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

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

Andes • Ganges • Limpopo • Mekong • Nile • Volta
Content
1. Main message
2. Background and Purpose
3. Methodology
4. Results
5. Lessons
6. Recommendations
2

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

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

What is an Innovation Platform?
Source: PAEPARD, October 2012
IP meeting in Lawra, June 27, 2013
5

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

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

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

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

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

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

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

 “A priori linear relationship between Structure, Conduct and Performance”
The SCP hypothesis
13

The regression model
14

Explanation of terms
15

4. Results
16

17
More descriptive

 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

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

Regression
Equation
Dependent
Variable
Explanatory
variables
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

Regression
Equation
Dependent
Variable
Explanatory
variables
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

Regression
Equation
Dependent
Variable
Explanatory
variables
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

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

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

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

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

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

Annex
28

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
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
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
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
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
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
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
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

Pictures from focus group discussions
37

Thank you!
Also credit to the contributors, CPWF,
CSIR-ARI, SNV, ILRI and others
38

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

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