DOI: 10.6084/m9.figshare.22215634
title: Firms in Mazovian region, Poland (2012)

description: <br>This dataset is a subsample of 50'000 observations (from 1 mln obs) of geo-localised firms for the Warsaw region in Poland (formally Mazovian NUTS1 voivodship). Data for 2012 were retrieved from the official register of Polish business entities (REGON) and included address, main sector (indicated with A:U letter and 5-digit code) and employment (in groups 1:5). Data were geo-coded from postal addresses to latitude /longitude. The dataset includes additional variables computed based on raw data:dist_core_10, dist_core_25, dist_core_50, dist_midsize_10, dist_midsize_25, dist_midsize_50,dist_regional_10, dist_regional_25, dist_regional_50, dist_localbig_10, dist_localbig_25, dist_localbig_50, dist_localsmall_10, dist_localsmall_25, dist_localsmall_50 – 15 dummy variable for each firm: 1 if a firm is located in a radius (10, 25 and 50 km) from the city centre. We considered 39 cities in 5 population size categories that are located within NUTS1 Mazovian region: core 1mln+ (1 city, Warsaw), midsize 100K+ (2 cities), regional 50K+ (4 cities), local big 30K+ (9 cities), local small 15-30K (23 cities).COREfirms, COREpopul - The dummy variable defines if a given point belongs to a high-density cluster from DBSCAN (of a radius of 0.03° what is equivalent to ca.3,3 km and minPts=75 firms / 500 persons).locAggA, locAggB, .., locAggU - A number of firms from a given sector (A:U) calculated in a radius of 500 m from a given firmlocAggAgri, locAggProd, locAggConstr, locAggServ - Aggregated local sectoral agglomeration values: Agriculture: sector A; Production: sectors B, C, D, E; Construction: sector F; Service: sectors G, H, I, J, K, L, M, N, O, P, Q, R, S, T, UlocAggTotal - A total number of firms calculated in the direct neighbourhood of 500 m of a given firmlocHH - Herfindahl-Hirsh index calculated in 500 m radiuslocLQ - Location Quotient calculated in 500 m radiuslocBIG - The number of firms classified as big in the direct neighbourhood of 500 m of a given firmlocPdens - A number of inhabitants in the direct neighbourhood of 500 m of the given firmAddress, latitude and longitude - Postal address of the firm from the REGON register, recoded into geo-coordinatesSEC_PKD7, PKD7 - Sectoral classification of firm’s main activity from the REGON registerempl, gr_empl - Employment size. Gr_empl reports five employment classes: up to 9 persons (gr.1), 10-49 persons (gr.2), 50-249 persons (gr.3), 250-1000 persons (gr.4) and 1000+ persons (gr.5). Empl gives the approximate mid-value of groups, respectively 5,30, 150, 600 and 1500dummy_if_highetch - Dummy variable if a firm can be classified as high-tech businessdummy_if_big - Dummy variable if a firm can be classified as big (employment above 250 persons, groups 4 and 5)dummy_agri, dummy_prod, dummy_constr, dummy_serv - Dummy variable if a firm belongs to one of four main sectors (see details for locAggAgri, locAggProd, locAggConstr, locAggServ)A Agriculture, forestry, hunting and fishingB Mining and explorationC Industrial processingD Producing and supplying in electricity, gas, steam, hot water and air conditioning systemsE Water supply; wastewater management, waste management and remediation activitiesF ConstructionG Wholesale and retail trade; repair of motor vehicles and motorcyclesH Transportation and storageI Activities related to accommodation and catering servicesJ Information and communicationK Financial and insurance activitiesL Activities related to real estate servicesM Professional, scientific and technical activitiesN Administration and support service activitiesO Public administration and defense; compulsory social securityP EducationQ Healthcare and social assistanceR Activities related to arts, entertainment and recreationS Other service activitiesT Private households hiring employees; households producing goods and providing services for their own needs,U Organisations and extraterritorial teams<br>This dataset is a part of a project titled "Modelling and forecasting business location in context of economies of density. Theoretical, methodological and empirical approach using spatial econometrics and spatial machine learning" financed by National Science Center, Poland (Krakow, Poland) [OPUS 21 call, grant number UMO-2021/41/B/HS4/00285].