Fixed land rent, sharecropping and Productivity of Teff in Ethiopia
Getu Hailu,
Department of Food, Agricultural and Resource Economics,
University of Guelph,
Guelph, Ontario, Canada, N1G 2W1,
Tel: 519-824-4120 ext 53890,
Fax: 519-767-1510,
e-mail: [email protected]
Alfons Weersink,
Department of Food, Agricultural and Resource Economics,
University of Guelph,
Guelph, Ontario, Canada, N1G 2W1,
e-mail: [email protected]
Bart Minten,
International Food Policy Research Institute (IFPRI),
Addis Ababa, Ethiopia
e-mail: [email protected]
B. James Deaton
Department of Food, Agricultural and Resource Economics,
University of Guelph,
Guelph, Ontario, Canada, N1G 2W1,
Tel: 519-824-4120 ext 52765,
Fax: 519-767-1510,
e-mail: [email protected]
First Draft May 14, 2015
Please do not cite or quote without permission
Selected Paper Prepared for Presentation at the 13th International Conference on the Ethiopian
Economy, July 22-25, 2015, Addis Ababa, Ethiopia.
Copyright 2015 by Getu Hailu, Bart Minten, Alfons Weersink, B. James Deaton All rights
reserved. Readers may make verbatim copies of this document for non-commercial purposes by
any means, provided that this copyright notice appears on all such copies
1 Fixed land rent, sharecropping and Productivity of Teff in Ethiopia
Getu Hailu, Bart Minten, Alfons Weersink, B. James Deaton
Abstract
The paper examines the relationship between forms of tenancy and teff land productivity in five
major teff growing zones in Ethiopia. Results show that sharecropped plots are no less
productive than owner-operated or fixed cash rent contracts. Fixed cash rent contract is,
however, more productive than own operated plots. Other drivers for productivity differences
appear to be the levels of input use, the management practices employed, the age of the operator,
the ease of access to markets, and the level of engagement in extension efforts. We conclude the
paper by discussing the potential policy implications of our findings for land rental market and
forms of tenancy.
Keywords: Land productivity, Teff, Fixed Land Rent, Sharecropping 2 1. Introduction
There is general consensus in the literature that secure property rights to land enhance
productivity by inducing greater effort on the part of the peasants. This paper examines the effect
of forms of tenancy on teff land productivity – own operated, land renting and sharecropping.
The Marshallian inefficiency literature argue that sharecropping is inefficient relative to fixedrent contract1 – productivity can be increased by moving from sharecropping to fixed-rent
contract. The Marshallian inefficiency hypothesis has a far reaching implication for forms of
land tenure policies target at reducing food insecurity and poverty through productivity
improvement. If the hypothesis holds, then, food insecurity and poverty are better under own
cultivated land, leading to the need to understand the underlying causes that lead to
sharecropping (e.g., lack of labour, oxen, and credit). On the other hand, if sharecropping leads
to better land productivity or has no effect, one would want to focus on other productivity
enhancing interventions. That said, the question is then why sharecropping, own-operated, wage,
and fixed-rent contracts do co-exist despite the inefficiency of sharecropping relative to other
contracts?
A large body of theoretical literature provide explanation for the occurrence of sharecropping:
Eswaran and Kotwal (1985), risk sharing (Newbery and Stiglitz 1979), effort contractibility
(Cheung 1969), safety-first (Sadoulet, Fukui, and de Janvry), equal sharing of all costs and
benefits (Braverman and Stiglitz 1982; Sharma and Drèze, 1996), and cooperation through a
repeated game (Sadoulet, Fukui, and de Janvry). The empirical evidence finds somewhat mixed
results. For example, and Hayami and Otsuka (1997) and Otsuka (2007) find that sharecropped
1
The Marhsallian disincentive effect- output sharing implies a disincentive to provide the inputs
which are not shared due to moral hazard in tenant effort. 3 lands have lower productivity that own-operated land. Recognizing a diverse nature of forms of
sharecropping, a number of recent studies find that sharecropped lands are as efficient as ownoperated or fixed-rent lands (e.g., Sadoulet et al. 1997; Pender and Fafchaps 2006; Kassie and
Holden, 2007; Jacoby and Mansuri 2009).
In this paper we examine the effect of contract choice on land productivity and production
efficiency. We use data on plots of teff from a survey of 1,200 stratified randomly selected
households across five major teff growing zones in Ethiopia. We find that there is no significant
differences between plots operated under sharecropping and other contracts.
2. Data and Empirical methods
2.1. Data
The data for the study come from 1,200 stratified randomly selected households survey
conducted by the Economic Development Research Institute (EDRI) and International Food
Policy Research Institute (IFPRI). The main purpose of the survey was to understand the teff
value chains to make recommendations to policymakers to improve the performance of value
chain for farmers, wholesalers, retailers, and consumers. The survey was conducted in 2012 in
sixty villages (Kebele) in twenty districts (Woreda) in five major teff producing zones (regions)
of Ethiopia (East Gojjam, West Gojjam, East Shewa, West Shewa, and South West Shewa). The
five zones represent 38% of national teff area and 42% of the commercial surplus (CSA 2012).
The average (and the coefficient of variation of) altitude in meters of the five zones are 2323
(10%) for East Gojjam, 2200 (5%) for West Gojjam, 1784 (24%) for East Shewa, 2099 (13%)
for West Shewa and 2188 (5%) for South West Shewa.
4 To ensure that data would be representative of teff areas cultivated in these five zones,
the following procedure was followed. First, within each production zone, the woredas were
ranked from smallest to largest producer in terms of area cultivated. These woredas were divided
in two; the less productive (cultivating all together 50 percent of the area) and the more
productive woredas (cultivating all together 50 percent of the area). Two woredas were randomly
selected from each group. Second, a list of all the kebeles of the selected woredas was then
obtained. Two kebeles were randomly chosen from the top 50 percent producing kebeles and one
from the low 50 percent producing kebeles. Third, a list of all teff producers in the selected
kebeles was then made. These were ranked from small to large teff producers (based on areas
cultivated). The farmers were then divided in two groups, the small production (cultivating all
together 50 percent of the area) and the large production farmers (cultivating all together 50
percent of the area). A total of 20 farmers were then selected: 10 from the small production and
10 from the large production farmers. In total, 240 farmers were interviewed per zone resulting
in 1,200 total respondents (240 households/zone * 5 zones).
The detailed cross-sectional data contains household and plot level information on teff
production. The number of teff plots per household range from 1 to 6 in the dataset with an
average of 1.2. In addition to teff production on each plot, the data includes information on six
sets of variables: (1) inputs (e.g., amount of seed, labor use, number of oxen, dap and urea
fertilizer applied, and amount of herbicide applied); (2) household characteristics (e.g., age,
gender, and the level of education of the household head, and distance from the nearest
marketplace); (3) plot characteristics (e.g., plot size, soil color, slope); (4) technological practices
(e.g., seed variety and color, crop rotation, mobile phone ownership); (5) extension activities
(e.g., co-operative membership, the numbers of community meetings attended, the number of
5 extension agent visits, and being a model farmer ); and (6) weather shocks (e.g., rainfall (timing
and extent), frost).
2.2. Empirical Methods
We use a simple competitive market tenant’s profit maximization model to motivate our study.
We assume that the share of the tenant and the fixed rental rate are exogenously determined,
where the tenant pays and choses the use of variables inputs. The profit maximization problem
is given by:
max,
,
1
, ; ̅
1
(1)
where s is the landlord’s share of output, r is the landlord’s share of variable costs other than
labour,
is the wage rate, l is labour input,
, ; ̅ production function,
variable inputs, x is other variable inputs, R is the fixed-rent, and
input price for
̅ is fixed land input. The first
order condition for the profit maximization problem is:
1
1
(2)
The above results suggest that the marginal product of labour is higher under
sharecropping contract, meaning the optimum labour effort is lower that what would be under
the own-operated or fixed-rent contract. In this study will test this hypothesis using plot level
data for teff.
The main objective of the study is to test the Marshallian inefficiency hypothesis. To
examine effect of contract choice on production efficiency we estimate the following CobbDouglas production function:
ln
6 ∑
ln
(3)
where
is output at for plot p for the h-th household in the k-th kebele in the w-th woreda
in z-th zone,
is the vector of inputs in the production function,
coefficients associated with the inputs,
,
,
,
is the vector of
are cluster specific random components,
represents the normally distributed random error. To estimate we use a linear multilevel
model (LMM). The LMM model handle data where plots’ productivity are not independent, and
allows to model correlated errors when for example predicted plot productivity and errors in
predicting them may be clustered by household, kebele, woreda, and zone. The linear mixed
model mixed model handles random effects at different level of clustering. Clustering of plots
within groups may lead to correlated error term, biased estimates of parameters, standard errors,
and possible substantive errors when interpreting the importance of salient covariates. Many
statistical analyses require the data to be statistically independent. OLS regression assumes error
terms are independent and have equal error variances, whereas when observations are nested by
groups, individual-level observations from the same upper-level group will not be independent
but rather will be more similar. In our case, the productivity of different plots owned by the
same farmers are likely to be more similar to each other than to productivity of plots owned by
another farmer - some farmers will have more people in the household, more oxen, more capital,
and these characteristics are likely to persist from one plot to the other. For these farmers, even
after allowing for different household types and other explanatory variables, their productivity
will generally tend to be larger than the average productivity and somewhat similar from one plot
to the other, whereas if productivity indices were independent, we might expect them to be above
average or below average with equal probability. If we perform a statistical analysis ignoring
this correlation between productivity different plots owned by the same farmers, our results can
be misleading.
7 3. Main Results and Discussions
Table 1 presents size of land owned by households by contract types and zones. Sharecropping is
most common in Gojjam than in Showa regions, but cash rent is more common in Shewa
regions. In addition, sharecroppers and renters tend to have smaller land than own operators.
Table 1: Average land size owned by households by contract type and zone (in hectare,
average)
zone
a
East
West
East
West
South West
Total
Contracts
Gojjam
Gojjam
Shewa
Shewa
Shewa
1.25
1.23
2.73
2.52
3.36
2.30
Own
cultivator
(109)
(151)
(152)
(176)
(164)
(752)
(autarky)
0.98
0.91
2.33
1.71
1.95
1.17
Sharecropper
(113)
(73)
(3)
(45)
(18)
(252)
1.12
0.86
1.61
1.52
2.02
1.62
Cash Renter
(15)
(16)
(84)
(19)
(57)
(191)
1.58
0.75
0.50
1.20
Borrowed
free
(3)
(0)
(1)
(0)
(1)
(5)
1.12
1.11
2.33
2.29
2.92
1.95
Overall
(240)
(240)
(240)
(240)
(240)
(1200
)
Note: Figures in the parentheses are the number of households. N=1200 households. a: contract refers to
land market participation status
Table 2: Average size of plots (in ha) operated under different contractual arrangements
and zones (n=1200)
Zone
East
West
East
West
South
Total
Contracts
Gojjam
Gojjam
Shewa
Shewa
West
Shewa
0.30
0.27
0.62
0.54
0.62
0.49
Own
0.32
0.30
1.58
0.50
0.48
0.38
Sharecropping
0.41
0.24
0.71
0.37
0.53
0.58
Rent
0.25
0.25
0.25
0.25
Borrow
0.31
0.27
0.65
0.52
0.60
0.48
Overall
8 Table 3 presents average plot level productivity by contract type and zones. Average Teff plot
productivity varies by zones and contract types.
Table 3. Average teff plot productivity in kilograms per hectare by zone and type of
contracts
zone
East
West Gojjam
East
West
South West Total
Contract
Gojjam
Shewa
Shewa
Shewa
1506
1072
1233
883
779
1073
Own
1427
1102
841
933
910
1211
Share
1854
1242
1324
931
884
1176
Rent
1400
800
300
1117
Borrow
1494
1086
1254
893
802
1106
Total
Table 3. Average teff plot yield, and inputs per hectare by contract type
Contracts yield labour seed urea dap herb oxen Quncho improved
1073
142
46
77 100 46
3
0.18
0.11
Own
1211
158
39
86
97
36
2
0.20
0.07
Share
1176
115
48
72 105 42
4
0.24
0.12
Rent
130
30
85 151 11
2
0.00
0.00
Borrow 1117
1106
141
45
78 100 44
3
0.19
0.11
Total
Note: Yield is teff output per hectare in kilograms per hectare, labour in man-equivalent per hectare, seed in
kilograms per hectare, urea in kilograms per hectare, DAP fertilizer in dollars per hectare, oxen in Quncho and
improved are proportion of high-yielding varieties.
Table 4 presents parameter estimates for a multilevel mixed linear and household fixed effect
regression Cobb-Douglas production functions. The estimates of our preferred model - a
multilevel mixed linear regression - show that the coefficients on owner operated and “the fixed
cash rental” are statistically insignificant, meaning that the productivity of owner operated plots
or rented plots are no better than sharecropped plots. This result does not support the Marshallian
sharecropping inefficiency theory, which states that production is less efficient under
sharecropping contract. The Marshallian sharecropping inefficiency theory states that the
9 optimum labour effort is lower than what would be under the own-operated or fixed-rent
contract. One interesting finding of the study is that when the reference group is changed to
“own operated land”, plots operated with rented land are more productive than own operated
plots and this relationship is statistically significant, meaning that teff yield on cash rented plot is
higher than that of own operated plots by approximately 4.1 percentage point. Still there is no
significant differences between sharecropped plots and owner-operated plots.
The fixed effect model that controls for unobservable household differences also show
that there is no statistically significant difference between sharecropped plots and cash rented
plots, and sharecropped plots and own operated pots. Contrary to the mixed linear model results,
we find that there is no statistically significant differences between own-operated plots and cash
rented plot.
Our results support results of a number of recent studies that find sharecropped lands are
as efficient as own-operated or fixed-rent lands (e.g., Sadoulet et al. 1997; Pender and Fafchaps
2006; Kassie and Holden, 2007; Jacoby and Mansuri, 2009). Other studies, however, find that
sharecropped lands are less productive than own-operated land (e.g., Hayami and Otsuka 1997;
Otsuka 2007; Ahmed et al 2002) while other find the opposite (Holden et al 2001). Pender and
Fafchaps (2006), for example, find that there is significant no difference in productivity between
tenants owned plots and sharecropped plots in the Arsi zone of the Oromia region of Ethiopia,
whereas Ahmed et al (2002) find that owner-operated plots outperformed sharecropped plots.
Holden et al (2001) find that barley land productivity was higher on sharecropped plots than on
owner-operated plots.
The LR test indicates that zone, woreda, kebele and household random effects are
statistically significant, which justifies the application of multi-level mixed linear model. For
10 example, we find intra-class correlations of 0.13 at the zone level and 0.66 at the household
level. These numbers represent the correlation in productivity between two plots in the same
zone, and between two plots for the same household (in the same zone, woreda, and kebele). We
can also conclude that 13% of the variation in plot productivity can be attributed to the
heterogeneity across zones and 66% to the heterogeneity across households (which includes the
zone, woreda, and kebele).
Table 4: The Relationship between forms of tenure and Teff Plot Productivity2
Variables
Multi-level Mixed Linear Regression
Variables
Coeff
Standard
Coeff
Standard
Coeff
Errors
Errors
Own operate
-0.0150
(0.02)
-0.00273
Sharecropped
0.015
(0.019)
Fixed rental
0.0250
(0.02)
0.040***
(0.012)
-0.00468
Borrowed
-0.0715
(0.06)
-0.057
(0.050)
0.0111
Log Plot Size
-0.411***
(0.05)
-0.448***
Log of Seed
0.182***
(0.009)
0.203***
Log of Labor
0.172**
(0.07)
0.244***
Log of DAP
0.00651
(0.004)
0.00107
Fertilizer
Log of UREA 0.00860***
(0.002)
0.000964
Fertilizer
Log of
0.00680***
(0.002)
0.0101**
Herbicide
Quncho
0.110***
(0.03)
0.0921*
Other
0.0598***
(0.02)
-0.0179
improved
White seed
-0.0371
(0.02)
-0.0466
Mix seed
-0.0664
(0.05)
-0.0357
Red seed
-0.0819***
(0.01)
-0.0808
Brown soil
0.0112
(0.04)
-0.0294
Black soil
0.0565
(0.04)
0.0331
Mix soil
0.0424*
(0.02)
0.0125
Terrain with
-0.0467***
(0.02)
-0.0658
hills
Steep slope
0.0314
(0.04)
0.0551
Crop rotation
0.0319
(0.02)
0.0276
Easy to plough 0.0605***
(0.02)
0.0359
Frequency of
0.0370***
(0.010)
0.0329
ploughing
Manure
0.0223*
(0.01)
-0.0318
applied
Household fixed effect model
Standard
Coeff
Standard
Errors
Errors
(0.03)
0.00273
(0.03)
(0.05)
-0.00195
(0.03)
(0.1)
0.0138
(0.1)
(0.07)
-0.448***
(0.07)
(0.06)
0.203***
(0.06)
(0.05)
0.244***
(0.05)
(0.008)
0.00107
(0.008)
(0.008)
0.000964
(0.008)
(0.004)
0.0101**
(0.004)
(0.05)
(0.06)
0.0921*
-0.0179
(0.05)
(0.06)
(0.05)
(0.07)
(0.06)
(0.05)
(0.04)
(0.05)
(0.05)
-0.0466
-0.0357
-0.0808
-0.0294
0.0331
0.0125
-0.0658
(0.05)
(0.07)
(0.06)
(0.05)
(0.04)
(0.05)
(0.05)
(0.1)
(0.04)
(0.04)
(0.03)
0.0551
0.0276
0.0359
0.0329
(0.1)
(0.04)
(0.04)
(0.03)
(0.06)
-0.0318
(0.06)
2
Note that we cannot completely rule out the possible endogeneity of the contract choice in the
equation for teff productivity. Though it is not addressed in the presented study, the instrumental
variable approach as in Pender and Fafchaps (2005), where the predicted probability of each
contract type is used as instrument.
11 Distance from
homestead
Low rain
High rain
Less logging
More logging
Less frost
More frost
Late rain
Early rain
Early planting
Late planting
Somewhat
fertile soil
Infertile soil
-0.000251
(0.0003)
0.0000470
(0.0007)
0.0000470
(0.0007)
-0.0134
0.00860
0.0912***
-0.0211
-0.0655
-0.191***
-0.0567*
-0.0167
-0.0338
-0.00147
-0.0406**
(0.02)
(0.06)
(0.03)
(0.02)
(0.04)
(0.03)
(0.03)
(0.06)
(0.05)
(0.04)
(0.02)
0.0350
-0.0332
0.138
-0.0410
-0.0473
-0.141
-0.350*
-0.0638
-0.0229
-0.0257
0.00196
(0.1)
(0.1)
(0.1)
(0.05)
(0.1)
(0.09)
(0.2)
(0.2)
(0.1)
(0.09)
(0.02)
0.0350
-0.0332
0.138
-0.0410
-0.0473
-0.141
-0.350*
-0.0638
-0.0229
-0.0257
0.00196
(0.1)
(0.1)
(0.1)
(0.05)
(0.1)
(0.09)
(0.2)
(0.2)
(0.1)
(0.09)
(0.02)
-0.0317
(0.04)
0.0469
6.825***
(0.04)
(0.2)
0.0469
6.822***
(0.04)
(0.2)
Estimated variance components
zone
0.190***
(0.078)
0.201***
(0.038)
woreda
0.142***
(0.017)
kebele
0.288***
(0.011)
household
0.300***
(0.016)
e
Adj. R2
0.722
0.722
Observations
2747
2747
Note: The dependent variable is yield per hectare. Robust standard errors in parentheses, * p < 0.10, ** p < 0.05,
*** p < 0.01. A zone random effect allows correlation in the error term across woreda, kebele, and households for a
given zone, with inter-class correlation of
. A woreda random effect allows correlation in the error
across zone, kebele and households for a given woreda. A kebele random effect allows correlation in the error term
across zone, woreda, and household for a given kebele. A household random effect allows correlation in the error
term across zone, woreda and kebele for a given household.
The findings in Table 6 provides a mixed results regarding the effect of form of tenancy on input
usage. For the mixed linear model, there are statistically significant differences in labour and
seed input usage between own operated and sharecropped plots, which provides evidence to
support the Marshallian inefficiency hypothesis. The household fixed effect models, however,
show no statistically significant differences in input use between sharecropped and own-operated
plots. The household fixed effect results are consistent with previous studies that find no
differences in input use between importers’ sharecropped and owner-operated plots (Pender and
Fafchamps 2005).
12 Table 5: The Relationship between forms of tenure and labour and seed usage3
Mixed Linear Model
Household Fixed Effect
Log(labou/ha)
Log(seed_ha)
Log(labou/ha)
Log(seed_ha)
Sharecropped
-0.0567*** (-2.79) -0.0203*** (-2.70)
-0.0531
(-1.39)
-0.0260
(-0.90)
Fixed rental
-0.0432
(-1.57)
-0.0180
(-0.99)
-0.0262
(-0.71)
-0.0276
(-0.92)
Borrowed
0.157*
(1.88)
-0.175*** (-10.42)
0.170
(0.91)
-0.181** (-2.05)
Quncho
-0.00940
(-0.27)
-0.0851
(-1.45)
0.00263
(0.06)
-0.0574
(-1.33)
Other improved
-0.0231
(-0.42)
0.0452**
(2.52)
0.0540
(0.57)
0.0508
(1.28)
White seed
0.00542
(0.22)
0.0210
(0.70)
0.0253
(0.66)
0.0369
(0.91)
Mix seed
-0.0480
(-0.90)
-0.0133
(-0.44)
0.0245
(0.41)
-0.00861 (-0.16)
Red seed
0.0123
(1.28)
0.0629
(1.60)
0.0374
(0.83)
0.0694
(1.47)
Brown soil
0.0257
(0.71)
-0.000725
(-0.02)
-0.0284
(-0.51)
0.00873
(0.20)
Black soil
-0.0117
(-0.45)
-0.00953
(-0.35)
-0.0364
(-0.73) -0.00660 (-0.19)
Mix soil
-0.00293
(-0.10)
0.00953
(0.33)
0.0246
(0.43)
0.0163
(0.40)
Terrain with hills
-0.0141
(-0.58)
0.0152
(0.41)
-0.0636
(-1.22) -0.00872 (-0.24)
Steep slope
-0.0214
(-0.30)
0.0615**
(1.96)
-0.0865
(-0.97)
0.0330
(0.50)
Crop rotation
0.0500**
(2.41)
0.0331***
(3.60)
0.0275
(0.70)
0.0147
(0.45)
Easy to plough
0.0284
(0.80)
0.00666
(0.65)
0.0423
(0.92)
0.0117
(0.35)
Frequency of ploughing
0.0186*
(1.65)
0.00843
(1.16)
-0.0178
(-0.57) -0.00781 (-0.39)
Manure applied
0.208***
(3.08)
0.0612
(1.60)
0.182*
(1.80)
0.0106
(0.28)
Distance from homestead
0.000439
(1.03)
-0.000126
(-0.38) 0.000481 (0.74) -0.000190 (-0.41)
Low rain
-0.0142
(-0.28)
0.00777
(0.26)
0.0169
(0.13)
0.0219
(0.22)
High rain
0.0284
(0.35)
0.0300
(0.46)
0.224
(1.24)
0.0541
(0.84)
Less logging
0.0138
(0.14)
-0.0132
(-0.52)
-0.0348
(-0.30)
0.0305
(0.34)
More logging
0.0160
(0.61)
-0.0342
(-1.18)
-0.0217
(-0.45)
-0.0205
(-0.41)
Less frost
0.000799
(0.01)
0.00340
(0.10)
-0.0811
(-0.78) -0.00530 (-0.07)
More frost
-0.0749*** (-3.24)
-0.0209
(-0.49)
-0.132*
(-1.81) -0.00184 (-0.03)
Late rain
-0.0475** (-2.15)
0.000847
(0.03)
-0.271
(-0.89)
-0.163
(-1.47)
Early rain
0.0655
(1.02)
0.0394
(1.10)
-0.606
(-1.47) -0.173** (-2.38)
Early planting
0.135
(1.51)
-0.0458
(-1.03)
0.0298
(0.30)
-0.0939
(-1.22)
Late planting
-0.0259
(-0.40)
-0.00995
(-0.29)
0.0263
(0.23)
0.00625
(0.07)
Somewhat fertile soil
0.00987
(0.60)
0.00506
(0.39)
-0.00133 (-0.05)
0.00586
(0.31)
Infertile soil
0.0481
(1.59)
-0.00147
(-0.11)
0.0309
(0.82)
-0.00192 (-0.07)
Constant
4.596***
(30.02)
3.633***
(27.03) 4.850*** (27.23) 3.727*** (33.17)
zone
0.221 (0.047)
0.196 (0.057)
0.132 (0.020)
0.181 (0.036)
woreda
kebele
0.059 (0.043)
0.151 (0.044)
0.394 (0.039)
0.323 (0.027)
household
e
0.324 (0.029)
0.236 (0.019)
Observations
2773
2775
2773
2775
t statistics in parentheses * p < 0.10, ** p < 0.05, *** p < 0.01. Robust standard errors in parentheses.
3
Note that we cannot completely rule out the possible endogeneity of the contract choice in the
equation for teff productivity. Though it is not addressed in the presented study, the instrumental
variable approach as in Pender and Fafchaps (2005), where the predicted probability of each
contract type may be used as instrument.
13 4. Concluding Remarks
The paper examines the relationship between sharecropping and plot productivity using unique
plot level data in five major teff growing zones of Ethiopia, where sharecropping and fixed cash
rent arrangements are common. We find that no systematic differences in teff productivity
between sharecropped and owned land between sharecropping and fixed rental after controlling
for other factors. Meanwhile, fixed cash rented plots are more productive than own operated
plots. Our teff productivity results does not provide support for the Marshallian sharecropping
inefficiency hypothesis, meaning that the land lease market is relatively well functioning the five
major teff growing zones of Ethiopia. On the contrary, the mixed linear model for input use lends
support to the Marshallian hypothesis
14 References
Ahmed, M. M. 2002. Measurement and Sources of Technical Efficiency of Land Tenure
Contracts in Ethiopia. Environment and Development Economics, 7(3): 507-27.
Ali, A., Abdulai, A., and Goetz, R. 2012. Impacts of Tenancy Arrangements on Investment and
Efficiency: Evidence from Pakistan. Agricultural Economics, 43(0): 85-97.
Benin, S., Ahmed, M. Pender, J. and Ehui, S. 2005. Development of Land Rental Markets and
Agricultural Productivity Growth: The Case of Northern Ethiopia. Journal of African
Economies, 14(1): 21-54.
Bhandari, R.. 2007. The Role of Social Distance in Sharecropping Efficiency: The Case of Two
Rice-Growing Villages in Nepal. Journal of Economic Studies, 34(3-4): 290-310.
Braido, L. H. B. 2008. Evidence on the Incentive Properties of Share Contracts. Journal of Law
and Economics, 51(2): 327-49.
Chattopadhyay, M., and Sengupta, A. 2001. Farm Size and Sharecropping Efficiency: Some
Theoretical Issues. Keio Economic Studies, 38(2): 65-73.
Deininger, K., Ali, D.A, and Alemu, T. 2013. Productivity effects of land rental market
operation in Ethiopia: evidence from a matched tenant–landlord sample, Applied
Economics, 45 (25) 3531-3551.
Deininger, K., Ali, D.A., and Alemu, T. 2008. Assessing the Functioning of Land Rental
Markets in Ethiopia. Economic Development and Cultural Change, 57(1): 67-100.
Deininger, K., Ali, D.A., and Alemu, T. 2011. Productivity effects of land rental markets in
Ethiopia : Evidence from a matched tenant-landlord sample. The World Bank, Policy
Research Working Paper Series: 5727.
Deininger, K., Jin, S. and Yadav, V. 2013. Does Sharecropping Affect Long-Term
Investment? Evidence from West Bengal’s Tenancy Reforms. American Journal of
Agricultural Economics, 95(3): 772-90.
Eswaran, M., and Kotwal, A. 1985. A Theory of Contractual Structure in Agriculture.
American Economic Review (75):352–67.
Fafchamps, M., and Pender, J. 2000. Land Lease Markets and Agricultural Efficiency: Theory
and Evidence from Ethiopia. University of Oxford, Department of Economics,
Economics Series Working Papers: WPS/2002-19.
Garrett, M. A., Jr., and Xu, Z. 2003. The Efficiency of Sharecropping: Evidence from the
Postbellum South. Southern Economic Journal, 69(3): 578-95.
15 Gersovitz, M. 2002. Review of Agriculture and economic growth: Theory and measurement, by
Yair Mundlak. Journal of Economic Literature 40 (1): 223-25.
Gebregziabher, G. and Holden, S.T. 2011. Distress rentals and the land rental market as a
safety net: contract choice evidence from Tigray, Ethiopia, Agricultural Economics 42:
supplement 45-60.
Hayami, Y. and Otsuka, K. 1993.. The Economics of Contract Choice: An Agrarian
Perspective.Oxford, Clarendon press.
Hoff, K. 1991. Land Taxes, Output Taxes, and Sharecropping: Was Henry George Right? World
Bank Economic Review, 5(1): 93-111.
Holden, S. T., Bekele, S., Pender, J., 2001. Market imperfections and land productivity in the
Ethiopian highlands. Journal of Agricultural Economics. 52, 53-79.
Holden, S.T, and Otsuka, K. 2014. The roles of land tenure reforms and land markets in the
context of population growth and land use intensification in Africa, Food Policy, 48, 88–
97.
Jabbar, M. A. 1975. Sharecropping and Economic Efficiency in BangladeshComment.Bangladesh Development Studies, 3(2): 253-59.
Jacoby, H. G., and Mansuri, G. 2007. Incentives, supervision, and sharecropper productivity.
The World Bank, Policy Research Working Paper Series: 4125.
Kassie, M., and Holden, S.. 2007. Sharecropping Efficiency in Ethiopia: Threats of Eviction
and Kinship. Agricultural Economics, 37(2-3): 179-88.
Laffont, J-J, and Matoussi, M.H.. 1995. Moral Hazard, Financial Constraints and
Sharecropping in El Oulja. Review of Economic Studies, 62(3): 381-99.
Lee, M-H. 2011. Land Ownership and Productivity in Early Twentieth-Century China: The Role
of Incentives. International Journal of Development Issues, 10(2): 141-53.
Nabi, I. 1986. Contracts, Resource Use and Productivity in Sharecropping. Journal of
Development Studies, 22(2): 429-42.
Otsuka, K. 2007. Efficiency and Equity Effects of Land Markets. In. Handbook of Agricultural
Economics, Elsevier.
Otsuka, K. and Hayami, Y. 1988. Theories of Share Tenancy: A Critical Survey. Economic
Development and Cultural Change 37(1): 32-68.
16 Pender, J., Fafchamps, M., 2001. Land lease and agricultural efficiency: theory and evidence
from Ethiopia. EPTD Discussion Paper No 1, International Food Policy Research
Institute, Washington, DC.
Pender, J., Fafchamps,M., 2006. Land lease markets and agricultural efficiency in Ethiopia.
Journal of African Economies. 15(2), 251–284.
Ray, T. 2005. Sharecropping, Land Exploitation and Land-Improving Investments. Japanese
Economic Review, 56(2): 127-43.
Reiersen, J. 2001. Bargaining and Efficiency in Sharecropping. Journal of Agricultural
Economics, 52(2): 1-15.
Sadoulet, E, Fukui, S. and de Janvry, A. 1994. Efficient share tenancy contracts under risk:
The case of two rice-growing villages in Thailand. Journal of Development Economics,
45(2): 225–243.
Sadoulet, E., de Janvry, A. and Fukui, S. 1997. The Meaning of Kinship in Sharecropping
Contracts. American Journal of Agricultural Economics 79(May): 394-407.
Sharma, N., and Dreze, J. 1996. Sharecropping in a North Indian Village. Journal of
Development Studies, 33(1): 1-39.
Stiglitz, J.E., and Braverman, A.. 1989. Credit rationing, tenancy, productivity, and the
dynamics of inequality. The World Bank, Policy Research Working Paper Series: 176.
Toufique, K. Ali. 1988. Sharecropping: Efficiency and Risk--A Note. Bangladesh Development
Studies, 16(2): 91-94.
Zaman, M. R.. 1973. Sharecropping and Economic Efficiency in Bangladesh. Bangladesh
Economic Review, 1(2): 149-72.
17