For constructing the typology (step IIa mentioned in Table 62 in the Annex), eight typology criteria have been selected from the 37 evaluation characteristics contained in the FPMM factsheets (see Table 61 and Table 63 for the overview of these criteria).
Table 19 contains these eight criteria used to classify the 17 approaches into the typology classes. This table also shows the evaluation categories they belong to as well as the optimal type of each criterion.9 These 'optimal types' are selected based on the insights gained from the comparative analysis in Section 2.2 Detailed characterisation of selected approaches and the intention of this analysis. They are used for classifying the 17 FPMM approaches and for characterising each typology class.
Table 19: Optimal types of the typology criteria Category Optimal criterion type
(as used in Table 20)
Explanation of optimal criterion type Institutional
context Graphical results and data available (output format: Which technical output formats are used?)
Providing the monitoring results in pdf format OR interactive graphs AND exportable data files thereby combining information with interpretations of results or accessible information in a graphical form with data availability for interested users.
Monitoring focus
>2 supply chain levels monitored (Supply chain levels monitored: Which supply chain levels are monitored?)
Monitoring at least three supply chain levels: farm, processing, and retail thereby providing insight into these supply chain levels by monitoring prices across all of them.
Data inputs Using panel data (Quantitative data inputs: Which types of quantitative data is the FPMMA based upon?)
Using panel data for the analysis which is the optimal combination for being able to assess temporal changes as well as cross-section structures.
Raw data available (Transparency of (raw) data: Are the raw data and numerical outputs made completely publicly available?)
Making the raw data publicly available to the user which makes the monitoring transparent.
Monitoring
results Price margins and/or costs and profits monitored (Quantitative results: Which types of quantitative results does the FPMMA publish?)
Publishing price margins and/or costs and profits along the supply chain either additionally to prices, price indices and/or simple indicators or exclusively being focused on that.
Indicators based on more than single price series (Indicators: Which indicators are calculated and published?)
Calculating and publishing indicators based on more than a single price series (multivariate price indicators) or based on quantities other than prices.
Illustrative graphical and commented results (Formats of graphical & commented results: What formats have the graphical results & commented qualitative analyses published?)
Providing comprehensive, detailed, qualitative and illustrative graphical and commented results on the supply chain structure helping the user to well understand it.
Results communicat ion
Time lag < 6 months (Time lag: How much times passes
approximately between the data gathering and the results publishing?)
Having a short to very short time lag of less than half a year between data gathering and monitoring results publication.
Source: Authors of this study.
Table 20 contains the typology of the 17 FPMM approaches which have been assessed in detail. It contains three classes which the existing monitoring approaches have been classified into as well as one class of a hypothetical optimal approach. Class 1, Class 2 and Class 3 are created by identifying which of the 17 observed approaches are most similar and classifying them into one class. Table 20 summarises each of the three typology classes and characterises each in terms of its typical class characteristics most of the approaches belonging to it fulfil as well as mentions which FPMM approaches belong to it. The most frequent advantages and disadvantages of each class are summarised in the next section.
Table 20: Typology of FPMM approaches
Typology
class
Typology criteria
Class 1
Class 2
Class 3
Class 4
Most
outstandi
ng
characteri
stics of
class
(that is,
belonging
to the
optimal
types of
the
indicated
typology
criteria)
Graphical results and
exportable data
X
>2 supply chain levels
monitored
X
X
Using panel data
X
X
Raw data available
X
X
Price margins and/or costs and
profits monitored
X
X
Indicators based on more than
single price series
X
X
X
X
Illustrative graphical and
commented results
X
X
Time lag < 6 months
X
X
Number of FPMM approaches belonging to
class
6
5
6
FPMM Approach belonging to class
BE1,
BG1,
BG2,
EU3,
LT2, US1
BE2,
EU2,
EU4,
LT1, US2
EU1, FR1,
FR2, NL,
ES1, ES2
Hypothetical
FPMM
approach
Source: Authors of this study.
Note: All background information about the steps taken to produce this typology in a transparent, reproducible and systematic fashion and the decision rules it is based upon are outlined in detail in AI.3 Methodology of Section 2.3, especially step IIIc in Table 62 and the explanation of the methodology thereafter in the Annex. An 'X' at the intersection of a column and a row indicates that the majority of approaches (i.e. at least 50%) belonging to that class show this optimal characteristic as commented on in Table 19. Therefore a cross indicates that a given typology characteristic is typical for a certain class. For details see step IIIc in Table 62 and the explanation of the methodology thereafter in the Annex. For details, see the example interpretation in Annex AI.3 Methodology of Section 2.3.
Table 20 shows that the FPMM approach which is deemed to be optimal with respect to the chosen typology (Class 4) satisfies all eight optimal typology criteria mentioned in Table 19, while the Class 3 approaches satisfy six, the Class 2 approaches satisfy two and Class 1 approaches satisfy one optimal criterion.
This typology suggests that the optimal (hypothetical) food price and margin monitoring approach Class 4 is characterised by:
1. It provides its monitoring results in pdf format OR interactive graphs AND also makes exportable data available to the user,
2. It monitors at least all three supply chain levels (farm, processing and retail), 3. It uses panel data for analysis,
4. It makes the raw data used for the calculations publicly available,
5. It publishes information about price margins, costs and profits along the supply chain additionally to prices and simple indicators,
6. it publishes multivariate price indicators or indicators based on quantities other than prices alone,
7. it provides comprehensive, detailed and illustrative graphical and commented results on the supply chain structure and, lastly,
8. It has a time lag of less than half a year until monitoring results are published. The six actually existing Class 3 approaches satisfy most of these optimal characteristics of Class 4, but they typically differ from the optimal monitoring approach by:
Not providing the optimal output format (which is monitoring results in pdf format OR interactive graphs AND exportable data) and
Not ensuring publication of monitoring results within the optimal time lag (which is less than half a year).
The five approaches belonging to Class 2 differ in their typical characteristics much stronger from the optimal approach of Class 4. Instead of all eight, they satisfy only the following two optimal characteristics:
Publishing multivariate price indicators or indicators based on quantities other than prices and
Having an optimal time lag of less than half a year until results publication. Lastly, the six approaches which belong to Class 1 show typically only one of the eight optimal characteristics by publishing multivariate price indicators or indicators based on quantities other than prices.