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Privacy and your donation

Whether a donor is an individual or an organisation, I take privacy issues seriously. In order to avoid breaching people's sense of privacy, unless I am directly contacted about a donation by the donor, I do not thank them unless having a direct conversation about a donation.

Having said this, most people become donors when they read that someone they respect suggests they do. Please take a moment every now and then and, if you are happy to be seen publicly supporting my work, share my work with your networks. While I am yet to receive a minimum wage for what I do I want people to know that I am grateful for both the financial and moral support I receive.


Marginal
Fairly Safe
Safe
MarginalFairly SafeSafe

Use the ☰ button to navigate between projects, filter content using options above or click to geo-locate results to your electorate.


Grants

Official portrait of Infrastructure, Transport, Regional Development and Comms

Infrastructure, Transport, Regional Development and Comms

Ministers responsible for the Infrastructure, Transport, Regional Development and Comms Portfolio awarded 22.74% of grants to FAIRLY SAFE electorates

Grants

Official portrait of Health

Health

Ministers responsible for the Health Portfolio awarded 21.97% of grants to FAIRLY SAFE electorates

Grants

Official portrait of Prime Minister and Cabinet

Prime Minister and Cabinet

Ministers responsible for the Prime Minister and Cabinet Portfolio awarded 21.53% of grants to FAIRLY SAFE electorates

Grants

Official portrait of Industry, Science, Energy and Resources

Industry, Science, Energy and Resources

Ministers responsible for the Industry, Science, Energy and Resources Portfolio awarded 16.05% of grants to FAIRLY SAFE electorates

Grants

Official portrait of Paul Fletcher

BRADFIELD

Between Jan 2018 & April 2021 orgs in the SAFE electorate of BRADFIELD held by LIBS MP Paul Fletcher received over $69.62 million in grants

Grants

Official portrait of Matt Thistlethwaite

KINGSFORD SMITH

Between Jan 2018 & April 2021 orgs in the FAIRLY SAFE electorate of KINGSFORD SMITH held by ALP MP Matt Thistlethwaite received over $1,299.03 million in grants

Grants

Official portrait of John Alexander

BENNELONG

Between Jan 2018 & April 2021 orgs in the FAIRLY SAFE electorate of BENNELONG held by LIBS MP John Alexander received over $688.42 million in grants

Grants

Official portrait of Helen Haines

INDI

Between Jan 2018 & April 2021 orgs in the MARGINAL electorate of INDI held by IND MP Helen Haines received over $188.33 million in grants

Charities

Official portrait of Keith Pitt

HINKLER

Based on ACNC data, in 2018 charities in the electorate of HINKLER held by LNP MP Keith Pitt declared over $272.04 million in revenue.

Charities

Official portrait of Paul Fletcher

BRADFIELD

Based on ACNC data, in 2018 charities in the electorate of BRADFIELD held by LIBS MP Paul Fletcher declared over $1,891.26 million in revenue.

Charities

Official portrait of Fiona Martin

REID

Based on ACNC data, in 2018 charities in the electorate of REID held by LIBS MP Fiona Martin declared over $898.57 million in revenue.

Charities

Official portrait of Steve Geogeanas

ADELAIDE

Based on ACNC data, in 2018 charities in the electorate of ADELAIDE held by ALP MP Steve Geogeanas declared over $4,818.95 million in revenue.

Franking Credits

Official portrait of Fiona Martin

REID

In the 17-18 tax year, residents in the electorate of REID held by LIBS MP Fiona Martin received franking credits worth $170.55 million.

Franking Credits

Official portrait of Michelle Landry

CAPRICORNIA

In the 17-18 tax year, residents in the electorate of CAPRICORNIA held by LNP MP Michelle Landry received franking credits worth $123.51 million.

Franking Credits

Official portrait of Anne Webster

MALLEE

In the 17-18 tax year, residents in the electorate of MALLEE held by NATS MP Anne Webster received franking credits worth $60.01 million.

Franking Credits

Official portrait of Peter Khalil

WILLS

In the 17-18 tax year, residents in the electorate of WILLS held by ALP MP Peter Khalil received franking credits worth $34.77 million.

Political Parties

NATS PARTY

Between 2013 and 2021 the NATS PARTY declared $86,869,126 to the AEC.

Political Parties

ALP PARTY

Between 2013 and 2021 the ALP PARTY declared $515,256,158 to the AEC.

Political Parties

UAP PARTY

Between 2013 and 2021 the UAP PARTY declared $142,067,828 to the AEC.

Political Parties

LIBS PARTY

Between 2013 and 2021 the LIBS PARTY declared $566,377,770 to the AEC.

Associated Entities

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LABOR HOLDINGS

Between 2013 and 2020 the ALP-affiliated associated entity LABOR HOLDINGS declared $78,964,691 to the AEC.

Associated Entities

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JOHN CURTIN HOUSE

Between 2013 and 2020 the ALP-affiliated associated entity JOHN CURTIN HOUSE declared $29,088,730 to the AEC.

Associated Entities

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LANEWAY ASSETS

Between 2013 and 2020 the NATS-affiliated associated entity LANEWAY ASSETS declared $837,099 to the AEC.

Associated Entities

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AFULE

Between 2013 and 2020 the ALP-affiliated associated entity AFULE declared $4,273,400 to the AEC.

Third Parties

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AUSTRALIAN AUTOMOBILE ASSOCIATION

Between 2013 and 2019 the un-affiliated 3rd party AUSTRALIAN AUTOMOBILE ASSOCIATION spent $1,207,018 on political messaging.

Third Parties

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DRAGOMAN

Between 2013 and 2019 the un-affiliated 3rd party DRAGOMAN spent $41,231 on political messaging.

Third Parties

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WILDERNESS SOCIETY

Between 2013 and 2019 the un-affiliated 3rd party WILDERNESS SOCIETY spent $229,108 on political messaging.

Third Parties

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RSPCA AUSTRALIA

Between 2013 and 2019 the un-affiliated 3rd party RSPCA AUSTRALIA spent $26,271 on political messaging.

Campaigners

NMA

Between 2018 and 2020 the un-affiliated campaigner NMA spent $364,922 on political messaging.

Campaigners

AUSTRALIAN CHAMBER OF COMMERCE AND INDUSTRY

Between 2018 and 2020 the un-affiliated campaigner AUSTRALIAN CHAMBER OF COMMERCE AND INDUSTRY spent $666,575 on political messaging.

Campaigners

ANIMALS AUSTRALIA FEDERATION

Between 2018 and 2020 the un-affiliated campaigner ANIMALS AUSTRALIA FEDERATION spent $515,879 on political messaging.

Campaigners

AMACSU

Between 2018 and 2020 the ALP-affiliated campaigner AMACSU spent $500,000 on political messaging.

Megacorps

SOUTH32

Between 2013 and 2021 SOUTH32 declared $17,473,759,905 revenue, paid $673,844,326 in tax & paid $321,027 to political orgs.

Megacorps

WSP

Between 2013 and 2021 WSP declared $1,722,474,444 revenue, paid $16,218,512 in tax & paid $187,000 to political orgs.

Megacorps

MELBOURNE IT

Between 2013 and 2021 MELBOURNE IT declared $625,001,231 revenue, paid $8,834,340 in tax & paid $78,010 to political orgs.

Megacorps

NATIONAL ROADS AND MOTORISTS ASSOCIATION

Between 2013 and 2021 NATIONAL ROADS AND MOTORISTS ASSOCIATION declared $3,178,471,106 revenue, paid $32,176,521 in tax & paid $44,000 to political orgs.

Payments from tax transparency list corps to political orgs includes donations & other receipts (subscriptions, rent, payment for services etc) to political parties, their associated entities, third parties, campaigners and political candidates.


Bill McLennan

FORMER HEAD, AUSTRALIAN BUREAU OF STATISTICS

I was impressed by the #Censusfail submission to this (Inquiry) Committee. It very clearly showed some good analysis that would have helped the ABS to run a better Census if it had done such research before developing the Census proposal. It also saved me from explaining the current thrust in government with the Government Data Linkage Project, and its likely links to the Census. Bill McLennan (Former Head of the ABS, Former Chairman United Nations Statistical Commission) in his Submission to the 2016 Inquiry.
Garry Brooke

FORMER DIRECTOR, APPROPRIATIONS MANAGEMENT TEAM, DEPARTMENT OF FINANCE

I’ve worked with Rosie Williams on budget data transparency since November 2013. Rosie has developed her project into the best government data transparency initiative I know... continually updated and improved to make underlying data intelligible and to focus on key aspects for analysis. My interest has particularly been on the transparency of federal budget data.
Scott Ludlam

FORMER SENATOR, THE AUSTRALIAN GREENS

We could hear from digital rights organisations like the Privacy Foundation, Digital Rights Watchand Electronic Frontiers Australia and from specialist researchers like Rosie Williams and Asher Wolf, who have led the debate online.

Sleeping Giants

ADVERTISING ACTIVISM

In late 2018, Sleeping Giants Australia, 'a community initiative to make racism, bigotry, misogyny and climate change denial less profitable', used data I provided to them on the parliamentarians who have accepted subscriptions to Foxtel in their campaign against racism. Sky took umbridge at this campaign and Sleeping Giants & similar campaigns have been the focus of media commentary ever since. My Tweet was pictured in a front-page diatribe in The Australian against Denise Shrivell who The Australian claimed is resonsible for the Sleeping Giants Australia account.



I launched the domain AusGov.info at Linux Conference Australia in January 2018, however this work is the result of years devoted to programming and transparency work beginning in 2012 and progressively expanded and improved upon over the intervening years.

Credits

The pecuniary interests register data was originally supplied by icacpls however I have updated it manually (or not as time permits) for the past couple of years.

Site icons by Flaticon.com

Flag icons by www.IconDrawer.com

Images from pixabay.com

Disclaimer

The figures in this site are either raw data at line-item level or totals/percentages - which are the result of algorithms or joins (programmatic matches between two lists) - written by myself in MySQL, which in turn are based on open data provided by Australian government agencies.

This data ranges in quality between datasets, is updated at different intervals and is published to different standards and in different formats.

The data you see in this site is edited by myself (unused fields are removed, names of programs/agencies/entities are spelled consistently within & between datasets). Data cleaning is a significant job. All care has been taken to represent every single figure accurately, however mistakes can be made either by the entity providing data to the government, the agency providing the data back to the public or at my end as I further transform this data for use.

It is important to understand that while opinions and inferences can be made based on the data on this site, that the data is not in and of itself an inference or an opinion. Inferences and opinions using data in this site remain the legal responsibility of the author of those opinions.

Media and Journalism

The Guardian Innovation Australia The New Daily Eureka Street AusVotes 2019 Independent Australia Electronic Frontiers Australia Crikey Open Australia Foundation AOGPN blog Open Knowledge Foundation EGovAu Blog The Conversation IdeasHoist ABC PM Radio Power to Persuade Croakey International Budget Partnership Sydney Morning Herald Online Opinion New Matilda No Fibs Sunday Life Magazine Read article

Keeping the bastards honest means filling in the missing pieces.

The Missing Piece


Keeping the bastards honest means filling in the missing pieces.

The Missing Piece

Your task is to solve a puzzle with an infinite number of pieces but you don't know what the puzzle will look like when it's done. No one has ever solved this puzzle on this scale before. But it gets worse, apart from having to figure out what you're actually trying to solve, you don't even have the pieces for this puzzle. You find some pieces online but some are missing or broken. You spend all your time trying to fix the broken pieces. At times you wonder if the effort will break you or if it is just all too hard.

Breakthroughs in solving this puzzle in the past have come largely from intentional leaks of specific infomation from the government to the media. No one has attempted a full scale matching of publicly available information through large scale data integration for the purposes of accountability and transparency.

Despite there being a key to make it all join together with complete accuracy and certainty, that key is not included in important datasets. This article is about that key and why it needs to be included in some primary datasets which would allow not only myself but journalists, researchers and the public to lock the pieces into place and form complete picture of who gives money to who and why.

This is something I've struggled to explain to my very patient Twitter and Patreon for years and felt very despondent over because I felt I could not easily explain what is so challenging about the data and programming aspects of joining administrative datasets at scale in order to give people an accurate picture of the challenges which frustrate me on a daily basis or explain in a palatable way what needs to be done to fix it - for everyone.

But there's a relatively recent story that provides a telling analogy for my goal, and that story revolves around the 2016 census.

When testing the waters for significant policy changes, the government sometimes has to present complex decisions to the public - like in 2015 when the ABS got a public relations firm to run focus groups on using future censuses to link government datasets - opening the door to them being able to trace individuals across datasets from the cradle to the grave in a way that I, or any researcher can only dream of when it comes to tracing the influence of the rich and powerful on government spending. This disparity is key:

While the government is building infrastrcuture to link our personal data from birth to death across federal agencies with the help of the census to form the missing link, the public is left trying to figure out how our taxes are being spent and who is inflencing those outcomes using the equivalent of a horse and cart.

The scale of investment in constructing that panopticon of data while keeping the public blind to improper influence in the policy process through publishing poor quality data on conflicts of interest and donations provides a glimpse of the scope of the challenges I face in tracing influence across the system for the public benefit.

This very contrast is precisely why I was able to understand what was being done with the 2016 census, because both the government and I are linking datasets with sometimes questionable data, it's just that the government is seeking to trace the behaviour of individuals. In contrast, I am tracing the behaviour of politicians and government decision makers.

I figure if the ABS can use a public relations firm to explain to a focus group of average Australians why it needs to store our names and addresses to create a 'gold' standard linkage 'key' between government datasets (about individuals), then I can use the same story to explain why we need to get the government to insert the already existing key they create for their own purposes- the ABN - into open datasets so the public can hold a mirror up to the government and end the information asymmetry.

Most people may only remember that there was a huge technical failure with the last census. In privacy circles, the 2016 census was the target of a major campaign because at the last minute (November 2015), off the back of a month long consultation, the government decided to introduce significant changes to the censuses going forward. On behalf of higher powers, the ABS wanted to use the census in a radical new way which went against its entire history and enabling legislation.

The Australian census had always been about anonymous statistics. Personal information was only used to make sure those anonymous statisics were correct. It was not used to give the personal information we share with the ABS to other agencies- for other uses. The 2016 census put a stop to all this ...privacy. The government thought what if they could join all the datasets from all the agencies together in one big...panopticon?

In the past, the government occasionally joined together data from one portfolio to another (say health and welfare datasets) but this was carried out sparringly and with strong oversight. Most of these efforts attracted little attention until the notorious linkage between ATO and Centrelink data resulted in the robodebt debacle. This provided a very public and harmful display of what happens when relying on questionable data.

Despite whatever intentions government had, data is a lot messier than we imagine and wouldn't you know it, when the government tried to create a unique number given to us on an 'Australian Card' in 1987 to police immigrants and catch welfare and tax cheats, instead of getting what they wanted, it produced the biggest nationwide protests ever seen, threatened to become 'the most divisive social issue at least since the Vietnam War', and instead gave birth to Australia's first Commonwealth privacy legislation, The Privacy Act 1988!

Fast forward a couple of decades and times have changed. Now we all provide social media giants enough personal information to make a government green with envy while their hands are tied when it comes to what it can collect, store and put to use. It was time to re-introduce long-shelved ideas about linking government data but to do this the government had a problem to solve (and I don't just mean getting this past an unsuspecting public), they needed new ways of joining datasets together because the previous ways, stymied by privacy legislation and secrecy provisions made matching one person across multiple datasets only vaguely accurate.

This problem is important because I, or any researcher face the same hurdle when matching organisations across datasets. If there is no accurate way to know 'x' in one dataset refers to 'x' in another dataset and not 'y' then you can't successfully trace the actions or outcomes of 'x' through the financial, economic and political system.

The 2014 ABS focus group described the improvement moving data linkage from the current 'bronze' to 'silver' or 'gold':

Gold standard – using name and address information to link records during the Census processing period; - Silver standard – Name is encrypted using a specific electronic key. This key is then used along with personal characteristics to link with; and - Bronze standard – linking only on personal characteristics like age and sex and date of birth, but not name and address. (Colmar Bruton focus group 2014)

Unlike the linking of data about organisations (and because of past public opposition and the privacy principles it gave birth to) there was no unique code identifying every Australian individual that could join datasets with accuracy high enough to draw research quality conclusions from. So they decided to use the personal information we must provide with our Census survey for this purpose and have been building the infrastructure to support this panopticon ever since.

The point of this story today is not to go over this old ground but to draw the now obvious comparison with efforts to trace organisations (which do not enjoy the same privacy protections as do individuals). Unlike individuals, organisations do have a unique number, an ABN which can be used to easily match them across tenders and grants for example. In an article published today, I used the ABN to join registered lobbyists and their clients (where the ABN hasn't been hidden from public inspection) with the Commonwealth tenders or grants they have received.

I could also identify which of these organisations are registered charities because charities data also contains ABN. Matching across these several datasets was relatively simple because tenders, grants and lobbyist data all contain ABN, though only about one in two lobbyist clients had allowed the Attorney-Generals department who registers lobbyists to publish their ABN.

Where it becomes a much harder challenge is cross referencing this data against other important datasets such as political donations published by the AEC and conflicts of interest data publised by the Parliamentary Departments. Likewise, Parliamentary Expenses Authority data includes only descriptions of items purchased instead of the ABN of the entity doing business with the MP.

In other examples, I recently devoted an enormous amount of time to linking top earning corporations to payments or donations they have made to political parties, their associated entities, political candidates, third parties or campaigners. It's possible I have made errors because while the ATO data includes ABN, the AEC annual returns filed for the aforementioned entities, do not.


$ to Politics Company Revenue (B) Taxable (B) Tax (B)
1 102,459,393 MINERALOGY 0.485 0.149 0.045
2 70,746,679 WESTPAC 226.638 62.978 17.753
3 43,038,186 ANZ 179.327 50.395 12.205
4 41,477,732 MEMBERS EQUITY BANK 6.279 0.638 0.181
5 39,308,747 COMMONWEALTH BANK OF AUSTRALIA 256.623 71.530 20.791
6 36,882,045 ROLEX 0.471 0.036 0.011
7 30,533,915 SLATER AND GORDON 1.439 0.040 0.011
8 20,957,494 MORGAN STANLEY 0.394 0.009 0.003
9 15,710,807 BEYOND BANK 0.265 0.029 0.009
10 15,687,686 WOOLWORTHS 304.789 13.879 3.863
11 11,380,786 GLENCORE 82.563 4.393 0.353
12 10,109,176 RIO TINTO 192.290 58.532 16.079
13 9,897,269 BHP 259.362 83.155 20.140
14 9,772,131 KNIGHT FRANK 0.875 0.046 0.014
15 9,032,792 COLES 21.876 0.708 0.208
16 8,523,245 ANGLO AMERICAN 26.004 4.542 0.825
17 6,082,280 BM ALLIANCE COAL OPERATIONS 2.351 0.000 0.000
18 5,689,778 AURIZON 21.532 2.349 0.651
19 5,672,510 PRATT HOLDINGS 13.261 0.387 0.019
20 5,637,234 WESFARMERS 378.740 21.810 6.303
21 5,015,772 MORGANS 1.237 0.131 0.039
22 4,632,482 SUNCORP 96.668 9.755 2.574
23 3,996,334 CARDTRONICS 0.456 0.000 0.000
24 3,590,934 ROYAL AUTOMOBILE CLUB OF VICTORIA RACV 3.989 0.597 0.000
25 3,077,852 G8 EDUCATION 3.741 0.582 0.175
26 2,867,625 YANCOAL 16.573 0.027 0.000
27 2,371,083 NEWCREST MINING 18.290 1.733 0.500
28 2,284,528 MAURICE BLACKBURN 0.673 0.053 0.016
29 2,200,000 PLATINUM ASSET MANAGEMENT 2.019 1.527 0.456
30 2,150,198 AGL ENERGY 80.161 3.451 1.017
31 2,106,553 GREATER BUILDING SOCIETY 0.591 0.099 0.028
32 2,022,583 TELSTRA 134.405 28.814 8.349
33 1,991,770 WOODSIDE 34.724 8.578 1.211
34 1,905,848 PWC 2.796 0.024 0.007
35 1,894,750 COLLIERS INTERNATIONAL 1.538 0.278 0.070
36 1,863,454 TYRO PAYMENTS 0.459 0.000 0.000
37 1,698,866 CBRE 3.000 0.267 0.074
38 1,619,824 ROADSHOW 4.105 0.228 0.064
39 1,540,000 ASX 5.617 3.976 1.165
40 1,333,568 WHITEHAVEN COAL 10.938 0.297 0.016
41 1,238,023 TRANSURBAN 13.740 0.594 0.000
42 1,234,447 CROWN 22.917 1.897 0.545
43 1,166,264 NEW HOPE CORPORATION 4.996 0.805 0.227
44 1,106,950 GOOGLE 4.889 0.885 0.159
45 1,022,060 METLIFE INSURANCE 4.234 0.135 0.040
46 1,016,554 ULAN COAL MINES 4.703 0.003 0.001
47 935,087 SANTOS 23.759 0.074 0.006
48 895,824 BANK OF QUEENSLAND 13.796 2.650 0.785
49 865,954 TABCORP 15.356 1.781 0.451
50 832,300 BUPA 36.087 1.945 0.568
51 804,162 SWISSE WELLNESS 1.122 0.263 0.072
52 775,293 BANK AUSTRALIA 0.785 0.142 0.039
53 759,188 QBE INSURANCE 7.871 0.062 0.000
54 724,337 AMP 182.245 40.751 4.337
55 635,519 ALLIANZ 28.102 3.304 0.924
56 634,279 IDP EDUCATION 2.240 0.308 0.049
57 628,449 STAR ENTERTAINMENT 9.239 1.141 0.336
58 618,257 UBER 1.592 0.066 0.020
59 598,034 TAHMOOR COAL 0.104 0.002 0.001
60 561,593 BLUESCOPE 25.799 1.239 0.000
61 521,500 TATA CONSULTANCY SERVICES 3.351 0.574 0.144
62 481,506 RICHARD CROOKES CONSTRUCTIONS 5.814 0.202 0.061
63 460,654 ORIGIN ENERGY 83.383 1.744 0.370
64 460,350 FORTESCUE METALS 59.910 14.918 4.241
65 458,100 CHEVRON 27.762 0.900 0.000
66 455,200 WORKPAC 5.457 0.095 0.028
67 435,701 MILTON CORPORATION 0.829 1.089 0.028
68 432,792 ST BARBARA 3.271 0.633 0.121
69 428,911 COCA COLA 21.395 2.201 0.621
70 420,613 BAE SYSTEMS 7.323 0.442 0.028
71 414,297 PREMIER COAL 2.541 0.000 0.000
72 408,687 ARGO INVESTMENTS 1.591 2.055 0.148
73 398,154 MMG 6.683 0.195 0.000
74 386,204 AUSTAL 1.819 0.153 0.018
75 383,800 NOVARTIS 7.070 0.544 0.098
76 366,358 PHILIP MORRIS 19.055 2.708 0.812
77 363,313 RAMSAY HEALTH CARE 26.736 3.463 1.030
78 337,971 DOWNER EDI 38.708 1.202 0.067
79 331,790 JARDINE LLOYD THOMPSON 1.446 0.346 0.103
80 330,500 NAVITAS 3.943 0.530 0.146
81 330,000 LION 20.522 1.118 0.250
82 329,529 BORAL 23.046 1.412 0.288
83 328,940 QBE 37.201 3.108 0.790
84 326,000 MEDIBANK 33.891 2.526 0.699
85 325,940 LAING OROURKE CONSTRUCTION AUSTRALIA 9.971 0.143 0.040
86 323,600 EMPLOYERS MUTUAL 1.250 0.050 0.006
87 323,051 LENDLEASE 53.446 0.000 0.445
88 321,027 SOUTH32 17.474 3.880 0.674
89 312,500 MINERAL RESOURCES 9.173 1.493 0.399
90 311,400 CALTEX 144.181 2.633 0.714
91 305,000 SONIC HEALTHCARE 12.651 1.113 0.224
92 304,260 SUEZ 6.733 0.202 0.055
93 301,283 MACQUARIE 57.952 7.021 1.478
94 300,000 CABCHARGE 1.851 0.339 0.100
95 293,571 JOHNSON AND JOHNSON 8.423 0.845 0.217
96 283,209 ADANI 2.650 0.013 0.004
97 280,899 SANOFI 4.891 0.235 0.030
98 275,000 PFD FOOD SERVICES 10.966 0.217 0.064
99 268,339 BRICKWORKS 5.719 1.029 0.112
100 249,167 QANTAS 78.386 0.432 0.011
101 244,465 ASCIANO 12.116 1.156 0.315
102 243,000 BANK OF CHINA 12.847 2.486 0.733
103 237,850 ALS 3.489 0.309 0.080
104 237,000 ALCOA 29.649 10.638 2.126
105 230,950 BEACH ENERGY 6.473 1.328 0.357
106 225,000 COOGEE CHEMICALS 1.556 0.197 0.045
107 222,727 GRAINCORP 21.327 0.159 0.005
108 222,631 CELGENE 0.634 0.030 0.009
109 220,000 DOW CHEMICAL 1.472 0.000 0.000
110 216,626 CSL 15.016 1.456 0.049
111 215,500 INCITEC PIVOT 15.322 0.365 0.054
112 211,967 PERILYA 1.755 0.000 0.000
113 210,450 NORTHERN STAR RESOURCES 5.155 1.075 0.313
114 207,000 SYDNEY AIRPORT 7.638 0.000 0.000
115 202,000 SEEK 3.423 1.272 0.252
116 201,500 PERRON 0.290 0.034 0.007
117 200,000 TEACHERS MUTUAL BANK 1.689 0.265 0.077
118 192,000 AUSTRALIAN UNITY 8.926 0.347 0.063
119 191,100 COCHLEAR 4.994 1.874 0.316
120 190,360 AUSTRALIAN FOUNDATION INVESTMENT COMPANY 2.279 2.621 0.124
121 189,000 MYOB 1.877 0.217 0.015
122 188,750 WELLARD 1.758 0.000 0.000
123 188,500 DXC TECHNOLOGY 4.547 0.001 0.000
124 187,000 MICROSOFT 8.352 0.974 0.292
125 187,000 MUFG BANK 1.823 0.254 0.076
126 187,000 JP MORGAN 6.612 0.451 0.126
127 187,000 WSP 1.722 0.107 0.016
128 187,000 JACOBS 3.135 0.001 0.000
129 187,000 AMCOR 19.036 0.038 0.000
130 187,000 ACCENTURE 10.876 0.780 0.220
131 187,000 CIMIC 43.382 2.952 0.751
132 187,000 ALUMINA 1.784 2.128 0.000
133 187,000 PERPETUAL 3.032 1.011 0.291
134 187,000 SIEMENS 7.958 0.368 0.096
135 187,000 SNOWY HYDRO 13.795 2.360 0.687
136 187,000 GWA 3.277 0.366 0.098
137 187,000 FLETCHER BUILDING 17.898 0.052 0.006
138 187,000 BRAMBLES 10.927 1.416 0.347
139 172,745 ADCO CONSTRUCTIONS 4.531 0.032 0.008
140 169,000 AIA 15.626 0.683 0.192
141 162,788 HANCOCK 15.052 5.779 1.725
142 149,700 ECHO ENTERTAINMENT 1.809 0.139 0.038
143 148,802 MACMAHON 3.871 0.057 0.003
144 148,500 BOEHRINGER INGELHEIM 2.180 0.164 0.034
145 146,500 VOCUS 5.336 0.303 0.090
146 142,878 QUEENSLAND COUNTRY CREDIT UNION 0.238 0.011 0.003
147 142,600 PRIMARY HEALTH CARE 6.610 0.782 0.215
148 138,908 BYERWEN COAL 0.446 0.000 0.000
149 138,025 DEUTSCHE 4.416 0.331 0.099
150 117,706 FACEBOOK 1.393 0.136 0.041
151 117,601 FAIRFAX MEDIA 8.669 0.434 0.073
152 114,944 MASTERMYNE 1.068 0.018 0.005
153 110,000 HELLOWORLD TRAVEL 0.677 0.066 0.018
154 108,600 BGC 12.295 0.389 0.110
155 108,050 BOOM LOGISTICS 1.155 0.000 0.000
156 106,822 FUJI XEROX 6.845 0.472 0.115
157 100,000 AUSTRALIAN PHARMACEUTICAL INDUSTRIES 24.491 0.338 0.086
158 100,000 WESTBOURNE CONSTRUCTIONS 0.201 0.001 0.000
159 93,500 EVANS DIXON 0.403 0.071 0.018
160 93,500 ORICA 15.021 0.964 0.131
161 93,500 EXXON 55.698 0.000 0.000
162 92,502 ABBVIE 2.002 0.181 0.025
163 91,844 CHU UNDERWRITING AGENCIES 0.134 0.025 0.007
164 90,195 COPYRIGHT AGENCY 0.590 0.000 0.000
165 87,100 INDUSTRY SUPER 1.822 0.216 0.063
166 85,100 ALINTA 10.263 0.001 0.000
167 84,073 WAM RESEARCH 1.615 0.147 0.034
168 82,500 CLOUGH 1.298 0.074 0.022
169 82,500 ADOBE SYSTEMS 1.419 0.067 0.020
170 78,710 PANORAMIC RESOURCES 0.869 0.000 0.000
171 78,010 MELBOURNE IT 0.625 0.050 0.009
172 75,900 SENEX ENERGY 0.782 0.000 0.000
173 74,850 ASPEN MEDICAL 0.902 0.086 0.026
174 70,000 EMPLOYERS MUTUAL MANAGEMENT 0.699 0.001 0.000
175 70,000 ACCOLADE WINES 2.270 0.000 0.000
176 69,941 CARDNO 3.419 0.204 0.025
177 68,891 ABC TISSUE 2.642 0.321 0.096
178 67,481 BLACKMORES 2.969 0.522 0.130
179 66,472 BIS INDUSTRIES 3.793 0.000 0.000
180 64,235 ASTRAZENECA 6.467 0.498 0.131
181 64,122 HEALTHSCOPE 13.062 0.156 0.041
182 61,768 TATTS GROUP 19.647 1.407 0.399
183 61,601 MIRVAC 11.530 0.023 0.000
184 59,980 GHD 4.651 0.457 0.122
185 59,963 DELTA 1.935 0.071 0.021
186 56,650 BRITISH AMERICAN TOBACCO 15.281 4.740 1.415
187 56,650 IMPERIAL TOBACCO 4.621 0.173 0.052
188 54,500 MCMILLAN SHAKESPEARE 2.587 0.669 0.198
189 52,520 MITSUBISHI DEVELOPMENT 41.007 8.428 2.475
190 47,086 BERKLEY INSURANCE 0.906 0.102 0.030
191 46,581 CLEANAWAY WASTE MANAGEMENT 6.930 0.453 0.080
192 46,000 METCASH 86.221 1.371 0.383
193 45,000 YELLOW BRICK ROAD 0.839 0.000 0.000
194 44,520 GRANT THORNTON 1.405 0.259 0.078
195 44,000 FRIENDLY SOCIETY MEDICAL ASSOCIATION 1.720 0.000 0.000
196 44,000 NATIONAL ROADS AND MOTORISTS ASSOCIATION 3.178 0.216 0.032
197 43,812 JAPARA HEALTHCARE 1.759 0.106 0.032
198 43,800 SVITZER 2.189 0.080 0.012
199 43,798 J HUTCHINSON 12.101 0.175 0.051
200 43,236 TREASURY WINE ESTATES 8.868 1.228 0.311
201 40,373 ERM POWER 16.443 0.136 0.040
202 40,000 TEN NETWORK 3.732 0.001 0.000
203 40,000 STW COMMUNICATIONS 0.269 0.054 0.001
204 39,000 DECMIL 2.759 0.209 0.023
205 39,000 SHELL 57.471 5.724 1.117
206 38,500 BAYER 6.652 0.537 0.119
207 36,500 INPEX 5.534 0.255 0.066
208 36,000 INFIGEN ENERGY 1.209 0.000 0.000
209 35,980 ENERGY DEVELOPMENTS 0.468 0.048 0.014
210 35,800 ISENTIA 0.225 0.062 0.008
211 34,500 HEALIUS 3.616 0.221 0.066
212 33,000 CUBIC TRANSPORTATION SYSTEMS 1.036 0.102 0.031
213 33,000 ROCHE PRODUCTS 3.811 0.327 0.055
214 31,941 NEXTDC 0.466 0.000 0.000
215 31,500 TOGA 2.475 0.205 0.054
216 31,123 TRANSFIELD SERVICES 2.817 0.016 0.000
217 31,000 SEVEN WEST MEDIA 10.495 0.921 0.233
218 30,592 CLINICAL LABORATORIES 1.459 0.000 0.000
219 30,132 PROGRAMMED MAINTENANCE SERVICES 8.744 0.044 0.013
220 30,036 NATIONAL STORAGE 0.420 0.007 0.002
221 29,586 AUSTRALIAN GOVERNMENT SOLICITOR 0.235 0.016 0.005
222 27,500 AUSTRALAND 1.023 0.000 0.000
223 27,000 SERCO 6.799 0.286 0.070
224 27,000 BIOGEN 0.114 0.006 0.002
225 26,257 LYNAS CORPORATION 0.374 0.000 0.000
226 25,000 COVENTRY 0.980 0.000 0.000
227 25,000 SHADFORTHS CIVIL 1.220 0.018 0.005
228 25,000 ASG 1.089 0.039 0.000
229 23,999 NBN CO 6.690 0.000 0.000
230 23,267 BENDIGO AND ADELAIDE BANK 18.836 3.578 1.025
231 22,843 MANDALAY RESOURCES 0.101 0.015 0.004
232 22,123 WOODSIDE PETROLEUM 8.199 1.992 0.000
233 22,000 COOPERS BREWERY 1.429 0.223 0.062
234 21,983 TECHNOLOGY ONE 1.552 0.494 0.058
235 21,200 ARROW ENERGY 1.894 0.000 0.000
236 20,300 PACIFIC HYDRO 0.214 0.000 0.000
237 20,000 ONESTEEL TRADING 3.990 0.041 0.006
238 20,000 HILLS 1.486 0.000 0.000
239 20,000 BC IRON 0.919 0.098 0.028
240 19,540 MAX SOLUTIONS 0.993 0.104 0.030
241 18,150 ILUKA RESOURCES 5.235 0.944 0.225
242 17,982 SPOTLESS 14.819 0.042 0.007
243 17,900 INTERNATIONAL SOS 1.128 0.012 0.004
244 17,761 PALADIN ENERGY 0.766 0.000 0.000
245 17,000 HANSEN YUNCKEN 5.971 0.087 0.004
246 16,500 CENTRAL EQUITY 1.350 0.409 0.123
247 16,500 APOLLO TOURISM AND LEISURE 0.469 0.002 0.000
248 15,000 PAYCE CONSOLIDATED 1.918 0.089 0.027
249 15,000 RURALCO 8.449 0.142 0.019
250 15,000 ZASHVIN 0.212 0.290 0.001
251 15,000 RESMED 12.757 2.698 0.530
252 15,000 PIACENTINI AND SON 0.752 0.039 0.012
253 14,978 BROADSPECTRUM 5.799 0.000 0.000
254 14,431 QANTAS AIRWAYS 17.503 0.943 0.259
255 14,300 BBC WORLDWIDE 0.533 0.034 0.007
256 14,200 AMAZON 2.536 0.219 0.066
257 13,555 TYNAN MOTORS 2.084 0.033 0.010
258 13,200 AFTERPAY 0.355 0.057 0.011
259 13,000 ATLAS IRON 4.866 0.000 0.000
260 12,600 CPA 1.056 0.004 0.000
261 12,500 AUSTRALIAN PACIFIC HOLDINGS 3.298 0.075 0.019
262 12,000 CALIBRE 3.228 0.024 0.000
263 11,000 BAXTER HEALTHCARE 3.737 0.248 0.073
264 11,000 TRU BLU BEVERAGES 0.879 0.012 0.004
265 11,000 VARIAN MEDICAL SYSTEMS 0.211 0.031 0.009
266 10,000 CONNECTIVE 0.218 0.006 0.001
267 10,000 AUSTRALIAN FINANCE GROUP 3.311 0.208 0.062
268 9,867 FREEDOM FOODS 1.344 0.042 0.007
269 8,800 AMAYSIM 2.003 0.084 0.015
270 8,650 IINET 2.292 0.185 0.054
271 8,625 IPH 0.568 0.158 0.045
272 8,529 CHARTER HALL 0.746 0.156 0.044
273 8,000 PAYPAL 2.450 0.073 0.022
274 8,000 TEYS 10.783 0.419 0.073
275 7,399 ADAIRS 1.436 0.167 0.050
276 7,150 ROBERT WALTERS 1.663 0.019 0.006
277 6,796 LOVISA 0.547 0.123 0.036
278 6,000 PICKLES AUCTIONS 1.901 0.162 0.048
279 5,500 COLLECTION HOUSE 0.892 0.213 0.061
280 4,950 ECLIPX 2.682 0.180 0.053
281 4,500 VEDA 0.742 0.079 0.000
282 4,000 GOODYEAR 5.262 0.076 0.023
283 3,900 BMD 6.235 0.002 0.000
284 3,900 VIRTUS HEALTH 1.265 0.250 0.065
285 3,600 DIAGEO 3.283 0.204 0.059
286 3,400 AMERICAN EXPRESS 6.773 0.232 0.068
287 3,300 TOPLACE 0.383 0.000 0.000
288 3,000 PHOSPHATE RESOURCES 0.344 0.096 0.028
289 2,519 BELLAMYS 1.156 0.172 0.046
290 2,074 REGIONAL EXPRESS 1.672 0.085 0.025
291 2,000 SUNNY QUEEN 1.971 0.076 0.021
292 2,000 DICK SMITH 0.754 0.001 0.000
293 2,000 WESTFIELD CORPORATION 2.299 0.020 0.005
294 1,865 REGIS HEALTHCARE 3.196 0.337 0.101
295 1,500 CSR 11.474 0.412 0.006
296 1,215 ORORA 10.504 0.464 0.084
297 1,100 G4S 1.729 0.064 0.019
298 1,000 BT INVESTMENT MANAGEMENT 0.769 0.156 0.047
?>

In fact, descriptions can often be quite open to interpretation and the fact that an underpaid, impoverished human being has to eyeball thousands of receipts because no ABN is supplied in order to tell what kind of an organisation has paid money to an entity that reports to the AEC, or often even who, precisely that organisation is, shows how problematic this lack of a unique identifier is in public records.

Another major issue exists with conflict of interest data which to this day are published solely as PDFs. But even if the data can be extracted, there is no requirement for Members of Parliament to list the ABN of the organisation providing them with a gift. If ABN was required, and it's hard to see why it should not be, gifts from the same entities could easily be listed or tallied and matched with other datasets like grants or tenders awarded to the same organisation.

I have devoted almost a decade to working with open data for the purposes of transparency and accountability but there is only so much civil society researchers can do to work with data that is missing crucial information to overcome data quality issues and provide the value to the public that the government claims is behind its very own open data policies.

No one with any decent income is going to do the monumental amount of tedious work and funding it is beyond the means of most organisations- at least at the rates normally paid for the multi-disciplinary expertise. It should not be such a Sisyphean effort of Herculean proportions. This is information the public is entitled to. It is after all, their money that decisions makers are playing with. It's time to put things right.



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