endogenous treatment effects model. Third, we discuss the sensitivity of our results to the inclusion of alternative explanatory variables.

# <u>Functional Upgrading and Specialization</u>

As a first robustness check, we examine the potential of functional upgrading and specialization as additional forms of structural transformation. This comes with a few caveats. First, f _unctional upgrading_ is only captured for contract farmers whereof we cannot estimate the effect of contract farming on structural transformation, but only of value chain participation as a direct proxy. Second, _specialization_ is underrepresented in the random sample, which leads to an estimation of the treatment effect of zero. Table 8 presents the PSM estimates as our preferred specification. Interestingly, we find positive and significant treatment effects of value chain participation on _functional upgrading_ and _specialization_ irrespective of the sample used. Under the circumstances that functional upgrading was only surveyed for contract farmers, the coefficient estimates now indicate that a contract farmer who sells the crop to a value chain is on average more likely to move to higher value-added tasks than a nonparticipant. Similarly, value chain participants seem to experience greater specialization in their output in contrast to nonparticipants. Given the small number of nontreated farmers in this setting and, hence, the lack of a true counterfactual, it is probably not surprising that the treatment effect is highly significant. Although the results point at a relatively strong impact of value chain participation on structural transformation, we treat this finding cautiously and sensitively.

**_<u>Table 8: Diversification and Functional Upgrading: PSM Results</u>_**

||**(1)**|**(2)**|
|---|---|---|
|**Outcome variable**|**Random sample**|**Purposive sample**|
|Functional upgrading|0.243***|0.070***|
|Diversification||0.013***|

_Note:_ The control variables are age of household head, education of household head, land area, and size of household, with country, region, and crop fixed effects included in each regression. The treatment effect is value chain participation. Significant: *** = 1% level.

# <u>Endogenous Treatment Effects Model</u>

Our PSM results may still be biased because of unobserved omitted variables that influence selfselection into value chain participation. Therefore, we control for the selection bias due to unobservables in an alternative setup. We estimate the average treatment effect and other parameters of a linear regression model with an endogenous binary treatment variable two-step estimator. The estimator allows for correlation between unobservable factors that affect the treatment as well as unobservable variables that affect the outcome.<sup>35</sup> The main challenge here is finding a valid selection variable. The variable most likely to meet the selection variable criteria is distance to the closest buyer. Distance to the closest buyer proxies for access to information on value chains. Therefore, it is most correlated with the endogenous variable but perhaps affects the outcome variable only through the farmers’ participation in value chains. Thus, the identifying assumption is that proximity to a buyer who has information on value chains for their product does not affect the farmers’ labor productivity and upgrading by itself per se. The advantage of the

> 35 <mark>The first-stage probit model is used to calculate the inverse Mills ratio, which is then used as an additional regressor in the second-stage OLS to provide adjusted estimates of the impact of the treatment on the outcome of interest.</mark>

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