ARTS AND SOCIAL SCIENCES - VOL. 01 ISSUE 01 PP. 13-23 (2020)
European Academy of Applied and Social Sciences – www.euraass.com
Arts and Social Sciences
*Corresponding author: Email: email@example.com, (Nsikak-Abasi A. Etim).
Available online: 30 December 2020
Journal reference: Arts and Social Sciences, 2020, 1(1): 13–23.
© European Academy of Applied and Social Sciences. Euraass – 2020. All rights reserved.
Migrant Remittances and Agricultural Production Under Climate
Change: Evidence From Rural Households in Nigeria
Nsikak-Abasi A. Etima*, Dorothy Thompsonb, Ubong A. Asaa
a Department of Agricultural Economics and Extension, University of Uyo, P. M. B 1017, Uyo, Akwa Ibom State, Nigeria.
b Department of Home Economics, University of Uyo, P. M. B 1017, Uyo, Akwa Ibom State, Nigeria.
Received: 05 September 2020 / Revised: 17 October 2020 / Accepted: 06 December 2020
Majority of Nigeria’s rural poor especially smallholder farmers who rely on agricultural production for their subsistence face
considerable difficulties in increasing productivity due to the adverse impact of changes in climate. But migrant remittance has
become an important part of the strategy for rural households to cope with negative environmental consequences through reduction
in vulnerability to climate variability, improvement in livelihood and expansion in production. An empirical study to measure the
impact of migrant remittances on small holder agricultural production was conducted. Through the multistage sampling technique,
120 smallholder farmers were selected and data were collected using questionnaire. Multiple regression analysis based on Cobb-
Douglas production function was used to determine the impact of migrant remittance on agricultural production. Result of the
analysis revealed that the most critical factors which positively and significantly (p<0.05) influenced the output of remittance
receiving households were education, experience, farm size and labour. Findings also showed that the same covariates also
influence output of non-remittance receiving households. Policies to increase the inflow of remittances to poorer households
engaging in agricultural production would be a rational decision.
Keywords: Migration, remittance, agriculture, output, climate.
© Euraass 2020. All rights reserved.
Migrant remittances are portion of workers’ earning or available income sent to their families back home (Asogwa, 2013;
Osondu et al., 2014; Davis & Lopez- Carr, 2014; Redehegn et al., 2019) and they are essential in adaptation to climate change
(Musah-Surugu,2018; Oronzo and Jewers, 2019). Remittances have been useful in stimulating local economies, reducing poverty
and improving agricultural production (Nwaru et al., 2011). Although, poor rural households are struggling with the challenge of
climate impacts Musah-Surugu et al. (2018) and the agricultural sector is constrained by changes in climate (Etim and Etim, 2020)
agricultural production and food security have been negatively impacted by these changes (Muller et al., 2011; Ndamani and
Watanabe, 2017). Furthermore, the adverse impacts of weather and climate vagaries coupled with the rising cost of agricultural
inputs, low farm income have greatly diminished the ability of rural farm families to expand production. Consequent upon this,
14 Arts and Social Sciences 2020, 1(1) 13–23
research has centered more and primarily on climate change. Studies by Chukwuome et al. (2007); Oseni & Winter, (2009) and
Babatunde and Martinelti (2010) have reported the impact of remittance on development, welfare, food security, poverty and
income inequality. There has been limited studies regarding the impact of remittance on agricultural production under changes in
climate and this has resulted in a huge lacuna. However, to formulate policies that will encourage the inflow of remittances which
will stimulate agricultural production in a tropical climate and enable farmers to cope with the adverse effect of changes in climate,
a study of migrant remittances and agricultural production deserves attention. This study was therefore conducted to estimate the
impact of remittances on agricultural production.
2.1 Study Area
This study was carried out in Akwa Ibom State, Nigeria. The state is located within the humid tropical rainforest region and lies
between latitude 4°33' and 5°53' North and longitude 7°25' and 8°25' East. According to the population estimates by National
Population Commission (2006), it has a population of 3,920,208 million people comprising 2,044,510 males and 1,875,698 females.
It covers a total land area of 8,412 square kilometers is bordered by Abia state on the North, Cross River State on the East, Rivers
and Abia States on the West and on the south by Atlantic Ocean. There are six (6) Agricultural Development Project (ADP) zones
viz; Uyo, Eket, Oron, Abak, Etinan and Ikot Ekpene and 2 distinct seasons viz; rainy season and short dry season. The mean
annual temperature in the state is between 260C and 290C while the average sunshine accumulates to 1,450 hours per annum. The
annual precipitation ranges between 2000mm to 3000mm per annum. The predominant occupation of most inhabitants is farming.
2.2 Sampling and Data Collection Technique
Multistage sampling technique was used in selecting the representative sample. The first stage involved the random selection
of 3 out of the 6 Agricultural Development Project (ADP) zones. The second stage involved the random selection of 2 blocks from
each ADP zone to make 6. Thirdly, 2 communities were randomly from each of the blocks to make 12. Finally 5 remittance and
non-remittance receiving households each were randomly selected from each of the communities to make 120 primary date were
obtained from farmers using the cost route method. Because most rural farmers do not keep farm records as they rely more on
mental recording, data were collected from farmers on a weekly basis. Data were collected with the aid of questionnaire.
Data were analyzed using multiple regression analysis based on Cobb-Douglas production chow and z-test.
The Z test statistics is given by Zcal = (x̅1-x̅2) / Sx̅1 - x̅2 ----------------------- (1)
where in equation (1) and (2), x̅1 and x̅2 are the mean values of the major socioeconomic variables of the migrant remittance
receiving and non-receiving households respectively; S2 x̅1 and S2 x̅2 are variances of the major socioeconomic variables of the
remittance receiving and non-receiving households respectively.; n1 and n2 are the number of households in each group
respectively; Sx1-x2 are sample standard error of the means to estimate the impact of migrant remittances on farmers output, a
Cobb-Douglas production function was specified for the two groups of households separately. The data were pooled and analyzed
in (see equation 3). The pooled data with dummy (equation 4) representing household type was also analysed. The model is
specified implicitly are
Y = f (X1i; X2i, X3i, X4i, X5i, X6i, X7i X8i) ----------------------- (3)
(i = 1,2)
Y = f(X1, X2, X3, X4, X5X6, X7, X8, D) ----------------------- (4)
where in equations (3) and (4) is the grain equivalent output of crops in kg (Olayemi, 1986); X1 = Age in years; X2 = Sex (D=1 if
male, 0 if female); X3 = Educational level in years; X4 = Experience in years, X5 = Farm size in hectares; X6 is labour measured in
man days; X7 = Agro chemicals in naira; X8 = Fertilizer in kilogram; e = error term and 1 the farm household group.
S2 x1/n1) + (S2 x2/n2) ----------------------- (2)
Sx̅1 - x̅2 =
16 Arts and Social Sciences 2020, 1(1) 13–23
Chow statistics was used to test if there was significant difference in production function of the two groups of households and is
computed following and Onyenweaku (1997) Olomola (1998). The chow test for production change (structural shift in production
function is given by
F* = [Ʃe23 – (Ʃe21 + Ʃe22] / [k3-k1-k2] ----------------------- (5)
(Ʃe21 + Ʃe22) / (k1+k2)
where in equation (5), Ʃe22 and k3 are the error sum of square and degree of freedom respectively of the sample of migrant
remittance receiving households; and Ʃe22 and k2 are the error sum of square and degree of freedom respectively of the sample of
non-remittance receiving households.
To test for homogeneity of slope, chow F – statistics was calculated as follows:
F* = [Ʃe24 – (Ʃe21 + Ʃe22] / [K4-K1-K2] ----------------------- (6)
(Ʃe21 + Ʃe24) /k1+k2)
where in equation (6), Ʃe24 and K4 = the error sum of square and degree of freedom respectively for the pooled data with a dummy
variable with a value of unity for remittance receiving households and zero for non-remittance receiving households, while other
variables were as previously defined.
To test for differences in intercept, chow F-statistics was calculated as follows:
F* = [Ʃe23 – (Ʃe21 + Ʃe24] / [K3-K4] ----------------------- (7)
where all variables in equation (7) were as previously defined.
The theoretical value of F is the value that defines the critical region of the test at the chosen level of confidence
(Koutsoyiannis, 2001). If the calculated F exceeds the tabulated F value, then the intercepts are assumed to be different between
the households. This test is conditional on a common slope, so the test for differences in slopes is performed first before testing for
differences in intercept (Onyenweaku, 1997).
3. Result and Discussion
3.1 Socioeconomic Attributes of Smallholder Farmers
Result of the socio economic characteristics of smallholder farmers revealed that majority (66.67%) of the sampled farmers
were males. Findings also showed that most (75%) of the farmers were within the economically active age of 20 to 60 years. About
73.33% of the farmers were married. Findings also revealed that more than 90 percent of the farmers had formal education. Result
showed that most farms were in small plots and majority of the farmers had more than 20 years’ experience in farming. Figure 1
reveals that most of (66.67%) of the farmers were men whereas only 33.33 percent were female. About 26.67% of the farmers
were single as shown in figure 3, whereas 73.33% were married. Figure 2 shows the most of the (75%) farmers were within
economically active and productive age. The educational background of the farmers is shown in figure 4.
The result revealed that most farmers had high literacy level (92%) had attained primary and post primary education. This is
indicative of the fact that rural farmers could make informed decisions on how to channel the remittances into meaningful
agricultural production. The experience in farming is shown in figure 5. Majority of the farmer (52%) had more than 20 years’
experience in farming whereas only (15%) had 1-10 years’ experience in farming. The result implies that most farmers had long
years of experience about the contribution of remittances to agricultural production.
Arts and Social Sciences 2020, 1(1) 13–23 19
Figure 5: Farming Experience of farmers.
The farm size of the farmers is shown in figure 6. Most of the farmers (73.33 percent) cropped farmlands less than 1 hectare
whereas 26.67% percent cropped farms between 1-2 hectares. This result suggests that majority of the farmers cultivated waterleaf
mainly for subsistence.
Figure 6: Farm Size of farmers.
3.2 Summary Statistics of the Socio-economic and Farm Specific Characteristics of remittance receiving and non-
remittance receiving households
Table 1 shows the mean, minimum, maximum value and standard deviations of some explanatory variables and output of the
remittance and output of the remittance and receiving and non-receiving households.
1-10 years 11-20 years More than 20
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Table 1: Summary Statistics of Socioeconomic Variables and Output.
Farm size 1
Farm size 2
Educational level 1
Educational level 2
Farming experience 1
Farming experience 2
Household Income 1
Household Income 2
Household size 1
Household size 2
Here household type 1 and 2 represent remittance receiving and non-remittance receiving households, respectively.
3.3 Production Function Estimates
The estimated production functions for the two groups of households, the pooled data and the pooled data with dummy is
presented in Table 2. The fact that all the F-ratios were statistically significant at 1 and 5 percent levels is indicative of goodness of
fit of the model. The coefficients of multiple determinations R2 were 0.7051, 0.6431, 0.7290 and 0.6911 for remittance receiving,
households, non-receiving households, the pooled data and the pooled data with dummy indicating household type respectively.
The result implies that 70.51 percent, 64.31 percent, 72.90 percent and 69.11percent variation in output of the remittance receiving
households, the non-receiving households, the pooled data and the pooled data with dummy indicating household type respectively
were accounted for by variables included in the models. The most critical factors influencing the output of the remittance and non-
remittance receiving households were education, experience, farm size and labour.
Education was significant (p<0.05) for remittance and (p<0.01) non-remittance receiving households respectively and positively
related to output. This implies that as households acquire more education; their agricultural output is likely to increase. This is
because knowledge acquired through formal educational system affords them the opportunity to make inform decisions on proper
allocation of productive resources in order to maximize output. Nwaru et al. (2011) reported that the education of household
members affords them the opportunity to use resources more efficiently and increase farm output and welfare. This result conforms
Arts and Social Sciences 2020, 1(1) 13–23 21
to a priori expectations and is synonymous with earlier empirical findings by Huy and Nonneman (2015).
The variable experience was significant (p<0.05) for both household types and positively related to output. This result implies
that both households’ types were engaged in agriculture over a long time and had acquired more experience about the farming
practice that resulted in increased outputs. Earlier empirical finding by Iheke (2014) corroborate with this result.
Farm size for both remittance receiving and non-remittance receiving households were significant (p<0.01 and p<0.05) and
positively related to output. This result implies that increasing the size of cultivable land would increase output level. Similar finding
was obtained by Iheke and Aniocha (2017). The variable labour was positively related to output and significant (p<0.05). This
conforms to a prior expectation and implies that increasing farm labour would result in higher output and income. This result is
contrary to earlier empirical findings by Iheke (2014).
The dummy representing household type was positively related to output and significant (p<0.05). This result implies that
remittance receiving households obtained higher output than the non-receiving households. This may be attributable to the fact that
portions of migrant earnings are sent home enable families to acquire additional resources, expand production and improve their
wellbeing. Gupta et al., (2009) reported that inflows of remittances increase the economic growth and reduce the poverty by
stimulating the income of the recipient country, reducing credit constraints, accelerating investment, enhancing human
development through financing better education and health. This result is consistent with earlier empirical findings by Etim and Edet
(2014) who reported that remittances may be a vehicle to reduce poverty levels by spending on improved nutrition, financial
children’s schooling or basic health care, or constructing their own home.
Table 2: Estimates of Cobb-Douglas Production functions
Pooled with dummy
Chow test for structural break at observations 30 were as follows:
Test statistics F(7,46) = 2.36892
With p-value p(F(7,46) > 2.36892 = 0.0373194
3.4 Hypothesis Testing
From the study, t-calculated (2.36892) was greater than t-tabulated (0.0373194) at 5 percent significance level. Therefore, Ho1
which states that there is no significant relationship between smallholder farmers output and migrant remittances is rejected. This
implies that migrant remittance influenced farmers output significantly. Also Ho2 states that there is no significant difference
between (farm size, labour, fertilizer, age, education farming experience) and output of remittance receiving and non-remittance
receiving households are rejected.
22 Arts and Social Sciences 2020, 1(1) 13–23
The study measured the impact of migrant remittances on agricultural production. Multiple regression based on Cobb Douglas
was used to analyse the data. Results revealed that remittances contributed significantly to increased agricultural output in
remittance receiving households. Findings also showed that education, experience, farm size, household type and labour were the
most important drivers of smallholder farmers agricultural output. Education is a vital asset for increasing agricultural output in both
remittance and non remittance receiving households. It is therefore essential to improve the human capital development of farmers.
Through education and training, farmers are well informed about climate change events and will be able to make rational decisions
regarding the allocation of remittances and other resources towards mitigating the negative effect of climate change. Apart from
using migrant remittances for construction and education, this study is suggestive that families could also gain if remittances are
ploughed into meaningful agricultural production. Remittances have been shown to impact agricultural output positively; therefore,
increasing the flow of remittance into the country would help smallholder farmers to expand agricultural production. Policy
measures should also be followed strictly to reduce the charges and constraints associated with receiving remittances from
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