Climate Change and Agriculture: Change in Yields
in a global CGE MIRAGE-CC
Presented by David Laborde at the AGRODEP Workshop on Analytical Tools for Climate Change Analysis
June 6-7, 2011 • Dakar, Senegal
For more information on the workshop or to see the latest version of this presentation visit: http://www.agrodep.org/first-annual-workshop
Since July 2006, UNDP/GEF-SGP has supported 75 projects in Ethiopia, out of which, 13 of them were being implemented in Dire Dawa Administration. Taking the already phased out four community projects as a reference, this study examines the socioeconomic impacts of the program in the Administration. A cross sectional survey of 160 households (80 from the project beneficiaries and 80 from non-beneficiaries) was undertaken to examine and evaluate the impacts of the program on households’ livelihood. Descriptive statistics coupled with an econometric model was used to analyze the data collected from different sources. The descriptive analysis of this study indicates that the societies are becoming better off in their livelihood due to the existence of the project despite the fact that it is associated with many challenges. Applying a propensity score matching technique, the study has figured out that the level of monthly income, asset and monthly consumption expenditure of the program beneficiaries are higher than that of non-beneficiaries. Generally, the major findings of the study showed the average effect of the program to be positive and statistically significant; suggesting that the program has achieved its stated objectives of improving the socio economic conditions of the local community and the environment. It is also suggested that the project would have the capacity to improve the livelihood of the beneficiaries further if corrective measures are taken to tackle the challenges faced by the project.
Climate Change and Agriculture: Change in Yields
in a global CGE MIRAGE-CC
Presented by David Laborde at the AGRODEP Workshop on Analytical Tools for Climate Change Analysis
June 6-7, 2011 • Dakar, Senegal
For more information on the workshop or to see the latest version of this presentation visit: http://www.agrodep.org/first-annual-workshop
Since July 2006, UNDP/GEF-SGP has supported 75 projects in Ethiopia, out of which, 13 of them were being implemented in Dire Dawa Administration. Taking the already phased out four community projects as a reference, this study examines the socioeconomic impacts of the program in the Administration. A cross sectional survey of 160 households (80 from the project beneficiaries and 80 from non-beneficiaries) was undertaken to examine and evaluate the impacts of the program on households’ livelihood. Descriptive statistics coupled with an econometric model was used to analyze the data collected from different sources. The descriptive analysis of this study indicates that the societies are becoming better off in their livelihood due to the existence of the project despite the fact that it is associated with many challenges. Applying a propensity score matching technique, the study has figured out that the level of monthly income, asset and monthly consumption expenditure of the program beneficiaries are higher than that of non-beneficiaries. Generally, the major findings of the study showed the average effect of the program to be positive and statistically significant; suggesting that the program has achieved its stated objectives of improving the socio economic conditions of the local community and the environment. It is also suggested that the project would have the capacity to improve the livelihood of the beneficiaries further if corrective measures are taken to tackle the challenges faced by the project.
Evaluating effects of cassava, R4D effects: market participation/sales,adoption, food security, ATE > per participant costs in DRC; ATE, ATT,ATU positive in Malawi, Replication studies, strong designs in future,test different contexts,ARIs.
Can improved food legume varieties increase technical efficiency in crop prod...africa-rising
Presented by Girma T. Kassie, Aden Aw-Hassan, Seid A. Kemal, Luleseged Desta, Peter Thorne, Kindu Mekonnen and Mulugeta Yitayih, ILRI, Addis Ababa, 29 September 2016
Vegetable Nursery business is running very well in these two states and it is dominant in some particular areas of both states. Farmers who produce vegetables usually have two options – to grow sapling on their own or to buy sapling from professional nursery growers. This research project addresses the particular segment in which farmers go for nursery grown sapling and focuses on different aspects mostly business aspects. One of the most important queries has been –should this vegetable nursery be considered as a new separate segment of vegetable seed market?
India is currently second largest producer of vegetables after china. Gujarat and Maharashtra are the two states which hold top rankings in vegetable production in India. Most of the vegetables are raised as sapling and then replanted in fields. Farmers have choice either to raise sapling on their own or to buy from professional nursery businessmen. Obviously, nursery business becomes linked to vegetable seed market directly which affects the sales of vegetable seeds. Culture of buying sapling from nurserymen is not always there in all areas. Somewhere farmers do on themselves and at other place mostly they buy from nurserymen. Reason varies area by area.
A good quality sapling production is very essential for getting higher yield and quality of produce. Vegetable sapling production is a specialized activity and farmers buy the saplings from these nurseries. Vegetable sapling production system has changed in recent years in the intensive vegetables growing areas. Sapling production has come up as a specialized enterprise in these areas.
So this vegetable nursery business creates specific equations for vegetable seed market in India which has not been much focused earlier. Due to hyper competition in this industry and entry of a number of players in India, understanding nursery business becomes vital. This will help to comprehend and draw the clear picture of different stake holders, prevalent culture and practices of nursery growers which will further give path to understand business aspects and its critical issues.
Every organization wants to be leader in its market. There are lots of MNCs as well as Indian companies, in vegetable seed market. There is existence of joint ventures of MNCs and Indian companies also. The company which understands more the market will enjoy more market share.
Despite being of utmost importance, vegetable nursery business has not been focused by companies so far. There has been a lot of research on vegetable nursery practices which deal on technical aspects of growing saplings but seldom if ever, study on business aspects has been done and therefore this study will handle this issue.
Dynamic Acreage Demand and Supply Response of Farm Households in Ethiopiaessp2
International Food Policy Research Institute (IFPRI) and Ethiopian Development Research Institute (EDRI) in collaboration with Ethiopian Economics Association (EEA). Eleventh International Conference on Ethiopian Economy. July 18-20, 2013
Powerpoint by Samuel Soki Harding, Daniel Santigie Fornah, and Edward S.A. Kargbo presented at the West Africa SRI Workshop in Ouagadougou, Burkina Faso on July 26-27, 2012.
Presentation by Bernardo Strassburg, the International Institute for Sustainability, Brazil, at the 2012 Agriculture and Rural Development Day in Rio de Janiero, Learning Event No. 1, Session 1: ‘Mato Grosso: realising the land-sparing potential from increased agricultural productivity’.
Winning solutions for climate-smart dairy animal nutrition in IndiaILRI
Presented by Jimmy Smith, With contributions from: Habibar Rahman, KSV Prasad, Ravi Devulapalli, Chris Jones, Padmakumar V. at the NDDB Animal Nutrition Research Advisory Committee meeting, May 2021
Transforming Maize-legume Value Chains –A Business Case for Climate-Smart Ag...CIMMYT
CIMMYT Senior Cropping Systems Agronomist Christian Thierfelder presented on climate-smart agriculture in southern Africa in a webinar titled Climate Resilient Agriculture Success Stories – Making a Case for Scale Up.
More Related Content
Similar to Market Participation Impacts of Improved Wheat Varieties in Ethiopia: Applications of Standard and Generalized Propensity Score Matching Methods
Evaluating effects of cassava, R4D effects: market participation/sales,adoption, food security, ATE > per participant costs in DRC; ATE, ATT,ATU positive in Malawi, Replication studies, strong designs in future,test different contexts,ARIs.
Can improved food legume varieties increase technical efficiency in crop prod...africa-rising
Presented by Girma T. Kassie, Aden Aw-Hassan, Seid A. Kemal, Luleseged Desta, Peter Thorne, Kindu Mekonnen and Mulugeta Yitayih, ILRI, Addis Ababa, 29 September 2016
Vegetable Nursery business is running very well in these two states and it is dominant in some particular areas of both states. Farmers who produce vegetables usually have two options – to grow sapling on their own or to buy sapling from professional nursery growers. This research project addresses the particular segment in which farmers go for nursery grown sapling and focuses on different aspects mostly business aspects. One of the most important queries has been –should this vegetable nursery be considered as a new separate segment of vegetable seed market?
India is currently second largest producer of vegetables after china. Gujarat and Maharashtra are the two states which hold top rankings in vegetable production in India. Most of the vegetables are raised as sapling and then replanted in fields. Farmers have choice either to raise sapling on their own or to buy from professional nursery businessmen. Obviously, nursery business becomes linked to vegetable seed market directly which affects the sales of vegetable seeds. Culture of buying sapling from nurserymen is not always there in all areas. Somewhere farmers do on themselves and at other place mostly they buy from nurserymen. Reason varies area by area.
A good quality sapling production is very essential for getting higher yield and quality of produce. Vegetable sapling production is a specialized activity and farmers buy the saplings from these nurseries. Vegetable sapling production system has changed in recent years in the intensive vegetables growing areas. Sapling production has come up as a specialized enterprise in these areas.
So this vegetable nursery business creates specific equations for vegetable seed market in India which has not been much focused earlier. Due to hyper competition in this industry and entry of a number of players in India, understanding nursery business becomes vital. This will help to comprehend and draw the clear picture of different stake holders, prevalent culture and practices of nursery growers which will further give path to understand business aspects and its critical issues.
Every organization wants to be leader in its market. There are lots of MNCs as well as Indian companies, in vegetable seed market. There is existence of joint ventures of MNCs and Indian companies also. The company which understands more the market will enjoy more market share.
Despite being of utmost importance, vegetable nursery business has not been focused by companies so far. There has been a lot of research on vegetable nursery practices which deal on technical aspects of growing saplings but seldom if ever, study on business aspects has been done and therefore this study will handle this issue.
Dynamic Acreage Demand and Supply Response of Farm Households in Ethiopiaessp2
International Food Policy Research Institute (IFPRI) and Ethiopian Development Research Institute (EDRI) in collaboration with Ethiopian Economics Association (EEA). Eleventh International Conference on Ethiopian Economy. July 18-20, 2013
Powerpoint by Samuel Soki Harding, Daniel Santigie Fornah, and Edward S.A. Kargbo presented at the West Africa SRI Workshop in Ouagadougou, Burkina Faso on July 26-27, 2012.
Presentation by Bernardo Strassburg, the International Institute for Sustainability, Brazil, at the 2012 Agriculture and Rural Development Day in Rio de Janiero, Learning Event No. 1, Session 1: ‘Mato Grosso: realising the land-sparing potential from increased agricultural productivity’.
Winning solutions for climate-smart dairy animal nutrition in IndiaILRI
Presented by Jimmy Smith, With contributions from: Habibar Rahman, KSV Prasad, Ravi Devulapalli, Chris Jones, Padmakumar V. at the NDDB Animal Nutrition Research Advisory Committee meeting, May 2021
Presented by Geremew Kefyalew at his MSc defense in International Economics, Addis Ababa University, December 2012
Similar to Market Participation Impacts of Improved Wheat Varieties in Ethiopia: Applications of Standard and Generalized Propensity Score Matching Methods (20)
Transforming Maize-legume Value Chains –A Business Case for Climate-Smart Ag...CIMMYT
CIMMYT Senior Cropping Systems Agronomist Christian Thierfelder presented on climate-smart agriculture in southern Africa in a webinar titled Climate Resilient Agriculture Success Stories – Making a Case for Scale Up.
Market Participation Impacts of Improved Wheat Varieties in Ethiopia: Applications of Standard and Generalized Propensity Score Matching Methods
1. 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
2. Outline of Presentation
I. Background
II. Objectives of the Study
III.Empirical Model
IV. Data Source
V. Empirical Results
VI. Conclusions and Implications
3. 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
4. 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
5. 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
6. 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
7. 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
8. 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
9. 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 After matching
matching NNM with KBM
caliper
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
11. 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
12. Figure 1 Impact of adoption of improved wheat
varieties on farm households’ probability of being net
buyer of wheat
Dose response function Treatment effect function
.2
.2
.1
.15
.1 0
.05 -.1
0 -.2
-.05 -.3
0 1 2 3 0 1 2 3
Area under improved wheat varieties (ha) Area under improved wheat varieties (ha)
Dose Response Low bound Treatment Effect Low bound
Upper bound Upper bound
Confidence Bounds at .95 % level Confidence Bounds at .95 % level
Dose response function = Probability of a positive outcome Dose response function = Probability of a positive outcome
Regression command = logit Regression command = logit
13. Figure 2 Impact of adoption of improved wheat
varieties on farm households’ probability of being
autarkic in wheat net market position
Dose response function Treatment effect function
.6
1
.5
.4
.2
0
-.5
0
0 1 2 3 0 1 2 3
Area under improved wheat varieties (ha) Area under improved wheat varieties (ha)
Dose Response Low bound Treatment Effect Low bound
Upper bound Upper bound
Confidence Bounds at .95 % level Confidence Bounds at .95 % level
Dose response function = Probability of a positive outcome Dose response function = Probability of a positive outcome
Regression command = logit Regression command = logit
14. Figure 3 Impact of adoption of improved wheat
varieties on farm households’ probability of being net
seller of wheat
Dose response function Treatment effect function
1
.5
.8
0
.6
-.5
.4
-1
.2
0 1 2 3 0 1 2 3
Area under improved wheat varieties (ha) Area under improved wheat varieties (ha)
Dose Response Low bound Treatment Effect Low bound
Upper bound Upper bound
Confidence Bounds at .95 % level Confidence Bounds at .95 % level
Dose response function = Probability of a positive outcome Dose response function = Probability of a positive outcome
Regression command = logit Regression command = logit
15. Figure 4 Impact of adoption of improved wheat
varieties on Marketed volume of wheat
Dose response function Treatment effect function
2000 3000
1500 2000
1000 1000
500 0
0 -1000
0 1 2 3 0 1 2 3
Area under imporved wheat varieties (ha) Area under improved wheat varieties (ha)
Dose Response Low bound Treatment Effect Low 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
16. 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
17. 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)