Using RegData to Answer Questions About Regulation
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RegData April 8, 2015 Regulation University Patrick A. McLaughlin
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Transcript of Using RegData to Answer Questions About Regulation
RegData
April 8, 2015
Regulation University
Patrick A. McLaughlin
Federal Regulatory Accumulation
Why do we need to understand regulatory accumulation?
Less innovation = less growth
$15.1 trillion
$53.9 trillion
0
10
20
30
40
50
60
Actual GDP Potential GDP
in t
rill
ion
s o
f re
al
do
lla
rs
What would GDP have been in 2011 if regulations had stayed at 1949 levels?
$0
$5,000
$10,000
$15,000
$20,000
$25,000
$30,000
$35,000
194
9195
1195
3195
51
95
7195
9196
1196
3196
5196
7196
91
97
1197
3197
5197
7197
9198
1198
31
98
5198
7198
9199
1199
3199
5199
71
99
9200
1200
3200
5
in b
illio
ns o
f d
ollars
Actual and Alternative Growth Paths
Hypothetical Economy
Actual Economy
0
600
1,200
1,800
0
30
60
90
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Spending for Elementary and Secondary Schools, 1960–2011bill
ions o
f re
al 2
013 d
olla
rsre
al 2
013 d
olla
rs
total outlays (left axis)
outlays per student (right axis)
0
400
800
1200
1997 2000 2003 2006 2009 2012
industr
y m
entions in the C
FR
Source: Regdata 2.0. regdata.mercatus.org. Accessed August 26, 2014.
Data note: Industry mentions measure the frequency with which the relevant industry is targeted by federal regulations.
Industry Mentions of Elementary and Secondary Education
in the Code of Federal Regulations
0
100
200
300
400
500
1978 1982 1986 1990 1992 1994 1996 1999 2004 2008 2012
Average Math Scores (out of 500)
age 17 age 13 age 9
0
100
200
300
400
500
1980 1984 1988 1990 1992 1994 1996 1999 2004 2008 2012
Average Reading Scores (out of 500)Age 17 Age 13 Age 9
Source: National Center for Education Statistics, "NAEP Long-Term Trend Assessments."
Without reading for 3 years, how can I know a regulation applies to me?
WWGD
Two methods of classifying CFR text
1. Human-assisted algorithm
2. Machine learning algorithms
Two methods of classifying CFR parts
1. Human-assisted algorithm
2. Machine learning algorithms
Method 1: Human-assisted algorithm
1. Devise search terms for each sector of the economy
2. Classify documents based on frequency of search terms
Top words in Title 30: Mineral Resources
Word Hits Rank
shall 17692 1be 17348 2is 7650 3
are 3990 4mining 3784 5
coal 3705 6surface 3423 7mine 3387 8
requirements 3294 9state 3195 10
Sample NAICS 3-digit Classifications
3-DigitCode Description
111 Crop Production
211 Oil and Gas Extraction
212 Mining (except Oil and Gas)
221 Utilities
236 Construction of Buildings
315 Apparel Manufacturing
316 Leather and Allied Product Manufacturing
321 Wood Product Manufacturing
322 Paper Manufacturing
334 Computer and Electronic Product Manufacturing
Permutation algorithm to create search
terms
Paper Manufacturing
Paper Manufacturer
Paper Manufacturers
Most Mentioned 2-Digit Industries in 2012
2-Digit NAICS Classification Search Term Count
Mining, Quarrying, and Oil and Gas Extraction 45,083
Transportation and Warehousing 44,712
Manufacturing 44,452
Finance and Insurance 28,683
Agriculture, Forestry, Fishing and Hunting 27,301
Real Estate and Rental and Leasing 26,282
Construction 22,261
Utilities 14,120
Educational Services 10,355
Retail Trade 4,311
Two methods of classifying CFR parts
1. Human-assisted algorithm
2. Machine learning algorithms
Two methods of classifying CFR parts
1. Human-assisted algorithm
2. Machine learning algorithms
Two methods of classifying CFR parts
1. Human-assisted algorithm
2. Machine learning algorithms
Average year: 8,100 parts
1. Train program to classify parts
2. Use training documents to teach what to look for
3. Assign probabilities for each industry-part-year
-hospitals (NAICS 622)-ambulatory health care services (NAICS 621)
Associate each document with industries
Train the program to look for similar documents
1. Each CFR unit receives a probability for each industry
2. Treated as the probability the part is targeting the industry
𝑟𝑖𝑠𝑘 = 𝑝𝑟𝑜𝑏𝑎𝑏𝑖𝑙𝑖𝑡𝑦 ∗ 𝑠𝑒𝑣𝑒𝑟𝑖𝑡𝑦
How to measure severity?
• Various options
– Multiply probability by restrictions
– Multiply probability by word counts
– Assume value of one
Weighted search term counts
crop production (code 111); animal production (code 112); mining (code 212); and gasoline stations (code 447
CFTC and Dodd-Frank
Search term counts for finance
Summing
• Each part has a number for each industry
– Search terms
– Search terms * severity (restrictions or words)
– Machine learning-based probability * severity
• Sum that industry-specific number across all parts in each year
• Alternatively, select a set of agencies
Railroads
Statistics in RegData 2.0
• Restrictions
• Words
• Search terms (2-, 3-, and 4-digit NAICS)
• Regulation index (search terms * restrictions)
• Years 1997 – 2012
• Agency-specific
Coming in RegData 2.1
• Machine learning-based probabilities
• Each part associated with underlying statute
• Years 1975 - 2014
What you can do with RegData
• Examine agency trends
• Examine industry trends
• Combine agencies and industries
Dept. of Labor – Word Count
OSHA – Restriction Count
Mining, Quarrying, Oil, and Gas
Mining, Quarrying, Oil, and Gas by EPA
OSHA regulation of Manufacturing
Railroads by FRA and PHMSA –Search Term Count
PHMSA Restrictions
Dept. of Treasury – Word Count
Treasury Breakdown - Customs
What you can do with RegData
• Examine agency trends
• Examine industry trends
• Combine agencies and industries
