Robustness Tests

In this sub-section, we carry out additional analyses to test the robustness of our results.

To start with, we undertake further analysis to find out whether matching farmers with different number of matches could bias our results. As discussed in section 2.4, not all farmers were matched with seven matches. Descriptive statistics on number of matches randomly allocated to each farmer show that 79% of the farmers were matched with five to seven farmers, whereas 21% were matched with less than five farmers. As a robustness check, we dropped all individual farmers matched with less than five matches (21%) and undertook analysis on the remaining sample of 700 dyads. Results of the reduced sample are very similar to those of the full sample. The two information link variables that had a positive and significant effect in the full sample also have a positive and significant effect in the reduced sample. The first information link variable “Both i& j are HVM farmers”

has a coefficient of 0.55 whereas “Farmer i is HVM, j is TM” has a coefficient of 0.43.

The magnitudes of both coefficients are very close to those from full sample (Table 2.2).

In the second robustness test, we are interested in finding out whether there are farmers who were only matched with TM-supplying farmers and not a single HVM-supplying farmer. If there are, this could have an influence on whether such households could have HVM information links or not. But could this be driving our results on effect of HVM information links on farmer’s own participation? Indeed we find 95 households

were not matched with any HVM-supplying farmer. As robustness test, we dropped these households and undertook our analysis with the reduced sample. Results of the probit model are shown in Table 2.5. Both information link variables have a positive and significant effect on participation in HVM. The magnitude of the marginal effects are also very close to those from the full sample (Table 2.3).

Table 2.5.Effect of HVM information links on probability of supplying HVM: Probit model results with reduced sample

Marginal effects

Robust std.

errors

HVM link within sample 0.118^*** (0.041)

HVM link outside sample 0.183^*** (0.043)

Trader as source of information 0.018 (0.038)

Complain of vegetable rejection 0.040 (0.040)

Participation in NGO activities 0.054 (0.051)

Male farmer 0.095^** (0.047)

Farmer education 0.014^** (0.007)

Age of farmer 0.014 (0.011)

Age of farmer squared -0.000 (0.000)

Household size 0.000 (0.012)

Off farm income 0.096^** (0.041)

Electricity access 0.151^*** (0.056)

Farm size (acres) 0.015^* (0.008)

Livestock ownership -0.035 (0.055)

Irrigation technology 0.026 (0.066)

Own means of transport -0.014 (0.060)

Access to credit -0.119^** (0.057)

Distance to the tarmac road -0.034^*** (0.009)

Limuru region -0.145^* (0.088)

Kikuyu/westlands/dagoretti region 0.051 (0.063)

Number of observations 236

Chi² 80.48***

Notes: ^*p< 0.10, ^**p< 0.05, ^***p< 0.01; HVM, high-value market; Households that were not matched with any HVM-supplying households are excluded in this analysis; All variables are lagged to 2008 except for the two information link variables, where we use 2012 data, because these data were not collected in 2008

Finally, we seek to find out whether the information link variables could be endogenous hence biasing the probit model results on effects of HVM information links on farmer’s own participation. One can think of various possible ways in which the information link variables could be potentially endogenous. Social interactions are symmetrical such that farmer i’s behavior affects the behavior of the network member, and vice versa. Moreover, a farmer could get to know a network member because they share similar characteristics or they supply in the same supply channel and therefore self-select into a specific social network group. Finally, unobserved attributes such as similar

Chapter 2. Informal Information Networks and Smallholder Participation in High-Value Markets

preferences and environment related factors affecting both the decision maker being modeled as well as the behavior of farmers in his/her network could also lead to biased estimates. In our probit model analysis, we have included various variables including soil characteristics, to control for such confounding factors. Nevertheless, we undertake further analysis to check for endogeneity of the information link variables as robustness check of our results.

We employ instrumental variable (IV) approach to address both observed and unobserved bias. The challenge of using IV approach is in finding valid instruments.

Instruments need to be exogenous, correlated with the endogenous variable, and uncorrelated with the outcome variable, i.e., the supply channel. We use various instruments that we believe to be valid as explained below.

As instrument for the first information link variable “HVM link within sample”

we use gender (male dummy) and average age (years) of the information network members. Characteristics of neighbors have recently been used as instrument for social capital (Andersson et al., 2015). In this recent article, the authors give an extensive discussion on why neighbor characteristics are not likely to affect farmers’ choice of supply channel directly, but indirectly through neighbors. Previous research show that farmer characteristics influence own choice of supply channel (Neven et al., 2009).

However, social network research show that characteristics of neighbors are not likely to affect farmer’s own participation decision (Matuschke and Qaim, 2009; Santos and Barrett, 2010). Therefore, we expect that characteristics of information networks are only likely to have an indirect effect on farmers’ participation decision through information networks.

For the second information link variable “HVM link outside sample”, we use average distance to other HVM-supplying farmers that the respondent has information link with outside our sample, as the instrument. As shown in the social network literature, distance is a key determinant in existence of an information link (Conley & Udry, 2010;

Maertens & Barrett, 2013). A social network link is more likely between farmers located near each other, since the cost of social interaction would be lower than when farmers are located far apart. At the same time, distance between information network members is not likely to have a direct effect on their choice of supply channel.

All our instruments are statistically significant in the first stage regression implying that they are important in explaining the specific information link variables. The test of over identifying restrictions fail to reject the null hypothesis that the instruments are uncorrelated with the error term (p-value=0.152). Furthermore, the Wald test of exogeneity fails to reject the null hypothesis that the two information variables are exogenous (p-value=0.153). Therefore, we conclude that the two information link variables are exogenous