | X, T � 1) � E 􏼐Y − Y X, T � 1􏼑 for agricultural production out of which 2 hectares is al- (7) 􏼌 􏼌 1 􏼌 0 􏼌 located to maize production. )is shows that maize pro- 􏼌 􏼌 � E􏼐Y X, T � 1􏼑 − E 􏼐Y X, T � 0􏼑. 􏼌 􏼌 duction is an important economic activity among rural dwellers. In Ghana, farm households depend on maize for food and income. Close to 43 percent of the respondents 2.3. Sampling Procedure and Sources of Data. )e data for the participated in off-farm work while 46 percent used credit in study were collected from smallholder maize farmers in 3 farming. In addition, 58 percent of the respondents are male communities in the Tolon District of Northern Ghana. Fifty indicating lower female participation in maize cultivation. farmers were selected from each community to give a total Maize is a food security crop and household heads, most of sample of 150. )e communities and respondents were whom are male, are anticipated to engage in its cultivation randomly selected. Face-to-face interviews were carried out for home consumption and cash sales. On average, farmers’ with each respondent using semistructured questionnaire. gross income from maize cultivation was GH¢ 2599 (ap- )e questionnaire contained both open- and close-ended proximately US$ 490). questions. )e questions covered farm, household and in- Table 3 presents the distribution of farmers’ gross in- stitutional factors, and off-farm and production decisions, come from maize cultivation. Majority (76%) of the farmers among others. obtained up to GH¢ 2500 as gross income from farming. )e figure is very low, considering that maize is the most dominant crop grown by nearly every household in northern 2.4. Description and Expected Signs of the Explanatory Ghana due to its important as a staple crop and also as cash Variables. )e description and expected signs of the vari- crop for large scale producers. Participants in off-farm work ables included in the analysis are provided in Table 1. Male had higher farm income compared with nonparticipants. farmers are expected to have higher farm income, but sex is hypothesized to have an indeterminate effect on participa- tion in off-farm work. Also, older farmers are expected to be 3.2. Results of the Heckman Selection Model. )e results of more experienced in farming and more endowed with the Heckman selection model are presented in Table 4. )e production resources than younger farmers, which is ex- log-likelihood ratio (LR) test is significant at 1% indicating pected to lead to a higher farm income. Older farmers, by the presence of selection bias, which justifies the use of the virtue of family obligations and dependents, are expected to Heckman model to correct for the bias. In other words, have higher participation in off-farm work. Education en- estimating separate equations for the selection and outcome hances employability and is therefore hypothesized to in- models would result in biased estimates. crease participation in off-farm work. At the same time, education enhances the human capital which is expected to 3.2.1. Factors Influencing Participation in Off-Farm improve farm income. Farmers with access to credit are Activities. )e estimates of the factors influencing par- expected to have lower participation in off-farm work since ticipation in off-farm work using the Heckman selection credit eases the financial burden of farm households. Credit model are presented in Table 4. )e study indicates that the also enhances farm production through acquisition of farm likelihood of maize farmers to participate in off-farm work inputs and financing of farm operations, which is expected is affected by age, sex, education, and total agricultural to enhance farm profits. Farmers with large farms are ex- land. pected to be progressive farmers and better-off compared to Participation in off-farm work is higher for women. those with small farm holdings, leading to less participation Women play several roles in the household apart from in off-farm work and higher farm income. In addition, a farming. )ey are involved in petty trading, arts, and craft higher number of dependents implies greater financial and are more disposed to take up extra income earning burden on households hence higher likelihood to take part activity to support the household’s income. )e marginal in off-farm work. However, the number of dependents is not effect of sex indicates that the probability of women en- expected to have any direct effect on farm income. gaging in off-farm work is 0.214 higher than men. Ahmed and Melesse [22] as well as Man and Sadiya [23] obtained 3. Results and Discussion similar results in their studies in eastern Ethiopia and 3.1. Characteristics of the Respondents. We first describe the Malaysia, respectively. As observed by Ahmed and Melesse characteristics of the sample as shown in Table 2. )e re- [22], female-headed households were more likely to engage spondents have an average age of 38 years and 6.5 years of in off-farm work compared with male-headed households formal education. )e respondents are therefore in their because female-headed families take part in off-farm work to youthful age, a situation which is conducive for agricultural offset their relative lower farm income compared with male- headed families. )e result however disagrees with the production. )is is because agriculture in most developing countries, including Ghana, involves a lot of drudgery. )e findings of Pramanik et al. [24] in their study on the rural Advances in Agriculture 5 Table 1: Description and expected signs of the explanatory variables included in the analysis. Expected sign Variable Description Model 1 Model 2 Sex Dummy � 1 if farmer is male; 0 otherwise ± + Age Age of farmer in years + + Education Years of formal education + + Credit access Dummy � 1 for credit access; 0 otherwise − + Maize farm size Maize farm size in hectares − + Dependents Number of dependents + NA 1 US$ � 5.4 Ghana Cedis (GH¢). Model 1 is the probit participation model. Model 2 is the farm income (outcome) equation. Table 2: Descriptive statistics of the variables used for the analysis. Full sample Participants Nonparticipants Variable Mean diff. Mean S. D. Mean S. D. Mean S. D. ∗∗∗ Farm income 2599 3490 3457 4313 1962 2570 1495 Sex 0.58 0.50 0.52 0.50 0.63 0.49 −0.11 Age 38.3 10.5 37.4 9.43 39.0 11.3 −1.64 ∗∗∗ Education 6.47 6.91 8.48 7.73 4.98 5.84 3.50 ∗∗∗ Maize farm size 2.02 2.12 2.63 2.80 1.56 1.26 1.07 Credit access 0.46 0.50 0.47 0.50 0.45 0.50 0.02 ∗∗∗ Dependents 2.80 2.31 2.17 2.11 3.27 2.36 −1.10 ∗ ∗∗ )e t value of the difference in means between participants and nonparticipants. Statistical significance at 10% level; statistical significance at 5% level; ∗∗∗ statistical significance at 1% level. Table 3: Distribution of gross farm income of the respondents. Full sample Participants Nonparticipants Variable Freq. (%) Freq. (%) Freq. (%) Up to 2,500 114 76.0 43 67.2 71 82.5 2,501–5,000 23 15.3 13 20.3 10 11.6 5,001–7,500 2 1.3 1 1.56 1 1.2 7,501–10,000 3 2.0 1 1.56 2 2.3 10,001–12,500 2 1.3 2 3.12 0 0 12,501–15,000 2 1.3 1 1.56 1 1.2 15,001–17,500 1 0.7 0 0 1 1.2 17,501–20,000 2 1.3 2 3.13 0 0 Above 20,000 1 0.7 1 1.56 0 0 Total 150 100 64 100 86 100 Mean 2,599 4,204 2,511 Minimum 140 480 140 Maximum 21,120 21,120 15,600 nonfarm economy in Bangladesh as well as Beyene [25] in a contribution of off-farm activities to farm income in Borno study to assess the determinants of off-farm participation State, Nigeria. decision of farm households in Ethiopia. Consistent with a priori expectation, the results indicate Younger farmers are more likely to engage in off-farm that educated farmers are more likely to work off-farm. work as shown by the coefficient of the age variable. As Education enhances the human capital and opens up op- farmers become older, they become less disposed to engage portunities for employment off the farm. An additional year in off-farm work. Younger people are more likely to find jobs of education increases the likelihood of participation in off- because they are more energetic. Due to the low poverty level farm work by 0.023. )e results are consistent with Matshe in most rural communities, off-farm employment for rural and Young [27] in their study on off-farm labour allocation decisions in Zimbabwe, as well as Abdulai and CroleRees people involves labour-intensive activities which are better suited to younger people. )e quadratic term for respon- [28] in their study on income diversification among dent’s age shows that participation in work off-farm in- households in Southern Mali. )e result also agrees with creases at a decreasing rate with age of the farmer. )e result Seng [29] in a study on the effect of nonfarm work on is consistent with Pramanik et al. [24] in their study in household food consumption in rural Cambodia, Pramanik Bangladesh as well as Bila et al. [26] in their study on the et al. [24] in their study on the rural nonfarm economy in 6 Advances in Agriculture Table 4: Results of the Heckman selection model of off-farm work and farm income. Probit model Outcome equation Independent variables Coefficient Std. error Coefficient Std. error ∗∗ Sex −0.523 0.241 437.2 552.0 ∗∗∗ ∗∗∗ Age 0.290 0.096 −814.0 223.9 ∗∗∗ ∗∗∗ Age squared −0.004 0.001 12.26 2.992 ∗∗∗ Education 0.054 0.018 −18.41 37.86 ∗∗∗ ∗∗∗ Farm size 0.590 0.127 849.6 150.3 ∗∗ Access to credit −0.116 0.236 1281 541.3 ∗∗ Number of dependents −0.121 0.050 ∗∗∗ ∗∗∗ Constant −5.610 1.901 14202 4283 Inverse Mill’s ratio (λ) −81.68 0.095 Rho −0.881 0.079 Sigma 2342.7 303.5 Lambda −2064.7 423.4 Selected observations 64 Unselected observations 86 Wald chi (6) 187 Prob> chi 0.00 2 2 ∗ ∗∗ LR test of independent equations (rho � 0): chi (1) � 8.94, prob> chi � 0.003. Statistical significance at 10% level. Statistical significance at 5% level. ∗∗∗ Statistical significance at 1% level. (6) and (1) indicate the degrees of freedom for the chi-squared estimation. Bangladesh, and Owusu et al. [5] in their study on nonfarm experience in farming, their production and income in- work and food security in northern Ghana. McCarthy and crease. )is is shown by the positive sign of the quadratic term of the age variable. As farmers gain experience in Sun [30] estimated separate models for men and women and observed that household education levels had positive effect farming, this is expected to translate into more efficient ways on both female and male participation in off-farm em- of production and informed decision-making to maximise ployment in rural northern Ghana. farm profits and income. Participation in off-farm work was found to be positively )e results further indicate that the farm size variable is related to farm size and significant at 1% level. )is indicates significant at 1% level and positively associated with farm that farmers with larger agricultural lands are more likely to income, which is consistent with a priori expectation. )is take up employment off the farm. )e result is contrary to a implies that an increase in farm size results in an increase in priori expectation and hard to explain. A unit increase in farm income. Larger farm operators are therefore able to total agricultural land increases the likelihood of working off increase their income from farming. In addition, the study the farm by 0.041. )e result agrees with Pramanik et al. [24] showed that the effect of credit on farm income was positive who found that farmers with larger plots were more likely to and significant at 10% level. Hence, access to credit enhanced participate in nonfarm activities in Bangladesh. McCarthy the farm income of maize farmers in the study area. )e and Sun [30] also observed that the size of owned land had a result is consistent with a priori expectation as credit eases positive influence on participation in off-farm employment farm liquidity constraints and acquisition of farm inputs in rural northern Ghana. while enabling timeliness in carrying out farm operations to )e number of dependents had a negative and significant maximise output and profits. effect on participation in off-farm work, which is contrary to expectation. Households with many dependents are ex- pected to have a higher propensity to participate in off-farm 3.3. Income Effect of Off-Farm Work. In order to quantify the income effect of off-farm work, we proceeded to estimate the work, but the result suggests otherwise. )e result is at variance with the findings of Anang [20] which indicated average treatment effect on the treated (ATT) using the that an increase in the number of dependents increased the nearest neighbour and kernel matching methods (Table 5). We used the two matching methods in order to compare and propensity of smallholder rice farmers to engage in off-farm work in northern Ghana. check for robustness of the estimates. )e results indicated that participation in off-farm work increases the farm in- come of participants by GH¢ 1702 using the nearest 3.2.2. Results of the Outcome (Farm Income) Model. )e neighbour method and GH¢ 1776 in the case of the kernel- estimates of the parameters of the farm income equation based method. )e result indicates a positive and significant th using the Heckman selection model are presented in the 4 effect of off-farm work on farm income. What the result th and 5 columns of Table 4. )e results indicate that farm implies is that participants in off-farm work are able to income initially decreases with age of the farmer as shown by increase their farm income by at least GH¢ 1702 as a result of income diversification. )e result is supported by Osarfo the negative coefficient of the age variable. In other words, the younger the farmer, the higher the income from maize et al. [31] who showed that participation in nonfarm work had a positive impact on the income of rural farm cultivation. However, as farmers become older and gain Advances in Agriculture 7 Table 5: Estimates of the average treatment effect of off-farm work on farm income. Estimation method Treated Control ATT Robust S.E. t statistic ∗∗ Nearest neighbour matching 64 33 1701.7 737.1 2.309 ∗∗∗ Kernel matching 64 79 1776.3 526.3 3.375 ∗∗ ∗∗∗ Note: the outcome variable is farm income. Statistical significance at 5% level. Statistical significance at 1% level. Agriculture—Statistics, Research and Information Director- households in the Upper East and Upper West Regions of Ghana. )e important role that off-farm income plays in ate (SRID), Accra, Ghana, 2013. [3] R. E. Namara, H. Horowitz, B. Nyamadi, and B. Barry, Ir- household income has been elaborated by other authors rigation Development in Ghana: Past Experiences, Emerging such as Ogbanje et al. 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Advances in AgricultureHindawi Publishing Corporation

Published: Sep 7, 2020

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