Ethiopian Development Research Institute (EDRI) and International Food Policy Research Institute (IFPRI), Seminar Series, May 24, 2012

1. Short-term Welfare Effects of Wheat Price
Changes on Farm Households in Ethiopia in
the Context of Increasing Intensity of
Adoption of Improved Wheat Varieties
Asfaw Negassa, Menale Kassie, Bekele Shiferaw
and Moti Jaleta
To be Presented at National Workshop on Food Price
Dynamics and Policy Implications in Ethiopia
Ethiopian Development Research Institute (EDRI)
24 May, 2012
Addis Ababa, Ethiopia

2. Outline of Presentation
I. Background
II. Objectives of the Study
III.Conceptual Framework
IV. Empirical Model
V. Data Source
VI. Key Results
VII.Conclusions and Implications

3. I Background
● Wheat is among the very important staple food crops
grown in Ethiopia and also large amount of it is annually
imported
● Given, its importance in the national economy, the
Ethiopian government has been also making large
investment in agriculture sector such as in the
development and extension of improved wheat
technologies
● Recently, the increased wheat price level and volatility
have been among the important public policy issues
facing developing countries like Ethiopia

4. I Background (Cont.)
● However, the welfare effects of wheat price changes on
wheat producers in the context of increasing intensity of
adoption of improved wheat varieties has not been
explored so far
● This has implications for the government’s effort to
stimulate wheat production through the adoption of
improved wheat varieties under the current conditions of
increasing wheat prices –is there impact?

5. I Background (Cont.)
Key research questions:
● Does increase in intensity improve the welfare
effects of wheat price increases?
● What is the threshold level of intensity of
adoption of improved wheat varieties beyond
which the farmers start having improved welfare
effect as a results of wheat price increases?
● What is the optimum level of intensity of adoption
which maximizes the welfare effect of wheat
price increases?

6. II Objectives of the Study
● The major objective of this study was to estimate the impact
of adoption of improved wheat varieties on welfare effects of
wheat price changes on farm households in Ethiopia.
Specific objectives:
● 1) To determine the impact of intensity of adoption of
improved wheat varieties on likelihood of the farm
households being in various net market positions
(net buyer, autarkic, or net seller) of wheat, and
● 2) To determine the impact of intensity of adoption of
improved wheat varieties on welfare effects of price
changes on farm households

7. III Conceptual Framework
● In standard neoclassical economic analysis, the first-order
welfare effects of food price changes on households is
measured using either consumer surplus or producer surplus
–this assumes households are either pure producers or pure
consumers
● However, the agricultural households could be both producer
and consumer of their own food and such single welfare
measures might not adequately capture the welfare effects
of price changes on agricultural households
● As a result, in order to evaluate the welfare effect of price
changes on agricultural households it is recommended that
farm households’ income, production and consumption must
be jointly considered Deaton (1989) and Budd (1993)

8. III Conceptual Framework (Cont.)
●

9. III Conceptual Framework (Cont.)
● The NBR takes in to account farmers net market position
 NBR < 0 for net buyers --welfare loss (gain) in case of price
increase(decrease)
 NBR = 0 for autarkic households --no welfare change
 NBR > 0 for net sellers --welfare gain (loss) in case of price increase
(decrease)
● It shows both the direction and magnitude of short-
run welfare effects of price changes
● We compare the NBR with independent variable of
interest (for example, the intensity of adoption) to
see its impact on welfare effects of price change

10. III Conceptual Framework (Cont.)
● However, there are two main weaknesses of NBR as a
welfare measure (Deaton, 1998)
 First, it only considers small price changes and may not give adequate
picture of the welfare effect of large price change
 Second, the effects of price changes might not just depend on amount
produced or consumed but also on second order effects such as
through labor wage market dynamics
● In general, the NBR does not show the general
equilibrium effects, or substitution effects
● Therefore, in the future, there is a need to explore
second-order welfare effects of wheat price
changes which take in to account the households’
supply and demand responses to the price changes

11. IV Empirical Model
● The key challenge in empirical impact evaluation is how to
remove or reduce biases in the estimated impact which
could arise when there are pre-treatment differences in
observed as well as unobserved covariates between control
and treatment groups as a result of non-random treatment
assignment
● Several parametric and non-parametric econometric
techniques have been developed and used to solve
selection bias problem including Heckman selectivity
correction, instrumental variable (IV), propensity score (PS)
matching methods, and error correction (EC) approaches.

12. IV Empirical Model (Cont.)
● Recently, in quasi experimental setting, the use of propensity
score (PS) matching has been very popular
● The PS matching was developed by Rosenbaum and Rubin
(1983) in order to overcome the dimensionality problem of
covariate adjusting
● However, the weakness of PS method is that it is binary and
it does not work well in situations where the treatment
variable is multivalued or continuous (Imbens, 2000; Hirano
and Imbens, 2004) --the binary treatment assumes the
effects are the same (homogenous) among the treatment
groups receiving different treatment levels

13. IV Empirical Model (Cont.)
● In this paper, we utilize the generalized propensity score
(GPS) matching method developed by Imbens (2000) and
Hirano and Imbens (2004) in order to reduce bias in
estimating the various impacts of intensity of adoption of
improved wheat varieties on farm households in Ethiopia
● The GPS extends the standard propensity score method
developed by Rosenbaum and Rubin (1983) for binary
treatment variables to the case of multi-valued or continuous
treatment variables
● Estimation involves three steps (technical details omitted)

14. V Data Sources
● For this study, cross-sectional survey data involving
nationally representative 2096 sample farm households
randomly selected from four major wheat growing regions in
Ethiopia: Amhara, Oromiya, Southern Nations Nationalities
and People (SNNP) and Tigray was used

15. VI Empirical Results
● Distribution of intensity of adoption of improved wheat
varieties
● Impacts on net wheat market positions
 Net buyer
 Autarkic
 Net seller
● Impacts on welfare effects of wheat price changes

16. Figure 1 Distribution of intensity of adoption of improved wheat
varieties
.025
.02
.015
Density
.01
.005
0
0 20 40 60 80 100
Intensity of adoption of wheat varieties (percent of total wheat area)
Kernel density estimate
Normal density
kernel = epanechnikov, bandwidth = 8.3536

17. Figure 2 Impact of intensity of adoption of improved wheat
varieties on farm households’ probability of being net
buyer of wheat
Dose-response function Treatment effect function
.15 .004
Change in probability of being net buyer
Probability of being net buyer
.002
.1
0
.05
-.002
0 -.004
0 20 40 60 80 100 0 20 40 60 80 100
Treatment level (intensity of adoption) Treatment level (intensity of adoption)
Dose Response Lower bound Treatment Effect Lower bound
Upper bound Upper bound
Confidence Bounds at .95 % level Confidence Bounds at .95 % level
Dose response function = Probability of positive outcome Dose response function = Probability of a positive outcome
Regression command = logit Regression command = logit

18. Figure 3 Impact of intensity of adoption of improved wheat
varieties on farm households’ probability of being
autarkic in wheat net market position
Dose-response function Treatment-effect function
.01
.4
Change in probability of being autarkic
Probability of being autarkic
.35
.005
.3
0
.25
.2 -.005
0 20 40 60 80 100 0 20 40 60 80 100
Treatment level (intensity of adoption) Treatment level (intensity of adoption)
Dose Response Lower bound Treatment Effect Lower bound
Upper bound Upper bound
Confidence Bounds at .95 % level Confidence Bounds at .95 % level
Dose response function = Probability of positive outcome Dose response function = Probability of a positive outcome
Regression command = logit Regression command = logit

19. Figure 4 Impact of intensity of adoption of improved wheat
varieties on farm households’ probability of being net
seller of wheat
Dose-response function Treatment-effect function
.75
.005
Change in probability of being net seller .7
Probability of being net seller
0
.65
.6
-.005
.55
.5 -.01
0 20 40 60 80 100 0 20 40 60 80 100
Treatment level (Intensity of adoption) Treatment level
Dose Response Lower bound Treatment Effect Lower bound
Upper bound Upper bound
Confidence Bounds at .95 % level Confidence Bounds at .95 % level
Dose response function = Probability of positive outcome Dose response function = Probability of a positive outcome
Regression command = logit Regression command = logit

20. Figure 5 Impact of intensity of adoption of improved wheat
varieties on farm households’ welfare effects of wheat
price changes
Dose-response function Treatment-effect function
.3
.005
Change in net benefit ratio
.2 0
Net benefit ratio
.1 -.005
0 -.01
0 20 40 60 80 100 0 20 40 60 80 100
Treatment (intensity of adoption) Treatment (intensity of adoption)
Dose Response Lower bound Treatment Effect Lower bound
Upper bound Upper bound
Confidence Bounds at .95 % level Confidence Bounds at .95 % level
Dose response function = Linear prediction Dose response function = Linear prediction

21. VI Conclusions and Policy Implications
● The results provide strong evidence for positive
but heterogeneous welfare effects of wheat price
changes based on the observed different levels
of intensity of adoption of improved wheat
varieties
● Increasing the intensity of adoption of improved
wheat varieties decreases the likelihood of
farmers being net buyers, decreases the
likelihood of being autarkic and increases the
likelihood of being net seller of wheat

22. VI Conclusions and Policy Implications (Cont.)
● At initial low levels of intensity of adoption, the impacts
could be low and decreasing while after certain threshold
level of intensity of adoption (about 20%) was achieved,
the positive welfare effects of wheat price changes
increase sharply
● It is observed that the farm households need to use
improved wheat varieties on about 80% of their total
wheat area in order for the improved wheat varieties
adoption to have maximum positive welfare effect as a
result of wheat price increases

23. VI Conclusions and Policy Implications (Cont.)
● Thus, given the current low level of intensity of adoption
of improved wheat varieties among the farm households,
there is a need to improve the farm households’ intensity
of adoption of improved wheat varieties in Ethiopia
● This study also indicates that the binary variable
treatment of adoption status of improved wheat varieties
in impact assessment assumes that the adopters are
homogeneous group in terms of their intensity of
adoption and leads to inaccurate impact estimates and
wrong conclusions and implications –impact varies by
level of intensity of adoption

24. Thank You