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Impact of innovation platforms on marketing relationships the case of volta basin integrated crop livestock value chains in ghana
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Impact of innovation platforms on marketing relationships the case of volta basin integrated crop livestock value chains in ghana

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by A. Zewdie, J. Cadilhon and C. Werthmann …

by A. Zewdie, J. Cadilhon and C. Werthmann
Presented at the Final Volta Basin Development Challenge Science Workshop, September 2013

Published in: Technology, Business

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  • 1. A Partner of Impact of Innovation Platforms on Marketing Relationships: The Case of Volta Basin Integrated Crop-Livestock Value Chains in Ghana Volta Basin Development Challenge Final Scientific Workshop Ouagadougou, Burkina Faso 17-19 September, 2013 Zewdie A., J. Cadilhonand C. Werthmann
  • 2. Andes • Ganges • Limpopo • Mekong • Nile • Volta Content 1. Main message 2. Background and Purpose 3. Methodology 4. Results 5. Lessons 6. Recommendations 2
  • 3. Andes • Ganges • Limpopo • Mekong • Nile • Volta Main messages  The IPs have created an additional option for value chain actors  The interaction through the platforms contributes to reduction in transaction costs and improvement in access to markets  No sizable market niche has been created although there is a potential for the future 3
  • 4. Andes • Ganges • Limpopo • Mekong • Nile • Volta 1. Background and purpose  The changing nature of Agricultural Research  Agricultural innovations as multi-dimensional and co-evolutionary processes  technological, organizational and institutional innovations  creating synergies  “convergence of agricultural sciences” (Huis et al. 2007)  Growing use of Innovation Platform (IP) approaches 4
  • 5. Andes • Ganges • Limpopo • Mekong • Nile • Volta What is an Innovation Platform? Source: PAEPARD, October 2012 IP meeting in Lawra, June 27, 2013 5
  • 6. Andes • Ganges • Limpopo • Mekong • Nile • Volta The Volta2 Innovation Platforms  Ghana Volta2 IPs were established in July 2011 Main goals of the Volta2 IP project:  to facilitate communication and collaboration among actors  to support value chains development  to serve as spaces for Participatory Action Research (PAR) 6
  • 7. Andes • Ganges • Limpopo • Mekong • Nile • Volta Purpose of the study  to assess the interrelationships between various actors  to investigate the impact of communication and information sharing on market access  test a new conceptual framework for evaluation of IPs 7
  • 8. Andes • Ganges • Limpopo • Mekong • Nile • Volta Why to use a new framework? Limitations with conventional methods  difficulty to check cause-effect relationships  only few econometric methods E.g. DID and IV methods, but not applied to IPs (and use control groups)  Statistics and record on agricultural data in LDCs is poor  Perception of respondents based on Likert-scale as an alternative? 8
  • 9. Andes • Ganges • Limpopo • Mekong • Nile • Volta 2. Methodology Data  Four communities (two IPs)  Lawra: Upper West region  Orbilli and Naburinye  Tolon-Kumbungu: Northern region  Digu and Golinga  43 IP members  34 farmers, 6 traders, 3 processors  9 key respondents Source: Diamenu and Nyaku 1998 9
  • 10. Andes • Ganges • Limpopo • Mekong • Nile • Volta Data cont…  Socio-economic information and 5 point Likert-scale data from IP members (1 = strongly disagree, 2 = disagree, 3 = undecided, 4 = agree, 5 = strongly agree)  Assumption: 5-4 = 4-3 = 3-2 = 2-1 10 Simplifying the Likert- scale, to ensure better response quality
  • 11. Andes • Ganges • Limpopo • Mekong • Nile • Volta Method of analysis Mixed methods using a new framework Qualitative Quantitative  response summaries and averages on various statements  factor analysis on the Likert-scale variables  Principal components factor  regression  Ordinary Least Squares (OLS) 11
  • 12. Andes • Ganges • Limpopo • Mekong • Nile • Volta The conceptual framework  Based on a mix of concepts from various disciplines  Industrial Economics Structure-Conduct-Performance (SCP) hypothesis  New Institutional Economics of Markets transaction costs governance structures  Concepts from the marketing literature value chain relationship 12
  • 13. Andes • Ganges • Limpopo • Mekong • Nile • Volta  “A priori linear relationship between Structure, Conduct and Performance” The SCP hypothesis 13
  • 14. Andes • Ganges • Limpopo • Mekong • Nile • Volta The regression model 14
  • 15. Andes • Ganges • Limpopo • Mekong • Nile • Volta Explanation of terms 15
  • 16. Andes • Ganges • Limpopo • Mekong • Nile • Volta 4. Results 16
  • 17. Andes • Ganges • Limpopo • Mekong • Nile • Volta 17 More descriptive
  • 18. Andes • Ganges • Limpopo • Mekong • Nile • Volta  Three factors* on communication and information sharing  Four factors* on market access Results of the factor analysis *Based on the Kaiser criteria is used 18
  • 19. Andes • Ganges • Limpopo • Mekong • Nile • Volta Results for regression equation 1 Regression Equation Dependen t Variable Explanatory variables Coefficient Beta t P>|t| 1 factor11 IP 0.0638 (0.617) 0.032 0.10 0.918 gender 0.2767 (0.349) 0.139 0.79 0.436 lnnbhous -0.0182 (0.542) -0.007 -0.03 0.973 age -0.0014 (0.011) -0.020 -0.13 0.896 incestm -0.0003 (0.000) -0.240 -1.19 0.247 focq50i 0.5782 (0.255) 0.365** 2.26 0.032 factor1 0.1543 (0.256) 0.156 0.60 0.553 factor2 -0.0642 (0.231) -0.069 -0.28 0.783 factor3 -0.1543 (0.619) -0.157 -0.95 0.349 constant -2.1627 (1.472) . -1.47 0.154 - Robust) standard errors are shown in brackets and betas are standardised coefficients. - * and ** represent statistical significance of betas at 1% and 5% levels of significance - focq50i = individual statement about improvement in communication in the past 2 years 19
  • 20. Andes • Ganges • Limpopo • Mekong • Nile • Volta Results for regression equation 2 - Robust) standard errors are shown in brackets and betas are standardised coefficients. - * and ** represent statistical significance of betas at 1% and 5% levels of significance - focq50i = individual statement about improvement in communication in the past 2 years Regression Equation Dependent Variable Explanatory variables Coefficient Beta t P>|t| 2 factor12 IP 0.6432 (0.443) 0.324 1.45 0.159 gender -0.2612 (0.296) -0.131 -0.88 0.387 lnnbhous 0.1248 (0.242) 0.054 0.51 0.611 age -0.0122 (0.012) -0.169 -0.96 0.344 incestm -0.0001 (0.000) -0.026 -0.17 0.867 focq50i -0.1968 (0.258) -0.124 -0.76 0.453 factor1 0.2535 (0.2252) 0.257 1.13 0.271 factor2 0.3339 (0 .117) 0.359* 2.84 0.009 factor3 -0.0460 ( 0.142) -0.047 -0.32 0.749 constant 1.068 (1.3416) . 0.80 0.433 20
  • 21. Andes • Ganges • Limpopo • Mekong • Nile • Volta Results for regression equation 3 - Robust) standard errors are shown in brackets and betas are standardised coefficients. - * and ** represent statistical significance of betas at 1% and 5% levels of significance - focq50i = individual statement about improvement in communication in the past 2 years Regression Equation Dependent Variable Explanatory variables Coefficient Beta t P>|t| 3 factor13 IP -0.3810 (0.553) -0.192 -0.69 0.497 gender -0.8305 (0 .381) -0.418** -2.18 0.039 lnnbhous 0.0440 (0.422) -0.418 0.10 0.918 age -0.0228 (0.014) 0.019 -1.55 0.132 incestm 0.0002 (0.000) -0.314 0.71 0.486 focq50i 0.3342 (0.322) 0.122 1.04 0.308 factor1 0.3007 (0.272) 0.305 1.10 0.279 factor2 0.0222 (0.162) 0.023 0.14 0.892 factor3 -0.1405 (0.223) -0.143 -0.63 0.534 constant 0.01651 (1.864) . 0.01 0.993 21
  • 22. Andes • Ganges • Limpopo • Mekong • Nile • Volta Results for regression equation 4 - Robust) standard errors are shown in brackets and betas are standardised coefficients. - * and ** represent statistical significance of betas at 1% and 5% levels of significance - focq50i = individual statement about improvement in communication in the past 2 years Regression Equation Dependent Variable Explanatory variables Coefficient Beta t P>|t| 4 factor14 IP 1.8330 (0.402) 0.923* 4.55 0.000 gender -0.2039 (0.297) -0.102 -0.69 0.499 lnnbhous -1.0078 (0.429) -0.438** -2.35 0.027 age 0.0123 (0.011) 0.170 1.14 0.265 incestm 0.0006 (0.000) 0.449* 3.02 0.006 focq50i -0.0157 (0.228) -0.009 -0.07 0.946 factor1 -0.1235 (0.165) -0.125 -0.75 0.463 factor2 -0.3224 (0.137) -0.347** -2.35 0.027 factor3 -0.0318 (0.118) -0.032 -0.27 0.790 constant 0.4784 (1.324) . 0.36 0.721 22
  • 23. Andes • Ganges • Limpopo • Mekong • Nile • Volta Regression results  Improvement in access to input and output markets is related to improvements in overall communication or interaction  Women have better access to market than men  Participants in Tolon-Kumbungu IP have better market access than those in Lawra: same information from baseline survey?  The higher the annual income the higher the level of market access  (log) household size (local indicator of wealth) is negatively related to market access  Use of the media reduces the likelihood of bypassing intermediaries although it improves access to market information and hence to markets 23
  • 24. Andes • Ganges • Limpopo • Mekong • Nile • Volta Results from qualitative information  Improved knowledge because of trainings and meetings  trainings by urban traders and processors to their rural counterparts and to farmers  value chain training by a marketing expert from ILRI  Knowledge on price standardization, commercialization and use of weighing scales  New, but limited additional market options because of the IP  members met potential trade partners due to the IP meetings  farmers and traders check prices before engaging in transactions 24
  • 25. Andes • Ganges • Limpopo • Mekong • Nile • Volta Results from qualitative information cont…  Improved knowledge on crop and livestock production  Improved post-harvest management and timing of sale  Knowledge on cooperatives Shelter for small ruminants, constructed after a training under the IP project : at Digu village 25
  • 26. Andes • Ganges • Limpopo • Mekong • Nile • Volta 5. Lessons  The IPs played a ‘role’ in improving communication and information sharing and opened new options Proximity to market centers and level of income of the members seems to be key determinants of access to market So, were the IPs helpful to the communities?  It is not time to judge whether the IPs had significant impact 26
  • 27. Andes • Ganges • Limpopo • Mekong • Nile • Volta 6. Recommendations  IPs should not be left alone at this stage  further education on commercial production, marketing, record keeping, cost benefit analysis and business plan development  more effort to link the value chain actors to engage in commercial transaction and hence creating new markets  further efforts on cooperative formation  linking them to other development partners  Evaluating the overall impact on livelihoods is also required 27
  • 28. Andes • Ganges • Limpopo • Mekong • Nile • Volta Annex 28
  • 29. Andes • Ganges • Limpopo • Mekong • Nile • Volta 29 Name of factor Statements contributing to the variances in the respective factors representing communication and information sharing Remark (assigning name to the factors) Factor1 I exchange information with my value chain partners about my on-going activities Information sharing? My value chain partners exchange information about their on-going activities with me Factor2 I listen to weekly radio announcements to get market information Using media to acquire information/better communication?I am satisfied with the quality of communication I was having with my business partners in the last two years Factor3 I am satisfied with the communication frequency I had with value chain actors in recent business relationships Frequent communication to obtain market information? I ask relatives and friends in the village for market information Underlying factors for communication and information sharing
  • 30. Andes • Ganges • Limpopo • Mekong • Nile • Volta 30 5 5 5 5 5 4 5 5 4 4 4 5 4 4 5 5 5 5 5 4 5 5 4 5 3 4 4 1 2 1 5 3 5 3 4 4 3 4 3 4 4 3 4 5 4 5 5 5 4 4 5 4 5 4 5 5 4 5 5 5 5 5 4 5 4 4 4 4 1 4 3 1 2 4 3 5 3 4 4 3 3 3 4 4 3 4 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 Relationship between responses on statements 28_a (I exchange information with my VC partners about my ongoing activities) and 28_b (my VC partners exchange information about their ongoing activities with me) which together constitute Factor1 (information 28_a 28_b
  • 31. Andes • Ganges • Limpopo • Mekong • Nile • Volta 31 Name of factor Statements contributing to the variances in the respective factors representing market access Remark (assigning name to the factors) Factor11 The number of marketing companies buying products from the villagers has increased in the past two years Improved access to input and output markets?My access to input markets has improved in the past two years My access to output market has improved in the past two years Factor12 Information on the market is easily accessible to value chain actors Better access to market information?Farmers in the IP negotiate with buyers as a group Factor13 I can now better negotiate market prices than two years ago Improved negotiation for better price? I am satisfied by the prices I get from my customers for my products Factor14 I sell my output directly to processers or consumers Bypassing market intermediaries?There is a ready market for farm produce during harvesting seasons in my area Underlying factors for market access
  • 32. Andes • Ganges • Limpopo • Mekong • Nile • Volta 32 5 4 5 5 4 4 5 4 5 4 4 5 5 4 3 4 4 4 4 4 4 3 5 5 5 5 4 3 5 4 5 3 4 2 4 4 4 4 4 5 4 4 4 5 3 5 5 5 4 5 4 5 5 5 5 4 4 3 4 2 4 1 4 4 5 4 5 5 5 5 4 4 4 4 4 4 4 4 2 4 4 4 5 3 5 4 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 Relationship between responses FocQG_55m (my access to input market has improved in the past two years) and FocQG_55n (my access to output market has improved in the past two years) FocQG_55m FocQG_55n
  • 33. Andes • Ganges • Limpopo • Mekong • Nile • Volta 33 Factor analysis Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy Bartlett’s test of sphericity Cronbach’s Alpha Chi-square p-value Conduct 0.748 142.887* 0.000 0.81 Performance 0.641 93.161* 0.000 0.72 H0: variables are not intercorrelated • * implies that the test rejects the null hypothesis at the 1% level of significance • KMO > 0.6; Cronbach’s alpha > 0.7 and P-value < 0.05 for Bartlett’s tests all suggest that conducting factor analysis is appropriate both for Conduct and Performance indicators Tests of factorability and reliability
  • 34. Andes • Ganges • Limpopo • Mekong • Nile • Volta 34 Variable VIF Tolerance = 1/VIF Variable VIF Tolerance = 1/VIF IP 2.84 0.352489 lnnbhous 1.24 0.808407 factor1 2.60 0.385266 age 1.22 0.822854 factor2 1.40 0.712389 incestm2 1.21 0.823299 gender 1.32 0.757049 factor3 1.13 0.888709 focq50i 1.29 0.776226 Mean VIF = 1.58 VIF < 5 implies absence of serious multicollinearity problem Multicollinearity test for explanatory variables using VIF
  • 35. Andes • Ganges • Limpopo • Mekong • Nile • Volta 35 Shapiro-Wilk W test for normality Breusch-Pagan / Cook-Weisberg test Variable W V Z P>Z Variable chi2(1) P>chi2 Resid1 0.957 1.794 1.235 0.108 fitted values of factor11 0.38 0.539 Resid2 0.946 2.256 1.719** 0.042 fitted values of factor12 3.99** 0.045 Resid3 0.941 2.434 1.880** 0.030 fitted values of factor13 1.32 0.249 Resid4 0.964 1.464 0.805 0.210 fitted values of factor14 0.16 0.687 Ho: error term is normally distributed Ho: dependent variable has constant variance - ** implies that the test rejects the null hypothesis at the 5% level of significance. - Resid refers to the residuals of the corresponding regression equations - No serious deviation from normality when the 1% level of significance is considered Tests of variance equality and residual normality in each of the four equations
  • 36. Andes • Ganges • Limpopo • Mekong • Nile • Volta 36 Regression Equation no. Dependent Variable F-value Prob > F R-squared 1 factor11 0.35 0.7920 0.3324 2 factor12 0.54 0.6584 0.5078 3 factor13 0.43 0.7315 0.2792 4 factor14 0.42 0.7396 0.5264 Ho: model has no omitted variables  All the four models are well specified, no serious problem of omitted variables  R-square is quite low particularly in equation 3 Ramsey regression equation error specification test (RESET) and overall fit of the models
  • 37. Andes • Ganges • Limpopo • Mekong • Nile • Volta Pictures from focus group discussions 37
  • 38. Andes • Ganges • Limpopo • Mekong • Nile • Volta Thank you! Also credit to the contributors, CPWF, CSIR-ARI, SNV, ILRI and others 38