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Production Risk in a Subsistence
Agriculture
a
A. G. Guttormsen & K. H. Roll
b
a
UMB School of Economics and Business, Norwegian University of
Life Science, Aas, Norway
b
Department of Industrial Economics and Risk Management,
University of Stavanger, Stavanger, Norway
Published online: 05 Apr 2013.
To cite this article: A. G. Guttormsen & K. H. Roll (2013): Production Risk in a
Subsistence Agriculture, The Journal of Agricultural Education and Extension,
DOI:10.1080/1389224X.2013.775953
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Journal of Agricultural Education and Extension
2013, 113, iFirst
Production Risk in a Subsistence
Agriculture
A. G. GUTTORMSEN* and K. H. ROLL**
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*UMB School of Economics and Business, Norwegian University of Life Science, Aas, Norway,
**Department of Industrial Economics and Risk Management, University of Stavanger, Stavanger,
Norway
ABSTRACT Purpose: In this article we illustrate the importance of understanding the risk
profiles of new technologies, in addition to the changes in productivity, to be able to determine
strategies for agricultural development.
Design/methodology/approach: The analysis is based on data obtained from a 2002 survey of
subsistence farmers in the Kilimanjaro region of Tanzania, and a Just and Pope (1978)
framework is used for modeling risk.
Findings: We find that even if extension services do not increase the mean production, it may
reduce production risk.
Practical implication and originality/value: During the past decades, agricultural extension and
subsidized conventional inputs such as high-yielding seed varieties, fertilizer and pesticides, have
become important components of agricultural aid programs in developing countries. However,
outcomes of this type of aid are somewhat ambiguous, and many donor countries have reduced
their support in response. For the most part, evaluation of these programs employs total factor
productivity analysis to estimate the changes in productivity resulting from investment in aid
programs. However, risk-averse, small-scale farmers will consider both the variance in output and
the expected mean. They may therefore choose input levels that differ from the optimal input
levels of risk-neutral producers, who consider only the expected mean. Programs can therefore
have a positive effect because they reduce risk, even if the direct impact on production is limited.
KEY WORDS: Subsistence agriculture, Risk, Production, Extension, Tanzania, Just and Pope
Correspondence address: K. H. Roll, Associate Professor, Department of Industrial Economics and Risk
Management, University of Stavanger, NO-4036 Stavanger, Norway. Tel: 47 51 83 22 58. Email:
[email protected]
1389-224X Print/1750-8622 Online/13/010001-13 # 2013 Wageningen University
http://dx.doi.org/10.1080/1389224X.2013.775953
2
A. G. Guttormsen and K. H. Roll
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1. Introduction
A common finding in agriculture is that small-scale farmers in developing countries
often use less fertilizer and other inputs than they would if they maximized expected
profits (Ramaswami 1992; Duflo, Kremer, and Robinson 2008). It is also common to
find that smallholders either do not adopt or only partially adopt new technologies,
even when these technologies could provide higher returns on land and labor than
any pre-existing technologies (Feder, Just and Zilberman 1985). One possible
explanation for this reluctance among smallholders in developing countries may be
the perceived risk profile associated with these technologies (Bond and Wonder 1980;
Tsur, Sternberg, and Hochman 1990; Leathers and Smale 1992; Feder and Umali
1993; Hardaker, Huirne and Anderson 2004).
An example of an input that could increase expected output but also increase risk
is fertilizer (Just and Pope 1979; Rosegrant and Roumasset 1985; Roumasset et al.
1987; Ramaswami 1992; Di Falco, Chavas, and Smale 2006). In developing countries,
the use of fertilizer is lower than elsewhere, primarily because of its high cost and the
inability of smallholders to obtain credit. It is therefore expected that an increase in
the use of fertilizer will increase yields and prevent the soil from becoming exhausted
(Evenson and Gollin 2003). Aid organizations and national governments have
accordingly often used free or heavily subsidized fertilizer as part of their
development programs. However, doubts persist as to the success of these sorts of
programs. Even when fertilizer is free or inexpensive, smallholders do not use it to the
extent expected. A possible explanation could be that the use of fertilizer will also
increase risk, as many farmers in developing countries do not receive the necessary
training to apply fertilizer properly. The misuse of fertilizer may even poison crops
(Feder 1980).
For the most part, the risk associated with increased fertilizer use is closely related
to the farmer’s knowledge and farming experience. Smallholders in developing
countries generally have little education: their farming practice is mostly based on
what they learned from their ancestors, who also had no experience with fertilizer.
Extension services then have major potential for improving agricultural productivity
and increasing farmer income, especially in developing economies (Leonard 1977;
Garforth 1982; Feder, Just, and Zilberman 1985; Jarrett 1985; Roberts 1989; Davis,
Ekboir, and Spielman 2008). Consequently, extension services and education have
become a large part of most aid programs.
The impact of extension on farm performance is mixed.1 Despite some notable
successes, there are many weaknesses which hamper the effectiveness of public
extension (Evenson 1997, 2001; Gautam 2000; Feder et al. 2001; Muyanga and Jayne
2008). In particular, the effectiveness of extension in enhancing productivity is not
always quantifiable (Birkhaeuser, Evenson, and Feder 1991). This difficulty in
attributing impact has, in many cases, weakened political support, leading to smaller
budgets and problems with fiscal sustainability (Omotayo, Chikwendu, and Adebayo
2001; Sulaiman and Hall 2002). Conversely, whereas extension services and learning
fail to increase overall production, a more experienced farmer may have the ability to
reduce risk associated with new technologies. An educated farmer will, for instance,
use fertilizer appropriately, thereby reducing the variability of production (Byerlee
1998); this practice will contribute to improving the welfare of risk-averse farmers.
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Production Risk in a Subsistence Agriculture
3
Accordingly, this particular aspect of extension services should be considered in their
evaluation.
Total factor productivity analysis has traditionally been the main tool for
evaluating the impact of aid programs. However, as effects other than maximizing
productivity influence farmers’ decisions and performance, other tools also should be
employed. By evaluating the impact of an aid program using total factor productivity
analysis, the researcher implicitly assumes that the utility function of the farmer has a
single argument; perceived income. For risk-neutral farmers, this is an accurate
specification. However, there is substantial evidence that subsistence farmers are risk
averse (Bromley and Chavas 1989; Ramaswami 1992; Fafchamps and Pender 1997;
Groom et al. 2008) and therefore we should incorporate production risk when
modeling farmer’s decision-making.
The econometric analysis in this article illustrates how smallholders use inputs to
enhance productivity and reduce yield variability. An important characteristic of
agricultural production processes is that we can observe random production shocks
only after input decisions. Hence, input levels influence both the expected level of
output and the level of output risk. Although we expect all inputs to increase output,
some inputs may reduce the level of output risk, whereas others may increase it
(Shankar, Bennet, and Morse 2008). In this article, we employ the framework in Just
and Pope (1978) to investigate how inputs influence production and the level of risk.
We specify a linear quadratic functional form to model the production function,
which we estimate together with a variance function. Using this approach, we
investigate the correlation between mean production and production risk on the one
hand and individual and socio-economic characteristics on the other. We also assess
the importance of risk to other sources of constraints in farm household production,
including credit market imperfections.
We base our analysis on data obtained from a 2002 survey2 of subsistence farmers
in the Kilimanjaro region of Tanzania. Tanzania is one of the poorest countries in the
world, with a per capita gross domestic product (GDP) of just US$1500. Since
independence in 1961, Tanzania has become one of the largest recipients of aid in
sub-Saharan Africa in absolute terms, and the country still receives considerably
more aid as a share of GDP than most other countries in the region (World Bank
2012; Therkildsen 2000). The Tanzanian economy is heavily dependent on
agriculture, which provides 85% of exports, and employs 80% of the work force.
Subsistence farmers, with average farm sizes of between 0.9 and 3.0 hectares,
dominate agriculture in Tanzania (Central Intelligence Agency 2012).
2. Theoretical background
Most studies dealing with production risk employ the Just and Pope (1978)
framework. In their seminal analysis, Just and Pope (1978) present eight postulates
for the stochastic production function that they argue are necessary for the function
to be able to reflect all potential risk structures. One of the requirements proposed is
that positive, zero and negative marginal risk in input levels should be possible. In
other words, inputs can increase or reduce the level of output risk. This contrasts with
the commonly used translog production function that restricts output risk to increase
4
A. G. Guttormsen and K. H. Roll
in input levels. The Just and Pope (JP) production function has the following
general form:
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y ¼ f ðx; aÞ þ hðz; bÞe;
(1)
where f() is the mean production function, h() is the variance (or risk) function, and
x and z are vectors of inputs (with parameters a and b), which may be identical or
have some unique elements. The exogenous stochastic disturbance or production
shock is represented by o, where E(o) 0 and varðeÞ ¼ r2e : One good feature of the
JP form is the separation of the mean and variance effects of changes in the input
levels. Mean output is given by E(y) f(x;a)u, whereas the variance of output is
2
given by varðuÞ ¼ ½hðz : bÞ r2e. This formulation is also useful from an econometric
viewpoint, because we can interpret the variance function as a heteroskedastic
disturbance term (Asche and Tveteras 1999).
3. Empirical specifications
The first task when analyzing production is to investigate whether any significant
production risk is present. Given that we specify production risk as being
heteroskedastic in the JP framework, we can use any test for the presence of
heteroskedasticity. A failure to detect heteroskedasticity is regarded as evidence
against production risk, and we can proceed within a conventional deterministic
framework. If production risk is detected, there are two issues of interest: the mean
production function f() and the variance function h().
In the present analysis, we specify a linear quadratic functional form to estimate
the production and variance function (Asche and Tveteras 1999). This particular
functional form allows the input elasticities to vary in input levels in the production
function f() and in the variance function h(). The linear quadratic production
function is given by:
X
XX
X
y ¼ a0 þ
ak xk þ 0:5
ajk xj xk þ
ad D d þ u
(2)
k
j
k
d
where the subscripts j and k refer to inputs. The subscript d refers to demographic
and socio-economic variables.
The output elasticity with respect to input k is then given by:
!
"
!#
X
xk
Ek ¼
ak þ 0:5
ajk xj
:
(3)
f ðxÞ
j
The general expression for returns to scale (RTS),
"
#
X @y
X
xk
Ek ¼
RTS ¼
;
@xk f ðxÞ
k
k
(4)
is equal to the sum of the k output elasticities. If the estimate for RTS is greater than
one, returns to scale are increasing; if the estimate for RTS is less than one, returns to
scale are decreasing; and if the estimate for RTS is equal to one, the returns to scale
are constant.
Production Risk in a Subsistence Agriculture
5
The variance function is a special case of Harvey’s (1976) variance function
specification, var(u) h(z) exp[zb], where the values of z are either input levels or
transformations of input levels. A nice property of the variance function in Harvey’s
(1976) formulation is that it also ensures positive output variance in the empirical
analysis. Note that in the JP model, var(y) var(u). In the specification, the
argument for the exponent is the following linear function:
"
varð yÞ ¼ exp b0 þ
X
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k
bk xk þ
X
#
bd Dd :
(5)
d
An important theoretical result provided by Ramaswami (1992) proves that for all
risk-averse producers, the marginal risk premium is positive if and only if the input is
risk-increasing. The importance of this result lies in the fact that it is sufficient to
obtain information on the marginal risk of an input in order to determine whether a
risk-averse producer uses less of the input than a risk-neutral producer. If the
marginal risk of an input is positive, then the risk-averse producer will use less of that
input, and if the marginal risk of an input is negative, the risk-averse producer will use
more of that input.
The marginal risk, represented by the output variance elasticity (VEk) in input k, is
given by the parameter estimate bk and represents the analogue to the input elasticity
Ek of the mean function. If input k is risk-increasing, VEk is greater than zero, and if
input k is risk-decreasing, then VEk is less than zero.
4. Background and data
4.1. Study area
The Kilimanjaro region is located in the northeastern part of mainland Tanzania,
immediately north of the equator. With a total surface area of about 13,309 km2, it
covers about 1.5% of the entire Tanzanian mainland, which makes it one of the
smallest regions on the mainland. However, it is the third most densely populated
area with 103 people per km2. The fertility of the land in the region partly explains
the high population density, which has led to land scarcity in the region. The total
population of the Kilimanjaro region is about 1.3 million, which is about 4% of the
Tanzanian mainland. Most of the region’s population is heavily dependent on
agriculture and livestock for their livelihood, with 79% living in rural areas. Farming
is the major economic activity of the region, and subsistence farmers dominate
(National Bureau of Statistics, Tanzania 2009).
The Kilimanjaro region comprises four ecological zones based on altitude, soil and
climate. These zones are the Peak of Kilimanjaro Mountain, the Highlands, and the
Intermediate and Lowland Plains zones. The Highlands lie between 1100 and 1800
meters above sea level and have very fertile soils derived from the remains of volcanic
rock rich in magnesium and calcium. The area is exceedingly well suited for
agricultural activities. The Intermediate zone lies between 900 and 1100 meters above
sea level, and has moderate soil fertility. The Lowland Plains zone lies below 900
meters with an average annual rainfall between 100 and 900 mm and temperatures
above 308C.
6
A. G. Guttormsen and K. H. Roll
4.2. Data collection
We estimate our model using cross-sectional data from a 2002 survey of Tanzanian
subsistence farmers in 11 villages in the Hai and Moshi rural districts in the
Kilimanjaro region. The villages were selected according to how well they represented
the two districts and the various ecological and agro-economic zones. The sample
villages Mabogini (Ma) and Himo (Hi) are in the Lowland Plain zone.3 Kariwa (Ka),
Shiri (Sh), Kware (Kw) and Roo (Ro)4 are in the Intermediate zone, and Kinde (Ki),
Wari (Wa), Umbwe (Um), Ng’uni (Ng) and Nronga (Nr)5 are in the Highlands (Land
Survey Department of the Regional Administrative Office). The survey was
conducted during summer, with 213 interviewed.
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4.3. Inputs
The production function is specified with six inputs: namely, labor (L), land (A),
fertilizer (F), pest control (P), seed (S) and irrigation (W). Table 1 provides summary
statistics for these variables. We measure labor as the number of hours spent farming
each year; this includes both family labor and hired labor. Land is the total area (in
acres) that the household has available for farming, including both owned and rented
land. The average total landholding is only 2.7 acres, which amply illustrates the
scarcity of land in the region. Use of fertilizer, pesticides and seeds is measured by
expenditure on these variables in Tshillings.6 The reason for this normalization is that
there are quality differences in these inputs, which we can control for by weighting the
quantity used by the price. The irrigation variable is a dummy variable that takes a
value of one if the farm has a functioning irrigation system, and zero otherwise.
The model also includes some demographic and socio-economic characteristics
thought to influence output and output risk, including the sex of the household head
(sex), the age of the decision-maker (age), years of education for the head of
household (edu), usage of extension services (ext) and access to credit (credit). The
sex variable takes a value of one if the head of the household is male and zero
otherwise; in the sample, most households are headed by a man (84.5%). The
education variable measures the education level of the household head, in number of
years of schooling, and the extension variable measures if the farm has received any
Table 1. Summary statistics
Variable
Output
Labor
Land
Fertilizer
Pest control
Seed
Irrigation
Sex
Age
Education
Extension services
Credit
Mean
Std dev.
308,282
4137
2.701
19,910
12,277
16,225
0.800
0.845
48.977
6.930
0.324
0.347
297,477
4076
2.213
31,391
29,114
27,408
1.247
0.363
15.250
2.932
0.469
0.477
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Production Risk in a Subsistence Agriculture
7
useful information from extension service or any other source (for example, a
research center) in the previous year. The credit variable is a dummy variable taking a
value of one if the farm has received any credit and zero otherwise.
Because of the diversity of the topography and the possibility of unequal
competence levels, different education and training opportunities and varying road
conditions, we also include village-specific effects in the model. We define output (y)
as total crop value. This value is a production value index, which we calculate by
multiplying the size of the crop by the market price for the relevant product.
While all the inputs in the model are expected to increase the output, some inputs
may reduce the level of output risk, while others may increase risk. Labor is expected
to be the most important input. The lack of machinery in the region means that
production depends heavily on human labor. Increasing the use of labor is expected
to have a risk-reducing effect, as the ability to detect unfavorable conditions early
increases the likelihood of discovering potential problems such as diseases, pests and
a lack of water or fertilizer as early as possible, and certainly before any significant
damage is incurred.
We expect that increasing land use will have a risk-increasing effect, ceteris paribus.
This is because when the land area increases, the time spent per square meter
decreases, and the ability to detect unfavorable conditions early decreases.7 In terms
of fertilizer, as the concentration of fertilizer increases, the size of the crop will
increase up to some point, after which further increase in fertilizer use may result in
crop poisoning and a reduction in yield. Many farmers in developing countries do
not receive the necessary training for applying fertilizer and as a result may poison
their crops. We justify this assumption by drawing on earlier work on production risk
in agriculture (Just and Pope 1979; Rosegrant and Roumasset 1985; Roumasset et al.
1987; Ramaswami 1992; Di Falco, Chavas and Smale 2006).
In general, we expect the increasing use of pesticides to keep the crop healthy and
improve crop resistance to pests. However, pesticide use has some disadvantages.
Besides the risk to human health, pesticides pose dangers to the environment, with
non-target organisms sometimes severely affected. In some cases where a pest insect is
normally controlled by beneficial insects or predators, insecticide use can kill both the
pest and the predator, and the control insect usually takes longer to recover than the
pest. Pesticides are also a factor in pollinator decline, which is a food security issue.8
Because of the lack of money, seed (along with fertilizer and pesticides) is a scarce
input factor. In subsistence agriculture, it is common to use one’s own seed instead of
buying seed. We expect commercial seed to result in less variation in crop quantity
and quality, and therefore to reduce production risk.9
5. Empirical results
We first estimated the linear quadratic mean production function using ordinary least
squares (OLS).10 The model fit is relatively good with an adjusted R2 of 0.72. Based
on the OLS estimates, we performed a number of heteroskedasticity tests to test for
the presence of significant marginal output risk in input levels.11 All of the tests
rejected the hypothesis of homoskedasticity at the 0.05 level, indicating the presence
of output risk in our small-scale agricultural production sample.
8
A. G. Guttormsen and K. H. Roll
As the heteroskedasticity tests provide evidence that production risk is present, we
re-estimated the production function together with the variance function using a
maximum likelihood estimator.12 Table 2 provides the parameter estimates for the
mean (ak) and variance (bk) functions. To obtain robust standard errors, we use the
covariance matrix calculation A 1BA 1 where A is the information matrix and B is
the outer product of the gradient (White 1982; Weiss 1986). These standard errors are
robust in the sense that we do not assume conditional normality of the errors.
Table 2. Parameter estimates for the mean production and variance function
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Mean production function
Variance function
Parameter Coefficient Std. error Parameter Coefficient Std. error
Input
Socio-economic
characteristics
Village-specific
effects
aL
aA
aF
aP
aS
aLL
aLA
aLF
aLP
aLS
aAA
aAF
aAP
aAS
aFF
aFP
aFS
aPP
aPS
aSS
aW
aSEX
aAGE
aEDUC
aEXT
aCREDIT
aHi
aKa
aKi
aUm
aKw
aNg
aNr
aRo
aSh
aWa
a0
0.154
0.232
0.160
0.137
0.262
0.039
0.185
0.000
0.088
0.062
0.135
0.057
0.050
0.008
0.008
0.021
0.076
0.033
0.010
0.058
0.031
0.071
0.000
0.001
0.015
0.213
0.039
0.120
0.244
0.012
0.110
0.310
0.129
0.024
0.125
0.311
0.123
0.0739*
0.1288
0.0536**
0.0396**
0.0619**
0.0281
0.0635**
0.0332
0.0342**
0.0190**
0.1342
0.0545
0.0352
0.0189
0.0406
0.0234
0.0097**
0.0116**
0.0129
0.0105**
0.0441
0.0433
0.0017
0.0093
0.0502
0.0626**
0.2082
0.1064
0.1040*
0.1750
0.1056
0.0996**
0.1044
0.0954
0.0768
0.0932**
0.1587
Notes: *Significant at 0.05 level; **significant at 0.01 level.
bL
bA
bF
bP
bS
0.290
0.427
0.318
0.167
0.268
0.1615
0.1441**
0.0937**
0.0315**
0.0553**
bW
bSEX
bAGE
bEDUC
bEXT
bCREDIT
bHi
bKa
bKi
bUm
bKw
bNg
bNr
bRo
bSh
bWa
b0
0.622
0.761
0.006
0.044
0.463
1.045
1.278
1.523
1.303
1.265
1.112
1.304
1.451
1.078
0.672
1.257
4.495
0.1871**
0.2151**
0.0080
0.0418
0.2279*
0.2694**
0.4420**
0.4454**
0.4645**
0.8153
0.3962**
0.6169*
0.3957**
0.5195*
0.4927
0.4283**
0.6174**
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Production Risk in a Subsistence Agriculture
9
To provide a meaningful interpretation of the estimated input parameters, we
calculate the elasticities. Table 3 details the elasticity estimates at the mean using the
estimated production function. As expected, the output elasticity, Ek, is positive for
all inputs, k. The sample average RTS is 0.8981, indicating decreasing RTS.13
However, the p-value of 0.14395, for a test that the elasticity is significantly different
from one, implies that we cannot reject the null hypothesis of constant RTS.
Credit is the only socio-economic characteristic that is found to influence
productivity at any conventional level of significance. Access to credit will increase
production, which we can partly attribute to the fact that a subsistence farmer with
access to credit uses intensive inputs more frequently. We find that the respective
quantities of fertilizer, pesticides and commercial seed used were 9.2%, 128% and
130% higher if the subsistence farmer had access to credit. In the survey, many
households identify disease as the greatest obstacle to a good coffee harvest. For
many, a lack of money to buy the necessary pesticide is a huge problem, with 81% of
the sample farmers responding that the high cost of pesticides was a problem in crop
cultivation. Neither of the estimated coefficients for education nor access to extension
is significant. Accordingly, our results support the findings in Evenson (1997, 2001)
and Gautam (2000) that extension investment has no measurable impact on farmer
efficiency or crop productivity.
We can discern the elasticities of the variance function by looking directly at the
parameter estimates from the variance function in Table 2. Based on the output
variance elasticities, seed has a risk-decreasing effect, whereas land, fertilizer and
pesticides have a risk-increasing effect. The effect of labor is statistically insignificant
in terms of risk. That seed is risk-reducing is in accordance with our expectations,
and supports the hypothesis that commercial seed results in less variation in crop
quantity and quality. Land usage appears to have a large effect on the level of risk,
with an elasticity of 0.43 for the sample average farm. This supports our a priori
expectation that an increase in the use of land will reduce farmers’ ability to detect
unfavorable conditions early, and will therefore increase production risk (Guan and
Wu 2009). Furthermore, irrigation increases production risk. Traditional irrigation
can be extremely labor intensive and wasteful of water, and may require communitywide infrastructure that is difficult to implement properly.
Of the individual and socio-economic characteristics, the sex of the household
head, use of extension services, and access to credit influence production risk.
Whereas access to credit increases risk in production, use of extension services
reduces risk. This latter finding agrees with our expectations, and supports the
hypothesis that extension services and learning can reduce the risk associated with
new technologies. That access to credit is risk-increasing may be a consequence of
Table 3. Sample average elasticity estimates from the mean function
Mean
Std. error
p-value
EL
EA
EF
EP
ES
RTS
0.12306
0.05139
0.01665
0.26951
0.05458
0.00000
0.24165
0.04391
0.00000
0.035974
0.035247
0.307430
0.22791
0.03809
0.00000
0.898104
0.069730
0.14395*
Notes: *Indicates if the RTS is significantly different from one.
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10
A. G. Guttormsen and K. H. Roll
subsistence farmers using risk-increasing inputs such as fertilizer and pesticide more
often. The sex dummy variable indicates that a male-headed household is more risky,
which is in accordance with the common understanding that women are relatively
more risk averse. This may be particularly true in this society, where women have
greater responsibility for providing and preparing food for the family and for caring
for children.
We also tested for the importance of village-specific effects on the levels of
production and output risk. For both the production and the variance functions,
Wald tests provide support for the use of village-specific parameters, with a x2(10)
statistic of 34.349 with a p-value of less than 0.001 in the production function, and a
x2(10) statistic of 35.722 with a p-value of less than 0.001 in the variance function.
Hence, the Kilimanjaro villages are heterogeneous with respect to both production
and the level of production risk. By looking more closely at the village-specific
parameters, it is possible to investigate whether there is some connection between the
different villages and the levels of production and/or production risk. When the
villages are sorted according to size and mean production, the five most efficient
villages are Wari, N ronga, Kinde, Roo and Umbwe, all of which are either in the
Highlands or in the upper Intermediate zone. This result is then not surprising
because these zones have very fertile soil that is exceedingly well suited for agriculture.
However, there is no correlation between production risk and village ecology or
between the districts and production or production risk in the villages.
6. Concluding remarks
When evaluating the impact of aid programs, conventional analyses do not consider
the fact that small-scale farmers are risk averse. For this reason, a number of aid
programs have been deemed unsuccessful because they appear to have failed to
increase farm productivity. In many cases, this has led to weakened political support,
smaller budgets and problems with fiscal sustainability (Sulaiman and Hall 2002).
However, production risk is of particular importance in developing countries. Nonfarm sources of income are few, and farmers must rely on their own production to
meet their food needs. Production variation may have grave consequences for both
the farmer and his or her family. Given that risk will adversely affect risk-averse
decision-makers, helping them to reduce their own risk exposure is a rational
approach.
This article provides information on the risk properties of inputs and examines
how production risk may influence the way a risk-averse producer chooses the
optimal level of inputs. Employing the Just and Pope (1978) framework, we
investigate how inputs influence production and the level of risk among smallholders
in the Kilimanjaro region of Tanzania. As expected, all inputs increase mean
production. However, according to the output variance elasticities, seed has a riskdecreasing effect, whereas land, fertilizer, pesticides and access to irrigation have a
risk-increasing effect. This may explain why risk-averse farmers decline to use
fertilizer even when it is free: even though use of fertilizer can be very profitable when
applied correctly, it may also increase the variance of yield, which may offset the
positive utility of increased production, thereby reducing the utility for a subsistence
farmer. Through extensive testing, we found that whereas access to credit and having
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Production Risk in a Subsistence Agriculture
11
a male household head increase output risk, the use of extension services reduces
output risk; we also found that villages are heterogeneous with respect to mean
production and production risk.
Our results show why it is important to focus not only on the mean production
function, but also on the variance of production when working with similar problems.
Extension services are one example. Even if extension services do not have a
significant impact in increasing mean production, the use of extension services may
reduce production risk and therefore increase the utility of subsistence farmers.
Although several studies have evaluated the impact of extension by measuring the
relationship between extension and farm productivity and profitability, the information generated does not present the full picture. Evaluation of the impacts of
extension (or any aid program) should consider not only the returns to the extension
program, but also any related improvements in farmer welfare, including risk
reduction, equity, poverty alleviation, environmental quality, food safety and
nutrition (Alston, Norton and Pardey 1995). However, there are few studies in this
area and more evaluative work is clearly required.
Notes
1
For a survey of this extensive literature, see Birkhaeuser, Evenson, and Feder (1991) and Anderson and
Feder (2004).
2
Since then Tanzania has had a substantial economic growth. Productivity in farming has also been
improved, but there are no indications of significant changes in farming practice, that is, fertilizer use,
etc. (World Bank 2012).
3
Mabogini and Himo are at altitudes of 762 and 869 meters above sea level, respectively.
4
Kariwa, Shiri, Kware and Roo are at altitudes of 914, 975, 1036 and 1052 meters above sea level,
respectively.
5
Kinde, Wari, Umbwe, Ng’uni, and Nronga are at altitudes of 1143, 1219, 1280, 1524 and 1676 meters
above sea level, respectively.
6
The Tanzanian national currency (Tanzanian shillings).
7
Increasing the land area is in many cases not possible, as most people already use their entire land share.
Land is becoming increasingly scarce in the region with increasing population pressure, especially in the
Highlands.
8
As an anonymous referee pointed out, we could regard this negative pesticide effect as a negative
externality. However, it would be difficult to determine this effect using cross-sectional data, since the
farmer using pesticides will typically do so for more than one year.
9
Even though we expect commercial seed to result in less production risk, it might have the opposite
effect on net income risk. This could occur because there is more upside potential in production (and
net income), but there is still the potential for no production and correspondingly more negative
income, since the farmers purchase high-priced seed as opposed to using their own seed from the
previous harvest. We thank an anonymous referee for presenting this argument.
10
SHAZAM (Professional Edition) software was used for all estimations.
11
The tests are White’s test, the ParkHarvey test, the Glejser test, and the BreuschPaganGodfrey test.
12
Two estimators provide consistent estimates of the production and variance function parameters: threestage feasible generalized least squares (FGLS) and maximum likelihood (ML). Most empirical studies
of production risk use the FGLS estimator (Just and Pope 1979; Griffiths and Anderson 1982; Hallam,
Hassan, and D’Silva 1989; Wan, Griffiths, and Anderson 1992; Hurd 1994; Traxler et al. 1995).
However, the ML estimator provides asymptotically more efficient estimates of the variance function
parameters (Saha, Havenner, and Hovav 1997).
13
Since irrigation is a dummy variable, the effect of irrigation is not included in the RTS expression. A
consequence of this is that the RTS measure can be considered a short-term measure, where irrigation is
considered semi-fixed.
12
A. G. Guttormsen and K. H. Roll
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