2.3 Methodology
2.3.3 Empirical Strategy
There are three issues we aim to analyze: first, determinants of the existence of information links; second, effects of having information links with farmers who previously supplied high-value markets (HVM) on farmers’ own probability of HVM participation; and third, the effects of having information links with farmers who previously supplied HVM on farmer’s own participation dynamics including joining and dropping from HVM.
Chapter 2. Informal Information Networks and Smallholder Participation in High-Value Markets
Analyzing Determinants of the Existence of Information Links
To empirically analyze determinants of existence of information links between a dyad of farmers, we follow Fafchamps and Gubert (2007) with adjustments to suit our study, and estimate the following model:
𝐿𝑖𝑗(2012)= 𝛽 + 𝛼1 (𝑥𝑖 − 𝑥𝑗)(2008)+ 𝛼2 (𝑥𝑖+ 𝑥𝑗)(2008)+ 𝛾𝑤𝑖𝑗(2008) +
𝜌𝑀𝑖𝑗(2008) + 𝜀𝑖𝑗 (2.4)
where Lij denotes the probability of existence of an information link between individuals i and j. The dyadic relationship is directional and therefore 𝐿𝑖𝑗 does not have to equal 𝐿𝑗𝑖. xi
and xj are characteristics of individuals i and j that are likely to influence probability of existence of a link, including the social distance characteristics. Since Lij is directional, regressors 𝑥𝑖 − 𝑥𝑗 enters the regression as such, not in absolute value (Fafchamps and Gubert, 2007). Parameter 𝛼1 measures effect of differences in attributes on 𝐿𝑖𝑗 while 𝛼2 measures effect of combined level of xi and xj on Lij. Variable 𝑤𝑖𝑗 captures link attributes of dyad i and j, including geographical distance, whereas 𝑀𝑖𝑗 denotes supply channel variables. All the explanatory variables are lagged to 2008 to avoid reverse causality.
Parameter 𝜌 will show the effect of past choice of supply channel of the dyads on probability of existence of an information link (𝐿𝑖𝑗). Finally, 𝜀𝑖𝑗 is the error term.
A potential problem of estimating equation (2.4) is that the error terms are likely to be inconsistent due to cross-observation correlation in the error terms involving same individuals. It is possible that at one time the respondent is the individual i, and in another instance the same respondent is identified as individual j. Therefore, there is need to correct the standard errors. Since our data were collected differently from Fafchamp and Gubert (2007), we are not able to follow their standard error correction method.
Therefore, we cluster the standard errors of the probit model based on farmers i and j following Petersen (2009).
This probit model will show determinants of existence of an information link. To understand the effects of information links on HVM participation, we undertake further analysis as explained in the following.
Analyzing Determinants of Participation in High-Value Markets
As discussed in the conceptual framework, farm households’ decision on the choice of the supply channel is an individual decision based on utility derived from each channel, and each household will choose to participate in the supply channel with the highest utility. Therefore, participation in HVM can be specified as follows:
𝐻 (2012) = 𝛽𝑍(2008)+ 𝛼𝑁(2012)+ 𝛾𝑂(2012)+ 𝜇 (2.5)
where H(2012) is a dummy variable equal to one if the household supplied HVM in 2012, and zero otherwise; Z(2008) is a vector of explanatory variables that we lag to 2008 to avoid reverse causality; N(2012) captures “HVM information link within sample”; a binary variable which is equal to one if the main person in the household responsible for vegetable production and marketing talked to at least one social network member about vegetable marketing options, and zero otherwise. The social network member came from our sample and had to have supplied HVM in 2008. 𝑂(2012) denotes “HVM information link outside sample”. This is also a binary variable which is equal to one if the main person in the household responsible for vegetable production and marketing talked to at least one other farmer currently supplying HVM, about vegetable marketing options, and zero otherwise. This refers to farmers other than those already randomly sampled and matched with the respondent. 𝛼 and 𝛾 are the parameters of interest, which show the effects of HVM information links on participation in HVM. 𝛽 is a vector of other parameters to be estimated, and 𝜇 captures stochastic disturbances, assumed to be normally distributed.
We draw on existing literature to identify explanatory variables to be included under Z. Previous studies have identified farmer characteristics such as age, gender, and education level; and physical capital as important determinants of supplying HVM (Hernández et al., 2007; Neven et al., 2009; Rao & Qaim, 2011; Andersson et al., 2015).
We also control for traders (proxy for other sources of market information) as farmers may receive vegetable marketing information from other sources than informal social networks. We include distance to tarmac road as a measure of infrastructure conditions.
Farmers who live close to tarmac roads may have easy access to transport hence easily market their produce compared to those living deep inside the villages (Hernández et al.,
Chapter 2. Informal Information Networks and Smallholder Participation in High-Value Markets
2007; Michelson, 2013). Furthermore, they may also easily receive more information about other marketing options. Finally, we include the region dummies to capture possible regional effects.
Analyzing Determinants of Dynamics of Participation in High-Value Markets Our third objective is to estimate effect of having information links with farmers that previously supplied high-value markets (HVM), on farmer’s own participation dynamics.
If farm households are faced with a decision to participate in HVM or traditional markets (TM) over two time period, they are likely to fall into four possible categories: Category 1=the household supplies HVM in both periods (HVM stayer); category 2=the household supplies TM in the first period and HVM in the second period (HVM newcomer);
category 3=the household supplies HVM in the first period and TM in the second period (HVM dropout) and category 4= the household supplies TM in both periods (TM stayer).
The probability that one alternative is chosen is the probability that the utility of that alternative exceeds the utility of all other available alternatives.
The choice of supply channel over the two time periods may be influenced by access to information on supplying HVM. As discussed in section 2.2, supplying HVM may require more information than supplying TM. We analyze the effect of having information links with previous HVM farmers on own participation dynamics using two information link variables (“HVM link within sample” and “HVM link outside sample”).
All other control variables discussed under determinants of supplying HVM are also used to analyze participation dynamics. We undertake our dynamic analysis using a multinomial logit model (Greene, 2008).