Research strategy for Incomplete markets, land and fertilizer use in Ethiopia
Donald Larson, Daniel Ali, Daniel Zerfu and Klaus Dininger
Background
Cereal yields and fertilizer use are low in Africa relative to other regions and there is a
widely expressed view that a Green Revolution in African agriculture is impossible
without a significant uptake in the use of fertilizers. Moreover, because supplies of
organic fertilizer and the scope for increased fallowing are limited in Africa, any
significant growth in fertilizer use will depend on an increase in the application of
chemical fertilizers (Morris et al., 2007).
Research from field studies in Africa often show that the economic returns to chemical
fertilizer is large relative to its cost in areas where application rates are nonetheless low.
For example, in a study on maize in Africa, Duflo et al. (2008) calculated the annualized
rate of return for fertilizer use at nearly 70 percent.
Several reasons are given why African farmers do not use fertilizers more intensively.
One notion is that, because infrastructure and natural transportation systems are weak and
population densities are low, transportation and other transaction costs are high in Africa.
This tends to lower the farm-gate price of maize for sale and increase the farm-gate price
for chemical fertilizers and this in turn limits economic viability. For example, Alene et
al. (2007) find evidence that distance to markets and participation in buying groups of
affected fertilizer use for maize in Kenya.
Other reasons offered for low fertilizer use have to do with farmer characteristics rather
than markets per se. General education levels, experience and advice from extension
agents are all expected to affect production technology choices and therefore fertilizer,
although the evidence is mixed. For example, Alene et al. and Alene and Hassan (2006)
provide evidence that extension matters for Kenya and Ethiopia; however Duflo et al.
(2008) claim that the advice provided to growers in Kenya did not make economic sense.
At the same time, the paper suggests that farmer behavior is not completely rational and
concludes ““there may be a role for non-fully rational behavior in explaining production
decisions.” (p. 487).”
Other studies point to the importance of farm and land characteristics. On the institutional
side, Deininger and Ali (2008) point out that incentives to make investments in soil
nutrients increase when tenure rights are secure and the study provides supporting
evidence from Ethiopia. Similarly, other studies show the importance of the organic
content of soils crop response rates to fertilizer, which in turn affects fertilizer application
rates. The paper by Matsumoto and Yamano (2008) provides examples from Kenya and,
to a degree, a counter-example from Uganda. This area of research has significant
implications for policy, since the high degree of heterogeneity in the soils of Africa due
to differences in natural endowments and compounded by past land management
practices that are affected by heterogeneous land rights makes it difficult to devise policy
instruments that can be broadly applied or even to provide effective extension services.
For example, Alene and Hassan (2008) show that farmers in Ethiopia used and benefited
from technology packets (seed, fertilizer and advice) in areas with productive land, but
that farmers chose to retain a variety of production techniques in low productivity areas.
Research question
The paper will examine the degree to which transaction costs and tenure status affect the
use of organic and chemical fertilizer for maize production in Ethiopia.
Data
The study will make use of a country-wide panel household survey conducted in 2004
and 2006 by the Ethiopian Economic Association (EEA) and the World Bank. The survey
consists of about 2,300 randomly selected households in 115 villages (kebeles) stratified
by agroecological zone and region to ensure coverage of all the agricultural production
systems of the country. After adjusting for missing data, there are observations on 2,140
matching households and two growing seasons for each year. The first round of the
survey focused on extension services while the core section was the land certification
program during the second round, so there are some differences in the questionnaires.
Both surveys contain plot level data; however, we encountered problems matching
sections of the first-round data such that only the household-crop level data is usable.
There are no similar problems for the second round.
We also have demographic and infrastructure data on Ethiopia that was assembled for a
World Bank study on infrastructure in Africa. By combining this with GIS data on the
location of the survey villages we can estimate travel time to population centers. We are
in the process of putting together data from weather stations gathered by the Ethiopian
weather agency so that we can include rainfall and temperature measures. If the time
series are lengthy enough, we intend to calculate climate (average weather) outcomes as
well. Otherwise, we will take our climate indicators from data used for more general
climate models.
Statistical model
Our current thinking is that we will focus on the second round of the survey where we
have plot level information. The statistical model is a variation of Heckman’s selection
model as described in Greene (2003):
N hpt  X hpt   e h  et  u 1hpt , subject to the selection equation Z hpt   u ht2  0 where
u1 ~ N (0, ) , u 2 ~ N (0,1) and corr(u1 , u 2 )   .
The dependent variable in the model is the observed plot-level (p) chemical fertilizer use
per hectare for each household (h) for each growing season (t). The components of X
include the relative price of chemical fertilizer, the use of organic fertilizer and other
inputs observed at the plot levels. We have information on whether the plot is owned or
rented-in, the distance of the plot from the farmer’s home and a subjective evaluation
about the quality of the land. We intend to include weather information, which repeats
over households from each village. We have information about the household, including
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measures of age, education and wealth. At this point, we are expect to use fixed-effects to
take-into account remaining differences in household efficiency and differences in
growing seasons.
The input-demand equation represents a desired level of use, which is affected by the
selection equation, so that sometimes chemical fertilizer is not applied, even when the
input-demand relationship suggests that it is desirable. From the literature, we expect that
whether the land is rented or owned will affect the decision the decision to use chemical
fertilizer since there are fewer incentives to preserve nutrients in the soils. Some costs
related to distance-from-markets is reflected in the price variables, but additional
transaction costs related to information and availability might also affect the decision to
use chemical fertilizer. We also have information on membership in social organization
that might provide access to credit or extension services. The capacity to produce organic
fertilizer, as indicated by livestock ownership might also affect the decision to use
chemical fertilizer, which is always purchased. Whether hybrid seeds are used is also
expected to affect the decision to use chemical fertilizers.
Help
There are two related problems about which the team could use some advice: how to
account for decisions taken about other crops and how to address the simultaneity
problem arising from input choices, especially organic fertilizer and hybrid seed use. The
dynamic panel approach taken by Matsumoto and Yamano is not open to us if we wish to
retain the plot level data. Similarly, household choices about producing other crops will
influence available land and labor for maize and should probably be included in the
input-demand equation. This however further complicates the simultaneity problem. Our
current thinking is to try to find appropriate instruments from the first round of the
survey. Are there better alternatives?
References
Alene Arega D., V.M. Manyong, G. Omanya, H.D. Mignouna, M. Bokanga and G.
Odhiambo. 2007. Smallholder market participation under transactions costs: Maize
supply and fertilizer demand in Kenya. Food Policy 33(4), 318-328.
Alene, Arega D. and Rashid M. Hassan. 2006. The efficiency of traditional and hybrid
maize production in Eastern Ethiopia: an extended efficiency decomposition
approach. Journal of African Economies 15(1):91-116.
Deininger, Klaus and Daniel Syalew Ali. 2008. Assessing the function of land rental
markets in Ethiopia. EDCC 57(1), 67-100.
Duflo, Esther, Michael Kremer and Jonathan Robinson. 2008. How high are rates of
return to fertilizer? Evidence from field experiments in Kenya. American Economic
Review 98(2), 482-488.
Greene, William H. 2003. Econometric Analysis. Upper Saddle River, NJ: Prentice-Hall.
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Matsumoto, Tomoya and Takashi Yamano. 2008. Soil fertility, fertilizer and the maize
green revolution in East Africa. Draft.
Morris, Michael, Valerie Kelly, Ron Kopichi and Derek Byerlee. 2007. Fertilizer Use in
African Agriculture: Lessons Learned and Good Practice Guidelines. World Bank.
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