Jackpot: How the gambling industry cashed in on political donations
Organisation: ABC News
Organisation size: Big
Publication date: 14/10/2021
Credit: Inga Ting, Nathanael Scott, Alex Palmer, , Katia Shatoba, Michael Workman, Anna Freeland, Stephen Hutcheon
Biography: Inga Ting (data journalist), Alex Palmer (designer), Nathanael Scott (developer), Katia Shatoba (developer), Michael Workman (researcher) and Stephen Hutcheon (supervising producer) are part of ABC New’s Digital Story Innovations team.
Anna Freeland (researcher) is a digital/data journalist with ABC Arts who joined the DSI team for this project.
Australians squander more on gambling per capita than any other nation.
Our series Hitting the Jackpot is the most comprehensive and detailed examination to date of the money flowing from Australia’s gambling industry into the political system.
Unlike most countries, Australia’s gambling industry extends far beyond lotteries and casinos. This is one of the reasons the industry’s political influence is so difficult to quantify.
Our project adopted a new approach to this problem, expanding on previous analyses to trace political payments from more than 370 gambling-related businesses and individuals over 22 years.
The project provided clear evidence of the monetary ties between the gambling industry and Australia’s political parties at a time of intense public scrutiny of the gambling sector’s links to money laundering and organised crime.
Using data from the Australian Electoral Commission (AEC) Transparency Register – a public database of annual disclosures about the financial dealings of political parties, candidates and others involved in the federal electoral process – we uncovered for the first time at least $81,769,853 in political payments linked to entities with a stake in gambling. This is more than twice the amount previously identified.
A key reason for this difference was that our project encompassed hundreds of organisations and businesses with interests in gambling, compared to the dozens identified in previous analyses.
This new data was used to build an unprecedented searchable database which visualised the flow of money through an connected bubble chart.
The series renewed calls for greater transparency and accountability of Australia’s federal political donations laws and provided impetus for the introduction of two separate bills to federal parliament.
These bills campaigned for, among other reforms:
* banning political donations from particular industries including gambling
* lowering the threshold for reporting political donations to $1,000
* capping donations and electoral spending
* requiring real-time disclosure of political payments.
We used a combination of Excel, Google sheets, OpenRefine and Tableau Prep to collect, share, catalogue, clean and join data.
We used Tableau Desktop to analyse and explore the data, as well as draft and create proof-of-concept visualisations.
We built a custom tool to scrape the data from the AEC website. We chose to scrape rather than download the data because the scrape picked up metadata (such as client IDs) which we knew would be invaluable not only for cleaning and joining our data to other datasets but also for future use of our work.
The custom tool was designed to pull details of all payments to and from our list of gambling “clients” (first-tier clients), whether declared by the donor or recipient.
It also fetched payments to/from associated entities, enabling us to detect vast amounts of money paid indirectly to parties.
All the charts were created using the D3 framework.
In Part 1, the tree chart adapts to various screen sizes/shapes to solve the issue of readability on small screens. In Part 2, we used a series of bee swarms to demonstrate how the pattern and timing of specific payments reveals clues about their purpose.
In both parts, we used a step-by-step “scrollyteller” to to avoid overwhelming people with detail. This format allowed us to guide users through the visualisations and zoom in on specific data points while providing relevant background and context to particular payments and clients.
This format also allowed us to give a sense of the sheer scale and number of payments.
Lastly, the interactive connected bubble chart (adapted from a network chart) in Part 1 allowed the user to explore the data more thoroughly and see how each of the groups are connected.
What was the hardest part of this project?
The hardest part of the project was creating a unique dataset of payments specific to the gambling sector.
We began by negotiating with three separate groups – the Democracy For Sale project, Monash University and the Centre for Public Integrity – to obtain their political donations data (specifically, their lists of clients categorised by industry).
We then compiled these three databases into a single “master dataset” of roughly 17,800 entities required weeks of cleaning because:
1. the databases didn’t have shared fields or IDs which would allow a straightforward join; and
2. donor and recipient names can have any number of variations (e.g. a single donor may be known as “Australian Hotels Association”, “Australian Hotels Association (NSW)”, Australian Hotels Association, NSW”, “AHA NSW”, etc.) making string matches difficult.
After compiling the master list we identified donors with gambling interests.This step was critical because previous analyses of political donations data have tended to categorise each donor by a single interest or industry However, this masks the true size and reach of Australia’s gambling industry, which penetrates into sectors far beyond casinos and lotteries, and includes individuals and businesses which may appear, at first glance, to have no obvious links to gambling.
It involved detailed research, including examining company records, annual reports, media archives, etc., and extensive consultation with researchers, gambling reform groups, and others with expertise in the topic.
We then forensically examined all payments connected to those entities, supplementing data scraped from the AEC website with data collected manually from hundreds of PDF forms. These contain key details – and in some cases, entire payments – missed or excluded from the Transparency Register’s online database.
The resulting dataset is the most comprehensive and detailed record to date of payments made by the gambling industry to Australia’s political parties.
What can others learn from this project?
This project demonstrates how journalism can build on previous work to develop new approaches and purpose-built datasets in response to the news of the day.
Our data-driven approach allowed us to not only create a new dataset from existing research, but to also generate new insights and break ground at a time of intense media focus on the gambling industry.
The series also shows that journalists can reveal important information even when the available data is incomplete, patchy or very messy. Australia has some of the weakest political donations laws in the developed world; multiple loopholes mean the source of more than a third of the money remains unknown. But detailed analysis of the available data reveals clues about how strongly Australia’s political parties and elected representatives are linked to the gambling industry.