Labour Market Areas STATISTICS PORTUGAL

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1 Labour Market Areas Interim Report for the European Commission within the Framework of the Grant Agreement for an Action: Agreement number STATISTICS PORTUGAL

2 TABLE OF CONTENTS 1. Overview Description of the work Timetable of the project Background information Application of the Portuguese Method to Census 2011 data Application of the European Method Description of the input data Tests and sensitive analysis Maps illustrating the results Main findings and future steps References

3 1. Overview This project aims to evaluate a harmonized methodology to define Labour Market Areas () for EU countries as proposed by the Task Force implemented in the Working Group on Regional Statistics and Rural Development under the framework of the European Statistical System. Considering the past experiences of Statistics Portugal on the development of labour market areas based on travel-to-work data and the discussions that took place in the National Statistical Council (where the stakeholders transmitted the need for further research on this matter), Statistics Portugal has committed to take part of this European project with the objective of testing the application of a different methodology as well as exploring the potentiality of using different data sources for the Portuguese territory. Thus, the main objectives of this project are: i) to assess the feasibility of implementing the European method in comparison with the national methodology; ii) to evaluate an alternative source to Census data for definition; iii) to define s based on the European method and based on the national methodology; and iv) to define and share the results. This report describes the activities that took place in the first 9 months of the action Description of the work The activities of this project are taking place at Statistics Portugal. Below there is a description of the main activities under implementation: a) Assess the feasibility of implementing the EU method in comparison with the national methodology. Describe the EU methodology and the national methodology including specifications of input data. Test the parameters of the proposed program based on a case study approach with Census data (LAU 1). Evaluate the use of an alternative source to Census data for definition based on a case study approach (LAU 2): EU and national methodologies, including parameterization aspects. Report on the results achieved according to the different approaches and input data. b) Define s based on the EU method and based on the national methodology. 3

4 Apply the EU method to LAU 1 and LAU 2 data for the entire territory (NUTS 1 Continente). Apply the national method to LAU 1 and LAU 2 data for the entire territory (NUTS 1 Continente). Discuss the results obtained taking into account the parameterization options and building blocks used (LAU 1 and LAU 2). Report on the composition of the based on the exercises developed and their feasibility. Characterization of in terms of size, self containment and employment. c) Disseminate and share the results. Present the results in the Standing Section for Territorial Base Statistics of the Statistical Council Timetable of the project In Portugal, the action is planned to run for 18 months and has started on March The main tasks completed up until now are: Application of the national methodology to create labour market areas using Census 2011 data (LAU 1 level); Test the national method using an alternative data source, namely administrative data derived from a National Statistical System project developed by the Ministry of Economy (List of Personnel linked to the Cadastre of Social Security) (LAU 1 level). Create a dataset based on Census 2011 data comparable with population covered by the administrative source. Test the European methodology (using the proposed program) with 2011 Census data. Test the European methodology (using the proposed program) with 2011 Census data comparable with administrative source. Test the European methodology (using the proposed program) with administrative data at LAU 1 level. Presentation of the results of labour market areas based on the national methodology and on the use of Census 2011 data in the Standing Section for Territorial Base Statistics of the Statistical Council this task took place in 12 th October The tasks foreseen for the upcoming months are: 4

5 Adequate data from the administrative source at LAU 2 level, namely according to the administrative organization of the territory at Census 2011 date. Test the European methodology (using the proposed program) with administrative data at LAU 2 level. Test the national methodology with administrative data at LAU 1 and LAU 2 level. Discuss the results obtained taking into account the parameterization options and building blocks used (LAU 1 and LAU 2 level). Report on the composition of the final geography based on the exercises developed and their feasibility. Characterization of the final geography in terms of size, self-containment and employment. Disseminate and share the final results in the Standing Section for Territorial Base Statistics of the National Statistical Council. 2. Background information In Portugal, the first experiences and outputs concerning the creation of labour market areas took place in the nineties using 1991 Census data at LAU 1 level. The study from Pisco (1997) called Unidades Geográficas de Emprego (Geographical units of employment) used a polarisation model with a previous determination of which municipalities were poles of the geographical units. High volumes of employment and high capacity of attraction of employees living in other municipalities firstly determined the cores of the new regions. The aggregation of the rest of the municipalities to the cores was made step by step using polarization rates between each municipality and the previous aggregation of poles with municipalities. According to this methodology 33 geographical units of employment were defined in the mainland of Portugal (NUTS 1 Continente). Another study, using a different methodology, was published by Pereira (1997): Bacias de emprego (Employment regions). In this case, the creation of labour market areas was based on an open system method using exclusively integration criteria with the purpose of obtaining regions as an empirical and operational expression of the labour markets. Census information on daily commuting from home to work was the basis for this study and the result was the partition of the Portuguese mainland territory in 40 employment regions based on values of interaction indicators and in employment size criteria. Based on the final data from Census 2001, Statistics Portugal tested a methodology for the development of functional urban areas. In this case, it is used an open system model and the main criterion for the aggregation of units (LAU 1) is the intensity of the commuting movements of the 5

6 workers between contiguous municipalities. The intensity of commuting is measured with an interaction indicator which is the ratio between the daily flows of the employed population between municipalities and the total resident population of those municipalities. The highest value of the interaction indicator determines the aggregation of the first pair of municipalities (contiguous) which will form a new territorial unit (or cluster). The agglomeration of new territorial units is then made through a hierarchical aggregation process between contiguous clusters. This model considers also two aggregate evaluation conditions that must be respected in the formation of new territorial units: i. Each new territorial unit must have a level of territorial integration sufficient enough to provide the necessary economic activities to the resident employed population in order to prevent them from moving to other territorial units this situation is measured by two self containment indicators: SC1 is the ratio between the employed resident population working in the cluster and the number of jobs provided in the cluster and SC2 is the ratio between the employed resident population working in the cluster and the total employed resident population of the cluster (independent of the place of work). The aggregation of territorial units is accepted if SC1 or SC2 is above 85% 1 in the new cluster. ii. A closing rule is established based on time-distance criteria: The maximum surface of each cluster must be Km 2 (except in the case of aggregation of isolated municipalities). This threshold corresponds to area of a circumference with a radius of 45Km, meaning that, in average, the residents of a region would have to travel 45Km (more or less one hour) to get to job (in maximum 90Km). This methodology was applied to the information on place of residence and place of work provided by Census 1991 and Census The output from the exercise using 1991 data was the partition of the Portuguese mainland in 19 functional urban regions and, concerning 2001 data, the result was 18 functional urban regions. The methodology and outputs of this project were analysed on the Standing Section for Territorial Base Statistics of National Statistical Council in The discussion in this forum focused on both the methodology to define these functional territories an on their analytical potentialities for policy decision-making. It was concluded that further studies should be developed, namely based on 2011 Census results. 1 Previous studies concerning the level of territorial integration of the municipalities (the basic building blocks in this methodology) showed that a significant number of municipalities had very high self-containment rates. So, when creating new functional regions it was expected to have a greater level of integration between them and a weaker integration with the rest of the territory. 6

7 3. Application of the Portuguese Method to Census 2011 data The application of the methodology previously tested by Statistics Portugal with Census 2001 data and Census 1991 data to the definitive data of Census 2011 required a preparation of the database of the workers living in NUTS 1 Continente (mainland) which is the geographical scope of this study. According to this approach, the first step was the construction of two commuting matrixes: i) commuting flows resulting from the comparison between the LAU 1 of residence and the LAU 1 of the place of work, and ii) contiguity matrix between LAU 1 territories. Additionally, it was necessary to have the total working population and the total surface for each LAU 1. According to Census 2011 data, there were workers in the Portuguese mainland territory. For about one third of these workers, the municipality of residence was different from the municipality of work. After having these data prepared, it was possible to run the algorithm based on the interaction indicator between all contiguous municipalities (LAU 1) and test different values for the selfcontainment restrictions. The figures below show the results of the exercise with a final value of 85% for the self-containment indicators and a maximum surface of Km 2. The final map according to this methodology has 20 functional areas (or labour market areas). The figures show that in almost all functional areas there are Portuguese cities and that this geography somehow reflects the delimitation of the most complex interaction relations of the Portuguese territory: in almost all regions it is possible to identify a central municipality and its hinterland. 7

8 Figure 1 Labour market areas (national exercise) and Portuguese cities Figure 2 Labour market areas (national exercise) and interaction relations between LAU1 Interaction rate LAU 1 (%) Cities > ] ; ] [ 0 ; ] Territorial Limits LAU ] 1,5 ; 11 ] ] 0,5 ; 1,5 ] ] 0,3 ; 0,5 ] ] 0,13 ; 0,3 ] ] 0 ; 0,13 ] Interaction between LAU 1 (%) ] 8 ; 18 ] ] 3 ; 8 ] ] 1,25 ; 3 ] Territorial Limits 0 50 km 0 50 km 8

9 Table 1 Comparative indicators between geographies Nr. of territorial units (t.u.) Surface (Km 2 ) Resident population (inhab.) Resident employed population (workers) Employment (jobs) SC1: Employed resident population working in the t.u./ Jobs in the t.u. SC2: Employed resident population working in the t.u./ workers of t.u. Municipalities NUTS 3 (2013) CV (%) Average Median Max Min CV (%) Average Median Max Min CV (%) Average Median Max Min CV (%) Average Median Max Min CV (%) Average Median Max Min CV (%) Average Median Max Min Application of the European Method 4.1. Description of the input data The implementation of the European method as proposed by the Task Force on harmonized Labour Market Areas is supported by an IT tool (initially a script based on open-source software R that was replaced by a package). This tool was developed by ISTAT in partnership with CBS but has been initially presented by Coombes and Bond (2008) and represents an adaptation of the methodology of the Travel-To-Work-Areas defined in Coombes et al. (1986). The developed program is a clustering algorithm that uses commuting data for the creation of new territorial units according to pre-determined parameters (self-containment levels and number of workers). As proposed in the final report of the Task Force, Census is the preferred data source to operate the EU method with the IT tool. In Portugal, Census data allows the comparison between the place of residence and the place of work for the resident employed population at LAU 1 level (308 9

10 municipalities). In fact, the place of residence is known as far as Census tracks, but LAU 1 (municipality) is the most detailed level of territorial disaggregation for the place of work. Thus, the 2011 input data for this exercise is a matrix containing the number of employed resident population (aged 15 or more) according to the LAU1 codes of place of residence and LAU1 codes of place of work for the mainland of Portugal (NUTS 1 Continente). Under the objective of testing an alternative data source, Statistics Portugal used administrative data derived from a National Statistical System project developed by the Ministry of Economy. This administrative data results from the Lists of Personnel 2 linked to the Cadastre of Social Security. The statistical project Lists of Personnel provides statistical data resulting from an administrative procedure. The obligation to deliver Lists of Personnel respects to all entities with employees, with exception of central, regional and local government and public institutions, as well as with the exception of employers of domestic service workers. Data resulting from the join between Lists of personnel (place of work) and the Cadastre of the Social Security allows the knowledge of the place of work and the place of residence by LAU 2 (4260 parishes at Census date), which is a more detailed level of territorial disaggregation than Census data. Nevertheless, the Lists of personnel database for 2010 covers a smaller part of the working population (about 65% of 2011 Census workers) because it excludes self-employed workers as well as employees of central, regional and local government and public institutions, and also domestic service workers. With this data, it was possible to run the package with data at LAU 1 level. IT is possible to obtain input data at LAU 2 level but this process still requires additional work to guarantee data quality and a total match with administrative structure and codes. Some tests were made to evaluate the level of association between Census data and administrative data ( Lists of Personnel ). It was observed that there is a strong association between most of the variables used in this exercise but, as expected, there are differences in terms of level when comparing both databases. Below there is a graphic example of the Proportion of the employed resident population living in the LAU 1, comparing these two data sources. Data from Lists of personnel underestimates the workers who live and work in the same LAU 1. 2 Information concerning List of Personnel integrates Annex A of the Single Report, which consists of an annual report on the information on the social activity of the company, delivered via an electronic form. 10

11 Proportion of Employed resident population working in LAU 1 (%) Lists of personnel 2010 Census 2011 With the objective of comparing the output of both sources (Census and Lists of Personnel ), Statistics Portugal created a comparable Census database by excluding data from Public administration and from self employees. Considering this exercise, the number of employees of the Lists of Personnel database for 2010 accounted for 89% of the comparable Census database Tests and sensitive analysis Taking into account the input data description made in the previous section, the sensitive analysis considered three input databases: Census, Lists of Personnel and Comparable Census database. Until now it was only possible to run tests using LAU 1 as the building block. The algorithm underlying the creation of in the package operates with two properties: a) Selfcontainment and b) Number of workers (persons employed). To run the algorithm for both properties, minimum and target values have to be defined according to the user s specifications. The four parameters to be defined are summarized in Table 2. Table 2. EU algorithm parameters Parameter minsz tarsz minsc tarsc Meaning Minimum number of employees for a cluster to be considered an Target value for the size of the cluster (in number of employees) i.e. the value for which we can accept a lower level of self-containment for an Level of self-containment that is acceptable for cluster of large sizes The minimum level acceptable for the minimum self-containment in order for a small cluster of communities to be considered an 11

12 Self-containment factor is measured by two indicators: Supply Side Self-containment (SSC) and Demand Side Self-containment (DSC). The SSC indicator represents the ratio between the number of people living and working in an area and the number of workers in the same area. The DSC indicator represents the ratio between the number of people living and working in an area and the number of jobs (residents and not residents employed) in the same area. The selection of the parameters does not follow a specific or predefined rule and, therefore, it is not an automatic process. The sensitivity analysis makes it possible to understand the effect of changing each single parameter on the output obtained. Therefore, four experiences were carried out by changing each parameter at a time and maintaining all of the other values fixed. Using Census 2011 data, the program runs for 308 municipalities with a total of workers in the Portuguese mainland territory. In the case of Lists of Personnel, the total of workers is and the number of employees resulting from the comparable Census database is Thus, it is expected to obtain different outputs as the total number of workers differs between the three databases. i. Reducing the number of small sized by changing only the minimum size of the clusters in terms of employees (minsz): Census Lists of personnel Census Comparable Database Test minsz minsc tarsz tarsc Number Number Number Min Emp Max Emp Min Emp Max Emp Min Emp Max Emp I , , I , , I , , I , , ii. Reducing the number of small sized by increasing the target size of self-containment indicators (tarsc): Census Lists of personnel Census Comparable Database Test minsz minsc tarsz tarsc Number Number Number Min Emp Max Emp Min Emp Max Emp Min Emp Max Emp II , , II , , II , , II , ,

13 iii. Reducing the number of very large sized by reducing the minimum size of selfcontainment indicators (minsc): Census Lists of personnel Census Comparable Database Test minsz minsc tarsz tarsc Number Number Number Min Emp Max Emp Min Emp Max Emp Min Emp Max Emp III , , III , , III , , III , , iv. Increasing the number of very large sized by increasing the target size of the clusters in terms of employees (tarsz): Census Lists of personnel Census Comparable Database Test minsz minsc tarsz tarsc Number Number Number Min Emp Max Emp Min Emp Max Emp Min Emp Max Emp IV , , IV , , IV , , IV , , In Annex I, a more detailed output of these tests is presented, organized by source (Census, Lists of Personnel and Comparable Census Database) with the indicators resulting from the functions CompareStat and StatClusterData provided by the package. 13

14 4.3. Maps illustrating the results Census data: same self containment, changing minimum and target number of workers Territorial Limits LAU km Census Parameter Census 0.80 Min SC Tar SC Min SZ Tar SZ Nr. of Min LAU by Max LAU by Mean EMP Mean EMP_Live_ Work Mean SC_Dema nd_side Mean SC_Supply _Side Min Area (KM 2 ) Max Area (KM 2 ) Territorial Limits LAU km Comparing sources: same parameters Territorial Limits LAU 0 50 km Census Parameter Lists of Personnel 0.70 Min SC Tar SC Min SZ Tar SZ Nr. of Min LAU by Max LAU by Mean EMP Mean EMP_Live_ Work Mean SC_Deman d_side Mean SC_Supply_ Side Min Area (KM 2 ) Max Area (KM 2 ) Territorial Limits LAU km 14

15 5. Main findings and future steps This project represents an opportunity for the Portuguese Statistical System to test different methodologies and functionalities concerning the creation of functional regions and thus corresponding to the needs already transmitted by the national stakeholders. Also, it is an opportunity to explore the potentialities of administrative data sources: more detailed building blocks which allow the definition of different objectives by returning different outputs and also the possibility of having a higher periodicity for the outputs. The project is going accordingly to the schedule. Up until now, it was possible to participate in some events associated with this European project. Besides the kick-off meeting that took place in Luxembourg in April 2016, the Portuguese staff was also in the training course organized by ISTAT in September 2016 under the name Development of Labour Market Areas and their use in Official Statistics. In this training course, it was possible to have a more in-depth knowledge of the potentialities associated with the package produced by ISTAT, namely the skills to use it and the best way to take advantage of all its functions. It was also an opportunity to share experiences with other countries concerning not only the experiences with the European methodology, but also different perspectives on the creation of labour market areas. On what concerns the Portuguese project, one of the difficulties for the operationalisation of is the decision on the parameters to use. The settlement asymmetries of the Portuguese mainland territory between North/South and Coast/Inland make it difficult to obtain a homogeneous geography in terms of population and area in the definition of functional regions. On the other hand, the two Portuguese metropolitan areas (Lisboa and Porto) have a very strong capacity of polarization of commuters from other municipalities, accounting for 40% of the Portuguese population. Other difficulty that also arose during the activities of this project is the creation of functional areas based on LAU 1 units (municipalities) with a very distinct size. The resident population of these building blocks varies between a minimum of residents and a maximum of half a million inhabitants. To overcome this difficulty, one of the next steps in this project is the possibility of using administrative data at LAU 2 level (parishes). Additionally, the treatment of administrative data is a challenge because this data is organized according to a very detailed geography. Being the greater detail of the geography one of the reasons for the exploration of this source, it is also a fact that the database received by the official department has some characteristics that require additional treatment, such as the case of incomplete records, namely missing information for the place of residence or the place of work, particularly at LAU 2 level. On the other hand, there are some situations of mismatch cases between the geographical codes of the database and the legal codes that were in force at the 15

16 reference date. This situation results from the legal changes of the territorial administrative codes that are not always followed in administrative records. Having completed this treatment, Statistics Portugal expects to have the possibility of running the European proposed program with building blocks at LAU 2 level using administrative data. The output is expected to be different from the one resulting from the use of LAU 1 building block data, either from Census database or from administrative records. Statistics Portugal will also run the national methodology with the administrative database. At a later stage, it is expected to have eight different scenarios resulting from the combination of sources, building blocks and methodologies: i) national methodology using Census data; ii) national methodology using Census comparable database with administrative data; iii) national methodology using administrative data at LAU 1 level; iv) national methodology using administrative data at LAU 2 level; v) European methodology using Census data; vi) European methodology using Census comparable database with administrative data; vii) European methodology using administrative data at LAU 1 level; and viii) European methodology using administrative data at LAU 2 level. Besides this specificity arising from the Portuguese project, there is also at EU level, the parameterisation paradox shall member states privilege the national urban system structure or an adequate portrait of across Europe; shall we promote a detailed geography of metropolitan areas labour markets or shall we look for labour market areas consistent with functional urban areas as defined in Urban Audit/OECD perspectives. Future discussions across member states and with Eurostat would be, therefore, crucial to further depict these issues and to ensure a comprehensive set of European territorial typologies. 6. References Coombes, M., Bond, S. (2008). Travel to Work Areas: the 2007 review. London: Office for National Statistics. Coombes, M.G., Green A.E., Openshow S., (1986). An efficient algorithm to generate official statistics report areas: the case of the 1984 Travel-to-Work Areas in Britain, The Journal of Operational Research Society, 37(10): Pereira, António Eduardo (1997) Bacias de Emprego em Portugal Continental, Revista de Estatística nº 4, 1º quadrimestre de 1997, INE, Lisboa. Pisco, Manuel Leal (1997) Migrações pendulares Unidades geográficas de emprego, DPP, Lisboa. 16

17 Annex I Sensitivity tests detailed results Census NbCluster UniqueCo m NbClust ersvalid Less1 NbCluste rsnocent ralcom Mean.S C_dema nd_side Std.SC_ demand _side Std.SC_ supply_ side Q1.Internal CohesionFl ows.25% Q2.Intern alcohesio nflows Q3.Internal CohesionFl ows.75% Q1.Interna lcohesion Link.25% Experience Test minsz mins C tarsz tarsc Number Mean.SC_s upply_side I I , , ,91 0,03 0,90 0,04 6,11 10,57 14,34 0,89 I I , , ,93 0,02 0,92 0,04 11,15 14,22 18,90 0,80 I I , , ,93 0,03 0,92 0,04 12,81 14,85 19,50 0,75 I I , , ,93 0,03 0,92 0,04 13,43 15,08 20,17 0,76 II II , , ,89 0,05 0,88 0,07 9,30 12,05 15,17 0,86 II II , , ,89 0,05 0,88 0,07 9,30 12,05 15,17 0,84 II II , , ,90 0,05 0,89 0,07 10,57 12,81 15,20 0,83 II II , , ,90 0,05 0,89 0,07 10,57 12,81 17,33 0,83 III III , , ,92 0,03 0,91 0,05 10,29 13,28 17,78 0,81 III III , , ,91 0,05 0,90 0,06 10,01 12,42 16,26 0,82 III III , , ,90 0,05 0,89 0,07 10,57 12,81 15,20 0,83 III III , , ,89 0,05 0,88 0,08 10,01 12,81 15,94 0,84 IV IV , , ,92 0,04 0,91 0,05 10,57 12,95 17,33 0,81 IV IV , , ,92 0,03 0,91 0,05 10,29 13,28 17,78 0,81 IV IV , , ,93 0,02 0,92 0,04 11,15 14,22 18,90 0,80 IV IV , , ,93 0,02 0,92 0,04 11,15 14,22 18,90 0,80 Q2.Inter nalcohe sionlink Q3.Interna lcohesionl ink.75% Q1.EMP _live.25 % Q3.EMP _live.75 % Q1.EMP _work.2 5% Q3.EMP _work.7 5% mins Q2.EMP_l Q2.EMP_w Q1.EMP_liv Q2.EMP_li Q3.EMP_liv Q_modula Experience Test minsz C tarsz tarsc ive ork e_work.25% ve_work e_work.75% rity I I , ,85 1,00 1, ,79 I I , ,85 0,96 0, ,80 I I , ,85 0,96 0, ,80 I I , ,85 0,97 0, ,80 II II , ,75 0,99 1, ,80 II II , ,80 0,98 1, ,80 II II , ,85 0,98 1, ,80 II II , ,90 0,98 1, ,80 III III , ,85 0,97 1, ,80 III III , ,85 0,98 1, ,79 III III , ,85 0,98 1, ,80 III III , ,85 0,98 1, ,80 IV IV , ,85 0,96 1, ,80 IV IV , ,85 0,97 1, ,80 IV IV , ,85 0,96 0, ,80 IV IV , ,85 0,96 0, ,80 1

18 Lists of Personnel Number NbClusterU NbClusters NbClustersN Mean.SC_de Std.SC_de Mean.SC_s Std.SC_su Q1.InternalCo Q2.InternalCo Q3.InternalCoh Q1.InternalCo Experience Test minsz minsc tarsz tarsc niquecom ValidLess1 ocentralcom mand_side mand_side upply_side pply_side hesionflows hesionflows esionflows hesionlink I I , , ,90 0,02 0,88 0,04 20,17 26,45 30,15 0,63 I I , , ,90 0,03 0,88 0,04 19,65 27,79 30,53 0,67 I I , , ,90 0,02 0,87 0,04 20,00 25,14 29,49 0,64 I I , , ,90 0,03 0,88 0,04 21,01 26,37 30,18 0,56 II II , , ,87 0,04 0,83 0,05 16,42 19,83 26,59 0,80 II II , , ,87 0,04 0,83 0,05 16,45 19,81 26,59 0,80 II II , , ,87 0,04 0,84 0,05 16,96 20,21 26,82 0,78 II II , , ,87 0,04 0,84 0,05 17,02 20,69 27,04 0,77 III III , , ,90 0,02 0,87 0,04 20,05 25,10 29,56 0,59 III III , , ,88 0,03 0,85 0,05 17,73 21,22 26,98 0,68 III III , , ,87 0,04 0,84 0,05 16,96 20,21 26,82 0,78 III III , , ,87 0,04 0,83 0,05 16,43 19,84 26,59 0,81 IV IV , , ,89 0,03 0,86 0,04 17,10 21,49 28,53 0,68 IV IV , , ,90 0,02 0,87 0,04 20,05 25,10 29,56 0,59 IV IV , , ,90 0,03 0,88 0,04 19,65 27,79 30,53 0,67 IV IV , , ,90 0,02 0,88 0,04 19,65 27,46 29,36 0,67 Q2.Inter Q3.Internal nalcohe CohesionLi Q1.EMP_liv Q1.EMP_w Q2.EMP_w Q3.EMP_ Q1.EMP_live_ Q2.EMP_live_ Q3.EMP_live_ Experience Test minsz minsc tarsz tarsc sionlink nk e Q2.EMP_live Q3.EMP_live ork ork work work work work Q_modularity I I , ,85 0,77 0, ,6996 I I , ,85 0,81 0, ,6972 I I , ,85 0,82 0, ,7174 I I , ,85 0,79 0, ,6898 II II , ,75 0, ,7276 II II , ,80 0, ,7276 II II , ,85 0,95 0, ,7279 II II , ,90 0,95 0, ,7293 III III , ,85 0,82 0, ,7181 III III , ,85 0,92 0, ,7297 III III , ,85 0,95 0, ,7279 III III , ,85 0, ,7281 IV IV , ,85 0,79 0, ,7140 IV IV , ,85 0,82 0, ,7181 IV IV , ,85 0,81 0, ,6972 IV IV , ,85 0,72 0, ,6959 2

19 Comparable Census database Number Mean.SC_d Std.SC_de Mean.SC_s Std.SC_su Q1.InternalCoh Q2.InternalCo Q3.InternalCo Q1.InternalCo Q2.InternalC Q3.InternalCo Experience Test minsz minsc tarsz tarsc emand_side mand_side upply_side pply_side esionflows hesionflows hesionflows hesionlink ohesionlink hesionlink I I , , ,91 0,02 0,90 0,03 10,83 14,94 19,09 0,78 0,95 0,99 I I , , ,92 0,02 0,91 0,04 15,24 17,80 23,85 0,73 0,87 0,97 I I , , ,92 0,02 0,91 0,04 15,25 18,01 23,83 0,73 0,92 0,98 I I , , ,93 0,02 0,92 0,04 16,36 20,40 23,89 0,73 0,82 0,94 II II , , ,88 0,05 0,86 0,07 12,83 15,69 20,88 0,83 0,98 1,00 II II , , ,88 0,05 0,86 0,07 12,96 16,51 21,38 0,89 0,99 1,00 II II , , ,91 0,03 0,89 0,05 13,77 17,47 21,78 0,77 0,95 0,98 II II , , ,91 0,03 0,89 0,05 13,77 17,47 21,78 0,77 0,95 0,98 III III , , ,92 0,02 0,91 0,04 14,74 17,80 23,77 0,73 0,92 0,98 III III , , ,91 0,03 0,90 0,05 13,66 17,59 21,78 0,77 0,95 0,98 III III , , ,90 0,04 0,88 0,07 13,77 17,59 22,65 0,80 0,96 1,00 III III , , ,89 0,05 0,87 0,08 13,14 17,41 22,10 0,83 0,97 1,00 IV IV , , ,92 0,02 0,91 0,04 14,51 17,88 22,69 0,75 0,95 0,98 IV IV , , ,92 0,02 0,91 0,04 14,74 17,80 23,77 0,73 0,92 0,98 IV IV , , ,92 0,02 0,91 0,04 15,24 17,80 23,85 0,73 0,87 0,97 IV IV , , ,92 0,02 0,91 0,04 15,24 17,80 23,85 0,71 0,87 0,97 Q1.EMP Q2.EMP_liv Q3.EMP_liv Q1.EMP_w Q2.EMP_w Q1.EMP_live_ Q2.EMP_live_ Q3.EMP_live_ Q_modularit Experience Test minsz minsc tarsz tarsc _live e e ork ork Q3.EMP_work work work work y I I , , ,77 I I , , ,77 I I , , ,78 I I , , ,77 II II , , ,78 II II , , ,78 II II , , ,78 II II , , ,78 III III , , ,78 III III , , ,78 III III , , ,78 III III , , ,78 IV IV , , ,78 IV IV , , ,78 IV IV , , ,77 IV IV , , ,77 3