Service Delivery Cost per Farmer vs Type of Service Provider

Key Messages

Our data shows remarkable results: service delivery cost per farmer is approximately double for local compared to global off-takers (definition of types of providers). Within our sector, a pervasive expectation is often the opposite – and this influences decision-making. A deeper understanding of investment patterns of global and local off-takers can help us to better allocate finite financial and other resources to achieve more inclusive, sustainable and commercially-viable smallholder inclusive markets. Our data shows remarkable results:

We believe the results are due to four main reasons:

  • Different context.
  • Development and CSR legacy.
  • Closer positioning to smallholder farmers.
  • Smaller and less mature models.

Keep reading to find out more.

Understanding how different types of companies invest in smallholder farmers

Until recently, much of the focus in the sustainability world had been on the supply chains of large global corporates and global (often cash crop) value chains. (Read more).

Going into this work, part of our team and some of the partners that we tested this with expected certain results when looking at the service delivery cost per farmer  patterns for different types of service providers. We expected that large multinational off-takers, often with pressure from sustainability-conscious consumers and highly engaged civil society actors, invest more than smaller and more regionally-active companies. The data shows us a different story: companies with a national or regional footprint have approximately double the service delivery cost compared to global off-takers.

READ MORE: What are the different types of organizations we consider in “type of provider”? Why did we exclude (for now) Farmer-led models and Specialized providers

The differences that we observe are substantial: the median service delivery cost per farmer for local off-takers is roughly double that of global off-takers. There is a broad spread of results; some global off-takers invest relatively much and some local off-takers invest relatively little. Nonetheless, there are strong patterns that suggest fundamental differences between global and local off-takers at an aggregate level.

From conversations with our partners we have identified a number of compelling reasons for these differences:

  1. Different contexts in which these models operate
  2. A stronger development-oriented or CSR legacy among global off-takers
  3. Closer positioning and stronger mutual dependence with farmers for local off-takers
  4. Different operating models

These results have been validated using machine learning methods. (Read more.)

When looking at the relationship between type of company and the other two outcomes analyzed in this Insights Hub, we find that:

  • For Direct Cost Recovery, higher investment does not appear to necessarily mean lower (percentage) direct cost recovery: the much higher service delivery cost by local companies is mirrored by much higher cost recovery percentages for those same companies. For local off-takers versus global off-takers, the median direct cost recovery percentages are 40% and 0.5%, respectively. Click here for more details
  • For Farmer Value Created, we find no appreciable differences when looking at different types of companies. Click here for more details

In the following sections, we dive deeper into the possible explanations and nuances behind these results, as well as their implications.

Diving deeper: what explains these results?

There are a number of compelling reasons for the results we see. The discussion below focuses on the difference between global off-takers and local off-takers.

  1. Different context – While the context differs for each individual business model, we find that global off-takers are more often active in contexts that reduce the need for or inhibit high investments in smallholder farmers
  2. Development and CSR legacy – The relatively lower service delivery cost by global off-takers reflects, in part, the legacy of development- and CSR-focused interventions compared to business-focused interventions  
  3. Distance to farmers – The closer positioning of local off-takers to farmers incentivizes them to invest more. This is also driven by the fact that global off-takers are overwhelmingly active in intercontinental value chains, meaning that they have a (much) higher diversity of sourcing options
  4. Organizational and business model – Local off-takers often run smaller and more recent models, meaning they can build on a lower scale of efficiencies, and existing corporate infrastructure

Clicking on each of the preceding reasons provides a longer overview of our thinking, including more supporting qualitative and quantitative insights.

Implications – so what does this mean for you?

Based on our findings till date on this topic, we see the following implications for different audiences:

Reflections on data limitations and further research

The Insights Hub is a living document that we are constantly updating with new data, new analysis, validation by our partners, etc. For the results on this page, we would like to emphasize the following:

Major caveats and limitations of our current approach

  1. Limited visibility of the role of the enabling environment
  2. Limited visibility on the role of philanthropic funding

Next steps that we have planned to update these findings in the near future

We have no immediate next steps planned to update the analyses in this section. We are considering conducting additional analysis on the interchange between the enabling environment and the activities of different types of off-takers, as well as strengthening our understanding and insights on Specialized service providers.  

Suggestions for additional research by our peers and partners

  1. Shine a further light on the role of public and philanthropic funding
  2. Build on our approach and methodology to study Specialized service providers

Strength of Relationship 3/5

  • Moderate relationship between driver and outcome variables
  • Results are not fully consistent across analytical models used
  • Few limitations regarding sample or indicator