FMO impact model

In 2019, we continued to use the FMO impact model to estimate the total number of direct and indirect jobs supported by FMO’s investments over the year. From 2020, we will use the Joint Impact Model, as described in chapter ‘Our commitments’. The jobs supported and GHG emissions avoided are estimated and reported in the year of commitment. The model makes use of data from international statistical sources and investment-specific information which we obtain from our customers’ annual accounts. We do not claim that the results of our model are exact, but they do provide insight in the progress made towards meeting our ambitious goal in a consistent manner.

Limitations of the model

The impact model allows quantifying the wider impact of investing in various economic regions and sectors, both directly and through financial institutions and funds. The impact model is an economic input-output model, which is a widely recognized academic method to depict inter-linkages between sectors, which enables the model to trace product and money flows through an economy. However, it is also important to point out the limitations of this methodology:

  1. The model produces ex-ante estimates of impact. Realized impact (ex-post) on the ground can differ from ex-ante expectations;

  2. Estimates of indirect impact are based on industry averages (via input/output tables). In reality indirect effects will be different at the individual company level due to differences in individual company characteristics. As a result, model outcomes become less accurate for smaller numbers of investments;

  3. Estimates are based on historical relations, while the methodology is based on the most recent (macro) economic data available;

  4. FMO’s investments are treated as investments from any other lender and it has been assumed that FMO’s financial support does not affect the relations of sectors within an economy;

  5. Given that the analysis is conducted for a specific moment in time, it does not take into account any structural changes in the economy (e.g. increased productivity). To better reflect economic changes, an update of the statistical data was conducted in 2017.

Taking the limitations of the model into account, we use the results only on the portfolio (and sub portfolio) level. In addition, we perform activities to provide insight in ex-post development effects, such as monitoring of direct effects, sector evaluations, effectiveness studies and impact evaluations. For more information on how we measure impact and FMO’s impact model, see FMO website. We refer to Annex 5 of the FMO Impact Model Methodology document for an overview of rules and assumptions. 

Macroeconomic update of the impact model

At the start of 2017 we updated macroeconomic data used in the FMO impact model. The update ensures that the estimation of jobs supported reflects the actual economic situation as much as possible. Because of economic progress, capital scarcity tends to decrease and labor productivity tends to increase over time in the countries we invest in. The update therefore resulted ceteris paribus in roughly 10% less jobs supported per euro invested.

FMO attribution rules

The number of supported jobs and the amount of avoided GHG are reported pro rata with FMO’s financing as part of the total (productive) assets or total project size. FMO’s financing includes the amount in euros that we have invested, and the third party amounts actively mobilized by FMO (‘mobilized funds’). The underlying assumption is that without FMO the third party would not have invested in the project. In 2019, the investments on behalf of third parties contributed to 16% of the number of jobs reported (2018: 30%) and to 33% of the amount of GHG avoided (2018: 35%).

Furthermore, to take into account the higher impact of equity products due to its higher leverage effects on client level, the model uses a multiplier of two for equity products: both the number of jobs supported and the amount of GHG avoided by equity investments are multiplied by two. The equity multiplier increases the number of jobs supported in 2019 by 15% (2018: 8%) and the amount of GHG avoided in 2019 by 19% (2018: 2%).

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