Proposal for Non‐Randomized Study of the Effectiveness of  

FAA Proposed Regulations Regarding Cockpit Crew Fatigue

August 16, 2011

Donald B. Rubin, PhD

Department of Statistics

Harvard University

1 Oxford Street

Cambridge, Massachusetts  02138

Telephone number:  617‐495‐5498

Email address: rubin@stat.harvard.edu
2 

This document describes the methodological steps that are needed to conduct a study,

using existing observational data, to estimate the causal effects, both intended and

unintended, of the FAA’s proposed regulations on flight crewmember duty and rest

requirements.   (A summary of the FAA proposal, from September 2010, is attached as

an Appendix to this Proposal.)   

The Use of Randomized Experiments – in General

In outlining those steps, it is important to understand that the challenge facing FAA is

not unique.    There are numerous comparable situations involving federal regulatory

agencies who must estimate such causal effects, many involving similar “interventions”

that propose to change such things as the standard of medical care or to implement new

job‐training programs.   Examples commonly occur within the context of the U.S. Food

and Drug Administration’s (FDA) approval process for new pharmaceuticals for

treatment of diseases or devices for the treatment of medical conditions, or within the

context of considering implementing    new educational policies.     Specific earlier

examples include the recommendations for how to treat certain types of breast cancer:  

should mastectomy be the standard of care, or should breast conservation be preferred

in some circumstances?  Or, is a new cholesterol‐reducing drug safe in general use and

effective for reducing heart disease?    

With such questions, there will not be generally accepted, scientifically‐based answers;

they are absent for a variety of reasons.    These include the presence of vaguely

understood causal factors operating that may affect the primary outcomes and the

unanticipated negative side‐effects of new interventions.    The literature discussing

interventions with humans is replete with examples.

In general, it is widely accepted that the most valid and reliable evidence about causal

effects is obtained from randomized experiments, in which some of the units being

studied are randomly assigned to the new intervention, here called the active treatment

group, and the others are assigned to the other one, here called the control group; for

simplicity of description, we assume that the choice is between two conditions, the

active treatment and the control, but in general there can be more than two treatment

conditions, especially when searching for a combination of factors that appears to be

most favorable.   

Often, however, such randomized experiments can be conducted only in specialized

populations of units, and as a result the information garnered from them needs to be

supplemented with evidence on the causal effects of the proposed new intervention,
3 

relative to the control, from non‐randomized or observational data.  A specific example

of such a situation ‐‐ involving the study of mastectomy versus breast conservation for

the treatment of breast cancer   ‐‐ is summarized in my (1997) description of a United

States General Accounting Office (GAO) investigation into that question using both

randomized and nonrandomized data. 1    In other common situations, there is no

opportunity to investigate the effects of proposed interventions using randomized

experiments.  This inability to randomize the units under study may be  due to ethical

concerns,  or to the long lead time needed to obtain answers from yet to be conducted

randomized experiments, or to practical obstacles created by implementation issues

such as the costs involved, financial or otherwise.    

For instance, it might be inappropriate or unwise to have flight crews on scheduled

passenger flights work prolonged shifts, or operate under otherwise arduous conditions,

for the purposes of experimentation about flight and duty period limits.    Consider

what happened in 2006 when, according to press reports, FAA officials permitted the

airline JetBlue to outfit a small number of pilots with devices to measure alertness and

then to assign some crews to work longer shifts in the cockpit  ‐‐ as many as 10 to 11

hours a day.  The study was designed to assess whether pilots could safely fly far longer

without exhibiting ill effects from fatigue.  After the study was disclosed, the airline and

FAA staff were roundly criticized, with an FAA official stating,  ʺWe donʹt allow

experiments with passengers on board, period.ʺ 2 If this study had instead randomly

selected crews to work    shorter versus    normal shifts, the havoc created with airline

schedules might have made the experiment impractical.  Similarly, because of the costs

incurred by air carriers in shortening the work day, subjecting some airlines, but not

others, to more restrictive rules in a randomized study might be unacceptable for  

competitive reasons.  

Although there are many situations where using randomized experiments to estimate

causal effects of proposed interventions may not be possible, it is imperative that the  

answers from studies of non‐randomized data  nevertheless approximate, as closely as

possible, the answers that would have been obtained from the corresponding

randomized experiment.    In fact, this was the unanimous conclusion of the opening

 1 See Rubin, D., Estimating Causal Effects from Large Data Sets Using Propensity Scores,   Annals of

Internal Medicine 127:(Supplement 2) 757‐763 (1997);    Breast Conservation Versus Mastectomy:    Patient

Survival in Day‐to‐Day Medical Practice and in Randomized Studies, U.S. General Accounting Office, Report

No. GAO/PEMD‐95‐9 (1994).

2 See A. Pasztor and S. Carey, Pilot‐Fatigue Test Lands JetBlue In Hot Water: Airline Pushed FAA

Limits On Cockpit Time but Failed To Tell Passengers on Planes, The Wall Street Journal (Oct. 21, 2006), p. 1.   
4 

session (on July 31, 2011) of the 2011 Joint Statistical Meetings (JSM) in Miami, which

was attended by representatives of Federal Statistical Agencies and their regulatory

counterparts (including the FDA and the Centers for Disease Control), academia

(Harvard University), and research organizations (National Institute of Statistical

Sciences).    And this is the situation that we find ourselves in when conducting an

evaluation of the proposed FAA regulations.  The general approach being advocated for

causal inference is now widely known as “Rubin’s Causal Model” (Holland, 1986) for

work initiated in Rubin (1974, 1975, 1977, 1978), and continuing through Rubin (2008,

2010).  

A Hypothetical Randomized Experiment of the FAA proposed Regulations

Even though we cannot conduct  randomized experiments to evaluate the effects of the

proposed FAA regulations, it greatly facilitates the identification of crucial ingredients

of a non‐randomized evaluation to consider, at least briefly, how a randomized

evaluation would be conducted.   

The first task is to identify the units of study that would be impacted by the new

regulations:  these would be individual flight segments.  In a randomized experiment, a

fraction, say half, of those segments would be randomly selected to be subject to the

new regulations; the remainder would not be subject to the new regulations but only to

the current regulations, the current “standard of care.”   In the aforementioned study of

the treatment of breast cancer for a certain category of woman, the units were women

with the disease, where the then‐current standard of care was mastectomy, whereas the

new intervention was breast conservation.   The randomization there would ensure that

the units subject to the new intervention and those subject to the control intervention

had the same distributions of all pre‐randomization characteristics, such as age, baseline

blood pressure, education level, marital status, region of the country, etc.    Baseline

variables like these are commonly called “covariates” because they “co‐vary” with the

outcome variables, such as mortality, that are of primary interest in the study of the

intervention.

In our setting, the units are flight segments, each one of which is currently operated

under current regulations but would be operated under the proposed regulations were

they in place.  Thus, if we could conduct a randomized evaluation for the effect of the

new regulations, we would toss coins to decide which of the segments would take place

under the old regulations and which take place under the new regulations.  We would

then record all sorts of outcome measurements on each flight segment, including

anything related to flight safety.  To improve the precision of the comparisons between

outcomes under the new and old regulations, and to allow for the detection of differing
5 

effects of the new versus old regulations under differing baseline conditions, we would

also record extensive covariate data.    These data are information about each flight

segment that is unaffected by the assignment to be operated under the old versus new

regulations, such as the type of aircraft, the experience of the crew, the weather

conditions, the planned flight time for the segment, etc.    Importantly, before the

examination of any outcome data, descriptions of the planned analyses of that data

under the two regulations would be provided and agreed on.   This plan would ensure

a fair assessment of the implementation of the new regulations in place of the old ones.

Even though a randomized experiment addressing the FAA question cannot be

conducted, at least at present, thinking about how to design one thus provides

important guidance on how to evaluate the specific proposed intervention.  If it were

possible to conduct a randomized experiment, ideally many factors  defining possible

interventions would be established first, and a factorial experiment (a way of evaluating

the effects and possible interactions of several factors or independent variables), or

better still, a fractional    factorial experiment would be designed to discover the best

combination of factors.   See Box, G. E., Hunter, W.G., Hunter, J.S., and Hunter, W.G.,

Statistics for Experimenters: Design, Innovation, and Discovery, 2d Ed., Wiley, 2005, ISBN

0471718130

By using existing FAA and industry data, and with the anticipated support of FAA,

OMB and industry and union representatives, the study of these non‐randomized data

described in this proposal could be designed to achieve answers that are fully objective

and that approximate the answers that would have been achieved with a randomized

experiment.    We would take advantage of the array of specific techniques that have

been developed over the decades to ensure the integrity of non‐randomized studies,

including propensity score methods,3 principal stratification,4 and various methods for  

 3 The propensity score is the probability of a unit (e.g., person, classroom, school) being assigned

to the active treatment in a study given a set of known covariates. Propensity scores are used to reduce

selection bias due to the lack of random assignment in an observational study by creating similar groups

based on these covariates.    Paul Rosenbaum and I introduced the propensity score to provide an

alternative method to help estimate treatment effects when treatment assignment is not formally

randomized, but can be assumed to be unconfounded (Rubin, 1975). The Central Role of the Propensity Score

in Observational Studies for Causal Effects. Biometrika, (1983) 70, pp. 41‐55. (With P. Rosenbaum).

4 Many scientific problems require that treatment comparisons be adjusted for post‐treatment

variables; however, the quantities being estimated by standard methods of adjusting for such variables

are not causal effects in general, but only in special cases. To address this deficiency, I developed, with

Constantine Frangakis, a general framework for comparing treatments adjusting for post‐treatment

variables that yields principal effects, causal  effects within principal strata. Principal stratification with

respect to a post‐treatment variable is a cross‐classification of subjects defined by the joint potential
6 

matching, such as Mahalanobis metric matching, 5 and propensity score sub‐

classification.   The ultimate goal in applying these methods is to estimate objectively

what the effects of the proposed regulations would have had on safety had they been

applied retroactively to flight segments that took place under existing rules.    The

purpose of the matching and sub‐classification is to create, in the observational data, a

group of treated units, i.e., flight segments that would have been forbidden under the

new regulations, and a “matching” group of control units, i.e., flight segments that

would not have been forbidden under the new regulations, where “matching” means

having the same (or nearly the same) distribution of all relevant covariates.

Steps Required for An Objective Evaluation of the Proposed Regulations

Our proposed analysis would proceed in six discrete steps, with an optional seventh

step:

1. Initial discussions.   In order for the project to succeed optimally in practice, it is

essential to have ‘buy‐in’ from all stakeholders on the appropriate methods and

metrics of the study.    To accomplish this consensus, we would initiate

discussions among the researchers, industry representatives, labor

representatives, OMB, the FAA, and other entities to determine what outcomes

would be measured and what variables, including baseline covariates, would

need to be considered.   

For instance, covariates such as flight crew experience, age, training background

(civilian versus military), and commuting practices might be considered.    In

terms of selecting outcomes for measurement, discussions might include how to

address various definitions of safely completed flights; for example, a flight crew

may opt to execute a missed approach or “go‐around” procedure to abort a

landing attempt before finally landing safely.  A missed approach or go‐around,

in certain circumstances, might be indicative of crew performance issues.    In

other cases, a missed approach or go‐around may indicate textbook decision

(continued...) 

values of that post‐treatment variable under each of the treatments being compared. “Principal

Stratification in Causal Inference.” Biometrics, (2002) 58, 1. pp. 21‐29. (With C. Frangakis).

5 In statistics, the Mahalanobis metric refers to a distance measure introduced by P. C.

Mahalanobis in 1936. It is a method that incorporates correlations between variables. It is the same as

Euclidean distance if the variables are uncorrelated, and it is also a scale‐invariant method , i.e., not

dependent on the scale of measurements.
7 

making and execution on the part of the crew, whereas, in many other cases, a

flight may be entirely uneventful.   The initial discussions would address how

the study should account for such variations and definitions.   

It is important that all parties with knowledge and perspective to contribute on

these subject areas be included in discussions on what outcome variables are to

be measured, what covariates should be included, the manner in which various

data sources will be used, and the analytical methods to be utilized.    The

inclusion of all parties will lead to a better‐informed and more refined study and

to a shared understanding among all parties that the sole purpose of the study is

to obtain the most accurate and objective answer possible.    

2. Data Gathering. Upon conclusion of initial discussions, we would begin

gathering data from existing sources and identify gaps requiring ab initio

collection efforts.   It is our understanding the FAA itself already collects vast

amounts of data through existing programs that could be used for the study

being proposed.    Moreover, we understand that a great deal of additional

operational data, relevant to the study, is kept by air carriers.   

For instance, the internationally recognized Line Operations Safety Audit

(“LOSA”) appears to be an excellent source of data.  LOSA is predicated on the

notion that “understanding the human contribution to successes and failures in

aviation can be better achieved by monitoring normal operations, rather than

accidents and incidents.”    See Line Operations Safety Audit (LOSA), Doc 9803

AN/761 (1st Ed. 2002), International Civil Aviation Organization, at 1‐2.   LOSA

consists of monitoring    crew performance during all phases of normally

conducted flights by an observer in the cockpit.  Unlike other data sources, the

generation of LOSA data is entirely independent of a specific outcome.  It is not

triggered by an accident or an incident.  Thus, LOSA appears to provide a wealth

of data to understand how flight crews perform in a spectrum of circumstances—

including circumstances in which a flight crew member may experience fatigue

but that does not result in an accident nor measurably reduce observable

margins of safety, and also those circumstances in which a crew member

experiences fatigue and consequently makes errors that might lead to an accident

in certain circumstances.    LOSA data certainly appear promising.    

Another promising source of FAA data consists of reports from the Aviation

Safety Action Program (“ASAP”).    The ASAP program collects voluntarily

submitted reports of safety lapses from airline employees.    Although ASAP

reports are outcome triggered and possibly suffer from biases because of their
8 

voluntary nature, they nonetheless are a source of data about systemic hazards

that could be used in a statistical analysis.    

There are numerous other data sources such as the Flight Operational Quality

Assurance (“FOQA”) program, which provides data directly from the aircraft

recorders and could capture instances of unsafe conditions, such as an unstable

approach, which could indicate degraded pilot performance.  

The FAA’s Aviation Safety Information Analysis and Sharing (“ASIAS”) System

ingests the data sources discussed above, as well as information from 43 other

databases.    We propose to consider all available FAA and available industry

data to determine what sources could be used in our analysis.   We note that

some data may be subject to confidentiality protections inuring to individuals or

employee groups.  At this stage, we are unable to ascertain what additional data

collection might be required.    The final decisions about  the data sources to be

used, and how they should be assembled, should involve subject matter experts,

and should have buy‐in approval from stakeholders before any analysis of

outcome data for causal effects is initiated.    

At the conclusion of this phase, the data should be separated into two parts,

covariate data and outcome data.  The outcome data would be safeguarded and

securely hidden from study participants by a third party until the completion of

all the analyses that are aimed at reconstructing the hypothetical randomized

experiment with matching treatment and control flight segments with respect to

the covariate data.     Segregation of outcome data is necessary to ensure the

objectivity of the resulting estimates of the causal effects of the proposed new

regulations.  This fact has been emphasized in several recent publications by me

(Rubin 2008, 2010) and others (D’Agostino and D’Agostino, 2010, NEJM), and, as

stated earlier, was unanimously agreed on in the opening session of the recently

concluded 2011 JSM.

3. Segment Classification as Treatment vs. Control. The third step would be to

classify the individual flight segments into ‘control’ and ‘treatment’ groups of

flight segments.   Flight segments falling into the ‘control’ group are those that

would remain legally permissible even if the FAA’s proposed regulations were

applied.    The treatment group segments are those flights that would become

legally impermissible with the application of the FAA’s proposed regulations.   

For instance, a crew at an all cargo airline can fly five consecutive nights under

existing regulations.  If the FAA proposal were adopted, they would be limited

to three consecutive nights.  Thus the flights operated on nights 1, 2 and 3 would
9 

be in the control group assuming those segments satisfied the other parts of the

new regulations, whereas the flights on nights 4 and 5, being  prohibited, would

thus be classified as being in the treatment group.   This exercise would reflect

the proposed regulations, as summarized in the included appendix, and would

be implemented with the agreement of the stakeholders by third parties using

approved software.    Moreover, whenever any doubt arose as to the methods

used, the stakeholder agreement would provide for a formal method of

adjudication.  This is the same process used in randomized medical experiments

for adjudicating so‐called adverse events, when the question is whether they

might be related to medical interventions.  

4. Segment Matching.    Once flights have been classified as treatment or control

segments, they must be matched on the covariates selected in Step 2, deemed

probative of flight crew performance and flight safety in general.  For example,

flights could be matched according to: aircraft type, origin & destination airports,

the ages of the crew, the season of the year, weather encountered, commuting

practices of the particular crews, and airline‐specific training and procedures. To

the extent that exact matches on all attributes are not found, and this is extremely

likely if there are more than a few covariates being considered, substantially

similar flights can be paired according to pre‐determined statistical methods.  It

is very likely that propensity score matching and sub‐classification will be used,

as well as other related techniques, some of which are summarized in the recent

book by me, Rubin (2006) on Matched Sampling for Causal Effects.    We can

assemble teams of individuals with substantial experience doing such matching

exercises for federal agencies, foreign regulatory agencies, and private companies.    

These techniques allow for the meaningful comparisons of treated and control

flight segments to estimate the consequences of the proposed FAA regulations.

At this point the created    matched groups must be assessed for balance with

respect to all covariates that are considered relevant.    This assessment can be  

time consuming, because many such matched samples will probably have to be

considered and assessed for balance.  It is also imperative that all stakeholders

provide their opinions on the adequacy of any final matched sample.  Recall that

all of this matching and assessing of balance is being conducted without any

party (except for the holder of outcome data) having access to outcome data, and

therefore without the benefit of answers about the estimated causal effects of the

proposed regulations.  The stakeholders’ buy‐in on the balancing of the groups ‐‐  

that is, the fairness of the comparison between treatment and control segments –

is assessed without knowledge of the consequences that the assessment has in

terms of  estimating the causal effects of the proposed regulations.
10 

I have had direct experience    overseeing such matching processes in other

projects.    One such project took place at the CDC’s Division of Reproductive

Health where the task was evaluating the consequences of different guidelines

for in vitro fertilization.  Another was for the Institute for Employment Research

at the German Federal Employment Agency, which was evaluating the

effectiveness of various job‐training programs.  Again, the process can be time‐

consuming, but it ensures that all stakeholders understand and accept that what

is being sought is an honest and fair assessment of the proposed interventions.   

When buy‐in is complete, and the treatment and control segments are considered

to be essentially at least as balanced with respect to the relevant covariates as if

they had been randomly divided, a protocol for the analysis of the outcome data

should then be agreed upon.    This step presents researchers with many

methodological options, but again, we have substantial experience with such

methods.  This is expanded upon in Step 5, which follows.           

5. Protocol for the Analysis of Data. The researchers would assess the efficacy of

the FAA’s proposal, i.e., whether it would improve flight safety, by comparing

and contrasting the matched control and treatment segments in light of the

outcome data.  Prior to obtaining the outcome data, we would write a protocol

describing, as precisely as possible, how outcome data would be analyzed.  This

process begins by carefully defining quantities to be estimated, known as

“estimands.”    The general task of defining estimands and identifying the

appropriate analysis plan to estimate them are critically important steps.

6. Analysis of the Outcome Data.    Once the protocol is written, outcome data

would be appended to the data set of matched control and treatment segments.  

Then, one would analyze the resulting data according to the protocol, for

example, by estimating accident/incident rates for the treated flights and for their

matching control flights.   If the rates are similar for the treated and matched

control flights, then there is no empirical evidence that the proposed regulations

will have any effect on flight safety.   If the rates are significantly lower in the

treated flights (those flights that would have been prohibited under the new

rules) than in the matched control flights, then there is evidence that the

regulations would have actually eliminated safer flights, i.e., that there are

negative effects of the proposed regulations; see the discussion below for some

possibilities.    If the accident/incident rate is significantly higher in the treated

flights than in the matched control flights, then there is empirical evidence that
11 

the proposed regulations would improve flight safety, which would suggest a

need to do a follow‐on cost‐benefit analysis of the regulations.   

7. Assessment of Other Possible Consequences of the Proposed Intervention

The data analysis discussed in Step 6 may reveal unintended adverse effects on

safety that stem from particular provision(s) of the proposed regulations; this

could suggest modifying those provisions if a decision is made to proceed with

the regulations.   However, that analysis could not fully address all the possible

unintended negative consequences of the regulations.    This optional Step 7

would use the outcome and covariate data from Steps 1 – 6 to examine such

potential consequences.

The risk and gravity of unintended consequences should never be discounted.  

As an illustrative example, with which I am personally familiar, the National

Institute of Health discovered, after extensive study based on randomized

experiments, that hormone replacement therapy, long believed to prevent

cardiac problems in post‐menopausal women, actually worsened them. 6   

Another example, from the transportation sector, was recently highlighted by the  

National Research Council of the National Academies in their new report on

airline pilot commuting. 7    Because their discussion is directly related to the

question at hand, I quote from the relevant portion of the report in its entirety:    

A major concern in establishing any regulation is designing it so that it

achieves its intended effect. Negative unintended consequences often

emerge when a seemingly simple regulation is implemented in a complex

system. Regulators may not have enough knowledge about the detailed

operation of the systems and so may adopt seemingly simple regulations

that fail to anticipate how the system will respond to those regulations. An

early analysis of the general problem of unintended consequences found

 6 An excellent discussion of these findings can be found in Risks and Benefits of Estrogen Plus

Progestin in Healthy Postmenopausal Women:  Principal Results from the Women’s Health Initiative Randomized

Controlled Trial, Journal of the American Medical Association, Vol. 288, No. 3 (2002), available at

www.jama.ama‐assn.org.

7 The Effects of Commuting on Pilot Fatigue, National Research Council (2011), available at

http://www.nap.edu/catalog.php?record_id=13201
12 

that one of its sources is “imperious immediacy of interest,” which is

where the intended consequence of an action is desired so strongly that

potential unintended effects are purposely ignored (Merton, 1936).  

The committee is concerned that a rush to establish regulation regarding

pilot commuting and fatigue without an adequate understanding of how

pilot commuting and fatigue interact with the aviation system might

trigger unanticipated and unintended consequences that have not yet

been carefully anticipated.  

Such unanticipated and unintended consequences can reduce the

effectiveness of the regulation in achieving its goal, and in some cases may

even result in a regulation having the opposite effect of what had been

intended. A noteworthy example occurred with the 55 mph speed limit,

established in March 1974 in response to the 1973 oil embargo (see

National Research Council, 1984). Following this adoption, highway

fatalities dropped. Although multiple factors contributed to the decline in

fatalities, the general consensus was that the reduced speed limits had

resulted in fewer highway fatalities. As the fuel shortage eased, the speed

limit was retained largely on the grounds of the increased safety it

apparently provided. However, in response to other pressures and

interests, in 1987 40 states raised the speed limit to 65: many anticipated

that highway fatalities would again increase due to the higher speeds.

Although highway fatalities did increase, so did vehicle miles traveled. A

study that examined statewide fatality rates, considering not only the

roads on which the speed limits were changed but also the non‐interstate

roads on which they were not, found that the higher 65 mph speed limit

reduced the statewide fatality rates by 3.4‐5.1 percent in comparison with

other states (Lave and Elias, 1994). It appears that this unexpected and

initially counterintuitive result was because enforcement and highways

are integrated systems. The federal government had threatened to impose

financial penalties if the 55 mph speed limit was not enforced, so states

devoted considerable patrol resources to rural interstates and reduced

both enforcement on other highways and other safety activities. In

addition, it appears that the higher level of enforcement on rural

interstates may have caused some drivers to switch to parallel non‐

interstate highways, which are more dangerous in terms of fatalities but

which had the same speed limit and less speed enforcement. Where the 55

mph speed limit was raised and the threat of federal financial sanctions

removed, highway patrols reallocated their activities to a better balance
13 

from a safety perspective and with the higher interstate speed limits, some

drivers switched from parallel rural roads to the safer interstate highways.  

This experience offers two cautionary lessons for safety regulation. One is

that complex systems may react to regulation in ways that were

unanticipated and, in this case, counter to the goal of improved highway

safety. The second is that patterns of enforcement can have important and

often unanticipated effects on how a system reacts to regulation.  

In aviation safety regulation, another possible unintended consequence of

regulation can come from modal shift effects. A regulation that increases

costs to the airline industry will likely result in some portion of those costs

being passed on to travelers in the form of higher airline ticket prices.

Higher airline ticket prices would cause some travelers to switch their

mode of travel from airplanes to automobiles. Since travel by private

automobile is more dangerous than travel by commercial airline, the

result of such a shift would be an increase in highway fatalities. Thus,

however many airline passenger and crew lives are saved by the airline

safety regulation; the net savings of life from the regulation would be less

because of the increase in highway fatalities. In some cases, the net effect

may actually be a net loss of lives from a regulation intended to save lives.  

This potential for an outcome other than that intended was forecast for a

proposed regulation to mandate the use of child safety seats on

commercial airlines. One study of the proposed regulation (Windle and

Dresner, 1991) concluded that more lives would be lost from the switch to

highway travel from the higher travel costs for families with children than

would be saved from the added safety benefit of child safety seats.8

  

Although that discussion takes place in the context of assessing the

potential side effects of regulating pilot commuting habits, in my

judgment their observations are equally applicable to an evaluation of the

FAA’s proposed rule on crewmember flight, duty and rest requirements.

In our situation, one expected result of the proposed rules (according to public

comments) would be the hiring of thousands of additional pilots by the airlines in order

to operate legally their current flight schedules.  We would need to assess the effects, if

 8  Id., at 6‐8 – 6‐9.
14 

any, on the incident/accident rate from the introduction of new pilots into the workforce.  

This would require a different matching process, because the evaluation (outlined in

Steps 1‐6, above) addresses only the immediate consequence of cancelling the

prohibited flights, as opposed to operating those flights with additional crews.   

Questions for examination would include:  As in the National Research Council report,

would the net result be an increase in ticket prices such that more people would drive

instead of fly?  Would the airlines be forced to hire less experienced pilots?  In the latter

case, we could try to assess the causal effects of pilot experience by matching segments

with experienced pilots and inexperienced ones.   

Projected Schedule for Completion of the Study

In my experience, I believe that, with the proper resources, the proposed study could be

completed within 24 months (two years) of commencement, but this is very dependent

on time for interested parties to agree to buy in, the ease of assembling data bases, the

ability to create matches, etc;  The estimated range of time involved in each phase is as

follows:

1. Initial Discussions              2 to 4 months

2. Data Gathering           3 to 5 months

3. Segment Classification         1 to 3 months

4. Segment Matching           5 to 7 months

5. Protocol for the Analysis of Data       1 month

6. Analysis of the Outcome Data       2 to 4 months

7. Assessment of Other Possible Effects    Currently beyond the scope

        Total:    13 to 24 months
15 

Proposed Research Team

Statisticians: D.B. Rubin and others acting under my direction.   

It would be necessary to hire a vendor specializing in database creation and

management.  These firms  vary in size  from relatively small with dozens of employees  

to relatively large with thousands of employees.  These entities include the Institute for

Social Research, Trinity Partners, Westat, National Opinion Research Corporation,

MITRE, and RAND.  I have worked with each of these vendors in the past.

Estimated Costs

Upon request, we can provide OMB with an estimate of our projected expenses.  

However, because of the variety of data sets available for the analysis, and the need to

identify experts who are familiar with manipulating these data sets  and the underlying

software systems, it will be difficult to provide a firm estimate until we have completed

Step 1, the initial discussions.    Those discussions would allow us to determine the

relative division of labor between the various stakeholder groups and our research team,

as well as outside third parties, to conduct the study.  

  

   
16 

APPENDIX:   SUMMARY OF THE 2010 FAA PROPOSAL

The FAA proposal has been broadly understood as a substantial and historic revision of existing

regulations governing flight crew duty and rest.    Many flight schedules and crew work

schedules permissible today would be rendered illegal.    In other cases, schedules that are

currently non‐viable may become a norm.  If promulgated, the regulations would pervasively

affect airline operations in term of flight schedules, staffing, route structures, training

requirements, and even the types of aircraft airlines choose to operate.    The FAA proposal

contains several elements that are summarized in the table below:

Types of Airline

Operation

The FAA proposal would eliminate historic distinctions between

“domestic”, “flag”, and “supplemental” operations which have had

separate rules for flight crew duty and rest.  “Domestic” operations are

generally those within the 48 contiguous United States. “Flag” operations

are generally those occurring between the United States and foreign

countries and also between the Hawaii/Alaska and points outside of

those states.    “Supplemental” operations are generally charter and

certain cargo operations.  Under the FAA proposal, the same regulations

would be applied to all operations for the first time.  Thus a pilot flying

only regional domestic routes   would be subject to the same rules as a

pilot operating an intercontinental flight for the first time.  

The Concept of “Flight

Duty Period” or “FDP”

The FAA historically has regulated how much flight time, or time spent

operating the aircraft, a crew member could perform.  The FAA proposal

would borrow from European regulations and regulate ‘duty’ periods to

encapsulate pre‐  and post‐flight obligations.    In contrast to European

regulations, the FAA would retain regulations of flight time.   Thus the

FAA would regulate both flight duty periods and flight time ‐‐which has

no regulatory precedent.  Pilots may run out of flight time with duty time

remaining in certain permutations.   The legally permissible length of a

flight duty period would depend upon the time of day a pilot actually

reports to work rather than the originally agreed upon schedule and thus

a pilot’s FDP could fluctuate overnight. Many flight schedules operated

today would become impermissible.

Acclimation Credits and

Penalties

The FAA proposal would establish an entirely new system to regulate

crew ‘acclimation’ which would determine which flight duty periods

apply.    The reference time zone for acclimation would be either the

home base or wherever the pilot commences their flying schedule.  

However it does not take into account that pilots often reside in and

report from a time zone other than home base before commencing duty.  

Many flight schedules operated today would become impermissible.
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Flight Time Limitations Under existing regulations, airlines are permitted to extend pilot flight

times to adapt to unforeseen delays caused by weather, air traffic control

delay, or other factors.  Under the FAA the proposal, airlines’ ability  to

adjust would be restricted.  Many flight schedules operated today would

no longer be permitted.   

Night and Day

Operations

The FAA would begin to distinguish between flight operations taking

place during daytime and nighttime hours  and apply different rules for

day and night operations.    The FAA would for the first time limit the

number of consecutive nighttime operations that may be flown by the

same crew.    Many crew schedules currently used would become

impermissible.   

Rest periods The FAA would increase the minimum required rest by 25% during any

seven consecutive day period and would increase minimum daily rest

from 8 to 9 hours.  For flight segments over certain lengths, the extended

rest periods would require changes to flight schedules.    

On board rest facilities The FAA proposes to mandate the installation of different categories of

flight crew facilities and regulate flight durations based on the type of

facility installed.  The effect of the proposal would be to limit or prohibit

the types of routes that may be legally operated under existing

regulations.     

Schedule reliability The FAA would require airlines to adjust crew duty periods within 60

days if actual duty periods exceeded scheduled duty periods more than

5% of the time any scheduled flight duty period exceeds the schedule

20% of the time.    This would be an entirely new requirement which

would render certain existing flight schedules impermissible.

Reserve pilots The FAA would require the use of a new system of scheduling pilots for

reserve duty during which they are ‘on call’ to substitute for crew unable

to fly due to illness, missed connection, or other reason.   The proposal

would create different categories of reserve duty such as

“airport/standby, short‐call, and long‐call” which would be new.