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Technical efficiency and productivity differential effects of land right certification: A quasi-experimental evidence

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Although theory predicts that better property rights to land can increase land productivity either through long-term investment effects and/or more efficient input use due to enhanced tradability of the land, empirical studies on the size and magnitude of these effects are very scarce. Taking advantage of unique quasi-experimental survey design, this study analyzes productivity impacts of the Ethiopian land certification program by identifying how investment effects (technological gains) would measure up against benefits from any improvements in input use intensity (technical efficiency). We adopted a data envelopment analysis-based Malmquist-type productivity index to decompose productivity differences into (1) within-group farm efficiency differences (technical efficiency effect, and (2) differences in the group production frontier (long- Term investment or technological effects). The results show that farms without a land use certificate, on aggregate, are less productive than those with formalized use rights. We found no evidence to suggest this productivity difference is due to inferior technical efficiency. Rather, the reason is down to technological advantages, or a favorable investment effect, from which farm plots with a land use certificate benefit when evaluated against farms not included in the certification program.
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Quarterly Journal of International Agriculture 54 (2015), No. 1: 1-31
Quarterly Journal of International Agriculture 54 (2015), No. 1; DLG-Verlag Frankfurt/M.
Technical Efficiency and Productivity Differential Effects
of Land Right Certification:
A Quasi-Experimental Evidence
Hosaena Ghebru
International Food Policy Research Institute, Washington, D.C., USA
Stein T. Holden
Norwegian University of Life Sciences, Aas, Norway
Abstract
Although theory predicts that better property rights to land can increase land
productivity either through long-term investment effects and/or more efficient input
use due to enhanced tradability of the land, empirical studies on the size and magnitude
of these effects are very scarce. Taking advantage of unique quasi-experimental survey
design, this study analyzes productivity impacts of the Ethiopian land certification
program by identifying how investment effects (technological gains) would measure up
against benefits from any improvements in input use intensity (technical efficiency).
We adopted a data envelopment analysis–based Malmquist-type productivity index to
decompose productivity differences into (1) within-group farm efficiency differences
(technical efficiency effect, and (2) differences in the group production frontier (long-
term investment or technological effects). The results show that farms without a land
use certificate, on aggregate, are less productive than those with formalized use rights.
We found no evidence to suggest this productivity difference is due to inferior
technical efficiency. Rather, the reason is down to technological advantages, or a
favorable investment effect, from which farm plots with a land use certificate benefit
when evaluated against farms not included in the certification program.
Keywords: low-cost land certification, land productivity, Malmquist productivity index,
Ethiopia
JEL: Q15
1 Introduction
Poor agricultural productivity and food insecurity are persistent features of many
developing countries. Governments and international agencies have therefore rightly
embraced agricultural intensification as the primary means for inducing technological
change in developing countries that have high population pressure and low agricultural
2 Hosaena Ghebru and Stein T. Holden
Quarterly Journal of International Agriculture 54 (2015), No. 1; DLG-Verlag Frankfurt/M.
productivity. Integral to this growing global interest in a public policy research and
development agenda is the issue of land tenure security (HOLDEN, DEININGER and
GHEBRU, 2010). Because of the conventional view that traditional or “customary” land
rights impede agricultural development (JOHNSON, 1972; GAVIAN and FAFCHAMPS,
1996), many developing countries and major multilateral organizations have advocated
formalization of land rights (through registration and certification of land rights) as a
top priority in their economic development agendas (ATWOOD, 1990; IFAD, 2001;
BONFIGLIOLI, 2003; DEININGER, 2003; HOLDEN, DEININGER and GHEBRU, 2011).
In theory, there are three routes through which secure property rights may influence
agricultural productivity. The first is by encouraging long-term land investment and
adoption of new technologies (BARROWS and ROTH, 1990; BESLEY, 1995; SJAASTAD
and BROMLEY, 1997; DEININGER and JIN, 2006). According to this hypothesis, afraid
of not recouping the investment made, the land user without formalized property rights
hesitates to spend resources on land-improving technologies (conservation, manure,
fertilizer, and so on). As a result, the demand for investment declines and productivity
suffers. Second, secure property rights are also thought to influence agricultural
productivity because they encourage efficient resource use (factor intensity). The
establishment of clear ownership of land, it is thought, lowers the cost and risk of
transferring the land, which improves factor intensity through reallocation of land to
more efficient producers. It has also been claimed that secure property rights can
stimulate efficient resource use by reducing land-related disputes (DEININGER and
CASTAGNINI, 2006; HOLDEN, DEININGER and GHEBRU, 2008) and may thereby
contribute to better access to credit if land can be used as collateral.
The fact that the literature on land tenure reforms is lacking in terms of empirical
assessment of the routes through which secure property rights can influence farm
productivity (i.e., the technological effect versus the factor intensity effect), this study
takes advantage of data from a quasi-experimental design of a household survey from
the northern highlands of Ethiopia to explain differential effects of the land
certification program in Ethiopia on farm level performances.1 Rather than simple
comparisons of relative productivity differentials between farms with and without a
certificate, this study utilizes an innovative approach decomposes such group
differences in productivity into (1) differences in within-group efficiency spread or
1 The recent land certification program in Ethiopia is arguably the largest land administration
program carried out over the last decade in Africa, and possibly the world (DEININGER, ALI and
ALEMU, 2011). This program departs from traditional titling interventions in developing countries
by issuing non-alienable use right certificates rather than full titles. See the previous study by
HOLDEN, DEININGER and GHEBRU (2009) for a detailed discussion of the land certification
program in the Tigray region of Ethiopia (the study area), the first region in the country to start the
certification program, in 1998.
Technical Efficiency and Productivity Differential Effects of Land Right Certification 3
Quarterly Journal of International Agriculture 54 (2015), No. 1; DLG-Verlag Frankfurt/M.
individual performance within each group (the catching-up effect or factor intensity
effect) and (2) differences in technology (the distance between group frontiers, or
technology effect). We accomplish this task of analyzing the group productivity
difference by constructing a nonparametric Malmquist productivity index based on
data envelopment analysis (DEA).
This methodology implies that the possible routes for performance improvements due
to the land certification program may involve the removal of pure technical in-
efficiencies (a catching-up effect), the removal of scale inefficiencies (adoption of
best-practice technology), or both. Comparing the performance of a group of farms
with formalized land use rights (a certificate) against those without a certificate, the
objectives of the study are, thus, twofold: (i) to examine whether or not there are any
productivity-enhancing benefits from land certification – i.e., assess the overall
productivity differential effects of the land certification program; (ii) to isolate and
examine the pathways through which land certification influences farm level
productivity. This later analysis is the core of the paper and provides insights into how
the investment effects (technological gains) of land certification would measure up
against the benefits from any improvements in input use intensity (technical
efficiency). We are not aware of any other study that analyzes and decomposes
efficiency (technical efficiency) and productivity (technological or scale) effects.
Based on the results from the DEA-based Malmquist productivity index, we find that
farms without a land use certificate are, on aggregate, less productive than those with
formalized use rights. Further, using the decomposed analysis, we find no evidence to
suggest that any productivity difference between the two groups of farms is due to
differences in technical efficiency. Rather, the reason comes down to “technological
advantages” or a favorable investment effect, from which farm plots with land use
certificates benefit when evaluated against those farms not included in the certificates.
The low level of within-group efficiency of farms in each group reinforces the
argument that certification programs need to be accompanied by complementary
measures such as an improved financial and legal institutional framework in order to
achieve the promised effects.
This paper is organized as follows. Section 2 reviews the conceptual framework for the
economic benefits of land reforms. The analytical approach adopted in this study to
measure productivity and productivity differences is discussed in Section 3. Section 4
describes the data sources and summary statistics, while the last two sections are
devoted to the discussion of results and concluding remarks.
4 Hosaena Ghebru and Stein T. Holden
Quarterly Journal of International Agriculture 54 (2015), No. 1; DLG-Verlag Frankfurt/M.
2 Literature Review:
Property Rights and Agricultural Productivity
Property rights theory does not emphasize who “owns” land but rather analyzes the
formal and informal provisions that determine who has a right to enjoy benefit streams
that emerge from the use of assets and who has no such rights (LIBECAP, 1989;
EGGERTSSON, 1990; BROMLEY, 1991). These rights need to be sanctioned by a
collective in order to constitute effective claims. Property rights to land can cover one
or more of the following: “access, appropriation of resources and products, provision
of management, exclusion of others, and alienation by selling or leasing,” with only
ownership conferring “the cumulation of all of these” (DE JANVRY et al., 2001, 2; see
also OSTROM, 2001). In various combinations or bundles, these rights are significant
for agricultural development inasmuch as they encourage different positive behaviors
toward land (investment) and toward other people (dispute resolution). The recent
literature on property rights over land and other natural resources commonly uses a
broad classification of property regimes: open access (no rights defined), public (held
by the state), common (held by a community or group of users), and private (held by
individuals or “legal individuals” such as companies).
Reflecting neoliberal thinking on private property rights and development, BESLEY
(1995) identified three channels through which farmers’ acquisition of clearly defined
property rights to land can, in principle, increase agricultural productivity: (1) tech-
nological change – long-term investment in land, (2) smooth functioning of the land
(rental) markets that lubricate factor-ratio adjustment, and (3) facilitating access to
(in)formal credit or informal collateral arrangements.
Tenure Security: Investment Effect: Farm households’ investment in practices that
enhance the long-term viability of agricultural production hinges significantly on
expectations regarding the length of time over which the investors (farmers) might
enjoy the benefits, which mostly are long-term. These expectations are affected by any
sense of tenure insecurity (whether through ownership disputes, eviction, or ex-
propriation by the government). Titling (ownership officially documented and verified
via land certificates) enhances the landholder’s sense of tenure security, boosting
incentives to invest in advancements that enhance long-term sustainability of agri-
cultural production (such as land improvements, conservation practices, and adoption
of new technology), which ultimately may increase farm productivity (GAVIAN and
FAFCHAMPS, 1996; HAYES, ROTH and ZEPEDA, 1997; GEBREMEDHIN and SWINTON,
2003; DEININGER and JIN, 2006; DEININGER et al., 2008; HOLDEN, DEININGER and
GHEBRU, 2009).
Technical Efficiency and Productivity Differential Effects of Land Right Certification 5
Quarterly Journal of International Agriculture 54 (2015), No. 1; DLG-Verlag Frankfurt/M.
Tenure Security: Market Efficiency Effect: In addition to its investment-enhancing
effects, formalization of land rights is also thought to influence agricultural
productivity through the tradability effect by facilitating the smooth functioning of
land transactions (in the Ethiopian context, land rental markets) because imperfections
in such markets (transaction costs and ownership uncertainties) may be more severe
when agents of the market lack formal land use rights. From the supply-side
perspective, for instance, without clear and definite claims to the land, farmers
(potential landlords) can be reluctant to transfer ownership to others (that is, to rent or
lease out land) for fear of losing the land through administrative redistribution
(DEININGER, ALI and ALEMU, 2008; GHEBRU and HOLDEN, 2013). In such
circumstances, it is possible that the landholder may operate the land by him- or
herself instead of transferring it even if the land’s productivity would be far better
under a different operator (the potential tenant) with better skill and complementary
farm inputs. Better property rights to land could therefore come to the rescue to reduce
the cost and risk of land transactions, ultimately improving factor mobility resource
allocation and, thus, farm productivity.
Access to Credit: Interlinked Collateral (Indirect Tenure Insecurity): Finally,
advocates of land titling have prioritized well-defined rights to landownership,
reasoning that land title can stimulate investment by means of a credit effect.
According to this hypothesis, turning land into a transferable commodity enables
farmers to use it as collateral to access the credit needed for productivity-enhancing
investments. However, since the land certificates in Ethiopia only provide use rights
with no rights to sell and mortgage the land, we are not able to evaluate the impact of
this channel in the Ethiopian context (a limitation reflected by the broken lines in
Figure 1).2 Despite the fact that land is not mortgageable in Ethiopia and hence cannot
formally be used as a loan guarantee, there are practices in the study area that make
use of agricultural land for informal mortgages.3 Under such arrangements, interlinked
with the informal land tenancy market, full use of the landholding is transferred from a
borrower (landlord) in exchange for an interest-free cash loan for the duration of the
credit period. As land registration and certification reduce boundary and ownership
disputes (HOLDEN, DEININGER and GEBHRU, 2008), the use of parcels with no
certificate as informal collateral can be minimal. Under these conditions, farmers who
have no registered and documented land rights may find it expensive, if not
impossible, to get access to this type of informal credit due to the lack of guarantee
2 See previous studies by HOLDEN, DEININGER and GHEBRU (2009) and by GHEBRU and HOLDEN
(2013) for detailed discussion of the evolution of the land tenure system in Ethiopia and the recent
land certification program in the Tigray region.
3 Our survey data shows few cases in which landlords rented their fields to tenants whom they had
borrowed money from.
6 Hosaena Ghebru and Stein T. Holden
Quarterly Journal of International Agriculture 54 (2015), No. 1; DLG-Verlag Frankfurt/M.
that informal money lenders look for. Formalizing land rights through land registration
and certification4 may reduce such liquidity constraints, enabling farmers to improve
variable input use and, in turn, increasing farm-level efficiency.
Figure 1. Land certification and agricultural productivity
Source: conceptual framework developed by authors
Against this backdrop, the formalization of land rights and the resultant tenure security
can be hypothesized to have an overall land productivity effect through two major
channels: (1) the “technological effects” via land-related investment and technology
adoption that have a lasting effect, causing a shift in a production frontier; (2) the
“factor intensity effect” via either a relative ease in farm factor-ratio adjustment
(enabling farms to operate at an optimal scale) facilitated through a reduction in
ownership uncertainty and a smoothing of land transactions or an improvement in
variable input use intensity through reducing the transaction cost of accessing the
informal credit market.
4 Ethiopian farmers, by law, are not landowners but holders of land use rights. Thus, the recent land
policy reform that formalizes land rights does not provide full titling to the holder but only registers
land and provides land use certificates. In this paper, we use the terms titling and certification inter-
changeably.
Tenure security Use of land as (in)formal
mort
g
a
g
e
Long-term land
investment
Conservation and
technolo
gy
ado
p
tion
Better access to land
(
rental
)
markets Better access to
(
in
)
formal credit
Smooth factor-ratio
ad
j
ustment
More variable
in
p
ut use
Higher farm productivity
Efficient resource use
(
factor intensit
y
effect
)
A frontier shift or
technolo
g
ical chan
g
e
Formalization of land rights
Re
istration and certification
Technical Efficiency and Productivity Differential Effects of Land Right Certification 7
Quarterly Journal of International Agriculture 54 (2015), No. 1; DLG-Verlag Frankfurt/M.
Although a growing body of literature explores the impact of tenure reforms on
investment, access to credit, and tradability of land in Africa (FEDER, ONCHAN and
CHALAMWONG, 1988; PINCKNEY and KIMUYU, 1994; BESLEY and COAST, 1995;
DEININGER and FEDER, 1998; LI, ROZELLE and BRANDT, 1998; PLACE and MIGOT-
ADHOLLA, 1998; SMITH, 2004; JACOBY and MINTEN, 2007; DO and IYER, 2008;
HOLDEN, DEININGER and GHEBRU, 2009, 2011), empirical assessments of the direct
effects of such interventions on land productivity are very scarce. Few exceptions
include studies by HOLDEN, DEININGER and GHEBRU (2009) and DEININGER, ALI and
ALEMU (2011), which assessed the overall land productivity impacts of the low-cost
land registration and certification program in Ethiopia. However, these studies have
analyzed farm productivity differentials entirely based on a method of pooling
decision-making units to form a common benchmark frontier, according to which
performances are evaluated – i.e., with no distinction between the routes through
which the intervention can influence agricultural productivity: technological effect
versus factor intensity effect. Such aggregate measure of performance pays little
attention to the aforementioned sources of productivity differences or changes.
Hence, in an attempt to fill this gap and characterize potential productivity differentials
in terms of pure technical efficiency difference and technological differences, the
objectives of the present study are twofold. First, we wish to examine whether or not
land certification produces any productivity-enhancing benefits. This analysis serves
as a vehicle for understanding the overall productivity differential effects of the land
certification program. Second, we aim to isolate and examine the pathways through
which land certification influences agricultural productivity. This analysis is the core
of the paper and provides insights into how the technological gains (investment
effects) of land certification would measure up against the benefits from improvement
in technical efficiency (factor intensity). To the best of our knowledge, there is no
other study on the productivity impacts of land reforms that has attempted to show
these decomposed effects.
3 Method of Analysis
To accomplish this objective, the present study adopts a combination of parametric
(stochastic frontier analysis – SFA) couple with a two-step nonparametric (Data
Envelopment Analysis – DEA) approaches – the later being the core method of
analysis for this study.5 Comparing the two approaches, while the SFA approach
5 Recent applications of the DEA method on the estimation and explanation of agricultural efficien-
cy in developing countries include SHAFIQ and REHMAN (2000) on Pakistani cotton farms;
DHUNGANA, NUTHALL and NARTEA (2004) on Nepali rice farms; and CHAVAS, PETRIE and ROTH
(2005) on Gambian farms.
8 Hosaena Ghebru and Stein T. Holden
Quarterly Journal of International Agriculture 54 (2015), No. 1; DLG-Verlag Frankfurt/M.
provides a convenient framework for conducting hypothesis testing since it uses
statistical techniques to estimate the parameters, the results can be sensitive to the
behavioral assumption and the functional forms chosen. Using the nonparametric
approach (DEA), in contrast, has the advantage of imposing no a priori parametric
restrictions on the underlying technology as it relies on a linear, piecewise function
without assuming any functional relationship between input and output. However, this
method is not flawless as the estimated efficiency scores from a DEA technique could
be biased if the production process is largely characterized by stochastic elements such
as outliers and data measurement errors.
However, since our main aim of this study is set out to investigate relative group
performances (comparing performances of groups of farms with and without land use
certificate) and not analyzing the efficiency level of these farms per se, we believe our
DEA based results remain robust regardless of the choice of methods (DEA versus
SFA) or model specifications (CRS versus VRS). In the latter case, while it is true a
higher order and more exible (translog) functional form is expected to t the data
more tightly, hence producing higher eciency estimates than the CRS assumption
which consistently generates lower eciencies, we argue that our results are free of
dependence on such choice of methods or model specification as we have no reason to
believe such model dependence will affect the estimated efficiency scores of the two
groups differently (or systematically) – hence affecting the efficiency ranking (ordinal
ranking) of farms across the two groups.6 As a result, our method of analysis in this
study consists a combination of the two approaches – the SFA approach for robustness
check and diagnostic assessment the prevalence of productivity differences, while a
two-step non-parametric DEA technique7 was used to conduct a more decomposed
analysis to explain potential sources of performance differences using a DEA-based
Malmquist productivity index which allows us to define group-specific frontiers in
order to compare relative performances. The basics of DEA estimation methods and
the adopted Malmquist index used in this study are explained below.
6 Comprehensive studies conducted on the sensitivity of efficiency measures to the choice of DEA
versus parametric approaches reveal that despite quantitative differences in the efficiency score
estimates between the two approaches, the ordinal efficiency rankings of farms obtained from the
two approaches appear to be quite similar (SHARMA, LEUNG and ZALESKI, 1999; WADUD and
WHITE, 2000).
7 developed by CHARNES, COOPER and RHODES (1978)
Technical Efficiency and Productivity Differential Effects of Land Right Certification 9
Quarterly Journal of International Agriculture 54 (2015), No. 1; DLG-Verlag Frankfurt/M.
Data Envelopment Analysis (DEA)
DEA is a linear programming technique for constructing a non-parametric piecewise
linear envelope to a set of observed output and input data (CHARNES, COOPER and
RHODES, 1978; FARE, GRASSKOPF and LOVELL, 1994). Assuming 12
( , ,..., )
iii i
M
M
XXX X

denotes the input vector to produce i
Ywhere i corresponds to a group a farm plot
belongs to8, the feasible production frontier that describes the technology of the
farming units can be defined in terms of correspondence between the output vector i
Y
and the input requirement set ()
ii
L
Ywhere:
(){ :( , ) ( )}
ii i ii i i
L
YXXYTX
(1)
The production possibility set ()
ii
LY provides all feasible input vectors that can
produce output vector i
Y where ()
ii
TX is the technology set of a group or government
program i showing i
X
can produce i
Y.
Assuming constant returns to scale, FARRELL (1957) proposed a radial measure of
technical efficiency in which efficiency is measured by radial reduction of levels of
inputs relative to the frontier technology holding output level constant.9 Stated
otherwise, Farrell’s input-oriented measure of technical efficiency estimates the
minimum possible expansion of i
X which is given by:
(,)min{: ()}
iii i ii
F
XY X LY


(2)
As formalized by FARE and LOVELL (1978), Farrell’s input-saving efficiency measures
are the same as the inverse of Shephard’s input distance function which provides a
theoretical basis for the ‘adopted’ Malmquist productivity index.10 Therefore, within a
context of input distance function, equation 2 can be rewritten as11:
(,)max{:( /) ()}
ij
ijj j ii
ij ij
DX Y X LY


(3)
8 As the emphasis of the study is to explain the potential productivity differentials with respect to the
land use certificate, from this on ward, we adopt two groups: Group 1: farms with no land use
certificate, and Group 2: those which are with formalized use rights (certificates).
9 The input-oriented model implicitly assumes cost-minimizing behavior and the output-oriented
DEA, on the other hand assumes revenue maximizing behavior of farmers. In our case, it is thus
reasonable to assume that farmers have a budget constraint and thus minimize costs.
10 1
(,)Min = [(,)]
ijj ijj
FXY DYY
,1,2ij
11 The expression (,)
ijj
D
XY is the maximum value by which the input vector can be divided and
still produce a given level of output vector y.
10 Hosaena Ghebru and Stein T. Holden
Quarterly Journal of International Agriculture 54 (2015), No. 1; DLG-Verlag Frankfurt/M.
where i, j = 1, 2; (, )
ij j
D
xy represents the input distance function for a farm in program
or group j with respect to the frontier technology of group i, the scalar ij
is the
maximum reduction (contraction) of the input vector of a farm plot belonging group or
program j (
j
X
) , the resulting deflated input vector (/)
j
ij
X
and the output vector
()
i
Y are on the frontier of the farming system under group or program i.
The Malmquist Index
The Malmquist index was introduced by CAVES, CHRISTENSEN and DIEWERT (1982)
and developed further by FARE, GRASSKOPF and LOVELL (1994). The index is
normally applied to the measurement of productivity change over time and can be
multiplicatively decomposed into an efficiency change index and a technical change
index. Similarly, the adapted Malmquist index (the performance index for program
evaluation) applied in this paper can be multiplicatively decomposed into an index
reflecting the efficiency spread among farms operating within each group (the internal
efficiency effect) and an index reflecting the productivity gap between the best-
practice frontiers of two different programs or groups (the technology effect). A recent
application of a DEA-based Malmquist index on cross-sectional micro-data is a study
by JAENICKE (2000), who analyzed the productivity differential effects of a crop
rotation farming system.
Taking best-practice farms under group i as reference (base) technology, with
being the number of farms in Group 1 (without certificate group) and number of
farms in Group 2 (with certificate group), the input-oriented Malmquist productivity
index developed by FARE, GRASSKOPF and LOVELL (1994) can be defined as:



,
1
1
,
,
,,,,
1
2
2
1
1
1
22
1
1
11
2121
i
i
i
i
C
C
j
jj
i
C
C
j
jj
i
jjjji
W
W
N
N
XYD
XYD
XXYYM
(4)
where i = 1,2. The above ratio evaluates the distance of the farms in each group from a
single reference technology i. The numerator evaluates the average (geometric/
arithmetic mean) distance of farms in Group 1 from frontier i while the denominator
evaluates the average distance of farms in Group 2 from frontier i. Since there is no
practical reason to prefer either frontier as a reference technology, the forthcoming
analysis is made based on the geometric/arithmetic mean of the two indexes generated
using each group’s frontier as reference. As a result, equation (4) can be rewritten as:
Technical Efficiency and Productivity Differential Effects of Land Right Certification 11
Quarterly Journal of International Agriculture 54 (2015), No. 1; DLG-Verlag Frankfurt/M.





, *
,
,
*
,
,
,,,,
2
1
21
22
11
12
2
1
1
1
22
1
1
112
1
1
221
1
1
111
2121
12
W
W
N
N
W
W
N
N
C
C
j
jj
i
C
C
j
jj
C
C
j
jj
C
C
j
jj
jjjj
XYD
XYD
XYD
XYD
XXYYM (5)
Thus, the two ratios inside the square brackets evaluate the distance of each farm from
a single reference frontier. The first ratio evaluates the average distance of farms in
Group 1 divided by the average distance of farms in Group 2 using a technology
defined by the best-practice farms from Group 1. The second ratio is a similar
quotient, taking Group 2’s frontier as reference. Also, when comparing the two groups,
to avoid the limitations associated with defining an “ideal” or “representative” farm to
represent each group, the aggregation of the distances or efficiency scores is conducted
using the geometric/arithmetic mean, which utilizes information from all farm plots.
A Malmquist index greater than 1 indicates a higher productivity of farms culti-
vated under the second property-rights group (plots with land use certificate) than plots
without a land certificate. This is so since the maximum reduction of an input vector of
a Group 1 farm necessary to reach the technology frontier under group i is always
higher than that of a corresponding Group 2 farm. The reverse holds true if is less
than 1, implying that farms under the first group or program are superior to those in
the second group.
With particular relevance to the theme of this study, the use of the Malmquist pro-
ductivity index provides an opportunity to further decompose the overall productivity
differences between groups into the following two subcomponents:
= fe MM 1212 * (6)
12
()M
12
M
12
()M







1
1
1
11
2
11 1 21 1 2 2 2
111
12 1 2
12 11
11 1 1 2 2
22 2
11
1
,,,
,, , . .
,,
,
Cn
nnw
nw
Cwnw
nw
w
CCC
CC
jj jj j j
jjj
jj j j
CC
CC
C
jj j j
jj jj
j
DYX D YX D Y X
MYYXX
DYX DY X
DYX


 
 

 
 

 

 



12
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The
C
p
roduc
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efficie
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that fa
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ractic
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etwee
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tes greate
r
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mponent
o
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the Mal
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etween t
h
dex comp
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plots with
w
ithout la
n
r
e four dif
f
of farms
certificat
e
t
echnolog
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M
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ntier-Shi
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2
. Similar
r
producti
v
c
hnical ef
f
o
f the inde
m
quist inde
x
e two gro
u
a
risons are
land use
c
d use cert
i
f
erent perf
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without
c
e
, Group
y
defined
b
f
the M
a
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ciency, or
o
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ecific fr
o
ff
iciency s
p
) and
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ogy (den
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is bigger
on avera
g
f
ficiency l
e
n
aggregat
e
of their o
w
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ter Effec
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s
ures the
d
to the fi
r
v
ity of the
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iciency di
f
e
x -

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(.) i
n
u
ps – the
d
predicate
d
c
ertificates
ificates. B
o
rmance
m
c
ertificate
B –
p
erf
o
by farms
a
lmquist
within-
i
thmetic
o
ntier or
p
read of
average
o
ted by
(
that is,
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vels of
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terms,
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-
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):
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istance
r
st sub-
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ference
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eq. (6)
d
istance
d
on the
uses an
ased on
m
easures
using a
o
rmance
without
Technical Efficiency and Productivity Differential Effects of Land Right Certification 13
Quarterly Journal of International Agriculture 54 (2015), No. 1; DLG-Verlag Frankfurt/M.
certificate, Group C – performance evaluation of farms with certificate using a
technology defined by farms with certificate, and Group D – performance evaluation
of farms without certificate using a technology defined by farms with certificate. As a
result, each index given by equations (5) and (6) is a function of four separate input
distance functions: two standard (within-group) distance function values and two inter-
group distance function values.
The main analytical problem with this kind of nonparametric approach is the difficulty
of testing the statistical significance of such indexes, which result only from the ratio
of the (arithmetic/geometric) means of group efficiencies. In order to obtain some
insights into the statistical significance of the DEA-based Malmquist indexes, we
invoke the concept of first-order stochastic dominance, which allows us to compare
and rank the distribution of measures of farm performance. For an empirical strategy
of testing whether group productivities are statistically different, we follow BANKER
(1996), adopting a nonparametric two-sided Kolmogorov-Smirnov (K-S) test.12 The
K-S test are shown in Table 6.
4 Data and Identification Strategy
In conducting the analysis of the productivity effects of the land use certification
program in the region, we came across a methodological challenge mainly due to
potential self-selection problems during program implementation, with reasons
ranging from administrative to household specific. Thus, we exercised utmost caution
to account for households that fail to collect land certificates for household-specific
reasons, which may cause correlations between the treatment (the certificate) and the
outcome (farm productivity as yield per hectare) variables. Thus, before applying a
random sampling exercise, we conducted a thorough investigation of the process of the
land registration and certification program in the region. To mitigate the methodlogical
challenge of potential self-selection, the household survey took advantage of the
coincidence that the land certification program was implemented during construction
of a micro-dam in the study area, resulting in a quasi-experimental setup.
The land certification program in Tigray was a one-shot, large-scale project, without
any major follow-up projects. However, for purpose of egalitarian distribution of the
high-quality land, the regional land regulation allows for future redistribution of
irrigated parcels; therefore no land certificates are issued for such parcels (TNRS,
1997). The fact that the 1998/99 land certification in the region coincided with
construction of a micro-dam in the sample area provided a unique opportunity for a
12 For details on the K-S test, see CONOVER (1999).
14 Hosaena Ghebru and Stein T. Holden
Quarterly Journal of International Agriculture 54 (2015), No. 1; DLG-Verlag Frankfurt/M.
quasi-experiment, since farm households from two communities (one upstream and
another downstream) were excluded from the certification program for administrative
reasons and not by the choice of the households. Thus, we were able to identify the
control group as households from the two excluded communities and the treatment
group as households from two contiguous communities. Although certainly not a
randomized controlled trial (RCT), the research design did ensure balance on a range
of pre-treatment (pre-land certification) covariates between the beneficiary and non-
beneficiary communities. In other words, it is reasonable to consider non-selected
communities as a plausible control group to isolate the causal effects of the land
certification program because they are believed to have socioeconomic, biophysical,
and agro-ecological attributes comparable to those of the selected communities (the
treatment group).
As a result, we took a random sample of 80 farm households from each of the four
communities (two each from the treatment and control areas), with a total sample of
320 farm households operating 1,356 parcels during the 2005/06 production season.
Though the adopted quasi-experimental approach enabled us to control for potential
household-level selection problems, the research design certainly did not meet the
rigorous standards of an RCT to maintain the comparability of parcels included in the
sample. Consequently, it was important to investigate whether parcels from the
treatment (with land use certificates) and the counterfactual (plots without land use
certificate) were comparable on observable plot characteristics. Such evidence would
further ensure that the selection criteria were “random”. Hence, to control for plot-
specific selection bias among parcels with and without land use certificate and
maintain the comparability of these two groups of parcels, we applied nonparametric
propensity score matching using observable plot characteristics (such as soil quality
and slope, crop grown, etc.) as conditioning/control variables (results of the detailed
matching algorithm is shown under Appendix). We ensured that the common support
and balancing properties were satisfied and, as a result, only 566 certified plots out of
the total of 827 were found to be comparable to the 476 plots (as shown in Table 1
below). This type of data preprocessing reduces model dependence to a potential
selection bias problems in the subsequent analysis of the outcome equation (HO et al.,
2007). In the context of the current study, this is particularly important as households
could react systematically in their decision of applying for and acquiring land use
certificate depending on the perceived tenure security on a particular parcel. For
instance, households may feel less insecure (and, thereby, less desperate to acquire a
certificate) for a homestead parcel as compared to a parcel located far. Thus, under
such circumstances, ignoring this form of selection bias when it is present may lead us
to understatement of the productivity of plots with certificate (in this case, distant
parcels) vis-à-vis parcels without certificate. The underlying assumption here is that in
the matched plots, the effects of exogenous physical factors on productivity is similar
Technical Efficiency and Productivity Differential Effects of Land Right Certification 15
Quarterly Journal of International Agriculture 54 (2015), No. 1; DLG-Verlag Frankfurt/M.
between plots with certificate as compared to parcels without land use certificate –
allowing, for comparative analysis.
Table 1. Distribution of plot characteristics for parcels operated by owner-cum-
sharecroppers – before and after propensity score matching
Before matching (unmatched) After propensity score matching
(PSM matched parcels)
Variables Plots with
certificate (827 )
Plots without
certificate (529 )
Mean
compar-
ison test
Plots with
certificate (566)
Plots without
certificate (476)
Mean
compar-
ison test
Mean (se) Mean (se) Mean (se) Mean (se)
Farm size
(Tsimdi+) 0.794 (0.02) 0.741 (0.021) * 0.796 (0.021) 0.751 (0.021)
Soil type -
clay 0.119 (0.016) 0.148 (0.013) 0.117 (0.013) 0.141 (0.016)
Soil type -
sandy 0.234 (0.019) 0.232 (0.018) 0.232 (0.018) 0.231 (0.019)
Soil type -
black 0.046 (0.01) 0.075 (0.009) ** 0.047 (0.009) 0.056 (0.011) *
Soil type -
mixed 0.601 (0.022) 0.566 (0.02) 0.605 (0.02) 0.573 (0.023)
Slope -
uphill 0.808 (0.015) 0.871 (0.016) *** 0.807 (0.016) 0.817 (0.016)
Slope -
foothill 0.084 (0.012) 0.074 (0.012) 0.084 (0.012) 0.075 (0.012)
Slope -
flat 0.091 (0.01) 0.047 (0.012) *** 0.072 (0.012) 0.064 (0.01)
Slope -
steep 0.017 (0.004) 0.008 (0.005) 0.017 (0.005) 0.009 (0.004)
Soil depth -
deep 0.309 (0.02) 0.277 (0.019) 0.312 (0.019) 0.278 (0.021)
Soil depth -
medium 0.381 (0.022) 0.436 (0.02) * 0.38 (0.02) 0.434 (0.023) *
Soil depth -
shallow 0.309 (0.02) 0.283 (0.019) 0.308 (0.019) 0.284 (0.021)
Distance
to plot 21.11 (1.013) 18.35 (0.724) ** 20.24 (0.731) 19.38
3 (1.032)
Plot is
homestead 0.108 (0.018) 0.193 (0.013) **** 0.115 (0.013) 0.132 (0.018)
Note: standard errors are in parentheses; + Tsimdi is local area measurement equivalent to quarter of
hectare; * shows significant at 10%; ** shows significant at 5%; *** shows significant at 1%;
and **** shows significant at 0.1%.
Source: authors’ computation using the 2005/06 survey data
16 Hosaena Ghebru and Stein T. Holden
Quarterly Journal of International Agriculture 54 (2015), No. 1; DLG-Verlag Frankfurt/M.
The results shown in Table 1 above (that compares plot characteristics before and after
the PSM matching procedure) concur with these observations as most of the
differences in parcel bio-physical features between matched owner-cultivated and
sharecropped plots are no longer statistically significant. Descriptive results from the
first two columns of Table 1 (mean comparisons before the PSM matching was
conducted) confirm the prevalence of difference in basic plot characteristics when
comparisons are made among plots with versus without land use certificates. Though
none of the soil type features were found to be significant, comparative results from
Table 1 shows, soil depth and plot slope of parcels with land use certificate were found
to be significantly different from plots without land use certificates. On average, plot
with land use certificate are more likely to be near-by plots and less flat in slope while
farm size difference was also found to be statistically significant when the two groups
were compared. In contrast, as shown under the last two columns of Table 1, none of
the mean difference of the conditioning key plot-specific variables were found to be
statistically significant between plots with versus without land use certificates. Thus,
using the quasi-experimental nature of the intervention in the study area coupled with
the PSM matching results ensures that our adopted identification strategy was effective
in showing plausible causal effects of the land use certification on farm productivity.
5 Results and Discussion
5.1 Descriptive Analysis
Table 2 summarizes some key characteristics of farm households based on their
possession of a land use certificate. Signifying the caution exercised while sampling
the respondents, the household characteristics in Table 2 show that farmers with and
without a certificate have comparable demographic and endowment variables such as
the sex and age of household head, the average size of household, the number of males
and females in the labor force, and key livestock endowment variables like cows and
oxen. Despite these similarities, there are marked differences in terms of long-term
land-related investments13 of households with a land use certificate versus those
without. The proportion of farm households who were engaged in conservation their
own plots is slightly higher, at 94.3 percent, for those with a land use certificate than
for those without the certificate, only 83.9 percent. Similarly, the percentage of
households who had considered improving or maintaining an existing conservation
structure is also significantly higher for those with a certificate, 40.7 percent,
compared with only 28.6 percent for households without a land certificate.
13 In this paper, long-term land-related investments are captured by household decisions on land-
improving technologies such as anti-erosion conservation measures, application of organic and
inorganic fertilizers, and adoption of new farming practices that entail long-term benefits.
Technical Efficiency and Productivity Differential Effects of Land Right Certification 17
Quarterly Journal of International Agriculture 54 (2015), No. 1; DLG-Verlag Frankfurt/M.
Table 2. Mean comparison tests for key household-level variables
With certificate Without certificate
Variable mean (standard error) mean (standard error)
Household demographic and endowment variables
Sex of the household head
(male=1; female=0) 0.721 (0.0380)
0.750 (0.0411)
Age of the household head 45.614 (1.1865) 45.045 (1.4799)
Size of the household 5.086 (0.2084) 4.830 (0.2261)
Number of oxen 1.164 (0.0933) 1.071 (0.0972)
Other livestock endowment+ 0.593 (0.0737) 0.357 (0.0738) >**
Off-farm income opportunity++ 0.079 (0.0228) 0.045 (0.0196)
Long-term land investment and modern input use
Investment in new conservation structures 0.943 (0.0197) 0.839 (0.0349) >**
Maintenance of conservation structures 0.407 (0.0417) 0.286 (0.0429) >**
Household’s use of chemical fertilizer 0.621 (0.0411) 0.500 (0.0475) >**
Household’s use of organic fertilizer 0.636 (0.0408) 0.625 (0.0460)
Household’s use of improved seed varieties 0.579 (0.0419) 0.464 (0.0473) >*
Number of observations 161 135
Notes: * significant at 10%, ** significant at 5%, *** significant at 1%, **** significant at 0.1%;
+ tropical livestock unit equivalent
Source: authors’ computation using the 2005/06 survey data;
A summary of plot-level variables used in both the stochastic frontier and DEA–based
Malmquist index analyses is provided in Table 3. As shown in the upper part of the
table, there is no significant difference between plots with a certificate and those
without a certificate in terms of output level and input use intensity. On average,
output value per tsimdi is slightly higher on farm plots with a land use certificate than
on those without a certificate, though the difference is not significant at a conventional
level.
A summary of plot-specific long-term land investments and new technology adoption,
presented in the bottom part of Table 3, reveals a significant difference between the
two groups of plots.14 Reinforcing the claim that land certification does improve tenure
security and encourage long-term land-related investments (see discussions in Section 2),
the result shows that a significantly larger proportion of farms with land certificates
has been conserved (56 percent) as compared with plots without land use certificates
(51 percent). The chance of improvement or maintenance of an existing conservation
14 All the variables summarized are in their dummy (dichotomy) form to show a shift or a jump in the
frontier, which may not be the case had their level form been considered.
18 Hosaena Ghebru and Stein T. Holden
Quarterly Journal of International Agriculture 54 (2015), No. 1; DLG-Verlag Frankfurt/M.
structure is also significantly higher on plots with a certificate (21 percent) than on
those without (15 percent). Showing the difference in new technology adoption, the
summary result also depicts a higher likelihood of application of chemical as well as
organic fertilizer (53 percent and 29 percent, respectively) on plots with a land
certificate than on plots without a certificate (only 46 percent and 23 percent,
respectively). These summary results are consistent with results of a study by
HOLDEN, DEININGER and GHEBRU (2009) that was conducted in a similar study area.
Table 3. Mean comparison tests for key plot-level variables
Plots with certificate Plots without certificate t-test
Variable mean
standard
error mean standard
error
Input intensity and output level
Total value of output/tsimdi+
(Ethiopian birr) 699.96 19.27 671.52 21.16
Total labor/tsimdi (no. of days) 34.53 1.02 33.23 0.99
Oxen/tsimdi (number of days) 14.25 0.47 17.36 0.56 <***
Seed cost/tsimdi (birr) 96.46 3.34 93.01 4.82
Chemical fertilizer/tsimdi (kg) 12.67 0.79 13.79 0.89
Long-term land investment and modern input use
Long-term land investment 0.56 0.020 0.51 0.023 >*
Improved conservation structures 0.21 0.017 0.15 0.016 >***
Well-maintained structures 0.23 0.017 0.25 0.020
Just maintained structures 0.04 0.008 0.05 0.010
Not maintained structures 0.10 0.012 0.13 0.015
Chemical fertilizer (dummy) 0.53 0.021 0.46 0.023 >**
Organic manure/compost (dummy) 0.29 0.019 0.23 0.019 >**
Seed type (1 = improved, 0 = otherwise) 0.22 0.017 0.20 0.018
Log of output value 5.82 0.053 5.59 0.084 >*
Number of observations 566 476
Notes: * significant at 10%, ** significant at 5%, *** significant at 1%, **** significant at 0.1%
Source: authors’ computation using the 2005/06 survey data
At the outset, the empirical evidence from the mean comparison tests of the two groups
of farms shows that there is a marked difference in terms of long-term land-related
investment and new technology adoption. We use this evidence as an empirical basis
for further testing of the productivity impact of land certification, considering separate
benchmarks (group-specific production frontiers) for each group of farm plots.
Technical Efficiency and Productivity Differential Effects of Land Right Certification 19
Quarterly Journal of International Agriculture 54 (2015), No. 1; DLG-Verlag Frankfurt/M.
5.2 Structural Efficiency Comparisons: Parametric Approach
Building on these descriptive results, we also conduct a diagnostic assessment whether
or not land certification has any potential productivity-enhancing effect using a para-
metric SFA by including an indicator variable certificate as a one of the covariate
alongside the conventional farm inputs. Since this variable is constructed as a dummy
variable (plots with a land certificate = 1, and 0 otherwise), any positive and signifi-
cant coefficient for this variable posits a frontier-shifter effect of land certification, a
preliminary empirical condition to proceed with the decomposed analysis of the DEA-
based Malmquist index approach.
Table 4. Stochastic production frontier estimates of plots with and without
certificates
Notes: * significant at 10%, ** significant at 5%, *** significant at 1%, **** significant at 0.1%
Source: authors’ computation using the 2005/06 survey data
Using the specifications of the Cobb-Douglas production function, the positive and
statistically significant certificate variable reported in Table 4 indicates that, on
average, the best-practice farms with a land use certificate perform better than the best-
practice farms without a certificate. In other words, the frontier defined by plots with a
land certificate is superior to the frontier defined by those without a land certificate.
This result supports the basic assumption of the analysis that production on farm plots
with a certificate uses different technology than production on farm plots without a
Pooled sample Without certificate With certificate
(n=1,042) (n=476) (n=566)
Variable Coefficient (st. err.) Coefficient (st. err.) Coefficient (st. err.)
CONSTANT 5.3933 (0.17)*** 5.1009 (0.23)*** 5.8103 (0.21)***
Log of cultivated area 0.3658 (0.05)*** 0.3081 (0.08)*** 0.4179 (0.07)***
Log of labor, man-days 0.2092 (0.03)*** 0.2720 (0.08)*** 0.2025 (0.04)***
Log of oxen-days 0.0624 (0.03)** 0.1019 (0.09) 0.0266 (0.04)
Log of seed cost, Ethiopian birr 0.2343 (0.03)*** 0.2624 (0.04)*** 0.1539 (0.04)***
Log of chemical fertilizer,
kilogram 0.0256 (0.01)*** 0.0195 (0.01)* 0.0276 (0.01)***
Certificate
(plot with certificate = 1) 0.1176 (0.05)** - - - -
sigma2 3.7082 (0.24) 4.2082 (0.30) 2.2778 (0.16)
Lambda 9.8764 (0.08) 11.576 (0.09) 4.1102 (0.07)
Log-likelihood -680.11 -720.11 -758.44
Technical efficiency score 0.45 0.41 0.47
20 Hosaena Ghebru and Stein T. Holden
Quarterly Journal of International Agriculture 54 (2015), No. 1; DLG-Verlag Frankfurt/M.
certificate. Regardless of whether the analysis was conducted either using the pooled
sample or separate productivity analysis on both groups of farm plots (plots with and
without certificate), farm output is most responsive to area under cultivation, labor,
and value of seed. Moreover, the equally low levels of average efficiency scores in
both farms with and without land certificate (efficiency scores of 47 percent and 41
percent, respectively) are indicative that there exists little difference between the two
groups’ within-group efficiency spreads.
The major aim of the study being to explain the source or cause of the productivity
differential effects of land certification by comparing the performance of farm plots
with and without a certificate, we further investigated whether any productivity
differential is (1) due to a mere difference in pure technical efficiency or within-group
efficiency spread (the ability to catch up with the best-practice farms of each
respective group) or (2) due to a technology gap (dominance of the frontier of one
group over that of the other). With results from parametric (SFA) estimates and the the
mean comparison tests discussed in Table 2 and Table 3 showing the prevalence of
differences in farm level productivity between the two groups of farms, by applying a
DEA-based Malmquist index approach, the section below is dedicated to explain
whether or not such differences are simply because of differences in technical
efficiency, technology gap or both.
5.3 Explaining Productivity Differences: DEA-Based Malmquist Index Approach
As shown in Section 3, choice of base (reference) technology when computing the
Malmquist index affects the outcome of the index and, thereby, the interpretation.
Therefore, we analyze the group productivity differences using the averages of results
when each group is used as a reference technology. For mere comparison, results of
the adapted Malmquist index are reported in arithmetic and geometric averages. As
discussed in Section 3, a value of the Malmquist index smaller than unity correspond-
ing to group i means that, on average, group i is more productive (performs better)
than the other group. From Table 5, the value of the index equal to 1.2367 corre-
sponding to the without certificate group shows that, on average, farm plots without a
land use certificate are less productive than plots with formalized land use rights; that
is, on average, plots without a certificate require 124 percent of the inputs required by
plots with a land use certificate to be equally productive (be on the same frontier). This
result is further elaborated by the index shown on the second row of Table 5. In this
case, the index value of 0.8086 means that, on average, the group of farm plots with a
land use certificate are more productive than their counterparts without a land certi-
ficate, requiring only 80.7 percent of the inputs required by those without a land
certificate to be equally productive.
Technical Efficiency and Productivity Differential Effects of Land Right Certification 21
Quarterly Journal of International Agriculture 54 (2015), No. 1; DLG-Verlag Frankfurt/M.
Table 5. Malmquist index for comparison of group performance (Mi
12) between
farms with and without land use certificate
Group/scenario Arithmetic mean Geometric mean
2
1 No certificate With certificate No certificate With certificate
No certificate 1 1.2367 1 1.1669
With certificate 0.8086 1 0.8570 1
Source: authors’ computation using the 2005/06 survey data
As mentioned before, the major analytical bottleneck that is common in this kind of
nonparametric analysis (DEA) is the difficulty of testing statistical significance. In
order to obtain some insights into the statistical significance of the productivity
difference results, we invoke the concept of first-order stochastic dominance, which
allows us to compare and rank the distribution of measures of farm performance.
Table 6. Test results of first-order stochastic dominance of two-sample
Kolmogorov-Smirnov test
P-values for two-sample Kolmogorov-Smirnov test
Group
Efficiency
scores
Overall productivity
difference
Difference
in technical
efficiency
Technology gap
(frontier
difference)
Mean/
standard
deviation)
Group A
Versus
Group B
Group C
Versus
Group D
Group B
Versus
Group C
Group B
Versus
Group D
Group A
Versus
Group C
Reference
technology
Performance
evaluation of: (1) (2) (3) (4) (5)
A Without
certificate
With
certificate
0.51
(0.329)
0.042 0.053 0.637 0.05 0.017
B Without
certificate
Without
certificate
0.451
(0.25)
C With
certificate
With
certificate
0.446
(0.234)
D With
certificate
Without
certificate
0.422
(0.27)
Notes: †H0: distributions are equal against; H1: distribution of first group dominates distribution of
second.
Source: authors’ computation using the 2005/06 survey data
22
Quarterly
J
The tw
p
roduc
t
Table
6
the tw
o
the fir
s
(show
n
domin
a
matter
w
The e
m
results
nents
o
differe
n
efficie
n
the gro
of the i
Figure
Source:
As sho
(1.045
1
have,
o
when
b
Stated
slight
e
best-
p
r
a
Hosaena
G
J
ournal of Inter
n
w
o-sided K
o
t
ivity diff
e
6
, the null
o
groups i
s
st
-order s
t
n
as effici
e
a
tes the pe
w
hich gro
u
m
pirical co
n
of the D
E
o
f the ind
e
n
ce is attr
i
n
cy spread
up frontie
r
ndex, resp
e
2. Cum
u
land c
authors’ co
m
wn in Ta
b
1
) shows t
h
o
n average
,
b
oth types
otherwise
,
e
dge over
p
actice far
m
G
hebru and S
t
n
ational Agricul
t
o
lmogoro
v
e
rence to b
e
hypothes
i
s
rejected
a
t
ochastic
d
e
ncy score
rformance
u
p was co
n
n
tribution
E
A-
b
ased
M
e
x, we ar
e
i
buted to
d
) and the
p
r
s (the tec
h
ectively.
u
lative dis
t
ertificate
(
m
putation us
i
b
le 7
b
elo
w
h
at farm
h
,
a relative
l
of farms
a
,
this resu
p
lots with
o
m
s.
t
ein T. Hold
e
t
ure 54 (2015),
N
v
-Smirnov
e
statistica
l
i
s of ident
i
a
t 5 perce
n
d
ominance
s
) of far
m
of those
p
n
sidered to
of this ap
p
M
almquis
t
e
able to
e
d
ifference
s
p
roductivi
t
h
nology g
a
t
ribution
f
(
first-ord
e
i
ng the 2005
/
w
, a value
h
ouseholds
l
y lower i
n
a
re evalua
t
l
t indicate
o
ut a certif
i
e
n
N
o. 1; DLG-Ve
r
(
K
-S) test
lly signifi
c
i
cal distri
b
n
t. This r
e
analysis
m
plots wit
h
p
lots with
o
o
define th
e
p
roach is
m
t
index ar
e
explain w
h
s
in pure
t
t
y gap tha
t
a
p,

).
T
f
unction f
o
e
r stochas
t
/06 survey
d
slightly g
r
belongin
g
n
ternal effi
c
t
ed agains
t
s that far
m
i
cate in ter
m
r
lag Frankfurt/
M
p
resented
c
ant. As re
p
b
ution of
o
e
sult is dia
in Figure
h
land us
e
o
ut certific
e
benchma
r
m
ore prodi
g
e
analyzed
h
ether the
t
echnical
e
t
is explai
n
T
ables 7 a
n
o
r the ove
r
t
ic domin
a
d
ata
r
eater than
g
to the gr
o
c
iency tha
n
t
their res
p
m
s with a
m
s of catc
h
M
.
in Table 6
p
orted in c
o
verall pro
g
rammati
c
2, where
e
certificat
e
a
tes. The
r
r
k frontier.
g
ious whe
n
. Using t
h
overall
g
e
fficiency
n
ed by a
d
d 8 report
r
all produ
a
nce)
1 for the
c
o
up witho
u
n
those wi
t
p
ective pr
o
land use
h
ing up w
i
shows th
e
c
olumns 1
a
ductivity
b
c
ally elabo
the perf
o
e
s unambi
r
esult is r
o
n
the dec
o
h
e two su
b
g
roup pro
d
(the withi
n
d
ifference
b
these co
m
u
ctivity ef
fe
c
atching-
u
u
t land cer
t
t
h land cer
t
o
duction f
r
certificate
i
th their re
s
e
overall
a
nd 2 of
b
etween
rated in
o
rmance
g
uously
o
bust no
mposed
b
compo-
d
uctivity
n
-group
b
etween
m
ponents
e
cts of
u
p effect
t
ificates
t
ificates
r
ontiers.
have a
s
pective
T
Table
7
Gr
o
2
1
N
o cer
t
With c
e
Source:
Howe
v
As re
p
distrib
u
techni
c
order
s
differe
n
p
aram
e
arithm
e
to unit
y
the ea
r
differe
n
Figure
Source:
The re
s
groups
T
echnical Ef
f
7
. A co
m
efficie
n
certifi
o
u
p
/scenari
o
2
t
ificate
e
rtificate
authors’ co
m
v
er, the tw
o
p
orted in
u
tions of
t
c
al efficien
c
s
tochastic
n
ce betw
e
e
ter. Resul
t
e
tic avera
g
y
(1.0059
a
r
lier resul
t
n
ce in inp
u
e
3. Cum
u
differ
e
authors’ co
m
s
ult comp
a
(the tech
n
f
iciency and
m
ponent o
f
n
cy sprea
d
cate
o
N
o
c
0
m
putation us
i
o
-sided K-
column 3
t
he two gr
c
y betwee
n
dominanc
e
e
en the t
w
t
s are eve
n
g
e, which
y
a
nd 0.994
1
t
s from t
h
u
t use inte
n
u
lative dis
t
e
nces
m
putation us
i
a
ring the r
e
n
ology ga
p
Productivit
y
Quarterly Jour
n
f
the Mal
m
d
(M
12
e
) –
Arithme
t
c
ertificate
1
.9941
i
ng the 2005
/
S test sho
w
of Tabl
e
o
ups sho
w
n
the two
g
e
analysis
w
o groups
n
more el
a
y
ields a val
u
1
, respecti
v
h
e mean
n
sity betw
e
t
ribution
f
i
ng the 2005
/
e
lative dist
a
p
) is show
n
y
Differentia
n
al of Internatio
n
m
quist ind
e
with-in f
a
t
ic mean
With certi
f
1.005
9
1
/06 survey
d
w
s the dif
f
e
6, the
K
w
s that th
e
g
roups is i
(Figure
3
based o
n
a
borated
w
u
e of the
d
v
ely, as re
p
comparis
o
e
en the gro
u
f
unction f
o
/06 survey
d
a
nce from
n
in Table
l Effects of
L
n
al Agriculture
5
e
x for co
m
a
rms with
f
icate
No
9
d
ata
f
erence to
K
-S test
e
null hyp
o
dentica
l
,
c
3
) also sh
o
n
the wit
h
w
hen the i
n
d
ecompose
p
orted in
T
o
n tests t
h
u
ps of far
m
o
r interna
l
d
ata
their prod
u
8. Similar
L
and Right
C
5
4 (2015), No. 1
m
parison
o
and with
o
Geome
o
certificate
1
0.9568
b
e statisti
c
for simil
a
o
thesis, di
s
c
annot be
r
o
ws that t
h
h
in-group
n
dex is c
o
d
index ap
p
T
able 7). T
h
h
at reveal
e
m
plots.
l
(technic
a
u
ction fro
n
to the int
e
C
ertification
; DLG-Verlag
F
o
f within-
g
o
ut land u
s
e
tric mean
With ce
r
1.0
4
1
c
ally insig
n
a
rity betw
e
s
tribution
r
ejected. T
h
h
ere is n
o
efficiency
o
mputed u
s
p
proximate
l
h
is result
s
e
d no si
g
a
l) efficien
n
tiers of re
s
e
rpretatio
n
23
F
rankfurt/M.
g
roup
s
e
r
tificate
4
51
1
n
ificant.
e
en the
of pure
h
e firs
t
-
o
t much
spread
s
ing the
l
y equal
s
upports
g
nificant
cy
s
pective
n
s of the
24
Quarterly
J
overall
that de
value
g
Table
8
Gr
o
1
N
o cer
t
With c
e
Source:
Consi
d
of Tab
l
input-s
a
multip
l
that, o
n
(opera
t
shows
t
an inp
u
with th
Figure
Source:
Hosaena
G
J
ournal of Inter
n
Malmqui
s
fines the
t
g
reater tha
n
8
. A co
m
betwe
e
use ce
r
o
u
p
/scenari
o
2
t
ificate
e
rtificate
authors’ co
m
d
ering the
g
l
e 8), the
v
aving par
a
l
ied and st
i
n
average
,
t
e on a hi
g
t
hat with
p
ut
-saving
p
ose with f
o
e
4. Cum
u
effect
s
authors’ co
m
G
hebru and S
t
n
ational Agricul
t
s
t index in
t
echnolog
y
n
1 indicat
e
m
ponent o
f
e
n the tw
o
r
tificate
o
N
o
c
0
m
putation us
i
g
roup of f
a
v
alue of th
e
a
meter, by
i
ll produc
e
,
plots wi
t
g
her front
i
p
roper inte
r
p
otential
fo
o
rmalized
l
u
lative dis
t
s
of land c
e
m
putation us
i
t
ein T. Hold
e
t
ure 54 (2015),
N
Table 6,
a
y
enjoys a
e
s inferiori
f
the Mal
m
o
group fr
o
Arithme
t
c
ertificate
1
0
.8134
i
ng the 2005
/
r
m plots
w
e
decompo
which in
p
e
the same
t
h a land
u
i
er) comp
a
r
ventions (
o
r those p
l
and use ri
g
t
ribution
f
e
rtificate
(
i
ng the 2005
/
e
n
N
o. 1; DLG-Ve
r
a
value sm
a
superior t
e
i
ty.
m
quist ind
e
o
ntiers (
M
tic mean
With certi
f
1.229
4
1
/06 survey
d
w
ith land u
s
o
sed comp
o
p
uts applie
level of o
u
u
se certifi
a
red with
(
in this par
t
p
lots with
o
g
hts.
f
unction f
o
(
first-ord
e
/06 survey
d
r
lag Frankfurt/
M
a
ller than
e
chnology
e
x for co
m
M
12
f
) - far
m
f
icate
No
4
d
ata
s
e certific
a
o
nent equa
d in plots
u
tput. Thi
s
cate enjo
y
plots wit
h
t
icular cas
e
o
ut a land
o
r technol
o
e
r stochas
t
d
ata
M
.
1
means t
h
(that is, a
m
parison
o
m
s with ve
r
Geom
e
o
certificate
1
0.8957
a
tes as refe
r
l
to 0.813
4
without a
s
is synon
y
y
a techno
h
out a lan
d
e
, land cer
t
use certif
i
o
gy (front
i
t
ic domin
a
h
e referen
c
frontier),
o
f product
r
sus with
o
e
tric mean
With c
e
1.1
1
r
ence (sec
o
4
is nothin
g
certificat
e
y
mous wit
h
o
logical a
d
d
certifica
t
t
ification),
i
cate as c
o
t
ier-shifti
n
a
nce)
c
e group
while a
i
vity
ut land
e
rtificate
165
1
o
nd row
g
but an
e
can be
h
saying
d
vantage
t
e. This
there is
o
mpared
n
g)
Technical Efficiency and Productivity Differential Effects of Land Right Certification 25
Quarterly Journal of International Agriculture 54 (2015), No. 1; DLG-Verlag Frankfurt/M.
The first-order stochastic dominance analysis, shown in Figure 4, supports this
evidence by showing the superiority of the frontier defined by best-practice farms with
a certificate over the frontier of those without a certificate. For instance, with
particular relevance to farm plots without a certificate, their relative performance
under the without certificate technology dominates their efficiency when evaluated
against the technology defined by the with certificate farms (shown in panel A of
Figure 4). On the other hand, the superiority in relative efficiency of plots with a
certificate is far greater in relation to the best-practice farms without certificate than it
is in comparison with the technology within their own group (shown in panel B of
Figure 4 below). Both of these nonparametric evidences show the superiority of the
with certificate frontier over the without certificate frontier. Results from the two-sided
Kolmogorov-Smirnov (K-S) test reported in Table 6 reaffirm this result. Both null
hypotheses – (1) identical distribution of relative performance of farms without a
certificate regardless of the benchmark technology15 (column 4 of Table 6) and (2)
identical distribution of relative efficiency of farms with a certificate regardless of the
benchmark technology (column 5 of Table 6) – are rejected with 5 percent level of
significance in favor of the dominance of the with certificate frontier over the without
certificate frontier.
6 Conclusions
Despite the fact that issues of land rights and tenure security are high on the global
policy agenda, comprehensive studies of how such new land reforms affect agri-
cultural productivity are scarce. Taking advantage of a detailed plot-specific household
survey from the northern highlands of Ethiopia, this study analyzes the productivity
impacts of the Ethiopian land certification program by identifying how the investment
effects (technological gains) would measure up against the benefits from any
improvements in input use intensity (technical efficiency).
Based on the results of a DEA-based Malmquist productivity index, we found that
farms without a land use certificate are, on aggregate, less productive than those with
formalized use rights. Using the decomposed analysis, we found no evidence to
suggest that this productivity difference between the two groups of farms is due to
differences in technical efficiency. Rather, the reason comes down to technological
advantages, or a favorable investment effect that farm plots with a land use certificate
benefit from when evaluated against those without a certificate. Results from a first-
15 Referring to second component of equation (6), this null hypothesis tests whether E11-E21 = 0 or,
more specifically, whether E11/E21 = 1. If we cannot reject the null hypothesis, then the two
frontiers intersect and there is no dominance of the one frontier over the other. The alternative
hypothesis is dominance of the distribution of the first efficiency measure over the second.
26 Hosaena Ghebru and Stein T. Holden
Quarterly Journal of International Agriculture 54 (2015), No. 1; DLG-Verlag Frankfurt/M.
order stochastic dominance analysis support the empirical findings, showing the
dominance in overall productivity of farm plots with a certificate over those plots
without a certificate.
Therefore, the recent wave of land certification projects in the country may not be an
ill-advised direction since such policy measures are found to improve farm
competitiveness and productivity. However, as indicated by results that show low
levels of within-group efficiency of farms in each group, the certification program by
itself may not achieve the promised effects of enhancing agricultural productivity
unless it is complemented by measures such as improving the financial and legal
institutional frameworks.
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Acknowledgement
This research was funded by the CGIAR Research Program on Policies, Institutions,
and Market (PIM), led by IFPRI and the Norwegian Agency for Development
Cooperation (NORAD): We would like to thank editor and two anonymous reviewers
for useful comments. Valuable comments to an earlier version of this paper were also
received from participants at the CSAE 25th Anniversary Conference 2011: Economic
Development in Africa, 20th-22nd March 2011, St Catherine’s College, Oxford, UK.
Corresponding author:
Hosaena Ghebru
International Food Policy Research Institute (IFPRI), 2033 K Street N.W., Washington, D.C. 20006, USA
e-mail: hosaenag@yahoo.com and h.g.hagos@cgiar.org
30 Hosaena Ghebru and Stein T. Holden
Quarterly Journal of International Agriculture 54 (2015), No. 1; DLG-Verlag Frankfurt/M.
Appendix
Stata program output of propensity score matching of plots with and without land
use certificate observable characteristics
****************************************************
Algorithm to estimate the propensity score
****************************************************
The treatment is certificate
1=yes | Freq. Percent Cum.
------------+-----------------------------------
0 | 529 39.01 39.01
1 | 827 60.99 100.00
------------+-----------------------------------
Total | 1,356 100.00
Estimation of the propensity score
Iteration 0: log likelihood = -735.35329
Iteration 1: log likelihood = -704.6413
Iteration 2: log likelihood = -704.35032
Iteration 3: log likelihood = -704.30895
Iteration 4: log likelihood = -704.29787
Iteration 5: log likelihood = -704.29462
Iteration 6: log likelihood = -704.29362
Iteration 7: log likelihood = -704.2933
Iteration 8: log likelihood = -704.29319
Iteration 9: log likelihood = -704.29316
Iteration 10: log likelihood = -704.29314
Iteration 11: log likelihood = -704.29314
Iteration 12: log likelihood = -704.29314
Iteration 13: log likelihood = -704.29314
Iteration 14: log likelihood = -704.29314
Iteration 15: log likelihood = -704.29314
Iteration 16: log likelihood = -704.29314
Probit regression Number of obs = 1356
LR chi2(12) = 62.12
Prob > chi2 = 0.0000
Log likelihood = -704.29314 Pseudo R2 = 0.0922
------------------------------------------------------------------------------
certificate | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
area_planted | .2039935 .0866108 2.36 0.019 .0342395 .3737476
homstad_plot | -.7245097 .124592 -5.82 0.000 -.9687055 -.4803138
slope_flat | -.5960804 .1655965 -3.60 0.000 -.9206435 -.2715173
slope _foot | -.3077846 .2125602 -1.45 0.148 -.7243951 .1088258
slope_steep | .0630793 .3894655 0.16 0.871 -.700259 .8264176
depth_shalow | 5.845854 .2492593 23.45 0.000 5.357314 6.334393
depth_meduim | 5.954099 .2566176 23.20 0.000 5.451137 6.45706
depth_deep | 5.934536 .2507117 23.67 0.000 5.44315 6.425921
stype_miixed | .0301215 .208445 0.14 0.885 -.3784233 .4386662
sttype_black | .1354379 .1993868 0.68 0.497 -.255353 .5262288
stype_clay | .1511897 .1850235 0.82 0.414 -.2114497 .5138291
plot_distanc | -.0094768 .0022706 -4.17 0.000 -.0139271 -.0050266
_cons | -5.25226 . . . . .
------------------------------------------------------------------------------
Note: the common support option has been selected
Technical Efficiency and Productivity Differential Effects of Land Right Certification 31
Quarterly Journal of International Agriculture 54 (2015), No. 1; DLG-Verlag Frankfurt/M.
The region of common support is [.18766866, .8727433]
Description of the estimated propensity score in region of common support
Estimated propensity score
-------------------------------------------------------------
Percentiles Smallest
1% .2682985 .1876687
5% .340772 .1876687
10% .3849107 .1876687 Obs 1042
25% .4865958 .2163665 Sum of Wgt. 1042
50% .5619563 Mean .5468721
Largest Std. Dev. .1133412
75% .6095591 .8437406
90% .662875 .8535808 Variance .0128462
95% .7397664 .8675736 Skewness -.284585
99% .8179659 .8727433 Kurtosis 3.537612
******************************************************
Step 1: Identification of the optimal number of blocks
Use option detail if you want more detailed output
******************************************************
The final number of blocks is 6
This number of blocks ensures that the mean propensity score is not different for
treated and controls in each block.
**********************************************************
Step 2: Test of balancing property of the propensity score
Use option detail if you want more detailed output.
**********************************************************
The balancing property is satisfied.
This table shows the inferior bound, the number of treated and the number of
controls for each block.
Inferior |
of block | 1=yes
of pscore | 0 1 | Total
-----------+----------------------+----------
.1666667 | 33 15 | 48
.3333333 | 130 98 | 228
.5 | 297 370 | 667
.6666667 | 14 79 | 93
.8333333 | 2 4 | 6
-----------+----------------------+----------
Total | 476 566 | 1,042
Note: the common support option has been selected.
*******************************************
End of the algorithm to estimate the pscore
*******************************************
The table below shows post-matching distribution of treatment (certificate).
1=yes | Freq. Percent Cum.
------------+-----------------------------------
0 | 476 45.68 45.68
1 | 566 54.32 100.00
------------+-----------------------------------
Total | 1,042 100.00
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