International Journal of Mechanical Engineering and Technology (IJMET)
Volume 10, Issue 04, April 2019, pp. 808-817. Article ID: IJMET_10_04_080
Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=10&IType=4
ISSN Print: 0976-6340 and ISSN Online: 0976-6359
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THE IMPACT OF MIGRATION ON RICE
FARMING TECHNICAL EFFICIENCY IN
INDONESIA
Defidelwina*
Ph.D Candidate on Agricultural Science Study Program, Universitas Gadjah Mada,
Yogyakarta, Indonesia
Agribusiness Study Program, Faculty of Agriculture, University of Pasir Pengaraian, Riau,
Indonesia
Jamhari, Lestari Rahayu Waluyati and Sri Widodo
Agricultural Science Study Program, Faculty of Agriculture, Gadjah Mada University,
Yogyakarta, Indonesia
*Corresponding Author
ABSTRACT
This study investigates the impact of migration on rice farming Technical efficiency
in the Rokan Hulu District Indonesia. It as a measure of rice farming performance and
a way to raise productivity without an increase in the inputs used. We investigate and
discuss the technical efficiency of migrants and indigenous farmers in terms of
characteristics and socio-economic conditions. We argue that it contributes to the
policy-making basis in increasing rural economic growth. Furthermore, existing
research focuses on rural-urban migration. But, we rarely found the literature on
reverse flows and its impact, especially staple food crops. The primary data were obtained
from interviews and analyzed using one step stochastic frontier production function. The study
shows that migration has a negative impact on technical efficiency and indigenous
farmers are more technically efficient than migrant ones. The rice farmers are expected to
increase their average technical efficiency level by 13%. To reach their potential
production, the farmers must improve their labor management.
Key words: Technical efficiency, stochastic frontier, indigenous farmers, migrant.
Cite this Article Defidelwina, Jamhari, Lestari Rahayu Waluyati and Sri Widodo, the
Impact of Migration on Rice Farming Technical Efficiency in Indonesia, International
Journal of Mechanical Engineering and Technology, 10(4), 2019, pp. 808-817.
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The Impact of Migration on Rice Farming Technical Efficiency in Indonesia
1. INTRODUCTION
Migration and agriculture are influenced by each other. Migration can occur in two directions,
come or out of the agricultural sector. In General, it causes by the pull factors such as
opportunities for economic improvement, social encouragement, political environment [1],
education, health and job [2]. So, migration policy and agricultural development have become
topical issues of interest to many researchers [3].
Whether migration will improve or worsen the conditions of agricultural households and
their communities, in the long run, the question that cannot be answered in a short time [4]. The
conditions of agricultural households will depend on the sustainability of the farming they
manage which is reflected in how farm perform.
One measure of farm performance is technical efficiency. Technical efficiency refers to
how land to get the maximum output from a bundle of inputs based on certain production
technologies[5];[6]. In case, assuming that the input used is the same, farming that is technically
inefficient tends to get lower production than to technically efficient ones. This means,
technically efficiency can raise farm productivity without having to increase the number of
input costs. Technical efficiency decision is very relevant to be used to increase productivity in
the limited economic conditions of the community. In addition, measuring the technical
efficiency of the farming will provide important information to decision-makers in formulating
agricultural policies [7];[8].
Some researchers have examined the effects of migration on technical efficiency. [9] use
the characteristics of stochastic frontier on average output and the level of technical efficiency.
[10] investigated the impact of international migration on technical efficiency, allocation of
resources and income from agricultural production in Albania. [11] investigated the relationship
between labor migration and agricultural production through its technical efficiency. [12] uses
a two-stage frontier estimation technique to capture the effects of migration on agricultural
efficiency.
Although the impact of migration on efficiency has been widely studied, in earlier, migrants
bear the costs of migration independently. Meanwhile, in Indonesia, one of the government's
regional development strategies is a migration program [13], through a balanced relocation of
population based on natural and environmental carrying capacity [14]. The Indonesian
government regulates and funds the migration process for middle to lower class citizens and
grants certain areas of land to support their lives in the destination village. Migrants use this
land for farming [15]. This makes migrants easier to work in the agricultural sector.
Even though many commodities make it possible for migrants to be planted, rice as a staple
food is an option, either as a main or side livelihood, to support their household food security.
This plant is also not an annual plant so that within 90-115 days, farmers can harvest and use it
to meet their household needs. This study fills in the existing gaps, how the impact of migration
on the technical efficiency of rice farming in people who migrate due to government programs.
We use the one step stochastic frontier production function to investigate the impact of
migration on the technical efficiency of rice farming in this region. This method is more
recommended than the two steps because it can avoid estimation bias [16].
2. METHODS
We chose Rokan Hulu District as a research area because of it one of the destinations for
migration that has been implemented by the government. We chose Rambah and Rokan IV
Koto sub-district as samples representing the regions and collected data through direct
interviews with farmers using a structured questionnaire. This survey collects information about
the characteristics and socio-economic of 100 farm households. We have chosen respondents
purposely according to the research goals. The number of samples taken is proportional to the
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Defidelwina, Jamhari, Lestari Rahayu Waluyati and Sri Widodo
rice farmers population in each sub-district, 55 and 45 respondents for Rambah and Rokan IV
Koto respectively
Data were analyzed using the stochastic frontier production function. Initially, the stochastic
frontier model was used by [17] and [18], depended on one side error term which is assumed
to be identical and independently distributed. Afterward, [19] used the stochastic frontier model
to allow for heterogeneous component errors, depending on differences in farmer
characteristics. This model has also been used by [20], [21] and [22]. In this study, the CobbDouglas model specification was used in the stochastic frontier production function to estimate
coefficient:
ln Yi   0  1 ln Landi   2 ln Seedsi   3 ln Labori   4 ln Fertilizeri   5 ln Pesticidei   6 DSAi  vi  ui
(1)
Yi is the actual production of rice (kg) and i mean farm i (i = 1,2,3, ..., 100), β1-β6 is the
parameter to be estimated, Land is the cultivated farmland (ha), Seeds is the amount of use of
seeds (kg), Labor is the number of labor use (man-day), Fertilizer is the amount of fertilizer use
(kg), Pesticide is the amount of pesticide use (l), DSA is dummy of a seed-aid (using seed-aid
from government =1, others=0), v is a random error which is assumed to be independent and
identically distributed N (0,  v2 ) and u is a non-negative random variable (the impact of
inefficiencies related to production). Since the farming process in the local area is still
conducted conventionally, DSA is used to capture technological differences between farmers.
The use of the Cobb Douglas model in this study was tested by the likelihood ratio test
statistic (λ) compared with the value of the chi-square distribution [23] with the degree of
freedom that corresponds to the restriction value. Hypothesis testing with a likelihood ratio
statistical test is defined as:
  2lnLH0  lnLH1 
(2)
L(H0) and L(H1) are the value of the likelihood of null and alternative hypothesis for the
Cobb Douglas model. The estimation results show that the Cobb Douglas model is fitted with
data on rice farming in Rokan Hulu District.
Model specifications used to identify the level of technical inefficiency of rice farming
households defined as:
ui   0  1 Agei   2 Educi   3 Expi   4 HS i   5FCi   6 MLi   7 Migrationi   i
(3)
Here, ui is the effect of technical inefficiency, δ1-δ7 is the coefficient that affects technical
inefficiencies, Age is age (years), Educ is education (years), Exp is the experience of farming
(years), HS is the household size, FC is the frequency of counseling (times), ML is the dummy
of the main livelihood as rice farming (the main livelihood as rice farming = 1, others = 0),
Migration is dummy of the migration (Migrant farmers = 1, indigenous farmers = 0) and 𝜖 is
an error term.
The technical efficiency of each farm is a comparison between actual production and
potential production, at the condition of the level of use of farm inputs. If the actual production
is located on the frontier, farming is technically efficient. However, when farming is under the
frontier, farming is said to be technically inefficient. Technical efficiency can be defined as:
TE i 
Yi
Yi *
Here,
(4)
Yi* is
the
potential production, while technical inefficiency is
TI i  1  TEi  1  exp ui  . The variance parameters of equations (3) and (4) are expressed
by  2   u2   v2 and    u2  2 . γ shows the effect of technical inefficiency where values lie
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The Impact of Migration on Rice Farming Technical Efficiency in Indonesia
between 0 and 1. t-test was used to test the individual significance of the parameters and test
the overall parameters, while to test the effects of the inefficiency model was used the likelihood
ratio (LR) test. The null hypothesis: there is no effect of technical inefficiency then
H 0 :    0  1  ... 7  0 and the coefficient on the model of the effect of inefficiency is
nothing H 0 : 1  ... 7  0 .
Production functions and technical inefficiency are estimated by Maximum Likelihood
Estimation (MLE) using the Frontier 4.1c program. This program will estimate both functions
simultaneously in one step [24].
3. RESULTS AND DISCUSSION
3.1. Characteristics of Rice Farmers
Table 1 presents the characteristics of migrants and indigenous farmers of rice farming in the
Rokan Hulu District. Overall, there were no significant differences except in the experience of
farming and the dummy of the main livelihood as rice farmers. Based on farming experience,
the migrant farmers have longer than indigenous ones with a difference of 14.5 years.
Experience is a reasonable proxy for farmers' skill levels and different skill levels may have
very different levels of technical efficiency. Based on rice farming as the main livelihood, 21%
of the indigenous farmers are more in number than the migrant ones.
Table 1. Characteristics of migrants and indigenous farmers of rice farming in Rokan Hulu Regency
Farmers
Characteristics
Age (Years)
Education (Years)
Farming Experience (Years)
Household size (People)
Dummy
Rice farming as the main livelihood (%)
Others (%)
Migrant
53.52
6.31
26.35
3
Indigenous
46.75
7.75
12.17
4.00
0.31
0.69
0.52
0.48
3.2. Production
Table 2 presents the use of each input per farm and per ha for migrant and indigenous farmers.
There is a fairly high difference between rice productivity of migrant farmers and indigenous
ones. Overall local farmers use input more intensively than migrant farmers except for land and
fertilizers. The land use of migrant farming is 1.84 times higher than local farmers. The average
land used of rice farming in the Rokan Hulu District is quite narrow with 0.268 hectares.
According to [25], farmers are included in the small-holder group. This is also in line with the
research of [26], which states that the agricultural land used is concentrated between 0.1-0.4
hectares per farm household. The use of fertilizers per ha of the migrant farmers is 22% higher
than that of the indigenous ones. The average of this fertilizer used in this area (269.30 kg per
ha) is lower than the recommendation for fertilizer use for the Rokan Hulu District (375 kg)
[27].
Conversely, local farmers are more intensive in using seeds, labor, and pesticides. The seeds
in this study were divided into two categories: aid from government and non-aid. The Number
of migrant farmers uses seed aid 5.67 times more in number than the indigenous ones. Seeds
are the initial determinant of farming productivity. The use of the correct quality and quantity
of seeds can be one way to make farming more efficient [28]. The used of seeds will greatly
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Defidelwina, Jamhari, Lestari Rahayu Waluyati and Sri Widodo
depend on the power of growing, the possibility of being attacked by pests, diseases, and
seasons. The use of labor per ha on indigenous farming is 25% higher than that of migrant ones.
The average labor use in this area (74.77 man-days per ha) is slightly higher than conventional
rice farming results of the study by [29], which were 69.92 man-days per ha per planting season.
On pesticide use, indigenous farmers use 4 times more in amount than migrant ones. The high
use of pesticides in indigenous farmers is due to the stages of cultivation carried out using more
such as cleaning the grass on land preparation, weeding and controlling pests and plant diseases.
Table 2. Use of per farm and per ha inputs of rice farming in Rokan Hulu District
Migrant Farmers
Indigenous Farmers
per farm
per ha
per farm
per ha
913.96
2,619.10 646.10 3,328.50
0.35
1.00
0.19
1.00
9.19
26.33
6.85
35.27
23.18
66.43
17.20
88.60
102.42
293.49
44.48
229.15
0.64
1.82
1.78
9.18
Pool
per farm
per ha
774.67 2,885.85
0.27
1.00
7.97
29.69
20.07
74.77
72.29
269.30
1.23
4.59
Dummy
Farmers
(people)
%
Farmers
(people)
%
Farmers
(people)
%
Seeds aid used (1=
Seeds aid used, 0=
Others)
17.00
17.00
3.00
3.00
20.00
20.00
Variable
Production (kg)
Land (ha)
Seed (kg)
Labor (man-days)
Fertilizer (kg)
Pesticide (l)
3.3. Estimating the Stochastic Frontier Production Function and Technical
Inefficiency
The estimation results of the stochastic frontier production function and technical inefficiency
are presented in Table 3. The sigma square value (σ2) of 0.28 is positive and significantly
different from zero at a significant level of 1%, indicating that the distribution used is in
accordance with the assumptions of the existing distribution with a half-normal distribution.
The parameter value γ is related to the variance of the effect of technical inefficiency estimated
from the stochastic frontier production function. The gamma variant value is 0.91 and is
significant at alpha 1%. This shows that 91% of the error term variation is affected by technical
inefficiency and only 9% is caused by noise. MLE value is greater than OLS/ordinary least
square (13.69 > -3.86). Thus, it is conclusive that the MLE model is good enough to represent
the existing conditions in the field. The likelihood ratio test is 35.09 and this is greater than the
table value of χ2 [23] at a significant level of 5% with a restriction value of 9 (16.27). This
shows that the Cobb Douglas stochastic frontier production function used can explain the
existence of technical inefficiency of farmers in the production process.
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The Impact of Migration on Rice Farming Technical Efficiency in Indonesia
Table 3. Estimated stochastic frontier production function and rice farming inefficiency in Rokan
Hulu District
Variables
Production Function
Constant
Land
Seed
Labor
Fertilizer
Pesticide
Dummy of Seed Subsidy
The Function of Technical
Inefficiency
Constant
Age
Education
Farming Experience
Household size
Frequency of counseling
Rice farming as the main
livelihood
Migration
Sigma-squared
Gamma
Log-likelihood function OLS
Log-likelihood function MLE
LR test of the one-sided error
Coefficient
Standard error
t-ratio
7.31***
0.61***
0.06
0.03
0.03**
-0.03
-0.03
0.51
0.10
0.06
0.14
0.01
0.03
0.06
14.45
5.85
0.87
0.19
2.39
-1.07
-0.44
-2.88*
0.03**
0.15**
-0.07**
-0.37*
0.49**
1.56
0.01
0.08
0.03
0.19
0.24
-1.84
2.00
2.02
-2.20
-1.93
2.02
0.29**
0.19
1.51
1.53**
0.28***
0.91***
-3.86
13.69
35.09
0.59
0.10
0.04
2.59
2.64
25.22
Description: *p<0.1, **p<0.05; ***p<0.01
The estimation result of the stochastic frontier production function shows that the land and
fertilizer have a significant effect on alpha 1% and 5%. Here, it is apparent that land is more
responsive to changes in production. The positive correlation between land and fertilizers on
production is in line with the research of [21], [30], [31], [32] and [33]. However, a different
result was revealed by [8] and [34] showing that fertilizer has no significant effect on
production.
All variables of technical inefficiency are significant in the alpha of 5% and 10%. These
results are in line with the results of [22] and [35]. On the other hand, [36], [37] found that age
has negative effect in inefficiency.
Variables of education also have an opposite sign to the expected one. The positive sign
shows that an increase in education level will increase the value of farmer inefficiency. This is
in line with the research of [21], [22] and [31].
The farming experience coefficient and household size are significant at the 5% and 10%
levels. This explains that the higher the experience of farming, the more technically efficient.
The household size also has a positive effect. In other words, more members of household size,
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the farmers more technically efficient because it will be easier to allocate the needed labor for
the farms. This finding is in line with the research of [37].
The frequency of counseling has a positive and significant coefficient at 5% alpha. This
shows that it does not have a positive impact on farming. The frequency of counseling per
planting season for indigenous farmers is higher (0.90 times) than that of migrant ones (0.88
times). The average frequency of counseling attended is 0.89 times per planting season.
In terms of the main livelihood, it is apparent that farmers whose main livelihood as rice
farming have a higher inefficiency level than other ones. This is in line with the research by
[38].
Migration variables indicate that indigenous farmers have a better level of efficiency than
migrant ones. This is in line with the research by [39].
3.4. Technical Efficiency Value of Rice Farming
The value of the technical efficiency of rice farmers in the Rokan Hulu District is presented in
Table 4. The average value of efficiency of farmers is 0.87, which indicates that farmers still
have the opportunity to increase production by 13%. The value of farmers' technical efficiency
has a wide range of 73% (0.97 - 0.24). Thus, farmers may improve their technical efficiency in
the range of 13% - 73%. [22] also find this wide range of levels. The migrant farmers have a
lower efficiency value of 5% (0.89 - 0.84) than indigenous ones. The results of this study are
in line with the research of [3], where non-migrant technical efficiency (0.68) is higher than
intercontinental migrants (0.61) and non-migrant technical efficiency is lower than continental
migrants (0.78)
Table 4. Frequency distribution of technical efficiency of rice farming
Interval
0.24
0.49
0.74
- 0.48
- 0.73
- 0.99
Total
Minimum
Maximum
Mean
Migrant Farmers
Frequency Percent
1
2.08
7
14.58
40
83.33
48
100.00
0.24
0.97
0.84
Indigenous Farmers
Frequency Percent
1
1.92
2
3.85
49
94.23
52
100.00
0.40
0.96
0.89
Pool
Frequency
2
9
89
100
Percent
2.00
9.00
89.00
100.00
0.24
0.97
0.87
4. CONCLUSION
This study examines the impact of migration on the technical efficiency of rice farming for
migrant and indigenous farmers. Overall, indigenous farmers are more technically efficient than
migrant ones. In addition, the use of inputs by migrant farmers is lower than that of indigenous
ones. Farmers' technical efficiency is wide-ranging with 73%, and an average of 0.87. The
average, farmers can still increase their technical efficiency by 13%. Using good labor
management, farmers are expected to achieve their potential production.
ACKNOWLEDGMENTS
We wish to thank the Ministry of Research, Technology and Higher Education of the Republic
of Indonesia (Kemenristekdikti RI) in collaboration with Education Fund Management Institute
(LPDP) for the financial support of our research.
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