PredictiveAnalytics withAdministrative Data from the Mine Safety and

HealthAdministration

Yuwen Dai, Natalie Patten, Albert Lee, George Cave1 

Summit Consulting, LLC 

601 New Jersey Avenue, NW 

Suite 400 

Washington, DC 20001 

Abstract 

The U.S. Department of Labor (DOL), Mine Safety and Health Administration (MSHA) would like to predict at an 

early stage which mine operating firms with health and safety violations are at risk of failing to pay their fines on 

time. Summit Consulting, LLC (Summit), in collaboration with the DOL Chief Evaluation Office (CEO), used 

MSHA internal administrative data to develop predictive models that identify mine operators who are at high risk of 

failing to make timely payments to MSHA. Summit also created a dynamic, user-friendly Microsoft Excel tool (the 

Early Detection tool) based on the delinquency risk scores calculated by the predictive models. 

The early identification of delinquent operators has three components: 1) identification of the probability to be

delinquent, and 2) the severity of delinquency, given delinquency, in terms of duration, and 3) the compounding 

effect of delinquency probability and severity. Summit used logistic regression and survival analysis to predict the 

delinquency probability and duration, respectively. We developed a composite risk score to rank the delinquency 

risk of violations based on these two models. The early detection model has demonstrated predictive power to 

discriminate operators with high delinquency risk and has uncovered key relationships in the delinquency behavior 

in a statistical and systematic way. Summit implemented the early detection model in a Microsoft Excel application 

so that MSHA can periodically use in-house resources to conduct the analysis and use the results for early 

enforcement activities. The automated tool requires minimum inputs and operations from users and has a userfriendly, dynamic interface. 

In this paper, we demonstrate that predictive analytics can increase MSHA’s internal capacity for conducting and 

implementing program-enhancing data analytics. This serves as a proof of concept for using predictive analytics to 

improve agency performance. 

Key Words: Predictive analytics, Administrative Data, Business Intelligence tool, Enforcement program-enhancing 

data analytics 

Introduction 

Under the Federal Mine Safety and Health Act of 1977 (Mine Act), the Mine Safety and Health Administration 

(MSHA) is entrusted to enforce compliance with mandatory safety and health standards at all mining and mineral 

processing operations in the United States. MSHA conducts regular inspections four times per year in each 

underground mine and twice per year in each surface mine, and may conduct spot inspections for locations 

1 Dai, Patten, Lee, and Cave: Summit Consulting, LLC. 

Disclaimer: This article is the result of the authors’ independent research and does not represent the views of 

Summit Consulting or the U.S. Department of Labor the Mine Safety and Health Administration. 

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warranting more intense scrutiny or in reaction to individual incidents and complaints [1-4]. MSHA assesses a civil 

penalty for every health and safety violation but is unable to collect about 10% of penalty fines. 

The Mine Improvement and New Emergency Response Act of 2006 (MINER Act) dictates the minimum and 

maximum penalty amounts for certain types of violations according to the following characteristics: 

 Size of the business of the operator charged, 

 History of previous violations, 

 Repeat violations of the same standard, 

 Negligence, 

 Gravity of the safety and health hazard(s), 

 Demonstrated good faith of the operator in abating the violation, and 

 The effect of the penalty on the operator’s ability to continue in business. 

MSHA calculates penalty points for each characteristic and uses a conversion table that determines the penalty 

amount according to the total number of points associated with a violation. “Regular” assessments, which MSHA 

levies for most violations, range between $100 and $70,000. MSHA imposes “special” assessments for violations of

extraordinary circumstances, which are not listed in the conversion table and can be valued at over $70,000. 

The mine operator may request a safety and health conference to review the citations and orders. After the 

conference, or if a conference is not requested, MSHA provides a form that lists the assessments for the violations, 

the instructions for paying or contesting them, and the payment due date, which is called the final order date. The 

penalty is typically due 30 days after the assessment. However, a firm’s successful contest of the violation or penalty

postpones the final order date. MSHA considers fines that firms do not pay by the final order date delinquent. Figure 

1 shows the process of fine delinquency or payment. 

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Figure 1: MSHA Assessment and Delinquency Process 

* For this analysis, the number of days delinquent is calculated as the data extraction date (in this case September 

11, 2014) minus the final order date, if the penalty remains unpaid at the time of extraction. 

When a fine is delinquent for 45 days, MSHA sends the delinquent firm a first demand letter. MSHA follows up 

with a second demand letter if the firm remains delinquent. If the mine operator has not paid the fine within 120 

days of the assessment (i.e. within 90 days of the final order date) and has not contested it, MSHA sends the debt to 

the Treasury Department (Treasury) for collection.2 

If the fine remains unpaid, Treasury sends a demand letter 30 days after receiving the delinquency notice from 

MSHA. After 90 days, Treasury sends the debt to a private collection agency. If the debt remains unpaid, Treasury 

sends it to a second private collection agency, and notifies the credit bureaus. The debt then enters the Treasury 

Offset Program, where any payments owed to the company, such as tax refunds, will go to Treasury. Finally, 

Treasury may choose to send the debt to the Department of Justice. 

Currently, MSHA uses the Scofflaw Program as an enforcement tool to pursue operators demonstrating egregious 

non-compliance in combination with delinquent civil penalties [5]. This program leverages legal action to prevent 

scofflaw operators from ignoring monetary penalties and to incentivize operators to improve mines’ safety and

health conditions. In the past three years, MSHA has brought nine scofflaw operators to federal district court and 

received judgments totaling over $3 million in penalties [6]. 

2 After 120 days, MSHA’s computer system automatically marks delinquencies. Those that are marked are sent to

Treasury once a week. MSHA may send delinquencies before or after the 120 day mark in certain cases. 

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allows MSHA to implement enforcement tools that increase timely payment of civil penalties. 

implemented in Microsoft Excel. 

However, this enforcement takes place after the non-compliance and non-payment has already occurred. To 

implement early intervention programs and tools that increase timely payment of civil penalties, MSHA must 

predict mine owners’ propensity for long term delinquencies at the time a violation occurs. Thus, for MSHA, the 

critical step to the success of the early intervention program is detecting at an early stage which operators with 

violations are at risk of failing to pay their fines in a timely manner before they appear on the list of operators and 

contractors with delinquent penalties. Predicting the propensity of long delinquency for a violation upon its issuance 

The early identification of delinquent operators has three components: 1) identification of the probability to be

delinquent, 2) the severity of delinquency, given delinquency, in terms of duration, and 3) the compounding effect of 

delinquency probability and severity. In collaboration with DOL CEO and MSHA, Summit developed three research 

questions to address each of the three components: 1) What is the predicted probability that a mine operator will be 

at least 90 days delinquent? 2) Given that an operator is delinquent, what is the predicted duration of delinquency? 

3) Which operators and contractors are at highest risk of long-term delinquency? In the next three sections, we first 

discuss the administrative data used in this paper, then discuss the predictive model methodologies and model 

results, and conclude by briefly describing the applications of the predictive model to MSHA and the automated tool 

Data 

We used historical administrative data from the MSHA Standardized Information System (MSIS). These data 

contain information on mine characteristics, mine status history, mine operator history, mine employment and 

production information, accidents and injuries information, violation assessment and penalty information, payment 

amounts and dates, and delinquent case status. In this study, the unit of analysis was a unique violation and we 

developed the predictive models at violation level. We used the violation-level data issued from January 1, 2011 to 

September 11, 2014. 

The outcome variables of interest were 1) a binary 90-day delinquency indicator and 2) the number of days in 

delinquency. Summit constructed both variables using the available data. 

90-Day Delinquency Indicator: To address the first research question, we constructed the outcome variable of a 

delinquency indicator variable which takes a value of 1 if the penalty associated with a violation is at least 90 days 

delinquent and 0 otherwise. A delinquency occurs if the mine operator makes the penalty payment after the final due 

date. A 90-day delinquency occurs if the actual penalty payment date is greater than 90 days past the final due date, 

which includes cases whose penalty is still not paid as of our data cutoff date, September 11, 2014. 

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Figure 2 shows the unconditional delinquency rate for each year of the data from January 1, 2011 to June 30, 2014. 

The delinquency rate is defined as the number of violations that were 90+ days delinquent in the quarter divided by 

the number of total violations in the quarter. 

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Figure 2: Rate of 90-Day Delinquencies by Year and Quarter 

18% 18% 19% 

18% 

17% 16% 

14% 13% 

12% 12% 

16% 

14% 

9% 

0% 0%

2% 

4% 

6% 

8% 

10% 

12% 

14% 

16% 

18% 

20% 

2011 2011 2011 2011 2012 2012 2012 2012 2013 2013 2013 2013 2014 2014 

Percentage of Violations that are 90+

Days Delinquent 

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 

Violation Issue Date 

Source: MSIS Administrative Data Files, January 1, 2011-June 30, 2014 

The figure shows that the percentage of violations that were delinquent for at least 90 days decreased each year from 

2011 to 2014. We would expect an overall decreasing trend across years, especially a significant drop in 2014. The 

MSHA data extract provides a snapshot of data as of September 11, 2014. It includes the delinquency history 

performance observed on and before September 11, 2014, and leaves the payment behavior on all the open 

violations unobserved. The data is right-censored, which means that the event of interest (90-day delinquency) 

happens after the time of observation (September 11, 2014) and is not collected in the observed data. The longer the 

observation period, the more likely the 90-day delinquency is observed. The violations issued in early years (e.g., 

year 2011) have longer observation periods, and more delinquencies are observed in the data. The violations issued 

in more recent years (e.g., 2014) have a very short observation time period and have fewer delinquencies (close to 

0%) observed. Some of those violations have not passed their payment due date by September 11, 2014 and most of 

them are still less than 90 days delinquent by September 11, 2014. Therefore, to avoid the right censoring issue in 

analyzing the 90 + days delinquency behavior, we used the violations issued between January 2011 and December 

2013 in the first research question. 

Time in Delinquency: To answer the second research question, we also created a new variable to indicate how long a 

payment is delinquent by taking the difference between the penalty due date and the actual payment date. We 

changed the time delinquent variable to zero if it had a negative value. A negative value would represent only those 

fines that firms paid before delinquency, so they have a delinquency duration of 0 days. This variable indicates how 

long a fine was delinquent before the violator paid it and how long a fine has been delinquent if it remains unpaid. 

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Figure 3 shows the distribution of time in delinquency for all the delinquent violations between January 2011 and 

December 2013, the same data as used in the research question 1. In the past, MSHA has collected about 90% of 

total penalties and about 10% have remained unpaid and in delinquency for more than six quarters. 

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0

.0005

.001

.0015

.002

.0025

0 500 1000 1500

Number of Days

Figure 4: Hazard Curve, 2011 Q1 through 2013 Q4 

We further analyzed the hazard curve by year to see if the hazard pattern is consistent for different time periods. 

Figure 5 shows the hazard curve by the year of the violation issue date for the observation period used for the 

survival model (2011 Q1 through 2013 Q4). The curves are similar for all three years, although the 2013 curve is 

shorter because the observation time for payment is shorter. For example, we can only observe a violation MSHA 

issued in September 2013 for one year, or about 365 days, because the data were pulled in September 2014. 

Figure 5: Hazard Curve by Year, 2011 Q1 through 2013 Q4 

0

.0005

.001

.0015

.002

.0025

0 500 1000 1500

Number of Days

year = 2011 year = 2012

year = 2013

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http://msha.gov/PROGRAMS/COAL.HTM. 

Assessments, 3-4. 

Interscience, 2002. 

[4] “Coal Mine Safety and Health.” Mine Safety and Health Administration. Accessed November 3, 2014, 

[5] Patricia W. Silvey to Jay Mattos, January 17, 2012, Memorandum; Scofflaw Program, MSHA Office of 

[6] “Response to NPR Series From the Mine Safety and Health Administration,” NPR, January 12, 2015, 

http://www.npr.org/2015/01/12/372490930/response-to-npr-series-from-the-mine-safety-and-health-administration 

[7] Kalbfleisch, John and Ross Prentice. The Statistical Analysis of Failure Time Data. Hoboken: Wiley15