How to cite

Lasarte-López, Jesús; Ronzon, Tévécia; M'barek, Robert; Carus, Michael; Tamošiūnas, Saulius (2023): Jobs and wealth in the EU bioeconomy / JRC - Bioeconomics. European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/7d7d5481-2d02-4b36-8e79-697b04fa4278

Related documents

• Ronzon, T. et al. Has the European Union entered a bioeconomy transition? Combining an output-based approach with a shift-share analysis. Environment, Development and Sustainability. 2022, 24, 8195-8217.
• Ronzon, T. et al. An output-based measurement of EU bioeconomy services: Marrying statistics with policy insight. Structural Change and Economic Dynamics. 2022, 60, 290-301.

Other Research Briefs relevant for the European Union Bioeconomy:

• Multiplying effects of the Bioeconomy
• The Sankey Biomass Diagram

Methodology

The methodology is described in Ronzon, T. et al. Developments of Economic Growth and Employment in Bioeconomy Sectors across the EU. Sustainability. 2020, 12 (11), 4507.
It is the result of a collaborative work between the nova-Institute and the EC JRC Seville. This methodology has been refined over time and former versions are described in the following scientific articles:

• Lasarte-López, J., M'barek, R., Ronzon, T., Tamosiunas, S. EU Bioeconomy Monitoring System indicator update. Jobs and Value Added in the EU Bioeconomy 2020. April 2023 release. Publications Office of the European Union, Luxembourg, forthcoming.
• Ronzon, T., Tamosiunas, S. and M`barek, R., Jobs and growth in the bioeconomy, Publications Office of the European Union, Luxembourg, 2022, ISBN 978-92-76-47130-1, doi:10.2760/323093, JRC128361.
• Ronzon, T.; M'Barek, R. Socioeconomic Indicators to Monitor the EU's Bioeconomy in Transition. Sustainability 2018, 10, 1745.
• Ronzon, T. et al. A systematic approach to understanding and quantifying the EU's bioeconomy. Bio-based and Applied Economics. 2017, 6(1), 1-17.
• https://doi.org/10.13128/BAE-20567.

Caveat

The use of multiple data sources led to situations where data on value added generated by the fisheries and aquaculture sector are reported for certain member States whereas the number of persons employed in that sector is not.

Data sources

Our estimates are based on the compilation of employment and turnover statistics in EU Member States (2008-2021) from the following datasets:

Employment and turnover data are reported by activity sector, following the NACE rev2 classification. For each of the NACE sectors contributing to the bioeconomy, bio-based shares have been applied in order to disentangle activities related to the manufacturing of biomass feedstock versus carbon fossil feedstock (e.g. the category Manufacturing of textiles includes both bio-based and synthetic fibres before application of a bio-based share). Thus, in this study bio-based sectors refer to the manufacturing of biomass feedstock only.

Bio-based shares have been estimated at product level through expert interviews held by the nova-Institute. The match between this classification by products (Combined Nomenclature, used in the EUROSTAT – Comext database) and the classification by NACE sectors in the above-mentioned datasets was possible thanks to the use of CN8 – NACE rev.2 correspondence tables.

The resulting estimates have been compiled in a single dataset, called JRC - Bioeconomics.

Definitions

Number of persons employed:
The number of persons employed is defined as the total number of persons who work in the observation unit (inclusive of working proprietors, partners working regularly in the unit and unpaid family workers working regularly in the unit), as well as persons who work outside the unit who belong to it and are paid by it (e.g. sales representatives, delivery personnel, repair and maintenance teams). It includes persons absent for a short period (e.g. sick leave, paid leave or special leave), and also those on strike, but not those absent for an indefinite period. It also includes part-time workers who are regarded as such under the laws of the country concerned and who are on the pay-roll, as well as seasonal workers, apprentices and home workers on the pay-roll.

Location quotient (LQ) = employment share in the bioeconomy of a Member State total divided by the employment share in the EU bioeconomy of the EU total.
LQ is a way of quantifying how "concentrated" the bioeconomy is in a Member State compared to the European Union.

Turnover (Million euros):
Turnover comprises the totals invoiced by the observation unit during the reference period, and this corresponds to market sales of goods or services supplied to third parties.

Value added (Million euros):
Value added refers to the value added at factor costs. It is the gross income from operating activities after adjusting for operating subsidies and indirect taxes. Value adjustments (such as depreciation) are not subtracted.

Legend

Data pre-processing

Due to missing values in the selected data sources, a data pre-processing is conducted prior to the application of bio-based shares to the target indicators. The data pre-processing addresses both, the filling in the missing data from the selected data sources as well as checking consistency of the imputed values among the different levels of the NACE sector classification (the 2-, 3- and 4-digit disaggregation).

Old data processing

The data filling procedure implemented in previous releases is based on the use of the newest information available in the dataset. Specifically, the missing data is first completed using the values of the more recent years that are available (last observation carried backward). As a secondary criterion, when no recent information is available (the last years are missing values), then the value of the nearest previous non-missing year is assigned to the more recent ones (last observation carried forward).

The main advantage of this process is that the most recent information is reflected in the target indicators. In addition, this process had an acceptable performance to represent the main trends of the EU bioeconomy sectors, largely explained by the context of economic and financial stability characterising the last decade. However, the COVID-19 pandemic shock caused breaks in the economic trends in most sectors, originating deviations between the estimated values and the actual trends for 2021.

New data processing

The new data pre-processing deals with the deviations problem by incorporating additional economic information to fill the missing data gaps. Accordingly, the estimated values better reflect the trends and dynamics of each sector and/or the economic context.

This new process is applied to data from Structural Business Statistics (SBS) and proceeds in two stages. The first stage estimates missing values corresponding to jobs and value added for the 2-digit NACE sectors. The second stage completes missing data for the 3- and 4- digit corresponding sectors, based on the estimated series in the previous stage.

Stage 1. Filling in the values from the 2-digit NACE sectors

For the imputation of missing values in the 2-digit level of NACE, we use estimates on employment (thousand persons) and value added (million euros, current prices), provided by National Accounts as auxiliary variables to compute missing values from the equivalent indicators in SBS. The applied procedure for this task is based on the use of both forward and backward growth rates derived from variables in National Accounts to compute missing values from SBS. The formula for this procedure is represented by Equation 1.

Where:

• Ŷ is the estimated value of the target indicator (missing in the target indicator).
• Y represents actual values from the variables
• The superscript SBS indicate that the corresponding value Y is obtained from Structural Business Statistics.
• The superscript NA indicate that the corresponding value Y is obtained from National Accounts.
• The subscript t indicates the specific year to be estimated (missing in the target indicator).
• The subscript p indicates the nearest previous year for which the target indicator is non-missing.
• The subscript s indicates the nearest subsequent year for which the target indicator is non-missing.

Using forward and backward growth rates where possible allows estimated values to be consistent with the actual level of the indicator in SBS, as well as to introduce trends shown by the equivalent indicator in National Accounts. Otherwise, if only a unique growth rate was used, the estimated values could cause artificial breaks in the target indicator in the event that SBS and National accounts show divergent trends (e.g. due to different estimation methods or rounding procedures from Eurostat). This does not apply when missing values have no more recent (past) observations available. In these cases, the series are projected (backward) using directly the growth rate derived from the equivalent indicator in National Accounts.

The advantage of using estimates from National Accounts as auxiliary variables is that we can estimate a high percentage of the missing values in SBS. The disadvantage is that the sectoral breakdown is less detailed than that of SBS, as some sectors are reported as an unique aggregate (e.g. National Accounts provide an aggregate value for food, beverages and tobacco, C10-C12, and for all textiles economic activities, C13-C15). This fact might lower the accuracy of the estimates for the affected sectors, as the aggregation can potentially hide diverging trends.

When the auxiliary variable cannot be used to estimate the target indicator, the following criteria are implemented as an alternative:

1. Interpolation procedures, in case the missing values of an indicator have both future and past non-missing observations.
2. Last observation carried backward, in the cases that missing values have future available observations but no past ones.
3. Last observation carried forward for those cases in which the missing values only have past observations available.

Stage 2. Filling in values for 3- and 4-digit NACE sectors

Some of the aggregates provided in the dataset require further sectoral disaggregation (liquid biofuels, generation of bio-electricity). For this purpose, a specific data filling procedure is also proposed for the 3- to 4-digit NACE sectors. No additional economic information is introduced at this stage, but the results from Stage 1 are used as a basis for the data filling procedure in this stage. The main steps are described below:

1. If there is only one missing subsector, the difference between the overarching sector (2- or 3- digit level) and the sum of the non-missing subsectors (3- or 4-digit, respectively) is assigned to the missing one. As Stage 1 finishes with an estimate for most missing values at the 2-digit level, the value for the missing subsector can be easily computed. This rule will not be applied when the gap is negative (i.e., when the sum of the subsectors is higher than the value assigned to the overarching sector).

2. When there are missing values for two or more subsectors within the same 2-digit NACE sector, the following procedures are applied in order of priority:

1. Interpolation procedures, in case the missing values of an indicator, have both future and past non-missing observations.
2. Last observation carried backward, in the cases that missing values have future available observations but no past ones.
3. Last observation carried forward for those cases in which the missing values only have past observations available.

3. A rescaling procedure is applied to the values of the more disaggregated NACE classification to ensure consistency among different levels of sectoral disaggregation. In case the overarching sector is missing, it is computed as the sum of the subsectors.

4. For each 2-digit NACE sector requiring further disaggregation, the complete process is reiterated until no more values can be completed.

Other minor changes

Some minor changes have been introduced at the time of elaborating the January 2022 version of socioeconomic indicators. They explain the differences with the previous versions for the period 2008-2019.

• Eurostat data sources. All calculations were updated with latest available Eurostat data. Considering that Eurostat implements regular revisions on past time series, changes in Eurostat data for the period 2008-2019 period could have caused some divergence with the numbers displayed in the previous release.

• Indicators computed only for the EU27 Member States. As the United Kingdom formally left the EU in 2020, estimates are no longer provided for this country.