market participation impacts of improved wheat varieties in ethiopia: applications of standard and...

Market Participation Impacts of Improved

Wheat Varieties in Ethiopia:

Applications of Standard and Generalized

Propensity Score Matching Methods

Asfaw Negassa and Bekele Shiferaw

To be Presented at Wheat for Food Security in Africa Conference

UNECA Conference Hall

October 8-12, 2012

Addis Ababa, Ethiopia

Outline of Presentation

I. Background

II. Objectives of the Study

III.Empirical Model

IV.Data Source

V. Empirical Results

VI.Conclusions and Implications

I Background: Why wheat in Ethiopia?

● Wheat is among the very important staple food crops

grown in Ethiopia

More than 4 million farm households are directly dependent on wheat

production (CSA, 2011)

Wheat is the third most important sources of per capita calorie

supply next to maize and sorghum, accounts for more than 12% the

total food calorie supply (Berhane et al., 2011)

● Wheat consumption is increasing due to increase in

population, rise in urbanization and income growth while

increase in wheat price levels and variability have been

observed

● Given, wheat’s strategic importance in the national

economy, the Ethiopian government has been making large

investment in the development and extension of improved

wheat technologies—several improved varieties have been

released

I Background (Cont.)

● However, the market participation and commercialization

impacts of the adoption of improved wheat varieties has not

been explored so far

Lack of evidence regarding to what extent past research and

development efforts has helped the wheat producers to participate in the

market and in generating marketable wheat quantities—interaction

between technological change and market participation

● This has implications for the government’s effort to stimulate

wheat production through the adoption of improved wheat

varieties to generate increased marketed volume of wheat to

feed the growing urban population under the current conditions

of increasing wheat prices

II Objectives of the Study

The major objective of this study was to estimate the impact of adoption of improved wheat varieties on market participation and marketed volume of wheat for wheat producers in Ethiopia

Specific objectives:

1) To determine the difference in the effect 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 marketed volume of wheat

2) To determine the impact of area under improved wheat varieties on the extent of market participation and marketed volume of wheat among adopters

III Empirical Model

● The key challenge in empirical impact evaluation using

observational studies is how to obtain unbiased treatment

effect in the presence of confounding factors which could

affect both the chances of receiving the treatment and the

outcome itself

bias 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

Treatment Outcome

Confounding factors

III Empirical Model (Cont.)

● Quasi-experimental methods developed to provide adequate covariate balance between treated and control groups—create adequate counterfactual comparison groups for the treated groups so that any difference between the treated and control groups is due to the treatment effect

● Two methods used

Propensity score matching (PSM) method (Rosenbaum and Rubin, 1983) –to see the treatment effect difference between adopters and non-adopters

Generalized propensity score matching (GPSM) method (Imbens, 2000; Hirano and Imbens, 2004) —to see the treatment effect difference among the adopters due to differential levels of technology use

IV Data Sources

● Data: For this study, cross-sectional survey data involving

nationally representative 2096 sample farm households

randomly selected from eight major wheat growing agro-

ecological zones of Ethiopia

● Covariates:

Household head characteristics (age, sex and education)

Household characteristics (family size and dependence ratio)

Household resources (land and cattle)

Institutions (access to formal and informal financial services)

Agroecological zones

V Empirical Results

A Results of PSM ● PS Matching quality (adequacy of counterfactual

comparison group) T-test of mean difference for individual covariates between

treated and control groups before and after matching Before matching – significant in 5 of 20 cases After matching significant only in 2 of 20 cases

Overall covariate balance test

Criteria Before

matching

After matching

NNM with

caliper

KBM

Pseudo R2 0.043 0.005 0.017

LR χ2 97.64 14.2 25.73

P-value χ2 0.000 0.819 0.138

Mean bias 7.8 2.8 4.2

Percent bias reduction 64 46

Impacts of adoption of improved wheat varieties on market participation

Outcome variable by

matching algorithm

Estimated outcome Average treatment effect (ATT)

Treated Controls Point estimate 95% confidence

interval

Unmatched comparison

Net buyer (%) 7 8 -1 --

Autarky (%) 26 42 -15 --

Net seller (%) 66 49 16 --

Marketed volume (kg) 367 163 204

NNM method

Net buyer (%) 7 9 -2 (2) -5 - 2

Autarky (%) 26 38 -11(3)*** -19 - (-5)

Net seller (%) 66 52 14(4)*** 5 - 22

Marketed volume (kg) 360 166 194 (28)*** 139 - 250

KBM method

Net buyer (%) 6 10 -4(3) -9 - 1

Autarky (%) 26 38 -11(4)*** -20 - (-3)

Net seller (%) 67 52 15(5)*** 5 - 25

Marketed volume (kg) 355 162 193(43)*** 109 - 277

B Results of GPSM

● GPS matching quality

Covariate balance violated in 27% of the cases before

matching

Covariate balance violated in 11% of the cases after

matching

● Dose-response functions

● Treatment effect functions

Figure 1 Impact of adoption of improved wheat

varieties on farm households’ probability of being net

buyer of wheat

-.05

0

.05

.1

.15

.2

Pro

ba

bili

ty o

f be

ing

ne

t bu

yer

of

whe

at

0 1 2 3Area under improved wheat varieties (ha)

Dose Response Low bound

Upper bound

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

Dose response function

-.3

-.2

-.1

0

.1

.2

Marg

inal c

han

ge

in p

roba

bili

ty o

f b

ein

g n

et buye

r o

f w

hea

t


Treatment Effect Low bound

Upper bound


Treatment effect function


varieties on farm households’ probability of being

autarkic in wheat net market position

0

.2

.4

.6

Pro

babi

lity

of b

eing

aut

arki

c



Upper bound



-.5

0.5

1

Mar

gin

al c

han

ge o

f pro

bab

ility

of b

ein

g a

utar

kic



Upper bound




varieties on farm households’ probability of being net

seller of wheat

.2

.4

.6

.8

1

Pro

babili

ty o

f bein

g n

et se

ller

0 1 2 3

Area under improved wheat varieties (ha)


Upper bound



-1-.

50

.5

Marg

inal c

han

ge

in p

roba

bili

ty o

f b

ein

g n

et se

ller

0 1 2 3



Upper bound




varieties on Marketed volume of wheat

0

500

1000

1500

2000

Mark

ete

d v

olu

me o

f w

heat (k

g)

0 1 2 3Area under imporved wheat varieties (ha)


Upper bound

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


-1000

0

1000

2000

3000

Marg

inal c

hange in

mark

ete

d v

olu

me o

f w

heat (k

g)

0 1 2 3



Upper bound

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


VI Conclusions and Policy Implications

● Significant difference between adopters and non-

adopters in terms of their market participation and

marketed volume of wheat

● Increasing the 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

and increases the market supply of wheat

● The results provide strong evidence for positive but

heterogeneous effects of adoption of improved wheat

varieties on farm households net market position and

marketed volume of wheat

VI Conclusions and Policy Implications (Cont.)

● Thus, given the current level of adoption of improved wheat

varieties at less than 70% among the farm households and

actual wheat area under improved varieties is also low, there is

a need to improve the farm households’ level 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 adoption and leads to inaccurate impact

estimates and wrong conclusions and implications –impact

varies by adoption status and level of adoption (area of

wheat under improved wheat varieties)

Thank You

market participation impacts of improved wheat varieties in ethiopia: applications of standard and...

Documents

major wheat

wheat producers

wheat consumption

wheat prices

net seller of wheat

wheat price levels

wheat production csa

matching nnm