Red Flags Analysis

Background

This analysis of UK PSC data that has been mapped to BODS format shows how we can answer common questions about 'red flags' in BODS datasets. These red flags are not necessarily an indication of anything wrong but may prompt further investigation.

This analysis has not been conducted in order to identify red flags in the UK data itself, but to show an example of how such analysis could be done.

Reading in data

#import qbods library from github !wget 'https://raw.githubusercontent.com/openownership/bodsanalysis/main/qbods.py' -O qbods.py #install required libraries !pip3 install -r requirements.txt #importing libraries import qbods import os import pandas as pd import numpy as np import gc import networkx as nx #change directory to /tmp (avoid disk space issues) os.chdir('/tmp') #importing bods data from OO website qbods.getBodsData('https://s3.eu-west-1.amazonaws.com/oo-bodsdata/data/UK_PSC/csv.zip')

3124850K .......... .......... .......... .......... .......... 98%  371M 1s
3124900K .......... .......... .......... .......... .......... 98%  379M 1s
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3138900K .......... .......... .......... .......... .......... 98% 56.3M 1s
3138950K .......... .......... .......... .......... .......... 98% 51.3M 1s
3139000K .......... .......... .......... .......... .......... 98% 84.9M 1s
3139050K .......... .......... .......... .......... .......... 98% 5.23M 1s
3139100K .......... .......... .......... .......... .......... 98%  109M 1s
3139150K .......... .......... .......... .......... .......... 98%  285M 1s
3139200K .......... .......... .......... .......... .......... 98%  245M 1s
3139250K .......... .......... .......... .......... .......... 98%  393M 1s
3139300K .......... .......... .......... .......... .......... 98% 18.1M 1s
3139350K .......... .......... .......... .......... .......... 98%  266M 1s
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3140000K .......... .......... .......... .......... .......... 98%  327M 1s
3140050K .......... .......... .......... .......... .......... 98%  225M 1s
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3140200K .......... .......... .......... .......... .......... 98%  333M 1s
3140250K .......... .......... .......... .......... .......... 98% 16.9M 1s
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3140350K .......... .......... .......... .......... .......... 98% 3.19M 1s
3140400K .......... .......... .......... .......... .......... 98% 29.7M 1s
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3140600K .......... .......... .......... .......... .......... 98% 91.4M 1s
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3141200K .......... .......... .......... .......... .......... 98%  194M 1s
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3141400K .......... .......... .......... .......... .......... 98%  631M 1s
3141450K .......... .......... .......... .......... .......... 98% 12.5M 1s
3141500K .......... .......... .......... .......... .......... 98% 80.7M 1s
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3141600K .......... .......... .......... .......... .......... 98%  423M 1s
3141650K .......... .......... .......... .......... .......... 98% 44.5M 1s
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3141800K .......... .......... .......... .......... .......... 98%  149M 1s
3141850K .......... .......... .......... .......... .......... 98% 58.9M 1s
3141900K .......... .......... .......... .......... .......... 98%  244M 1s
3141950K .......... .......... .......... .......... .......... 98% 53.8M 1s
3142000K .......... .......... .......... .......... .......... 98% 74.5M 1s
3142050K .......... .......... .......... .......... .......... 98% 5.23M 1s
3142100K .......... .......... .......... .......... .......... 98%  508M 1s
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3142300K .......... .......... .......... .......... .......... 99% 18.1M 1s
3142350K .......... .......... .......... .......... .......... 99%  470M 1s
3142400K .......... .......... .......... .......... .......... 99% 40.3M 1s
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3142500K .......... .......... .......... .......... .......... 99%  394M 1s
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3143200K .......... .......... .......... .......... .......... 99%  609M 1s
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3143750K .......... .......... .......... .......... .......... 99% 5.48M 1s
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3144050K .......... .......... .......... .......... .......... 99% 89.5M 1s
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3145050K .......... .......... .......... .......... .......... 99% 85.7M 1s
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3145850K .......... .......... .......... .......... .......... 99% 38.6M 1s
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3146500K .......... .......... .......... .......... .......... 99% 17.2M 1s
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3147050K .......... .......... .......... .......... .......... 99% 96.0M 1s
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3148050K .......... .......... .......... .......... .......... 99% 86.8M 1s
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3151100K .......... .......... .......... .......... .......... 99% 97.4M 1s
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3151200K .......... .......... .......... .......... .......... 99%  138M 1s
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3151500K .......... .......... .......... .......... .......... 99%  530M 1s
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3152000K .......... .......... .......... .......... .......... 99%  412M 1s
3152050K .......... .......... .......... .......... .......... 99% 85.4M 1s
3152100K .......... .......... .......... .......... .......... 99% 22.8M 1s
3152150K .......... .......... .......... .......... .......... 99%  310M 1s
3152200K .......... .......... .......... .......... .......... 99%  278M 1s
3152250K .......... .......... .......... .......... .......... 99%  385M 1s
3152300K .......... .......... .......... .......... .......... 99%  377M 1s
3152350K .......... .......... .......... .......... .......... 99%  498M 0s
3152400K .......... .......... .......... .......... .......... 99%  314M 0s
3152450K .......... .......... .......... .......... .......... 99%  178M 0s
3152500K .......... .......... .......... .......... .......... 99%  442M 0s
3152550K .......... .......... .......... .......... .......... 99% 3.65M 0s
3152600K .......... .......... .......... .......... .......... 99% 28.7M 0s
3152650K .......... .......... .......... .......... .......... 99% 49.6M 0s
3152700K .......... .......... .......... .......... .......... 99%  165M 0s
3152750K .......... .......... .......... .......... .......... 99% 5.37M 0s
3152800K .......... .......... .......... .......... .......... 99% 69.6M 0s
3152850K .......... .......... .......... .......... .......... 99%  106M 0s
3152900K .......... .......... .......... .......... .......... 99% 80.8M 0s
3152950K .......... .......... .......... .......... .......... 99%  118M 0s
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3153050K .......... .......... .......... .......... .......... 99%  184M 0s
3153100K .......... .......... .......... .......... .......... 99%  291M 0s
3153150K .......... .......... .......... .......... .......... 99% 3.53M 0s
3153200K .......... .......... .......... .......... .......... 99% 68.9M 0s
3153250K .......... .......... .......... .......... .......... 99%  419M 0s
3153300K .......... .......... .......... .......... .......... 99%  234M 0s
3153350K .......... .......... .......... .......... .......... 99%  307M 0s
3153400K .......... .......... .......... .......... .......... 99%  416M 0s
3153450K .......... .......... .......... .......... .......... 99%  344M 0s
3153500K .......... .......... .......... .......... .......... 99%  381M 0s
3153550K .......... .......... .......... .......... .......... 99%  514M 0s
3153600K .......... .......... .......... .......... .......... 99%  476M 0s
3153650K .......... .......... .......... .......... .......... 99%  439M 0s
3153700K .......... .......... .......... .......... .......... 99%  516M 0s
3153750K .......... .......... .......... .......... .......... 99%  515M 0s
3153800K .......... .......... .......... .......... .......... 99%  455M 0s
3153850K .......... .......... .......... .......... .......... 99%  238M 0s
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3153950K .......... .......... .......... .......... .......... 99%  352M 0s
3154000K .......... .......... .......... .......... .......... 99%  431M 0s
3154050K .......... .......... .......... .......... .......... 99%  444M 0s
3154100K .......... .......... .......... .......... .......... 99%  107M 0s
3154150K .......... .......... .......... .......... .......... 99% 5.38M 0s
3154200K .......... .......... .......... .......... .......... 99%  449M 0s
3154250K .......... .......... .......... .......... .......... 99% 93.8M 0s
3154300K .......... .......... .......... .......... .......... 99% 9.65M 0s
3154350K .......... .......... .......... .......... .......... 99% 47.8M 0s
3154400K .......... .......... .......... .......... .......... 99%  467M 0s
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3154650K .......... .......... .......... .......... .......... 99%  347M 0s
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3154750K .......... .......... .......... .......... .......... 99%  416M 0s
3154800K .......... .......... .......... .......... .......... 99% 91.2M 0s
3154850K .......... .......... .......... .......... .......... 99% 97.5M 0s
3154900K .......... .......... .......... .......... .......... 99%  188M 0s
3154950K .......... .......... .......... .......... .......... 99%  371M 0s
3155000K .......... .......... .......... .......... .......... 99%  300M 0s
3155050K .......... .......... .......... .......... .......... 99% 84.3M 0s
3155100K .......... .......... .......... .......... .......... 99% 14.8M 0s
3155150K .......... .......... .......... .......... .......... 99%  235M 0s
3155200K .......... .......... .......... .......... .......... 99%  356M 0s
3155250K .......... .......... .......... .......... .......... 99%  126M 0s
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3155350K .......... .......... .......... .......... .......... 99%  521M 0s
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3155550K .......... .......... .......... .......... .......... 99% 4.79M 0s
3155600K .......... .......... .......... .......... .......... 99% 12.8M 0s
3155650K .......... .......... .......... .......... .......... 99% 53.0M 0s
3155700K .......... .......... .......... .......... .......... 99% 60.7M 0s
3155750K .......... .......... .......... .......... .......... 99%  231M 0s
3155800K .......... .......... .......... .......... .......... 99% 5.50M 0s
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3155950K .......... .......... .......... .......... .......... 99% 76.3M 0s
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3156050K .......... .......... .......... .......... .......... 99%  123M 0s
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3156150K .......... .......... .......... .......... .......... 99%  170M 0s
3156200K .......... .......... .......... .......... .......... 99% 3.97M 0s
3156250K .......... .......... .......... .......... .......... 99% 33.6M 0s
3156300K .......... .......... .......... .......... .......... 99%  533M 0s
3156350K .......... .......... .......... .......... .......... 99%  180M 0s
3156400K .......... .......... .......... .......... .......... 99%  253M 0s
3156450K .......... .......... .......... .......... .......... 99% 93.8M 0s
3156500K .......... .......... .......... .......... .......... 99%  352M 0s
3156550K .......... .......... .......... .......... .......... 99% 50.5M 0s
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3156650K .......... .......... .......... .......... .......... 99%  147M 0s
3156700K .......... .......... .......... .......... .......... 99%  247M 0s
3156750K .......... .......... .......... .......... .......... 99%  142M 0s
3156800K .......... .......... .......... .......... .......... 99%  109M 0s
3156850K .......... .......... .......... .......... .......... 99%  110M 0s
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3156950K .......... .......... .......... .......... .......... 99%  114M 0s
3157000K .......... .......... .......... .......... .......... 99%  191M 0s
3157050K .......... .......... .......... .......... .......... 99%  205M 0s
3157100K .......... .......... .......... .......... .......... 99%  352M 0s
3157150K .......... .......... .......... .......... .......... 99% 8.77M 0s
3157200K .......... .......... .......... .......... .......... 99%  239M 0s
3157250K .......... .......... .......... .......... .......... 99% 90.1M 0s
3157300K .......... .......... .......... .......... .......... 99% 9.22M 0s
3157350K .......... .......... .......... .......... .......... 99% 67.8M 0s
3157400K .......... .......... .......... .......... .......... 99%  225M 0s
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3157650K .......... .......... .......... .......... .......... 99%  442M 0s
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3157750K .......... .......... .......... .......... .......... 99%  408M 0s
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3157850K .......... .......... .......... .......... .......... 99% 96.3M 0s
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3158000K .......... .......... .......... .......... .......... 99%  317M 0s
3158050K .......... .......... .......... .......... .......... 99% 64.8M 0s
3158100K .......... .......... .......... .......... .......... 99% 13.7M 0s
3158150K .......... .......... .......... .......... .......... 99% 80.3M 0s
3158200K .......... .......... .......... .......... .......... 99% 34.0M 0s
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3158300K .......... .......... .......... .......... .......... 99%  122M 0s
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3158550K .......... .......... .......... .......... .......... 99%  200M 0s
3158600K .......... .......... .......... .......... .......... 99% 6.79M 0s
3158650K .......... .......... .......... .......... .......... 99% 12.7M 0s
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3158850K .......... .......... .......... .......... .......... 99% 5.52M 0s
3158900K .......... .......... .......... .......... .......... 99% 36.2M 0s
3158950K .......... .......... .......... .......... .......... 99% 67.1M 0s
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3159050K .......... .......... .......... .......... .......... 99% 84.1M 0s
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3159200K .......... .......... .......... .......... .......... 99% 3.97M 0s
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3161000K .......... .......... .......... .......... .......... 99%  313M 0s
3161050K .......... .......... .......... .......... .......... 99% 73.4M 0s
3161100K .......... .......... .......... .......... .......... 99% 12.6M 0s
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3161200K .......... .......... .......... .......... .......... 99% 13.9M 0s
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3161800K .......... .......... .......... .......... .......... 99% 33.3M 0s
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3162000K .......... .......... .......... .......... .......... 99% 71.6M 0s
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3162500K .......... .......... .......... .......... .......... 99%  561M 0s
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3164050K .......... .......... .......... .......... .......... 99% 77.8M 0s
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3164150K .......... .......... .......... .......... .......... 99% 11.1M 0s
3164200K .......... .......... .......... .......... .......... 99% 16.7M 0s
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3165450K .......... .......... .......... .......... .......... 99% 82.0M 0s
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3166200K .......... .......... .......... .......... .......... 99%  583M 0s
3166250K .......... .......... .......... .......... .......... 99% 13.6M 0s
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3166350K .......... .......... .......... .......... .......... 99% 9.83M 0s
3166400K .......... .......... .......... .......... .......... 99% 32.3M 0s
3166450K .......... .......... .......... .......... .......... 99%  123M 0s
3166500K .......... .......... .......... .......... .......... 99% 97.1M 0s
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3167050K .......... .......... .......... .......... .......... 99%  127M 0s
3167100K .......... .......... .......... .......... .......... 99% 12.3M 0s
3167150K .......... .......... .......... .......... .......... 99%  272M 0s
3167200K .......... .......... .......... .......... .......... 99%  374M 0s
3167250K .......... .......... .......... .......... .......... 99% 11.0M 0s
3167300K .......... .......... .......... .......... .......... 99%  268M 0s
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3167600K .......... .......... .......... .......... .......... 99%  560M 0s
3167650K .......... .......... .......... .......... .......... 99% 12.4M 0s
3167700K .......... .......... .......... .......... .......... 99% 20.0M 0s
3167750K .......... .......... .......... .......... .......... 99% 27.6M 0s
3167800K .......... .......... .......... .......... .......... 99%  385M 0s
3167850K .......... .......... .......... .......... .......... 99% 45.0M 0s
3167900K .......... .......... .......... .......... .......... 99% 5.15M 0s
3167950K .......... .......... .......... .......... .......... 99% 42.8M 0s
3168000K .......... .......... .......... .......... .......... 99% 35.4M 0s
3168050K .......... .......... .......... .......... .......... 99%  344M 0s
3168100K .......... .......... .......... .......... .......... 99%  170M 0s
3168150K .......... .......... .......... .......... .......... 99%  164M 0s
3168200K .......... .......... .......... .......... .......... 99% 3.99M 0s
3168250K .......... .......... .......... .......... .......... 99% 78.3M 0s
3168300K .......... .......... .......... .......... .......... 99% 45.7M 0s
3168350K .......... .......... .......... .......... .......... 99%  372M 0s
3168400K .......... .......... .......... .......... .......... 99%  184M 0s
3168450K .......... .......... .......... .......... .......... 99% 78.1M 0s
3168500K .......... .......... .......... .......... .......... 99%  142M 0s
3168550K .......... .......... .......... .......... .......... 99%  377M 0s
3168600K .......... .......... .......... .......... .......... 99% 10.1M 0s
3168650K .......... .......... .......... .......... .......... 99% 28.4M 0s
3168700K .......... .......... .......... .......... .......... 99%  218M 0s
3168750K .......... .......... .......... .......... .......... 99%  304M 0s
3168800K .......... .......... .......... .......... .......... 99%  247M 0s
3168850K .......... .......... .......... .......... .......... 99%  132M 0s
3168900K .......... .......... .......... .......... .......... 99%  447M 0s
3168950K .......... .......... .......... .......... .......... 99%  337M 0s
3169000K .......... .......... .......... .......... .......... 99%  415M 0s
3169050K .......... .......... .......... .......... .......... 99%  276M 0s
3169100K .......... .......... .......... .......... .......... 99%  235M 0s
3169150K .......... .......... .......... .......... .......... 99%  393M 0s
3169200K .......... .......... .......... .......... .......... 99%  482M 0s
3169250K .......... .......... .......... .......... .......... 99% 23.6M 0s
3169300K .......... .......... .......... .......... .......... 99%  145M 0s
3169350K .......... .......... .......... .......... .......... 99% 9.75M 0s
3169400K .......... .......... .......... .......... .......... 99%  469M 0s
3169450K .......... .......... .......... .......... .......... 99% 30.4M 0s
3169500K .......... .......... .......... .......... .......... 99% 67.1M 0s
3169550K .......... .......... .......... .......... .......... 99%  329M 0s
3169600K .......... .......... .......... .......... .......... 99%  296M 0s
3169650K .......... .......... .......... .......... .......... 99%  421M 0s
3169700K .......... .......... .......... .......... .......... 99%  149M 0s
3169750K .......... .......... .......... .......... .......... 99%  191M 0s
3169800K .......... .......... .......... .......... .......... 99%  177M 0s
3169850K .......... .......... .......... .......... .......... 99%  348M 0s
3169900K .......... .......... .......... .......... .......... 99%  158M 0s
3169950K .......... .......... .......... .......... .......... 99%  249M 0s
3170000K .......... .......... .......... .......... .......... 99%  127M 0s
3170050K .......... .......... .......... .......... .......... 99%  231M 0s
3170100K .......... .......... .......... .......... .......... 99% 12.8M 0s
3170150K .......... .......... .......... .......... .......... 99%  319M 0s
3170200K .......... .......... .......... .......... .......... 99%  169M 0s
3170250K .......... .......... .......... .......... .......... 99% 8.89M 0s
3170300K .......... .......... .......... .......... .......... 99%  497M 0s
3170350K .......... .......... .......... .......... .......... 99%  319M 0s
3170400K .......... .......... .......... .......... .......... 99%  242M 0s
3170450K .......... .......... .......... .......... .......... 99%  338M 0s
3170500K .......... .......... .......... .......... .......... 99%  500M 0s
3170550K .......... .......... .......... .......... .......... 99%  483M 0s
3170600K .......... .......... .......... .......... .......... 99%  544M 0s
3170650K .......... .......... .......... .......... .......... 99% 15.6M 0s
3170700K .......... .......... .......... .......... .......... 99% 19.2M 0s
3170750K .......... .......... .......... .......... .......... 99% 29.2M 0s
3170800K .......... .......... .......... .......... .......... 99%  248M 0s
3170850K .......... .......... .......... .......... .......... 99% 49.8M 0s
3170900K .......... .......... .......... .......... .......... 99% 5.29M 0s
3170950K .......... .......... .......... .......... .......... 99% 38.4M 0s
3171000K .......... .......... .......... .......... .......... 99%  482M 0s
3171050K .......... .......... .......... .......... .......... 99% 29.0M 0s
3171100K .......... .......... .......... .......... .......... 99%  190M 0s
3171150K .......... .......... .......... .......... .......... 99% 91.4M 0s
3171200K .......... .......... .......... .......... .......... 99% 4.17M 0s
3171250K .......... .......... .......... .......... .......... 99%  405M 0s
3171300K .......... .......... .......... .......... .......... 99% 64.9M 0s
3171350K .......... .......... .......... .......... .......... 99% 49.4M 0s
3171400K .......... .......... .......... .......... .......... 99%  187M 0s
3171450K .......... .......... .......... .......... .......... 99% 69.7M 0s
3171500K .......... .......... .......... .......... .......... 99% 92.0M 0s
3171550K .......... .......... .......... .......... .......... 99%  333M 0s
3171600K .......... .......... .......... .......... .......... 99% 10.7M 0s
3171650K .......... .......... .......... .......... .......... 99% 16.1M 0s
3171700K .......... .......... .......... .......... .......... 99%  345M 0s
3171750K .......... .......... .......... .......... .......... 99%  497M 0s
3171800K .......... .......... .......... .......... .......... 99%  465M 0s
3171850K .......... .......... .......... .......... .......... 99%  122M 0s
3171900K .......... .......... .......... .......... .......... 99%  517M 0s
3171950K .......... .......... .......... .......... .......... 99%  457M 0s
3172000K .......... .......... .......... .......... .......... 99%  456M 0s
3172050K .......... .......... .......... .......... .......... 99%  408M 0s
3172100K .......... .......... .......... .......... .......... 99%  479M 0s
3172150K .......... .......... .......... .......... .......... 99%  522M 0s
3172200K .......... .......... .......... .......... .......... 99%  483M 0s
3172250K .......... .......... .......... .......... .......... 99%  435M 0s
3172300K .......... .......... .......... .......... .......... 99% 32.1M 0s
3172350K .......... .......... .......... .......... .......... 99% 9.92M 0s
3172400K .......... .......... .......... .......... .......... 99%  502M 0s
3172450K .......... .......... .......... .......... .......... 99% 30.3M 0s
3172500K .......... .......... .......... .......... .......... 99% 70.1M 0s
3172550K .......... .......... .......... .......... .......... 99%  252M 0s
3172600K .......... .......... .......... .......... .......... 99%  414M 0s
3172650K .......... .......... .......... .......... .......... 99%  172M 0s
3172700K .......... .......... .......... .......... .......... 99%  239M 0s
3172750K .......... .......... .......... .......... .......... 99%  170M 0s
3172800K .......... .......... .......... .......... .......... 99%  220M 0s
3172850K .......... .......... .......... .......... .......... 99%  255M 0s
3172900K .......... .......... .......... .......... .......... 99%  403M 0s
3172950K .......... .......... .......... .......... .......... 99%  173M 0s
3173000K .......... .......... .......... .......... .......... 99%  131M 0s
3173050K .......... .......... .......... .......... .......... 99%  100M 0s
3173100K .......... .......... .......... .......... .......... 99% 13.3M 0s
3173150K .......... .......... .......... .......... .......... 99%  448M 0s
3173200K .......... .......... .......... .......... .......... 99%  177M 0s
3173250K .......... .......... .......... .......... .......... 99% 8.86M 0s
3173300K .......... .......... .......... .......... .......... 99%  310M 0s
3173350K .......... .......... .......... .......... .......... 99%  508M 0s
3173400K .......... .......... .......... .......... .......... 99% 7.42M 0s
3173450K .......... .......... .......... .......... .......... 99%  208M 0s
3173500K .......... .......... .......... .......... .......... 99%  366M 0s
3173550K .......... .......... .......... .......... .......... 99%  492M 0s
3173600K .......... .......... .......... .......... .......... 99%  484M 0s
3173650K .......... .......... .......... .......... .......... 99%  428M 0s
3173700K .......... .......... .......... .......... .......... 99%  475M 0s
3173750K .......... .......... .......... .......... .......... 99%  482M 0s
3173800K .......... .......... .......... .......... .......... 99%  593M 0s
3173850K .......... .......... .......... .......... .......... 99% 64.3M 0s
3173900K .......... .......... .......... .......... .......... 99% 47.8M 0s
3173950K .......... .......... .......... .......... .......... 99% 4.73M 0s
3174000K .......... .......... ..                              100%  437M=73s

2024-02-28 16:39:34 (42.2 MB/s) - ‘csv.zip’ saved [3250198723/3250198723]

Archive:  csv.zip
  inflating: csv/person_statement.csv  
  inflating: csv/person_identifiers.csv  
  inflating: csv/person_names.csv    
  inflating: csv/person_nationalities.csv  
  inflating: csv/person_addresses.csv  
  inflating: csv/entity_statement.csv  
  inflating: csv/entity_addresses.csv  
  inflating: csv/entity_identifiers.csv  
  inflating: csv/ooc_statement.csv   
  inflating: csv/ooc_interests.csv   
  inflating: datapackage.json

How many entities have foundingDate and dissolutionDate where the difference between these dates is less than a year?

Assumptions

Companies with the same name, founding date and dissolution date are likely to be unique.

Company names are consistent in the data set (other than variations in case).

note - very limited data from pre-2016. I suspect that this is due to records of companies dissolved before then not being routinely digitised.

#read in entity statements df = pd.read_csv('csv/entity_statement.csv',usecols=['foundingDate','dissolutionDate','name']) #drop any companies not yet dissolved df = df.dropna(subset=['dissolutionDate']) #make company names lowercase df['name'] = df['name'].str.lower() #group to remove duplicates df = df.drop_duplicates() #calculate years between founding and dissolution df['foundingDate'] = pd.to_datetime(df['foundingDate'],yearfirst=True) df['dissolutionDate'] = pd.to_datetime(df['dissolutionDate'],yearfirst=True) df['yearsOperating'] = (df['dissolutionDate']-df['foundingDate'])/np.timedelta64(365, 'D') #filter to companies operating for less than one year df = df[df['yearsOperating']<1] df['yearFounded'] = df['foundingDate'].dt.to_period('Y') #filter to 2017 onwards df=df[df['yearFounded']>='2017'] #filter to pre-2022 df=df[df['yearFounded']<'2022'] #return answer print('Number of companies (founded from 2017 onwards) dissolved within a year of founding - '+str(len(df.index))) df = df.groupby(by=['yearFounded'],as_index=False).size()

Number of companies (founded from 2017 onwards) dissolved within a year of founding - 109912

How many entities have ownership chains which have involved natural persons or entities in countries on the EU list of non-cooperative tax jurisdictions?

Assumptions

A person is 'in' a country if their 'registered' address is in that country (UK PSC data doesn't record other address types).

An entity is 'in' a country if their 'registered' address is in that country (UK PSC data doesn't record other address types) or if they were incorporated in that country.

This includes companies that have been dissolved / interests that have ended

Company numbers have been used in place of entity statement IDs as statement IDs are not unique to each company

Technical note - UK PSC data only has one level of reporting - there are no 'component' statements and there's no overarching OOC that lists all the component statements included. With properly formatted BODS data it would be easy to use that overarching OOC statement to get the countries of every component entity / person.

Here we have used networkx to construct a graph of the ownership networks. This required more time and expertise than would have been needed for a complete set of BODS data.

#list of country codes countrylist = ['AS','AI','aS','VG','CR','FJ','GU','MH','PW','PA','RU','WS','TT','TC','VI','VU'] #read in entity statements entity_st = pd.read_csv('csv/entity_statement.csv', usecols=['_link','statementID','incorporatedInJurisdiction_code']) #filter to incorporated in country list entity_bl = entity_st[entity_st['incorporatedInJurisdiction_code'].isin(countrylist)] entity_bl = entity_bl.drop(['_link','incorporatedInJurisdiction_code'],axis=1) entity_bl = entity_bl['statementID'].values.tolist() entity_st =entity_st.drop(['incorporatedInJurisdiction_code'],axis=1)

#get company numbers for UK companies df_chunk = pd.read_csv('csv/entity_identifiers.csv',usecols=['_link_entity_statement','id','scheme'],chunksize=1000000) chunk_list = [] for chunk in df_chunk: #filter to company house number only chunk = chunk[(chunk['scheme']=='GB-COH') | (chunk['id'].str.startswith('https://opencorporates.com/companies/gb/',na=False))] chunk = chunk.drop(['scheme'],axis=1) chunk['id'] = chunk['id'].str[-8:] chunk = chunk.drop_duplicates() chunk_list.append(chunk) #free up memory del chunk gc.collect()

#join chunks iden = pd.concat(chunk_list) iden = iden.drop_duplicates() #merge with entity statements iden = iden.merge(entity_st,left_on=['_link_entity_statement'],right_on=['_link']) iden = iden.drop(['_link','_link_entity_statement'],axis=1) #free up memory del entity_st gc.collect()

# read in OOC statements ooc = pd.read_csv('csv/ooc_statement.csv',usecols=['subject_describedByEntityStatement','interestedParty_describedByEntityStatement']) # filter to company owning company ooc = ooc[ooc['interestedParty_describedByEntityStatement']!=''] #merge ooc and ent statements to get link number for subject ooc = ooc.merge(iden, how='left', left_on=['subject_describedByEntityStatement'],right_on=['statementID']) #merge ooc and ent statements to get link number for owner ooc['interestedParty_describedByEntityStatement'] = ooc['interestedParty_describedByEntityStatement'].astype('uint64') ooc = ooc.merge(iden, how='left', left_on=['interestedParty_describedByEntityStatement'], right_on=['statementID'],suffixes = ['_subject','_interestedparty']) ooc = ooc.drop(['statementID_interestedparty','statementID_subject'],axis=1)

#fill missing IDs with statement ID ooc['id_interestedparty'] = ooc['id_interestedparty'].fillna(ooc['interestedParty_describedByEntityStatement']) #tidy columns ooc = ooc.drop(['subject_describedByEntityStatement','interestedParty_describedByEntityStatement'],axis=1) #drop duplicates ooc = ooc.drop_duplicates() #create networkx graph G = nx.from_pandas_edgelist(ooc, source='id_subject',target='id_interestedparty',create_using=nx.DiGraph) #free up memory del ooc gc.collect()

#read in person statements person_st = pd.read_csv('csv/person_statement.csv', usecols=['_link','statementID']) #read in person addresses person_ad = pd.read_csv('csv/person_addresses.csv', usecols=['_link_person_statement','country']) #filter to country list only person_ad = person_ad[person_ad['country'].isin(countrylist)] #drop countries person_ad = person_ad.drop(['country'],axis=1) #drop duplicates person_ad = person_ad.drop_duplicates()

#merge to get ID for people in country list person_st = person_st.merge(person_ad,left_on=['_link'],right_on=['_link_person_statement']) #drop link columns person_st = person_st.drop(['_link','_link_person_statement'],axis=1) #create list of people IDs person_bl = person_st['statementID'].tolist() #free up memory del person_ad gc.collect()

# read in OOC statements df_chunk = pd.read_csv('csv/ooc_statement.csv',usecols=['subject_describedByEntityStatement','interestedParty_describedByPersonStatement'],chunksize=1000000) chunk_list = [] for chunk in df_chunk: # filter to person owning company chunk = chunk[chunk['interestedParty_describedByPersonStatement']!=''] chunk_list.append(chunk) #join up chunks ooc = pd.concat(chunk_list)

#merge with country list IDs ooc['interestedParty_describedByPersonStatement'] = ooc['interestedParty_describedByPersonStatement'].astype('uint64') ooc = ooc.merge(person_st,left_on=['interestedParty_describedByPersonStatement'],right_on=['statementID']) ooc = ooc.drop(['statementID'],axis=1) #free up memory del person_st del chunk gc.collect() #merge with iden to get CH numbers ooc = ooc.merge(iden, left_on=['subject_describedByEntityStatement'],right_on=['statementID']) ooc = ooc.drop(['statementID','subject_describedByEntityStatement'],axis=1) #free up memory del iden gc.collect()

#create networkx graph H = nx.from_pandas_edgelist(ooc, source='id',target='interestedParty_describedByPersonStatement',create_using=nx.DiGraph) #free up memory del ooc gc.collect() #combine both graphs R = nx.compose(G, H) #free up memory del G, H gc.collect()

#create a set of all entity IDs with a connection to a entity or person in country connected_entities = set() bl=entity_bl+person_bl for x in bl: try: connected_entities.update(nx.ancestors(R,x)) except: pass

#create a set of all entity IDs with a connection to a entity or person in country owned_entities = [] for x in bl: try: owned_entities.append(nx.ancestors(R,x)) except: pass

chain_length = pd.DataFrame(columns=['entity','BO','distance']) for (x,y) in zip(bl,owned_entities): for i in y: try: newrow = {'BO':x,'entity':i,'distance':len(nx.shortest_path(R,source=i,target=x))-1} except: pass chain_length.loc[len(chain_length)]=newrow chain_length_count = chain_length.groupby(by=['distance']).count().reset_index()

print('There are '+str(len(entity_bl))+' companies and '+str(len(person_bl))+' people located in EU list of non-cooperative tax jurisdictions countries')

There are 2504 companies and 10103 people located in EU list of non-cooperative tax jurisdictions countries

#convert set to dataframe connected_entities = pd.DataFrame(connected_entities) connected_entities = connected_entities[connected_entities[0].str.len()==8] connected_entities = connected_entities.drop_duplicates() print('There are '+str(len(connected_entities.index))+' companies registered with Companies House that are owned (indirectly or directly) by a company or person located in a listed country')

There are 13375 companies registered with Companies House that are owned (indirectly or directly) by a company or person located in a listed country

The majority of ownership relationships were direct - the longest chain between a company and the owner in a listed country contains 12 edges.

How many entities declare that they have no beneficial owners?

In properly formatted BODS data this would be identified in the primary OOC statement under interestedParty -> unknown -> noBeneficialOwners.

The PSC register itself does include an option for companies to declare they have no beneficial owners. For example - https://find-and-update.company-information.service.gov.uk/company/01382560/persons-with-significant-control

However, we don't have an indicator for this in our mapping of the PSC data. The example above does have an entity statement in the data set but no OOC statement.

df = pd.read_csv('csv/person_statement.csv',usecols=['statementID','personType'])

df.groupby(by=['personType']).count()

statementIDint64

knownPerson

9069253

df_chunk = pd.read_csv('csv/entity_identifiers.csv',usecols=['_link_entity_statement','id','scheme'],chunksize=5000000) chunk_list = [] for chunk in df_chunk: #filter to company house number only chunk = chunk[chunk['scheme']=='GB-COH'] chunk = chunk.drop(['scheme'],axis=1) chunk_list.append(chunk) iden = pd.concat(chunk_list)

print(iden[iden['id']=='04906854'])

df_chunk = pd.read_csv('csv/entity_statement.csv',chunksize=2000000) for chunk in df_chunk: chunk = chunk[chunk['_link']==5331961] if not chunk.empty: print(chunk)

ooc = pd.read_csv('csv/ooc_statement.csv',usecols=['subject_describedByEntityStatement','interestedParty_describedByEntityStatement','interestedParty_describedByPersonStatement'])

print(ooc[ooc['subject_describedByEntityStatement']==16558284086462596647])

print(ooc[ooc['subject_describedByEntityStatement']==7478522944795189817])

df = pd.read_csv('csv/person_names.csv',usecols=['fullName'])

df[df['fullName'].str.contains('Unknown')]

fullNameobject

7722733

Tanaka Unknown

7809326

First Name Unknown Abdul Hanan

How many entities are there where their beneficial owners are unknown?

In properly formatted BODS data this would be identified in the primary OOC statement under interestedParty -> unknown and we have functions in qbods to summarise this data.

In this data set we could theoretically iterate over the OOC statements, construct an ownership chain and identify where chains don't have an end-point that is a person. This also might not work depending on the reporting thresholds of the country.

eg - Live Nation Limited is a UK company owned by a US parent company. We don't have any information on who owns Live Nation Entertainment Inc.

https://register.openownership.org/entities/59b927b267e4ebf340072a1a/graph

This is what that looks like in the mapped PSC data

Further investigation

Investigate PSC data at source to understand what is within the scope of unknown beneficial owner.

We could look into the mapping code to understand how the incoming data is being handled

The company number for Live Nation UK is 0380556

This corresponds to several entity statements as Live Nation UK is also an owner of several other companies.

print(iden[iden['id']=='03805556'])

          _link_entity_statement        id
585567                    199965  03805556
7069935                  2400753  03805556
19943843                 6748085  03805556

These are the relevant statementIDs for the entities

ent = pd.read_csv('csv/entity_statement.csv',usecols=['statementID','_link'])

print(ent[ent['_link'].isin([199965,2400753,6748085])])

           _link           statementID
199965    199965  12284762247333522994
2400753  2400753   1715278187371176618
6748085  6748085  10428407949385890365

ooc = pd.read_csv('csv/ooc_statement.csv',usecols=['subject_describedByEntityStatement','interestedParty_describedByEntityStatement'])

There's one OOC statement listing Live Nation UK as a subject - the interested party entity statement relates to the US parent company

print(ooc[ooc['subject_describedByEntityStatement'].isin([12284762247333522994,1715278187371176618,10428407949385890365])])

        subject_describedByEntityStatement  \
236619                12284762247333522994   

       interestedParty_describedByEntityStatement  
236619                       14713919030763430437

print(ent[ent['statementID']==14713919030763430437])

         _link           statementID
199980  199980  14713919030763430437

print(iden[iden['_link_entity_statement']==199980])

Empty DataFrame
Columns: [_link_entity_statement, id]
Index: []

There's no ownership statement with the parent company as a subject. Therefore we don't know who the beneficial owners of Live Nation UK are.

print(ooc[ooc['subject_describedByEntityStatement']==14713919030763430437])

Empty DataFrame
Columns: [subject_describedByEntityStatement, interestedParty_describedByEntityStatement]
Index: []

ent_ad=pd.read_csv('csv/entity_addresses.csv',usecols=['_link_entity_statement','country'])

print(ent_ad[ent_ad['_link_entity_statement']==199980])

        _link_entity_statement country
199980                  199980      US

We could iterate over the OOC statements until the chain either reaches a person or reaches a dead end but that would be quite complicated. Having properly formatted BODS data would make this process relatively simple.

How many entities are there where their beneficial owners are unknown?

ent = pd.read_csv('csv/entity_statement.csv',usecols=['statementID','name','_link']) ooc = pd.read_csv('csv/ooc_statement.csv',usecols=['subject_describedByEntityStatement'])

ent[ent['_link']==5737536]

_linkint64

statementIDuint64

5737536

14933364169972089000

ooc[ooc['subject_describedByEntityStatement'].astype('str').str.contains('14933364169')]

Execution error

KernelInterrupted: Execution interrupted by the Jupyter kernel.

ent[ent['statementID'].isin(ooc['subject_describedByEntityStatement'])==False]

statementIDuint64

nameobject

5366517385087977000

Moreland Solutions Ltd

16819144986572624000

Tilley & Daniells Limited

2337035965993638000

Brian Kelly

105

3532179607741949400

Brent Group Ltd

152

46512034808298584

Redbrick Survey & Valuation Ltd

153

3702828803448023600

Dsj Homes Limited

154

3337986983714143000

Greenfields B.V.

175

14301966157565490000

The Midcounties Co-Operative Properties Limited

218

2159641316737475800

Centreco (Holdings) Limited

219

5924124683398733000

Anderson Mann Group Ltd

iden = pd.concat(chunk_list)

Execution error

KernelInterrupted: Execution interrupted by the Jupyter kernel.

iden.head()

_link_entity_statementint64

idobject

05515124

10298061

SC345418

07719278

08594594

iden[iden['id']=='09490140']

_link_entity_statementint64

idobject

16964387

5737536

09490140

How many entities have complex ownership chains? (e.g. 5+ observable layers of ownership)

This could be done more efficiently using properly formatted BODS data. You would take the overarching OOC statement and count how many components are listed then use the relevant component statements to construct the actual chain.

Statement IDs are not unique to each entity. One entity can have multiple entity statements. To mitigate this when joining up the ownership chains I have used company numbers where possible. Where a company number wasn't available (e.g foreign companies) I have used the statement ID.

Not all UK companies had a company number under the 'GB_COH' scheme in the identifiers table. Where possible the company number has been taken from the open corporates link in the same table.

End Date in interests is up to date

Layer of ownership - a company owned directly by a person has 1 layer of ownership

Company A has '5 layers of ownership'

Company A -> Company B -> Company C -> Company D -> Company E -> Person A

#read in OOC interests interests = pd.read_csv('csv/ooc_interests.csv',usecols=['_link_ooc_statement','endDate']) #remove OOC interests that have ended interests = interests[interests['endDate'].isna()] #remove duplicates interests = interests.drop_duplicates() #merge with OOC to get only active statements ooc = ooc.merge(interests, left_on=['_link'],right_on=['_link_ooc_statement']) ooc = ooc.drop(['_link','_link_ooc_statement','endDate'],axis=1) #release memory del interests gc.collect()

#read in ent statements ent = pd.read_csv('csv/entity_statement.csv',usecols=['statementID','_link']) #merge ooc and ent statements to get link number for subject ooc = ooc.merge(ent, left_on=['subject_describedByEntityStatement'],right_on=['statementID']) #merge ooc and ent statements to get link number for owner ooc['interestedParty_describedByEntityStatement'] = ooc['interestedParty_describedByEntityStatement'].astype('uint64') ooc = ooc.merge(ent, left_on=['interestedParty_describedByEntityStatement'], right_on=['statementID'],suffixes = ['_subject','_interestedparty']) ooc = ooc.drop(['subject_describedByEntityStatement','interestedParty_describedByEntityStatement'],axis=1) #release memory del ent gc.collect()

#get company numbers for UK companies df_chunk = pd.read_csv('csv/entity_identifiers.csv',usecols=['_link_entity_statement','id','scheme'],chunksize=5000000) chunk_list = [] for chunk in df_chunk: #filter to company house number only chunk = chunk[(chunk['scheme']=='GB-COH') | (chunk['id'].str.startswith('https://opencorporates.com/companies/gb/',na=False))] chunk = chunk.drop(['scheme'],axis=1) chunk['id'] = chunk['id'].str[-8:] chunk = chunk.drop_duplicates() chunk_list.append(chunk) iden = pd.concat(chunk_list) iden = iden.drop_duplicates()

#merge company numbers with OOC statements ooc = ooc.merge(iden, how='left',left_on=['_link_subject'],right_on=['_link_entity_statement']) ooc = ooc.merge(iden, how='left',left_on=['_link_interestedparty'],right_on=['_link_entity_statement'],suffixes=['_subject','_interestedparty']) #free up memory del iden gc.collect()

#tidy columns ooc = ooc.drop(['_link_subject','_link_interestedparty','_link_entity_statement_subject','_link_entity_statement_interestedparty'],axis=1)

print(ooc.head())

    statementID_subject  statementID_interestedparty id_subject  \
0  12402454016991056867         16819144986572623535   01935622   
1  10848264383995424230         16819144986572623535   01939322   
2   2175043006849223560          3702828803448023565   09531644   
3     49154567025947370          3702828803448023565   10072291   
4  17038059142250203902          3337986983714143282   06969820   

    id_interestedparty  
0             00670616  
1             00670616  
2             04955962  
3             04955962  
4  3337986983714143282

#fill missing IDs with statement ID ooc['id_interestedparty'] = ooc['id_interestedparty'].fillna(ooc['statementID_interestedparty'])

ooc = ooc.drop_duplicates()

ooc = ooc.drop(['statementID_subject','statementID_interestedparty'],axis=1)

levels = ooc i = 0 while i < 3: levels = levels.merge(ooc,left_on=['id_interestedparty'],right_on=['id_subject'],suffixes=['_x','']) levels = levels.drop(['id_subject'],axis=1) i += 1

print(levels.head())

  id_subject_x id_interestedparty_x id_interestedparty_x id_interestedparty_x  \
0     06327087             08760444             11469039             11469226   
1     06327041             08760442             11469039             11469226   
2     03236336             08042035             11469039             11469226   
3     01388951             08042035             11469039             11469226   
4     00571662             08042035             11469039             11469226   

  id_interestedparty  
0           11470070  
1           11470070  
2           11470070  
3           11470070  
4           11470070

print('Theres are '+str(len(levels['id_subject_x'].drop_duplicates().index))+' companies with ownership chains of 5 or more levels')

Theres are 71774 companies with ownership chains of 5 or more levels