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KEEPING GOVERMENTS ACCOUNTABLE WITH OPEN DATA SCIENCE
Cezary Podkul, ProPublica | @Cezary
5/31/2015 ODSC 2015 | Boston 1
Quick Word About ProPublica
• We are a non-profit investigative news-room focused on accountability journalism
• We publish stories, develop news apps, tools and open source a lot of our code at: github.com/propublica
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Accountability Journalism
• There is a growing need for it general, in public finance in particular:
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- Detroit Free Press, April 5, 1993
- Chicago Tribune, Nov. 1, 2013
- The Bond Buyer, Feb. 12, 2014
- ProPublica, Aug. 7, 2014
- Boston Herald, June 10, 2012
- BenefitsPro Feb. 12, 2015 - USA Today, Dec. 3, 2013
- Wall Street Journal, Jan. 26, 2010
- Voice of San Diego, Aug. 6, 2012
The Good News
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• A lot of data already exists on the finances of state and local governments: – Governments that borrow money from
investors provide bond offering documents and other disclosures on EMMA
– They must also produce annual filings called “Comprehensive Annual Financial Reports” which detail all of their financials
The Good News: EMMA
• What is EMMA? – Electronic Municipal Market Access
• Since 2009, the official repository for muni bond offering documents and continuing disclosures
• Run by the Municipal Securities Rulemaking Board (MSRB)
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• What’s in EMMA?
– Data on more than 1.2 million muni bonds:
• Official statements; ongoing financial disclosures; advance refunding documents; event notices, voluntary disclosures, and more
– Real-time trade data for nearly every municipal bond bought and sold
– Political contribution disclosures (here)
– Documents, documents, more documents
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The Good News: EMMA
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The Bad News
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• EMMA is great repository of info, but little of it is easily accessible: – PDFs, PDFs and more PDFs
• Sell a bond? Submit a PDF
• Material event happened? Tell us via PDF
• File financials? File a PDF
– No standardized reporting templates • Important info scattered in different places
– No machine-readable bulk download • XBRL? You wish
Things Could Be Better
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• The SEC’s EDGAR database makes a wealth of info available about corporations: – Bulk download of filings available via FTP:
• http://datahub.io/dataset/edgar • ftp://ftp.sec.gov/
– The agency is also moving away from text-based submissions to XBRL filings: • http://www.sec.gov/info/edgar/edgartaxonomies.shtml
– No PDFs … seriously: • “Only documents submitted to the EDGAR system in
either plain text or HTML are official filings. PDF documents are unofficial copies of filings. Filers may not use the unofficial PDF copies instead of plain text or HTML documents to meet filing requirements.”
The Result
• When IBM files its annual form 10-K, you get this: – XBRL:
• http://www.sec.gov/Archives/edgar/data/51143/000104746915001106/ibm-20141231_pre.xml
– Text: • http://www.sec.gov/Archives/edgar/data/51143/0001047469-15-
001106.txt
– Even an interactive data explorer, with Excel download:
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The Result
• When Detroit files its Comprehensive Annual Financial Report with EMMA, you get this:
– http://emma.msrb.org/ER789294-ER614016-ER1015978.pdf
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Happy Hunting
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• So how do you spot anomalies like these and write about them in a systematic way?
$0
$500,000,000
$1,000,000,000
$1,500,000,000
$2,000,000,000
$2,500,000,000
$3,000,000,000
$3,500,000,000
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Ohio Series 2007B Tobacco Settlement Bonds
Principal Accreted Interest
$191.3m borrowed, with $3.2bn due at maturity in 2047. Interest accrues at 7.25% interest rate, compounded. No option to redeem until 2017
Example: Tobacco Bonds
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• That’s what I wanted to do for my series on tobacco bonds – state and local debts backed by payments from the 1998 legal settlement with Big Tobacco
Example: Tobacco Bonds
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• Problem: How do you define the sample universe?
– How many bonds are there, which ones are the anomalies?
– Searching on EMMA wasn’t much help; just links to PDFs
• Solution: Asked a data vendor, Thomson Reuters SDC, for their list:
Source: Thomson Reuters SDC
Example: Tobacco Bonds
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• Problem: How do you vet the data?
– Need to ensure completeness and accuracy
• Solution: Lots, and lots of reading
– Re-created Thomson Reuters database from paper filings, zeroing-in on 38 deals that included the anomalous bonds
– Logged all the terms and conditions we needed to calculate the amounts owed on the debt
Example: Tobacco Bonds
• Why not do it programmatically?
Wish we could have, but:
– Data often buried in scanned PDFs like this ->
– Even if you OCR, data do not appear in same place across documents
– Different labels, different conventions for reporting
– Sometimes, repayment amounts not reported at all
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Example: Tobacco Bonds
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• Results:
– Calculated that, in aggregate, state and local governments promised to repay $64 billion on $3 billion they raised by borrowing using these bonds
– Money from tobacco settlement was supposed to go for healthcare, instead turned into multi-generational debt
– The bonds are now heading for default, prompting some state and local governments to bail out bondholders
– Focused attention on this issue, spurred additional local, state and national media coverage
Source: GoComics
Next Steps
• The Financial Transparency Act of 2015 (HR 2477) has some helpful provisions in it:
• But for now it’s up to us to liberate the data
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Source: Data Transparency Coalition
Example: Treasury.io
• API for daily spending, revenue and debt operations data for U.S. Treasury
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Developed by csv soundsystem with grant from Knight-Mozilla Open News Code Sprint Grant
Example: Treasury.io
• Turns text:
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• Into structured csv:
• Parser code available at: https://github.com/csvsoundsystem/federal-treasury-api
Next Challenge
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• The U.S. Treasury publishes even more useful data in its monthly statement:
– http://www.fiscal.treasury.gov/fsreports/rpt/mthTreasStmt/backissues.htm
• I am looking for developers interested in helping liberate the data
– Is that you? Code repo available here:
https://github.com/csvsoundsystem/monthly-treasury-statements
