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R quality checks for DCF data submission Exploratory Data Analysis for Fishing Fleet economic data call

Cristina Castro Ribeiro & Arina Motova 2015 Report EUR 27456

European Commission Joint Research Centre Institute for the Protection and Security of the Citizen (IPSC) Contact information Castro Ribeiro Cristina Address: European Commission, Joint Research Centre (JRC), Institute of the Protection and Security of the Citizen (IPSC), Maritime Aairs Unit G03, Via Enrico Fermi 2749, 21027 Ispra (VA), Italy) E-mail: [email protected] Tel.: +39 0332 78 9329 JRC Science Hub https://ec.europa.eu/jrc Legal Notice This publication is a Technical Report by the Joint Research Centre, the European Commission’s in-house science service. It aims to provide evidence-based scientific support to the European policy-making process. The scientific output expressed does not imply a policy position of the European Commission. Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use which might be made of this publication. All images © Arina Motova

JRC97428 EUR 27456 ISBN 978-92-79-51780-8 ISSN 1831-9424 (online) doi:10.2788/02897 Luxembourg: Publications Office of the European Union, 2015 © European Union, 2015 Reproduction is authorised provided the source is acknowledged. Abstract The JRC-IPSC under it Administrative Arrangement with DG MARE, amongst many other activities, has to call data from the Member States, give support to MS on the continuos improvement of data quality, make this data available to the Scientific, Technical and Economic Committee for Fisheries (STECF) and then curate these data to ensure it long-term usability. Amongst the calls launched by JRC-IPSC, the call for fishing fleet economic data is launched every year since 2005. Since then a policy on data quality has been implemented, however, lately, due to the existence of more data intensive processes and a progressively implementation of an open data policy and a data reusability policy, additional effort has been done to further streamline the process of assessing/improving the data quality. In this sequence, since 2013 a new tool was developed in support of the data quality assessment. This is a tool based on the generation of dynamic reports based in knitr/Sweave (R packages). This report presentes the Data Quality Report . A tool developed in R /Latex language that on the fly fetchs data from a database where data is uploaded by the MS, cleans the data, reprocess the data, produce the outputs to support the data quality analysis and, at the end, generates a pdf report where the coding, outputs and analysis are putted together. This tool has revealed to be of major efficiency - less time consuming, error free, reproducible at any time and based on a policy of transparence (code and outputs all made available together). Therefore the same methodology will be used on support of the data policy in the JRC-IPSC in the future. For that further enhancements might be sought such has producing the outputs as interactive web application instead of a pdf document.

Contents 1 Summary

2

2 Introduction

2

3 Material and Methods

3

3.1

Software needed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3

3.2

The datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4

3.3

The work flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4

4 Results - The Data Quality Report

6

4.1

Define global options for the output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

6

4.2

Data fetching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

6

4.3

Data coverage analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

12

4.3.1

Fleet segments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

12

4.3.2

Clusters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

14

4.3.3

Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

18

Analyses by variable Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

25

4.4.1

Capacity

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

25

4.4.2

Employment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

34

4.4.3

Fishing Enterprises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

39

4.4.4

Effort - Data by Fleet Segment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

42

4.4.5

Landings - Data by Fleet Segment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

45

4.4.6

Income

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

49

4.4.7

Expenditure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

56

4.4.8

Capital and Investment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

68

4.4

5 Lists of acronyms

74

6 Discussion and Conclusion

74

7 Annex 1

75

8 References

75

1

1

Summary

The JRC-IPSC under it Administrative Arrangement with DG MARE, amongst many other activities that has to call data from the Member States, give support to MS on the continuos improvement of data quality, make this data available to the Scientific, Technical and Economic Committee for Fisheries (STECF) and then curate these data to ensure it long-term usability. Amongst the calls launched by JRC-IPSC, the call for fishing fleet economic data is launched every year since 2005. Since then a policy on data quality has been implemented, however, lately, due to the existence of more data intensive processes and a progressively implementation of an open data policy and a data reusability policy, additional effort has been done to further streamline the process of assessing/improving the data quality. In this sequence, since 2013 a new tool was developed in support of the data quality assessment. This is a tool based on the generation of dynamic reports based in knitr/Sweave (R packages). This report presentes the Data Quality Report. A tool developed in R/Latex languages that on the fly fetchs data from a database where data is uploaded by the MS, cleans the data, reprocess the data, produce the outputs to support the data quality analysis and, at the end, generates a pdf report where the coding, outputs and analysis are putted together. This tool has revealed to be of major efficiency - less time consuming, error free, reproducible at any time and based on a policy of transparence (code and outputs all made available together). Therefore the same methodology will be used on support of the data policy in the JRC-IPSC in the future. For that further enhancements might be sought such has the conversion of the outputs from a pdf document to an interactive web application.

2

Introduction

Through the Administrative Arrangement No SI2.704986 with DG-MARE, the Institute for the Protection and Security of the Citizen of the Joint Research Center, JRC-IPSC, on behalf of DG MARE, launches annually between 3 to 6 data calls to the EU Member States to support the production of scientific advice to the Common Fisheries Policy by the Scientific, Technical and Economic Committee for Fisheries (STECF).The datasets collected this way are kept at JRC databases where they must be maintained, curated and stored for a long term usability. These are data collected under the remit of the Data Collection Framework (DCF; (EC) No 199/2008), which is also impose certain rules for the managing and disseminating of data collected. Therefore JRC needs to ensure a proper management and curation of the data throughout it life cycle amongst which ensure that data used to support the development of scientific knowledge is properly available, adhere to quality standards and is properly documented for further uses. Further information about the Call for data launched by JRC-IPSC under this remit is available on https://datacollection.jrc.ec.europa.eu/data-calls Amongst these six calls for data, the Call for Fleet Economic scientific data is launched every year since 2005. It aims at gather yearly data on the fishing vessels activity and their economic performance with the objective of being available to the STECF Expert Working Group on the preparation of the Annual Economic Report (AER) which serves to inform the Common Fisheries Policy (CFP) and support the process of decision making policy. The latest call for this data has been launched in 2015, for the years 2008-2014 (Ref.Ares(2015)421690), and has requested the Member States (MS) to submit 9 data sets in the format of Excel templates. These templates are defined based on the Variable group level of the Appendix VI of Commission Decision 2010/93/EU Adopting a Multiannual Community Programme for the collection, management and use of data in the fisheries sector for the period 2011-2013. Full details about the templates, the variables, levels of disaggregation, codes, etc., can be found on: http://datacollection.jrc.ec.europa.eu/dc/fleet/templates. The implementation of a data quality policy to support data sets management process has been developed progressively since JRC first assigned the role of data manager, in 2005. However, lately, due to more data intensive processes and a progressively implementation of an open data policy and a data reusability policy, additional effort had been done to streamline it. In this sequence, since 2013 a new tool was developed to support the data quality assessment - The Data Quality Reports.

2

The Data Quality Report is a tool that consist of several R code scripts, embeded in a latex document, a Sweave (Rnw) file that at the end is compiled as a pdf file. This is a unique tool that allows the production of a statistical report on the fly, including data entry and storage, data cleaning (including checking for, resolving, correcting data entry errors), data preparation (including transforming/recoding variables, creating new variables, creating necessary subsets), performing the proposed statistical analyses, including generating desired graphs; recording/saving the desired results/graphs and, finally, produce the results report, which include documentation text, tables and graphs. Essentially the aim of the Data Quality Report is to create a map of the values and relationships of the data in order to create graphical representations that will support an easy identification of possible errors, extreme fluctuations and outliers in the data sets. By doing this JRC provides the MS with one single file, that can be compiled in real time after on-line data submission, and can be easily regenerated when the data or analyses change - all of the results/tables/figures are automatically updated. On the context of the quality policy of the data calls, the same methodology has been implemented for two other economic data calls: the data call for Aquaculture and the data call for the Processing Industry. The current document covers only the work done with the fishing fleet economic data. This Technical Report aims to give insight description of the process developed to produce the Data Quality Report. this is a process that ensures a complete track of the data processing methodology and a transparent framework of the data handling process run by JRC when producing the feedback to the MS. Simultaneoulsy it aims at demonstrating the exraordinary advantage of the use of these dynamic documents to ensure the reproducibility of the processes, decrease errors and save time.

3

Material and Methods

The Reporting tool was developed in Knitr/Sweave. A Sweave file is a flexible system for creating dynamic reports and reproducible research using LaTeX, that enables the embedding of R code within LaTeX to generate a PDF file that includes the text, figures, code and the output from the code computation. This process enables the creation of dynamic reports that are updated automatically if the data or the analysis changes. The process of ”weaving” an Rnw file involves executing R code and inserting it into the document. The idea is to combine program source code (code chunks) and the corresponding documentation (documentation chunks) into a single file. Code is normally stored as R Script. For the documentation portion it is used LaTeX (.tex) and then it’s used a library like Sweave or knitr to put together the code chunks in R’s noweb format (.rnw) along with the documentation (in LaTeX) to create an output in PDF.By tradition the method of weaving Rnw files is the version of Sweave that is included within the base distribution of R. In addition to Sweave, RStudio also supports using the knitr package to weave Rnw files. The Knitr package is distributed with the RStudio. Additional information about the use of Sweave and Knitr may be found: • http:// yihui.name/knitr • https://support.rstudio.com/hc/en-us/articles/200552056-Using-Sweave-and-knitr. . .

3.1

Software needed

To use Knitr/Sweave a working installation of RStudio is required. The following code was run using RStudio Version 0.98.1028. Also, to use Sweave and knitr to create PDF reports, LaTeX should be installed in the system. LaTeX can be installed following the directions on the LaTeX project page http://latexproject.org/ftp.html.

3

Other librarys need to be loaded to run the Data Quality Report. These are: RPostgreSQL, sqldf, plyr, psych, Hmisc, memisc, reshape, lattice, latticeExtra, xtable and knitr. The package RPostgreSQL provides a Database Interface (DBI) compliant driver for R to access the PostgreSQL database where the data is stored. Packages such as sqldf, plyr, psych, reshape provide several functions to manipulate and work data; the packages latticeand latticeExtra provide a powerful and elegant high-level data visualization system inspired by Trellis graphics. The packages Hmisc, memisc and xtable are used to create and manage tables in Latex. All packages are available on the CRAN mirror network and can be installed via install.packages() from within R. Additionally to these R packages, also two functions developed specifically to these report have to be sourced to the script, these are: out fun and out fun list. These function identifies the outliers from a distribution of values and create a list of these outliers, respectively. (Annex 1)

3.2

The datasets

The datasets analyzed with the R Data Quality Report are the datasets identified on the JRC website for Fleet Economic data call http://datacollection.jrc.ec.europa.eu/dc/fleet/templates;here one can find the list of variables, its description, aggregation levels and codifications. These datasets are uploaded by the 23 non-land locked countries into a database located at JRC-IPSC once a year after a formal data call launched by DG MARE. There is a total of 9 main data sets fetched directly from the database, these are:

1. Capacity 2. Effort 3. Employment 4. Fishing Enterprises 5. Effort 6. Landings 7. Income 8. Costs 9. Capital and Investments. . .

3.3

The work flow

While executing the report the general work flow is as follows: 1. Open the Rnw file in Rstudio, 2. Define global options for the output 3. Source the functions and packages 4. Fetching the data from the Postgres database through an authenticated connection, 5. Data cleaning and correction 6. Data Analysis (tabulate summary statistics and graph data series) 7. Compile the .Rnw file into latex and then pdf

4

8. Report automatically save in the working dir. After the report is saved and produced in the JRC it is send to the MS with comments, identifying questionable data. In case the data is not good and MS identify the need to reupload or update data sets provided, MS are reuploading the data throught uploading facility, after which the new cycle of quality checking procedure starts. The overall work flow looks like this:

database

Fetch data

Clean data

Process Datasets

update data

Data summaries and Graphs

Compile Report

no

is data ok?

yes

stop

5

Send report

4 4.1

Results - The Data Quality Report Define global options for the output

General option for the default global options in a document: 1 2 3

o p t s c h u n k $ s e t ( f i g . a l i g n = ” c e n t e r ” , f i g . p o s = ”H” , f i g . k e e p = ” l a s t ” , c a c h e = FALSE, message = FALSE, e r r o r = FALSE, w a r n i n g s = FALSE, t i d y = TRUE, width = 5 0 , s i z e = ” small ” )

Load the packages: Sources the function to list outliers. The complete script is shown in annex (annex 1). 1

source ( ” out fun.R ” )

The theme used for the output is the ggplot2-like theme for Lattice: The variable MS identifies the Member State for which the report will be produced. The variable MS is then used throughout the report to subset the data.

4.2

Data fetching

Establish the connection to the database: 1 2 3

drv ← d b D r i v e r ( ” PostgreSQL ” ) con ← dbConnect ( drv , h o s t = ”XXXXXXXX” , dbname = ” dc−economic ” , u s e r = ” app−xls ” , password = ”XXXXXXX” )

The SQL statement is submitted for synchronous execution to the server connected through the ’con’ object, as parameterised above. The DBMS executes the statement,and then data is trasfered to R. 1

c l u s t e r ← dbSendQuery ( con , ”SELECT c o u n t r y c o d e , f i s h i n g t e c h , s u p r a r e g , year , v e s s e l l e n g t h , c l u s t e r FROM (SELECT c o u n t r y c o d e , f i s h i n g t e c h , s u p r a r e g , year , v e s s e l l e n g t h , CASE WHEN s u b s t r ( c l u s t e r , 1 , 3 ) NOT IN ( ’ARE’ , ’OFR’ , ’NON’ ) THEN s u p r a r e g | | c l u s t e r ELSE c l u s t e r END FROM ( SELECT c a p a c i t y . c o u n t r y c o d e , c a p a c i t y . f i s h i n g t e c h , c a p a c i t y . s u p r a r e g , capacity.year , c a p a c i t y . v e s s e l l e n g t h , replace ( replace ( replace ( cluster name , ’ ’ , ’ ’) , ’ ’ , ’ ’ ) , ’ ∗ ’ , ’ ’ ) AS c l u s t e r FROM PUBLIC.capacity WHERE l e n g t h ( c l u s t e r n a m e ) > 2 ) AS c a p a c i t y UNION ALL SELECT c o u n t r y c o d e , f i s h i n g t e c h , s u p r a r e g , year , v e s s e l l e n g t h , ’ U n c l u s t e r e d ’ AS c l u s t e r FROM PUBLIC.capacity WHERE l e n g t h ( c l u s t e r n a m e ) ≤ 2 ) AS h e r e i s m e GROUP BY c o u n t r y c o d e , f i s h i n g t e c h , s u p r a r e g , year , v e s s e l l e n g t h , c l u s t e r ” )

2 3

c l u s t e r M S 1 3 ← f e t c h ( c l u s t e r , n = −1)

4 5

c l u s t e r o n l y ← dbSendQuery ( con , ”SELECT c o u n t r y c o d e , ’ c a p a c i t y ’ AS template , f i s h i n g t e c h , s u p r a r e g , year , v e s s e l l e n g t h , CASE WHEN s u b s t r ( c l u s t e r , 1 , 3 ) NOT IN ( ’ARE’ , ’OFR’ ) THEN s u p r a r e g | | c l u s t e r ELSE c l u s t e r END FROM (SELECT c a p a c i t y . c o u n t r y c o d e , capacity.fishing tech , capacity.supra reg , capacity.year , capacity.vessel length , replace ( r e p l a c e ( r e p l a c e ( c l u s t e r n a m e , ’ ’ , ’ ’ ) , ’ ’ , ’ ’ ) , ’ ∗ ’ , ’ ’ ) AS c l u s t e r FROM PUBLIC.capacity WHERE l e n g t h ( c l u s t e r n a m e ) > 2 AND f i s h i n g t e c h NOT IN ( ’ INACTIVE ’ ) ) AS c a p a c i t y GROUP BY c o u n t r y c o d e , f i s h i n g t e c h , s u p r a r e g , year , v e s s e l l e n g t h , CASE WHEN s u b s t r ( c l u s t e r , 1 , 3 ) NOT IN ( ’ARE’ , ’OFR’ ) THEN s u p r a r e g | | c l u s t e r ELSE c l u s t e r END” )

6 7

c l u s t e r o n l y ← f e t c h ( c l u s t e r o n l y , n = −1)

8 9

e f f o r t ← dbSendQuery ( con , ” \nSELECT ’ e f f o r t ’ AS template , t e f f o r t m s . c o u n t r y c o d e , t e f f o r t m s . y e a r , sum ( t e f f o r t m s . t o t t r i p s ) AS t o t t r i p s , sum ( t e f f o r t m s . t o t e n e r c o n s ) AS t o t e n e r c o n s , sum ( t e f f o r t m s . t o t f i s h d a y s ) AS t o t f i s h d a y s , sum ( t e f f o r t m s . t o t g t f i s h d a y s ) AS t o t g t f i s h d a y s , sum ( t e f f o r t m s . t o t k w f i s h d a y s ) AS t o t k w f i s h d a y s , sum ( t e f f o r t m s . t o t s e a d a y s ) AS t o t s e a h d a y s FROM P U B L I C . t e f f o r t m s GROUP BY t e f f o r t m s . c o u n t r y c o d e , t e f f o r t m s . y e a r ” )

10 11

e f f o r t M S 1 3 ← f e t c h ( e f f o r t , n = −1)

12

6

13

c a p a c i t y ← dbSendQuery ( con , ”SELECT ’ c a p a c i t y ’ AS template , t c a p a c i t y m s . c o u n t r y c o d e , t capacity ms.year , t capacity ms.totves , t capacity ms.totkw , t capacity ms.totgt , t c a p a c i t y m s . a v g l o a , t c a p a c i t y m s . a v g a g e FROM PUBLIC.t capacity ms ” )

14 15

capacity MS13 ← f e t c h ( c a p a c i t y , n = −1)

16 17

employment ← dbSendQuery ( con , ”SELECT ’ employment ’ AS template , t e m p l o y m e n t m s . c o u n t r y c o d e , t e m p l o y m e n t m s . y ea r , t e m p l o y m e n t m s . t o t j o b , t e m p l o y m e n t m s . t o t n a t f t e , t e m p l o y m e n t m s . t o t h a r m f t e FROM PUBLIC.t employment ms ” )

18 19

employment MS13 ← f e t c h ( employment , n = −1)

20 21

income ← dbSendQuery ( con , ”SELECT ’ income ’ AS template , t i n c o m e m s . c o u n t r y c o d e , t income ms.year , t income ms.totlandginc , t income ms.totrightsinc , t income ms.totdirsub , t i n c o m e m s . t o t o t h e r i n c FROM PUBLIC.t income ms ” )

22 23

income MS13 ← f e t c h ( income , n = −1)

24 25

e x p e n d i t u r e ← dbSendQuery ( con , ”SELECT ’ e x p e n d i t u r e s ’ AS template , t expenditure ms.country code , t expenditure ms.year , t expenditure ms.totcrewwage , t expenditure ms.totunpaidlab , t expenditure ms.totenercost , t expenditure ms.totrepcost , t expenditure ms.totvarcost , t expenditure ms.totnovarcost , t expenditure ms.totrightscost , t e x p e n d i t u r e m s . t o t d e p c o s t FROM PUBLIC.t expenditure ms ” )

26 27

e x p e n d i t u r e s M S 1 3 ← f e t c h ( e x p e n d i t u r e , n = −1)

28 29

c a p i n v ← dbSendQuery ( con , ”SELECT ’ capinv ’ AS template , t c a p v a l i n v e s t m s . c o u n t r y c o d e , t capvalinvest ms.year , t capvalinvest ms.totdephist , t capvalinvest ms.totdeprep , t c a p v a l i n v e s t m s . t o t r i g h t s , t c a p v a l i n v e s t m s . t o t i n v e s t , t c a p v a l i n v e s t m s . f i n p o s FROM PUBLIC.t capvalinvest ms ” )

30 31

c a p i n v e s t M S 1 3 ← f e t c h ( c a p i n v , n = −1)

32 33

l a n d i n g s ← dbSendQuery ( con , ”SELECT ’ l a n d i n g s ’ AS template , t l a n d i n g s m s . c o u n t r y c o d e , t l a n d i n g s m s . y e a r , sum ( t o t w g h t l a n d g ) AS to twg htl and g , sum ( t o t v a l l a n d g ) AS t o t v a l l a n d g FROM PUBLIC.t landings ms GROUP BY t l a n d i n g s m s . c o u n t r y c o d e , t l a n d i n g s m s . y e a r ” )

34 35

landings MS1 3 ← f e t c h ( l a n d i n g s , n = −1)

Some data tunning, melting and merging to create the nat_tot_2013 dataset (data on MS level): 1

capacity MS13 $ y e a r ← f a c t o r ( capacity MS13 $ y e a r )

2 3

capacity MS13 ← melt ( capacity MS13 , i d v a r s = 1 : 7 , na.rm = TRUE)

4 5

capinvest MS13 $ year ← f a c t o r ( capinvest MS13 $ year )

6 7

c a p i n v e s t M S 1 3 ← melt ( capinvest MS13 , i d v a r s = 1 : 7 , na.rm = TRUE)

8 9

effort MS13 $ year ← f a c t o r ( effort MS13 $ year )

10 11

e f f o r t M S 1 3 ← melt ( e f f o r t M S 1 3 , i d v a r s = 1 : 7 , na.rm = TRUE)

12 13

employment MS13 $ y e a r ← f a c t o r ( employment MS13 $ y e a r )

14 15

employment MS13 ← melt ( employment MS13 , i d v a r s = 1 : 7 , na.rm = TRUE)

16 17

expenditures MS13 $ year ← f a c t o r ( expenditures MS13 $ year )

18 19

e x p e n d i t u r e s M S 1 3 ← melt ( expenditures MS13 , i d v a r s = 1 : 7 , na.rm = TRUE)

20 21

income MS13 $ y e a r ← f a c t o r ( income MS13 $ y e a r )

22 23

income MS13 ← melt ( income MS13 , i d v a r s = 1 : 7 , na.rm = TRUE)

24 25

landings MS1 3 $ y e a r ← f a c t o r ( landings MS13 $ y e a r )

26

7

27

landings MS1 3 ← melt ( landings MS13 , i d v a r s = 1 : 7 , na.rm = TRUE)

28 29 30

n a t t o t 2 0 1 3 ← r b i n d ( capacity MS13 , capinvest MS13 , e f f o r t M S 1 3 , employment MS13 , expenditure s MS13 , income MS13 , landings MS13 )

Fetch the datasets with lower level of aggregation - Data on a fleet segment level: 1 2

e f f o r t ← dbSendQuery ( con , ”SELECT ’ e f f o r t ’ AS template , t e f f o r t f s . c o u n t r y c o d e , t e f f o r t f s . y e a r , upper ( t e f f o r t f s . s u p r a r e g ) AS s u p r a r e g , t e f f o r t f s . f i s h i n g t e c h , t effort fs.vessel length , t effort fs.tottrips , t effort fs.totenercons , t effort fs.totfishdays , t effort fs.totgtfishdays , t effort fs.totkwfishdays , t e f f o r t f s . t o t s e a d a y s FROM P U B L I C . t e f f o r t f s ” )

3 4

e f f o r t 2 0 1 3 ← f e t c h ( e f f o r t , n = −1)

5 6

c a p a c i t y ← dbSendQuery ( con , ”SELECT ’ c a p a c i t y ’ AS template , t c a p a c i t y . c o u n t r y c o d e , t c a p a c i t y . y e a r , upper ( t c a p a c i t y . s u p r a r e g ) AS s u p r a r e g , t c a p a c i t y . f i s h i n g t e c h , t capacity.vessel length , t capacity.totves , t capacity.totkw , t capacity.totgt , t c a p a c i t y . a v g l o a , t c a p a c i t y . a v g a g e FROM PUBLIC.t capacity GROUP BY t c a p a c i t y . c o u n t r y c o d e , t c a p a c i t y . y e a r , upper ( t c a p a c i t y . s u p r a r e g ) , t c a p a c i t y . f i s h i n g t e c h , t capacity.vessel length , t capacity.totves , t capacity.totkw , t capacity.totgt , t capacity.avgloa , t capacity.avgage ”)

7 8

c a p a c i t y 2 0 1 3 ← f e t c h ( c a p a c i t y , n = −1)

9 10

employment ← dbSendQuery ( con , ” SELECT ’ employment ’ AS template , t e m p l o y m e n t . c o u n t r y c o d e , t e m p l o y m e n t . y e a r , upper ( t e m p l o y m e n t . s u p r a r e g ) AS s u p r a r e g , t e m p l o y m e n t . f i s h i n g t e c h , t employment.vessel length , t employment.totjob , t employment.totnatfte , t e m p l o y m e n t . t o t h a r m f t e FROM PUBLIC.t employment ” )

11 12

employment2013 ← f e t c h ( employment , n = −1)

13 14

income ← dbSendQuery ( con , ” SELECT ’ income ’ AS template , t i n c o m e . c o u n t r y c o d e , t i n c o m e . y e a r , upper ( t i n c o m e . s u p r a r e g ) AS s u p r a r e g , t i n c o m e . f i s h i n g t e c h , t i n c o m e . v e s s e l l e n g t h , t i n c o m e . t o t l a n d g i n c , t i n c o m e . t o t r i g h t s i n c , t i n c o m e . t o t d i r s u b , t i n c o m e . t o t o t h e r i n c FROM PUBLIC.t income ” )

15 16

income2013 ← f e t c h ( income , n = −1)

17 18

e x p e n d i t u r e ← dbSendQuery ( con , ”SELECT ’ e x p e n d i t u r e s ’ AS template , t e x p e n d i t u r e . c o u n t r y c o d e , t e x p e n d i t u r e . y e a r , upper ( t e x p e n d i t u r e . s u p r a r e g ) AS s u p r a r e g , t e x p e n d i t u r e . f i s h i n g t e c h , t expenditure.vessel length , t expenditure.totcrewwage , t expenditure.totunpaidlab , t expenditure.totenercost , t expenditure.totrepcost , t expenditure.totvarcost , t e x p e n d i t u r e . t o t n o v a r c o s t , t e x p e n d i t u r e . t o t r i g h t s c o s t , t e x p e n d i t u r e . t o t d e p c o s t FROM PUBLIC.t expenditure ” )

19 20

e x p e n d i t u r e 2 0 1 3 ← f e t c h ( e x p e n d i t u r e , n = −1)

21 22

c a p v a l i n v e s t ← dbSendQuery ( con , ” SELECT ’ c a p i n v ’ AS template , t c a p v a l i n v e s t . c o u n t r y c o d e , t c a p v a l i n v e s t . y e a r , upper ( t c a p v a l i n v e s t . s u p r a r e g ) AS s u p r a r e g , t capvalinvest.fishing tech , t capvalinvest.vessel length , t capvalinvest.totdephist , t capvalinvest.totdeprep , t capvalinvest.totrights , t capvalinvest.totinvest , t c a p v a l i n v e s t . f i n p o s FROM P U B L I C . t c a p v a l i n v e s t ” )

23 24

c a p v a l i n v e s t 2 0 1 3 ← f e t c h ( c a p v a l i n v e s t , n = −1)

25 26

l a n d i n g s ← dbSendQuery ( con , ”SELECT l a n d i n g s f a o . c o u n t r y c o d e , l a n d i n g s f a o . s p e c i e s , landings fao.year , landings fao.supra reg , landings fao.fishing tech , l a n d i n g s f a o . v e s s e l l e n g t h , sum ( l a n d i n g s f a o . t o t w g h t l a n d g ) s w e i g t h , sum ( l a n d i n g s f a o . t o t v a l l a n d g ) s v a l u e FROM P U B L I C . l a n d i n g s f a o GROUP BY landings fao.country code , landings fao.species , landings fao.year , landings fao.supra reg , landings fao.fishing tech , landings fao.vessel length ”)

27 28

l a n d i n g s 2 0 1 3 ← f e t c h ( l a n d i n g s , n = −1)

29

8

30

fish ent ← fishent fishent fishent

dbSendQuery ( con , ” SELECT f i s h e n t t o t . c o u n t r y c o d e , f i s h e n t t o t . y e a r , sum ( t o t . o n e v e s ) AS oneves , sum ( f i s h e n t t o t . t w o f i v e v e s ) AS t w o f i v e v e s , sum ( t o t . s i x m o r e v e s ) AS s i x m o r e v e s FROM P U B L I C . f i s h e n t t o t GROUP BY tot.country code , fishent tot.year ”)

31 32

f i s h e n t N 2 0 1 3 ← f e t c h ( f i s h e n t , n = −1)

Some data tunnings, melting and merging to create the dataset fs_var_2013: 1 2

capacity2013 $ year ← f a c t o r ( capacity2013 $ year )

3 4

c a p a c i t y 2 0 1 3 ← melt ( c a p a c i t y 2 0 1 3 , i d v a r s = 1 : 7 , na.rm = TRUE)

5 6

capvalinvest2013 $ year ← f a c t o r ( capvalinvest2013 $ year )

7 8

c a p v a l i n v e s t 2 0 1 3 ← melt ( c a p v a l i n v e s t 2 0 1 3 , i d v a r s = 1 : 7 , na.rm = TRUE)

9 10

e f f o r t 2 0 1 3 $ year ← f a c t o r ( e f f o r t 2 0 1 3 $ year )

11 12

e f f o r t 2 0 1 3 ← melt ( e f f o r t 2 0 1 3 , i d v a r s = 1 : 7 , na.rm = TRUE)

13 14

employment2013 $ y e a r ← f a c t o r ( employment2013 $ y e a r )

15 16

employment2013 ← melt ( employment2013 , i d v a r s = 1 : 7 , na.rm = TRUE)

17 18

expenditure2013 $ year ← f a c t o r ( expenditure2013 $ year )

19 20

e x p e n d i t u r e 2 0 1 3 ← melt ( e x p e n d i t u r e 2 0 1 3 , i d v a r s = 1 : 7 , na.rm = TRUE)

21 22

income2013 $ y e a r ← f a c t o r ( income2013 $ y e a r )

23 24

income2013 ← melt ( income2013 , i d v a r s = 1 : 7 , na.rm = TRUE)

25 26

# all datasets ready to rbind and create a unique dataset

27 28 29

f s v a r 2 0 1 3 ← r b i n d ( c a p a c i t y 2 0 1 3 , c a p v a l i n v e s t 2 0 1 3 , e f f o r t 2 0 1 3 , employment2013 , e x p e n d i t u r e 2 0 1 3 , income2013 )

30 31

names ( f s v a r 2 0 1 3 ) [ 4 ] ← c ( ” s u p r a r e g ” )

32 33 34

f s v a r 2 0 1 3 u ← r b i n d ( c a p a c i t y 2 0 1 3 , c a p v a l i n v e s t 2 0 1 3 , e f f o r t 2 0 1 3 , employment2013 , e x p e n d i t u r e 2 0 1 3 , income2013 )

35 36

names ( f s v a r 2 0 1 3 ) [ 4 ] ← c ( ” s u p r a r e g ” )

Data cleaning and correction: 1 2

# ## Further analysis is made on clustered data level , therefore we keep # ## unclustered data set to allow the exploration of clusters.

3 4 5

m a i n c l u s t e r ← s u b s e t ( c l u s t e r o n l y , c l u s t e r == p a s t e ( c l u s t e r o n l y $ s u p r a r e g , c l u s t e r o n l y $ f i s h i n g t e c h , c l u s t e r o n l y $ v e s s e l l e n g t h , sep = ”” ) )

6 7

8

f s . m c l u s t e r ← s q l d f ( ”SELECT a . c o u n t r y c o d e , a . y e a r , a . s u p r a r e g , b . s u p r a r e g c s u p r a , a . f i s h i n g t e c h , b . f i s h i n g t e c h c f t , a . v e s s e l l e n g t h , b . v e s s e l l e n g t h c v l , b . c l u s t e r FROM c l u s t e r o n l y a , m a i n c l u s t e r b WHERE a . c o u n t r y c o d e = b . c o u n t r y c o d e AND a . c l u s t e r = b . c l u s t e r AND a . y e a r = b . y e a r GROUP BY a . c o u n t r y c o d e , a . y e a r , a . s u p r a r e g , b . s u p r a r e g , a.fishing tech , b.fishing tech , a.vessel length , b.vessel length , b.cluster ” , drv = ” SQLite ” )

9 10 11

f s v a r 2 0 1 3 . c ← ( merge ( f s v a r 2 0 1 3 , f s . m c l u s t e r , by = c ( ” c o u n t r y c o d e ” , ” y e a r ” , ” f i s h i n g t e c h ” , ” v e s s e l l e n g t h ” , ” s u p r a r e g ” ) , a l l . x = TRUE) )

12 13

14

fs var 2013.c [ i s . n a ( fs var 2013.c $ c f t ) , 10] ← c ( subset ( fs var 2013.c , i s . n a ( fs var 2013.c $ c ft ) , fishing tech ))

9

15 16

17

fs var 2013.c [ i s . n a ( fs var 2013.c $ c vl ) , 11] ← c ( subset ( fs var 2013.c , i s . n a ( fs var 2013.c $ c vl ) , vessel length ) )

18 19

20

fs var 2013.c [ i s . n a ( fs var 2013.c $ c supra ) , 9] ← c ( subset ( fs var 2013.c , i s . n a ( fs var 2013.c $ c supra ) , supra reg ) )

21 22 23

new ← s u b s e t ( f s v a r 2 0 1 3 . c , s e l e c t = c ( ” c o u n t r y c o d e ” , ” y e a r ” , ” t e m p l a t e ” , ” c supra ” , ” c v l ” , ” c f t ” , ” variable ” , ” value ” ) )

24 25

new ← rename ( new , c ( c s u p r a = ” s u p r a r e g ” ) )

26 27

new ← rename ( new , c ( c v l = ” v e s s e l l e n g t h ” ) )

28 29

new ← rename ( new , c ( c f t = ” f i s h i n g t e c h ” ) )

30 31

32

new a ← s q l d f ( ”SELECT template , c o u n t r y c o d e , year , s u p r a r e g , f i s h i n g t e c h , v e s s e l l e n g t h , v a r i a b l e , sum ( v a l u e ) AS v a l u e FROM new WHERE v a r i a b l e NOT IN ( ’ a v g l o a ’ , ’ avgage ’ ) GROUP BY c o u n t r y c o d e , year , s u p r a r e g , f i s h i n g t e c h , v e s s e l l e n g t h , v a r i a b l e ” , drv = ” SQLite ” )

33 34

f s v a r 2 0 1 3 ← new a

35 36

# data.frame to evaluate coherence of dimensions across templates :

37 38

39

dim dc13 ← s q l d f ( ”SELECT c o u n t r y c o d e , template , y e a r | | ’ . ’ | | s u p r a r e g | | ’ . ’ | | f i s h i n g t e c h | | ’ . ’ | | v e s s e l l e n g t h AS dims , count (DISTINCT v a r i a b l e ) AS n v a r FROM f s v a r 2 0 1 3 GROUP BY c o u n t r y c o d e , t e m p l a t e , y e a r | | ’ . ’ | | s u p r a r e g | | ’ . ’ | | f i s h i n g t e c h | | ’ . ’ | | vessel length ” , drv = ” SQLite ” )

40 41

42

capa1 ← s q l d f ( ”SELECT a . c o u n t r y c o d e AS c o u n t r y c o d e , a . y e a r AS year , a . s u p r a r e g AS s u p r a r e g , ’ Mean LOA ’ AS v a r i a b l e , sum ( a . v a l u e ∗ b . v a l u e ) / sum ( a . v a l u e ) m e t r i c FROM (SELECT c o u n t r y c o d e , year , s u p r a r e g , f i s h i n g t e c h , v e s s e l l e n g t h , v a l u e FROM f s v a r 2 0 1 3 WHERE v a r i a b l e = ’ t o t v e s ’ ) a , ( SELECT c o u n t r y c o d e , year , s u p r a r e g , f i s h i n g t e c h , v e s s e l l e n g t h , v a l u e FROM f s v a r 2 0 1 3 WHERE v a r i a b l e = ’ a v g l o a ’ ) b WHERE a . y e a r = b . y e a r AND a . c o u n t r y c o d e = b . c o u n t r y c o d e AND a . s u p r a r e g = b . s u p r a r e g AND a . f i s h i n g t e c h = b . f i s h i n g t e c h AND a . v e s s e l l e n g t h = b . v e s s e l l e n g t h GROUP BY a . c o u n t r y c o d e , a . y e a r , a.supra reg ” , drv = ” SQLite ” )

43 44

45

capa2 ← s q l d f ( ”SELECT a . c o u n t r y c o d e AS c o u n t r y c o d e , a . y e a r AS year , a . s u p r a r e g s u p r a r e g , ’ Mean Age ’ AS v a r i a b l e , sum ( a . v a l u e ∗ b . v a l u e ) / sum ( a . v a l u e ) m e t r i c FROM (SELECT c o u n t r y c o d e , year , s u p r a r e g , f i s h i n g t e c h , v e s s e l l e n g t h , v a l u e FROM f s v a r 2 0 1 3 WHERE v a r i a b l e = ’ t o t v e s ’ ) a , ( SELECT c o u n t r y c o d e , year , s u p r a r e g , f i s h i n g t e c h , v e s s e l l e n g t h , v a l u e FROM f s v a r 2 0 1 3 WHERE v a r i a b l e = ’ avgage ’ ) b WHERE a . y e a r = b . y e a r AND a . c o u n t r y c o d e = b . c o u n t r y c o d e AND a . s u p r a r e g = b . s u p r a r e g AND a . f i s h i n g t e c h = b . f i s h i n g t e c h AND a . v e s s e l l e n g t h = b . v e s s e l l e n g t h GROUP BY a . c o u n t r y c o d e , a . y e a r , a . s u p r a r e g ” , drv = ” SQLite ” )

46 47

48

capa3 ← s q l d f ( ”SELECT c o u n t r y c o d e , year , s u p r a r e g s u p r a r e g , ’ N r V e s s e l s ’ AS v a r i a b l e , sum ( v a l u e ) AS m e t r i c FROM f s v a r 2 0 1 3 WHERE v a r i a b l e IN ( ’ t o t v e s ’ ) GROUP BY c o u n t r y c o d e , year , supra reg , variable ” , drv = ” SQLite ” )

49 50

51

capa4 ← s q l d f ( ”SELECT c o u n t r y c o d e , year , s u p r a r e g s u p r a r e g , ’ Total Gt ’ AS v a r i a b l e , sum ( v a l u e ) AS m e t r i c FROM f s v a r 2 0 1 3 WHERE v a r i a b l e IN ( ’ t o t g t ’ ) GROUP BY c o u n t r y c o d e , y e a r , supra reg , variable ” , drv = ” SQLite ” )

52 53

54

capa5 ← s q l d f ( ”SELECT c o u n t r y c o d e , year , s u p r a r e g s u p r a r e g , ’ Total kW ’ AS v a r i a b l e , sum ( v a l u e ) AS m e t r i c FROM f s v a r 2 0 1 3 WHERE v a r i a b l e IN ( ’ totkw ’ ) GROUP BY c o u n t r y c o d e , y e a r , supra reg , variable ” , drv = ” SQLite ” )

10

55 56

capa ← r b i n d ( capa1 , capa2 , capa3 , capa4 , capa5 )

57 58

# create data.frame to analyse variations

59 60 61

v a r i a t i o n s d c 1 3 ← cast ( fs var 2013 , template + supra reg + country code + f i s h i n g t e c h + v e s s e l l e n g t h + v a r i a b l e ∼ year , sum )

62 63 64

v a r i a t i o n s d c 1 3 ← s u b s e t ( v a r i a t i o n s d c 1 3 , v a r i a b l e != ” a v g l o a ” & v a r i a b l e != ” avgage ” )

65 66 67

names ( v a r i a t i o n s d c 1 3 ) [ 6 : 1 3 ] ← c ( ” d c v a r ” , ” y e a r 0 8 ” , ” y e a r 0 9 ” , ” y e a r 1 0 ” , ” y e a r 1 1 ” , ” year12 ” , ” year13 ” , ” year14 ” )

68 69

t e s t e ← a s . d a t a . f r a m e ( t ( apply ( v a r i a t i o n s d c 1 3 [ , −1 : −6 ] , 1 , f u n c t i o n ( xx ) xx [ −1 ] / xx [ −length ( xx ) ]) ))

70 71

names ( t e s t e ) ← c ( ” y0809 ” , ” y0910 ” , ” y1011 ” , ” y1112 ” , ” y1213 ” , ” y1314 ” )

72 73

v a r i a t i o n s d c 1 3 e x t r ← cbind ( v a r i a t i o n s d c 1 3 , t e s t e )

74 75

v a r i a t i o n s d c 1 3 ← cbind ( v a r i a t i o n s d c 1 3 [ 1 : 6 ] , t e s t e )

76 77

v a r i a t i o n s d c 1 3 . m ← s u b s e t ( melt ( v a r i a t i o n s d c 1 3 , rm.na = TRUE) , v a l u e != ( I n f ) )

78 79 80

nves ← s u b s e t ( c a p a c i t y 2 0 1 3 , v a r i a b l e == ” t o t v e s ” , s e l e c t = c ( ” c o u n t r y c o d e ” , ” year ” , ” supra reg ” , ” f i s h i n g t e c h ” , ” v e s s e l l e n g t h ” , ” value ” ) )

81 82

names ( nves ) [ 6 ] ← c ( ” t o t v e s ” )

83 84 85

t o t g t ← s u b s e t ( c a p a c i t y 2 0 1 3 , v a r i a b l e == ” t o t g t ” , s e l e c t = c ( ” c o u n t r y c o d e ” , ” year ” , ” supra reg ” , ” f i s h i n g t e c h ” , ” v e s s e l l e n g t h ” , ” value ” ) )

86 87

names ( t o t g t ) [ 6 ] ← c ( ” t o t g t ” )

88 89 90

totkw ← s u b s e t ( c a p a c i t y 2 0 1 3 , v a r i a b l e == ” totkw ” , s e l e c t = c ( ” c o u n t r y c o d e ” , ” year ” , ” supra reg ” , ” f i s h i n g t e c h ” , ” v e s s e l l e n g t h ” , ” value ” ) )

91 92

names ( totkw ) [ 6 ] ← c ( ” totkw ” )

93 94 95

new capa ← j o i n ( t o t g t , nves , by = c ( ” c o u n t r y c o d e ” , ” y e a r ” , ” s u p r a r e g ” , ” f i s h i n g t e c h ” , ” vessel length ”) )

96 97 98

new capa ← j o i n ( new capa , totkw , by = c ( ” c o u n t r y c o d e ” , ” y e a r ” , ” s u p r a r e g ” , ” fishing tech ” , ” vessel length ”) )

99 100

a v g . g t v e s s e l ← new capa $ t o t g t / new capa $ t o t v e s

101 102

a v g . k w v e s s e l ← new capa $ totkw / new capa $ t o t v e s

103 104

names ( new capa ) [ 3 ] ← c ( ” s u p r a r e g ” )

105 106

capacity MS ← c b i n d ( new capa , a v g . g t v e s s e l , a v g . k w v e s s e l )

107 108

# compare total value of landings with total income from landings

109 110

111

income sum ← s q l d f ( ”SELECT year , c o u n t r y c o d e , f i s h i n g t e c h , v a r i a b l e , sum ( v a l u e ) m e t r i c FROM f s v a r 2 0 1 3 WHERE t e m p l a t e = ’ income ’ GROUP BY y e a r , c o u n t r y c o d e , f i s h i n g t e c h , v a r i a b l e ” , drv = ” SQLite ” )

112 113

114

i n c v a l u e ← s q l d f ( ”SELECT year , c o u n t r y c o d e , ’ income ’ AS template , sum ( m e t r i c ) e u r o FROM income sum WHERE v a r i a b l e = ’ t o t l a n d g i n c ’ GROUP BY y e a r , c o u n t r y c o d e ” , drv = ” SQLite ” )

115 116

l a n v a l u e ← s q l d f ( ”SELECT year , c o u n t r y c o d e , ’ l a n d i n g s ’ AS template , sum ( s v a l u e ) e u r o FROM l a n d i n g s 2 0 1 3 GROUP BY y e a r , c o u n t r y c o d e ” ,

11

117

drv = ” SQLite ” )

118 119

l a n v a l u e $ year ← a s . f a c t o r ( l a n v a l u e $ year )

120 121

inc land ← rbind ( inc value , lan value )

122 123

124

l a n v o l ← s q l d f ( ”SELECT year , c o u n t r y c o d e , ’ l a n d i n g s ’ AS template , sum ( s w e i g t h ) / 1000000 t h t o n , sum ( s v a l u e ) / 1000000 MM Euro FROM l a n d i n g s 2 0 1 3 GROUP BY year , c o u n t r y c o d e ” , drv = ” SQLite ” )

125 126

l a n v o l $ year ← f a c t o r ( l a n v o l $ year )

127 128

a v g p r i c e ← l a n v o l $MM Euro/ l a n v o l $ t h t o n

129 130

l a n v o l ← cbind ( l a n v o l , a v g p r i c e )

131 132

l a n d v o l . m ← melt ( l a n v o l )

133 134

avg price ← landings 2013 $ s value / landings 2013 $ s weigth

135 136

land ← cbind ( landings 2013 , a v g p r i c e )

137 138

# summary missing values ( both euro or tonnes ) include 0?

139 140 141

m i s s v a l u e . s u m ← s t a t s : : : a g g r e g a t e . f o r m u l a ( s w e i g t h ∼ c o u n t r y c o d e + year , data = s u b s e t ( l a n d i n g s 2 0 1 3 , i s . n a ( s w e i g t h ) | i s . n a ( s v a l u e ) ) , sum )

142 143 144

m i s s v a l u e . l e n g t h ← s t a t s : : : a g g r e g a t e . f o r m u l a ( s w e i g t h ∼ c o u n t r y c o d e + year , data = s u b s e t ( l a n d i n g s 2 0 1 3 , i s . n a ( s w e i g t h ) | i s . n a ( s v a l u e ) ) , l e n g t h )

145 146 147

m i s s t o n n e . l e n g t h ← s t a t s : : : a g g r e g a t e . f o r m u l a ( s w e i g t h ∼ c o u n t r y c o d e + year , data = s u b s e t ( l a n d i n g s 2 0 1 3 , i s . n a ( s w e i g t h ) | i s . n a ( s v a l u e ) ) , l e n g t h )

148 149 150

m i s s t o n n e . s u m ← s t a t s : : : a g g r e g a t e . f o r m u l a ( s w e i g t h ∼ c o u n t r y c o d e + year , data = s u b s e t ( l a n d i n g s 2 0 1 3 , i s . n a ( s w e i g t h ) | i s . n a ( s v a l u e ) ) , sum )

151 152 153

m i s s w e i g h t ← merge ( m i s s v a l u e . l e n g t h , m i s s v a l u e . s u m , by = c ( ” c o u n t r y c o d e ” , ” year ” ) )

154 155 156

m i s s v a l u e ← merge ( m i s s t o n n e . l e n g t h , m i s s t o n n e . s u m , by = c ( ” c o u n t r y c o d e ” , ” year ” ) )

157 158

# analyse average prices - the distribution

159 160

l a n d a v g p ← d r o p l e v e l s ( n a . o m i t ( s u b s e t ( l a n d [ c ( 1 : 3 , 5 , 7 : 9 ) ] , s w e i g t h != 0 ) ) )

161 162

n a t t o t 2 0 1 3 $ v a l u e ← round ( n a t t o t 2 0 1 3 $ v a l u e , 0 )

163 164 165 166

compare data ← merge ( s t a t s : : : a g g r e g a t e . f o r m u l a ( round ( v a l u e , 0 ) ∼ t e m p l a t e + c o u n t r y c o d e + y e a r + v a r i a b l e , data = f s v a r 2 0 1 3 , sum ) , n a t t o t 2 0 1 3 , by = c ( ” t e m p l a t e ” , ” c o u n t r y c o d e ” , ” y e a r ” , ” v a r i a b l e ” ) , a l l = TRUE)

167 168

names ( compare data ) [ 5 : 6 ] = c ( ” FS data ” , ” N a t t o t a l ” )

169 170

171

c o m p a r e d a t a . d ← s q l d f ( ” SELECT ∗ FROM compare data WHERE ( FS data / N a t t o t a l > 1 . 1 OR FS data / N a t t o t a l < 0 . 9 ) AND v a r i a b l e NOT LIKE ( ’ avg % ’) ” , drv = ” SQLite ” )

172 173

# Prepare file for Fishing Enterprises

174 175

n f t e h f t e ← s q l d f ( ” SELECT a . s u p r a r e g , a . c o u n t r y c o d e , a . y e a r , a . f i s h i n g t e c h , a . v e s s e l l e n g t h , round (HFTE / NFTE, 2 ) METRIC FROM ( SELECT s u p r a r e g , c o u n t r y c o d e , year , f i s h i n g t e c h , v e s s e l l e n g t h , v a l u e AS NFTE FROM f s v a r 2 0 1 3 WHERE v a r i a b l e = ’ t o t n a t f t e ’ AND f i s h i n g t e c h NOT IN ( ’ INACTIVE ’ ) ) a , ( SELECT s u p r a r e g , c o u n t r y c o d e , year , f i s h i n g t e c h , v e s s e l l e n g t h , v a l u e AS HFTE FROM f s v a r 2 0 1 3 WHERE v a r i a b l e = ’ t o t h a r m f t e ’AND f i s h i n g t e c h NOT IN ( ’ INACTIVE ’ ) ) b WHERE a . s u p r a r e g = b . s u p r a r e g AND a . c o u n t r y c o d e = b . c o u n t r y c o d e AND

12

a . y e a r = b . y e a r AND a . f i s h i n g t e c h = b . f i s h i n g t e c h \tAND a . v e s s e l l e n g t h = b . v e s s e l l e n g t h GROUP BY a . s u p r a r e g , a . c o u n t r y c o d e , a . y e a r , a . f i s h i n g t e c h , a . v e s s e l l e n g t h ” , drv = ” SQLite ” )

176 177 178

179

v a r u n i t v a l u e s ← s q l d f ( ”SELECT a . t e m p l a t e , a . c o u n t r y c o d e , a . y e a r , a . s u p r a r e g , a . f i s h i n g t e c h , a . v e s s e l l e n g t h , a . v a r i a b l e , a . v a l u e / b . v a l u e AS r a t i o FROM (SELECT template , c o u n t r y c o d e , year , s u p r a r e g , f i s h i n g t e c h , v e s s e l l e n g t h , v a r i a b l e , v a l u e FROM f s v a r 2 0 1 3 ) a , ( SELECT c o u n t r y c o d e , year , s u p r a r e g , f i s h i n g t e c h , v e s s e l l e n g t h , v a l u e FROM f s v a r 2 0 1 3 WHERE v a r i a b l e = ’ t o t v e s ’ ) AS b WHERE a . c o u n t r y c o d e = b . c o u n t r y c o d e AND a . s u p r a r e g = b . s u p r a r e g AND a . f i s h i n g t e c h = b . f i s h i n g t e c h AND a . v e s s e l l e n g t h = b . v e s s e l l e n g t h AND a . y e a r = b . y e a r GROUP BY a . t e m p l a t e , a . c o u n t r y c o d e , a . y e a r , a . s u p r a r e g , a . f i s h i n g t e c h , a.vessel length , a.variable ” , drv = ” SQLite ” )

180 181

# Check the fluctuations of averages #

182 183 184

variations unv ← cast ( var unit values , a.template + a.supra reg + a.country code + a . f i s h i n g t e c h + a . v e s s e l l e n g t h + a . v a r i a b l e ∼ a . y e a r , sum )

185 186 187

v a r i a t i o n s u n v ← s u b s e t ( v a r i a t i o n s u n v , a . v a r i a b l e != ” a v g l o a ” & a . v a r i a b l e != ” avgage ” & a . v a r i a b l e != ” f i n p o s ” )

188 189 190 191

names ( v a r i a t i o n s u n v ) [ 1 : 1 3 ] ← c ( ” t e m p l a t e ” , ” s u p r a r e g ” , ” c o u n t r y c o d e ” , ” f i s h i n g t e c h ” , ” v e s s e l l e n g t h ” , ” d c v a r ” , ” y2008 ” , ” y2009 ” , ” y2010 ” , ” y2011 ” , ” y2012 ” , ” y2013 ” , ” y2014 ” )

192 193

t e s t e ← a s . d a t a . f r a m e ( t ( apply ( v a r i a t i o n s u n v [ , −1 : −6 ] , 1 , f u n c t i o n ( xx ) xx [ −1 ] / xx [ −length ( xx ) ]) ))

194 195

names ( t e s t e ) ← c ( ” y0809 ” , ” y0910 ” , ” y1011 ” , ” y1112 ” , ” y1213 ” , ” y1314 ” )

196 197

v a r i a t i o n s u n v e x t r ← cbind ( variations unv , t e s t e )

4.3

Data coverage analysis

Section coverage aims to provide an overview of the MS fleet based on the data submitted. This section is built around 3 major components: fleet segments, clusters and indicators. Each of the components provides an overview of the data provided during the data call and helps to identify inconsistencies in segmentation/clustering and missing variables. 4.3.1

Fleet segments

The following table shows the number of vessels over the period 2008-2014, for each DCF fleet segment provided in the Capacity Template. The Table provides an overview in terms number of vessels (’totves’), which also helps to assess stability of the fleets segmentation over the analysed period and identify possible confidentiality issues and needs for clustering. 1 2

c a p a c i t y M S f l ← s u b s e t ( capacity MS , c o u n t r y c o d e == MS, s e l e c t = c ( ” c o u n t r y c o d e ” , ” supra reg ” , ” f i s h i n g t e c h ” , ” v e s s e l l e n g t h ” , ” year ” , ” totves ” ) )

3 4 5

1

2 3 4

capacity MS fl ← cast ( capacity MS fl , country code + supra reg + f i s h i n g t e c h + v e s s e l l e n g t h ∼ year , sum ) p r i n t ( x t a b l e ( c a p a c i t y M S f l , c a p t i o n = ” F l e e t segment development o v e r t h e p e r i d 2008 −2014 ( i n number o f v e s s e l s ) . ” , d i g i t s = 0 ) , c a p t i o n . p l a c e m e n t = ” top ” , i n c l u d e . r o w n a m e s = FALSE, l a b e l = ” tab : t a b l e 1 ” , f o r m a t . a r g s = l i s t ( b i g . m a r k = ” , ” ) , t a b u l a r . e n v i r o n m e n t = ” l o n g t a b l e ” , f l o a t i n g = FALSE, N A . s t r i n g = ”−” )

Table 1: Fleet segment development over the perid 2008-2014 (in number of vessels).

13

country code POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL

supra reg AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 NONE NONE NONE NONE NONE OFR

fishing tech DFN DFN DFN DTS DTS DTS DTS DTS DTS HOK HOK HOK HOK PG PG PG PMP PMP TM TM TM TM INACTIVE INACTIVE INACTIVE INACTIVE INACTIVE TM

vessel length VL1012 VL1218 VL1824 VL0010 VL1012 VL1218 VL1824 VL2440 VL40XX VL1012 VL1218 VL1824 VL2440 VL0010 VL1012 VL1218 VL1218 VL1824 VL1218 VL1824 VL2440 VL40XX VL0010 VL1012 VL1218 VL1824 VL2440 VL40XX

14

2008 0 67 4 0 0 57 33 25 2 0 15 0 0 491 72 0 0 0 0 2 54 0 30 2 5 2 2 1

2009 0 24 1 5 6 45 22 10 1 0 32 4 1 451 58 0 0 0 0 7 53 0 48 10 38 4 9 3

2010 0 20 2 0 10 45 20 10 1 0 29 2 1 459 58 0 0 0 1 5 41 0 46 17 25 6 5 3

2011 0 15 0 4 11 56 14 4 1 0 22 5 0 450 67 1 0 0 3 10 42 1 43 16 17 6 2 3

2012 0 36 2 1 14 60 31 5 1 0 3 0 0 450 94 0 3 0 4 7 38 1 20 12 4 0 2 2

2013 0 28 0 1 11 60 28 4 1 1 4 0 0 455 97 0 1 1 2 17 41 1 17 21 4 1 0 2

2014 11 12 0 1 0 74 33 0 1 0 2 0 0 488 106 0 2 0 0 17 46 1 20 17 4 1 0 2

4.3.2

Clusters

This analysis provides some insight into the fleet segments and how the clustering information provided by the MS will result after all data sets have been analysed. In the table below note that:

1. The Cluster name may not be exactly the same as the name provided during the data upload. As the guidelines for clustering are not always universally adopted, the cluster names provided by the MS have been harmonised where necessary according to these guidelines http://datacollection.jrc.ec.europa.eu/documents/1 For example, when special characters have been used, such as *, ,’ ’, these have been removed, e.g. DTSVL1824* is modified to DTSVL1824. 2. Cluster names are sometimes provided by MS without identifying the Supra-region. Given that one specific fleet segment (fishing techology + vessel length group) may be present in more than one supraregion, it is essential that the supra-region to which the cluster belongs is also identified in the name! In cases where the supra-region was not included in the cluster name, the supra-region identified in the capacity table for the fleet segment was added to complement the cluster name, e.g DTSVL1824 becomes AREA27DTSVL1824. 3. Note, that if the same cluster name is identified in more than one supra-region, this procedure will produce two different clusters, e.g HOKVL0010 may produce AREA27HOKVL0010 and OFRHOKVL0010.

If any of the outcomes presented in the table below resulting from the information provided appear incorrect, please contact us! The second table of the chapter is built on the basis of information about clusters from the capacity template and also the names found out in other templates with economic and transversal data. The information is organized in a way that shows what fleet segments have been merged and the final designation of this merged fleet segments and for which years the information have been provided in this format. Only clustered fleet segments are included in the following analysis. 1

2

c l u s t e r i n l a n d i n g s ← s q l d f ( ” s e l e c t ’ l a n d i n g s ’ a s template , c o u n t r y c o d e , year , s u p r a r e g , f i s h i n g t e c h , v e s s e l l e n g t h from l a n d i n g s 2 0 1 3 group by c o u n t r y c o d e , year , s u p r a r e g , fishing tech , vessel length ” , drv = ” SQLite ” )

3 4

5

c l u s t e r i n d a t a s e t s ← s q l d f ( ” s e l e c t template , c o u n t r y c o d e , year , s u p r a r e g , f i s h i n g t e c h , v e s s e l l e n g t h from f s v a r 2 0 1 3 u where t e m p l a t e not i n ( ’ c a p a c i t y ’ ) group by template , c o u n t r y c o d e , year , s u p r a r e g , f i s h i n g t e c h , v e s s e l l e n g t h ” , drv = ” SQLite ” )

6 7

c l u s t e r i n d a t a s e t s ← rbind ( c l u s t e r i n d a t a s e t s , c l u s t e r i n l a n d i n g s )

8 9

10

cluster data ← sqldf (” select c o u n t r y c o d e , year , supra reg | | f i s h i n g t e c h | | v e s s e l l e n g t h as f l e e t s e g , c l u s t e r from c l u s t e r o n l y group by c o u n t r y c o d e , year , s u p r a r e g | | f i s h i n g t e c h | | vessel length , cluster ” , drv = ” SQLite ” )

11 12

c l u s t e r ← s u b s e t ( c l u s t e r i n d a t a s e t s , c o u n t r y c o d e == MS)

13 14

c l u s t e r c a p a c i t y ← s u b s e t ( c l u s t e r o n l y , c o u n t r y c o d e == MS)

15 16 17

c l u s t e r 2 ← merge ( c l u s t e r c a p a c i t y , c l u s t e r , by = c ( ” f i s h i n g t e c h ” , ” s u p r a r e g ” , ” v e s s e l l e n g t h ” , ” year ” , ” country code ” ) )

18 19

c l u s t e r 2 ← c l u s t e r 2 [ , −6 ]

20

15

21 22

I c l u s t e r ← count ( c l u s t e r 2 , c ( ” c o u n t r y c o d e ” , ” s u p r a r e g ” , ” f i s h i n g t e c h ” , ” v e s s e l l e n g t h ” , ” c l u s t e r ” , ” year ” , ” template.y ” ) )

23 24 25

I c l u s t e r ← cast ( I c l u s t e r , country code + c l u s t e r + supra reg + f i s h i n g t e c h + v e s s e l l e n g t h + year ∼ template.y )

16

1 2 3

p r i n t ( x t a b l e ( I c l u s t e r , c a p t i o n = ”Map o f c l u s t e r e d f l e e t segments and t h e i r u s e i n each d a t a s e t t y p e . ” , d i g i t s = 0 ) , c a p t i o n . p l a c e m e n t = ” top ” , i n c l u d e . r o w n a m e s = FALSE, l a b e l = ” tab : t a b l e 2 ” , t a b u l a r . e n v i r o n m e n t = ” l o n g t a b l e ” , f l o a t i n g = FALSE, N A . s t r i n g = ”−” )

Table 2: Map of clustered fleet segments and their use in each dataset type.

17

country code POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL

cluster AREA27DFNVL1218 AREA27DFNVL1218 AREA27DFNVL1218 AREA27DFNVL1218 AREA27DFNVL1218 AREA27DFNVL1218 AREA27DTSVL1218 AREA27DTSVL1218 AREA27DTSVL1218 AREA27DTSVL1218 AREA27DTSVL1218 AREA27DTSVL1218 AREA27DTSVL1824 AREA27DTSVL1824 AREA27DTSVL1824 AREA27DTSVL1824 AREA27HOKVL1218 AREA27HOKVL1218 AREA27HOKVL1218 AREA27PGVL0010 AREA27PGVL0010 AREA27PGVL0010 AREA27PGVL0010 AREA27TMVL1824 AREA27TMVL1824 AREA27TMVL2440 AREA27TMVL2440 AREA27TMVL2440 AREA27TMVL2440 AREA27TMVL2440 AREA27TMVL2440 AREA27TMVL2440

supra reg AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27

fishing tech DFN DFN DFN DFN DFN DFN DTS DTS DTS DTS DTS DTS DTS DTS DTS DTS HOK HOK HOK PG PG PG PG TM TM TM TM TM TM TM TM TM

vessel length VL1218 VL1218 VL1218 VL1218 VL1218 VL1218 VL1218 VL1218 VL1218 VL1218 VL1218 VL1218 VL1824 VL1824 VL1824 VL1824 VL1218 VL1218 VL1218 VL0010 VL0010 VL0010 VL0010 VL1824 VL1824 VL2440 VL2440 VL2440 VL2440 VL2440 VL2440 VL2440

year 2008 2009 2010 2012 2013 2014 2009 2010 2011 2012 2013 2014 2011 2012 2013 2014 2009 2010 2011 2011 2012 2013 2014 2011 2013 2008 2009 2010 2011 2012 2013 2014

cap inv 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 -

effort 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

employment 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 -

expenditures 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 -

income 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

landings 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

The next table is providing an overview of all fleet segments data (clusters and non clusters) and theirs consistency during analysed period. 1 2 3

c l u s t e r M S ← n a . e x c l u d e ( s u b s e t ( c l u s t e r M S 1 3 , c o u n t r y c o d e == MS, s e l e c t = c ( f i s h i n g t e c h , supra reg , v e s s e l l e n g t h , c l u s t e r , year ) ) )

4 5

cluster MS $ year ← f a c t o r ( cluster MS $ year )

6 7

cluster MS $ c l u s t e r ← f a c t o r ( cluster MS $ c l u s t e r )

8 9

cluster MS $ f i s h i n g t e c h ← f a c t o r ( cluster MS $ f i s h i n g t e c h )

10 11

cluster MS $ v e s s e l l e n g t h ← f a c t o r ( cluster MS $ v e s s e l l e n g t h )

12 13

cluster MS $ supra reg ← f a c t o r ( cluster MS $ supra reg )

14 15

l e v e l s ( c l u s t e r M S $ y e a r ) ← c ( ” 08 ” , ” 09 ” , ” 10 ” , ” 11 ” , ” 12 ” , ” 13 ” , ” 14 ” )

16 17 18

I c l u s t e r ← count ( c l u s t e r M S , c ( ” s u p r a r e g ” , ” f i s h i n g t e c h ” , ” v e s s e l l e n g t h ” , ” c l u s t e r ” , ” year ” ) )

19 20 21

1

2 3 4

I c l u s t e r ← cast ( I cluster , cluster + supra reg + vessel length + fishing tech ∼ year ) p r i n t ( x t a b l e ( I c l u s t e r , c a p t i o n = ”Map o f f l e e t segments and c l u s t e r e d f l e e t segments o v e r t h e time s e r i e s . ” , d i g i t s = 0 ) , c a p t i o n . p l a c e m e n t = ” top ” , i n c l u d e . r o w n a m e s = FALSE, l a b e l = ” tab : t a b l e 2 ” , f o r m a t . a r g s = l i s t ( b i g . m a r k = ” , ” ) , t a b u l a r . e n v i r o n m e n t = ” l o n g t a b l e ” , f l o a t i n g = FALSE, N A . s t r i n g = ”−” )

Table 3: Map of fleet segments and clustered fleet segments over the time series. cluster AREA27DFNVL1218 AREA27DFNVL1218 AREA27DFNVL1218 AREA27DFNVL1218 AREA27DFNVL1218 AREA27DFNVL1218 AREA27DTSVL1218 AREA27DTSVL1218 AREA27DTSVL1218 AREA27DTSVL1218 AREA27DTSVL1218 AREA27DTSVL1824 AREA27DTSVL1824 AREA27HOKVL1218 AREA27HOKVL1218 AREA27HOKVL1218 AREA27PGVL0010 AREA27PGVL0010 AREA27TMVL1824 AREA27TMVL1824 AREA27TMVL1824 AREA27TMVL2440 AREA27TMVL2440 AREA27TMVL2440 Unclustered Unclustered

supra reg AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27

vessel length VL1012 VL1012 VL1218 VL1218 VL1218 VL1824 VL0010 VL1012 VL1218 VL1218 VL1218 VL1824 VL2440 VL1218 VL1824 VL2440 VL0010 VL0010 VL1218 VL1824 VL1824 VL1824 VL2440 VL40XX VL0010 VL0010 18

fishing tech DFN HOK DFN HOK PMP DFN DTS DTS DTS PMP TM DTS DTS HOK HOK HOK DTS PG TM PMP TM TM TM TM DTS PG

08 1 1 1 1 1 1

09 1 1 1 1 1 1 1 1 1 1 1

10 1 1 1 1 1 1 1 1 1 1

11 1 1 1 1 1 1 1 1 1 1 1 1 -

12 1 1 1 1 1 1 1 1 1 1 1 1 1 1 -

13 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 -

14 1 1 1 1 1 1 1 1 1 1 -

Unclustered Unclustered Unclustered Unclustered Unclustered Unclustered Unclustered Unclustered Unclustered Unclustered Unclustered Unclustered Unclustered Unclustered Unclustered

4.3.3

AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 NONE NONE NONE NONE NONE OFR

VL1012 VL1012 VL1218 VL1218 VL1218 VL1824 VL1824 VL2440 VL40XX VL0010 VL1012 VL1218 VL1824 VL2440 VL40XX

DTS PG DFN DTS PG DTS TM DTS DTS INACTIVE INACTIVE INACTIVE INACTIVE INACTIVE TM

1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1

1 1 1 1 1 1 1

1 1 1 1 1 1 1 1

Indicators

This subsection provides tables describing the data sets in terms of number of values provided by fleet segment and template. The aim of this analysis is to check if all variables which have to be submitted in different templates are provided. There is no cross check with the capacity template, or attempt to check what should be provided against what have been provided. In case there is not information provided by fleet segment in the template the fleet segment is not listed in the Tables and in case there was no data provided for certain year ’-’ sighn is used. All templates, submitted during the data call are analysed in this sub-chapter. The number of obligatory variables is listed in the headings of all tables. 1

I c a p a c i t y ← s u b s e t ( c a p a c i t y 2 0 1 3 , c o u n t r y c o d e == MS)

2 3 4

I c a p a c i t y ← count ( I c a p a c i t y , c ( ” c o u n t r y c o d e ” , ” y e a r ” , ” s u p r a r e g ” , ” f i s h i n g t e c h ” , ” vessel length ”) )

5 6

I c a p a c i t y ← rename ( I c a p a c i t y , c ( f r e q = ” c a p a c i t y ” ) )

7 8 9

1

2 3 4

I capacity ← cast ( I capacity , country code + supra reg + f i s h i n g t e c h + v e s s e l l e n g t h ∼ year ) p r i n t ( x t a b l e ( I c a p a c i t y , c a p t i o n = ”Number o f v a r i a b l e s p r o v i d e d by f l e e t segment i n t h e Capacity template (5 o b l i g a t o r y v a r i a b l e s ) . ” , d i g i t s = 0 ) , c a p t i o n . p l a c e m e n t = ” top ” , , i n c l u d e . r o w n a m e s = FALSE, l a b e l = ” tab : t a b l e 2 ” , f o r m a t . a r g s = l i s t ( b i g . m a r k = ” , ” ) , t a b u l a r . e n v i r o n m e n t = ” l o n g t a b l e ” , f l o a t i n g = FALSE, N A . s t r i n g = ”−” )

Table 4: Number of variables provided by fleet segment in the Capacity template (5 obligatory variables). country code POL POL POL POL POL POL POL POL POL

supra reg AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27

fishing tech DFN DFN DFN DTS DTS DTS DTS DTS DTS

vessel length VL1012 VL1218 VL1824 VL0010 VL1012 VL1218 VL1824 VL2440 VL40XX 19

2008 5 5 5 5 5 5

2009 5 5 5 5 5 5 5 5

2010 5 5 5 5 5 5 5

2011 5 5 5 5 5 5 5

2012 5 5 5 5 5 5 5 5

2013 5 5 5 5 5 5 5

2014 5 5 5 5 5 5

POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL

AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 NONE NONE NONE NONE NONE OFR

HOK HOK HOK HOK PG PG PG PMP PMP TM TM TM TM INACTIVE INACTIVE INACTIVE INACTIVE INACTIVE TM

VL1012 VL1218 VL1824 VL2440 VL0010 VL1012 VL1218 VL1218 VL1824 VL1218 VL1824 VL2440 VL40XX VL0010 VL1012 VL1218 VL1824 VL2440 VL40XX

5 5 5 5 5 5 5 5 5 5 5

5 5 5 5 5 5 5 5 5 5 5 5 5

5 5 5 5 5 5 5 5 5 5 5 5 5 5

5 5 5 5 5 5 5 5 5 5 5 5 5 5 5

5 5 5 5 5 5 5 5 5 5 5 5 5

5 5 5 5 5 5 5 5 5 5 5 5 5 5 5

5 5 5 5 5 5 5 5 5 5 5 5

It is also possible to calculate number of indicators missing in the template. However this way of the analysis could be used only in cases all variables in the template are obligatory. In this case number between 0 and -3 will indicate number of missing variables, however in case none of the variable submitted ’-’ are inserted (see the example below). 1

I i n c o m e ← s u b s e t ( income2013 , c o u n t r y c o d e == MS)

2 3 4

I i n c o m e ← count ( I i n c o m e , c ( ” c o u n t r y c o d e ” , ” y e a r ” , ” s u p r a r e g ” , ” f i s h i n g t e c h ” , ” vessel length ”) )

5 6

I income $ fre q ← I income $ fre q − 4

7 8

I i n c o m e ← rename ( I i n c o m e , c ( f r e q = ” income ” ) )

9 10 11

1

2 3 4

I income ← c a s t ( I income , country code + supra reg + f i s h i n g t e c h + v e s s e l l e n g t h ∼ year ) p r i n t ( x t a b l e ( I i n c o m e , c a p t i o n = ”Number o f v a r i a b l e s m i s s i n g f o r f l e e t segment i n t h e Income template. ” , d i g i t s = 0 ) , c a p t i o n . p l a c e m e n t = ” top ” , i n c l u d e . r o w n a m e s = FALSE, l a b e l = ” tab : t a b l e 3 ” , f o r m a t . a r g s = l i s t ( b i g . m a r k = ” , ” ) , t a b u l a r . e n v i r o n m e n t = ” l o n g t a b l e ” , f l o a t i n g = FALSE, N A . s t r i n g = ”−” )

Table 5: Number of variables missing for fleet segment in the Income template. country code POL POL POL POL POL POL POL POL POL POL

supra reg AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27

fishing tech DFN DTS DTS DTS DTS HOK PG PG TM TM

vessel length VL1218 VL1012 VL1218 VL1824 VL2440 VL1218 VL0010 VL1012 VL1824 VL2440

20

2008 0 0 0 0 0 0 0

2009 0 0 0 0 0 0 0 0

2010 0 0 0 0 0 0 0 0 0

2011 0 0 0 0 0 0 0 0

2012 0 0 0 0 0 0

2013 0 0 0 0 0 0 0

2014 -3 -3 -3 -3 -3 -3 -3

1

I e x p e n d i t u r e ← s u b s e t ( e x p e n d i t u r e 2 0 1 3 , c o u n t r y c o d e == MS)

2 3 4

I e x p e n d i t u r e ← count ( I e x p e n d i t u r e , c ( ” c o u n t r y c o d e ” , ” y e a r ” , ” s u p r a r e g ” , ” fishing tech ” , ” vessel length ”) )

5 6

I e x p e n d i t u r e ← rename ( I e x p e n d i t u r e , c ( f r e q = ” c o s t s ” ) )

7 8 9

1

2 3 4

I expenditure ← cast ( I expenditure , country code + supra reg + f i s h i n g t e c h + v e s s e l l e n g t h ∼ year ) p r i n t ( x t a b l e ( I e x p e n d i t u r e , c a p t i o n = ”Number o f v a r i a b l e s p r o v i d e d by f l e e t segment i n t h e Expenditure template (8 o b l i g a t o r y v a r i a b l e s ) . ” , d i g i t s = 0 ) , c a p t i o n . p l a c e m e n t = ” top ” , i n c l u d e . r o w n a m e s = FALSE, l a b e l = ” tab : t a b l e 4 ” , f o r m a t . a r g s = l i s t ( b i g . m a r k = ” , ” ) , t a b u l a r . e n v i r o n m e n t = ” l o n g t a b l e ” , f l o a t i n g = FALSE, N A . s t r i n g = ”−” )

Table 6: Number of variables provided by fleet segment in the Expenditure template (8 obligatory variables). country code POL POL POL POL POL POL POL POL POL POL 1

supra reg AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27

fishing tech DFN DTS DTS DTS DTS HOK PG PG TM TM

vessel length VL1218 VL1012 VL1218 VL1824 VL2440 VL1218 VL0010 VL1012 VL1824 VL2440

2008 7 7 7 7 7 7 7

2009 7 7 7 7 7 7 7 7

2010 7 7 7 7 7 7 7 7 7

2011 8 8 8 8 8 8 8 8

2012 8 8 8 8 8 8

2013 8 8 8 8 8 8 8

I employment ← s u b s e t ( employment2013 , c o u n t r y c o d e == MS)

2 3 4

I employment ← count ( I employment , c ( ” c o u n t r y c o d e ” , ” y e a r ” , ” s u p r a r e g ” , ” f i s h i n g t e c h ” , ” vessel length ”) )

5 6

I employment ← rename ( I employment , c ( f r e q = ” employment ” ) )

7 8 9

1

2 3 4

I employment ← c a s t ( I employment , c o u n t r y c o d e + s u p r a r e g + f i s h i n g t e c h + v e s s e l l e n g t h ∼ year ) p r i n t ( x t a b l e ( I employment , c a p t i o n = ”Number o f v a r i a b l e s p r o v i d e d by f l e e t segment i n t h e Employment t e m p l a t e ( 3 o b l i g a t o r y v a r i a b l e s ) . ” , d i g i t s = 0 ) , c a p t i o n . p l a c e m e n t = ” top ” , i n c l u d e . r o w n a m e s = FALSE, l a b e l = ” tab : t a b l e 5 ” , f o r m a t . a r g s = l i s t ( b i g . m a r k = ” , ” ) , t a b u l a r . e n v i r o n m e n t = ” l o n g t a b l e ” , f l o a t i n g = FALSE, N A . s t r i n g = ”−” )

Table 7: Number of variables provided by fleet segment in the Employment template (3 obligatory variables). country code POL POL POL POL POL POL POL POL POL POL

supra reg AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27

fishing tech DFN DTS DTS DTS DTS DTS HOK PG PG TM

vessel length VL1218 VL1012 VL1218 VL1824 VL2440 VL40XX VL1218 VL0010 VL1012 VL1824 21

2008 3 3 3 3 3 3 3 -

2009 3 3 3 3 3 3 3 3 -

2010 3 3 3 3 3 3 3 3 3 -

2011 3 3 3 3 3 3 3 3

2012 3 3 3 3 3 3 -

2013 3 3 3 3 3 3 3

POL POL 1

AREA27 OFR

TM TM

VL2440 VL40XX

3 3

3 3

3 3

3 3

3 3

3 3

I c a p ← s u b s e t ( c a p v a l i n v e s t 2 0 1 3 , c o u n t r y c o d e == MS)

2 3 4

I c a p ← count ( I c a p , c ( ” c o u n t r y c o d e ” , ” y e a r ” , ” s u p r a r e g ” , ” f i s h i n g t e c h ” , ” vessel length ”) )

5 6

I c a p ← rename ( I c a p , c ( f r e q = ” c a p i t a l ” ) )

7 8 9

1

2 3 4

I cap ← cast ( I cap , country code + supra reg + f i s h i n g t e c h + v e s s e l l e n g t h ∼ year ) p r i n t ( x t a b l e ( I c a p , c a p t i o n = ”Number o f v a r i a b l e s p r o v i d e d by f l e e t segment i n t h e C a p i t a l and I n v e s t m e n t t e m p l a t e ( 4 o b l i g a t o r y v a r i a b l e s f o r a c t i v e f l e e t s , 1 f o r i n a c t i v e ) . ” , d i g i t s = 0 ) , c a p t i o n . p l a c e m e n t = ” top ” , i n c l u d e . r o w n a m e s = FALSE, l a b e l = ” tab : t a b l e 6 ” , f o r m a t . a r g s = l i s t ( b i g . m a r k = ” , ” ) , t a b u l a r . e n v i r o n m e n t = ” l o n g t a b l e ” , f l o a t i n g = FALSE, N A . s t r i n g = ”−” )

Table 8: Number of variables provided by fleet segment in the Capital and Investment template (4 obligatory variables for active fleets, 1 for inactive). country code POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL 1

supra reg AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 NONE NONE NONE NONE NONE

fishing tech DFN DTS DTS DTS DTS HOK PG PG TM TM INACTIVE INACTIVE INACTIVE INACTIVE INACTIVE

vessel length VL1218 VL1012 VL1218 VL1824 VL2440 VL1218 VL0010 VL1012 VL1824 VL2440 VL0010 VL1012 VL1218 VL1824 VL2440

2008 4 4 4 4 4 4 4 1 1 1 1 1

2009 4 4 4 4 4 4 4 4 1 1 1 1 1

2010 4 4 4 4 4 4 4 4 4 1 1 1 1 1

2011 4 4 4 4 4 4 4 4 1 1 1 1 1

2012 4 4 4 4 4 4 1 1 1 1

2013 4 4 4 4 4 4 4 1 1 1 1 -

I e f r t ← s u b s e t ( e f f o r t 2 0 1 3 , c o u n t r y c o d e == MS)

2 3 4

I e f r t ← count ( I e f r t , c ( ” c o u n t r y c o d e ” , ” y e a r ” , ” s u p r a r e g ” , ” f i s h i n g t e c h ” , ” vessel length ”) )

5 6

I e f r t ← rename ( I e f r t , c ( f r e q = ” e f f o r t F S ” ) )

7 8 9

1

2 3 4

I e f r t ← cast ( I e f r t , country code + supra reg + f i s h i n g t e c h + v e s s e l l e n g t h ∼ year ) p r i n t ( x t a b l e ( I e f r t , c a p t i o n = ”Number o f v a r i a b l e s p r o v i d e d by f l e e t segment i n t h e E f f o r t template (2 o b l i g a t o r y v a r i a b l e s ) . ” , d i g i t s = 0 ) , c a p t i o n . p l a c e m e n t = ” top ” , i n c l u d e . r o w n a m e s = FALSE, l a b e l = ” tab : t a b l e 6 ” , f o r m a t . a r g s = l i s t ( b i g . m a r k = ” , ” ) , t a b u l a r . e n v i r o n m e n t = ” l o n g t a b l e ” , f l o a t i n g = FALSE, N A . s t r i n g = ”−” )

Table 9: Number of variables provided by fleet segment in the Effort template (2 obligatory variables). country code

supra reg

fishing tech

vessel length 22

2008

2009

2010

2011

2012

2013

2014

POL POL POL POL POL POL POL POL POL POL POL POL

AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 OFR

DFN DTS DTS DTS DTS DTS HOK PG PG TM TM TM

VL1218 VL1012 VL1218 VL1824 VL2440 VL40XX VL1218 VL0010 VL1012 VL1824 VL2440 VL40XX

6 6 6 6 5 6 6 6 3

6 6 6 6 5 6 6 6 6 5

4 6 6 6 6 5 6 6 6 6 5

4 6 6 5 6 6 6 6 6 5

6 6 6 5 6 6 6 5

6 6 6 5 6 6 6 6 5

5 5 5 5 5 5 5 5 5

In order to check correspondence between weight and value per fleet and species provided in the template ’Landings’ additional check is performed for landings weight and value submitted. The values/weight by specie and segment are compared against weight/value by specie and segments. In case some species are missing tables, identifying the fleet segment, year, number of species missing and weight/value provided are printed. In cases there is no data missing message ”No missing values found for Landings in weight/value and corresponding Landings in value/weight” is displayed. Note: the check is done on the fleet segment level and doesn’t check for sub region and gear type. 1 2

I ws ← subset ( landings 2013 , s e l e c t = c ( ” country code ” , ” s p e c i e s ” , ” year ” , ” supra reg ” , ” fishing tech ” , ” vessel length ” , ” s weigth ”) )

3 4

t e s t 2 ← s u b s e t ( I ws , s w e i g t h != 0 )

5 6

I w e i g h t ← s u b s e t ( t e s t 2 , c o u n t r y c o d e == MS)

7 8 9

I w e i g h t ← count ( I w e i g h t , c ( ” c o u n t r y c o d e ” , ” y e a r ” , ” s u p r a r e g ” , ” f i s h i n g t e c h ” , ” vessel length ”) )

10 11

I w e i g h t ← rename ( I w e i g h t , c ( f r e q = ” w e i g t h ” ) )

12 13 14

I v l ← subset ( landings 2013 , s e l e c t = c ( ” country code ” , ” s p e c i e s ” , ” year ” , ” supra reg ” , ” fishing tech ” , ” vessel length ” , ” s value ”) )

15 16

t e s t ← s u b s e t ( I v l , s v a l u e != 0 )

17 18

I v a l u e ← s u b s e t ( t e s t , c o u n t r y c o d e == MS)

19 20 21

I v a l u e ← count ( I v a l u e , c ( ” c o u n t r y c o d e ” , ” y e a r ” , ” s u p r a r e g ” , ” f i s h i n g t e c h ” , ” vessel length ”) )

22 23

I v a l u e ← rename ( I v a l u e , c ( f r e q = ” v a l u e ” ) )

24 25 26 27 28

# ### calculation of missing values for weight and value... s p e c i e s ← merge ( I w e i g h t , I v a l u e , b y . x = c ( ” c o u n t r y c o d e ” , ” y e a r ” , ” s u p r a r e g ” , ” f i s h i n g t e c h ” , ” v e s s e l l e n g t h ” ) , by.y = c ( ” country code ” , ” year ” , ” supra reg ” , ” f i s h i n g t e c h ” , ” v e s s e l l e n g t h ” ) , a l l . x = TRUE, a l l . y = TRUE)

29 30

s p e c i e s $A ← s p e c i e s $ w e i g t h − s p e c i e s $ v a l u e

31 32

s p e c i e s ← s p e c i e s [ , −6 ]

33 34 35

1 2 3

I s p e c i e s ← cast ( species , country code + supra reg + f i s h i n g t e c h + v e s s e l l e n g t h ∼ year ) # summary missing values ( both euro or tonnes ) miss euro.length ← s t a t s : : : aggregate.formula ( s weigth ∼ country code + supra reg + f i s h i n g t e c h + v e s s e l l e n g t h + year , data = s u b s e t ( l a n d i n g s 2 0 1 3 , i s . n a ( s v a l u e ) |

23

4

s v a l u e == 0 & ( s w e i g t h != 0 & s v a l u e != 0 ) ) , l e n g t h )

5 6 7 8

miss euro.sum ← s t a t s : : : aggregate.formula ( s weigth ∼ country code + supra reg + f i s h i n g t e c h + v e s s e l l e n g t h + year , data = s u b s e t ( l a n d i n g s 2 0 1 3 , i s . n a ( s v a l u e ) | s v a l u e == 0 & ( s w e i g t h != 0 & s v a l u e != 0 ) ) , sum )

9 10 11

m i s s e u r o ← merge ( m i s s e u r o . l e n g t h , m i s s e u r o . s u m , by = c ( ” c o u n t r y c o d e ” , ” s u p r a r e g ” , ” f i s h i n g t e c h ” , ” v e s s e l l e n g t h ” , ” year ” ) )

12 13

m i s s e u r o ← rename ( m i s s e u r o , c ( s w e i g t h . x = ” n u m b e r s p m i s s i n g ” ) )

14 15

m i s s e u r o ← rename ( m i s s e u r o , c ( s w e i g t h . y = ” w e i g h t p r o v i d e d ” ) )

16 17 18

19

20 21 22 23

24

i f ( nrow ( s u b s e t ( m i s s e u r o , c o u n t r y c o d e == MS) ) != 0 ) { p r i n t ( x t a b l e ( s u b s e t ( m i s s e u r o , c o u n t r y c o d e == MS) , c a p t i o n = ” T o t a l l a n d i n g s w e i g h t and t o t a l number o f rows i n t h e l a n d i n g s data s e t \ n f o r which l a n d i n g s w e i g h t i s d i f f e r e n t from z e r o and v a l u e o f l a n d i n g s i s z e r o o r NA, i . e . , l a n d e d w e i g h t w i t h o u t v a l u e . ” , d i g i t s = 0 ) , c a p t i o n . p l a c e m e n t = ” top ” , i n c l u d e . r o w n a m e s = FALSE, l a b e l = ” tab : t a b l e 1 ” , format.args = l i s t ( big.mark = ” , ” ) , tabular.environment = ” l o n g t a b l e ” , f l o a t i n g = FALSE, N A . s t r i n g = ”−” ) } else { p r i n t ( ”No m i s s i n g v a l u e s found f o r Landings i n v a l u e and c o r r e s p o n d i n g Landings i n w e i g h t . ”) }

Table 10: Total landings weight and total number of rows in the landings data set for which landings weight is different from zero and value of landings is zero or NA, i.e., landed weight without value. country code POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL 1 2 3

supra reg AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 OFR OFR OFR OFR OFR OFR OFR

fishing tech DTS DTS DTS DTS DTS DTS DTS PG TM TM TM TM TM TM TM

vessel length VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL0010 VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX

year 2,008 2,009 2,010 2,011 2,012 2,013 2,014 2,010 2,008 2,009 2,010 2,011 2,012 2,013 2,014

number sp missing 11 9 9 11 9 8 16 1 7 13 16 20 20 26 16

weight provided 5,424,840 4,254,231 5,309,367 5,257,011 5,340,650 7,261,398 6,792,987 16 26,102,407 76,494,747 55,361,589 63,889,490 53,788,163 54,137,233 45,258,882

miss kg.sum ← s t a t s : : : aggregate.formula ( s v a l u e ∼ country code + supra reg + f i s h i n g t e c h + v e s s e l l e n g t h + year , data = s u b s e t ( land , i s . n a ( s w e i g t h ) | s w e i g t h == 0 & ( s w e i g t h != 0 & s v a l u e != 0 ) ) , sum )

4 5 6 7

miss kg.length ← s t a t s : : : aggregate.formula ( s value ∼ country code + supra reg + f i s h i n g t e c h + v e s s e l l e n g t h + year , data = s u b s e t ( land , i s . n a ( s w e i g t h ) | s w e i g t h == 0 & ( s w e i g t h != 0 & s v a l u e != 0 ) ) , l e n g t h )

8 9 10

m i s s k g ← merge ( m i s s k g . l e n g t h , miss kg.sum , by = c ( ” c o u n t r y c o d e ” , ” s u p r a r e g ” , ” f i s h i n g t e c h ” , ” v e s s e l l e n g t h ” , ” year ” ) )

11 12

m i s s k g ← rename ( m i s s k g , c ( s v a l u e . x = ” n u m b e r s p m i s s i n g ” ) )

13 14

m i s s k g ← rename ( m i s s k g , c ( s v a l u e . y = ” v a l u e p r o v i d e d ” ) )

15

24

16 17

18

19 20 21 22

23

i f ( nrow ( s u b s e t ( m i s s k g , c o u n t r y c o d e == MS) ) != 0 ) { p r i n t ( x t a b l e ( s u b s e t ( m i s s k g , c o u n t r y c o d e == MS) , c a p t i o n = ” T o t a l v a l u e ( e u r o ) and t o t a l number o f rows i n t h e l a n d i n g s data s e t f o r which v a l u e o f l a n d i n g s i s d i f f e r e n t from z e r o and l a n d i n g s w e i g h t i s z e r o o r NA, i . e . v a l u e o f l a n d i n g s w i t h o u t c o r r e s p o n d i n g l a n d i n g s weight. ” , d i g i t s = 0 ) , c a p t i o n . p l a c e m e n t = ” top ” , i n c l u d e . r o w n a m e s = FALSE, l a b e l = ” tab : t a b l e 1 ” , format.args = l i s t ( big.mark = ” , ” ) , tabular.environment = ” l o n g t a b l e ” , f l o a t i n g = FALSE, N A . s t r i n g = ”−” ) } else { p r i n t ( ”No m i s s i n g v a l u e s found f o r Landings i n w e i g h t and c o r r e s p o n d i n g Landings i n v a l u e . ”) }

[1] ”No missing values found for Landings in weight and corresponding Landings in value.” The following table was created additionally in order to provide an overview of the fleet segments and number of species landed. 1 2 3

I value ← cast ( I value , country code + supra reg + f i s h i n g t e c h + v e s s e l l e n g t h ∼ year )

1 2

3 4 5

p r i n t ( x t a b l e ( I v a l u e , c a p t i o n = ”Number o f s p e c i e s p r o v i d e d by f l e e t segment i n t h e Landings t e m p l a t e ( by l a n d e d v a l u e ) . ” , d i g i t s = 0 ) , c a p t i o n . p l a c e m e n t = ” top ” , i n c l u d e . r o w n a m e s = FALSE, l a b e l = ” tab : t a b l e 6 ” , f o r m a t . a r g s = l i s t ( b i g . m a r k = ” , ” ) , t a b u l a r . e n v i r o n m e n t = ” l o n g t a b l e ” , f l o a t i n g = FALSE, N A . s t r i n g = ”−” )

Table 11: Number of species provided by fleet segment in the Landings template (by landed value). country code POL POL POL POL POL POL POL POL POL POL

supra reg AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27

fishing tech DFN DTS DTS DTS DTS HOK PG PG TM TM

vessel length VL1218 VL1012 VL1218 VL1824 VL2440 VL1218 VL0010 VL1012 VL1824 VL2440

25

2008 17 16 13 9 33 16 8

2009 13 15 14 9 9 34 17 9

2010 15 12 14 12 8 10 38 17 11

2011 13 14 10 5 34 19 8 9

2012 13 18 12 32 19 10

2013 13 18 16 33 19 10 9

2014 20 18 15 32 22 16 7

4.4

Analyses by variable Groups

Each template/variable group analysis includes several basic checks: • comparison between the sum of the data by segment and the totals provided at MS level. These figures due to confidentiality might not equate. Since it’s not always clear when these situations might occur, whenever the difference is found to be of significance (more than 10 percent) the table with the relevant indicators and years is displayed, otherwise the message ”No significant differences were found while comparing national totals and the sum over data by fleet segment!” is shown. • plotting of the indicators provided as totals and by main fishing techniques, • plotting of averages per vessels (calculated on the clusters level) with outliers table (optional), • plotting of variations by variable between cosequative years, • tables with the lists of significant variations across years. The basic assumptions behind the Tables with the Lists of significant variations across years is stability of the time series. It is assumed that the data should not fluctuate more then 100 percent in case of totals per fleet and 50 percent in case of averages per vessel. In cases annual fluctuations from one year to another are exceeding these thresholds the fleet segments are included in the table and MS can check the data. The initial threshold have been chosen based on quite high variability of economic data and could be updated in the script. 4.4.1

Capacity

The following subsection includes results from the exploratory analysis on the capacity data sets. Totals from both data sets (national and fleet segment level) are compared. Output is produced in a table format whenever significant differences are found. The figure shows variable totals by year and supra-region and aims to facilitate visualisation of the data series. Additionally, two boxplots are provided for: (1) average vessel gross tonnage and (2) average vessel engine power. Values identified as outliers are listed in a table below. This exercise serves mainly to highlight the presence of extreme values, which may be reasonable in some cases. Finally, a plot of variations across years highlights the (in)stability of vessel characteristics over the time series. A list of all significant variations, if detected, is presented in the subsequent tables. 1 2 3 4

5 6 7 8 9

10

i f ( nrow ( s u b s e t ( c o m p a r e d a t a . d , c o u n t r y c o d e == MS & t e m p l a t e == ” c a p a c i t y ” ) ) != 0) { p r i n t ( x t a b l e ( s u b s e t ( c o m p a r e d a t a . d , c o u n t r y c o d e == MS & t e m p l a t e == ” c a p a c i t y ” ) , c a p t i o n = ” T o t a l v a l u e s by v a r i a b l e . Nat \\ t o t a l shows t h e t o t a l taken from t h e n a t i o n a l d a t a s e t and FS\\ d a t a shows t h e t o t a l v a l u e c a l c u l a t e d from t h e f l e e t segment and s u p r a − r e g i o n d a t a s e t . T h e o r e t i c a l l y both v a l u e s s h o u l d e q u a t e . The t a b l e , when p r o v i d e d , l i s t s t h e v a r i a b l e s where s i g n i f i c a n t d i f f e r e n c e s were found ( $>$ 0 . 1 ) . ” ) , c a p t i o n . p l a c e m e n t = ” top ” , i n c l u d e . r o w n a m e s = FALSE, l a b e l = ” tab : t a b l e 7 ” , format.args = l i s t ( big.mark = ” , ” ) , tabular.environment = ” l o n g t a b l e ” , f l o a t i n g = FALSE, N A . s t r i n g = ”−” ) } else { p r i n t ( ”No s i g n i f i c a n t d i f f e r e n c e s were found w h i l e comparing n a t i o n a l t o t a l s and t h e sum o v e r data by f l e e t segment ! ” ) }

[1] ”No significant differences were found while comparing national totals and the sum over data by fleet segment!” 1 2 3 4 5 6

d o t p l o t ( y e a r ∼ m e t r i c | v a r i a b l e , data = s u b s e t ( capa , c o u n t r y c o d e == MS) , h o r i z o n a t l = TRUE, a u t o . k e y = l i s t ( cex = 0 . 8 , columns = 3 ) , s c a l e s = l i s t ( x = l i s t ( r e l a t i o n = ” f r e e ” , cex = 0 . 5 ) , y = l i s t ( cex = 0 . 5 ) ) , l a y o u t = c ( 3 , 1 ) , g r o u p s = f a c t o r ( s u p r a r e g ) , y l a b = l i s t ( l a b e l = ” Year ” , cex = 0 . 5 ) , x l a b = l i s t ( cex = 0 . 5 ) , a s p e c t = c ( 1 ) , o r i g i n = T, p a r . s t r i p . t e x t = l i s t ( cex = 0 . 6 ) )

26

AREA27

NONE

●

Year

Nr_Vessels 2014

●●

2013

●●

2012

●●

2011

● ●

●

2010

●

●

●

2009

●

●

2008

●●

0

●

●

● ●

● ●

●

●

●●

● ●

●

●

●

●●

●

600

●

●

●

● ●

400

●

Total_kW

●

●

200

OFR

●

Total_Gt

800

● ● ●

● ●

●

●●

●●

●●

●

●

●

0

5000 10000150002000025000

● ●

0

● ● ● ● ●

20000 40000 60000 80000

metric

Figure 1: Total values for Capacity Data by Supra-region and Year.

1 2 3

b o x p l o t ( a v g . g t v e s s e l ∼ year , data = s u b s e t ( capacity MS , c o u n t r y c o d e == MS) , l o g = ”y” , x l a b = ” Year ” , y l a b = ” g t / v e s s e l ” , c e x . l a b = 0 . 7 5 , c e x . a x i s = 0 . 7 5 , cex = 0 . 7 5 , boxwex = 0 . 2 , s t a p l e w e x = 0 . 5 )

27

5000 10000

●

●

●

●

●

●

●

●

●

●

●

2011

2012

2013

2014

●

●

●

●

●

●

100 5

10

50

gt/vessel

500

1000

●

2008

2009

2010

Year

Figure 2: Boxplots showing the average GT per vessel. 1 2

o u t t a b l e ← a s . d a t a . f r a m e ( b o x p l o t . a d d . o u t l i e r . l i s t ( s u b s e t ( capacity MS , c o u n t r y c o d e == MS) , ” y e a r ” , ” a v g . g t v e s s e l ” , ” t o t g t ” ) )

3 4 5

names ( o u t t a b l e ) ← c ( ” Country code ” , ” Year ” , ” S u p r a r e g ” , ” f i s h i n g t e c h ” , ” v e s s e l l e n g t h ” , ” t o t g t ” , ” t o t v e s ” , ” totkw ” , ” a v g . g t . v e s s e l ” , ” a v g . k w . v e s s e l ” )

6 7 8 9

# ### additional changes to sort the data.... o u t t a b l e 2 ← s u b s e t ( o u t t a b l e , s e l e c t = c ( ” Country code ” , ” S u p r a r e g ” , ” f i s h i n g t e c h ” , ” v e s s e l l e n g t h ” , ” Year ” , ” t o t g t ” , ” t o t v e s ” , ” totkw ” , ” a v g . g t . v e s s e l ” , ” a v g . k w . v e s s e l ” ) )

10 11 12 1 2 3 4

o u t t a b l e 2 ← s o r t ( o u t t a b l e 2 , p a r t i a l = c ( ” Country code ” , ” S u p r a r e g ” , ” f i s h i n g t e c h ” , ” v e s s e l l e n g t h ” ) , Y e a r . i n c r e a s i n g = TRUE) p r i n t ( x t a b l e ( o u t t a b l e 2 , c a p t i o n = ” L i s t o f o u t l i e r s ” , c a p t i o n . p l a c e m e n t = ” bottom ” , ) , c a p t i o n . p l a c e m e n t = ” t o p ” , i n c l u d e . r o w n a m e s = FALSE , l a b e l = ” t a b : t a b l e 1 ” , f o r m a t . a r g s = l i s t ( b i g . m a r k = ” , ” ) , t a b u l a r . e n v i r o n m e n t = ” l o n g t a b l e ” , f l o a t i n g = FALSE , N A . s t r i n g = ”−” )

28

Table 12: List of outliers Country code POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL

Supra reg AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 OFR OFR OFR OFR OFR OFR OFR

fishing tech DTS DTS DTS DTS DTS DTS DTS TM TM TM TM TM TM TM TM TM TM TM

vessel length VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX

Year 2008 2009 2010 2011 2012 2013 2014 2011 2012 2013 2014 2008 2009 2010 2011 2012 2013 2014

totgt 4,876.00 1,805.00 1,805.00 1,805.00 1,805.00 1,805.00 1,805.00 468.00 468.00 468.00 468.00 3,861.00 19,431.00 19,471.00 19,471.00 15,610.00 15,610.00 15,446.00

totves 2.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 3.00 3.00 3.00 2.00 2.00 2.00

1 b o x p l o t ( a v g . k w v e s s e l ∼ y e a r , d a t a = s u b s e t ( c a p a c i t y M S , c o u n t r y c o d e == MS) , 2 l o g = ” y ” , x l a b = ” Year ” , y l a b = ”kW/ v e s s e l ” , v a r w i d t h = T, c e x . l a b = 0 . 7 5 , 3 c e x . a x i s = 0 . 7 5 , c e x = 0 . 7 5 , boxwex = 0 . 2 , s t a p l e w e x = 0 . 5 )

29

totkw 6,905.00 3,375.00 3,375.00 3,375.00 3,375.00 3,375.00 3,375.00 740.00 740.00 740.00 740.00 3,200.00 14,416.00 15,040.00 15,040.00 11,840.00 11,840.00 11,216.00

avg.gt.vessel 2,438.00 1,805.00 1,805.00 1,805.00 1,805.00 1,805.00 1,805.00 468.00 468.00 468.00 468.00 3,861.00 6,477.00 6,490.33 6,490.33 7,805.00 7,805.00 7,723.00

avg.kw.vessel 3,452.50 3,375.00 3,375.00 3,375.00 3,375.00 3,375.00 3,375.00 740.00 740.00 740.00 740.00 3,200.00 4,805.33 5,013.33 5,013.33 5,920.00 5,920.00 5,608.00

5000

●

●

●

●

●

●

●

●

●

2011

2012

2013

2014

●

●

●

●

●

500 20

50

100

200

kW/vessel

1000

2000

● ●

● ●

2008

2009

2010

Year

Figure 3: Boxplots showing the average kW per vessel. 1 o u t t a b l e ← a s . d a t a . f r a m e ( b o x p l o t . a d d . o u t l i e r . l i s t ( s u b s e t ( c a p a c i t y M S , c o u n t r y c o d e == 2 MS) , ” y e a r ” , ” a v g . k w v e s s e l ” , ” totkw ” ) ) 3 4 names ( o u t t a b l e ) ← c ( ” C o u n t r y c o d e ” , ” Year ” , ” S u p r a r e g ” , ” f i s h i n g t e c h ” , ” v e s s e l l e n g t h ” , 5 ” t o t g t ” , ” t o t v e s ” , ” totkw ” , ” a v g . g t . v e s s e l ” , ” a v g . k w . v e s s e l ” ) 6 7 # ### a d d i t i o n a l c h a n g e s to sort the d a t a . . . . 8 o u t t a b l e 2 ← subset ( o u t t a b l e , s e l e c t = c ( ” Country code ” , ” Supra reg ” , ” f i s h i n g t e c h ” , 9 ” v e s s e l l e n g t h ” , ” Year ” , ” t o t g t ” , ” t o t v e s ” , ” totkw ” , ” a v g . g t . v e s s e l ” , ” a v g . k w . v e s s e l ” ) ) 10 11 o u t t a b l e 2 ← s o r t ( o u t t a b l e 2 , p a r t i a l = c ( ” C o u n t r y c o d e ” , ” S u p r a r e g ” , ” f i s h i n g t e c h ” , 12 ” v e s s e l l e n g t h ” ) , Y e a r . i n c r e a s i n g = TRUE) 1 2 3 4

p r i n t ( x t a b l e ( o u t t a b l e 2 , c a p t i o n = ” L i s t o f o u t l i e r s ” , c a p t i o n . p l a c e m e n t = ” bottom ” ) , c a p t i o n . p l a c e m e n t = ” t o p ” , i n c l u d e . r o w n a m e s = FALSE , l a b e l = ” t a b : t a b l e 1 ” , f o r m a t . a r g s = l i s t ( b i g . m a r k = ” , ” ) , t a b u l a r . e n v i r o n m e n t = ” l o n g t a b l e ” , f l o a t i n g = FALSE , N A . s t r i n g = ”−” )

Table 13: List of outliers Country code

Supra reg

fishing tech

vessel length

Year

30

totgt

totves

totkw

avg.gt.vessel

avg.kw.vessel

POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL

1 2 3 4 5 6 7 8 9 10 11

AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 OFR OFR OFR OFR OFR OFR OFR

DTS DTS DTS DTS DTS DTS DTS TM TM TM TM TM TM TM TM TM TM TM

VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX

2008 2009 2010 2011 2012 2013 2014 2011 2012 2013 2014 2008 2009 2010 2011 2012 2013 2014

4,876.00 1,805.00 1,805.00 1,805.00 1,805.00 1,805.00 1,805.00 468.00 468.00 468.00 468.00 3,861.00 19,431.00 19,471.00 19,471.00 15,610.00 15,610.00 15,446.00

2.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 3.00 3.00 3.00 2.00 2.00 2.00

6,905.00 3,375.00 3,375.00 3,375.00 3,375.00 3,375.00 3,375.00 740.00 740.00 740.00 740.00 3,200.00 14,416.00 15,040.00 15,040.00 11,840.00 11,840.00 11,216.00

2,438.00 1,805.00 1,805.00 1,805.00 1,805.00 1,805.00 1,805.00 468.00 468.00 468.00 468.00 3,861.00 6,477.00 6,490.33 6,490.33 7,805.00 7,805.00 7,723.00

s t r i p p l o t ( v a l u e ∼ v a r i a b l e | d c v a r , d a t a = ( s u b s e t ( v a r i a t i o n s d c 1 3 . m , v a r i a t i o n s d c 1 3 . m $ c o u n t r y c o d e == MS & v a r i a t i o n s d c 1 3 . m $ t e m p l a t e == ” c a p a c i t y ” & v a r i a t i o n s d c 1 3 . m $ v a r i a b l e != ” y1314 ” ) ) , l a y o u t = c ( 3 , 1 ) , , j i t t e r . d a t a = TRUE, c o l . l i n e = ” r e d ” , c o l . s y m b o l = ” b l a c k ” , panel = function (x , y , j i t t e r . d a t a , . . . ) { p a n e l . g r i d ( h = 0 , v = −1) p a n e l . s t r i p p l o t ( x , y , j i t t e r . d a t a = TRUE, . . . ) p a n e l . a v e r a g e ( x , y , f u n = mean , . . . ) } , x l a b = l i s t ( l a b e l = ” Year ” , c e x = 0 . 5 ) , y l a b = l i s t ( l a b e l = ” (%) ” , c e x = 0 . 5 ) , p a r . s t r i p . t e x t = l i s t ( cex = 0 . 6 ) , aspe ct = c ( 1 ) , s c a l e s = l i s t ( x = l i s t ( r o t = 45 , cex = 0 . 5 ) , y = l i s t ( r e l a t i o n = ” f r e e ” , cex = 0 . 5 ) ) )

31

3,452.50 3,375.00 3,375.00 3,375.00 3,375.00 3,375.00 3,375.00 740.00 740.00 740.00 740.00 3,200.00 4,805.33 5,013.33 5,013.33 5,920.00 5,920.00 5,608.00

totkw

totgt

8

totves ●

●

8

10

●

6

8

6

●

●

● ● ●

6

●

●

● ● ● ● ● ● ●● ● ● ● ● ●●

● ● ● ●● ● ●

21

3

●

y1

2 11

1 01 y1

y0

91

0

●

●

● ● ● ● ● ●● ● ● ●● ●

y1

● ● ● ● ● ● ● ● ●●

9

● ● ●● ● ● ● ●

80 y0

21

3

●

0

● ●● ● ● ● ●●

y1

2 11

1 01 y1

91

0

●

y0

y0

80

9

0

● ● ● ● ● ● ● ● ● ● ●● ●●

● ● ● ● ● ● ● ● ● ● ● ● ● ●

y1

2 3

●

● ● ● ●● ● ● ● ● ●● ●

●● ● ● ●● ● ● ●

● ● ●● ● ● ● ● ● ●

21

2

2

●

● ● ● ● ● ●● ● ● ● ● ●

11

1 01 y1

0 91 y0

9 80

● ●● ● ● ● ●● ● ● ● ● ●●

●

0

●

y1

● ● ● ● ● ●● ● ● ● ● ●

● ● ● ● ● ●● ●

●

●

●

y1

● ●

y0

4

●

●

2

4

4

(%)

●

Year

Figure 4: Plot of variations per variable between consecutive years. Comparison is made between fleet segments at supra-region level.

32

1 2 3 4 5 6 7 8 9

p r i n t ( x t a b l e ( s u b s e t ( v a r i a t i o n s d c 1 3 e x t r , c o u n t r y c o d e == MS & t e m p l a t e == ” c a p a c i t y ” & ( a b s ( y0809 ) > ( 1 0 0 ) | ( a b s ( y0910 ) > ( 1 0 0 ) ) | ( a b s ( y1011 ) > ( 1 0 0 ) ) | ( a b s ( y1112 ) > ( 1 0 0 ) ) | ( a b s ( y1213 ) > ( 1 0 0 ) ) | ( a b s ( y1314 ) > ( 1 0 0 ) ) ) , s e l e c t = c ( s u p r a r e g , country code , f i s h i n g t e c h , v e s s e l l e n g t h , dc var , year08 , year09 , year10 , y e a r 1 1 , y e a r 1 2 , y e a r 1 3 , y e a r 1 4 , y0809 , y0910 , y1011 , y1112 , y1213 , y1314 ) ) , c a p t i o n = ” L i s t o f s i g n i f i c a n t v a r i a t i o n s ( $>$ 100 p e r c e n t ) a c r o s s y e a r s − C a p a c i t y . ” , d i g i t s = 0 ) , c a p t i o n . p l a c e m e n t = ” t o p ” , i n c l u d e . r o w n a m e s = FALSE , l a b e l = ” t a b : t a b l e 1 ” , f o r m a t . a r g s = l i s t ( b i g . m a r k = ” , ” ) , t a b u l a r . e n v i r o n m e n t = ” l o n g t a b l e ” , f l o a t i n g = FALSE , N A . s t r i n g = ”−” )

Table 14: List of significant variations (>100 percent) across years - Capacity.

33

supra reg AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 NONE NONE NONE

country code POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL

fishing tech DTS DTS DTS DTS DTS DTS HOK HOK HOK PG PG PG TM TM TM INACTIVE INACTIVE INACTIVE

vessel length VL0010 VL0010 VL0010 VL1012 VL1012 VL1012 VL1218 VL1218 VL1218 VL1218 VL1218 VL1218 VL1824 VL1824 VL1824 VL1824 VL1824 VL1824

dc var totves totkw totgt totves totkw totgt totves totkw totgt totves totkw totgt totves totkw totgt totves totkw totgt

year08 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 456 81

year09 5 495 48 0 0 0 37 6,206 1,339 0 0 0 0 0 0 4 716 198

year10 0 0 0 10 1,070 127 32 4,546 1,125 0 0 0 0 0 0 6 1,361 365

year11 0 0 0 0 0 0 27 3,772 958 1 72 18 13 3,397 867 6 1,638 360

year12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

year13 0 0 0 0 0 0 0 0 0 0 0 0 18 4,955 1,265 1 227 51

year14 0 0 0 0 0 0 0 0 0 0 0 0 17 4,403 1,443 1 121 37

y0809 Inf Inf Inf Inf Inf Inf 2 2 2

y0910 0 0 0 Inf Inf Inf 1 1 1 2 2 2

y1011 0 0 0 1 1 1 Inf Inf Inf Inf Inf Inf 1 1 1

y1112 0 0 0 0 0 0 0 0 0 0 0 0

y1213 Inf Inf Inf Inf Inf Inf

y1314 1 1 1 1 1 1

1 2 3 4 5 6 7 8

p r i n t ( x t a b l e ( s u b s e t ( v a r i a t i o n s u n v e x t r , c o u n t r y c o d e == MS & t e m p l a t e == ” c a p a c i t y ” & ( a b s ( y0809 ) > ( 5 0 ) | ( a b s ( y0910 ) > ( 5 0 ) ) | ( a b s ( y1011 ) > ( 5 0 ) ) | ( a b s ( y1112 ) > ( 5 0 ) ) | ( a b s ( y1213 ) > ( 5 0 ) ) ) , s e l e c t = c ( s u p r a r e g , c o u n t r y c o d e , f i s h i n g t e c h , v e s s e l l e n g t h , d c v a r , y2008 , y2009 , y2010 , y2011 , y2012 , y2013 , y2014 , y0809 , y0910 , y1011 , y1112 , y1213 ) ) , c a p t i o n = ” L i s t o f s i g n i f i c a n t v a r i a t i o n s ( $>$ 50 p e r c e n t ) d i g i t s = 0 ) , c a p t i o n . p l a c e m e n t = ” t o p ” , i n c l u d e . r o w n a m e s = FALSE , l a b e l = ” t a b : t a b l e 1 ” , f o r m a t . a r g s = l i s t ( b i g . m a r k = ” , ” ) , t a b u l a r . e n v i r o n m e n t = ” l o n g t a b l e ” , f l o a t i n g = FALSE , N A . s t r i n g = ”−” )

of

averages

( per

vessel )

across

years − Capacity. ” ,

Table 15: List of significant variations (>50 percent) of averages (per vessel) across years - Capacity.

34

supra reg AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 NONE NONE NONE

country code POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL

fishing tech DTS DTS DTS DTS DTS DTS HOK HOK HOK PG PG PG TM TM TM INACTIVE INACTIVE INACTIVE

vessel length VL0010 VL0010 VL0010 VL1012 VL1012 VL1012 VL1218 VL1218 VL1218 VL1218 VL1218 VL1218 VL1824 VL1824 VL1824 VL1824 VL1824 VL1824

dc var totgt totkw totves totgt totkw totves totgt totkw totves totgt totkw totves totgt totkw totves totgt totkw totves

y2008 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 40 228 1

y2009 10 99 1 0 0 0 36 168 1 0 0 0 0 0 0 50 179 1

y2010 0 0 0 13 107 1 35 142 1 0 0 0 0 0 0 61 227 1

y2011 0 0 0 0 0 0 35 140 1 18 72 1 67 261 1 60 273 1

y2012 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

y2013 0 0 0 0 0 0 0 0 0 0 0 0 70 275 1 51 227 1

y2014 0 0 0 0 0 0 0 0 0 0 0 0 85 259 1 37 121 1

y0809 Inf Inf Inf Inf Inf Inf 1 1 1

y0910 0 0 0 Inf Inf Inf 1 1 1 1 1 1

y1011 0 0 0 1 1 1 Inf Inf Inf Inf Inf Inf 1 1 1

y1112 0 0 0 0 0 0 0 0 0 0 0 0

y1213 Inf Inf Inf Inf Inf Inf

4.4.2

Employment

The following results are based on the employment variables taken from the Employment template. The purpose of this analysis is to easily visualise the data and highlight values that are missing for each variable or, for several reasons, appear incoherent. To avoid an over production of figures, the analysis focuses on yearly totals and fishing techniques only. Comparison between the national totals (by variable and year) and totals derived from fleet segment values are presented as well as the ratio between National FTE and harmonised FTE. Given the nature of the two variables, a constant ratio by year is expected. Across years, differences in the ratios are only justified if the national threshold has been modified. 1 2 3 4

5 6 7 8 9

10

i f ( nrow ( s u b s e t ( c o m p a r e d a t a . d , c o u n t r y c o d e == MS & t e m p l a t e == ” employment ” ) ) != 0) { p r i n t ( x t a b l e ( s u b s e t ( c o m p a r e d a t a . d , c o u n t r y c o d e == MS & t e m p l a t e == ” employment ” ) , c a p t i o n = ” T o t a l v a l u e s by v a r i a b l e . Nat \\ t o t a l shows t h e t o t a l taken from t h e n a t i o n a l d a t a s e t and FS\\ d a t a shows t h e t o t a l v a l u e c a l c u l a t e d from t h e f l e e t segment and s u p r a r e g i o n d a t a s e t . T h e o r e t i c a l l y both v a l u e s s h o u l d e q u a t e . The t a b l e , when p r o v i d e d , l i s t s t h e v a r i a b l e s where s i g n i f i c a n t d i f f e r e n c e s were found ( $>$ 0 . 1 ) . ” ) , c a p t i o n . p l a c e m e n t = ” top ” , i n c l u d e . r o w n a m e s = FALSE, l a b e l = ” tab : t a b l e 1 ” , format.args = l i s t ( big.mark = ” , ” ) , tabular.environment = ” l o n g t a b l e ” , f l o a t i n g = FALSE, N A . s t r i n g = ”−” ) } else { p r i n t ( ”No s i g n i f i c a n t d i f f e r e n c e s were found w h i l e comparing n a t i o n a l t o t a l s and t h e sum o v e r data by f l e e t segment ! ” ) }

[1] ”No significant differences were found while comparing national totals and the sum over data by fleet segment!” 1 2

3

employment sum ← s q l d f ( ” s e l e c t year , c o u n t r y c o d e , \ n v a r i a b l e , sum ( v a l u e ) m e t r i c \ nfrom f s v a r 2 0 1 3 \ nwhere t e m p l a t e =’employment ’ \ ngroup by year , c o u n t r y c o d e , v a r i a b l e ” , drv = ” SQLite ” )

4 5 6

7 8

b a r c h a r t ( y e a r ∼ m e t r i c | v a r i a b l e , data = s u b s e t ( employment sum , c o u n t r y c o d e == MS) , l a y o u t = c ( 3 , 1 ) , a s p e c t = c ( 1 ) , x l a b = l i s t ( l a b e l = ” No. Jobs , No. N a t i o n a l FTE and No. Harmonised FTE” , cex = 0 . 7 5 ) , y l a b = l i s t ( l a b e l = ” Year ” , cex = 0 . 7 5 ) , p a r . s t r i p . t e x t = l i s t ( cex = 0 . 6 ) , o r i g i n = 0 , s c a l e s = l i s t ( x = l i s t ( cex = 0 . 5 ) , y = l i s t ( cex = 0 . 5 ) ) )

35

0

totjob

500

1000

1500

2000

2500

3000

totnatfte

totharmfte

2013

Year

2012

2011

2010

2009

2008

0

500

1000

1500

2000

2500

3000

0

500

1000

1500

2000

2500

3000

No. Jobs, No. National FTE and No. Harmonised FTE

Figure 5: Total Employment by year 1

2

emp sum ← s q l d f ( ” s e l e c t year , c o u n t r y c o d e , f i s h i n g t e c h , \ n v a r i a b l e , sum ( v a l u e ) m e t r i c \ nfrom f s v a r 2 0 1 3 \ nwhere t e m p l a t e =’employment ’ \ ngroup by year , c o u n t r y c o d e , f i s h i n g t e c h , v a r i a b l e ”, drv = ” SQLite ” )

3 4 5 6 7 8

b a r c h a r t ( v a r i a b l e ∼ m e t r i c | f i s h i n g t e c h , data = d r o p l e v e l s ( s u b s e t ( emp sum , c o u n t r y c o d e == MS) ) , g r o u p s = year , a u t o . k e y = l i s t ( columns = 4 , cex = 0 . 7 ) , a s p e c t = c ( 1 ) , x l a b = l i s t ( cex = 0 . 7 5 ) , y l a b = l i s t ( l a b e l = ” F i s h i n g t e c h n i q u e ” , cex = 0 . 7 5 ) , p a r . s t r i p . t e x t = l i s t ( cex = 0 . 6 ) , s c a l e s = l i s t ( x = l i s t ( cex = 0 . 6 ) , y = l i s t ( cex = 0 . 6 ) ) )

36

2008 2009

2010 2011 0

2012 2013 500

1000

PG

TM

DFN

DTS

totharmfte

Fishing technique

totnatfte

totjob

HOK

totharmfte

totnatfte

totjob

0

500

1000

0

500

1000

metric

Figure 6: Data on employment by fishing technique and year. 1 2 3

b o x p l o t (METRIC ∼ a . y e a r , data = s u b s e t ( n f t e h f t e , a . c o u n t r y c o d e == MS) , l o g = ”y” , x l a b = ” Year ” , y l a b = ”NFTE/HFTE” , boxwex = 0 . 2 , s t a p l e w e x = 1 )

37

10.0 5.0 2.0 1.0 0.1

0.2

0.5

NFTE/HFTE

2008

2009

2010

2011

2012

2013

Year Figure 7: Ratio between National FTE and Harmonised FTE. 1 2 3 4

o u t t a b l e ← a s . d a t a . f r a m e ( b o x p l o t . a d d . o u t l i e r . l i s t ( s u b s e t ( n f t e h f t e , a . c o u n t r y c o d e == MS) , ” a . y e a r ” , ”METRIC” , ”METRIC” ) ) names ( o u t t a b l e ) ← c ( ” S u p r a r e g ” , ” Country code ” , ” Year ” , ” f i s h i n g t e c h ” , ” v e s s e l l e n g t h ” , ” Outlier ?” )

5 6 7 8

# ### additional changes to sort the data.... o u t t a b l e 2 ← s u b s e t ( o u t t a b l e , s e l e c t = c ( ” S u p r a r e g ” , ” Country code ” , ” f i s h i n g t e c h ” , ” v e s s e l l e n g t h ” , ” Year ” , ” O u t l i e r ? ” ) )

9 10 11

o u t t a b l e 2 ← s o r t ( o u t t a b l e 2 , p a r t i a l = c ( ” Country code ” , ” S u p r a r e g ” , ” f i s h i n g t e c h ” , ” v e s s e l l e n g t h ” ) , Y e a r . i n c r e a s i n g = TRUE)

12 13 14 15 16

p r i n t ( x t a b l e ( o u t t a b l e 2 , c a p t i o n = ” L i s t o f o u t l i e r s ” , c a p t i o n . p l a c e m e n t = ” bottom ” ) , c a p t i o n . p l a c e m e n t = ” top ” , i n c l u d e . r o w n a m e s = FALSE, l a b e l = ” tab : t a b l e 1 ” , f o r m a t . a r g s = l i s t ( b i g . m a r k = ” , ” ) , t a b u l a r . e n v i r o n m e n t = ” l o n g t a b l e ” , f l o a t i n g = FALSE, N A . s t r i n g = ”−” )

38

Table 16: List of outliers Supra reg

Country code

fishing tech

vessel length

Year

Outlier?

1 2 3

4 5 6

bwplot ( r a t i o ∼ a . y e a r | a . v a r i a b l e , data = s u b s e t ( v a r u n i t v a l u e s , a . c o u n t r y c o d e == MS & a . t e m p l a t e == ” employment ” ) , l a y o u t = c ( 3 , 1 ) , a s p e c t = c ( 1 ) , s c a l e s = l i s t ( x = l i s t ( rot = 45 , cex = 0 . 5 ) , y = l i s t ( r e l a t i o n = ”same” , cex = 0 . 5 ) ) , a l t e r n a t i n g = TRUE, p a r . s t r i p . t e x t = l i s t ( cex = 0 . 6 ) , x l a b = l i s t ( l a b e l = ” Year ” , cex = 0 . 5 ) , y l a b = l i s t ( l a b e l = ” nr / v e s s e l ” , cex = 0 . 5 ) )

totharmfte

totjob

●

totnatfte

●

●

250

nr/vessel

200

150

100 ●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

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50

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3 20 1

2 20 1

1 20 1

0 20 1

9 20 0

8 20 0

3 20 1

2

1

20 1

20 1

0 20 1

9

8

20 0

20 0

3 20 1

2 20 1

1 20 1

0 20 1

9 20 0

20 0

8

0

Year

Figure 8: Average employment per vessel 1 2 3

v a r . l i s t ← l e v e l s ( f a c t o r ( v a r u n i t v a l u e s [ v a r u n i t v a l u e s $ a . t e m p l a t e == ” employment ” & v a r u n i t v a l u e s $ a . c o u n t r y c o d e == MS, ” a . v a r i a b l e ” ] ) )

39

1 2 3 4 5 6 7

d ← data.frame () for ( i in v a r . l i s t ) { d ← rbind (d , as.data.frame ( b o x p l o t . a d d . o u t l i e r . l i s t ( subset ( var unit values , a . c o u n t r y c o d e == MS & a . t e m p l a t e == ” employment ” & a . v a r i a b l e == i , s e l e c t = c ( a.year , a.supra reg , a . f i s h i n g t e c h , a . v e s s e l l e n g t h , a.variable , ratio ) ) , ” a.year ” , ” ratio ” , ” ratio ”) ) ) }

8 9 10

names ( d ) ← c ( ” Year ” , ” S u p r a r e g ” , ” F i s h i n g t e c h ” , ” v e s s l e l e n g t h ” , ” V a r i a b l e ” , ” O u t l i e r ? ( avg by v e s s e l ) ” )

11 12 13 14

# ### additional changes to sort the data.... d2 ← s u b s e t ( d , s e l e c t = c ( ” S u p r a r e g ” , ” F i s h i n g t e c h ” , ” v e s s l e l e n g t h ” , ” V a r i a b l e ” , ” Year ” , ” O u t l i e r ? ( avg by v e s s e l ) ” ) )

15 16 17

d2 ← s o r t ( d2 , p a r t i a l = c ( ” S u p r a r e g ” , ” f i s h i n g t e c h ” , ” v e s s e l l e n g t h ” , ” V a r i a b l e ” ) , Y e a r . i n c r e a s i n g = TRUE)

18 19 20 21 22

p r i n t ( x t a b l e ( d2 , c a p t i o n = ” L i s t o f o u t l i e r s ” , d i g i t s = 0 , c a p t i o n . p l a c e m e n t = ” bottom ” ) , c a p t i o n . p l a c e m e n t = ” top ” , i n c l u d e . r o w n a m e s = FALSE, l a b e l = ” tab : t a b l e 1 ” , f o r m a t . a r g s = l i s t ( b i g . m a r k = ” , ” ) , t a b u l a r . e n v i r o n m e n t = ” l o n g t a b l e ” , f l o a t i n g = FALSE, N A . s t r i n g = ”−” )

Table 17: List of outliers Supra reg AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 OFR OFR OFR OFR OFR OFR OFR OFR OFR OFR OFR OFR OFR OFR OFR OFR OFR

Fishing tech DTS DTS DTS DTS DTS DTS DTS DTS DTS DTS DTS DTS DTS DTS DTS TM TM TM TM TM TM TM TM TM TM TM TM TM TM TM TM TM

vessle length VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX

Variable totharmfte totharmfte totharmfte totharmfte totharmfte totjob totjob totjob totjob totjob totnatfte totnatfte totnatfte totnatfte totnatfte totharmfte totharmfte totharmfte totharmfte totharmfte totharmfte totjob totjob totjob totjob totjob totjob totnatfte totnatfte totnatfte totnatfte totnatfte 40

Year 2008 2009 2010 2011 2013 2008 2009 2010 2011 2013 2008 2009 2010 2011 2013 2008 2009 2010 2011 2012 2013 2008 2009 2010 2011 2012 2013 2008 2009 2010 2011 2012

Outlier?(avg by vessel) 40 40 40 36 37 40 40 40 40 37 40 40 40 36 37 270 86 90 90 90 68 270 90 90 90 90 90 270 86 90 90 90

OFR

4.4.3

TM

VL40XX

totnatfte

2013

68

Fishing Enterprises

The purpose here is again to enable easy visualisation of the data, highlighting values that appear incoherent and/or missing data. Data on Fishing enterprises is only requested at the national level. 1 2 3

1 2

3 4 5

f i s h e n t 2 0 1 3 ← s u b s e t ( f i s h e n t N 2 0 1 3 , c o u n t r y c o d e == MS) fish ent sum ← reshape ( fish ent2013 , varying = l i s t ( 3 : 5 ) , idvar = c ( ” year ” , ” c o u n t r y c o d e ” ) , d i r e c t i o n = ” l o n g ” , v.names = ” m e t r i c ” , t i m e s = names ( f i s h e n t 2 0 1 3 ) [ 3 : 5 ] ) d o t p l o t ( f a c t o r ( y e a r ) ∼ m e t r i c | time , data = d r o p l e v e l s ( s u b s e t ( f i s h e n t s u m , c o u n t r y c o d e == MS) ) , a s p e c t = c ( 0 . 7 ) , s t a c k = TRUE, l a y o u t = c ( 3 , 1 ) , s c a l e s = l i s t ( x = l i s t ( cex = 0 . 5 ) , y = l i s t ( cex = 0 . 5 ) , axs = ” r ” ) , type = c ( ”p” , ”h” ) , y l a b = l i s t ( l a b e l = ” Year ” , cex = 0 . 6 ) , x l a b = ” ” , p a r . s t r i p . t e x t = l i s t ( cex = 0 . 6 ) , s t r i p . l e f t = TRUE, s t r i p = FALSE)

41

0

●

2010

sixmoreves

2011

● ● ●

2009

●

2008

0

200

400

600

800

●

●

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oneves

Year

2012

600

●

●

2013

400

●

twofiveves

2014

200

● ● ●

● ● ●

●

●

●

●

800

0

200

400

600

800

Figure 9: Total number of Fishing Enterprises by enterprise type and Year. 1 2 3 4

5

b a r c h a r t ( p r o p . t a b l e ( x t a b s ( m e t r i c ∼ y e a r + time , data = s u b s e t ( f i s h e n t s u m , c o u n t r y c o d e == MS) , d r o p . u n u s e d . l e v e l s = TRUE) , margin = 1 ) , a u t o . k e y = l i s t ( a d j = 1 , columns = 3 , cex = 0 . 7 ) , a s p e c t = c ( 0 . 5 ) , x l a b = l i s t ( l a b e l = ” P r o p o r t i o n ” , cex = 0 . 7 5 ) , s c a l e s = l i s t ( x = l i s t ( cex = 0 . 5 ) , y = l i s t ( cex = 0 . 5 ) ) , y l a b = l i s t ( l a b e l = ” Year ” , cex = 0 . 7 5 ) , p a r . s t r i p . t e x t = l i s t ( cex = 0 . 6 ) )

42

oneves

sixmoreves

twofiveves

2014

2013

Year

2012

2011

2010

2009

2008

0.0

0.2

0.4

0.6

0.8

1.0

Proportion

Figure 10: Proportion of each enterprise type to the total No. of Fishing Enterprises.

43

4.4.4

Effort - Data by Fleet Segment

Effort data is analysed at the national total and fleet segment levels. Differences, if any, between the two datasets are shown in the table below. The plots by year and fishing technique for each variable allow for easy inspection of the data series and consistency of the values submited. 1 2 3 4

5 6 7 8 9

10

i f ( nrow ( s u b s e t ( c o m p a r e d a t a . d , c o u n t r y c o d e == MS & t e m p l a t e == ” e f f o r t ” ) ) != 0) { p r i n t ( x t a b l e ( s u b s e t ( c o m p a r e d a t a . d , c o u n t r y c o d e == MS & t e m p l a t e == ” e f f o r t ” ) , c a p t i o n = ” T o t a l v a l u e s by v a r i a b l e . Nat \\ t o t a l shows t h e t o t a l taken from t h e n a t i o n a l d a t a s e t and FS\\ d a t a shows t h e t o t a l v a l u e c a l c u l a t e d from t h e f l e e t segment and s u p r a r e g i o n d a t a s e t . T h e o r e t i c a l l y both v a l u e s s h o u l d e q u a t e . The t a b l e , when p r o v i d e d , l i s t s t h e v a r i a b l e s where s i g n i f i c a n t d i f f e r e n c e s were found ( $>$ 0 . 1 ) . ” ) , c a p t i o n . p l a c e m e n t = ” top ” , i n c l u d e . r o w n a m e s = FALSE, l a b e l = ” tab : t a b l e 1 ” , format.args = l i s t ( big.mark = ” , ” ) , tabular.environment = ” l o n g t a b l e ” , f l o a t i n g = FALSE, N A . s t r i n g = ”−” , d i g i t s = 0 ) } else { p r i n t ( ”No s i g n i f i c a n t d i f f e r e n c e s were found w h i l t comparing n a t i o n a l t o t a l s and t h e sum o v e r data by f l e e t segment ! ” ) }

[1] ”No significant differences were found whilt comparing national totals and the sum over data by fleet segment!” 1

2

e f f o r t s u m ← s q l d f ( ” s e l e c t year , c o u n t r y c o d e , f i s h i n g t e c h , \ n v a r i a b l e , sum ( v a l u e ) m e t r i c \ nfrom f s v a r 2 0 1 3 \ nwhere t e m p l a t e =’ e f f o r t ’ \ ngroup by year , c o u n t r y c o d e , f i s h i n g t e c h , variable ” , drv = ” SQLite ” )

3 4 5 6

7

b a r c h a r t ( y e a r ∼ m e t r i c | f i s h i n g t e c h + v a r i a b l e , data = s u b s e t ( e f f o r t s u m , c o u n t r y c o d e == MS) , s t a c k = FALSE, s c a l e s = l i s t ( x = l i s t ( r o t = 4 5 , cex = 0 . 4 , r e l a t i o n = ” f r e e ” ) , y = l i s t ( cex = 0 . 5 ) ) , a u t o . k e y = TRUE, y l a b = l i s t ( l a b e l = ” E f f o r t Variables ” , cex = 0 . 4 ) , x l a b = l i s t ( cex = 0 . 4 ) , p a r . s t r i p . t e x t = l i s t ( cex = 0 . 5 ) , o r i g i n = 0 )

44

1

4 5 6

bwplot ( r a t i o ∼ a . y e a r | a . v a r i a b l e , data = s u b s e t ( v a r u n i t v a l u e s , a . c o u n t r y c o d e == MS & a . t e m p l a t e == ” e f f o r t ” ) , a s p e c t = ” f i l l ” , x l a b = l i s t ( l a b e l = ” y e a r ” , cex = 0 . 7 ) , y l a b = l i s t ( l a b e l = ” Average v a l u e p e r v e s s e l ” , cex = 0 . 7 ) , s c a l e s = l i s t ( y = l i s t ( r e l a t i o n = ” f r e e ” , cex = 0 . 5 ) , x = l i s t ( cex = 0 . 5 ) ) , p a r . s t r i p . t e x t = l i s t ( cex = 0 . 7 ) )

45

00 00 40

00 30

Figure 11: Summary data for Effort variables by Year and Fishing Technique.

3

80

00 60

00 00 20

0

00 10

0 50

00

0 40

00

0 00

40

00

0 20 00 00 0 40 00 00 0 60 00 00 0 80 00 00 10 0 00 00 00 12 00 00 00

0

0

20 00 00

15 00 00

0 10 00 00 0 30

0

00 20

0

00 10

0

tottrips TM

metric

2

80 00

00 10 00 00 00 15 00 00 00 20 00 00 00

0

50 00 0

0 50 00 00 0 10 00 00 00 15 00 00 00 20 00 00 00 25 00 00 00 0

20

50 00 0

30 00 0 40 00 0

00 15

00

0 10 00 0 20 00 0 0 50 00 00

totenercons TM

tottrips PG

60

60 00

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50 00 0

80 00 0 80 00

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0

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20 00 10 0 40

60 00 0

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0 10 00 0 20 00 0

20 00

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10 00

50 0

0 0

10 00

0e +0 0 1e +0 5 2e +0 5 3e +0 5 4e +0 5 5e +0 5 0

00

totfishdays TM

totenercons PG

tottrips HOK

60

00 40

00 20

0

00 20

00 15

0

00 10

0

00 10 00 0 12 00 0

00

80

00

60

40

00

0

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0 20 00 00 40 00 00 60 00 00 80 00 00 10 00 00 0 12 00 00 0 0e +0 0 1e +0 6 2e +0 6 3e +0 6 4e +0 6 5e +0 6 6e +0 6 0

50

totgtfishdays TM

totfishdays PG

totenercons HOK

tottrips DTS

totkwfishdays TM

totgtfishdays PG

totfishdays HOK

totenercons DTS

tottrips DFN

2014 2013 2012 2011 2010 2009 2008

0

3e +0 6 15 00 00 0

10 00 00 0

50 00 00

0

40 00 0 00 40

00

totenercons DFN

2014 2013 2012 2011 2010 2009 2008

totseadays TM

totkwfishdays PG

totgtfishdays HOK

totfishdays DTS

30

00

20

10

0

2014 2013 2012 2011 2010 2009 2008

00

totfishdays DFN

totseadays PG

totkwfishdays HOK

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30 00 0

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1e +0 6

0e +0 0

0

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totkwfishdays DTS

totgtfishdays DFN

2014 2013 2012 2011 2010 2009 2008

totseadays HOK

10 00 0

50 00

0

50 00

40 00

20 00

0

10 00

totseadays DTS

totkwfishdays DFN

2014 2013 2012 2011 2010 2009 2008

Effort Variables

30 00

totseadays DFN

2014 2013 2012 2011 2010 2009 2008

6e+06

●

tottrips 100

totseadays 500

totkwfishdays ●

80

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●

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totfishdays

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8e+06

250000

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●

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6e+06

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200

200000

Average value per vessel

2e+06

●

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●

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4e+06

100000

150

●

●

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●

50

50000

2e+06

100

●

●

0

0e+00

●

2008 2009 2010 2011 2012 2013 2014

2008 2009 2010 2011 2012 2013 2014

●

●

●

●

2008 2009 2010 2011 2012 2013 2014

year

Figure 12: Boxplot of average effort variables by vessel. 1 2

v a r . l i s t ← l e v e l s ( f a c t o r ( v a r u n i t v a l u e s [ v a r u n i t v a l u e s $ a . t e m p l a t e == ” e f f o r t ” & v a r u n i t v a l u e s $ a . c o u n t r y c o d e == MS, ” a . v a r i a b l e ” ] ) )

1 2 3 4 5 6 7 8

d ← data.frame () for ( i in v a r . l i s t ) { d ← rbind (d , as.data.frame ( b o x p l o t . a d d . o u t l i e r . l i s t ( subset ( var unit values , a . c o u n t r y c o d e == MS & a . t e m p l a t e == ” e f f o r t ” & a . v a r i a b l e == i , s e l e c t = c ( a . y e a r , a.supra reg , a.fishing tech , a.vessel length , a.variable , ratio ) ) , ” a.year ” , ” ratio ” , ” ratio ”) ) ) }

9 10 11

names ( d ) ← c ( ” Year ” , ” S u p r a r e g ” , ” F i s h i n g t e c h ” , ” v e s s l e l e n g t h ” , ” V a r i a b l e ” , ” O u t l i e r ? ( avg by v e s s e l ) ” )

12 13 14

# ### additional changes to sort the data.... d2 ← s u b s e t ( d , s e l e c t = c ( ” S u p r a r e g ” , ” F i s h i n g t e c h ” , ” v e s s l e l e n g t h ” , ” V a r i a b l e ” ,

46

15

” Year ” , ” O u t l i e r ? ( avg by v e s s e l ) ” ) )

16 17 18

d2 ← s o r t ( d2 , p a r t i a l = c ( ” S u p r a r e g ” , ” f i s h i n g t e c h ” , ” v e s s e l l e n g t h ” , ” V a r i a b l e ” ) , Y e a r . i n c r e a s i n g = TRUE)

19 20

21

22

p r i n t ( x t a b l e ( d2 , c a p t i o n = ” L i s t o f o u t l i e r s ” ) , c a p t i o n . p l a c e m e n t = ” top ” , i n c l u d e . r o w n a m e s = FALSE, l a b e l = ” tab : t a b l e 1 ” , f o r m a t . a r g s = l i s t ( b i g . m a r k = ” , ” ) , t a b u l a r . e n v i r o n m e n t = ” l o n g t a b l e ”, f l o a t i n g = FALSE, N A . s t r i n g = ”−” , d i g i t s = 0 )

Table 18: List of outliers Supra reg AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 OFR OFR OFR OFR OFR OFR OFR OFR OFR OFR OFR OFR OFR OFR OFR

Fishing tech DTS DTS DTS DTS DTS DTS DTS DTS DTS DTS DTS DTS DTS DTS DTS DTS DTS DTS DTS DTS DTS DTS DTS TM TM TM TM TM TM TM TM TM TM TM TM TM TM TM TM TM TM TM

vessle length VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL2440 VL2440 VL2440 VL2440 VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX

Variable totfishdays totfishdays totfishdays totfishdays totgtfishdays totgtfishdays totgtfishdays totgtfishdays totgtfishdays totgtfishdays totkwfishdays totkwfishdays totkwfishdays totkwfishdays totkwfishdays totkwfishdays totseadays totseadays totseadays totseadays totseadays totseadays tottrips totenercons totenercons totenercons totenercons totfishdays totfishdays totfishdays totfishdays totfishdays totfishdays totgtfishdays totgtfishdays totgtfishdays totgtfishdays totgtfishdays totgtfishdays totkwfishdays totkwfishdays totkwfishdays 47

Year 2008 2009 2010 2013 2008 2009 2010 2011 2013 2014 2008 2009 2010 2011 2013 2014 2008 2009 2010 2011 2013 2014 2014 2010 2011 2012 2013 2008 2009 2010 2011 2013 2014 2009 2010 2011 2012 2013 2014 2009 2010 2011

Outlier?(avg by vessel) 136.50 222.00 263.00 212.00 665,574.00 400,710.00 474,715.00 314,070.00 382,660.00 335,730.00 942,532.50 749,250.00 887,625.00 587,250.00 715,500.00 627,750.00 339.50 268.00 308.00 223.00 281.00 229.00 5.00 217,178.25 237,828.51 254,064.67 219,509.69 280.00 219.00 173.33 216.00 230.50 223.00 7,778,244.00 3,374,973.33 4,205,736.00 2,864,435.00 8,948,240.50 3,212,561.00 5,799,077.33 2,606,933.33 3,248,640.00

OFR OFR OFR OFR OFR OFR OFR OFR OFR OFR

4.4.5

TM TM TM TM TM TM TM TM TM TM

VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX VL40XX

totkwfishdays totkwfishdays totkwfishdays totseadays totseadays totseadays totseadays totseadays totseadays tottrips

2012 2013 2014 2008 2009 2010 2011 2013 2014 2014

2,172,640.00 6,536,058.00 2,367,871.00 488.00 292.67 300.33 243.00 259.00 284.50 14.00

Landings - Data by Fleet Segment

Landings data are analysed at the fleet segment and national total levels. Differences, if any, between the two datasets are listed in the table below. Additional analysis on the total landings weight, value and average first-sale price is presented. Extreme annual average prices per species are identified and listed. 1 2 3 4

5 6 7 8 9

10

i f ( nrow ( s u b s e t ( c o m p a r e d a t a . d , c o u n t r y c o d e == MS & t e m p l a t e == ” l a n d i n g s ” ) ) != 0) { p r i n t ( x t a b l e ( s u b s e t ( c o m p a r e d a t a . d , c o u n t r y c o d e == MS & t e m p l a t e == ” l a n d i n g s ” ) , c a p t i o n = ” T o t a l v a l u e s by v a r i a b l e . Nat \\ t o t a l shows t h e t o t a l taken from t h e n a t i o n a l d a t a s e t and FS\\ d a t a shows t h e t o t a l v a l u e c a l c u l a t e d from t h e f l e e t segment and s u p r a r e g i o n d a t a s e t . T h e o r e t i c a l l y both v a l u e s s h o u l d e q u a t e . The t a b l e , when p r o v i d e d , l i s t s t h e v a r i a b l e s where s i g n i f i c a n t d i f f e r e n c e s were found ( $>$ 0 . 1 ) . ” ) , c a p t i o n . p l a c e m e n t = ” top ” , i n c l u d e . r o w n a m e s = FALSE, l a b e l = ” tab : t a b l e 1 ” , format.args = l i s t ( big.mark = ” , ” ) , tabular.environment = ” l o n g t a b l e ” , f l o a t i n g = FALSE, N A . s t r i n g = ”−” ) } else { p r i n t ( ”No s i g n i f i c a n t d i f f e r e n c e s were found w h i l e comparing n a t i o n a l t o t a l s and t h e sum o v e r data by f l e e t segment ! ” ) }

[1] ”No significant differences were found while comparing national totals and the sum over data by fleet segment!” 1 2 3

4 5

6 7 8

i n c v a l u e ← s q l d f ( ” s e l e c t year , c o u n t r y c o d e , ’ income ’ a s template , sum ( m e t r i c ) e u r o from income sum where v a r i a b l e =’ t o t l a n d g i n c ’ group by year , c o u n t r y c o d e ” , drv = ” SQLite ” ) l a n v a l u e ← s q l d f ( ” s e l e c t year , c o u n t r y c o d e , ’ l a n d i n g s ’ a s template , sum ( s v a l u e ) e u r o from l a n d group by year , \ n c o u n t r y c o d e ” , drv = ” SQLite ” ) l a n v a l u e $ year ← f a c t o r ( l a n v a l u e $ year ) inc land ← rbind ( inc value , lan value )

9 10 11 12 13

b a r c h a r t ( f a c t o r ( y e a r ) ∼ e u r o / 1 e +06 , group = template , data = s u b s e t ( i n c l a n d , c o u n t r y c o d e == MS) , a u t o . k e y = l i s t ( a d j = 1 , cex = 0 . 7 , columns = 2 ) , a s p e c t = 0 . 7 , x l a b = l i s t ( l a b e l = ” 000 . 0 0 0 e u r o s ” , cex = 0 . 6 ) , y l a b = l i s t ( l a b e l = ” Year ” , cex = 0 . 6 ) , s c a l e s = l i s t ( y = l i s t ( cex = 0 . 6 ) , x = l i s t ( cex = 0 . 6 ) ) )

48

income

landings

2014

2013

Year

2012

2011

2010

2009

2008

35

40

45

50

55

000.000 euros

Figure 13: Total Income from landings (totlandginc) and total value of landings (totvallandg). 1 2

3 4 5 6 7 8 9 10 11 12 13

# analyse total volume and value of landings and average price l a n v o l ← s q l d f ( ” s e l e c t year , c o u n t r y c o d e , ’ l a n d i n g s ’ a s template , \ nsum ( s w e i g t h ) / 1000000 t h t o n , sum ( s v a l u e ) / 1000000 MM Euro from l a n d group by year , c o u n t r y c o d e ” , drv = ” SQLite ” ) l a n v o l $ year ← f a c t o r ( l a n v o l $ year ) a v g p r i c e ← l a n v o l $MM Euro/ l a n v o l $ t h t o n l a n v o l ← cbind ( l a n v o l , a v g p r i c e ) l a n d v o l . m ← melt ( l a n v o l ) a v g p r i c e ← land $ s v a l u e / land $ s weigth l a n d ← c b i n d ( land , a v g p r i c e ) d o t p l o t ( t h t o n + MM Euro + a v g p r i c e ∗ 10 ∼ year , data = s u b s e t ( l a n v o l , c o u n t r y c o d e == MS) , type = ” o ” , a s p e c t = 0 . 7 , a u t o . k e y = l i s t ( l i n e s = TRUE, columns = 3 , cex = 0 . 7 ) , x l a b = l i s t ( l a b e l = ” Year ” , cex = 0 . 5 ) , y l a b = l i s t ( l a b e l = ” ” , cex = 0 . 5 ) , s c a l e s = l i s t ( y = l i s t ( cex = 0 . 5 ) , x = l i s t ( cex = 0 . 5 ) ) )

49

th_ton

MM_Euro

●

avg_price * 10

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200 ●

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0

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Year

Figure 14: Total landings weight, Value and average first-sale price. 1 2

# analyse average prices - the distribution

3 4 5

l a n d a v g p ← d r o p l e v e l s ( n a . o m i t ( s u b s e t ( l a n d [ c ( 1 : 3 , 5 , 7 : 9 ) ] , s w e i g t h != 0 ) ) ) l a n d a v g p $ a v g p r i c e ← round ( l a n d a v g p $ a v g p r i c e , 2 )

6 7 8 9

10

11 12 13 14 15

bwplot ( a v g p r i c e ∼ f i s h i n g t e c h | f a c t o r ( y e a r ) , data = s u b s e t ( land avgp , c o u n t r y c o d e == MS) , a s p e c t = 0 . 7 , x l a b = l i s t ( l a b e l = ” F i s h i n g Technique ” , cex = 0 . 5 ) , y l a b = l i s t ( l a b e l = ” Average p r i c e ( e u r o / kg ) ” , cex = 0 . 5 ) , s c a l e s = l i s t ( y = l i s t ( cex = 0 .5 ) , x = l i s t ( r o t = 4 5 , cex = 0 . 5 ) ) , p a r . s t r i p . t e x t = l i s t ( cex = 0 . 5 ) , p a n e l = f u n c t i o n ( . . . , box.ratio ) { p a n e l . v i o l i n ( . . . , c o l = ” l i g h t b l u e ” , v ar wi d th = FALSE, b o x . r a t i o = b o x . r a t i o ) p a n e l . b w p l o t ( . . . , c o l = ” b l a c k ” , cex = 0 . 8 , pch = ” | ” , f i l l = ” gray ” , box.ratio = 0 .1 ) })

50

2014 15 ● ●

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10 ●

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5

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0 2011

2012

2013 ● ●

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Average price (euro/kg)

15

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10

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5 ● ●

0

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2009

2010

15

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5

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TM

PG

K O H

TS D

FN D

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PG

K O H

TS D

FN D

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TS D

D

FN

0

Fishing Technique

Figure 15: Violin plot of the average first-sale price, calculated by specie, supra-region and LOA category; and group by fishing technique. 1 2 3 4

p r i n t ( x t a b l e ( s u b s e t ( land avgp , c o u n t r y c o d e == MS & a v g p r i c e > q u a n t i l e ( s u b s e t ( land avgp , c o u n t r y c o d e == MS) $ a v g p r i c e , p r o b s = c ( 0 . 9 9 ) ) ) ) , c a p t i o n . p l a c e m e n t = ” top ” , i n c l u d e . r o w n a m e s = FALSE, l a b e l = ” tab : t a b l e 1 ” , t a b u l a r . e n v i r o n m e n t = ” l o n g t a b l e ” , f l o a t i n g = FALSE, N A . s t r i n g = ”−” )

country code POL POL POL POL POL POL POL

species ELE ELE ELE ELP ELE ELE ELP

year 2013 2012 2014 2014 2013 2012 2014

fishing tech DTS DTS PG TM PG PG PG 51

s weigth 177.50 23.00 1407.50 1.00 1549.50 1891.00 39.40

s value 2524.15 351.00 17331.88 12.38 22575.71 30008.38 487.44

avg price 14.22 15.26 12.31 12.38 14.57 15.87 12.37

POL POL 1 2 3 4 5 6 7 8 9

10 11

ELE ELE

2013 2012

PG PG

46904.40 28956.50

701394.06 441851.10

14.95 15.26

# analyse average prices - the distribution sum.pmed ← summaryBy ( round ( a v g p r i c e , 2 ) ∼ year , data = s u b s e t ( land avgp , c o u n t r y c o d e == MS) , FUN = f u n c t i o n ( x ) { c ( Length = l e n g t h ( x ) , Mean = round ( mean ( x ) , 2 ) , Median = round ( median ( x ) , 2 ) , SD = round ( sd ( x ) , 2 ) , Min = min ( x ) , Max = max( x ) ) }) Names ← c ( ” Year ” , ” Length ” , ”Mean” , ” Median ” , ”SD” , ”Min” , ”Max” ) names ( sum.pmed ) ← Names p r i n t ( x t a b l e ( sum.pmed , c a p t i o n = ”Summary s t a t i s t i c s on t h e a v e r a g e f i r s t − s a l e p r i c e ( e u r o / kg ) by Supra−region , \ n f l e e t segment , LOA c a t e g o r y and s p e c i e s . ” , d i g i t s = 2 ) , c a p t i o n . p l a c e m e n t = ” top ” , i n c l u d e . r o w n a m e s = FALSE, l a b e l = ” tab : t a b l e 1 ” , t a b u l a r . e n v i r o n m e n t = ” l o n g t a b l e ” , f l o a t i n g = FALSE, N A . s t r i n g = ”−” )

Table 20: Summary statistics on the average first-sale price (euro/kg) by Supra-region, fleet segment, LOA category and species. Year 2008 2009 2010 2011 2012 2013 2014

4.4.6

Length 112.00 120.00 137.00 112.00 104.00 118.00 130.00

Mean 1.74 1.72 2.13 2.14 2.29 2.11 2.00

Median 0.90 0.82 1.01 1.03 1.08 0.80 0.90

SD 1.88 1.85 2.41 2.33 2.94 2.84 2.58

Min 0.15 0.10 0.01 0.13 0.12 0.12 0.12

Max 10.41 8.38 10.47 11.70 15.87 14.95 12.38

Income

Income data is analysed at the fleet segment and national total levels. Differences, if any, between the two datasetas are shown in the table below. Income structure at the national level is presented. Boxplot of average income variables per vessel are presented for easy identification of extreme values and for comparing the distribution range across years. Plots of income variables by year and fishing technique are also displayed. 1 2 3 4

5 6 7 8 9

10

i f ( nrow ( s u b s e t ( c o m p a r e d a t a . d , c o u n t r y c o d e == MS & t e m p l a t e == ” income ” ) ) != 0) { p r i n t ( x t a b l e ( s u b s e t ( c o m p a r e d a t a . d , c o u n t r y c o d e == MS & t e m p l a t e == ” income ” ) , c a p t i o n = ” T o t a l v a l u e s by v a r i a b l e . Nat \\ t o t a l shows t h e t o t a l taken from t h e n a t i o n a l d a t a s e t and FS\\ d a t a shows t h e t o t a l v a l u e c a l c u l a t e d from t h e f l e e t segment and s u p r a r e g i o n d a t a s e t . T h e o r e t i c a l l y both v a l u e s s h o u l d e q u a t e . The t a b l e , when p r o v i d e d , l i s t s t h e v a r i a b l e s where s i g n i f i c a n t d i f f e r e n c e s were found ( $>$ 0 . 1 ) . ” ) , c a p t i o n . p l a c e m e n t = ” top ” , i n c l u d e . r o w n a m e s = FALSE, l a b e l = ” tab : t a b l e 1 ” , format.args = l i s t ( big.mark = ” , ” ) , tabular.environment = ” l o n g t a b l e ” , f l o a t i n g = FALSE, N A . s t r i n g = ”−” ) } else { p r i n t ( ”No s i g n i f i c a n t d i f f e r e n c e s were found w h i l e comparing n a t i o n a l t o t a l s and t h e sum o v e r data by f l e e t segment ! ” ) }

[1] ”No significant differences were found while comparing national totals and the sum over data by fleet segment!” 1

2

income sum ← s q l d f ( ” s e l e c t year , c o u n t r y c o d e , f i s h i n g t e c h , \ n v a r i a b l e , sum ( v a l u e ) m e t r i c \ nfrom f s v a r 2 0 1 3 \ nwhere t e m p l a t e =’ income ’ \ ngroup by year , c o u n t r y c o d e , f i s h i n g t e c h , variable ” , drv = ” SQLite ” )

3 4 5

b a r c h a r t ( p r o p . t a b l e ( x t a b s ( m e t r i c ∼ y e a r + v a r i a b l e , data = s u b s e t ( income sum , c o u n t r y c o d e == MS) , d r o p . u n u s e d . l e v e l s = TRUE) , margin = 1 ) , a u t o . k e y = l i s t ( columns = 4 ,

52

6 7

8

cex = 0 . 7 ) , a s p e c t = 0 . 7 , x l a b = l i s t ( l a b e l = ” P r o p o r t i o n ” , cex = 0 . 7 5 ) , y l a b = l i s t ( ” Year ” , cex = 0 . 7 5 ) , p a r . s t r i p . t e x t = l i s t ( cex = 0 . 6 ) , s c a l e s = l i s t ( x = l i s t ( cex = 0 . 6 ) , y = l i s t ( cex = 0 . 6 ) ) )

totlandginc

totrightsinc

totdirsub

tototherinc

2014

2013

Year

2012

2011

2010

2009

2008

0.0

0.2

0.4

0.6

0.8

1.0

Proportion

Figure 16: Proportion of each income variable to total annual income. 1 2 3 4 5

bwplot ( r a t i o / 1000 ∼ a . y e a r | a . v a r i a b l e , data = s u b s e t ( v a r u n i t v a l u e s , a . c o u n t r y c o d e == MS & a . t e m p l a t e == ” income ” ) , a s p e c t = ” f i l l ” , x l a b = l i s t ( l a b e l = ” y e a r ” , cex = 0 . 7 5 ) , y l a b = l i s t ( l a b e l = ” Average v a l u e p e r v e s s e l ( 0 0 0 u n i t s ) ” , cex = 0 . 7 5 ) , s c a l e s = l i s t ( y = l i s t ( r e l a t i o n = ” f r e e ” , cex = 0 . 5 ) , x = l i s t ( cex = 0 . 5 ) ) , p a r . s t r i p . t e x t = l i s t ( cex = 0 . 6 ) )

53

tototherinc

totrightsinc

3

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4

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600

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Average value per vessel (000 units)

2

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Figure 17: Average income per vessel 1 2

1 2 3 4 5 6 7

v a r . l i s t ← l e v e l s ( f a c t o r ( v a r u n i t v a l u e s [ v a r u n i t v a l u e s $ a . t e m p l a t e == ” income ” & v a r u n i t v a l u e s $ a . c o u n t r y c o d e == MS, ” a . v a r i a b l e ” ] ) ) d ← data.frame () for ( i in v a r . l i s t ) { d ← rbind (d , as.data.frame ( b o x p l o t . a d d . o u t l i e r . l i s t ( subset ( var unit values , a . c o u n t r y c o d e == MS & a . t e m p l a t e == ” income ” & a . v a r i a b l e == i , s e l e c t = c ( a . y e a r , a.supra reg , a.fishing tech , a.vessel length , a.variable , ratio ) ) , ” a.year ” , ” ratio ” , ” ratio ”) ) ) }

8 9 10

names ( d ) ← c ( ” Year ” , ” S u p r a r e g ” , ” F i s h i n g t e c h ” , ” v e s s l e l e n g t h ” , ” V a r i a b l e ” , ” O u t l i e r ? ( avg by v e s s e l ) ” )

11 12 13 14

# ### additional changes to sort the data.... d2 ← s u b s e t ( d , s e l e c t = c ( ” S u p r a r e g ” , ” F i s h i n g t e c h ” , ” v e s s l e l e n g t h ” , ” V a r i a b l e ” , ” Year ” , ” O u t l i e r ? ( avg by v e s s e l ) ” ) )

54

15 16 17

d2 ← s o r t ( d2 , p a r t i a l = c ( ” S u p r a r e g ” , ” f i s h i n g t e c h ” , ” v e s s e l l e n g t h ” , ” V a r i a b l e ” ) , Y e a r . i n c r e a s i n g = TRUE)

18 19

20

21

p r i n t ( x t a b l e ( d2 , c a p t i o n = ” L i s t o f o u t l i e r s ” ) , c a p t i o n . p l a c e m e n t = ” top ” , i n c l u d e . r o w n a m e s = FALSE, l a b e l = ” tab : t a b l e 1 ” , f o r m a t . a r g s = l i s t ( b i g . m a r k = ” , ” ) , t a b u l a r . e n v i r o n m e n t = ” l o n g t a b l e ”, f l o a t i n g = FALSE, N A . s t r i n g = ”−” , d i g i t s = 0 )

Table 21: List of outliers Supra reg AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27

Fishing tech DFN DTS TM TM TM TM TM TM TM TM

vessle length VL1218 VL1218 VL1824 VL2440 VL2440 VL2440 VL2440 VL2440 VL2440 VL2440

Variable tototherinc tototherinc tototherinc totdirsub totlandginc totlandginc totlandginc totlandginc totlandginc totlandginc

Year 2010 2008 2013 2012 2008 2009 2010 2011 2012 2013

Outlier?(avg by vessel) 2,885.71 4,648.18 1,935.68 63,992.94 245,088.07 274,117.52 363,999.75 463,794.17 525,866.65 573,333.49

1 2 3 4

5 6

b a r c h a r t ( y e a r ∼ m e t r i c / 1 e+06 | f i s h i n g t e c h , data = d r o p l e v e l s ( s u b s e t ( income sum , c o u n t r y c o d e == MS) ) , a u t o . k e y = l i s t ( columns = 4 , a d j = 1 , s p a c e = ” top ” , cex = 0 . 7 ) , g r o u p s = v a r i a b l e , a s p e c t = 1 , s t a c k = TRUE, x l a b = l i s t ( l a b e l = ” 000 . 0 0 0 E u r o s ”, cex = 0 . 7 5 ) , y l a b = l i s t ( l a b e l = ” Year ” , cex = 0 . 7 5 ) , p a r . s t r i p . t e x t = l i s t ( cex = 0 . 6 ) , s c a l e s = l i s t ( x = l i s t ( cex = 0 . 5 ) , y = l i s t ( cex = 0 . 5 ) ) )

55

totlandginc

totrightsinc 0

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PG

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2014 2013 2012 2011 2010 2009

Year

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HOK

2014 2013 2012 2011 2010 2009 2008

0

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30

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000.000Euros

Figure 18: Totals by Income variable across Fishing Techniques. 1 2

3 4 5 6 7 8 9 10 11

s t r i p p l o t ( v a l u e ∼ v a r i a b l e | d c v a r , data = ( s u b s e t ( v a r i a t i o n s d c 1 3 . m , v a r i a t i o n s d c 1 3 . m $ c o u n t r y c o d e == MS & v a r i a t i o n s d c 1 3 . m $ t e m p l a t e == ” income ” & v a r i a t i o n s d c 1 3 . m $ v a r i a b l e != ” y1314 ” ) ) , l a y o u t = c ( 3 , 1 ) , j i t t e r . d a t a = TRUE, c o l . l i n e = ” r e d ” , c o l . s y m b o l = ” b l a c k ” , a s p e c t = c ( 1 . 1 ) , s c a l e s = l i s t ( x = l i s t ( r o t = 4 5 , cex = 0 . 5 ) , y = l i s t ( r e l a t i o n = ” f r e e ” , cex = 0 . 5 ) ) , p a n e l = f u n c t i o n ( x , y , j i t t e r . d a t a , . . . ) { p a n e l . g r i d ( h = 0 , v = −1) p a n e l . s t r i p p l o t ( x , y , j i t t e r . d a t a = TRUE, . . . ) p a n e l . a v e r a g e ( x , y , fun = mean , . . . ) } , x l a b = l i s t ( l a b e l = ” Years ” , cex = 0 . 7 5 ) , y l a b = l i s t ( l a b e l = ”(%)” , cex = 0 . 7 5 ) , p a r . s t r i p . t e x t = l i s t ( cex = 0 . 6 ) )

56

totdirsub

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(%)

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Years

Figure 19: Plot of variations by variable between consecutive years. Comparison made between fleet segments (supra-region, vessel length and fishing technology). 1 2

v a r . l i s t ← l e v e l s ( f a c t o r ( v a r i a t i o n s d c 1 3 [ v a r i a t i o n s d c 1 3 $ t e m p l a t e == ” income ” & v a r i a t i o n s d c 1 3 $ c o u n t r y c o d e == MS, ” d c v a r ” ] ) )

57

1 2 3 4 5 6 7 8

p r i n t ( x t a b l e ( s u b s e t ( v a r i a t i o n s d c 1 3 e x t r , c o u n t r y c o d e == MS & t e m p l a t e == ” income ” & ( a b s ( y0809 ) > ( 1 0 0 ) | ( a b s ( y0910 ) > ( 1 0 0 ) ) | ( a b s ( y1011 ) > ( 1 0 0 ) ) | ( a b s ( y1112 ) > ( 1 0 0 ) ) | ( a b s ( y1213 ) > ( 1 0 0 ) ) ) , s e l e c t = c ( s u p r a r e g , c o u n t r y c o d e , f i s h i n g t e c h , v e s s e l l e n g t h , dc var , year08 , year09 , year10 , year11 , year12 , y e a r 1 3 , y0809 , y0910 , y1011 , y1112 , y1213 , y1314 ) ) , c a p t i o n = ” L i s t o f s i g n i f i c a n t v a r i a t i o n s d i g i t s = 0 ) , c a p t i o n . p l a c e m e n t = ” t o p ” , i n c l u d e . r o w n a m e s = FALSE , l a b e l = ” t a b : t a b l e 1 ” , f o r m a t . a r g s = l i s t ( b i g . m a r k = ” , ” ) , t a b u l a r . e n v i r o n m e n t = ” l o n g t a b l e ” , f l o a t i n g = FALSE , N A . s t r i n g = ”−” )

across

years − Income. ” ,

Table 22: List of significant variations across years - Income. supra reg AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27

58

1 2 3 4 5 6 7 8 9

country code POL POL POL POL POL POL POL POL POL POL POL

fishing tech DFN DTS DTS DTS HOK HOK HOK TM TM TM TM

vessel length VL1218 VL1012 VL1012 VL1824 VL1218 VL1218 VL1218 VL1824 VL1824 VL1824 VL2440

dc var tototherinc totlandginc totdirsub tototherinc totlandginc totdirsub tototherinc totlandginc totdirsub tototherinc tototherinc

year08 107,919 0 0 50,566 0 0 0 0 0 0 12,190

year09 39,313 0 0 32,719 1,389,997 2,630,854 43,524 0 0 0 13,278

year10 63,486 642,455 289,578 0 1,472,605 1,913,672 38,818 0 0 0 0

p r i n t ( x t a b l e ( s u b s e t ( v a r i a t i o n s u n v e x t r , c o u n t r y c o d e == MS & t e m p l a t e == ” income ” & ( a b s ( y0809 ) > ( 5 0 ) | ( a b s ( y0910 ) > ( 5 0 ) ) | ( a b s ( y1011 ) > ( 5 0 ) ) | ( a b s ( y1112 ) > ( 5 0 ) ) | ( a b s ( y1213 ) > ( 5 0 ) ) ) , s e l e c t = c ( s u p r a r e g , c o u n t r y c o d e , f i s h i n g t e c h , v e s s e l l e n g t h , d c v a r , y2008 , y2009 , y2010 , y2011 , y2012 , y2013 , y0809 , y0910 , y1011 , y1112 , y1213 ) ) , c a p t i o n = ” L i s t o f s i g n i f i c a n t v a r i a t i o n s ( $>$ 50 p e r c e n t ) o f d i g i t s = 0 ) , c a p t i o n . p l a c e m e n t = ” t o p ” , i n c l u d e . r o w n a m e s = FALSE , l a b e l = ” t a b : t a b l e 1 ” , f o r m a t . a r g s = l i s t ( b i g . m a r k = ” , ” ) , t a b u l a r . e n v i r o n m e n t = ” l o n g t a b l e ” , f l o a t i n g = FALSE , N A . s t r i n g = ”−” )

year11 0 0 0 51,699 515,923 2,051,398 108,131 1,464,143 927,983 0 92,264

averages

year12 47,077 0 0 59,155 0 0 0 0 0 0 11,765

( per

year13 0 0 0 0 0 0 0 4,389,683 400,390 34,842 6,385

vessel )

y0809 0 1 Inf Inf Inf 1

across

y0910 2 Inf Inf 0 1 1 1 0

y1011 0 0 0 Inf 0 1 3 Inf Inf Inf

y1112 Inf 1 0 0 0 0 0 0

y1213 0 0 Inf Inf Inf 1

years − Income. ” ,

Table 23: List of significant variations (>50 percent) of averages (per vessel) across years - Income. supra reg AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27

country code POL POL POL POL POL POL POL POL POL POL POL

fishing tech DFN DTS DTS DTS HOK HOK HOK TM TM TM TM

vessel length VL1218 VL1012 VL1012 VL1824 VL1218 VL1218 VL1218 VL1824 VL1824 VL1824 VL2440

dc var tototherinc totdirsub totlandginc tototherinc totdirsub totlandginc tototherinc totdirsub totlandginc tototherinc tototherinc

y2008 1,255 0 0 1,532 0 0 0 0 0 0 218

y2009 1,573 0 0 1,487 71,104 37,567 1,176 0 0 0 221

y2010 2,886 28,958 64,246 0 59,802 46,019 1,213 0 0 0 0

y2011 0 0 0 2,872 75,978 19,108 4,005 71,383 112,626 0 2,146

y2012 1,148 0 0 1,643 0 0 0 0 0 0 256

y2013 0 0 0 0 0 0 0 22,244 243,871 1,936 152

y0809 1 1 Inf Inf Inf 1

y0910 2 Inf Inf 0 1 1 1 0

y1011 0 0 0 Inf 1 0 3 Inf Inf Inf

y1112 Inf 1 0 0 0 0 0 0

y1213 0 0 Inf Inf Inf 1

y1314 1 0 0 0

4.4.7

Expenditure

Expenditure data are analysed by fleet segment and national total levels. Differences, if any, between the two datasets are shown in the table below. Expenditure by variable and the cost structure at the national level are presented. Boxplots of the average cost variable per vessel are also displayed for identifying extreme values and for comparing the distribution range across years. 1 2 3 4

5 6 7 8 9

10

i f ( nrow ( s u b s e t ( c o m p a r e d a t a . d , c o u n t r y c o d e == MS & t e m p l a t e == ” e x p e n d i t u r e s ” ) ) != 0) { p r i n t ( x t a b l e ( s u b s e t ( c o m p a r e d a t a . d , c o u n t r y c o d e == MS & t e m p l a t e == ” e x p e n d i t u r e s ” ) , c a p t i o n = ” T o t a l v a l u e s by v a r i a b l e . Nat \\ t o t a l shows t h e n a t i o n a l t o t a l taken from t h e n a t i o n a l d a t a s e t and FS\\ d a t a shows t h e t o t a l v a l u e c a l c u l a t e d from t h e f l e e t segment and s u p r a r e g i o n d a t a s e t . T h e o r e t i c a l l y both v a l u e s s h o u l d e q u a t e . The t a b l e , when p r o v i d e d , l i s t s t h e v a r i a b l e s where s i g n i f i c a n t d i f f e r e n c e s were found ( $>$ 0 . 1 ) . ” ) , c a p t i o n . p l a c e m e n t = ” top ” , i n c l u d e . r o w n a m e s = FALSE, l a b e l = ” tab : t a b l e 1 ” , format.args = l i s t ( big.mark = ” , ” ) , tabular.environment = ” l o n g t a b l e ” , f l o a t i n g = FALSE, N A . s t r i n g = ”−” ) } else { p r i n t ( ”No s i g n i f i c a n t d i f f e r e n c e s were found w h i l e comparing n a t i o n a l t o t a l s and t h e sum o v e r data by f l e e t segment ! ” ) }

[1] ”No significant differences were found while comparing national totals and the sum over data by fleet segment!” 1

2

c o s t s s u m ← s q l d f ( ” s e l e c t year , c o u n t r y c o d e , \ n v a r i a b l e , sum ( v a l u e ) m e t r i c \ nfrom f s v a r 2 0 1 3 \ nwhere t e m p l a t e =’ e x p e n d i t u r e s ’ \ ngroup by year , c o u n t r y c o d e , v a r i a b l e ” , drv = ” SQLite ” )

3 4 5 6 7 8

d o t p l o t ( y e a r ∼ m e t r i c / 1 e+06 | v a r i a b l e , data = s u b s e t ( c o s t s s u m , c o u n t r y c o d e == MS) , o r i g i n = 0 , type = c ( ”p” , ”h” ) , i n d e x . c o n d = f u n c t i o n ( x , y ) median ( x ) , l a y o u t = c ( 4 , 2 ) , a s p e c t = c ( 1 ) , x l a b = l i s t ( l a b e l = ” 000 . 0 0 0 Euros ” , cex = 0 . 7 5 ) , y l a b = l i s t ( l a b e l = ” Year ” , cex = 0 . 7 5 ) , p a r . s t r i p . t e x t = l i s t ( cex = 0 . 6 ) , s c a l e s = l i s t ( x = l i s t ( cex = 0 . 5 ) , y = l i s t ( cex = 0 . 5 ) ) )

59

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000.000 Euros

Figure 20: National level Costs by variable and Year. 1 2 3

4 5 6

b a r c h a r t ( p r o p . t a b l e ( x t a b s ( m e t r i c ∼ y e a r + v a r i a b l e , data = d r o p l e v e l s ( s u b s e t ( c o s t s s u m , c o u n t r y c o d e == MS) ) , d r o p . u n u s e d . l e v e l s = TRUE) , margin = 1 ) , a u t o . k e y = l i s t ( columns = 3, s p a c e = ” top ” , cex = 0 . 7 ) , a s p e c t = 0 . 7 , x l a b = l i s t ( l a b e l = ” P r o p o r t i o n ” , cex = 0 . 7 5 ) , y l a b = l i s t ( l a b e l = ” Year ” , cex = 0 . 7 5 ) , p a r . s t r i p . t e x t = l i s t ( cex = 0 . 6 ) , s c a l e s = l i s t ( x = l i s t ( cex = 0 . 5 ) , y = l i s t ( cex = 0 . 5 ) ) )

60

totcrewwage totunpaidlab totenercost

totrepcost totvarcost totnovarcost

totrightscost totdepcost

2013

2012

Year

2011

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0.0

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Proportion

Figure 21: Annual cost structure. 1

2 3 4 5 6 7 8 9 10

s t r i p p l o t ( v a l u e ∼ v a r i a b l e | d c v a r , data = ( s u b s e t ( v a r i a t i o n s d c 1 3 . m , v a r i a t i o n s d c 1 3 . m $ c o u n t r y c o d e == MS & v a r i a t i o n s d c 1 3 . m $ t e m p l a t e == ” e x p e n d i t u r e s ” & v a r i a t i o n s d c 1 3 . m $ v a r i a b l e != ” y1314 ” ) ) , j i t t e r . d a t a = TRUE, c o l . l i n e = ” r e d ” , c o l . s y m b o l = ” b l a c k ” , p a n e l = f u n c t i o n ( x , y, jitter.data , . . . ) { p a n e l . g r i d ( h = 0 , v = −1) p a n e l . s t r i p p l o t ( x , y , j i t t e r . d a t a = TRUE, . . . ) p a n e l . a v e r a g e ( x , y , fun = mean , . . . ) } , x l a b = l i s t ( l a b e l = ” Years ” , cex = 0 . 7 6 ) , y l a b = l i s t ( l a b e l = ”(%)” , cex = 0 . 7 6 ) , s c a l e s = l i s t ( x = l i s t ( r o t = 4 5 , cex = 0 . 6 ) , y = l i s t ( r e l a t i o n = ” f r e e ” , cex = 0 . 6 ) ) , p a r . s t r i p . t e x t = l i s t ( cex = 0 . 7 ) )

61

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Years

Figure 22: Plot of variations by variable between consecutive years. Comparison is made between fleet segments (supra-region, vessel length and fishing technique).

62

1 2 3 4 5 6 7 8

p r i n t ( x t a b l e ( s u b s e t ( v a r i a t i o n s d c 1 3 e x t r , c o u n t r y c o d e == MS & t e m p l a t e == ” e x p e n d i t u r e s ” & ( a b s ( y0809 ) > ( 1 0 0 ) | ( a b s ( y0910 ) > ( 1 0 0 ) ) | ( a b s ( y1011 ) > ( 1 0 0 ) ) | ( a b s ( y1112 ) > ( 1 0 0 ) ) | ( a b s ( y1213 ) > ( 1 0 0 ) ) ) , s e l e c t = c ( s u p r a r e g , c o u n t r y c o d e , f i s h i n g t e c h , v e s s e l l e n g t h , dc var , year08 , year09 , year10 , year11 , year12 , y e a r 1 3 , y0809 , y0910 , y1011 , y1112 , y1213 ) ) , c a p t i o n = ” L i s t o f s i g n i f i c a n t v a r i a t i o n s a c r o s s d i g i t s = 0 ) , c a p t i o n . p l a c e m e n t = ” t o p ” , i n c l u d e . r o w n a m e s = FALSE , l a b e l = ” t a b : t a b l e 1 ” , f o r m a t . a r g s = l i s t ( b i g . m a r k = ” , ” ) , t a b u l a r . e n v i r o n m e n t = ” l o n g t a b l e ” , f l o a t i n g = FALSE , N A . s t r i n g = ”−” )

years − Expenditures. ” ,

Table 24: List of significant variations across years - Expenditures.

63

supra reg AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27

1 2 3 4 5 6 7 8 9 10

country code POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL

fishing tech DFN DTS DTS DTS DTS DTS DTS DTS DTS HOK HOK HOK HOK HOK HOK HOK PG PG TM TM TM TM TM TM TM TM

vessel length VL1218 VL1012 VL1012 VL1012 VL1012 VL1012 VL1012 VL1218 VL1824 VL1218 VL1218 VL1218 VL1218 VL1218 VL1218 VL1218 VL0010 VL1012 VL1824 VL1824 VL1824 VL1824 VL1824 VL1824 VL1824 VL2440

dc var totunpaidlab totcrewwage totenercost totrepcost totvarcost totnovarcost totdepcost totunpaidlab totunpaidlab totcrewwage totunpaidlab totenercost totrepcost totvarcost totnovarcost totdepcost totunpaidlab totunpaidlab totcrewwage totunpaidlab totenercost totrepcost totvarcost totnovarcost totdepcost totunpaidlab

year08 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

year09 0 0 0 0 0 0 0 0 0 806,525 0 170,532 159,684 182,495 126,946 33,508 0 0 0 0 0 0 0 0 0 0

year10 0 132,249 130,888 63,536 71,688 89,771 3,405 0 0 902,481 0 236,356 143,299 277,325 224,476 80,848 0 0 0 0 0 0 0 0 0 0

p r i n t ( x t a b l e ( s u b s e t ( v a r i a t i o n s u n v e x t r , c o u n t r y c o d e == MS & t e m p l a t e == ” e x p e n d i t u r e s ” & ( a b s ( y0809 ) > ( 5 0 ) | ( a b s ( y0910 ) > ( 5 0 ) ) | ( a b s ( y1011 ) > ( 5 0 ) ) | ( a b s ( y1112 ) > ( 5 0 ) ) | ( a b s ( y1213 ) > ( 5 0 ) ) ) , s e l e c t = c ( s u p r a r e g , c o u n t r y c o d e , f i s h i n g t e c h , v e s s e l l e n g t h , d c v a r , y2008 , y2009 , y2010 , y2011 , y2012 , y2013 , y0809 , y0910 , y1011 , y1112 , y1213 ) ) , c a p t i o n = ” L i s t o f s i g n i f i c a n t v a r i a t i o n s ( $>$ 50 p e r c e n t ) o f d i g i t s = 0 ) , c a p t i o n . p l a c e m e n t = ” t o p ” , i n c l u d e . r o w n a m e s = FALSE , l a b e l = ” t a b : t a b l e 1 ” , f o r m a t . a r g s = l i s t ( b i g . m a r k = ” , ” ) , t a b u l a r . e n v i r o n m e n t = ” l o n g t a b l e ” , f l o a t i n g = FALSE , N A . s t r i n g = ”−” )

year11 60,694 0 0 0 0 0 0 137,598 55,618 288,254 16,103 189,424 175,316 115,569 85,522 148,434 342,480 120,759 432,024 50,701 452,015 198,311 164,317 155,971 104,932 257,518

averages

year12 111,922 0 0 0 0 0 0 135,621 52,828 0 0 0 0 0 0 0 457,898 97,326 0 0 0 0 0 0 0 756,585

( per

year13 99,569 0 0 0 0 0 0 271,087 67,754 0 0 0 0 0 0 0 622,108 112,172 437,839 39,962 1,036,273 439,976 401,265 349,279 182,412 585,345

vessel )

across

y0809 Inf Inf Inf Inf Inf Inf -

y0910 Inf Inf Inf Inf Inf Inf 1 1 1 2 2 2 -

y1011 Inf 0 0 0 0 0 0 Inf Inf 0 Inf 1 1 0 0 2 Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf

y1112 2 1 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 3

y1213 1 2 1 1 1 Inf Inf Inf Inf Inf Inf Inf 1

years − Expenditures. ” ,

Table 25: List of significant variations (>50 percent) of averages (per vessel) across years - Expenditures. supra reg AREA27 AREA27 AREA27

country code POL POL POL

fishing tech DFN DTS DTS

vessel length VL1218 VL1012 VL1012

dc var totunpaidlab totcrewwage totdepcost

y2008 0 0 0

y2009 0 0 0

y2010 0 13,225 340

y2011 4,046 0 0

y2012 2,730 0 0

y2013 2,928 0 0

y0809 -

y0910 Inf Inf

y1011 Inf 0 0

y1112 1 -

y1213 1 -

AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27

POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL POL

DTS DTS DTS DTS DTS DTS HOK HOK HOK HOK HOK HOK HOK PG PG TM TM TM TM TM TM TM TM

VL1012 VL1012 VL1012 VL1012 VL1218 VL1824 VL1218 VL1218 VL1218 VL1218 VL1218 VL1218 VL1218 VL0010 VL1012 VL1824 VL1824 VL1824 VL1824 VL1824 VL1824 VL1824 VL2440

totenercost totnovarcost totrepcost totvarcost totunpaidlab totunpaidlab totcrewwage totdepcost totenercost totnovarcost totrepcost totunpaidlab totvarcost totunpaidlab totunpaidlab totcrewwage totdepcost totenercost totnovarcost totrepcost totunpaidlab totvarcost totunpaidlab

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 21,798 906 4,609 3,431 4,316 0 4,932 0 0 0 0 0 0 0 0 0 0

13,089 8,977 6,354 7,169 0 0 28,203 2,526 7,386 7,015 4,478 0 8,666 0 0 0 0 0 0 0 0 0 0

0 0 0 0 1,938 3,090 10,676 5,498 7,016 3,167 6,493 596 4,280 761 1,802 33,233 8,072 34,770 11,998 15,255 3,900 12,640 5,989

0 0 0 0 1,674 1,467 0 0 0 0 0 0 0 1,015 1,035 0 0 0 0 0 0 0 16,447

0 0 0 0 3,714 2,117 0 0 0 0 0 0 0 1,364 1,156 24,324 10,134 57,571 19,404 24,443 2,220 22,292 13,937

Inf Inf Inf Inf Inf Inf -

Inf Inf Inf Inf 1 3 2 2 1 2 -

0 0 0 0 Inf Inf 0 2 1 0 1 Inf 0 Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf

1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 3

2 1 1 1 Inf Inf Inf Inf Inf Inf Inf 1

64

1 2 3 4 5 6

bwplot ( r a t i o / 1000 ∼ a . y e a r | a . v a r i a b l e , data = s u b s e t ( v a r u n i t v a l u e s , a . c o u n t r y c o d e == MS & a . t e m p l a t e == ” e x p e n d i t u r e s ” ) , a s p e c t = ” f i l l ” , x l a b = l i s t ( l a b e l = ” y e a r ” , cex = 0 . 7 5 ) , y l a b = l i s t ( l a b e l = ” Average v a l u e p e r v e s s e l ( 0 0 0 u n i t s ) ” , cex = 0 . 7 5 ) , s c a l e s = l i s t ( x = l i s t ( cex = 0 . 5 , r o t = 4 5 ) , y = l i s t ( r e l a t i o n = ” f r e e ” , cex = 0 . 5 ) ) , p a r . s t r i p . t e x t = l i s t ( cex = 0 . 7 ) )

totunpaidlab

totvarcost ●

15

50

●

●

●

●

totrightscost ●

50

50

●

●

●

0.2

●

40

40

●

0.4

totrepcost

●

60

totnovarcost

0.0

30

30

●

0

−0.2

0

−0.4

10

10

20

20

●

totdepcost

totenercost

150

●

150

●

50

●

60

totcrewwage

200

●

●

40

● ●

100

Average value per vessel (000 units)

0

0

10

5

20

30

10

40

●

30

100

● ● ●

20

●

13 20

12 20

11 20

10 20

09 20

20

08

0 13 20

12 20

11 20

10 20

09 20

08 20

13 20

12 20

11 20

10 20

09 20

20

08

0

0

10

50

●

50

●

●

year

Figure 23: Boxplot of the average costs per vessel. 1 2 3

v a r . l i s t ← l e v e l s ( f a c t o r ( v a r u n i t v a l u e s [ v a r u n i t v a l u e s $ a . t e m p l a t e == ” e x p e n d i t u r e s ” & v a r u n i t v a l u e s $ a . c o u n t r y c o d e == MS, ” a . v a r i a b l e ” ] ) )

1 2 3 4 5

d ← data.frame () for ( i in v a r . l i s t ) { d ← rbind (d , as.data.frame ( b o x p l o t . a d d . o u t l i e r . l i s t ( subset ( var unit values , a . c o u n t r y c o d e == MS & a . t e m p l a t e == ” e x p e n d i t u r e s ” & a . v a r i a b l e ==

65

i , s e l e c t = c ( a.year , a.supra reg , a . f i s h i n g t e c h , a . v e s s e l l e n g t h , a.variable , ratio ) ) , ” a.year ” , ” ratio ” , ” ratio ”) ) )

6 7 8

}

9 10 11

names ( d ) ← c ( ” Year ” , ” S u p r a r e g ” , ” F i s h i n g t e c h ” , ” v e s s l e l e n g t h ” , ” V a r i a b l e ” , ” O u t l i e r ? ( avg by v e s s e l ) ” )

12 13 14 15

# ### additional changes to sort the data.... d2 ← s u b s e t ( d , s e l e c t = c ( ” S u p r a r e g ” , ” F i s h i n g t e c h ” , ” v e s s l e l e n g t h ” , ” V a r i a b l e ” , ” Year ” , ” O u t l i e r ? ( avg by v e s s e l ) ” ) )

16 17 18

d2 ← s o r t ( d2 , p a r t i a l = c ( ” S u p r a r e g ” , ” F i s h i n g t e c h ” , ” v e s s e l l e n g t h ” , ” V a r i a b l e ” ) , Y e a r . i n c r e a s i n g = TRUE)

19 20 21

22

23

p r i n t ( x t a b l e ( d2 , c a p t i o n = ” L i s t o f o u t l i e r s ” ) , c a p t i o n . p l a c e m e n t = ” top ” , i n c l u d e . r o w n a m e s = FALSE, l a b e l = ” tab : t a b l e 1 ” , f o r m a t . a r g s = l i s t ( b i g . m a r k = ” , ” ) , t a b u l a r . e n v i r o n m e n t = ” l o n g t a b l e ”, f l o a t i n g = FALSE, N A . s t r i n g = ”−” , d i g i t s = 0 )

Table 26: List of outliers Supra reg AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27 AREA27

Fishing tech TM TM TM TM TM TM TM TM TM TM TM TM TM TM TM TM TM TM TM TM TM TM TM TM TM TM TM TM TM

vessle length VL2440 VL2440 VL2440 VL2440 VL2440 VL2440 VL2440 VL2440 VL2440 VL2440 VL2440 VL2440 VL2440 VL2440 VL2440 VL2440 VL2440 VL2440 VL2440 VL2440 VL2440 VL2440 VL2440 VL2440 VL2440 VL2440 VL2440 VL2440 VL2440

Variable totcrewwage totcrewwage totcrewwage totcrewwage totcrewwage totcrewwage totdepcost totdepcost totdepcost totdepcost totenercost totenercost totenercost totenercost totnovarcost totnovarcost totnovarcost totnovarcost totnovarcost totrepcost totrepcost totrepcost totrepcost totrepcost totunpaidlab totunpaidlab totvarcost totvarcost totvarcost

66

Year 2008 2009 2010 2011 2012 2013 2010 2011 2012 2013 2010 2011 2012 2013 2008 2010 2011 2012 2013 2009 2010 2011 2012 2013 2012 2013 2010 2011 2012

Outlier?(avg by vessel) 81,079.69 62,523.19 94,550.45 106,517.63 125,411.26 189,079.47 17,163.39 22,898.10 50,028.42 59,862.16 92,298.75 136,085.24 180,850.74 144,004.12 33,641.47 35,848.06 54,031.39 46,642.95 53,661.48 25,301.61 35,401.13 54,077.64 50,374.87 57,739.58 16,447.50 13,936.79 31,098.38 50,847.70 39,583.29

1 2

3

c o s t c o n s ← s q l d f ( ” s e l e c t a . s u p r a r e g , a . y e a r , a . c o u n t r y c o d e , a . f i s h i n g t e c h , a . v e s s e l l e n g t h , a . v a r i a b l e , c o s t , c o n s \ nfrom ( select s u p r a r e g , c o u n t r y c o d e , year , f i s h i n g t e c h , v e s s e l l e n g t h , v a r i a b l e , sum ( v a l u e ) a s c o s t from f s v a r 2 0 1 3 WHERE t e m p l a t e =’ e x p e n d i t u r e s ’ and v a r i a b l e =’ t o t e n e r c o s t ’ and f i s h i n g t e c h not i n ( ’ INACTIVE ’ ) group by s u p r a r e g , c o u n t r y c o d e , year , f i s h i n g t e c h , v e s s e l l e n g t h , v a r i a b l e ) a , ( s e l e c t s u p r a r e g , c o u n t r y c o d e , year , f i s h i n g t e c h , v e s s e l l e n g t h , v a r i a b l e , sum ( v a l u e ) a s c o n s from f s v a r 2 0 1 3 where t e m p l a t e =’ e f f o r t ’ and v a r i a b l e =’ t o t e n e r c o n s ’ and f i s h i n g t e c h not i n ( ’ INACTIVE ’ ) \ ngroup by s u p r a r e g , c o u n t r y c o d e , year , f i s h i n g t e c h , v e s s e l l e n g t h , v a r i a b l e ) b where a . s u p r a r e g=b . s u p r a r e g \nand a . c o u n t r y c o d e=b . c o u n t r y c o d e and a . f i s h i n g t e c h=b . f i s h i n g t e c h and a . v e s s e l l e n g t h=b . v e s s e l l e n g t h \nand a . y e a r=b . y e a r group by a . s u p r a r e g , a . y e a r , a.country code , a.year , a . f i s h i n g t e c h , a . v e s s e l l e n g t h , a . v a r i a b l e , cost , cons ” , drv = ” SQLite ” )

4 5 6 7

r e s ← lm ( c o n s ∼ c o s t , data = s u b s e t ( c o s t c o n s , c o u n t r y c o d e == MS) ) p l o t ( c o n s ∼ c o s t , data = s u b s e t ( c o s t c o n s , c o u n t r y c o d e == MS) ) abline ( res )

67

1.2e+07 1.0e+07

●

8.0e+06

●

●

6.0e+06

●

4.0e+06 2.0e+06 0.0e+00

68

cons

●

●

● ● ●

● ●● ● ● ● ● ● ●●● ●● ●●●●●● ●● ●● ● ● ●● ● ● ● ●

0e+00

2e+06

4e+06

6e+06

cost Figure 24: Plot of pairs Energy consumption, Energy Costs.

8e+06

1

summary ( r e s )

1 2 3 4

Call : lm ( formula = cons ∼ cost , data = subset ( cost_cons , country_code == MS ) )

5 6 7 8

Residuals : Min 1Q -1764412 -159188

Median -76826

3Q 58141

Max 3086432

9 10 11 12 13 14 15

Coefficients : Estimate Std . Error t value Pr ( >| t |) ( Intercept ) 7.91 e +04 1.14 e +05 0.69 0.49 cost 1.61 e +00 5.03 e -02 31.94