Paul B. Rogers

Stephen J.H. Véronneau

Connie L. Peterman

James E. Whinnery

Estrella M. Forster

Civil Aerospace Medical Institute

Federal Aviation Administration

Oklahoma City, OK 73125

May 2009

Final Report

An Analysis of the U.S. Pilot 

Population From 1983-2005:

Evaluating the Effects of

Regulatory Change

DOT/FAA/AM-09/9

Office of Aerospace Medicine

Washington, DC 20591

OK-09-0434-JAH

Federal Aviation

Administration
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assumes no liability for the contents thereof.

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Technical Report Documentation Page 

1. Report No. 2. Government Accession No. 3. Recipient's Catalog No. 

DOT/FAA/AM-09/9 

4. Title and Subtitle 5. Report Date 

An Analysis of the U.S. Pilot Population From 1983-2005: May 2009 

Evaluating the Effects of Regulatory Change 6. Performing Organization Code 

 

7. Author(s) 8. Performing Organization Report No. 

Rogers PB, Véronneau SJH, Peterman CL, Whinnery JE, Forster EM 

9. Performing Organization Name and Address 10. Work Unit No. (TRAIS) 

FAA Civil Aerospace Medical Institute 

P.O. Box 25082 11. Contract or Grant No. 

Oklahoma City, OK 73125 

12. Sponsoring Agency name and Address 13. Type of Report and Period Covered 

Office of Aerospace Medicine 

Federal Aviation Administration 

800 Independence Ave., S.W. 

Washington, DC 20591 14. Sponsoring Agency Code 

15. Supplemental Notes 

 

16. Abstract 

Introduction. The size of the U.S. civil aviator community has been of interest to researchers, policy makers, and special 

interest groups. A strict definition for membership in the U.S. pilot population was used that was based on Scientific 

Information System principles. This approach provides methods for scientists to describe, quantify, and predict changes in 

this population over the 23-year study period. The Bioinformatics Research Team at the Civil Aerospace Medical Institute 

(CAMI) analyzed and modeled the counts of the U.S. pilot population using a segmented linear regression model. 

Methods. A dataset was constructed, based upon the methods prescribed by Scientific Information System principles of 

data construction, from 1983 to 2005. This methodology was selected since the data represent the entire population of 

pilots, rather than just a sample. Thus, the statistical results are population parameters, rather than estimates, and are not 

subject to sampling error. The airmen population was constructed and examined for each year of the study period. The 

criterion for membership of the U.S. civil pilot population is based on the medical examination that each airman must pass 

to hold a pilot certificate. A segmented linear regression model was chosen because of its flexibility in accounting for any 

policy changes that occurred over the 23-year study period.

Discussion. The CAMI Scientific Information System provided the foundation to build a segmented linear regression 

model pertaining to the counts of the U.S. civil pilot population; from these results it was possible for the first time to

explain the changing frequencies over time and make fact-based predictions concerning future population numbers. The 

capability now exists to categorize the population by gender, medical class, age, and experience over a two-decade time 

period, which may provide hints at some of the changes taking place within the aviation community as a whole. 

Conclusion. The model constructed clearly shows a decline in the overall U.S. civil aviator community. This decline is 

most evident in second-and third-class medical certificate holders. The percentage of women in the largely male-dominated 

population remained relatively stable over the study years. The age composition of both men and women changed 

substantially from the beginning of the study in 1983 to the end in 2005. Both segments of this population have grown 

significantly older. As a group, men were older than women over the study period. Therefore, when average flight time was 

calculated and categorized by medical class and gender, men were shown to have more flight experience. 

 

17. Key Words 18. Distribution Statement 

 

 

Segmented Linear Regression, U.S. Civil Pilot Population, 

Scientific Information System, Database, Bioinformatics, Data 

Analysis, Aviation Safety, Epidemiology, Gender, Medical Class, 

Age, Experience

Document is available to the public through the 

Defense Technical Information Center, Ft. Belvoir, VA 

22060; and the National Technical Information 

Service, Springfield, VA 22161 

19. Security Classif. (of this report) 20. Security Classif. (of this page) 21. No. of Pages 22. Price 

Unclassified Unclassified 23 

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized 

iii

Contents

INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

BACKGROUND. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

METHODS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

RESULTS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Population Frequencies From 1983-2005 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Population Frequencies by Gender. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Population Frequencies by Medical Class. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Population Frequencies by Age. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Population Measures of Experience. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

DISCUSSION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

CONCLUSIONS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

REFERENCES. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

An Analysis of the U.S. Pilot Population From 1983-2005: 

Evaluating the Effects of Regulatory Change

Scientific data are not taken for museum purposes; they are taken as a basis for doing something. If nothing is to be done with the 

data, then there is no use in collecting any. The ultimate purpose of taking data is to provide a basis for action or a recommendation for action. The step intermediate between the collection of data and the action is prediction.

— William Edwards Deming (1)

INTRODUCTION

The demographics of the U.S. general population, of

which the U.S. civil pilot population is a subset, have

changedconsiderablyoverthelastseveraldecades. Arethe

changes observed intheU.S. general populationreflected

in the nation’s civil aviator community? This question

interests researchers, regulators, safety policy makers,

and special-interest groups. To better examine changes

over time, a longitudinal view of the aviator population

is needed to analyzethelong rangeeffects of regulations,

policies, and changing demographics. 

Estimates of the U.S. civil pilot population’s numbers

have suggested that it has been in an overall decline in

recent decades (2). Quantifying and modeling changes

in the U.S. civil pilot population was conducted to

improve the analysis of this decline. A good statistical

model will permit researchers to describe the changes

in this population and make predictions relative to its

future characteristics. The development of this model

was based on the data available for the U.S. civil pilot

population from 1983 through 2005. A model covering

such a large span of time requires flexibility to accommodate any policy changes that have occurred that may

affect thelength of time an airman could be considered a

member of the population. For this reason, a segmented

linear regression model was developed to describe the

changes in the overall counts of the U.S. civil airman

population. 

In addition to modeling the size of the U.S. pilot

population, this study examined the trends for factors

such as gender, age, experience, and differences in the

numbers of medical certificates by class. The reasons for

changes in these characteristics within the U.S. civil pilot

population were also analyzed and discussed at length.

The data forthestudy periodwere constructed on Scientific Information System (SIS) principles, as described

in Development of an Aeromedical Scientific Information 

System for Aviation Safety (3). The primary purpose of

this system was to enhance the feasibility of aviation

epidemiological studies by increasing the accuracy of

calculating specificdiseaseprevalenceovertime. Studying

theepidemiology ofspecific diseases or conditionswithin

the U.S. civil pilot population and the corresponding accident/incident outcomes can determine if segments of

the population with these conditions are at greater risk

of an accident or incident.

BACKGROUND

Aviatorswho hold a valid medical certificate in a given

year arereferred to as active airmen. A medical certificate,

without limitations, allowed an airman to be classified an

active airman for as long as two years prior to 1996 and

up to three years after 1996. The change in the duration

of medical class is based on an FAA policy amendment

enacted in1996that increasedthelengthofthe certificate’s

validity based upon the airman’s age.

The distribution of the U.S. civil aviator community

by age, medical class,experience, and gender can further

describethedemographic changes occurringovertime. In

this study, the U.S. civil pilot population was categorized

using these variables to define how they have changed

over a 23-year period (1983 – 2005). Many researchers

haveextensively studied age and measures ofexperience,

seeking a correlation between these variables and adverse

outcomes such as accidents and incidents.

This study was not the first attempt to examine the

active airmenpopulationusingexistingelectronic aviation

records. An effort to observe the U.S. civil pilot population using the accessibleelectronic aviation records ofthe

day was done as early as 1968 by Booze (4). His attempt

to merge the existing datasets into a usable, coherent

whole was not possible given the technologies of the

time. Regarding these restrictions, Booze stated “Both

the airman certification andmedical certification systems

are automated and the obvious solution, which would

involve matching the two tape files and categorizing the

airman by rating and medical certification status, would

greatly simplify the problem. However, this solution is

not feasible due to lead/lag considerations, workload

considerations and backlogs, incompatibility of input

control data under the separate systems, differences in

the degree of automation, and further magnification of
discrepancies between thetwo tapesystems dueto input

errors.” These limitations required Booze to restrict his

study period to examining the U.S. civil pilot population over a single year from October 1, 1966 to October

1, 1967. In 1977, Booze achieved the construction of

an epidemiological database by restricting his data to a

sampling ofthe U.S. pilot population for aviation studies

but covered a limited timeframe(5). Improved computer

and database technologies have allowed the successful

linking of the numerous existing datasets into a single,

coherent database structure. The SIS, with its 23 years

of integrated longitudinal data, permitted us to examine

the active airman population year by year and construct a

segmented linear regression model describing changes in

overall numbers. The SIS liberated us from having to use

any sampling techniques or generating point estimates,

since the entire population is available for analysis. 

Age has been a controversial topic in aviation medicine for many years and has been the focus of numerous

studies. Li and Baker, in their retrospective cohort study

of professional pilots (air taxi/air carriers), reported that

aviation accident rates did not significantly change as

commercial pilots aged from theirlateforties to their late

fifties (6). They concluded that the medical certification

processeffectively screenedoutpilotsthatexperiencedthe

most age-related declines in health and cognitive ability. 

In essence, the absence of association between age and

accident rates was described as a result of the “healthy

worker effect;” those pilots not meeting minimum

standards of health are forced out of the system. They

argued that the amount of experience, in terms of number of flight hours an individual attains (5,000 – 9,999

hours), is a more likely determinant of an incident than

age. They found that pilots with less than 5,000 hours

of total flight time were twice as likely to experience an

accident and that an additional increase in cumulative

flight time beyond 10,000 hours was not associated with

any decrease in crash risk.

Booze also conducteda study of 4,491general aviation

accidents from 1974 and reported that age appears to

be a factor in accidents and that the common trend for

general aviation is increased risk of accidentwith increas-

ing age (5). Booze pointed out that greater cumulative

experience must reduce accidents, but at some point any

special benefit gained from cumulative experience is lost

and becomes a risk factor as increasing time in the air

is measured as increased exposure to the hazards of the

environment. He demonstrated this finding by showing

that more than half of all airmen with 200 hours or less

only accounted for a third of all accidents. He did note

that the one exception to this observation was for airline

pilots, who had the highest cumulativeexperience of any

group and maintained a very low accident rate. He concluded that “Risk analysis based on high age alone or in

any combination not also associated with high exposure

are somewhat inconclusive.” He then went on to say,

“The frequently observed relationships of greater risk of

accident at higher ages is seen to result from a dilution

of younger age rates by a large number in this age group

with little or no exposure.” He also adds a gender component to the age argument, noting that the data seem

to indicatethat women experiencesignificantly fewer accidentsproportionately thantheirmale counterparts. This

observation on gender has been supported morerecently

by Baker et al. (7). One of the issues acknowledged by

Booze in his research is, “A recognized difficulty in all

the studies cited has been the availability of appropriate

and accurate denominator data for rate computation.”

This is one of the primary purposes of the SIS: to provide

accurate andconsistentdenominator andnumeratordata

for any aviation epidemiological study.

A study by Bruckart examined a selection of demographics concerning the U.S. pilot population over a 20-

year period (8). He evaluated the U.S. pilot population

by categorizing pilots into ageranges and found themean

pilot population age increased from 35 to 40 years and

that the number of pilots over the age of 60 increased

five-fold over the years spanning 1968 to 1987. Since

1980, he noted, there has been a steady decline in new

pilots under the age of 35.

Age continues to be a controversial topic in aviation

medicine, as age determines how long an airman’s medical certificate is valid and also plays a key factor in the

roles an airman can perform. The “Age 60 Rule” (Title

14 of theCode of FederalRegulations(CFR) §121.383),

in place since 1960, determined that an airman cannot

perform the duties as a pilot or copilot of a commercial

passenger or cargo aircraft with ten or more passenger

seats or 7,500 payload-pounds of capacity after turning

60 years of age. The purpose of the rule was to diminish

the risk of an in-flight incapacitating medical condition

such as heart attack or stroke, which are associated with

advancing age. The primary effect of this rule was seen in

the commercial airline industry, where pilots wereforced

into retirement or different jobs once they reached 60

years of age. 

Since 1994, the FAA has sponsored studies that

examined the question of age and pilot performance,

particularly in regard to age 60. Both a database consolidation study (9) and an experimental evaluation of

pilot performance (10) yielded no conclusive evidence

that pilots aged 60 or over caused a greater proportion

of accidents or incidents. 
In a speech by then-FAAadministratorMarionBlakey

at the National Press Club in January 2007, it was announced that the age limit of 60 would be raised to 65

(11),meaning thatreaching age60wouldnolongersignal

theend of a commercial aviator’s career. Some considered

the Age 60 rule obsolete, as foreign pilots older than 60

were allowed to fly in U.S. airspace. Upon reaching 60,

many American pilots simply changed employers from a

domestic carrier to a foreign-based airline and continued

flying. Raising the limit from 60 to 65 years of age puts

the FAA in compliance with InternationalCivil Aviation

Organization (ICAO) standards. As of December 13,

2007, commercial aviatorswere allowed to fly beyond the

age of 60. That is the date when the Fair Treatment for

Experienced Pilots Act was passed, which amended the

age standard of Title 14 of the Code of Federal Regulations (CFR) §121.383(c) from 60 to 65, to be the same

as the ICAO standard (12).

METHODS

A population database was constructed based upon

the methods described by Peterman et al. (3) in Development of an Aeromedical Scientific Information System 

for Aviation Safety, covering the time frame from 1983

through 2005. The electronic medical records, collected

on all airmen, are hosted at the Civil Aerospace Medical

Institute (CAMI) and served as the foundation for this

unique data construct. The primary purpose of using

Scientific Information System principles to construct

study datasets was to enhance aviation epidemiological

research; this is what makes the subject database distinct

from a typical management information system (MIS). 

The database consisted of a table for each year under

study, populated with therecords ofeach individual who

qualified as an active airman for that year in the U.S. 

civil pilot population. The criterion for membership in

the U.S. civil pilot population is based on the medical

examination that each airman must pass to exercise the

privileges of a pilot certificate. 

A member of the population of U.S. pilots was defined originally as one who passed a flight physical from

a certified Aviation Medical Examiner (AME), leading

to a third-, second-, or first-class medical certificate. The

level of medical certificate, the year it was earned, and

the age of the airman at the time of the medical exam

determines the length of time the airman is qualified to

remain in the population. Within the time frame of this

study, an airman can remain in the population for aslong

as three years, based upon a single exam. Disqualifying

conditions andthedifferenttypes anddurationofmedical

certificates are described intheGuide for Aviation Medical 

Examiners (13). 

Medical certificates have varying durations:

A first-class airman medical certificate is required to

exercisetheprivilegesof anairlinetransportpilot(ATP)

certificate. This certificate is valid fortheremainder of

the month of issuance, plus six months, for activities

requiring a first-class medical certificate. After that

time, the certificate is valid for another six months for

activities requiring a second-class medical certificate,

plus an additional 12 to 24 months for activities requiring a third-classmedical certificate, depending on

the age of the pilot at the time of the exam. 

A second-class airman medical certificate is required

for commercial, non-airline duties(e.g., for crop dusters or corporate pilots) and is valid for the remainder

of the month of issuance, plus one year. After that

time, the certificate is valid for an additional 12 to 24

months for activities requiring a third-class medical

certificate, depending on the age of the pilot at the

time of the exam. Those exercising the privileges of a

•

•

Table I. Duration of Medical Certificates by Class 

Prior to September 16, 1996 

Certificate Class First Second Third 

Privilege ATP Commercial Private 

Duration 6 Months + 1 Year + 2 Years +

+ The remainder of the month of issuance

Age

Post-September 16, 1996, Amendment  40 < 40 

Certificate Class First Second Third Third 

Privilege ATP Commercial Private Private 

Duration 6 Months + 1 Year + 2 Years + 3 Years + 

+ The remainder of the month of issuance 
flightengineer certificate, a flightnavigator certificate,

or acting as an air traffic control tower operator must

hold a second-class airman medical certificate. 

A third-class airman medical certificate is required to

exercise the privileges of a private pilot, recreational

pilot, or a student pilot. This certificate is valid for

the remainder of the month of issuance, plus three

years for pilots under age 40, or two years for pilots

40 and over. Prior to September 16, 1996, before the

FAA revised 14 CFR, §61.23, the third-class medical

certificate was valid for two years, regardless of age

(Table I). Individuals were defined as active during a

specific year aslong astheirmedical certificatewas valid

for at least one month of that year. Theexact length of

time an airman was considered active was captured in

a variable calledMonths Contributed,whichrecordsthe

number of months in the current year that the airman

was considered an active member of the U.S. civil

airman population (3). On 24 July, 2008, the durations of first- and third-class medical certificates were

extended to one and five years, respectively, for pilots

under the age of 40 (14). This study cannot research

the effects of those changes until new data, generated

under the 2008 policy changes, are available.

The airmen records within the SIS were, in turn,

linked to other datasets such as the NationalTransportation Safety Board (NTSB), the FAA Accident Incident

Database System (AIDS), and the FAA Comprehensive

Airman Information System (CAIS). The Structured

Query Language (SQL) scripts and the order of their

execution in building and defining the resulting airman

population within the SIS is referred to as the Active 

Airmen Algorithm (3). The data in the SIS represent the

entire population of pilots, rather than just a sample. 

Thus, the statistical results are population parameters,

rather than statistical estimates, and are not subject to

sampling error (15).

The variables of interest in ourstudywere age, gender,

medical class, measures ofexperience, and the overallsize

of the U.S. civil pilot population. Each of these will be

examined to determine how they have changed over the

study period. Some of these changes are due to policy

decisions; others are due to changing demographics,

while others can be attributed to fluctuations in the

aviation industry.

In building the dataset and observing the number of

medicalexams conductedperyear,data anomaliesforyears

1986, 1989, and 1993wererevealed, asseen by the “dips”

in Figure 1. The 1986 anomaly was first observed during

the consolidated database portion of the Age 60 project

in 1994 and was omitted in the calculation of accident

rates for that study (9). There is no explanation for the

•

missing records during the years 1986 and 1993. It was

deemed thatenough recordsremained in 1986 and 1993

to be included in the analysis of age and active aviator

counts by year in our study, as there were not enough

missing records to significantly affect the annual median

age or the coefficient of determination in our segmented

linear regression model for aviator counts. 

The 1989 anomaly was due to technical issues in the

electronic systemsoftheday. Forexample,dataweremanually entered into these systems, and only pilot medicals

that required little medical review (those from primarily

younger applicants) were forwarded for electronic submission. This approach resulted in approximately 75%

of the 1989 electronic exams to go missing in the dataset, an anomaly severe enough to introduce significant

bias into our model regarding the counts and the ages

of the U.S. civil pilot population. When we examined

the median age for only those airmen who had a flight

physical in a given year, we confirmed that the surviv-

ing exams for year 1989 are from younger pilots (Figure

2). The length of active airman membership for each

individual was calculated based on the date of their most

recently passed medical examination that was required

to exercise the privileges of a pilot certificate. An airman

who passed his/her aviation 1989 medical examination

without limitations was qualified to remain in the active

airmen population for the remainder of the month issued, plus two years, before another exam was required. 

The electronic medical records from 1989 were used

to determine which airmen were active for years 1989,

1990, and 1991. Therefore, we considered thesummary

statistics for age and overall counts to be biased for these

three years due to the way our active pilot population

was defined in the SIS dataset.

The datawere modeled using Statistical Analysis Software (SAS) version 9.1.3 and Insightful Miner version

7.0; the database scripts were written in Transact-SQL

and Procedural Language SQL (PL/SQL) to accommodate both the Microsoft SQL Server 2000 and Oracle

version 8.1.7 platforms. Graphs were produced in the

SAS, Insightful Miner, and Microsoft Excel software

packages. 

Adjusting forthe knowndata anomalies, amathematical model was constructed to explain, describe, and predict the size of the U.S. civil pilot population. Statistical

significance was defined at an alpha of 0.05 (α = 0.05). 

We also examined theresults in terms of age, gender, and

measures of experience to identify trends over time.

Added to the difficulties posed by the data anomalies

werethe problems introduced by policy changes affecting

eligibility in our defined population using the active airman algorithm. This is why our model was based upon a

segmented regression analysis involving interrupted time
0

100,000

200,000

300,000

400,000

500,000

600,000

19831984198519861987198819891990199119921993199419951996199719981999200020012002200320042005

Year

Number

Figure 1. Number of Medical Exams per Year. 

30

32

34

36

38

40

42

44

46

48

19831984198519861987198819891990199119921993199419951996199719981999200020012002200320042005

Year

Age

Figure 2. Median Age (in Years) of Airmen Who Received a Flight Physical, 1983-2005. 
series. The model must be able to compensate for these

policy changes to accurately predict their effects upon

the size of the U.S. civil airman population.

RESULTS

Population Frequencies From 1983-2005

Observing thefrequencies oftheU.S. civil pilot population via the active airmen algorithm tells us a great deal

about the size and demographics of this population and

how it has changed over time. The SIS data revealed the

1989 electronic medical records anomaly, as well as the

effects of the rule change in 1996 that significantly affected membership in our population. The data anomaly

caused by the lack of medical exams for the year 1989

created a dip in our overall active pilot population from

1989 through 1991 (Figure 3). For the purposes of our

analysis and modeling efforts these years were treated as

being biased in regard to overall counts and age.

InSeptemberof1996, a rule changeextendedthelength

of the medical certificates for people under the age of 40. 

Pilots not yet 40 at the time of their medical exams were

certified for amaximumofthree years, as opposed to two. 

Airmen who had been issued a medical certificate in late

1996 or 1997 normally would have been dropped from

our activepopulationunlessthey renewedtheir certificate,

but they were allowed to remain an additional year in

the population due to this policy change, resulting in an

artificially increasing trend in overall numbersfrom1999

through 2000. The population, as defined by our active

airmen algorithm, appeared to decline when the initial

impact of the policy change on the population was over,

beginning in 2001. However, even with the rule change

that allowed younger pilots less than 40 years of age to

remain an extra year as members of our defined pilot

population, the number of pilots declined over the 23-

yearperiod(1983-2005)by approximately200,000. Had

the rule not changed in 1996, we would have observed a

largerdisparitybetween1983 and2005. Thus,the overall

numbers of the U.S. civil pilot population is declining. 

This finding differs from Booze’s 1968 finding, that the

U.S. civil pilot population was on the increase (4). This

is an indication that the industry has gonethrough deepseated changes over the last 40 years.

We modeled the frequencies of our pilot population

to extrapolate changes and predictthe population’ssize at

future points. To accomplish this, we accounted for the

impact of the 1996 policy change regarding the length

of time an airman can retain a medical certificate. This

change inpolicyaffectedthesizeofthepopulation,starting

in 1999, the year we demonstrated the displacement in

frequencies. Taking this policy change into account, we

Figure 3. Number of Active Airman per Year. Figure 3. Number of Active Airman per Year.
modeled the frequencies using segmented linear regression involving interrupted times series analysis (16). The

model accounted for the policy changes and still allowed

testing of our data for a linear relationship. The model

initially took the form:

Y = β0 + β1 * (time) + β2 * (policy change) + β3 * (time 

after policy change) + error term

In the model, time represents any of the 23 years of

the study. The policy change is an indicator variable that

takes a value of 0 for years before 1999 and 1 in 1999

and beyond. The time after policy change represents the

number of years post-policy change(1999). Theerror term

represents the random variability that our model could

not explain. The regression coefficient β0 represents the

y-intercept; β1 is the slope of the population over time

before the effect of the policy change. The regression

coefficient β2 represents the offset at the point at which

the policy change took effect. That is, the difference in

numbers causedby thepolicy change intheU.S. civilpilot

populationfrom1999forward. Theregressioncoefficient

β3 is similar to β1 in that it represents the slope in the

population over time but after the year 1999; therefore,

we were able to determine if there was a statistically

significant change in the overall slope of the model after

the policy change took effect.

Modeling the frequencies of the U.S. pilot population using segmented regression analysis and omitting

the biased years (1989, 1990, and 1991) generated the

results in Table II.

The p-value for β3 was greater than 0.05; therefore,

it was considered statistically insignificant, meaning that

the rate of decline did not change significantly after the

year 1999. Whenthis insignificanttermwasdropped,the

final most parsimonious regression model became:

Y = β0 + β1 * (time) + β2 * (policy change) + error 

term

The model was considered a second time using only

the significant terms to generate the results for the parsimonious model (Table III).

Giventhisresult, our predicted regressionmodel looks

like Figure 4, where our equation:

Y = β0 + β1 * (time) + β2 * (policy change) + error 

term 

becomes: Ŷ= 25,136,096.54 + (-12,238.28)(time) + 

(85,692.80)( policy change)

Notethat inTableIII, our coefficient of determination

(R-square) is0.913,whichmeansthemodelhas accounted

for 91% of the variability in the dataset. Based on this

model, we calculated where the population will be in

the future, if existing conditions hold. InTable IV it was

estimated how largethe active airman population would

be through 2008, with a 95% confidence band.

1

Figure 5 represents the predicted values from the

parsimonious regression model. The grey lines indicate

the 95% confidence band for the predicted population

values. From this regression model, we concluded that

the 1996 policy change, which was designed to allow

younger airmen to keep a valid medical certificate for a

longer period of time, did not change the rate of decline

ofthe active pilot population. Ifthiswerethe finalstate of

our model, then Figure 5 would represent our predicted

1

Hand calculations may differ slightly from those estimated by SAS

since the regression coefficients are computed to only two significant

figures.

Table II. Full Segmented Regression Model 

Coefficient Standard Error t-statistic p-value R-square

Intercept ȕ0 26054379.92 2008185.039 12.97 <.0001 0.926 

Baseline Trend ȕ1 -12699.69 1008.823 -12.59 <.0001

Level Change ȕ2 66885.70 18081.045 3.70 0.0019

Trend Change ȕ3 6014.72 3642.740 1.65 0.1182

Table III. Parsimonious Segmented Regression Model 

Coefficient Standard Error t-statistic p-value R-square

Intercept ȕ0 25136096.54 2025246.102 12.41 <.0001 0.913

Baseline Trend ȕ1 -12238.38 1017.393 -12.03 <.0001

Level Change ȕ2 85692.80 14738.029 5.81 <.0001
Table IV. Population Projections With Upper (UCL) and Lower (LCL) Confidence 

Intervals Through 2008 

Year Population Size LCL UCL

2006 671595 653874 689315 

2007 659356 640502 678211 

2008 647118 626964 667272 

Figure 4. Parsimonious Regression Model. 
valuesthrough 2015, but thereweremore policy changes

in 2008 that are not covered by our current dataset. These

policies also affected the duration of medical certificates

(14). Additional data are needed to allow the modeling

of the regulatory changes implemented in 2008 and to

finalize our regression model. 

Population Frequencies by Gender

When gender was considered, we found that women

comprise a small percentage of the active airmen population, and this percentageremains fairly constant over the

23-year interval (Figure 6). The percentage of women

ranges from 6.4% to 7% over this period with their

greatest numbers at the start of the study period in 1983

(58,847). At theend of thestudy, in 2005, women numbered 42,536. Thelowest number of women occurred in

1990 at 40,327, but this is one of the years affected by

the bias caused by the missing medical records; the next

lowest year occurred in 1998 at 41,063.

To better view the variation in the numbers of female

pilots over the 23-years of the study, Figure 7 shows the

same graph of women from Figure 6 on a condensed

scale. The same data anomalies and policy changes that

affected the number of active airmen per year (Fig. 3)

are also evident in Figure 7.

Population Frequencies by Medical Class

Medical class is a dynamic variable that can change

annually for any individual airman. Airmen with a firstclass certificate can have that certificate become secondclass, and it, in turn, can drop to a third-class certificate

if the airman fails to renew it as required for each class. 

A third-class certificate, if not renewed prior to its expiration date, leaves the airman without a valid medical

certificate and legally unable to fly.

2 To gain an accurate

understanding of the distribution of the three medical

classes, a constant point in the year was chosen, and an

algorithm was developed for determining the current

medical class held by all individuals at that time; this

approach led to what was referred to as Effective Class. 

Effective Class is essentially the point prevalence of the

 The exception to this is the rule regarding Sport Pilots, which was

enacted in 1994 under 14 CFR §61.23.

Figure 5. Parsimonious Regression Line With 95% Confidence Band.
10

Figure 6. Active Airman per Year, by Gender. 

Figure 7. Active Female Aviators, by Year. 
11

three classes of medical certificates. For this study, the

last day of each year was used to calculate this variable. 

Considering the prevalence of medical certificates (Fig. 

8) over the 23-year span of this study reveals a tale of

contrasts.

There were more third-class medical certificate holders than any other, but those numbers were in decline. 

Second-class medical certificate holders numbered less

than half that of third-class medical certificate holders,

andthey toowere indecline. First-classmedical certificate

holders initially numbered less than either second- or

third-class medical certificate holders but were generally

increasing and were close to overtaking second-class

medical certificate holders in recent years, in terms of

overall numbers.

Population Frequencies by Age

The population dataset has the recorded age of the

pilots reported at the time of their medical exam, which

makes it a static variable, just as it was for Bruckart’s

study (8). An algorithm was used that incorporated the

age of the pilot as a dynamic variable and computed the

exact age of every individual on the last day of each year. 

Figure 2 displayed the median age of airmen who had

successfully passed a physical in the given year. Figure 9

shows the median age of all airmen considered active in

the given year. When we view the median age of active

airmen by year (Fig. 9), we see that age is increasing year

to year, just as Bruckart observed. Thescarcity of medical

records for 1989 is reflected in the median age for that

year; those numbers are overwhelmed by airmen who

had medical exams from previous years and were still

considered active in 1989. The median age ranged from

37 in 1983 to 45 in 2005. Clearly, the U.S. civil airman

population is growing older.

When our population was categorized by gender, it

seemed the trend affects men and women alike, as seen

in Figure 10.

It is evident that the median age increased for both

men and women from 1983 through 2005. For men, the

minimummedian agewas 37 in 1983, and themaximum

was 45 in 2005. The women’s minimum median age was

32 in 1983; the maximum was 38 in 1998, 1999, and

2005. Althoughwomen, as a group,were gradually aging,

they were still younger than male aviators. The explanation for why female aviators, as a group, were younger

than their male counterparts remains unknown.

To considerhowthepopulationchangedregarding the

different age categories, refer to the population pyramids

in Figures 11 and 12. The graphs display how the age

distribution for men and women changed from the start

and the last year of the study.

3

Population Measures of Experience

Measuring levels of experience for individual airmen

involves many factors. For the purposes of this study, we

observedthetotalnumberofflighthoursreportedby each

airman on their most recent medical exam and classified

them by gender, age, and class of medical certificate. At

the time of their medical exam, airmen were asked how

many total hours they had accumulated. The answer

was assumed to be an estimate or an approximation by

the airman based upon when they last tallied the flight

hours in their logbook.

Weexpected that age and reported flight hoursshould

be highly correlated. That is, the longer an airman had

maintained a medical certificate, the more accumulated

total flight time would be acquired. Since female aviators tended to be younger than their male counterparts,

we categorized total flight time by gender. Breaking

our analysis down by gender revealed that, since female

pilots were younger than their male counterparts, their

accumulated flight time was lower (Figure 13). The fact

that the few medical records for 1989 belonged primar-

ily to younger, inexperienced aviators is reflected in the

average hours calculated for that year. 

Wefurthersubdivided our analysis by gender and class

ofmedicalexam. Figures 14, 15, and 16 showthe average

hours reported at the time of the airman’s medical exam

by these variables. 

The trends shown in Figures 14, 15, and 16 were

very similar, suggesting that all female medical classes,

as a group, tended to be younger than their male counterparts, and, therefore, have accumulated fewer flight

hours than men. 

DISCUSSION

One of the purposes of this study was to develop a

method to track changes in the counts of various data

categoriesthatmay be of interestto aviation safety regulators, accident investigators, and researchers. When there

are factors that change during the course of a study, it is

importanttotry totakethese changes intoaccount. Inthis

study, the only regulatory change occurred in 1996, the

effect ofwhichwasdelayeduntil1999. Otherresearchers,

Wagner et al., for example, have found that regulatory

effects upon purchasing patterns, adverseevents, or other

measurable activities can befollowedwith segmented linearregressionmodeling. Recentexamples include changes

3 The number of individuals holding medical certificates under the

age of 16 is not displayed since they were few in number.
12

Figure 8. Effective Medical Class, by Gender. 
13

Figure 9. Median Age of Active Airmen at End of Year. 

Figure 10. Median Age of Active Airmen at End of Year, by Gender (M = Male and F = Female). 
14

Figure 11. Population Pyramid of Female Pilots, by Age Category and Year. 

Figure 12. Population Pyramid of Male Pilots, by Age Category and Year. 
15

Figure 13. Average Hours Reported at Time of Medical, by Gender (M = Male and F = Female). 

Figure 14. Average Hours Reported at Time of Third-Class Medical, by Gender. 
16

Figure 15. Average Hours Reported at Time of Second-Class Medical, by Gender. 

Figure 16. Average Hours Reported at Time of First-Class Medical, by Gender. 
17

in insurancepolicies andmedicationuse(16). Withinour

study, we noted that after theregulatory change of 1996,

it was immediately apparent that the otherwise straight

line marking the counts of active pilots had an upward

bump in 1999. If one were to try to make predictions

using the entire data series from 1983 to 2005, there

would be unexplained variationand error intheestimates

predicted by a standard regression model. 

Segmented linear regression allows the modeling of

before and after changesto examine and predict, using all

of the data available, leading to more accurate estimates. 

This type of model can accommodate multiple changes

since more than one segment can be employed in the

analysis. It is important to note that the technique does

not auto-identify the segments; it is up to the researcher

to closely correlate the known factors, such as regulatory

change, and include an indicator variable(s) to allow the

segmentedregressiontoproceed. One variableestablishes

the presence or absence of a segment(s), and another variable marks the years after the change occurs. Thus, for

our model, zeros were used to precede the change, and

ones were used to indicatethat the change had occurred. 

Similarly, afterthe change, another indicator variablewas

used in increments of one for each year after the effect

of the regulatory change (1, 2, 3, 4, and so on). These

indicator variables create structure that linear regression

models can useto account for the initialeffect of thefactor and the possible prolonged effect over the years after

the factor change. In this study, only the initial effect of

the regulatory change was statistically significant. When

newdata become availablefrom2008 forward,weexpect

to see a jump in the number of active pilots, similar to

what was seen in 1999, due to the increased duration of

first- and third-class medical certificates. In other words,

2008will becomethenext yearwe place a segmentfor our

regression model. With this new segment, we can once

again test to determine if the new policies have any effect

on the rate of decline for our population. The benefit of

oursegmentedlinearregressionmodel isthat anynumber

and type of regulatory change that affects the size of the

population can be incorporated in the analysis.

Afterthe1996regulatory changetoPart61(extending

the duration of validity of a third-class medical certificate

for the under age 40 group), there are two possible explanations for the effect upon the count of active pilots. 

The first is that more pilots may have become interested

in obtaining a medical certificate due to the attention

being paid to the regulatory change. From the counts of

the medical exams submitted (Fig. 1), it is possible to

see that there was a temporary increase in the number of

applicants. Thesecond explanation is that theregulatory

change had the direct effect of elevating the number of

active pilots by increasing the validity period, beginning

in exams submitted in 1996 and reflected in 1999 (Fig. 

3). This threshold effect means, in modeling terms, that

there would be a different Y-intercept for post-1999 data

compared to the pre-1999 data. While the overall effect

was to increase the number of active pilots by ~86,000,

most of this increase came as a result of the extension

of the certificates of existing pilots, and a much smaller

contribution (whichwaned asthe yearswent by)was due

to more pilots applying for a medical certificate.

The rate of loss of pilots before the regulatory change

in 1996 and the rate of loss after the effect of the regulatory change appears in 1999 is not statistically different. That is, there was a statistically significant change

in the number of pilots after the initial effects of the

regulatory change, but the slopes of the model (rate of

loss) remained the same. In practical terms, the rate at

which pilots are leaving the medical certification system

was unaffected after the regulatory change. It is likely

that the change in the regulation was sought to increase

the number of pilots by decreasing the frequency of the

mandatorymedicalexamfor younger pilots,saving them

time and expense without compromising aviation safety. 

The exam frequency was decreased, but the number of

pilots not maintaining a medical certificate continued its

multi-year decline. Operators of gliders, ultralights, balloons, and, morerecently, sport aircraft, do not require a

medical certificateto fly; increases in the number of such

pilots would not show up in our study of active airmen,

which includes only pilotswith validmedical certificates. 

Perhaps pilots who previously might have held a medical certificate and flew in general aviation activities are

changing to perform flying activities that do not require

a medical certificate.

There may be other impediments to flying such as the

increasing costs of aircraft, fuel, maintenance, insurance,

landing and hangarfees, all ofwhichmay be contributing

to the decreasing number of active pilots. This study did

not have access to pilot surveys or other data that might

beexamined to explain the prolonged trend of decreasing

numbers of active pilots.

CONCLUSIONS

Based on the results of this study, the U.S. civil aviator population is indisputably in an overall decline. The

segmented linear regression model developed to help

describe and quantify the numbers of the U.S. civil pilot

population overthe 23 years of thestudy periodwas used

to project future numbers through the year 2008 (Table

IV). If current conditions hold, the model predicts that

the U.S. civil pilot population will continue to decline

and fall to new lows. The model displays the declining

numbers, alongwiththeeffectsofregulationsthat changed
18

the length of validity of a medical certificate for aviators

underthe age of 40. Themodelwill bemodified to reflect

future policy decisions, both in 2008 and beyond, when

data concerning those years are available.

When we categorized the population by Effective

Class over the 23 years of the study, we are able to see

third- and second-class medical certificate holders are

in decline. Holders of first-class medical certificates

are increasing. These trends in medical class may be

representative of changes within both the general and

commercial aviation industries. Perhaps there is a wan-

ing number of the general public interested in general

aviation, for which only a third-class medical certificate

is needed. Non-airline commercial operations may also

be diminishing, which would explain the decreasing

numbersofsecond-classmedical certificateholders. More

first officers may beseeking first-class medical certificates

to be able to upgrade to captain status or may be fulfill-

ing requirements from their companies that they hold

higher medical certificates than required by the federal

regulations. Finally, commercial operations requiring a

first-class medical certificate such as airline operations

may be expanding, which is why we have observed an

increase in this category. Our findings suggest that one

ormore general aviationcomponents are declining,while

air carrier and other commercial operations requiring a

first-class medical certificate are growing (2).

In our initial look at the parameters of the U.S. civil

pilot population, we have definitiveevidence of an aging

population that is shrinking in overall numbers. This

population, althoughgradually growing older, confirmed

that women, as a group, are generally younger than their

male counterparts. We found that female aviators, althougha very smallpartoftheoverallpopulation,differed

in median age by as much as seven years from men by

the year 2005. The data suggest that in the younger age

categories, there are proportionately more women than

men. Within the older age categories, the proportion of

women drops off considerably. The reason for this age

difference between the genders is unknown. 

Measures of experience show that total accumulated

flight time, asreported by aviators at thetime of their last

medical exam, is greatest for holders of a first-, second-,

and third-class medical certificate, respectively. These

differences may be due to the involvement of holders of

first- and second-class medical certificates in commercial

aviation. Pilots involved incommercial activitieswouldbe

expected to earn more flight time than infrequent fliers. 

It is not surprising that the greatest average flight time

was held by those with a first-class medical certificate. 

This group of aviators is composed largely of professional

pilots that log many hours of flight time annually. The

data suggests that women logged hours at the same rate

as men, but since women, as a group, tend to be younger

than their male counterparts, the accumulated female

flightexperience, asmeasured intotalflighttime,wasless. 

This trend was also observed for the different classes of

medical certificates. Total annual flight times for women

lagged behind those reported by men in all categories of

medical certificates because women pilots, on average,

are younger than their male counterparts and generally

have fewer years of aviation experience. 

As people are living longer and with the aging of the

Baby Boomers, the U.S. general population is becoming

older (17). Increasing age is one of the risk factors for

many chronicdiseases. Otherfactors includetobaccouse,

lack of physical activity, and poor nutrition. Therefore,

the incidence ofmany chronicdiseases intheU.S. general

population is increasing. As bettertreatmentsfor chronic

disease and disability are developed, we would expect

an increase in life expectancy. This, in turn, will lead to

a corresponding increase in the prevalence of chronic

disease and disability. U.S. demographic projections

indicate that the proportion of people 65 and older will

swell from 12% in 1995 to 20% by 2050 (18). Since

the U.S. civil pilot population is a subset of the overall

U.S population, we would expect to seesimilar results in

terms of increasing prevalence and incidence of chronic

disease. Future epidemiological studies will target some

of these chronic conditions, along with their prevalence

and effects within the aviation community to include

flight safety.

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