Does an increase in agricultural output price leads to an increase in supply and efficiency?
In the case of Ethiopia
Anbes Tenaye Kidane
Norwegian University of Life Sciences, Ås, Norway
[email protected]
Eirik Romstad
Norwegian University of Life Sciences, Ås, Norway
[email protected]
Abstract
This paper estimates the supply response and technical efficiency change to an increase in price.
Ethiopian Rural Household Survey panel data of 1994-2009 is used for this study. The true
random and true fixed effect models proposed by Greene (2005a) are used for stochastic frontier
analysis. These specifications allow differentiating time-varying technical inefficiency from
unit specific time invariant unobserved heterogeneity. The Cobb-Douglas technology is
employed for estimating stochastic frontier. It also measures the level and determinants of
technical efficiency before and after the price increase. The supply responses of major crops are
estimated taking before and after the price increase since 2006.
Generalized Method of Moments (GMM) is used to estimate two closely related dynamic panel
data models. The first is the Arellano-Bond (1991) estimator, which is also available without
the two-step standard error correction. It is sometimes called "difference GMM." The second is
an augmented version outlined by Arellano and Bover (1995) and fully developed by Blundell
and Bond (1998). It is known as "system GMM." Roodman (2009) provides introduction to
these estimators. The estimators are designed for dynamic "small-T, large-N" panels that may
contain fixed effects and separate from those fixed effects from idiosyncratic errors that are
heteroskedastic and correlated within but not across individuals.
It is found that there are moderate supply responses and technical efficiency change to an
increase in price of major crops in Ethiopia since 2006. Own-price output elasticity is estimated
at 5.58 for tef, 0.62 for barely and 25.79 for wheat.
1
There is technical efficiency gain after 2006 where a general increase in price of agricultural
products are observed.
Farm size, labour, traction power, fertilizer and precipitation attain significant farm output
elasticity. Land quality, extension program participation, credit and hoe use are also
significantly affect farm output. Five major agro-ecological zones included gained from Hicksneutral technological improvements in the period. Average level of farm production efficiency
for the surveyed farmers is 0.56, indicating that an average farmer produces 56 per cent of the
value of output produced by the most efficient farmer using the same technology and inputs.
The scale coefficient is 1.33.
Participation in off-farm activity, participation in extension program, credit use, household sizelabour interaction, number of plot-farm size interaction, agroecology-year interaction and farm
size-labour interaction significantly affect farm household efficiency.
Key words: stochastic frontier analysis, true fixed effect model, true random effect model, price
increase, supply response, technical efficiency, Ethiopia
Introduction
Ethiopia is a country that has already adopted and implementing a policy strategy of agricultural
development led industrialization (ADLI). The strategy states that first to develop the leading
sector, agriculture, and then the industry sector. The rationale behind this policy strategy is that
agriculture is everything to the country that can bring about rapid, sustainable, and equitable
economic growth of the economy. However, the strategy gives equal emphasis to the industry
sector as well since September 2010. Devaluation of Ethiopian currency by about 16.7% since
September 1st, 2010 is part of this new strategy.
2
Coupled with ADLI policy direction, the Ethiopian government design and implement different
programs to raise production and productivity. Like, Sustainable Development and Poverty
Reduction Plan (SDPRP), where the policy considers agriculture believed to be a potential
source to generate primary surplus to fuel the growth of other sectors of the economy (industry)
(MoFED, 2002). Plan for Accelerated and Sustained Development to End Poverty (PASDEP),
which was the strategic framework for the five-year period 2005/06-2009/10, also put emphasis
to the agricultural sector. The PASDEP represents the second phase of the Poverty Reduction
Strategy Program (PRSP), which has begun under the Sustainable Development and Poverty
Reduction Program (SDPRP), which covered 2002/03-2004/05. The Participatory Demonstration
and Training Extension Systems (PADETS), instituted in the mid-1990s, were especially
designed to increase agricultural production through demonstrations of seed-fertilizer
technologies.
The Ethiopian government further designs a five year Growth and Transformation Plan (GTP),
since September 2010. The GTP aims to increase the economy from the current 460 billion birr
to one trillion birr and wants to achieve vast growth in different sectors in the next five years.
This leads to a rapid development of agro-industries in the country. As the agro-industry and
industrial sectors develop fast, the demand for agricultural output would be increased.
A rapid increase in food prices are another concern of Ethiopian agriculture. According to Minot
(2010), the increases in food price were quite large (over 150%) in Ethiopia and Malawi over
2007-2008 that were associated with the surge in global agricultural commodity prices.
According to Rashid (2010), domestic prices of wheat and maize went above the import parity
price in 2008 by as much as US$300 per ton. Rashid (2010), further analyzed that the nominal
3
prices of teff and wheat rose gradually over the period 2005-2007 before more than doubling
between mid-2007 and mid-2008.
Given the various agricultural programs and policies implemented over the last 20 years to raise
farmers’ efficiency and productivity, it then becomes imperative to quantitatively measure the
current level of and determinants of technical efficiency, whether or not the increase in
agricultural output price since 2006 leads to an increase in productivity/efficiency.
According to Minot (2010), the world prices of maize, wheat, and soybeans more than doubled,
while world rice prices tripled between January 2006 and early 2008. However, food prices
increases in Ethiopia and Malawi were actually larger than the increase in world markets for the
same commodities. He points out that staple food prices in sub-Saharan Africa have raised
rapidly since 2006, even in US dollar terms.
According to Rashid (2010), Ethiopian food price increase has been different from in many other
developing countries. Domestic price rise in Ethiopia was not related to world price rise, unlike
other countries. Rashid (2010), explains that the price rise began with rapid growth in the money
supply relative to overall economic growth. This was later aggravated by a balance of payment
crisis that resulted in government rationing of foreign exchange and increase in fuel price.
Several efforts have been undertaken to raise production and productivity of farmers so as to
achieve food security. These include building the capacity of farmers and extension agents,
introduction of high-yielding varieties through agricultural research and extension services,
establishment of appropriate marketing systems and expansion of small and medium-scale
irrigation systems. As the population density increases and some lands are leased out for foreign
investor (land grab), farmers must produce even more food than before. Hence, raising
4
productivity and efficiency coupled with reducing production loss in the agricultural sector is
unquestionable.
In spite of its tremendous potential, the capacity of the agriculture in generating increased
production to meet domestic, and export requirements has been handicapped by low production
and productivity. It is in fact, a paradox that a country with such immense agricultural resources
could not feed its population and has to rely on external food aid and imports.
Despite the fact that various agricultural programs and policies have been implemented over the
last 20 years to raise farmers’ efficiency, production and productivity; the expansion of agroindustries, emerging of industries and increase in population leads to an increase in demand for
agricultural output. This increase in demand can be filled either by an increase in production
efficiency and/or diminish output loss. There is no such study revealed the supply response to
price changes in agricultural products since 2006. This study estimates supply response for major
crops before and after 2006. It also measures the level and determinants of technical efficiency
before and after the price increase in 2006.
The Study Area and Data
For this study, The Ethiopia Rural Household Survey (ERHS) is used. ERHS data sets cover a
number of villages in rural Ethiopia. It is panel data collected in 1994, 1999, 2004 and 2009
from four major regions of the nine administrative regions in Ethiopia. This survey covers 18
farmers associations (FAs) and 1477 households. These surveys cover 15 of the 389 woredas
(districts) in the four major regions. One FA was selected from each of the woredas, except for
5
one large woreda in the Amhara region, Debre Birhan, from which four FAs were included in
the sample. The surveys are conducted on a sample that is stratified over the country’s three
major agricultural systems found in five agroecological zones (Dercon and Hoddinott 2004).
The first agroecological zone is known as northern highlands. This zone includes two villages in
the Tigray region, Geblen and Harresaw, and one from the Amhara region, Shumsheha. The
northern highlands are characterized by poor resource endowments, adverse climatic conditions,
and frequent drought. The central highlands agroecological zone is represented by the villages
of Dinki, Yetmen, and Debre Birhan, all located in the Amhara region, and Turufe Ketchema in
the Oromia region. The Arussi/Bale agroecological zone includes the villages of Koro Degaga
and Sirbana Godeti, both found in Oromiya. Adele Keke is the sole survey site found in the
Hararghe agroecological zone of Oromiya. The remaining five villages of Imdibir, Aze Deboa,
Gara Godo, Adado, and Doma are found in the Enset growing agroecological zone located in
the Southern Nations, Nationalities and People region.
Additional data from Central Statistics Authority (CSA) and National Meteorological Service
Agency of Ethiopia is used.
6
Theoretical Model
There are two general approaches for analyzing supply response in economics literature. The
first approach is a Nerlovian expectation model, which deals with the analysis of both the rate
and the level of adjustment of actual acreage and yield toward desired acreage. The second is the
supply function approach, derived from the profit-maximizing framework. Just (1993) and
Sadoulet and de Janvry (1995) review these approaches. The supply function approach needs all
input prices. In Ethiopia, the agricultural input markets for example land and labour are not
functioning in a competitive environment. For this reason, this study uses a Nerlovian approach.
The pioneering work of Nerlove (1958) on supply response enables one to determine short run
and long run elasticities. Production decisions have to be based on the prices farmers expect to receive
several months later, at harvest time. Nerlovian models are built to examine the farmers’ output reaction
based on price expectations and partial area adjustment (Nerlove 1958). The Nerlovian supply response
approach enables us to determine short- and long-run elasticities. The Nerlovian model gives
flexibility to introduce non-price shift variables in the model. According to the Nerlove-Koyck
adjustment model, the desired acreage Atd is a function of ‘expected normal price’, while the
actual acreage At adjusts to the desired acreage with some lag1. The model is as follows:
Models of the supply response of crops can be formulated in terms of yield or area response. For
instance, the desired area to be planted in a crop in period t is a function of expected relative
prices P and a number of exogenous shifters Z.
Atd = a1 + a2 Pt e + a3Zt +Ut ..............................................................................................(1)
Where Atd is the desired cultivated area in period t; Pte is the expected prices of the crop and of
other competing crops; Zt is a set of other exogenous shifters, including weather, etc.; Ut
accounts for unobserved random factors affecting the area under cultivation; and αi is the
parameter to be estimated. Specifically, α2 is the long-run coefficient of supply response.
Because full adjustment to the desired allocation of land may not be possible in the short run, the
actual adjustment in area will be only a fraction δ of the desired adjustment.
At - At-1 = d (Atd - At-1 )+ vt ...........................................................................................................(2)
Where At is the actual area planted in the crop; δ is the partial-adjustment coefficient; and νt is a
random term, E(νt)=0.
The price that the producer expects to prevail at harvest time cannot be observed. Therefore, one
has to specify a model that explains how the agent forms expectations based on actual and past
prices and other observable variables. For example, farmers adjust their expectations as a
fraction of the deviation between their expected price and the actual price in the last period, t–1.
e
P et - pet-1 = g (Pt-1 - P t-1
) + wt .........................................................................................................(3)
0 £ g £1
Where Pte the expected price for period t; Pt-1 is the price that prevails when decisionmaking for
production in period t occurs; γ is the adaptive-expectations coefficient; and wt is a random term,
E(wt)=0.
Since Atd and Pte are unobservable, we eliminated them from the system. Substitution of
Equation (1) and (3) into Equation (2) and rearrangement gives the reduced form.
8
Descriptive statistics of the explanatory variables
This paper uses four rounds of panel data set from 1994 to 2009 from four major regions of
Ethiopia. It compiles 15 woreda 2 and 18 peasant associations. Farmers produce more than 60
types of crop products and all of them are taken into this analysis. Some of the major crops are
teff, maize, wheat, barley, sorghum, coffee, chat, enset, legumes and vegetables. Sole cropping is
the most common agricultural practice followed by mixed cropping.
Total value outputs are obtained from crops and animal products. The inputs for agricultural
production are land, labour, traction power, fertilizer, precipitation, soil fertility, credit, hoe and
extension service. All local measurement units take in to account to bring them to a common
standard unit. This enables us to aggregate and compare farm output and inputs within and
between households. Total farm income can be considered as composed of value of more than 60
types of crop products. All values measures are expressed in 1994 price index for farm products
and for some of the inputs.
Table 1 provides descriptive statistics of variables in the stochastic model. Land is the basic asset
of farmers in Ethiopia. The average farm size is about 1.52 ha, with 0.01 ha being the minimum
and 11.5 ha being the maximum. Land is very scarce resource in Ethiopia. About 75 % of rural
households operated 2 hectares and less; whereas 50 % of them cultivated farms less than 1.2
hectare; while 25% operated land sizes of 0.5 hectare and less in the 1994-2009 cropping
seasons. Soil fertility of the plots is medium on average. Average number of plot per household
is 5. This shows that the farmers have so fragmented operated land.
2
Woreda: Ethiopian administrative unit, equivalent to district
9
Next to land, livestock is the most important asset for rural households in Ethiopia. It is used as
a source of food, draft power, income and energy. Moreover, livestock is an index of wealth and
prestige in rural community of the country. About 86% of the sample households reared
livestock, which consists of cattle, small ruminants, back animals and poultries. On average the
households own 3.4 tropical livestock units.
Labour refers to the total number of family members of the household that is converted to labour
equivalent unit. This is done by conversion factor that takes sex and age into account. The larger
the number of family members, the more the labour force available for production purpose. This
is true if the dependency ratio of the household is small. The average age of the household head
is about 49.74. The average family size of the sample is about 6.71. The average labour force
contribution to the production process is 4.03 labour equivalent unit over the years. About 22%
of the sample households are female-headed households, while the remaining 78% are maleheaded households. The proportion of female headed households are about 20% in 1994, 21% in
1999, 27% in 2004 and 23% in 2009.
Education has an important role to enhance the utilization of farm inputs and adopt new
technologies. However, only thirty eight percent of the households have schooling. Among
whom 12.92% have some religious and adult education training that enable them read and write,
12.74% have studied grade 1 to 4, 7.95% have grade 5 to 8, 5.80% have grade 9 to 12 and 0.26%
have higher education. Sixty two percent of the sample households had no schooling. This
shows that the level of education in the study area is very low.
Fertilizer application for production is measured by fertilizer expense of the households. Below
half of the households (47%) is used fertilizer for production. The average expense of fertilizer
10
was 341.7 Ethiopian birr. This fertilizer application rate is far below the recommended rate of
150 kg per hectares (UNDP, 1993).
11
Table 1: Definitions and units of measurement of variables in the stochastic frontier and
efficiency models
Variable Code
Description
Sex
1, if the household head is male and
0, otherwise
Hhsize
Total number of family members
6.71+3.17
Age
Age of the household head (years)
49.74+15.39
Educ
1, if the household head is literate
and 0, otherwise
Farmsz
Total farm size operated by the
household (hectares)
Soil fertility
1, if the fertility status is bad, 2 if the
fertility status is fair and 3, if it is
good.
Labour
Adult equivalent unit
4.03+2.35
TLU
Tropical livestock unit owned (TLU)
3.42+4.04
Fertilizer
Total real value of fertilizer
expenditure of the household (Birr)
341.71+369.86
Credit
Total real value of credit taken by the
household (Birr)
204.87 + 475.20
Extensiondummy Whether or not visited by extension
agent for technical support
Hoe
AEZ
Precipitation
Output value
The number hoe(s) owned by the
household
Agro-ecology zone: 1 if AEZ is
Northern highlands, 2 if it is enset
growing area (hoe farming), 3 if it
is Hararghe (oxen farming), 4 if it
is Arussi/Bale and 5 if it is
Central highlands
Rain fall amount in mm
Sum of the real values of crops and
livestock products (Birr)
% With a Value 1
Mean+ SD
77.74
37.57
1.52+1.28
11.62, 42.00,
31.43
1.23 + 1.54
17,32,7,14,30
85.56 + 28.87
2857.378+4117.79
Source: by authors´ calculation SD=standard devation
12
*Birr is Ethiopian currency: 1USD=5.22 birr in 1994, 1USD=7.81 birr in 1999, 1USD=8.34 birr in
2004 and 1USD=11.53 birr
in 2009 when the data was collected. Source:
http://www.gocurrency.com/v2/historic-exchange-rates.php
** Subscripts for household number (i) and year (t) are omitted to improve readability.
13
Table 2: Summary of input-output data used in stochastic production frontier 1994-2009
Production Variables
Year
1994
Total
Credit
(birr)
(mm)
Soil
Hoe
Fertility number
Extension
1602.08
1.45
5.12
2.68
249.45
48.67
88.26
2.22
0.80
0.51
830.00
1.00
4.76
1.80
0.00
0.00
82.63
2.00
1.00
1.00
Max
22956.17
9.00
19.10
61.85
10563.58
1056.36
159.50
3.00
9.00
1.00
Mean
2407.80
1.27
5.05
2.97
118.85
160.50
87.97
2.39
0.64
0.10
Median
1685.03
1.00
4.82
2.40
25.49
46.16
81.15
2.00
0.00
0.00
33639.82 11.50
14.60
17.50
2038.94
1618.41
143.79
3.00
10.00
1.00
Mean
(birr)
Fertilizer Precipitation
(AEU)
dummy
Mean
3374.30
1.56
3.52
2.90
197.41
187.64
80.29
2.32
1.20
0.22
Median
1547.39
1.25
3.20
2.05
0.00
0.00
82.62
2.00
1.00
0.00
45821.48 10.90
14.30
29.30
5580.54
2790.27
176.99
3.00
10.00
1.00
Max
2009
Draft
power
(TLU)
(ha)
Max
2004
Land Labour
(birr)
Median
1999
Out put
Mean
4043.80
1.80
2.42
5.25
258.73
243.29
86.12
2.46
2.34
0.45
Median
2654.63
1.43
2.30
3.62
48.49
55.24
78.89
3.00
2.00
0.00
Max
47541.46
8.63
8.00
46.62
5819.03
3782.37
129.90
3.00
12.00
1.00
Mean
2857.38
1.52
4.03
3.42
204.87
160.13
85.56
2.35
1.23
0.31
Median
1556.59
1.18
3.62
2.30
14.55
0.00
82.06
2.00
1.00
0.00
47541.46 11.50
19.10
61.85
10563.58
3782.37
176.99
3.00
12.00
1.00
Max
14
The prices of major crops started to rise as early as 2004, the sharpest increase occurred in the years
2006-08.
Price trends of major crops in Ethiopia (1994-2009)
15
Table 3: Parameter estimates of the stochastic frontier analysis under true fixed models
True fixed model-Sfpanel
Variables
LnLabour
LnFarmSize
LnTotalLivestockUnit
LnFertilizer
LnPrecipitation
CreditDummy
SoilFertilityCategorical
HoeUseDummy
ExtensionDummy
AgroecologicalZone
Northern highlands
Enset, hoe
Hararghe, oxen
Arussi/Bale
Central highlands
TimeDummy
Usigma_constant
Vsigma_constant
sigma_u
sigma_v
Lambda
Wald chi2 (14)= 9.75E+09
Prob > chi2 = 0.0000
Log likelihood = -4916.3
Coefficient
0.582***
0.210***
0.013***
0.016***
0.508***
0.247***
0.660***
0.511***
0.110***
S.E.
0.00008
0.00004
0.00002
0.00001
0.00009
0.00025
0.00017
0.00018
0.00018
Z-value
6972.440
5170.130
558.590
1244.770
5470.580
976.200
3860.780
2912.790
593.500
1.465***
1.920***
1.667***
1.636***
0.661***
-0.040
-30.365
0.980***
0.000000255
3846195***
0.00024
0.00026
0.00030
0.00032
0.00022
0.02824
19.37216
0.01384
2.47E-06
0.0138375
6008.000
7509.600
5526.900
5074.630
3009.400
-1.420
-1.570
70.830
0.100
2.80E+08
Source: by authors´ calculation S.E=standard error
Estimation and Results
The farm size, labour, traction power, fertilizer and precipitation variables are converted to log
so that the first-order parameters can be interpreted as elasticities while the rest are categorical
variables. A maximum likelihood estimator, as implemented in the STATA 12 ® module sfpanel
and xtfrontier (StataCorp 2012), are used to estimate stochastic frontier equation. All input
variables are significant at the 1% level of significance. Cobb-Douglas technology, time variant
16
technical efficiency and different distribution of technical inefficiency give different estimates.
More over, the true fixed model and time-varying model estimate for the SFA and inefficiency
analysis are given in Table 3 and Table 4, respectively.
Parameter Estimates
The first-order parameters of inputs have the expected sign and are statistically significant at 1%
level of significance. High elasticities are found for labour (0.58), precipitation (0.50) and land
(0.21) where as low elasticities are found for traction power (0.013) and fertilizer (0.016) in true
fixed model. Farmers, who use hoe, get extension service, get credit and have better soil fertility
have greater production than their counterparts.
Technical Efficiency
The time-varying technical efficiency scores of each farmer are obtained from the composite
error term (Jondrow et al., 1982). The parameter γ shows the share of technical inefficiency out
of the total error variance. The higher value suggests the appropriateness of the frontier approach
as compared to least squares. It is about 75% in the case of true fixed model. The average
technical efficiency score is 56% with standard deviation of 0.36 in the true fixed model
specification. This indicates that an average farmer produces 56 percent of the value of output
produced by the most efficient farmer using the same technology and inputs. The range of the
efficiency score is (0.0000584-0.9999989) in the true fixed model. Farmers in Ethiopia can
improve the technical efficiency of to fully utilize the existing inputs. They can reduce the input
requirement of producing the average output by 44% if their operation becomes technically
efficient. The scale coefficient is 1.33 that indicates that the farmers are operated under
increasing returns to scale.
17
Table 4: Factors affecting inefficiency of the smallholder farmers
Inefficiency from True fixed model
Coefficient
Variables
Sex
0.044
Age
0.001
Education
0.001
Hhsize_labour
0.001**
Oxendummy
-0.043**
Creditdummy
0.114***
OffincomeDummy
0.046***
TLU
0.001
Extensiondummy
0.041***
Monthdummy
-0.093***
Plot_farmsz
-0.002***
Farmsz_labour
-0.056***
AEZ_year
-0.001***
Constant
0.416***
Sigma_u
0.208
Sigma_e
0.376
Rho
0.234
F (13,3490) = 10.63
Prob > F = 0.0000
S. E.
0.0359
0.0008
0.0050
0.0003
0.0168
0.0142
0.0131
0.0026
0.0140
0.0252
0.0007
0.0059
0.0004
0.0478
t-value
1.23
-0.14
0.24
2.00
-2.55
8.08
3.52
0.29
2.95
-3.68
-2.81
-9.39
-3.93
8.70
Source: by authors´ calculation S.E=standard error
18
Figure2: Area trends of major crops in Ethiopia (1994-2009)
19
Figure3: Area trends of major crops in Ethiopia (1994-2009)
20
Figure4: Real Value trends of major crops in Ethiopia (1994-2009)
21
Table 5: Elasticity estimates of supply response for Tef, Barely and wheat
Area Response
Yield Response
VARIABLES
lntefArea
lnbarelyArea
lnwheatArea
lnrtefvalue
lnrbarelyvalue
lnrwheatvalue
Lagged Dep. Variable
0.218***
0.550***
0.627***
0.115
0.167***
0.499***
(0.0650)
(0.0841)
(0.0607)
(0.0749)
(0.0477)
(0.0713)
6.583
27.44***
12.40***
-5.772
11.81*
0.812
(4.018)
(8.175)
(4.327)
(10.88)
(6.508)
(8.198)
1.078
-2.754
-3.633**
6.991*
1.175
10.27***
(1.535)
(2.361)
(1.629)
(4.244)
(1.649)
(2.150)
-2.041***
1.131*
-1.587***
-3.407***
0.619
-1.441***
(0.480)
(0.668)
(0.321)
(1.012)
(0.441)
(0.337)
-0.0539
-0.0706
-0.125**
0.202
0.0365
-0.321**
(0.0638)
(0.117)
(0.0633)
(0.170)
(0.0829)
(0.125)
-4.930
21.79***
10.05***
-26.70**
-1.186
-25.79***
(3.292)
(7.343)
(3.450)
(11.68)
(5.435)
(7.627)
-0.187**
0.641***
0.0464
-0.683***
0.0422
-0.787***
(0.0905)
(0.181)
(0.0788)
(0.212)
(0.128)
(0.183)
4.695***
7.568***
-0.848
4.885
-0.0934
-7.345***
(1.198)
(2.401)
(1.106)
(3.392)
(1.710)
(1.873)
-1.678***
0.806
-0.558
-4.114***
-0.105
-2.719***
lntef_p
L.ltef_p (-1)
lnbarely_p
L.lbarely_p (-1)
lnwheat_p
L.lwheat_p (-1)
lnPrecipitation
Timedummy*Price
22
Time2
Time3
(0.454)
(0.940)
(0.556)
(1.296)
(0.502)
(0.898)
-48.99**
187.4***
63.16**
-225.7***
15.18
-186.7***
(23.82)
(57.71)
(25.34)
(85.27)
(40.42)
(55.87)
-17.91**
72.19***
18.80**
-84.21***
14.12
-64.28***
(7.911)
(21.96)
(9.044)
(26.88)
(13.48)
(19.34)
9.492**
-2.548*
-5.696**
(4.172)
(1.527)
(2.729)
Educdummy
-3.471
-124.4***
-19.18
96.74**
-26.08
76.72**
(13.20)
(33.84)
(13.96)
(41.62)
(22.15)
(30.40)
AR(1)_p-value
0.106
0.05
0.027
0.801
0.000
0.399
AR(2)_p-value
-
-
-
-
-
-
Sargan test of overid
restrictions
0.99
0.99
0.99
0.99
.99
0.99
Observations
3,170
3,170
3,170
3,170
3,170
3,170
Number of hhs
1,323
1,323
1,323
1,323
1,323
1,323
Constant
Source: Authors’ calculation.
Note: Z-statistics in parentheses, *** p<0.01, ** p<0.05, * p<0.1
23
Conclusions
The true random effect (TRE) and true fixed effect (TFE) models are used with Cobb-Douglas
production function setting. These specifications allow differentiating time-varying technical
inefficiency from unit specific time invariant unobserved heterogeneity.
This study estimates supply response for major crops before and after the price increase since
2006. It also measures the level and determinants of technical efficiency before and after the
price increase.
It is found that there is moderate supply response and technical efficiency change to an increase
in price of major crops in Ethiopia since 2006. There is technical efficiency gain after 2006
where a general increase in price of agricultural products are observed.
Farm size, labour, traction power, fertilizer and precipitation attain significant farm output
elasticity. Land quality, participation in extension program, credit and hoe use that are
categorical variables are also significantly affect farm output. Five major agro-ecological zones
included gained from Hicks-neutral technological improvements in the period. The average
technical efficiency score is 56% in the true fixed model specification model. This indicates that
an average farmer produces 56 percent of the value of output produced by the most efficient
farmer using the same technology and inputs. Farmers in Ethiopia can improve the technical
efficiency of to fully utilize the existing inputs. They can reduce the input requirement of
producing the average output by 44% if their operation becomes technically efficient. The scale
coefficient is 1.33 that indicates that the farmers are operated under increasing returns to scale.
Participation in off-farm activity, participation in extension program, credit use, household sizelabour interaction, number of plot-farm size interaction, and farm size-labour interaction
significantly affect farm household efficiency model. These results suggest that there is
considerable room to increase agricultural output without additional inputs and given existing
technology.
24
It is also found that there are moderate supply responses and technical efficiency change to an
increase in price of major crops in Ethiopia since 2006. The true fixed effect (TFE) models are
used with Cobb-Douglas production function setting show that there is technical efficiency gain
after 2006. In Stochastic Frontier Analysis specification, parameters of inputs have the expected
sign and are statistically significant at 1% level of significance. High elasticities are found for
labour (0.58), precipitation (0.50) and land (0.21) where as low elasticities for traction power
(0.013) and fertilizer (0.016) in true fixed model. Farmers, who use hoe, get extension service,
get credit and have better soil fertility have greater production than their counterparts. Oxen
dummy, plot-farm size, farm size-labour and AEZ-year interactions decrease inefficiency
whereas household size, off-farm activity participation and extension participation increases
inefficiency. The average technical efficiency score is 56% before and 59% after price increase,
and it is statistically significantly difference. The scale coefficient is 1.33 that indicates that the
farmers are operated under increasing returns to scale. These results suggest that there is
considerable room to increase agricultural output without additional inputs and given existing
technology.
Area and yield Supply response for major crops are responsive to price incentives/increase price
since 2006. However, Area response is more consistent than yield. The elasticity for the
household level crops area planted, with respect to own-price are positive and significant.
Whereas yield response approximated by value are less response to own price and cross price
elasticities.
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