EMPLOYING THE EU METHODOLOGY TO DEFINE LABOUR MARKET
EMPLOYING THE EU METHODOLOGY TO DEFINE LABOUR MARKET AREAS IN POLAND Statistical Office in Bydgoszcz Luxembourg, 2017. 06. 28
Schedule o Data source for LMAs o Defining LMAs in Poland o. Choosing the final set of parameters o Fine tuning of LMAs o Final LMA division for Poland o Statistics by LMAs for years 2011 -2014 o Problems o Overall project experience – lessons learned o Main challenges o Changes to method or terminology used o. Presenting systematically data on LMAs o. Future plans regarding the LMAs
Data source for LMAs In Poland the source for developing Labour Market Areas is Population Census 2011. Data are available at gminas (LAU-2) level. National Census of Population and Housing 2011 in Poland: o decision to use the “mixed” method – direct interview and 28 administrative sources o use of public administration registers and information systems as the census-data source o internet self-enumeration o the first general statistical survey carried out only with the use of electronic questionnaires o census carried out using hand-held terminals, on the basis of electronic forms - paper forms completely dropped o use of GIS (Geographic Information Systems) tools
Defining LMAs in Poland o data from Population Census 2011 based on registers o persons aged 15 years and more o insurance code as the criterion of being employed or not o excluding persons not employed, working abroad or those for whom it is impossible to define place of work from registers o specifying for each employed person two LAU-2 codes: living_code and working_code o for all farmers living_code=working_code o for persons who did not declare travelling to work in tax registry living_code=working_code o creating a matrix of commuting flows between living_code and working_code (around 278 000 links)
Choosing the final set of parameters Selecting possible values of parameters on the basis of area, population, density of population and number of LAU-2 s in Great Britain, Italy and Poland. First tests performed for the following values of parameters: • min. SZ = {1 000, 2 000, 3 500, 4 000, 5 000} • tar. SZ = {7 500, 10 000, 15 000, 16 000, 17 000, 18 000, 19 000, 20 000, 35 000} • min. SC = {0. 5, 0. 55, 0. 667} • tar. SC = {0. 667, 0. 75, 0. 85, 0. 9}
Choosing the final set of parameters – basic data on Italy, Great Britain and Poland in 2011 Italy Great Britain Poland Population (persons) 59, 433, 744 63, 182, 180 38, 044, 565 Population of 15 years & more (persons) 51, 107, 701 52, 082, 285 32, 262, 995 Economically active (persons) 25, 985, 295 32, 442, 335 17, 576, 246 Employed (persons) 23, 017, 840 30, 008, 635 15, 443, 421 2, 967, 455 2, 433, 705 2, 132, 825 302, 073 248, 528 312, 679 197 254 122 Number of building blocks 8, 092 10, 399 3, 081 Minimal size of LMA (working residents) 1, 000 3, 500 4, 000 10, 000 25, 000 30, 000 0. 667 Target self-containment of LMA 0. 75 0. 8 Number of LMAs 611 228 339 Average population in a LMA (persons) 97, 273 277, 114 112, 226 Average area of a LMA(thousands km 2) 494 1, 090 922 13 46 9. 1 Unemployed (persons) Area (thousands km 2) Density of population (persons/km 2) Target size of LMA (working residents) Minimal self-containment of LMA Average number of building blocks in a LMA Source: own work on the basis of Eurostat, Europa. eu portal, Istat, INSEE, Office for National Statistics
Choosing the final set of parameters First tests performed for the following values of parameters: • min. SZ = {1 000, 2 000, 3 500, 4 000, 5 000} • tar. SZ = {7 500, 10 000, 15 000, 16 000, 17 000, 18 000, 19 000, 20 000, 35 000} • min. SC = {0. 5, 0. 55, 0. 667} • tar. SC = {0. 667, 0. 75, 0. 85, 0. 9}
Choosing the final set of parameters o single test made with EURO_script_Eurostat R-program lasted from one week at the beginning to about 30 hours after introducing new version of R-program o the results were not satisfying o revision of the method of deciding whether persons travel to work (on the basis of tax register) and thereby revision of flow matrix o decision not to use minimal self-containment lower than 0. 6 and to use target self‑containment equal or higher than 0. 75
Choosing the final set of parameters o improvement of efficiency after receiving Labour. Market. Areas R-package - single test lasted about 30 minutes o decision to rerun tests for some sets of parameters o testing R-package (version 2. 0) with 144 sets of parameters: • min. SZ = {2000, 3000, 4000, 5000} • tar. SZ = {15000, 20000, 25000, 30000} • min. SC = {0. 6, 0. 667, 0. 7} • tar. SC = {0. 75, 0. 85}
Choosing the final set of parameters o sensitivity analysis – checking and comparing sets of parameters using functions Stat. Cluster. Data, Stat. Reserve. List, Compare. LMAs. Stat o analysis of the results and maps focusing on LMAs covering area of three voivodships and their capital cities: kujawsko-pomorskie (Bydgoszcz), mazowieckie (Warsaw) and wielkopolskie (Poznan) o analysis of non-contiguous LMAs
Choosing the final set of parameters – avoiding red and choosing green
Choosing the final set of parameters Rank analysis (method introduced by Zdzisław Hellwig) of sets of parameters according to following characteristics ([-] – negative correlation, [+] – positive correlation): o number of communities in a reserve-list [-] o percent of the number of clusters with an unique community [-] o percent of the number of clusters with validity smaller than 1 [-] o percent of the number of clusters with no communities having a centrality measure greater than 1 [-] o mean of the demand side self-containment of the clusters in the partition [+] o mean of the supply side self-containment of the clusters in the partition [+] o median of the percentage of internal flows (excluding flows having the same node as origin and destination) of the LMA between different communities with regard to the total internal flows [+] o median of the ratio between number of links between communities inside LMA, excluding itself, and the maximum number of possible links [+] o Q_modularity index [+]
Choosing the final set of parameters Four combinations of weights were created. In each of the rankings the following set of parameters turned out to be in the first place: min. SZ = 4000 tar. SZ = 30000 min. SC = 0. 667 tar. SC = 0. 8
Choosing the final set of parameters Choosing proper set of parameters is one of the hardest part of defining LMAs – there is no unequivocal method. Analyses may not give the same results. In Poland, the sensitivity analysis and rank analysis gave similar results, whereas the analysis of voivodships gave slightly different set of parameters for every voivodship.
Choosing the final set of parameters min. SZ = 4 000 tar. SZ = 30 000 min. SC = 0. 667 tar. SC = 0. 8
Fine tuning of LMAs Decision to perform fine tuning manually due to necessity to correct more LMAs than indicated by Fine. Tuning function (e. g. LMAs with holes).
Fine tuning of LMAs – LMA 1613 o One special case – LMA 1613: LMA 1613 consisted of two distant parts: • part one – 9636 residents in 4 gminas • part two – 7795 residents in 3 gminas. The first idea was to split this LMA into two LMAs, but SC for part two was lower than min. SC, therefore part two could not become a valid LMA. For part one X-equation was not met.
Fine tuning of LMAs – LMA 1613
Final LMA division for Poland number of LMAs mean self-containment mean size mean number of gminas forming the LMA mean validity number of LMAs with validity < 1 number od links between LMAs number of LMAs with no gminas having a centrality measure > 1 339 0. 816 41, 818 9. 1 1. 12 1 46, 167 41 mean SC_demand_side 0. 902 std SC_demand_side 0. 050 mean SC_supply_side 0. 822 std SC_supply_side 0. 055
Statistics by LMAs for years 2011 -2014 LMA Registered unemployed to working age population ratio by labour market areas 2011 POLAND 1 BOLESŁAWIEC 9 DZIERŻONIÓW 16 GŁOGÓW 28 JAWOR 44 KAMIENNA GÓRA 50 KŁODZKO 78 LUBAŃ 87 LUBIN 104 MILICZ 106 OLEŚNICA 118 OŁAWA 145 ŚWIDNICA 176 WAŁBRZYCH 180 WOŁÓW 199 ZĄBKOWICE ŚLĄSKIE 206 ZGORZELEC 216 ZŁOTORYJA Source: own work 2012 8, 01% 7, 22% 9, 05% 7, 55% 11, 99% 10, 97% 12, 58% 11, 85% 6, 14% 9, 24% 7, 99% 7, 40% 6, 76% 9, 69% 10, 97% 11, 51% 6, 51% 13, 40% 2013 8, 68% 8, 18% 9, 62% 8, 23% 12, 88% 11, 79% 13, 96% 12, 20% 6, 60% 9, 86% 8, 73% 8, 57% 8, 24% 10, 71% 12, 44% 12, 21% 7, 38% 13, 89% 2014 8, 84% 7, 83% 9, 42% 7, 94% 12, 36% 10, 56% 14, 28% 11, 77% 6, 69% 10, 22% 8, 79% 9, 23% 7, 61% 10, 46% 12, 07% 12, 05% 7, 66% 14, 64% 7, 53% 5, 81% 7, 38% 9, 40% 8, 04% 12, 03% 9, 60% 5, 54% 8, 17% 6, 53% 6, 96% 6, 01% 8, 37% 9, 83% 10, 13% 6, 23% 12, 26%
Employed to working age population ratio in 2011 Registered unemployed to working age population ratio in 2011
Problems with data source o links between gminas situated on two edges of the country o frequent cases of minor number of flows between remote gminas
Problems with data source Possible causes: o in insurance registry people are considered to be working in the headquarters instead of working in the actual place o data errors (e. g. using names of localities instead of unique locality identifiers) Possible solutions in future: o analyzing a distance between gminas, and eliminating insignificant links between gminas situated too far from each other o introducing a threshold for number of flows between gmina_live and gmina_work dependent on number of residents and eliminating links beneath the threshold We intend to test these solutions after next Population Census.
Other problems o too big building blocks (average surface of gmina about 101, 5 km 2) o diverse number of residents in gminas (minimum= 164 , maksimum= 726 245) o administrative islands The decision was to accept such situations.
Other problems o 3 different territorial identifiers for urban-rural set of gminas: one for town one for rural area one for town and rural area together Usually we treat urban-rural gminas as two different gminas, but in some datasets they are treated as one.
Overall project experience – lessons learned othe general rule: minimal self-containment equal or higher than 0. 6 and target self‑containment equal or higher than 0. 75 oproposals of particular quality measures in choosing final set of parameters o the solution: treating urban-rural gminas separately in the algorithm and providing the contiguity in the fine tuning process o possibility to use centrality index in the fine tuning process
Main challenges o choosing the optimal set of parameter values – no unequivocal method ourban-rural gminas in defining LMAs oproblems with defining the actual place of work osingle cases of non-contiguous LMAs and LMAs with holes
Changes to method or terminology used o unintuitive name of size parameter o introducing a threshold of number of flows between gmina_live and gmina_work dependent on number of residents and eliminating links beneath the threshold
Presenting systematically data on LMAs o Survey-based data concerning employment in enterprises of 10 persons and more is available annually at LAU-2 level. The division by NACE rev. 2 economic activity sections possible at the LAU-2 level, but due to statistical confidentiality, it is recommended to group the NACE sections. o. The number of the unemployed persons available at LAU-2 level twice a year from data of Ministry of Family, Labour and Social Policy. o. Since 2016 data concerning employed and unemployed with unique identifiers from National Insurance System are available four times a year at the LAU-2 level. o Both, the employed and unemployed may be presented according to the gender, age, residence. Additionally, the employed can be presented according to NACE rev. 2 economic activity sections, wages, size of the enterprise. All data available at LAU-2 level can be obtained at LMA level.
Future plans regarding the LMAs o A publication concerning labour market areas in Poland. o Defining labour market areas on the basis of the Population Census 2021 data. o Comparison with Labour Market Areas defined on the basis of of the Population Census 2011 data. o Considering elimination of insignificant links between distant gminas. o Introduction and dissemination of the Labour Market Areas by occupational categories, gender, age groups, earning groups, mode of travel to work, NACE rev. 2 economic activity sections and others.
Thank you for your attention
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