indirect jobs


Capturing the uncountable

One of our strategic goals is to support jobs and foster economic growth in our markets. Assessing tho what extent this occurs is complex, especially when considering the various trickle-down effects of investments—whether through the supply chain or employees spending their wages.

Jobs generated through this latter process are referred to as ‘indirect jobs’, and they are more challenging to capture. Rather than visiting each of our clients' suppliers and related businesses to gauge the number of supported jobs, a model is used to estimate these indirect effects. The model relies on existing data per country and sector to fill in missing information pieces.

As such, FMO, in close cooperation with other DFIs and MDBs, developed the Joint Impact Model (JIM) as a tool to estimate economy-wide effects, including total jobs supported. As part of the harmonization process among European Development Finance Institutions, several DFIs have collaborated to ensure ensure consistent and comparable measurement and reporting of the effects using JIM.

The JIM can estimate impact indicators based on the customer's revenues, sector and country. This information feeds into models that, combined with large publicly available datasets, arrive at estimated total jobs supported. This includes jobs supported through trickle-down effects in the supply chain or local salary expenditures of employees.

We use the JIM to estimate the effects of investments in, among others, the agricultural sector, a key sector for the Building Prospects fund. FMO has been managing Building Prospects on behalf of the Dutch government since 2002. The fund’s objective is to support private sector development and job creation in developing countries through investments in the agricultural sector and the enabling infrastructure. Recognizing agriculture's essential role for promoting economic development and creating local employment, Building Prospects finances high-risk investments along the agribusiness value chain in emerging markets.

To better understand the indirect effects of our investments in the Agribusiness, Food & Water department, FMO commissioned a study to assess the JIM and its use in our agri portfolio. To this end, the study:

  • Provides a review of the JIM and its applicability to the agri portfolio.
  • Estimates the development results in terms of employment and value-added achieved by the agri investments along the agri value chain.
  • Provides recommendations to the JIM and FMO on how to improve both the measurement of effects in terms of jobs and value-added and the development results that originate from these investments.

The evaluation’s methodological approach is based on a desk review of documentation, model assessment, and ten case studies.

Aligned with current market practices

The evaluation confirms that the JIM methodology is on par with current market practices, striking an appropriate balance between precision and practicality. The basic assumption of the JIM and input/output modelling is sound: positive upstream effects (i.e., contribution to jobs through the supply chain or through expenditures of employee income) are linked to positive demand shocks, reflecting a key mechanism for the materialization of indirect effects.

Having reviewed four alternative databases the JIM could have used, the evaluation confirmed that the JIM uses those databases with which it can obtain a comprehensive coverage of the countries and sectors of interest to FMO. Because the JIM operates with minimal data requirements, it relies to a large extent on sector averages. This essentially means assuming these enterprises are equivalent to the sector in which they are active, after they have been adjusted for the size of their revenues.

At the same time however, the evaluation found structural differences between our clients and the average companies in the databases. For example, because of above-average productivity of our clients, intermediate consumption (and thereby indirect jobs) is underestimated. On the other hand, our clients also tend to import a larger share of their supplies compared to the average. This in turn means local indirect jobs are likely overestimated.

FMO’s choice to include additional customer-level data beyond the minimum requirements, specifically direct jobs and taxes, mitigates this to some extent and clearly enhances the quality of the JIM estimations. Therefore, it is necessary that we continue collecting addtional primary data among our client as these feed directly into these inputs.

A strong multiplier effect

As part of the evaluation, additional information was included in the model to improve the accuracy of the estimated numbers. The results show that companies in agri-manufacturing and processing have a strong multiplier effect through the supply chain. This is in line with our investment approach, emphasizing the importance of such companies.

The effects of investing in low-income countries are even more significant: for every EUR million invested in a low-income country, 813 jobs are estimated to be supported, compared to the agri portfolio average of 161.  At the same time, the evaluation emphasized the importance of local sourcing, to ensure this multiplier effect really materializes in our markets.


The evaluation offers external validation for both the strengths and limitations of the JIM tailored to the agri-portfolio, providing further detailed insights. Furthermore, the case studies exemplified the underlying mechanisms that generate indirect effects: suppliers of our clients who were interviewed expressed that their revenues grew proportionally with the revenue of our clients.

As the JIM has already stretched the trade-offs between more accuracy and practical feasibility, the recommendations made for more accuracy to the model are not easy to directly implement. However, there are certainly various items which can be studied more closely in the years to come. One way in which FMO and other DFIs can increase the accuracy of the model is by adding additional input. For example, including information on wages for the most relevant clients in the portfolio increases the accuracy. Alternatively, a correction factor which may mitigate the more structural biases could be implemented. Setting an accurate correction factor would benefit from further funding.

With respect to our investment strategy, this evaluation pointed out just how much an investment in low-income countries can contribute to more job creation. This further strengthens the case for the increased business envisioned in Least Developed Countries, as part of FMO’s strategy 2030.

Taken together, the study presents a visible reminder of the trickle-down nature of enabling entrepreneurs to make a difference. Capturing the full extent is not easy, but the JIM shows a way to start.


Summary of report

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