Market Participation Impacts of Improved      Wheat Varieties in Ethiopia:Applications of Standard and Generalized  Propen...
Outline of PresentationI. BackgroundII. Objectives of the StudyIII.Empirical ModelIV. Data SourceV. Empirical ResultsVI. C...
I     Background: Why wheat in Ethiopia?● Wheat is among the very important staple food crops  grown in Ethiopia  More th...
I       Background (Cont.)● However, the market participation and commercialization  impacts of the adoption of improved w...
II     Objectives of the StudyThe major objective of this study was to estimate the impact ofadoption of improved wheat va...
III     Empirical Model● The key challenge in empirical impact evaluation using  observational studies is how to obtain un...
III    Empirical Model (Cont.)● Quasi-experimental methods developed to provide  adequate covariate balance between treate...
IV      Data Sources● Data: For this study, cross-sectional survey data involving  nationally representative 2096 sample f...
V      Empirical Results  A   Results of PSM● PS Matching quality (adequacy of counterfactual  comparison group)  T-test ...
Impacts of adoption of improved wheat varieties on market participationOutcome variable by      Estimated outcome       Av...
B     Results of GPSM● GPS matching quality    Covariate balance violated in 27% of the cases before     matching    Cov...
Figure 1                   Impact of adoption of improved wheat                           varieties on farm households’ pr...
Figure 2                  Impact of adoption of improved wheat                          varieties on farm households’ prob...
Figure 3                 Impact of adoption of improved wheat                         varieties on farm households’ probab...
Figure 4               Impact of adoption of improved wheat                       varieties on Marketed volume of wheat   ...
VI    Conclusions and Policy Implications● Significant difference between adopters and non-  adopters in terms of their ma...
VI    Conclusions and Policy Implications (Cont.)● Thus, given the current level of adoption of improved wheat  varieties ...
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Market Participation Impacts of Improved Wheat Varieties in Ethiopia: Applications of Standard and Generalized Propensity Score Matching Methods

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Presentation by Dr. Asfaw Negassa (CIMMYT, Ethiopia) at Wheat for Food Security in Africa conference, Oct 9, 2012, Addis Ababa, Ethiopia.

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Market Participation Impacts of Improved Wheat Varieties in Ethiopia: Applications of Standard and Generalized Propensity Score Matching Methods

  1. 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. 2. Outline of PresentationI. BackgroundII. Objectives of the StudyIII.Empirical ModelIV. Data SourceV. Empirical ResultsVI. Conclusions and Implications
  3. 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. 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. 5. II Objectives of the StudyThe major objective of this study was to estimate the impact ofadoption of improved wheat varieties on market participationand marketed volume of wheat for wheat producers in EthiopiaSpecific 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 wheat2) To determine the impact of area under improved wheat varieties on the extent of market participation and marketed volume of wheat among adopters
  6. 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. 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. 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. 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
  10. 10. Impacts of adoption of improved wheat varieties on market participationOutcome variable by Estimated outcome Average treatment effect (ATT)matching algorithm Treated Controls Point estimate 95% confidence intervalUnmatched comparison Net buyer (%) 7 8 -1 -- Autarky (%) 26 42 -15 -- Net seller (%) 66 49 16 -- Marketed volume (kg) 367 163 204NNM 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 - 250KBM 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
  11. 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. 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. 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. 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. 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. 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. 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)
  18. 18. Thank You

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