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

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 Shiferawand 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

Outline of Presentation

I. Background II. Objectives of the StudyIII.Conceptual FrameworkIV. Empirical ModelV. Data SourceVI.Key ResultsVII.Conclusions and Implications

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

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?

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?

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

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)

III Conceptual Framework (Cont.)

● Deaton (1989) derived the net benefit ratio (NBR) which measures the short-term welfare effects of food price changes for agricultural households as:

●

● where w is considered as a social welfare function, is social marginal utility of money, Pj is the price of food j,

yij is the production of food j by household i,

qij is the amount of food j consumed by i,

and xi is consumption expenditure for household i,

zi is relevant characteristic for household i.


● 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


● 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

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.

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

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)

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

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

Figure 1 Distribution of intensity of adoption of improved wheat varieties

0.0

05.0

1.0

15.0

2.0

25D

ens

ity

0 20 40 60 80 100Intensity of adoption of wheat varieties (percent of total wheat area)

Kernel density estimate

Normal density

kernel = epanechnikov, bandwidth = 8.3536

Figure 2 Impact of intensity of adoption of improved wheat varieties on farm households’ probability of being net buyer of wheat

0

.05

.1

.15

Pro

bab

ility

of be

ing

net b

uyer

0 20 40 60 80 100Treatment level (intensity of adoption)

Dose Response Lower bound

Upper bound

Confidence Bounds at .95 % levelDose response function = Probability of positive outcome Regression command = logit

Dose-response function

-.004

-.002

0

.002

.004

Ch

an

ge

in

pro

ba

bili

ty o

f b

ein

g n

et bu

ye

r


Treatment Effect Lower bound

Upper bound

Confidence Bounds at .95 % levelDose response function = Probability of a positive outcomeRegression command = logit

Treatment effect function

Figure 3 Impact of intensity of adoption of improved wheat varieties on farm households’ probability of being autarkic in wheat net market position

.2

.25

.3

.35

.4

Pro

bab

ility

of b

ein

g a

uta

rkic



Upper bound

Confidence Bounds at .95 % levelDose response function = Probability of positive outcomeRegression command = logit


-.005

0

.005

.01

Ch

an

ge

in p

roba

bili

ty o

f b

ein

g a

uta

rkic



Upper bound


Treatment-effect function

Figure 4 Impact of intensity of adoption of improved wheat varieties on farm households’ probability of being net seller of wheat

.5

.55

.6

.65

.7

.75

Pro

bab

ility

of be

ing

net selle

r

0 20 40 60 80 100Treatment level (Intensity of adoption)


Upper bound

Confidence Bounds at .95 % levelDose response function = Probability of positive outcomeRegression command = logit


-.01

-.005

0

.005

Ch

an

ge

in p

roba

bili

ty o

f b

ein

g n

et se

ller

0 20 40 60 80 100Treatment level


Upper bound



Figure 5 Impact of intensity of adoption of improved wheat varieties on farm households’ welfare effects of wheat price changes

0

.1

.2

.3

Ne

t be

ne

fit r

atio

0 20 40 60 80 100Treatment (intensity of adoption)


Upper bound

Confidence Bounds at .95 % levelDose response function = Linear prediction


-.01

-.005

0

.005

Ch

an

ge

in n

et b

en

efit ra

tio

0 20 40 60 80 100Treatment (intensity of adoption)


Upper bound

Confidence Bounds at .95 % levelDose response function = Linear prediction


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

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

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

Thank You

Technology

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