Direct Cost Recovery from Services vs Type of Service Provider

Key Messages

Our data shows remarkable results: Direct cost recovery from services is several times higher for local compared to global off-takers (definition of types of providers). These results are, however, not uniform. While there are many more global off-taker models not charging farmers at all for services provided (i.e., a direct cost recovery of 0%), there are plenty of global off-takers recovering part or even most of their costs, as well as those who recover (close to) 0% of their costs. Unsurprisingly, our data also shows that nearly all specialized providers recover near or even above 100% of their costs.

The data suggests that these results are due to three key factors:

  • Nature of the business model
  • Indirect sources of value
  • Development and aid-focused legacy

Keep reading to find out more.

Understanding how different types of companies charge SHFs for services

In the perception of development organizations, impact investors and NGOs, but also in the eyes of many consumers, the provision of goods and services to smallholder farmers is an activity designed to improve livelihoods and has been closely associated with development and aid. It is very unusual to charge beneficiaries for the aid that is provided to them.

Related, a pervasive belief in our sector is that the provision of goods and services to smallholder farmers is most often a loss-making endeavor that can only be sustained with internal CSR or sustainability funding or with external (often development) funding, and certainly cannot be profitable on its own, for instance by charging farmers.

Going into this work, we expected direct cost recovery to be low across the board regardless of the type of provider – with the exception being specialized service providers whose business model depends entirely on service provision and its associated revenues, and who are therefore much more likely to charge farmers. Our data broadly confirms these results; almost half the models that we have analyzed charge farmers nothing or heavily subsidize their service delivery to these farmers, while cost recovery is very high for specialized service providers. However, cost recovery for local off-takers is 20 times higher on average than for global off-takers.

Nevertheless, there is a lot of nuance behind these results - so keep reading to find out more!

READ MORE: What are the different types of organizations we consider in “type of provider”? Why did we exclude farmer-led providers?

The differences observed are substantial: Specialized service providers have by far the highest direct cost recovery, with a median of 86% (and a mean of 75%). The median direct cost recovery by local off-takers is far higher than that of global off-takers (40% versus 0.4%). Looking at the spread of results, we see three main clusters of models:

  1. Models charging farmers nothing or almost nothing. This includes mainly global but also some local off-takers
  2. Models charging farmers at least 50% of the cost of service provision. This includes mainly local but also a small number of global off-takers
  3. Models charging farmers nearly all, all, or even more than their costs

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 the FarmFit Insights Hub, we find that:

  • For Service Delivery Cost per Farmer, we find that results mirror those of direct cost recovery: median cost per farmer is higher for local compared to global off-takers. This means that on average local off-takers invest more per farmer and also recover more of these costs from farmers. 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 possible explanations and nuances behind these results, as well as their implications.

Diving deeper: what do we think explains these results

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

  1. Internal factors: Nature of the business model – The business of local and global off-takers are organized differently. Global off-takers have larger corporate behind them, separate sustainability teams, and offer less complex services, all of which allow (or lead) these companies to see service delivery less as a direct revenue-generating activity.
  2. External factors: Indirect sources of value – Global off-takers are more often able to subsidize their service provision through indirect sources of value, including sourcing, premium markets, and compliance-related value
  3. Development and aid-focused legacy – Global off-takers’ models often follow a legacy of development- or aid-focused interventions, in which charging farmers – who are seen as beneficiaries rather than customers – is not a norm

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

Implications – what does this mean for you?

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

Reflections on data limitations and further research

The FarmFit Insights Hub is an interactive resource which 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 on the sources of “hidden value”
  2. Limited FarmFit data on the role of philanthropic funding

Updating these findings: Next steps

  1. Inclusion of sourcing profitability into future analyses 

Suggestions for additional research by our peers and partners

  1. Build on our approach and methodology to study specialized service providers

Strength of Relationship 3/5

  • Strong relationship between driver and outcome variables
  • Results are largely consistent across analytical models used
  • Several limitations regarding sample or indicator