Political Economy of Monitoring and Enforcement in the Coal Mining - PowerPoint PPT Presentation

Political Economy of Monitoring and Enforcement in the Coal Mining Industry R.J. Briggs, Anastasia Shcherbakova, Suman Gautum Pennsylvania State University 30 August 2012

Motivation • Upper Big Branch Mine (Massey Energy) – West Virginia • Coal dust explosion at 15:27 • 29 of 31 miners on site were killed • Worst accident in U.S. since 1970 • MSHA found that safety violations contributed to explosion • Mine permanently closed April 2012

Motivation • 2009 – Upper Big Branch mine received 515 citations for safety violations • U.S. Mine Safety and Health Administration (MSHA) was blamed for failing to enforce safety measures • March 2012: former superintendent, Gary May, pleaded guilty to impeding MSHA’s enforcement efforts • Investigation reported that Massey Energy had a lot of political power in the state • FBI launched investigation of criminal negligence and possible bribery of federal regulators

Motivation • Political connections were a factor in the disaster • Massey Energy contributed over $307,000 to federal political candidates since the 1990 election cycle • Former CEO Don Blankenship contributed tens of thousands more • The mining industry as a whole tripled its lobbying expenditures from $10.2 million in 2004 to $30.8 million in 2008 • With respect to the effect of political activity on monitoring and enforcement, was Massey Energy the exception or the rule?

Research Question • What effect, if any, does lobbying have on coal mine violation and fine outcomes?

Link Between Lobbying and Profits • Firms maximize profits. Costly regulations may reduce profit potential by increasing cost of doing business. If firms can successfully lobby against such regulations, they will increase profits. • Lobbying represents firm’s efforts to fight costly regulations (Jaffe & Palmer, 1995) • Political expenditures signal a firm’s willingness to fight against its political disinterests. As a consequence, large donors are inspected less and violate more (Gordon & Hafer, 2005) • Firms lobby to raise an agency’s cost of performing its functions

Literature Review • Most prior research finds that political connections have little value in developed economies (Jones and Olken 2005; Fisman, Fisman, Galef, and Khurana 2006) • But corporate lobbying can affect firm outcomes (Drope and Hansen 2006, Brasher and Lowery 2006, Kim 2008) • Corporate spending on lobbying efforts exceeds direct or PAC contributions to campaigns (Milyo et al. 2000) • Lobbying can provide continuous rather than periodic influence, regardless of which political party is in charge • Lobbying is more prevalent in regulated industries; firms that lobby “tend to outperform the market average and, to a lesser degree, the average peer in the same industry” (Kim, 2008)

Research Contribution • Combine MSHA data on coal mine inspections and violations with CRP data on lobbying expenditures by coal mine controllers • To our knowledge, this study is: • The first analysis of impacts of lobbying on regulatory outcomes • The first empirical political economy examination of monitoring and enforcement • The first to separate inspections from violations in MSHA coal mine data

Research Design • Interested in four main outcomes: • Probability of receiving a violation • Number of violations received during a year • Probability of securing a fine reduction • Aggregate financial penalties accrued for all of year’s fines • Main conditioning variables: • Controller lobbying expenditures • Mine inspections (examine only inspected firms)

Research Design • Mine decides whether or not to comply with MSHA regulations • Decision based on which is greater, expected cost of compliance or expected cost of violation • Cost of compliance unobserved; we assume it exceeds expected cost of violating for every instance in which a citation is issued     1 if [ | 1 ] [ | 0 ] viol E C violation E C violation  0 otherwise viol

Research Design • Expected cost of a single violation    ( | 1 ) ( ) ( ) E C viol prob detection E fine    ( ) ( insp successful ly identifies violation ) prob insp prob E(fine)  • Since we only observe inspected mines, ( ) 1 prob insp • Total annual cost of violation:     ( | 1 ) ( insp successful ly identifies violation ) E TC viol #OwnInsp prob E(fine)

Research Design • Consider : ( fine ) E • According to MSHA guidelines, financial penalty assigned to citation is based on: 1. History of previous violations 2. Size of operator’s business 3. Operator’s negligence 4. Gravity of violation 5. Operator’s good faith in trying to correct violation promptly 6. Effect of penalty on operator’s ability to stay in business Inspector allowed to propose alternative penalty when above • criteria results in “insufficient” fine

Research Design • Consider : ( fine ) E • Other factors to influence expected fine: • Differences between regional inspection offices • Individual inspector characteristics • MSHA budgetary circumstances • Etc. • So likely to vary from inspection to inspection and ( fine ) E year to year, not directly observed by mine. • Absorbed into coefficient on own inspections

Research Design • Abstracting from improvements in detection mechanism (except through increased inspection rates), and introducing lobbying activities, probability of choosing to violate regulations is     ( | 1 ) | ( ) E TC viol #OwnInsp E(fine lobbying) f lobbying • First term picks up indirect monitoring and financial effects of lobbying, second term accounts for remaining direct effect of lobbying on violation decisions • And probability of violation will be a decreasing function of the expected annual cost of violating    ( 1 ) [ ( | 1 )] prob viol f E TC viol

Econometric model 1. Exploratory Granger causality tests on main variables of interest 2. Basic econometric specification:             Y Lobby Insp X Z , 0 1 , 2 , 3 , 4 , , i t c t i t i t n t i t Y i,t is one of four main outcomes of interest Lobby c,t is controller ‐ level lobbying covariates Insp i,t is mine ‐ level inspection covariates X i,t is mine ‐ level characteristics Z n,t is county ‐ level characteristics

Econometric model • Two concerns: • Regulator’s inspection activities may not be random (targeting) • Decision to lobby may be endogenous (moral hazard) • Instrumental variable approach: • Inspections 1. Average annual inspection rate of each mine’s county peers 2. Distance from each mine to its regional inspection office • Lobbying 1. Number of mines in controller’s portfolio

Econometric model • Instrumenting for own inspections 1. Average annual inspection rate of each mine’s county peers (Shimshack & Ward 2005) • Mine i ’s violation behavior may affect its own inspection rate, but will not affect inspection rates of all other mines in county. • A rise in inspection rates of all other mines in county signals a general increase in regulator activity and will be correlated with violation decision of mine i .

Econometric model • Instrumenting for own inspections 2. Distance from each mine to its regional inspection office (Almeida & Carneiro 2008) • Inspectors must visit a company’s facilities in person, so physical distance between inspector and mine to be inspected is good proxy for regulator’s cost of inspection. Inspections will be less frequent where they are more costly. • A mine’s location is determined exogenously by location of coal deposits. Assuming regulators don’t locate inspection offices strategically, distance between mine and regulator is also determined exogenously. • So distance will have no direct influence on a mine’s decision to violate, but will affect frequency of inspector visits. • Inspection frequency will in turn affect a mine’s compliance decision.

Econometric model • Instrumenting for own lobbying 1. Number of mines in controller’s portfolio (Figueiredo & Silverman 2006) • Studies point to positive correlation between a firm’s income and profitability measures and engagement with lobbyists (more disposable cash gives a firm more resources to devote to lobbying activities). • In our data, lobbying is done by mines’ controllers; we do not have financial information on mine controllers, but know how many mines each oversees every year. • Since lobbying is done by controllers, number of mines in controller’s portfolio should not influence mine ‐ specific compliance decisions. • But controllers in charge of many mines face greater potential benefits of favorable industry laws, and therefore greater incentives to engage in lobbying.

Summary Statistics Variable Mean Median Std dev Min Max N Number of inspections 12.8 6.0 24.2 1 190 3738 Number of violations 44.8 10.0 114.3 0 1609 3738 Number of S&S violations 15.1 3.0 37.7 0 445 3738 Number of non ‐ S&S violations 29.7 6.0 79.6 0 1242 3738 Final fines due (‘000 USD) 25.2 1.0 136.3 0 3993 3738 Final S&S fines (‘000 USD) 19.7 0.51 109.2 0 3067 3738 Final non ‐ S&S fines (‘000 USD) 5.5 0.4 29.3 0 926 3738