ECONOMICS OF MARITIME DISASTERS ESSAYS ON THE TITANIC … · examine the sinking‘s of the R.M.S....

ECONOMICS OF MARITIME DISASTERS:

ESSAYS ON THE TITANIC AND LUSITANIA

David A. Savage

BIT BBus

Primary Supervisor: Professor Benno Torgler

Submitted in fulfilment of the requirements for the degree of

Master of Business (Research)

Centre for Learning Innovation

Faculty of Education

Queensland University of Technology

Submitted December, 2009

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Economics of Maritime Disasters: Essays on the Titanic and Lusitania i

Keywords

Altruism and Self-Interest; Decision under Pressure; Excess Demand; Disasters; Life

and Death; Lusitania; Quasi-Natural Experiment; Social Norms; Survival of the

Fittest; Titanic; Tragic Events; Women and Children First

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ii Economics of Maritime Disasters: Essays on the Titanic and Lusitania

Abstract

This work seeks to fill some of the gap existing in the economics and behavioural

economics literature pertaining to the decision making process of individuals under

extreme environmental situations (life and death events). These essays specifically

examine the sinking‘s of the R.M.S. Titanic, on 14th

April of 1912, and the R.M.S.

Lusitania, on 7th

May 1915, using econometric (multivariate) analysis techniques.

The results show that even under extreme life and death conditions, social norms

matter and are reflected in the survival probabilities of individuals onboard the

Titanic. However, results from the comparative analysis of the Titanic and Lusitania

show that social norms take time to organise and be effective. In the presence of such

time constraints, the traditional ―homo economicus‖ model of individual behaviour

becomes evident as a survival of the fittest competition.

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Economics of Maritime Disasters: Essays on the Titanic and Lusitania iii

Table of Contents

Keywords .................................................................................................................................................i

Abstract .................................................................................................................................................. ii

Table of Contents .................................................................................................................................. iii

List of Tables........................................................................................................................................... v

Dedication .............................................................................................................................................. vi

Statement of Original Authorship ......................................................................................................... vii

Acknowledgments ............................................................................................................................... viii

CHAPTER 1: INTRODUCTION ........................................................................................................ 1

CHAPTER 2: THEORETICAL BACKGROUND ............................................................................ 5

2.1 Panic ............................................................................................................................................ 6

2.2 Fight and Flight ............................................................................................................................ 9

2.3 Social and Moral Norms ............................................................................................................ 11

2.4 Queuing Behavior ...................................................................................................................... 13

CHAPTER 3: METHODOLOGICAL BACKGROUND ............................................................... 17

3.1 Experimentation: laboratory vs Field ......................................................................................... 17

3.2 Essays ......................................................................................................................................... 19

CHAPTER 4: NOBLESSE OBLIGE? DETERMINANTS OF SURVIVAL IN A LIFE AND

DEATH SITUATION ......................................................................................................................... 23

4.1 Introduction ................................................................................................................................ 24

4.2 Theoretical Background ............................................................................................................. 26

4.3 Empirical Results ....................................................................................................................... 31

4.4 Conclusions ................................................................................................................................ 38

4.5 Table .......................................................................................................................................... 41

CHAPTER 5: SURVIVING THE TITANIC: ECONOMIC, NATURAL AND SOCIAL

DETERMINANTS .............................................................................................................................. 43

5.1 Introduction ................................................................................................................................ 44

5.2 Theoretical Hypotheses about who is Expected to be Saved ..................................................... 47 5.2.1 Economic Determinants .................................................................................................. 50 5.2.2 Natural Determinants ...................................................................................................... 52 5.2.3 Social Determinants ........................................................................................................ 55

5.3 The Data ..................................................................................................................................... 57

5.4 Econometric Estimates and Results............................................................................................ 59 5.4.1 Testing Economic Determinants ..................................................................................... 59 5.4.2 Testing Natural Determinants ......................................................................................... 61 5.4.3 Testing Social Determinants ........................................................................................... 61

5.5 Conclusions ................................................................................................................................ 65

5.6 Table .......................................................................................................................................... 66

CHAPTER 6: SHOULD I STAY OR SHOULD I GO NOW? AND TIME AND TIDE:

CONSTRAINED ALTRUISM? ......................................................................................................... 67

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iv Economics of Maritime Disasters: Essays on the Titanic and Lusitania

6.1 Introduction ................................................................................................................................ 68

6.2 Data and Model .......................................................................................................................... 72

6.3 Results ........................................................................................................................................ 74 6.3.1 Is self-interest dominant in the Lusitania case? .............................................................. 74 6.3.2 Pro-social behaviour and social power in the Titanic? ................................................... 77

6.4 Conclusions ................................................................................................................................ 79

CHAPTER 7: CONCLUDING REMARKS .................................................................................... 81

BIBLIOGRAPHY ............................................................................................................................... 85

APPENDICES 103 Appendix A: Publications ........................................................................................................ 103

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Economics of Maritime Disasters: Essays on the Titanic and Lusitania v

List of Tables

Table: 4.1 Survival Probability and Pro-Social Behaviour ...................................... 35

Table: 4.2 Survival of Women ................................................................................. 38

Table: 4.3 Mean Values ............................................................................................ 41

Table: 5.1 Economic and Natural Determinants ...................................................... 60

Table: 5.2 Social Determinants of Survival ............................................................. 62

Table: 5.3 Robustness Tests Including Interaction Terms........................................ 64

Table: 5.4 Summary Statistics .................................................................................. 66

Table: 6.1 Lusitania vs. Titanic ................................................................................ 70

Table: 6.2 Determinants of Passengers‘ Survival on Lusitania ............................... 76

Table: 6.3 Determinants of Passengers‘ Survival on Titanic ................................... 78

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vi Economics of Maritime Disasters: Essays on the Titanic and Lusitania

Dedication

I would like to dedicate this work to the victims and their families, who have died

tragically in disasters. Specifically, I wish to acknowledge the victims, families and

loved ones of those who lost their lives onboard the Titanic and Lusitania, and whose

untimely passing was neither forgotten nor in vain. It is my hope that this work will

add to the understanding of human behaviour in these disasters so as to reduce future

losses of life.

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Economics of Maritime Disasters: Essays on the Titanic and Lusitania vii

Statement of Original Authorship

The work contained in this thesis has not been previously submitted to meet

requirements for an award at this or any other higher education institution. To the

best of my knowledge and belief, the thesis contains no material previously published

or written by another person except where due reference is made.

Signature: _________________________

Date: _________________________

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viii Economics of Maritime Disasters: Essays on the Titanic and Lusitania

Acknowledgments

One of the most difficult voyages an individual can undertake is that of self

discovery, there are no charts or maps to provide direction, nor are there other

explorers whose prior paths one can follow. At best one is able find a friend with

whom you can share the journey and the occasional guide to assist with course

corrections. The old idiom states that nothing of value is easily obtained, it is the

struggle and the effort that makes the prize all the better for winning. Similarly the

journey for knowledge and understanding is equally fraught with problems and

pitfalls, but this makes achievement all the sweeter. For me, these journeys have been

simultaneous, and I have indeed been fortunate to have both a dear friend and partner

with whom to share the voyage and a mentor to direct and share.

First and foremost I would like to thank my best friend and partner, Deborah Stewart.

Thank you for not only allowing me to undertake this journey but for coming along

and sharing it with me, success is meaningless without someone to share it with.

Secondly I would like to thank my supervisor, mentor, teacher and friend, Professor

Benno Torgler, and partner in crime Dr. Clevo Wilson. Without whose incessant

badgering and thinly veiled suggestions, I would never have taken up this challenge.

Thank you both from the bottom of my heart, I hope you continue to identify and

pick out the annoying students and convince them to stay and grow. And I hope that I

can follow your shining example and show future students the same level of empathy

and commitment as you both showed me. Additionally, I would like to thank

Manuela, for allowing me to ‗borrow‘ so much of your husband‘s time and being so

welcoming and friendly. I would like to include thanks to the ―Disciples‖ whose

friendship and support made the journey a much better experience. I expect that one

day the number of ―Disciples‖ will swell into a fully fledged cult, with members

spread the world over, mixing research with camaraderie.

Additionally I would like to thank the School of Economics and Finance, The Faculty

of Business and Queensland University of Technology staff. Specifically I would like

to thank the Head of School, Tim Robinson and Professor Stan Hurn for their initial

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Economics of Maritime Disasters: Essays on the Titanic and Lusitania ix

support, allowing my entry into the post graduate program when others turned me

away. To Professor Uwe Dulleck for showing me how much I really didn‘t know

then helping me understand much of it and Professor Paul Frijters for opening my

eyes and demonstrating the Dutch method of asking questions, I thank you both.

Additionally, I would like to thank my co-authors, collaborators and conference

attendees for their input and feedback on these and other works. Specifically I would

like to thank: Professor Bruno S. Frey, Professor Benno Torgler, Dr Lionel Page;

Martin Gächter, Marco Piatti, and the attendees at brown bag sessions, the Australian

Conference of Economists ‘09, the ANU/QUT cross institutional PhD workshop and

the Faculty of Business Research Colloquium.

And finally I need to thank all the very patient economists, post graduate

students and random rail commuters, to whom I have posed all the truly stupid

questions.

Chapter 1: Introduction 1

Chapter 1: Introduction

“This awful catastrophe is not the end but the beginning. History does not end so. It

is the way its chapters open.”

St. Augustine

On the morning of December 6th

1917, an unfortunate series of events and bad luck

set into motion a chain reaction that reduced the port city of Halifax to a freezing,

wet and smouldering ruin. On this morning a French freighter the Mont Blanc was

leaving the harbour laden with 2,500 tons of explosives bound for the war in Europe.

While attempting leaving the harbour an accidental collision with another vessel, the

Imo, started a fire on the French ship turning it into a floating time bomb. The

ensuing explosion, the largest in history, shattered windows up to 60 miles away and

rained down fiery molten death, killing many and setting the city ablaze. However, as

almost prophetically stated by St. Augustine, this was not the end but the beginning

of the destruction. The explosion also created a massive tidal wave that swamped the

shore, drowning many of those along the shoreline and all but destroying the port. In

a final unfortunate piece de résistance, that night Mother Nature wracked the city

with a blizzard; the final death toll from the event was 1,963 people (Prince, 1920).

Bearing witness to this event was Samuel Henry Prince, a pastor who 5 years earlier

had performed burials at sea for another world shaking disaster, the sinking of the

Titanic. Prince observed the behaviour of the citizens of Halifax and was greatly

puzzled, he commented on the ―…utter and complete social disintegration which

followed (…) Old traditional social lines were hopelessly mixed and confused (…)

Rich and poor, debutante and chambermaid, official and bellboy met for the first time

as victims of a common calamity‖ (Prince, 1920: pp 31-32). Parents did not

recognise their children, even in the morgue; individuals underwent painful surgery

with little or no anaesthetic without complaint or outcry; and the first triage station

was setup by a troupe of actor‘s not trained medical staff. From this observer‘s

perspective, nothing was as it should have been.

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2 Chapter 1: Introduction

Prince went on after these two disasters to write a PhD thesis analysing the behaviour

of people in disasters, which for the most part languished on the dusty shelves of

libraries until very recently. In the last few decades a slowly growing resurgence in

the behaviour of individuals in these disaster situations has occurred. In the wake of

the September 11 attacks on the World Trade Centre and the evacuation debacle of

Hurricane Katrina, an explosion of research attempted to understand how and why

individuals behave in disasters. Prince‘s work had returned with renewed vigour and

purpose, almost 80 years prior Prince foresaw the need for the continuance of his

research stating "This little volume on Halifax is offered as a beginning; don't let it

be the end. Knowledge will grow scientific only after the most faithful examination

of many catastrophes." (Prince, cited in Ripley 2008: pp. xi) In this work, I return to

Princes vision and begin anew, with a ―faithful‖ and detailed examination of human

behaviour during disasters using the rigour of an economics perspective. The specific

events under investigation in this work are the sinking‘s of the Titanic on April 14th

1912 and the Lusitania on May 7th

1915.

The disaster scenario is a staple of the Hollywood movie industry, when the end of

the world scenario ensures the depicted behaviour is stereotypical and matches the

traditional view, which is that of mass panic (see e.g. Armageddon (1998), 2012

(2010), War of the Worlds (1938, 1953, 2005), Deep Impact (1998) or The Day After

Tomorrow (2004)). The fictional behaviour in disasters movies closely matches that

of the traditional disaster literature, where the expected behaviour is that of mass

panic entailing some chaotic or random actions. Within this panic behaviour, movies

portray the full spectrum of emotive action, including: quiet resigned acceptance of

death; immobilized panic inaction; frenzied action without direction or coordination;

the obligatory looting and random killings; to the desperate seeking out of loved

ones. But do individuals in real world disaster situations, behave in this manner or

are the popular myths, the traditional panic literature and Hollywood completely

wrong and engaged in good hype?

This work began life as an interesting detour prior to starting my ‗real‘ research, as I

began dissecting the Titanic and observed the event for what it was, an excess of

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Chapter 1: Introduction 3

demand (for survival) market, my interest rapidly grew. Here was, an almost perfect

natural field experiment, designed for analysing the decision making process of

individuals with a shortage of supply (life boat seats) under high stakes (life or

death). The initial research examined the determinants of survival, or the factors

determining the allocation of the scarce lifeboat resource. Some of the results were

unsurprising given the common familiarity of the myth, but others were somewhat

inexplicable. Such as why, given the standard homo economicus model, did social

norms prevail when surely a survival of the fittest competition should prevail for the

allocation of such a life saving resource? This research sparked a plethora of other

questions that I suddenly needed to find answers for, unfortunately I found a rather

large gap in the economics literature. Nowhere to be found was an economic analysis

of the decision making process in extreme environments, as all the existing

experimental literature was performed under normal everyday environmental

conditions. Furthermore, the Titanic occurred almost 100 years ago, would the social

norms observed in this event still hold today? As this research was presented at

economics conferences and seminars interest in the subject material was clearly

evident, but this raised a raft of further questions. Questions ranging from cultural

norms, interdependent preferences, biological instinct, parental investment, a

plethora of questions presented themselves and demanded to be accounted for.

The initial paper received an amazing amount of attention from the world‘s

mainstream media, published in print in over 50 countries around the world, as well

as numerous broadcast media outlets (such as ABC, BBC and CBC national radio‘s).

The topic became a hotbed of discussion on blogs and discussion boards, raising

even more questions and demands for further empirical evidence. It quickly became

evident that this topic had supplanted my original research topic to become the focus

of my post graduate study. The initial discussions led me to question which of the

many theories of human behaviour were evident within this event, this inevitably led

to the second paper: Surviving the Titanic: Economic, Natural and Social

Determinants (see chapter 5). From these finding, supporting social normative

behaviour a new aspect of the research appeared. Has the behaviour of individuals

changed over time, such that if a Titanic type event happened in another time period

would the outcomes remain the same?

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4 Chapter 1: Introduction

From the questions investigations into the Lusitania (1915), Andrea Doria (1956)

and Estonia (1994) began, 4 events over the last century provided an opportunity to

examine the change in social norms. Beginning with the Lusitania, this was sunk by

a German u-boat in less than 18 minutes, on 7th May 1915, killing 1198 of the 1950

on board. Given the closeness in history between the Titanic and Lusitania and the

close demographical makeup of the ships, it became clear that the only major

difference was the time the vessels took to sink. The research resulted in: Should I

stay or Should I go Now? and Time and Tide: Constrained Altruism? (see chapter 6).

Chapter 2: Theoretical Background 5

Chapter 2: Theoretical Background

Traditionally the economist‘s sole interest in disaster analysis was to calculate the

economic costs associated with these events over the short, medium, and long term,

in line with the seminal contributions by Hirshleifer (1963) or Dacy and Kunreuther

(1969). Other fields, such as psychology and sociology, have focused on the actual

behaviour of the people involved in the disaster itself, often as a group such as a

collective action problem or mass panic (e.g. Aguirre et al. 1998; Elster 1985; Fehr et

al. 2002; Johnson 1988; Kelley et al. 1965; Mawson 1980; Quarantelli 2001; Smelser

1963). Much of this literature rejects the commonly held assumptions about

individual behaviours in these types of events, such as: the inability to act rationally

(the so-called ―disaster syndrome‖); or the reign of chaos, panic, social breakdown

and antisocial behaviour, such as: crime, looting, or exploitation (e.g. Brown 1954;

Drabek 1986; Goldthorpe 1988; Gwynne et al. 2006; der Heide 2004; Howard

1966p; Johnson 1988; Mawson 1978; Mintz 1951; Quarantelli 1972 & 2001). Indeed

it has been found using empirical analysis that morals, loyalty, respect for law and

customs, and tenets of acceptable behaviour do not instantly break down with a

disaster. This is consistent with some of the findings of the newly emerged field of

behavioural economics, which shows that people do not necessarily exploit an

opportunity presented to them when it can hurt other people. Rather, they are often

inclined to help other people. Substantial evidence has been generated that motives

such as altruism, fairness, or morality affect the behaviour of many individuals (e.g.

Becker 1974; Camerer et al. 2004; Drago & Garvey 1998; Elster 2007; Fehr &

Schmidt 1999; Frey 1997; Kahneman et al. 1986; Thaler 2000).

Utilizing a behavioural economics approach in this way can provide some new and

important insights into the disaster and behavioural literature, by using improved

structural design, up to date econometrical and experimental modelling tools.

Economics employs the scientific rigour of laboratory experimentation and

implements this expertise into the world of natural and field experimentation,

providing better, more realistic results. Moreover, economics, and more specifically

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6 Chapter 2: Theoretical Background

behavioural economics, has failed to explore the decision making process of

individuals in high cost environments (such as life and death events). At best only

some theoretical conjecture has been offered on the subject, even though a large

number of laboratory experiments have been carried out (such as ultimatum and

dictator games numerous settings). One of the problems stems from a core tenet of

decision theory, namely the rationality condition, where economists assume that all

individuals making decisions are rational and able to correctly weigh options.

However, experimentation has shown that under pressure and duress this function

can break down leading to inefficient, sub-optimal outcomes (Jamal 1984; Keinan

1987; Meichenbaum 2007; Schultz 1966; Wright 1997).

Before examining the individual behaviour on board the Titanic or Lusitania, I begin

this work by examination of some of the underlying behavioural literature, which

includes: panic; fight or flight; social norms (altruism, fairness, reciprocity retaliation

and helping behaviours); queuing behaviours and field experimentation. However,

this section will not contain a complete theoretical discussion, given that some

concepts are discussed in detail in the literature section of the papers that make up

each of the chapters. These broad concepts discussions will be examined further and

in more detail in each of the essays, in a more contextual setting. But they are

provided here as an overview of the surrounding literature on individual and groups

behaviours in extreme life and death events.

2.1 PANIC

Panic is perhaps one of the most misunderstood, misinterpreted and contradictive of

all human behaviour, the traditional and common understanding of the phenomenon

is erroneous and based on myth rather than reality. The basis of the word comes from

the ancient Greek deity ―Pan‖, who was said to be able to instil an irrational and

unfounded fear into individuals. The use of the term ‗panic‘ is extremely prolific in

disaster literature, but has conjured up as many various definitions and causes as the

depicted behaviour ranging from: any fear, flight, or uncoordinated activity (Heide,

2004); fear and/or flight behaviour that is considered inappropriate, excessive or


irrational (Mawson, 2005); a reaction involving terror, confusion and irrational

behaviour (Goldenson, 1984); to a breakdown of social ties and social order

(Johnson, 1988). The most common threads in the popular panic mythology are

irrationality and social breakdown. Traditional models state that individual behaviour

in these types of events becomes illogical, without reason, random or nonsensical and

the breakdown of social order is oft repeated. Additionally, the typical behavioural

response depicted is either: self-preservative aggression or flight (Brown, 1954;

Cannon, 1929a, 1929b); or flight towards objective safety and away from danger

(Smelser, 1963). Some of the traditional literature indicates that physical dangers are

considered to be more disturbing than other (psychological) kinds of events, such that

seeing the danger is greater than the threat of danger. Additionally, they indicate that

flight behaviour is supposedly prevented in danger situations by social control, such

that social norms and other controls regulate or constrain individual‘s natural

tendency to flee danger. Problems arise from these contradictive models, given that it

is from these traditional perceptions of behaviour that governments and other

agencies build and design disaster plans, as well as create building and evacuation

codes and provide disaster relief.

This can obviously present serious problems, as Heide (2004) states, ―Disaster

planning is only as good as the assumptions it is based upon. Unfortunately this

planning is often based upon a set of conventional beliefs that have been shown to be

inaccurate or untrue when subjected to empirical analysis…It is more efficient to

learn what people tend to do naturally in disasters and plan around that, rather than

design your plan and then expect people to conform to it.‖ (Heide 2004: p340). He

also denoted the 4 conditions that must be present for panic to occur, which include:

1) Victims perceive an immediate threat of entrapment; 2) Escape routes appear to be

rapidly closing; 3) Flight seems the only way to survive; and 4) No one is available to

help. Heide (2004) further states that because this combination of conditions is so

uncommon in disasters, panic is also rare. Mawson (2005) extended these concepts

through his investigation into mass panic and collective behaviour, which reinforced

that some of these panic conditions are also required for group flight to occur. He

states that conditions required for group flight include: 1) people believe that major

physical danger is present or imminent; and 2) that escape routes are either limited or

8


rapidly closing. He adds a caveat to this by saying that mass flight from community

disasters is uncommon and that ‗organized and altruistic behaviour is the rule‘

(Mawson, 2005: 97). Panic and flight in these traditional models of behaviour, are

representative of pure self interest or survival behaviour, where the individual flees in

order to preserve their own life, possibly even at the expense of others.

However, the social attachment model of human behaviour in disasters (see Mawson,

1978, 1980) states that maintaining proximity to the familiar (people and locations) is

a dominant behaviour. Therefore flight can be considered an anti affiliative

behaviour, thus the flight-and-affiliate behaviour depends on the degree of social

contact and the degree of danger, such that the presence of the familiar influences the

perception of danger and the measure of response, where close proximity diminishes

fear responses. ―The most extreme stresses, including drowning at sea, can be calmly

faced if the individual is not separated from his fellows … conversely being alone in

an unfamiliar environment or with strangers heightens the response to stress and

increase the probability of flight‖ (Mawson 2005). Here we observe that contrary to

the expected social breakdown or irrational behaviour we observe collective

behaviour and an affiliative response to danger. This point is neatly concluded by

Quarantelli (2001), that despite major evidence to the contrary, panic remains part of

the popular imagination and continues to be evoked as part of disaster management

plans worldwide. This flight and affiliate behaviour is more in line with socially

normative behaviour, where individuals run to those they have the strongest social

bond (family, friends etc.).

This affiliative behaviour was observable in the investigation by Aguirre et al. (1998)

into group responses during the 1993 World Trade Centre (WTC). After the

explosion inhabitants of the WTC experienced numerous after effects including:

power failure (computers and lighting); phone lines were cut; and the majority of

people felt the explosion (which created a crater approximately 120m wide and 7

stories deep). Over 75% of the survey respondents indicated that they knew

something serious had occurred but only 8.7% of the groups1 surveyed chose to act

1 The vast majority of the groups were formed by persons who knew each other prior to the event.


immediately and evacuate. All the remaining groups delayed evacuation, 63% of the

groups sought additional information2 before committing to a course of action, this

information gathering cost several minutes. The investigation also found that larger

groups took much longer to organize, on average over 6.7 minutes longer before

beginning evacuate, additionally the research found that familiarity with the WTC

was not a significant factor for evacuation. Additionally Rosenkoetter et al. (2007)

has investigated what are the factors that determine the evacuation of the elderly and

supports the fight or flight vs. social norms arguments. The results show that living

alone, gender (females are more sensitive to danger) and fear for their own safety are

the best predictors for evacuation. This holds with the concept that individuals are

more prone to flight if they are not part of a social group and are more willing to do

so if they fear for their own safety.

Thus, research has shown that the supposed panic reaction of the population is almost

exclusively a mass media creation, and that pro-social rather than anti-social

behaviours dominate in such situations (see Johnson, 1988; Johnson et al., 1994;

Mawson, 2005, 2007; Quarantelli, 1960, 1972, 2001; Schultz 1966). Through

investigation of the Titanic and Lusitania events, it can be empirically shown that

contrary to popular myth, mass panic did not occur. Additionally, the evidence

supports non-random actions and behaviour, which would indicate rational and thus

predictable decision making. However, we still observe that under specific conditions

in some events, flight behaviour occurs. This raises the questions about why

individuals flee, given the affliliative behaviour described above.

2.2 FIGHT AND FLIGHT

Humans, like all animals, have an automatic biological response system that triggers

to threats and danger. When threatened the body chemistry is altered, such that they

are ready to run from danger or fight it, to better understand this instinctive behaviour

process included here are some limited biology and neuroscience theory (see, e.g.,

2 26% sought additional information and advice from those in the area, 25.5% tried to phone for

help/information and 11.3% turned to the media for information.

10


Cannon, 1929a, 1929b 1935; Selye, 1936; Bracha, 2004). The biological make up of

the human brain consists of three distinct and separate sections that had developed at

different times for specific tasks, known as the Triune Brain (MacLean 1990). The

oldest and centre portion is the brain stem or Proto-Reptilian Complex, found in

lower order animals, is primarily concerned with instinctive behaviour and biological

function including self preservation3. The next layer which surrounds the brain-stem

is the limbic system or Paleo-Mammalian Complex, which account for warm-

bloodedness, nursing, infant care and extended social bonding4. The newest addition

and outermost layer is the neo-cortex or the Neo-Mammalian Complex5, which

allows for complex social interaction, an awareness of self and a sense of time

(which in turn allows for planning).

The speed at which the brain processes information and the reaction process as each

portion of the brain interprets the information varies depending on level of the brain

it needs to reach. Fight or flight6 behaviour is a survival mechanism, believed to be

controlled by the oldest portions of the brain. The flight or fight survival mechanism

has traditionally been viewed as ―irrational‖ behaviour, but as Johnson (1988, as

cited in Quarantelli, 2001) illustrates that if this behaviour is taken from the

perspective of those inside the disaster area an alternative view is constructed. To the

actors inside the event the action is meaningful and purposeful and is thus far from

the concept of ―irrationality‖. Grays updated version of flight or fight includes a pre-

stage of freeze, when danger is first sensed then a freeing action is undertaken, this

act is used to assess and hide from the threat.7 Under time sensitive conditions, an

extended freeze period uses valuable time, and the first to enter flight mode would

have a greater probability of survival.

3 Lower order animals generally refer to reptiles/snakes and the like, for this reason this brain function

if generally known as the reptilian complex (R-Complex) 4 Social Bonding of this level is that of the pack or herd, is observable in the lower mammalian species

wolves, lions etc. 5 The Neo-Cortex is visible in all primates to some degree, but only humans has developed this far

enough for language and cognitive generalizations. Primates have some sense of individualism but not

that of ego or id. 6 Fight or Flight originally formulated by Cannon (929) and extended and refined by Gray (1988)

7 Predator species, especially males, eyesight has developed to detect movement. Freezing in place

increases the chance of non-detection


This is instinctual behaviour and as it only needs to pass into one portion of the brain

is by far the fastest processed and is immediately acted upon. For fight or flight

instinct to be counteracted by social norms, the brain must pass the problem from the

brain-stem into the limbic system, then onto the neo-cortex. It is within the neo-

cortex that an individual must assess the situation and either maintain the flight or

fight behaviour or attempt to over ride it with cognitive reasoning from either moral

or social norms. There currently exists a gap in neuroscience literature pertaining to

duration of flight behaviours as well as the requirements for individuals to overcome

the fight or flight instinct. This work provides some evidence to support that while

instinctual flight occurs in some situations, given some (unknown) period of time

individuals are able to overcome this and reapply social normative behaviour. This is

observable in the variation in survival rates between the Titanic and Lusitania, where

the defining factor between socially normative behaviour and survival of the fittest

behaviour, is time (see chapter 6). However, the existing literature does demonstrate

that humans, like all other animals, instinctively respond to danger by either flight or

fight. This is a survival mechanism, which would satisfy a survival of the fittest or

self interest behavioural models.

2.3 SOCIAL AND MORAL NORMS

As discussed in the panic section, affiliative or social behaviours are clearly observed

and seem to predominate in extreme life and death, disaster type situations. Social

norms are the generally accepted conditions under which society operates, not only

how individuals are expected to act and behave towards each other but the moral and

ethical compass with which to navigate existence. Social norms direct both

individual and group behaviours under normal conditions, specifically the group herd

mentality once some members of society conform to the social norm others will

follow (Banerjee, 1992). Social norms are enforced society and are not always in the

best interest of a particular individual, but are in general better for the group as a

whole (Elster, 1985, 2006). While this societal enforcement works in nearly all cases

an example of this not holding can be observed in Germany during the Second World

War. It is this enforcement of the social norm by others of the group that would make

12


it possible for individuals to take a course of action that would otherwise seem

irrational by either shaming or forcing them into the required action (Elster, 1989,

1998). Elster (2007) differentiates moral and social norms into either unconditional

or conditional respectively. Moral norms are guided by the individuals own internal

belief systems which would be completely encompassed within the neo-cortex and

does not require exogenous stimuli8. This means that it takes little or no time to

determine a ―moral‖ course of action for an individual. Social norms are conditional

on social situation and as such require social interaction between individuals to form

social groups before decision making.

Models of rational action show that actions are driven by the desires (values) and

beliefs of an individual. The desires or values of an individual are derived via the

influence of factors such as: moral norms; social norms; religious ideology; and

political ideology (Elster 2007). These factors directly influence beliefs systems

which in turn re-influence the desires and values of individuals. Beliefs can skew

information and knowledge assimilation, reinforcing the desires leading to action.

Social norms, or the ―collective consciousness‖, are the set of values and beliefs

shared by a society‘s members (Elster 2007). These norms are the accepted, enforced,

conditions under which a society functions, guiding how individuals act and behave

towards each other. These behaviours can include selfishness (homo economicus) or

deviations away from selfish behaviour. In the last few decades these deviations to

homo economicus have been the subject of much study which has included: altruism,

extreme altruism (self sacrifice), fairness, helping behaviour, punishment and

reciprocity (see e.g. Batson, (1992); Camerer et al., (2004); Elster, (1985, 1996, 2006,

2007); Fehr et al., (2002); Fehr, and Fischbacher, (2003); Henrich, (2004);

Kahneman, Knetsch, and Thaler (1986a, 1986b); Krebs (1991); Piliavin and Charng,

(1990)). The inter-relationship amongst individuals as defined by their roles and

statuses is the social structure of a society. It is the interactions between structure,

status and culture that we use to define a society (Schooler 1996). It could be argued

that actions and behaviours are the observable physical manifestations of these social

norms.

8 Moral norms require a sense of self and as such must be located with the neo-cortex.


Individuals are immersed in the social norms of society, codes of behaviour and

expectations, which are ingrained and adhered to throughout their life (Foucault

1979). Long lived norms eventually become institutionalized, which has been used to

explain some cross-national values and behaviour (Frank, Meyer, & Miyahara 1995).

When social norms are internalized they become an integral part of an individual‘s

personality and are used as a reference in the interplay between actors within that

society (Parsons 1964). Internalized norms have a stronger influence on behaviour

while social norm violations do not engender a sense of guilt or shame. It is the

observation of the transgression, by a third party, that triggers shame in the

transgressor not the violation itself. Norms that are internalized become more like

moral norms, such that any transgression would elicit a sense of shame and guilt in

the individual, observed or not (Elster 1985). Once norms are identified as being

shared by other members of society they cannot be easily disregarded, individuals

will follow the prescripts of a social norm even when it is clearly not in their own

best interest to do so (Elster 1989). An analogy used by Weber likened the adherence

to norms to be like the use of a taxi, such that individuals cannot embark or

disembark at will, for to do so would be deemed irrational (Weber 1930). This raises

both a temporal and biological problem, social norms are interactionary and should

take time to evolve and enforce behaviour, and additionally we observe that

instinctive behaviour in the face of danger is to flee. This is observed in the analysis

of the Lusitania and Titanic (chapter 6), where a short event horizon results in a clear

survival of the fittest competition and a long horizon leads to stable social normative

outcomes.

2.4 QUEUING BEHAVIOR

A specific instance of socially interactional behaviour is that of queuing, which has

its own set of rules, principles and culturally instituted norms. Given the nature of

disaster evacuations where time can be crucial factor in determining survival,

queuing may be useful in explaining some behavioural outcomes. Queuing behaviour

can be viewed as an attempt at solving several competing issues: firstly it can be seen

as an attempt to maximise social welfare (Hassin & Haviv 2006); secondly resolution

14


of the scarcity of resources; or ordered queues are used in an attempt to maintain

some level of social justice or fairness (Avi-Itzhak & Levy 2004; Avi-Itzhak, Levy,

& Raz 2005; Larson 1987). While the allocation of scarce resources is a basic tenet

of economics, only in the last few decades has this discussion included the aspect of

fairness. Surveys have shown that using pricing controls for allocation under scarcity

was viewed as being unfair (Kahneman, Knetsch, & Thaler 1986a, 1986b). Within

most western societies, formal laws have been instituted to penalise vendors who

take advantage of shortages after disasters by raising prices (Camerer & Fehr 2006).

In a shortage situation an allocation process following the queuing tradition of first

come, first served (FIFO) is perceived as being the fairest (Camerer, Loewenstein, &

Rabin 2004; Savage & Torgler 2008).

This type of allocation purely a function of time of arrival (time in queue) and is not

dependant on other factors like age, gender, class etc. In times of scarcity individuals

can be viewed as having two competing and diametrically opposed natures:

Individualism based on rational self-interest; and Egalitarianism based on social

justice and fairness (Singer 1999). It would stand to reason that cultural variations

could have great affect on the creation of, the willingness to enter and the adherence

to queuing culture. However Mann‘s (1969) work on queue culture posed that

egalitarianism by itself does not relate to respect for queue etiquette or adherence to

queue priority. Stating that the English are famous for their strictly democratic

queuing behaviour, despite the rigid class structure, where as American‘s are much

less likely to join or acknowledge queuing rules and behaviours (Mann 1969). If as

suggested by Mann (1969) that the English are by nature willing to queue where

Americans are not, then this should be observable in a greater probability of survival

of for non-queuers.

This concept of fairness and queuing may have been importantly evident with the

context of the Titanic disaster, much in line with the Kahneman et al. (1986)

examinations. The Titanic had a catastrophic design flaw, namely the severe shortage

of lifeboats, where at best only 50% of those onboard would have been able to obtain

a seat. This would indicate that some allocation system had to have been used in the

evacuation process. Given that those individuals who did not secure a lifeboat seat


were guaranteed a death sentence, it was in the best interest of all individuals on the

vessel to secure a seat (survival). Failure to secure a seat or refusal to compete/queue

for one resulted in the same, worst possible outcome that is death of the individual.

The first come first served (FCFS) system was deemed to be the fairest method of

allocation for a scarce resource over any other method (Frey & Pommerehne, 1988;

Savage & Torgler, 2008). If this FCFS system was implemented we would possibly

see a much more random survival effect and not such a strong social norm effect.

However that is not to say that no queuing effect was evident, and may in part

explain the much higher rate of British deaths.

Chapter 3: Methodological Background 17

Chapter 3: Methodological Background

3.1 EXPERIMENTATION: LABORATORY VS FIELD

The analyses of these events are done post mortem, as opposed to peri mortem and

unlike laboratory experimentation, disasters do not afford the researcher the ability to

formulate questions or model the event beforehand. These result in less than perfect

data, with missing data or pertinent questions not posed to respondents. Therefore,

complex or multivariate analysis is required to formulate clearer pictures about what

happened post event. Given that it was not possible to observe the event directly, any

analysis of this event must in a sense work backwards, here the outcome is known

and the analysis attempts to determine the factors that are most likely to be

responsible for it. This is in essence what the first work is about (Chapter 4)

estimating the determinants of survival during the Titanic disaster. In other research

areas, it is possible to create simulations or laboratory experimentations to test

hypothesis under controllable conditions. However, several serious problems arise

when conducting experiments on these types of events, such as believability and

natural reaction. Amato (1990) has criticised that ―Researchers who value the rigor of

the laboratory have been reluctant to extend the study of prosocial behaviour to

everyday life, where the possibility of control is minimal‖ (p. 31). It is well

understood that individuals alter behaviour under experimental conditions if they are

aware of being observed. Such that the actions taken are not an accurate or

representative reflection of that individual‘s normal behaviour. Additionally, these

types of laboratory experiments do not create the stress and pressures needed to

simulate the life and death nature of true disasters. Both these factors result in

inaccurate and often mixed reactions to experimental modelling.

Another criticism is the use of field data in a multiple regression context instead of

non-random experimental data. Multiple regressions are not fully able to estimate

without noise the single estimate for the effect of stress on performance as it is

18

18 Chapter 3: Methodological Background

impossible to measure all the variables that might conceivably affect performance.

Allison (1999, p. 20) nicely points out ―No matter how many variables we include in

a regression equation, someone can always come along and say, ―Yes, but you

neglected to control for variable X and I feel certain that your results would have

been different if you had done so‖. The question now arises whether we are able to

find work environments that are close to an experimental setting.

Thus these events can be considered a quasi-natural experiment where subjects are

acting in the natural environment instead of an artificial laboratory environment

(natural incentives to perform). It has been shown that experiments performed in an

environment where the test subjects are keenly aware that their behaviour is being

monitored are prone to change their normal behaviour such that it is difficult to

generalize the results (Levitt and List, 2009). Moreover, selection effects are also

visible when recruiting subjects for (lab) experiments (e.g., ―scientific do-gooders‖

interested in research). Individuals in these events compete in an actual high stakes

contest (life and death), in a very controlled environment. This realism provides

researchers with a clear advantage over laboratory, self-reporting and other forms of

experiments while maintaining the randomness of natural data (Reiley and List,

2007). Additionally, laboratory experiments are unable to replicate or even

approximate the levels of stress and danger to provide sufficient threat of panic in

test subjects. The disaster events are also relatively controlled events where all

participants encounter the same environmental variables, which allow for a large

number of the exogenous (external) factors to be controlled, such that the

environmental and situational conditions were identical for every individual onboard

these vessels.

However, these natural field experiments are not with their limitations, by gaining the

randomness and natural behaviour of the participants the experimenter loses some

control over the experiment. This is especially true in the case of the Titanic, the

event was not observed firsthand and analysis is done post mortem without the

chance to implement experimental controls. Thus this is not a true natural field

experiment but is very close in this instance. Each event was impacted by an

exogenous shock that affected everyone onboard in the same manner, individuals

19


were not able to abstain from or remove themselves from the effects of the event.

This meant that all individuals had to partake, those not willing to participate in the

event received the same outcome as those who did participate and failed to secure a

safe seat, death. In addition, we can largely exclude that potential helping behaviour

could have been driven by future reciprocity. Such a life-and death-situation can be

seen as a ―one-shot game‖.

The Titanic and Lusitania disasters fit remarkably well into the model of a quasi-

natural field experiment, every individual was involved in the event (willing or not)

and the situation was enclosed, all individuals in the experiment were known and no

one was able to leave. This type of experiment would not have worked as well for an

open type of event, where the number of individuals involved in the experiment was

unknown, such as the September 11 World Trade Centre (WTC) attacks. Where

number of victims is known but the exact numbers of individuals within the complex

at the time of the initial incident means it is not possible to perform such a

determinant analysis.

3.2 ESSAYS

While the unfortunate and tragic loss of life from these types of disasters is indeed

sorrowful, these events do provide me with an excellent source of data that has

enabled examination of the decision-making processes of individuals under extreme

pressure. In these types of life and death events individuals are forced to make

choices that will affect their probability of surviving. Both events demonstrated

similar excess of demand situations for the limited lifeboats available, albeit for

slightly different reasons that will be discussed further. Failure to evacuate or failure

to secure a lifeboat seat virtually guaranteed death of an individual. Additionally all

individuals were required to make a decision about competing for lifeboat seats as

failure to make a decision carried the same costs as failing to secure a seat, a high

probability of death.

20

20 Chapter 3: Methodological Background

The essays presented in this work are the first steps into a much larger study, into

which the decision making process of individuals will be examined in many extreme

environmental conditions. The essays are presented such that they form a logical

experimental progression, beginning with a determinants study, to hypothesis testing

and finally to imposing an experimental treatment. The three essays presented here

are collaborative works, co-authored by Professor Bruno S. Frey, Professor Benno

Torgler and myself. The first essay, entitled ―Noblesse Oblige? Determinants of

Survival in a Life and Death Situation,‖ is forthcoming in the Journal of Economic

Behaviour and Organisation (JEBO) and as such has been included as submitted.

Additionally the second essay, entitled ―Surviving the Titanic: Economic, Natural

and Social Determinants,‖ has been included as submitted. The final essay included

in this work is an amalgamation of two papers, entitled ―Should I stay or Should I go

Now?‖ and Time and Tide: Constrained Altruism?‖ Both of these papers have been

included in this format so both economic and non-economic perspectives into the

comparative Lusitania event can be considered.

The first essay explores what determines the survival of people in a life–and-death

situation. The sinking of the Titanic allows us to inquire whether pro-social

behaviour matters in such extreme situations. This event can be considered a quasi-

natural experiment. The empirical results suggest that social norms such as ‗women

and children first‘ are persevered during such an event. Women of reproductive age

and crewmembers had a higher probability of survival. Passenger class, fitness, group

size, and cultural background also mattered. The second essay develops a simple

theoretical framework that allows us to develop nine hypotheses (arranged according

to whether they belong to what can be called ―economic,‖ ―natural,‖ or ―social‖

factors) that can be tested using the data on who survived and who perished in the

Titanic disaster. The motivation for the economic, natural and social factors is

developed within the paper and thus not included here (see chapter 5).

Finally, the third essay explores the interaction of natural survival instincts and

internalized social norms using data on the sinking of the Titanic and the Lusitania.

We show that time pressure is crucial when explaining behaviour under extreme

conditions of life and death. Even though the two vessels and the composition of

21


their passengers were quite similar, the behaviour of the individuals on board was

dramatically different. On the Lusitania, selfish behaviour dominated (which

corresponds to the classical homo economicus); on the Titanic, social norms and

social status (class) dominated, which contradicts standard economics. This

difference can be attributed to the fact that the Lusitania sank in 18 minutes, creating

a situation in which the short-run flight impulse dominates behaviour. On the slowly

sinking Titanic (2 hours, 40 minutes), there was time for socially determined

behavioural patterns to re-emerge.

Knowing human behaviour under extreme conditions allows us to gain insights about

how varied human behaviour can be depending on differing external conditions. The

study of the Titanic sinking may also have major policy consequences beyond that

implemented shortly after the disaster (e.g. life boat regulations and evacuation

training drills). Do more stringent safety regulations crowd out intrinsically moral

behaviour, and could they possibly lead to worse outcomes than less strict

regulations? These events demonstrate that behaviour of individuals in disaster

events does not follow the traditional mythology of mass panic. Their behaviour is

neither random nor inexplicable and as such it can be accounted for by using

economic analysis. ―Disaster planning is only as good as the assumptions it is based

upon. Unfortunately this planning is often based upon a set of conventional beliefs

that have been shown to be inaccurate or untrue when subjected to empirical

analysis…It is more efficient to learn what people tend to do naturally in disasters

and plan around that, rather than design your plan and then expect people to conform

to it.‖ (Heide 2004: p340). These works demonstrate that economic analysis can

account for human behaviour in such situations. And those simple models of

behaviour do not fully predict survival outcomes. It could be that a mixed strategy

approach may provide better modelling outcomes. Through these better models,

better plans can be made to help after disasters, which will in turn improve the

survivability of individuals caught in these types of events.

Chapter 3: Noblesse Oblige? Determinants of Survival in a Life and Death Situation 23

Chapter 4: Noblesse Oblige? Determinants

of Survival in a Life and Death

Situation

Statement of Contribution of Co-Authors for

Thesis by Published Paper

The authors listed below have certified* that:

1. they meet the criteria for authorship in that they have participated in the conception, execution, or

interpretation, of at least that part of the publication in their field of expertise;

2. they take public responsibility for their part of the publication, except for the responsible author

who accepts overall responsibility for the publication;

3. there are no other authors of the publication according to these criteria;

4. potential conflicts of interest have been disclosed to (a) granting bodies, (b) the editor or publisher

of journals or other publications, and (c) the head of the responsible academic unit, and

5. they agree to the use of the publication in the student‘s thesis and its publication on the

Australasian Digital Thesis database consistent with any limitations set by publisher requirements.

In the case of this chapter:

Noblesse Oblige? Determinants of Survival in a Life or Death Situation.

October 2008 - Forthcoming in Journal of Economic Behavior and Organization.

Contributor Statement of contribution*

David A. Savage

Has equally contributed to all aspects of this paper, including research, analysis

and writing

Date 3/11/2009

Bruno S. Frey*


and writing

Benno Torgler*


and writing

Principal Supervisor Confirmation

I have sighted email or other correspondence from all Co-authors confirming their certifying

authorship.

Benno Torgler 09/11/2009 _______________________ ____________________ ______________________

Name Signature Date

24

24 Chapter 3: Noblesse Oblige? Determinants of Survival in a Life and Death Situation

How selfish soever man may be supposed, there are evidently some principles in his

nature, which interest him in the fortune of others, and render their happiness

necessary to him, though he derives nothing from it, except the pleasure of seeing it.

The Theory of Moral Sentiments (Smith, 1790)

4.1 INTRODUCTION

At the very core of economics lies the question of scarcity, or ―how society makes

choices concerning the use of limited resources‖ (Stiglitz, 1988). To achieve utility-

maximization from a limited set of resources, traditional economic models assume

that individuals are exclusively pursuing their material self-interest. The Homo

Economicus theory has shown to be useful in many cases. However, substantial

evidence has been generated that suggests that other motives, such as altruism,

fairness, and morality profoundly affect the behaviour of many individuals. People

may punish others who have harmed them or reward others who have helped them,

sacrificing their own wealth (Camerer, Loewenstein & Rabin, 2004). People donate

blood or organs without being compensated; they donate money to charitable

organizations. During wartime many individuals volunteer to join the armed forces

and are willing to take high risks as soldiers (Elster, 2007). Citizens vote in elections

incurring higher private costs than benefits and people show greater tax compliance

than a traditional economics-of-crime model would predict (Torgler, 2007).

Individuals also help others in many situations in the workplace (Drago & Garvey,

1998). In many experiments subjects have shown to care about aspects as fairness,

reciprocity, and distribution. Ultimatum experiments have shown that the modal offer

is (50, 50) and that the mean offer is somewhere around (40, 60). This also

demonstrates that the smaller the offer, the higher the probability that the offer will

be rejected (Ochs & Roth, 1989; Roth, 1995). We also observe helping to be a key

element in our work environment ―Within every work group in a factory, within any

division in a government bureau, or within any department of a university are

countless acts of cooperation without which the system would break down. We take

these everyday acts for granted, and few of them are included in the formal role

prescriptions for any job‖ (Katz & Kahn, 1966).

25


Individuals compare themselves to their environment and care greatly about their

relative position, which can influence individual choices. Thus, not only is the

absolute level of an individual‘s situation important (e.g. income), but also the

relative position. Researchers have included the concept of interdependent

preferences to allow for social comparison (e.g., Akerlof & Yellen, 1990; Becker,

1974; Clark et al., 2008; Easterlin, 1974; Frank, Meyer & Miyahara 1995; Pollak,

1976; Schelling, 1978; Scitovsky, 1976). Frank (1999) emphasizes that research

provides ―compelling evidence that concern about relative position is a deep-rooted

and ineradicable element in human nature‖ (Frank, 1999:p. 145).

Thus, several approaches try to take into account the deviation of a self-interested

model by extending the motivation structure (e.g. Andreoni & Miller, 2002; Becker,

1974; Bolton & Ockenfels, 2000; Dufwenberg & Kirchsteiger, 2004; Fehr &

Schmidt, 1999; Frey, 1997; Rabin, 1993; Sobel, 2005). In general, Thaler (2000)

stresses that the Homo Economicus will evolve to Homo Sapiens: ―As economists

become more sophisticated, their ability to incorporate the findings of other

disciplines such as psychology improves‖ (Thaler, 2000:p. 140).

Despite the large number of studies in this area, there is hardly any empirical

evidence that demonstrates that interdependent preferences and pro-social behaviour

matter in extreme situations such as life-and-death situations. This paper tries to

rectify this shortcoming by exploring this question using data from the sinking of the

RMS Titanic, the most recognizable maritime disaster in history. While the

unexpected loss of life from this tragedy was indeed sorrowful, the event provides us

with data that help us to better understand decision-making processes under extreme

pressure. Individuals are forced to make choices that affect their probability of

surviving. What makes the event interesting for research is that it is a contained and

controlled event; much like a natural field experiment would be designed, wherein

the majority of the exogenous factors are controlled and the endogenous factors can

be tested and investigated. The environmental or situational conditions were identical

for every person on board the Titanic. This allows us to explore behavioural reactions

to an external shock, as well as to investigate people‘s behaviour under scarcity. The

26


issue of scarcity or shortage arose, as there existed a severe lack of lifeboats. The

Titanic carried only 20 lifeboats adequate for 1178 people (or 53% of the passengers

on board). The problem was exacerbated further by the panicked deck crew, who

began launching lifeboats that had not been loaded to capacity. This meant an excess

demand situation as people wishing to survive had to compete with others on board

for a place on the lifeboats. A failure to secure a seat virtually guaranteed death as the

average water temperature of the surrounding ocean was approximately 2 degrees

Celsius (35 Fahrenheit). Anyone left in the freezing water would quickly succumb to

hypothermia and drown. We can expect a certain level of agreement among those

already in a lifeboat and those still waiting to board a lifeboat to limit the lifeboat to

its maximum safe load to avoid the boat falling into serious danger (Martin, 1978). In

addition, we can largely exclude that potential helping behaviour could be motivated

by future reciprocity, a key element in the helping literature (e.g. Batson et al., 1979;

Gouldner, 1960). A life-and-death situation can be seen as a ―one-shot game‖.

Moreover, previous research has shown that legitimacy affects helping behaviour.

Legitimate need elicits more help than does illegitimate need (e.g. own laziness)

(Berkowitz, 1969; Schwartz & Fleishman, 1978). In our case, people were

confronted with an ―external shock‖ which in a substantial manner helps to control

legitimacy.

Thus, the intention of the paper is to investigate the decisions made under these

extreme conditions and see if the survival outcomes fit with the literature on

interdependent preferences. The key question is whether we are able to observe social

norms, fairness and social preferences in a life or death situation.

4.2 THEORETICAL BACKGROUND

Previous studies have explored the link between fairness and shortage using survey

data. In telephone surveys of randomly selected residents of two Canadian

metropolitan areas, Kahneman, Knetsch & Thaler (1986a) has shown that people

consider the use of prices to eliminate the excess of demand to be unfair. This is

consistent with the observation that firms do not adjust prices and wages as often as

traditional economic theory would suggest. Moreover, we also observe formal laws

27


that penalize vendors who take advantage of shortages by increasing prices for water,

fuel and other necessities after a natural disaster (Camerer et al., 2004). Frey &

Pommerehne (1993) and Savage & Torgler (2008) replicated the study using

European samples, and found similar results. In a shortage situation an allocation

process in line with tradition (first-come, first-served) is perceived to be fairest,

followed by administrative allocation procedures. However, in contrast to these

studies, which consider attitudes we explore behavioural consequences of excess

demand in a life-and-death situation.

Our research focus is closely linked to the question in line with the traditional

economic approach, whether people behave according to the notion ―every man for

himself‖ or whether a ―helping hand‖ effect can be observed. Interestingly, the

willingness to help others in such situations is not uncommon. (Perlow & Weeks,

2002) stress that helping behaviour is required within organizations for increased

efficiency, flexibility, learning and innovation: ―Therefore, it has never been more

important for us to understand why people help each other at work and why they

don‘t‖ (Perlow & Weeks, 2002:p. 343). Shotland and Stebbins (1983) refer to two

lines of thoughts: firstly an ―altruism school‖ with the premise that people have a

need (innate or acquired) to help others in need; secondly a ―hedonistic base‖ that

suggests that people weigh the benefits and costs to themselves to reach the decision

to help or not (Shotland & Stebbins, 1983:p. 36). The second one is close to a

traditional economic approach.

Helping behaviour is not only linked to altruism (Piliavin & Charng, 1990), but also

to reciprocity or exchange (Fehr, Fischbacher, & Gachter, 2002; Henrich, 2004;

Oberholzer-Gee, 2007). The idea of reciprocity is helping those who have helped us.

Exchange not only focuses on direct reciprocity but also on expectations that lead to

solidarity and indirect reciprocity in more anonymous settings such as, helping lost

tourists (Rabinowitz et al., 1997). However, as discussed in the introduction, in the

case of the sinking of the Titanic, we are able to exclude such motivation due to the

nature of the event studied.

28


Altruistic motivation has been defined as the desire or motivation to enhance, as the

ultimate goal, the welfare of others even at a net welfare loss to oneself (Batson,

1992; Elster, 1996). An additional definition of an altruistic act is ―an action for

which an altruistic motivation provides a sufficient reason‖ (Elster, 1996). However,

altruistic behaviour is often framed described as being somewhat selfish. It is stressed

that what appears to be motivated by a concern for others is often ultimately driven

by selfish motives (Piliavin & Charng, 1990). The differentiation between motivation

and act is useful, as identifying altruistic motivation is problematic. For example, a

so-called ―warm glow effect‖ can be observed when people give. Giving makes

people feel good. Piliavin and Charng (1990) when summarizing the literature refer

to a ―paradigm shift‖ that emphasizes the importance of altruistic behaviour: ―The

central point we attempt to make in this review is that the data from sociology,

economics, political science, and social psychology are all at least compatible with

the position that altruism is part of human nature. People do have ―other regarding

sentiments‖, they do contribute to public goods from which they benefit little, and

they do sacrifice for their children and even for others to whom they are not related‖

(Piliavin & Charng, 1990:p. 29).

When people sacrifice their life or when they increase the fitness or the survival

possibility of others in the Titanic disaster, at the expense of their own survival

chances, we are observing altruistic behaviour. Self-sacrifice can be seen as an

extreme form of altruism. For example, Krebs (1991) stresses: ―On my definition of

altruism, behaviours directed toward the enhancement of the welfare of another

increase in altruism in proportion to the anticipated costs to self: Risking your life to

save a drowning person is more altruistic than throwing him or her a lifesaver‖

(Krebs, 1991:p. 137). A person could have done better for herself not helping others

and therefore ignoring the effects of her choice on others (Margolis, 1982). Such a

notion is consistent with the definition of altruism in social biology (Wilson, 1975).

There are various approaches to model altruistic behaviour. An altruistic individual i

would have the following function:

29


Ui = Ui (si, sj), (1)

where si, sj measure the survival probability of i and other individuals j. If i were an

egoist the utility function only depends on his own survival. This can be modelled

using the following specific utility function:

jij

ijii sssU )( (2)

ij is a factor that shows how much individual i cares about j. If i doesn‘t care at all,

i‘s utility only depends on the own survival. A positive ij reflects altruism. The

utility of i increases when individual j survives. On the other hand, a negative ij

reflects spite (Sobel, 2005). The utility of i decreases if individual j has a higher

probability of surviving. The degree of ij depends on the relation (closeness)

between i and j. Higher positive values are expected for family members and friends.

Personal and societal norms are implicated in altruism (Piliavin & Charng, 1990).

Altruistic motivation may be driven by moral norms such as sharing equitably or

helping others in distress (Elster, 2006). Norms are the generally accepted conditions

under which society functions, guiding how individuals act and behave towards each

other. They are adopted and enforced by members of that society and are not always

in the best interest of the individual within that society (Elster, 1985). Elster (2007)

sees moral norms as unconditional while social norms are conditional and therefore

influenced by the presence or the behaviour of other people (Elster, 2007:p. 104.). A

key norm that we are going to explore is ―women and children first‖. Interestingly, no

international maritime law requires that women and children are rescued first. Such a

social norm was first documented during the sinking of HMS Birkenhead in 1852.

The Birkenhead sank only twenty-five minutes after having struck a rock off the

South African coast. The seven women and thirteen children onboard were rowed

away from the wreck to safety. Captain Seton drew his sword ordering men to ―stand

an‘ be still‖ (Kipling, 1892) to avoid men rushing to the lifeboats putting the life of

women and children in danger. Similar norms have been found in other areas where

people had to be evacuated. Humanitarian agencies often first evacuate ―vulnerable‖

30


and ―innocent‖ civilians such as women, children and the elderly. The Geneva

Convention provides special protection and evacuation priority for pregnant women

and mothers of young children (Carpenter, 2003).

How can we explain that such a social norm may arise? Helping children and women

as their caregivers serves to strengthen the chances of their survival and thereby helps

to guarantee the survival of future generations. This may explain why it was also

considered vital for women to be rescued. Behavioural evidence is consistent with

the norm of social responsibility. For example, studies report that motorists are more

willing to stop on a busy street for a woman who is pushing a baby carriage than for a

woman who is pushing a grocery cart (Harrell, 1994). Helping behaviour is also

exhibited during common threat situations (Batson et al., 1979). An increased level

of helping behaviour may be observed during situations of common threat that may

generate ―we-feelings‖ and as a consequence a concern for the welfare of others

(Worman, 1979). In other words closeness strongly correlates to helping behaviour

(Amato, 1990) and being connected during an external and shocking event may

induce closeness.

Eagly and Crowley (1986) in their meta-study report that traditional male gender

roles may matter and encourage chivalrous and heroic acts. The results show that

men may be predisposed to being more helpful than women during situations which

women judge to be more dangerous than men do. Moreover, women usually receive

more help than men and males believe themselves to be more competent and more

comfortable helping than females. This would suggest a higher probability of

survival among females.

In addition, socio-biology also stresses the relevance of the ―procreation instinct‖.

The survival of a species relies on its progeny; thus a high value must be placed upon

females of reproductive age as a valuable resource. Social norms may be created to

protect the reproductive and child-rearing role of women. It is an attempt to protect

children rather than the desire to help a woman. A potential shortage of women

would limit the number of offspring, while a shortage of men would not (Felson,

2000).

31


In humans the period of peak reproduction is between the age of 15 and 35

(American Society for Reproductive Medicine, 2003). Prior to age 15 females on

average are not yet reproductively functional and after the age of 35 the reproductive

cycle begins to slow until at about 50 the reproductive function is lost. Others also

stress that the emergence of a social norm, which gives preference to women, may be

related to an increased physical and structural vulnerability in women (Felson, 2000).

Females may also have a strong incentive to guarantee the survival of their children.

In the study of anthropology “parental investment” is an important concept. The

study argues that females of most species invest more in the survival of their

offspring than males do. Females invest a whole range of benefits, over a period of

time, on their offspring starting with the gestation period, lactation, predatory

protection and education (Geary, 1998) whereas a male‘s investment is much

smaller. Because of this much larger investment the opportunity costs of losing

offspring are much higher and the drive to ensure offspring survival is therefore

much stronger (Campbell, 1999). It has been shown that the mortality rates of

children with a surviving mother are 1.4 times lower than those without (Voland,

1998). The survival rates of offspring can be directly linked to maternal survival

(Bjorklund & Shackelford, 1999). Under these conditions it would be expected that

females with children would be much more wary of possible danger and would

aggressively fight other females to ensure a safe haven (Cashdan, 1997). Moreover, it

has been stressed that the sex that puts in greater parental investment to promote the

prosperity of offspring, is the more valued resource (Eswaran & Kotwal, 2004;

Trivers, 1972).

4.3 EMPIRICAL RESULTS

Amato (1990) criticizes that a large amount of literature in this area of helping is

laboratory-based: ―Researchers who value the rigor of the laboratory have been

reluctant to extend the study of prosocial behaviour to everyday life, where the

possibility of control is minimal‖ (Amato, 1990:p. 31). Working with the Titanic data

provides an alternative strategy to explore whether ―social norms of helping‖ survive

32


in a real life-and-death situation. We cannot observe the detailed rescue process but

we can evaluate the overall outcome, which provides an indication about the level of

social norms or altruism among crew and passengers.

We use a probit model of the survival probability for a typical Titanic passenger:

Pr (y = 1 | x1, x2, …, xk ) = ( + 1x1 + 2x2 + … + kxk). (3)

Here y is a dummy variable indicating whether the passenger survived (y = 1) or not

(y = 0); the variables (x1, x2, …, xk) are explanatory variables such as gender, age,

etc; ( , 1, 2, … k) are parameters to estimate; and is the cumulative standard

normal distribution function. The role of , which is increasing in its argument, is to

keep the probability Pr(y = 1) in the zero to one interval. Each passenger contributes

one observation on (y, x1, x2, …, xk). From a sample of such observations, assumed

independent, the parameters can be estimated by maximum likelihood. This is a

standard probit model.

Since the coefficients are difficult to interpret directly, the marginal effect of a

continuous explanatory variable xj will, as usual, be interpreted through the partial

derivative

Pr(y 1 | x1,x2,...,xk )

x jj ( 1x1 2x2 kxk ) , (4)

evaluated at the means, where is the standard normal density function (not the

cumulative density ). Since > 0, the sign of the marginal effect is the same as the

sign of j. For a discrete xj, a difference rather than a derivative will be used in place

of (4).

Tables 4.1 and 4.2 present the results. For each coefficient of each probit, we report

the maximum likelihood estimates of the coefficient (first value), the z-statistic (ratio

of coefficient to its standard deviation, in italics), and the marginal effect (in bold).

33


At the bottom of the table, for each probit, we also report the sample sizes and the

pseudo-R2s. The pseudo-R2 is 1–(L1/L0), where L0 is the log-likelihood value for

the null model excluding all explanatory variables, and L1 is the log-likelihood value

for the fitted model.

Our gender variable (female=1) will be a key factor that we will explore. We predict

that the coefficient is statistically significant with a positive sign. In addition, we will

observe whether children and women with children have a higher probability to

survive. To measure the age range of a child we use the United Nations provisional

guidelines of standard international age classifications (United Nations, 1982). The

guidelines classify children as up to the age of 15. Moreover, to develop further age

dummies we rely on an age notion that the British Royal Commission used in 1870-

74 and which appeared in a subsequent Act in 1875 in regard to age benefits. The

transition into ―old age‖ was defined to begin at 50 (e.g. Arias, 2004; Boyer, 1988;

Eysenck, 2004; Gorsky, 1998). We will also explore whether females in their

reproductive age are more likely to survive compared to other women. Moreover, we

will examine (check) whether individuals or females with a larger potential pool of

helpers (family members) have a higher probability of surviving.

In addition to controls for gender, age and family or travel group size9, we also

explore the following independent variables: passenger-class, crew member, and

nationality. The data was generated from numerous sources, in particular the

Encyclopaedia Titanica (Encyclopaedia Titanica, 2008). Passengers were separated

into three different classes, namely: first class, second class and third class. It can be

expected that first class passengers tried to obtain preferential treatment. A higher

level of (bargaining) power, better access to information about imminent danger,

persons of power and decision makers such as leading crew members may facilitate

(lead to a better) access to lifeboats and therefore raise the probability of survival.

Moreover, first class cabins were closest to the boat deck. We control for nationality

9Singles, singles with children, singles with servants, couples, couples with children, couples with

servants, families/friends, families/friends with children and families/friends with servants. The

families/friends groups include extended family groups and groups of friends travelling together as a

34


as previous studies on helping behaviour did report cultural differences (Perlow &

Weeks, 2002). Moreover, it is worthwhile to explore differences between the crew

and the passengers. Crew members are better prepared for a catastrophic event and

are also in the position of obtaining the information earlier than the passengers. They

could use this information advantage to generate a higher survival rate. They have

also better access to important resources such as lifeboats. On the other hand, they

are restrained by the expectation to be among the very last to leave the sinking ship.

Table 4.1 presents the empirical results for the first set of estimations. We begin by

first examining if we can find the expected gender effect. In the first four

specifications we only include the coefficient FEMALE in the specification, focusing

on all the individuals on board the Titanic (see specification 1), only passengers (2),

crew members (3), and couples (4). The results indicate that a strong gender effect

exists. Being female rather than male increases the probability of surviving between

23.7% (specification 3) and 53.9% (specification 4).

In a next step we explore whether children also have a higher probability of

surviving. In specification (5) we focus only on passengers, controlling for passenger

class using the age dummies AGE Sub 15 (age 15 and below), AGE 16-50 and

AGE51+ (which is the reference group) to explore the age-survival relationship. The

results support the notion that children have a higher probability of survival than

other age groups reporting the largest marginal effects. Being a child rather than a

person AGE 51+ (reference group) increases the probability of survival by 32%.

Moreover, the coefficient AGE 16-50 is also statistically significant. Thus, we find a

negative relationship between age and survival probability.

Specification (5) and the following ones in Table 4.1 also show that first and second-

class passengers have a higher probability of survival. Being a first class passenger as

opposed to a third class passenger (which is the reference group) increases the

party families/friends groups include extended family groups and groups of friends travelling together

as a party.


Table: 4.1 Survival Probability and Pro-Social Behaviour

Probit

All Passenger Crew Couple Passenger All All Couples All All All

[1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11]

FEMALE 1.413**

*

1.462*** 1.858**

*

1.477**

*

1.469*** 1.493**

*

1.488**

*

1.702**

*

1.517**

*

1.509*** 1.512***

20.22 18.34 5.50 10.29 17.39 18.29 18.16 9.8 18.11 17.98 17.84 0.517 0.529 0.237 0.539 0.530 0.542 0.541 0.605 0.550 0.547 0.548

AGE SUB 15 0.832*** 0.764**

*

0.758**

*

0.745**

*

0.745*** 0.753***

4.12 3.94 3.89 3.76 3.75 3.77 0.322 0.293 0.291 0.286 0.285 0.289

AGE 16 - 50 0.474*** 0.416**

*

0.445**

*

0.463**

*

0.462*** 0.469***

3.01 2.84 3.01 3.11 3.10 3.14 0.162 0.131 0.139 0.143 0.143 0.145

CREW 0.536**

*

0.546**

*

0.493**

*

0.649*** 0.631***

6.51 6.61 5.42 5.62 5.37

0.189 0.193 0.174 0.229 0.223

1st CLASS 1.149*** 1.140**

*

1.122**

*

0.833**

*

1.194**

*

1.173*** 1.136***

10.95 10.92 10.68 3.85 10.91 10.67 9.36 0.432 0.429 0.422 0.320 0.448 0.440 0.427

2nd CLASS 0.409*** 0.407**

*

0.390**

*

1.577**

*

0.412**

*

0.481 0.454***

3.93 3.9 3.72 7.9 3.89 4.34 3.97 0.156 0.150 0.144 0.569 0.153 0.179 0.169

HAS CHILD/

CHILDREN

0.523**

*

0.596**

*

0.713**

*

0.688 0.682***

2.69 2.77 3.39 3.26 3.22 0.199 0.234 0.274 0.264 0.261

SMALL

GROUP

(Couples)

-0.274** -0.254 -0.252***

-2.47 -2.28 -2.25 -0.090 -0.084 -0.084

LARGE

GROUP

(Families)

-0.479 -0.033 -0.023

-0.47 -0.33 -0.22 -0.017 -0.012 -0.008

ENGLAND

(1143)

-

0.201***

-2.20

-0.070

IRELAND

(114)

0.140

0.85 0.050

SWEDEN

(106)

0.068

0.40 0.024

USA (424) 0.236**

2.18 0.085

ALL OTHER

NATIONS

(399)

0.206*

1.89 0.040

Obs. 2186 1300 886 376 1300 2186 2186 376 2186 2186 2186

Pseudo R2 0.161 0.211 0.041 0.221 0.286 0.209 0.212 0.389 0.214 0.216 0.216

Notes: z- values in italics, marginal effects in bold. The symbols *, **, *** represent statistical significance at the 10%, 5%

and 1% levels, respectively.

36


probability of survival by about 40%. Thus, more (bargaining) power, better access to

information and lifeboats increases the probability of survival quite substantially.

In specification (6) we work with the entire data set using a CREW dummy variable.

The results show that crew-members had a higher probability of survival which may

indicate their taking advantage of their increased opportunities (better possibilities) to

acquire resources and to be informed which promoted their survival rate. Thus, such

a result is more in line with a self-interested approach.

In a next step specification (7) and in the following ones we explore whether having

a child increases the survival rate of an individual. This is indeed the case. Having a

child increases the probability of survival by 20%. This effect in part explains not

only the social norm of ―children first‖, but also the parental investment norm.

Having children motivates parents (especially mothers - women being the main

caregivers at that time) to fight harder for their child‘s survival. Helping children

increases the possibility of guaranteeing the survival of future generations.

In specification (8) we again focus on couples only. We find that passenger class and

having children also mattered. In this specification we observe the strongest gender

effect. A possible explanation could be that husbands and fathers fought to secure a

place on a lifeboat for their wives and children but perished as they did not attain a

seat for themselves. Specifications (9) to (11) allow us to explore whether being

active within a small or large group increases the probability of survival. Joint efforts

may lead to a higher probability of survival, but they may also lead to a lower level of

flexibility during critical situations. The results indicate that both coefficients, the

one for small groups (couples) and large groups (families), are negative. Thus, people

acting alone have a higher probability of survival. There is even a statistically

significant difference for the smaller group.

Finally, in the last two specifications in Table 4.1 we control for nationality. First we

include a dummy for the single largest group on board: people from England. We

find that English people had a lower probability of survival. To deal with the

heterogeneous structure of the reference group in specification (10) we use people

37


from England as the reference group in specification (11) and compare them with

other nationalities such as the US Americans, Irish, Swedes and the remaining

countries. Interestingly, the results show that Americans had ceteris paribus the

highest probability of surviving.

Next we investigate the survival factors among females. This allows us to test, for

example, if indeed a higher priority is placed upon females in their prime

reproductive age. We therefore construct three dummy variables, namely age 16-35,

age below 16 and age 36+. Table 4.2 presents the results. The findings indeed

indicate that women in their prime reproductive age were more likely to survive.

Compared to the reference group (age 36+) their probability increases by more than

16% (see specification 12). This result remains robust after including further factors

(see other specifications).

We again observe a passenger class effect. Table 4.2 shows that the class coefficients

report the largest marginal effects. Being a first class passenger increased the

probability of surviving among women by around 40%. Interestingly, there exists no

statistically significant difference between children and the reference group. One

reason could be that several women above the reproductive age may be active as

caregivers.

Specifications (14) to (18) show that having a child increases, ceteris paribus, the

probability of surviving among women. Interestingly, we observe that female

crewmembers also had a higher probability of survival. The quantitative difference is

quite substantial (close to 20%). On the other hand, being in a small group (with only

a partner) reduces the probability of survival while being part of a larger group

(family) does not lead to a statistically significant difference in relation to women

who are travelling alone. Finally, Table 4.2 shows that nationality does not matter.

Thus, the advantage of being a US citizen disappears when the focus lies on women

only.

38


Table: 4.2 Survival of Women

Probit

Passenger All All Couples All All All

[12] [13] [14] [15] [16] [17] [18]

AGE SUB15 0.147 0.060 0.086 0.993 -0.011 -0.012 0.054

0.61 0.25 0.35 1.15 -0.04 -0.05 0.21 0.044 0.017 0.023 0.089 -0.003 -0.003 0.015

AGE 16 – 35 0.528*** 0.421** 0.457** 0.272 0.473*** 0.472*** 0.425**

2.85 2.39 2.55 0.83 2.62 2.60 2.29 0.169 0.125 0.132 0.048 0.135 0.135 0.121

CREW DUMMY 1.177*** 1.22*** 1.007*** 1.014*** 1.031***

3.41 3.54 2.81 2.64 2.66 0.200 0.194 0.174 0.175 0.177

1st CLASS 1.964*** 2.001*** 1.99*** 2.899*** 2.170*** 2.168*** 2.138***

7.96 8.45 8.21 6.04 8.74 8.69 7.89 0.415 0.403 0.389 0.527 0.408 0.407 0.403

2nd CLASS 1.131*** 1.118*** 1.111*** 1.168*** 1.202*** 1.205*** 1.188***

6.40 6.37 6.25 3.77 6.43 6.11 5.80 0.274 0.241 0.231 0.136 0.240 0.241 0.238

HAS CHILD / CHILDREN 1.024** 1.45*** 1.457*** 1.456*** 1.536***

2.37 2.98 3.18 3.17 3.16 0.186 0.154 0.215 0.215 0.220

SMALL GROUP (Couples) -0.661*** -0.660*** -0.623***

-3.43 -3.40 -3.18 -0.197 -0.196 -0.185

LARGE GROUP (Families) -0.167 -0.166 -0.154

-0.95 -0.94 -0.86 -0.047 -0.047 -0.044

ENGLAND -0.009

-0.05 -0.003

IRELAND 0.203

0.76 0.052

SWEDEN -0.413

-1.40 -0.130

USA 0.016

0.07 0.0040

ALL OTHER

NATIONS

0.045

0.21 0.012

Obs. 433 482 482 169 482 482 482

Pseudo R2 0.2198 0.2338 0.2466 0.4505 0.2683 0.2683 0.2761

Notes: z- values in italics, marginal effects in bold. The symbols *, **, *** represent statistical significance at the 10%, 5%

and 1% levels, respectively.

4.4 CONCLUSIONS

There has been little evidence available that illuminates whether interdependent

preferences or prosocial behaviours matter in extreme situations such as life-and-

39


death events. This paper tries to address this shortcoming by exploring these

questions using data from the sinking of the Titanic. This data set allows us to

explore not only the behavioural consequences of an extraordinary event, but also

provides evidence of an individual‘s reaction in a situation where there exists an

excess of demand due to the shortage of lifeboats. Moreover, the analysed event can

be considered a quasi-natural experiment. The environmental or situational

conditions were identical for every person on board the Titanic. The event can be

seen as an external shock that affected everyone on board in the same manner. In

addition, we can largely exclude that potential helping behaviour could have been

driven by future reciprocity. Such a life-and death-situation can be seen as a ―one-

shot game‖.

The results offer strong support for the assumption that social norms and altruism

matter. The adherence to the norm ―women and children first‖ is apparent in such a

life and death situation. Being female rather than male increases the probability of

survival between 23.7% and 53.9%, depending on the specification used. This is a

large quantitative effect. Interestingly, females had a lower probability to survive

among crew-members than among passengers. However, the effect is still quite

substantial (23.7%). Moreover, the survival rate of females increases when focusing

only on couples. Similarly, being a child rather than a person of AGE 51+ (reference

group) increases the probability of survival by about 30%. Having a child and being

of reproductive age strongly raises the survival probability. Having a child also

increases the probability of survival when males are considered also. Such results are

in line with socio-biological theories (e.g. procreation instincts or parental

investment) that were discussed in the theoretical part. The findings are also

consistent with previous results that report that males are more willing to help in

critical situations (e.g. chivalrous and heroic behaviour).

Social class has a strong effect. Passengers of the first and second class had a higher

probability of survival. Preferential treatment, a higher level of (bargaining) power,

better access to information about imminent danger, persons of power and decision

makers such as leading crew members, tend to raise the probability of survival as

they allow for better access to lifeboats. Moreover, these passengers were closer to

40


the boat deck10

. Similarly, it seems that crew-members used their information

advantage and their superior access to resources (e.g. lifeboats) to generate a higher

probability of survival.

In sum, the intention of the paper was to investigate the decisions made under these

extreme conditions and to see if the survival outcomes correspond with the literature

on interdependent preferences and social norms. Helping behaviour is common and

altruism or social and moral norms seem to play a central role in such a risky and

extreme situation. For example, social norms such as ―women and children first‖ are

maintained during such external shocks that create life and death situations. Such an

effect is only observable when both, crew and passengers agree to defer to such

norms. Otherwise, it would have been easy for male passengers to revolt against such

a norm. Actions are guided by norms and rationality in the sense that society profits

when a large number of females and offspring survive. The social norms are strong

enough to keep the ―public good‖ problems under control, limiting individual self-

interested behaviour although people also take advantage of their relative situation as

can be seen by the higher survival rate of crew and first and second class passengers.

Our findings clearly show the importance of working with Richard Thaler‘s notion of

a Homo Sapiens able to understand an individual‘s behaviour in a life-and-death

situation.

10

Unfortunately, there are only very sketchy data on where the cabins of passengers were located on

the Titanic. We could only collect information on the distance to the lifeboats in meters for 325

persons of which 64 percent survived. As the overall survival rate is 32 percent, this sample is likely to

be highly skewed; that is, the information on the distance to the lifeboats comes predominantly from

passengers saved. Nevertheless, using this questionable and small sample, the estimates of the

determinants discussed are robust: the effects of gender, cabin class, and reproductive age remain

statistically significant and of similar magnitude.

41


4.5 TABLE

Table: 4.3 Mean Values

Variables Mean

SURVIVED 0.319

FEMALE 0.220

AGE SUB 15 0.052

AGE 16 - 50 0.891

CREW 0.405

1st CLASS 0.146

2nd

CLASS 0.129

HAS CHILDREN 0.031

SMALL GROUPS (Couples) 0.171

LARGE GROUPS (Families) 0.167

ENGLAND 0.529

IRELAND 0.052

SWEDEN 0.048

USA 0.191

ALL OTHER NATIONALITIES 0.180

FEMALE AGE 16-35 0.121

Chapter 5: Surviving the Titanic: Economic, Natural and Social Determinants 43

Chapter 5: Surviving the Titanic: Economic,

Natural and Social Determinants














Surviving the Titanic Disaster: Economic, Natural and Social Determinants.

January 2009 - Working Paper: Submitted for Publication.


David A. Savage


and writing

Date

Bruno S. Frey*


and writing

Benno Torgler*


and writing



authorship.

Benno Torgler 09/11/2009 _______________________ __________________ ______________________

Name Signature Date

44

44 Chapter 5: Surviving the Titanic: Economic, Natural and Social Determinants

5.1 INTRODUCTION

During the night of April 14, 1912, the Titanic collided with an iceberg on her

maiden voyage. Two hours and forty minutes later she sank, resulting in the loss of

1,517 lives—more than two-thirds of her 2,207 passengers and crew.11

This remains

one of the deadliest peacetime maritime disasters in history and by far the most

famous.12

It is one of those rare events that are imprinted on human memory, like

President Kennedy‘s assassination, the first moon landing, and the terrorist attacks on

the Twin Towers on 9/11. The disaster came as a great shock to many because the

vessel was equipped with the most advanced technology at that time, had an

experienced crew, and was thought to be (practically) ―unsinkable.‖13

The myths surrounding the Titanic disaster were kept alive by the many attempts to

find her wreckage. It was not until 1985 that a joint American-French expedition, led

by Jean-Louis Michel and Dr. Robert Ballard, located the wreckage and collected

approximately 6,000 artefacts, which were later shown in a successful exhibition that

toured the world.

The Titanic‘s fame was enhanced by the considerable number of films made about it,

especially the 1997 production of Titanic, which was directed by James Cameron and

11

For accounts of the disaster, see, for example, Eaton & Haas (1994); Lord (1955, 1988); Quinn

(1999); Ruffman (1999) as well as the Encyclopaedia Titanica (www.encyclopedia–titanica.org) and

the information provided by RMS Titanic, Inc. that were granted ―salver-in-possession‖ rights to the

wreck by the U.S. District Court for the Eastern District of Virginia (www.titanic-online.com). 12

The Titanic‘s death toll was exceeded by the explosion and sinking of the steamboat Sultana on the

Mississippi River in 1985 when 1,700 people perished. The worst peacetime maritime disaster

happened in 1987 when the passenger ferry Doña Paz collided with an oil tanker and caught fire. The

sinking of the ferry claimed between 1,500 and 4,000 lives. However, the worst maritime disasters

happened during wartime. For instance, the sinking of the Wilhelm Gustloff by Soviet submarines in

January 1945 caused the deaths of between 7,000 and 9,000 people. The Titanic is not the only major

vessel that did not survive her maiden voyage. The British RMS Tayleur in 1854 and the Danish Hans Hedthoft in 1995 were also technically innovative vessels that sank on their first trip. The famous

Gustav Vasa met with the same fate in 1628; it capsized while still in port at Stockholm. 13

In contrast to popular mythology, the Titanic was never described as ―unsinkable‖ without

qualification. The notion entered the public‘s consciousness only after the sinking see Howell (1999)

or in general, Tierney (2006).

http://www.encyclopedia–titanica.org/

http://www.titanic-online.com/

45


starred Leonardo Di Caprio and Kate Winslet.14

It was (at the time) the most

expensive film ever made, costing approximately US$200 million, and was funded

by Paramount Pictures and 20th Century Fox. The film was a major commercial and

critical success. It is the highest grossing film of all time, earning US$1.8 billion, and

it won eleven Academy Awards, tying with Ben Hur and The Lord of the Rings: The

Return of the King for the most Oscars won by a movie.

The extent of the tragedy is mainly because there were too few lifeboats on the

Titanic. The vessel carried only 20 lifeboats, which could accommodate 1,178

people, or 52 percent of the people aboard.15

As the Titanic did not show any signs of

being in imminent danger, passengers were reluctant to leave the apparent security of

the vessel to board small lifeboats. The panicking deck crew exacerbated the

situation further at the beginning by launching lifeboats that were partially empty. As

a consequence, there was an even greater demand for lifeboat places when the

remaining passengers finally realized that the ship was indeed sinking. People

struggling to survive had to compete with other people aboard for a place in the few

remaining lifeboats. Failure to secure a seat virtually guaranteed death because the

average ocean temperature was about 2 degrees Celsius (35 degrees Fahrenheit); any

survivors of the sinking vessel left in the water would have quickly frozen to death.

Only a handful of swimmers were rescued from the water.16

This paper analyses the determinants of who is more likely to survive such a tragic

event. This is an interesting issue in itself as the probability of survival differs greatly

between individuals. For example, according to the official casualty figures, men

travelling first class were much more likely to survive than men in second and third

class, and nearly all women travelling in first class survived compared to women

14

For example, Saved from the Titanic (1912), In Nacht und Eis (1912), Atlantic (1929), Titanic (1943 and 1953), A Night to Remember (1958), Raise the Titanic! (1980). In addition, there were

several TV movies and series. 15

There were more lifeboats than required by the rules of the British Board of Trade, which were

drafted in 1894 and which determined the number of lifeboats required by a ship‘s gross register

tonnage, rather than the number of persons aboard. 16

Anecdotal evidence taken from (Subcommittee of the Committee on Commerce, 1912).

46


travelling in the other two classes.17

Yet, the Titanic disaster is also relevant in a

more general context. It allows us to analyse behaviour under extraordinary

conditions, namely in a life and death situation. Do human beings behave more in

line with the selfish homo economicus, where everybody is out for himself or herself

and possibly even puts other people‘s lives in danger? If that were the case, we would

expect that physically stronger people, that is, adult males, would have a higher

probability of survival than women, children, and older people. Otherwise, when it

comes to a life or death decision, are human beings capable of unselfishness and

perhaps even chivalrous behaviour? The answer to this question is open.18

Some economists argue that the tendency to act selfishly arises when the stakes are

high; in particular, when survival is at stake. Other economists are less certain.19

In

contrast, socio-biologists argue that under such circumstances genetic influences

become more powerful, resulting in more women of childbearing age being saved

than those not of childbearing age or men. The study of the sinking of the Titanic

may also have major policy consequences beyond what was implemented shortly

after the disaster.20

Thus, provided unselfish behaviour can be identified, the question

then becomes—Do more stringent safety regulations crowd out intrinsically moral

behaviour, and could they possibly lead to worse outcomes than less strict

regulations? The data available to us can be considered to be the outcome of a quasi-

natural experiment; the disaster occurred due to an exogenous event, and the

resulting life and death situation affected all persons aboard equally. The tragic event

17

Titanic Disaster: Official Casualty Figures and Commentary (http://www.anesi.com/

titanic.htm). 18

Helping behaviour has been shown to exist under particular circumstances; see, for example, Amato

(1990); Batson et al. (1979); Harrell (1994); Worman (1979), and for a survey Eagly & Crowley

(1986). 19

This issue has been debated and experimentally analysed in the context of high-stakes games. See,

for example, Camerer (2003); Camerer & Fehr (2006); Fehr et al. (2002). For life or death decisions,

see more generally Howard (1966, 1980); Shepard & Zeckhauser (1984); Slonim & Roth (1998);

Smith & Keeney (2005). 20

The sinking of the Titanic led to the first International Convention for the Safety of Life at Sea in

London on November 12, 1913, resulting in a treaty that was to go into effect on July 1, 1915, but

which was delayed by World War I. It established the International Ice Patrol to monitor and report on

the location of North Atlantic icebergs that could pose a threat to shipping. In addition, it was agreed

that all passenger vessels must have sufficient lifeboats for everyone aboard, safety drills must be

instituted, and radio communication must be operated 24 hours a day.

http://www.anesi.com/titanic.htm

http://www.anesi.com/titanic.htm

47


occurred in a closed environment, undisturbed by the outside intervention of other

agents.

We proceed by first developing the theoretically grounded hypotheses of what

determined the survival probability of the people aboard the Titanic. Section II

discusses the data we use, and Section III presents the econometric estimates,

including various robustness tests. The first set of hypotheses relate to economic

determinants. Our estimates suggest that the first-class passengers‘ income and

wealth greatly helped in saving their lives as compared to the second-class

passengers, and even more so the third-class passengers. The crew had access to

more informational and relational resources and therefore had a higher survival

chance than the passengers, in particular, the deck crew. The second set of

hypotheses deal with natural determinants. We find that people in their prime (ages

15–35) had a higher chance of survival than older people. Women of reproductive

age and women with children also had a higher probability of being rescued, which

speaks for the socio-biological approach. The third set of hypotheses refers to various

social determinants of survival. It seems that (at least to some extent) the social norm

that ―women and children first‖ was followed in this situation, overcoming

completely selfish behaviour. The British passengers did not, or could not, take

advantage of being on a British ship; indeed, passengers from the USA had a higher

survival probability than citizens of other nations. Section IV concludes by drawing

general consequences for the behaviour of human beings in life or death situations.

5.2 THEORETICAL HYPOTHESES ABOUT WHO IS EXPECTED TO BE

SAVED

Economists have mainly studied the consequences of disasters by analysing the

effects for the short, medium, and long term, following the path-breaking

contributions by Hirshleifer (1963) or Dacy and Kunreuther (1969).21

Psychologists

and sociologists, on the other hand, focus more on the behaviour of people during

48


disasters. Much of the latter literature rejects the idea that during a disastrous event

many people are stunned, become immobilized, and are unable to act rationally (the

so-called ―disaster syndrome‖). This literature also rejects the concept that in the

event of a disaster chaos, panic, social breakdown and antisocial behaviour, such as

crime, looting, or exploitation, often occur. Indeed, it has been found that morals,

loyalty, respect for law and customs, and tenets of acceptable behaviour do not

instantly break down with a disaster.22

This is consistent with the empirical evidence

accumulated in behavioural economics (or economic psychology), which shows that

people do not necessarily exploit an opportunity presented to them when it can hurt

other people. Rather, they are often inclined to help other people. Substantial

evidence has been generated that motives such as altruism, fairness, or morality

affect the behaviour of many individuals. People sometimes punish others who have

harmed them or reward those who have helped them, sacrificing their own wealth

(Camerer et al., 2004). People donate blood or organs without being paid and give

money for charitable purposes. In wartime, many individuals volunteer and are

willing to take high risks as soldiers (Elster, 2007). Citizens‘ vote in elections,

incurring more private costs than benefits and people are paying more taxes than a

traditional economics-of-crime model would predict (Torgler, 2007). Individuals also

help others in many situations on the job (Drago & Garvey, 1998).23

For our purpose, we develop a simple theoretical framework that allows us to

develop nine hypotheses (arranged according to whether they belong to what can be

called ―economic,‖ ―natural,‖ or ―social‖ factors) that can be tested using the data on

who survived and who perished in the Titanic disaster. The factual knowledge about

the conditions aboard the Titanic has been gathered from various sources, most

21

Other contributions are, for instance, Albala-Bertrand (1993); De Alessi (1975); Grossi & Kureuther

(2005); Kunreuther & Pauly (2005); Sorkin (1982). Particular attention has been paid to insurance

against natural disasters, for example, Kunreuther (1996); Kunreuther & Roth (1998). 22

See, for example, Aguirre, Wenger, & Vigo (1998); Drabek (1986); Hancock & Szalma (2008);

Johnson (1988); Johnson, Feinberg, & Johnston (1994); Quarantelli (1960); Quarantelli (1972);

Tierney, Lindell, & Perry (2001). 23

See, for example, Meier (2006, 2007) for an extensive survey; Camerer (2003); Camerer & Thaler,

(1995); Frey & Meier (2004); Ledyard (1995) specifically for voluntary contributions to public goods;

and Andreoni & Miller (2002); Eckel & Grossman (1996); Henrich et al. (2001) for dictator and

ultimatum games. Surveys on the related topic of fairness are provided, for example, by Camerer

(2003); Fehr & Schmidt (1999); Konow (2003).

49


importantly from the Encyclopedia Titanica and various official accounts as well as

monographs.24

The hypotheses should be understood in the ceteris paribus sense.

They are not mutually exclusive, but can occur simultaneously. The theoretical

framework is influenced by tournament theory (see Kräkel, 2008; Lazear & Rosen,

1981; Nalebuff & Stiglitz, 1983) and biological theories on efforts to understand

fitness in a cooperative animal society, such as the wasp (see Cant & Field, 2001).

Surviving the Titanic disaster can be modelled as a tournament with two risk averse

contestants i and j. Survival (s) can be described as a production function is = ii ae

and jjj aes 25 where e is the effort expended to save oneself, and a is the ability

to do so. 1

, jis indicates that individual i or j survives and 0

, jis that the individual does

not survive. The ability difference ∆a between individual j and i is: ∆a = ij aa . We

assume that ∆a 0 . Exerting effort imposes costs on an individual, described by the

function c( ie ) and c( je ) with c(0) = 0, c ( jie ,) > 0 and c ( jie ,

) > 0. The utility

functions can be written as:

)()()1()()( 01

iiiiiii ecsupsupeUii

(5)

)()()()()1()( 01

jjijjijj ecsupsupeUj

(6)

with aeeFssprobp jijii ()( ). In other words, the probability is a

cumulative distribution based on individual effort and ability difference (Kräkel,

2008). We normalize the utility of those persons not surviving to )( 0

isui = 0 and

)( 0

jj su =0. Thus, we can reformulate equations (5) and (6) as:

24

Official British and American inquiries by The Wreck Commissioner‘s Court (Wreck

Commissioner's Court, 1912) and The Committee on Commerce (Subcommittee of the Committee on

Commerce, 1912). 25

The production function is also affected by noise or random shocks, but we assume that both

subjects are affected identically.

50


)()()( 1

iiiii ecsupeUi

(7)

)()()1()( 1

jjijj ecsupeUj

(8)

Agents choose their efforts in order to maximize equations (7) and (8). The first-

order condition can be written as:

0)()( *1**

iiji ecuaeef (9)

0)()( *1**

jjji ecuaeef (10)

Equations (9) and (10) indicate that the flatter the density f(.), or in other words the

higher the survival rate and the steeper the cost function, the lower the equilibrium

effort of an agent will be. Moreover, the stronger the ability disadvantage, a , the

higher the survival rate. On the other hand, the more i tries to generate a relative

effort advantage, (**

ji ee ), the lower the survival rate. Furthermore, an individual‘s

incentive to survive increases with an increase in the value of surviving because

0/ 1

,

1

, jiji su . In addition, an individual requires less effort to survive if his

marginal costs are lower. These findings allow us to develop several testable

hypotheses with regard to economic and natural determinants.

5.2.1 ECONOMIC DETERMINANTS

The 1,316 passengers on the Titanic were divided into three different classes: 325 in

first class, 285 in second class, and 706 in third class. It is to be expected that the

first-class passengers tried to obtain the same preferential treatment with respect to

lifeboat access that they generally received on the vessel. People with more income

and wealth, such as first-class passengers, are more able to secure a place on a

lifeboat than people of lesser economic means. Thus, they have a relative ability

advantage compared to the second- and third-class passengers. They were used to

giving orders to employees (in this case the crew), and they were better able to

51


bargain, in the extreme case even offering financial rewards. They were also in closer

contact with the upper echelon crewmembers (in particular, First Officer Murdoch,

who commanded the loading of lifeboats on the starboard side, and Second Officer

Lightoller, who did the same on the port side). Moreover, the first-class passengers

had better access to information about the imminent danger and were aware that the

lifeboats were located close to the first-class cabins. Thus, their marginal effort costs

to survive were lower. In contrast, most third-class passengers had no idea where the

lifeboats were located (safety drills for all passengers were introduced after the

Titanic disaster), and they did not know how to reach the upper decks where the

lifeboats were stowed. A relative advantage in the ability, the effort, and the marginal

costs raises the probability of survival, leading to the following hypotheses:

Hypothesis E1: First-class passengers have a higher probability of survival than

second-class passengers; second-class passengers, in turn, have a higher probability

of survival than third-class passengers.

One would expect the experienced crew of 886 men and women to be better prepared

for a catastrophic event, to be earlier and better informed about the location of

lifeboats and the danger of sinking, and to have closer personal contacts with the

crewmembers in charge of loading the lifeboats. This gives them a relative advantage

over passengers regarding saving their own lives (relative ability and effort/cost

advantage). On the other hand, it is their duty to help save passengers, and they are

only supposed to abandon a sinking ship when that task has been fulfilled. We expect

that in life or death situations, such as that encountered on the Titanic, selfish

interests tend to dominate.

Hypothesis E2: Crewmembers have a higher probability of survival than passengers.

Not all crewmembers benefited from the same favourable conditions. Some of the

conditions just mentioned are more likely to apply to the deck crew (who was, for

instance, in charge of manning the lifeboats) or the engine crew (who had

information about the damage done to the ship). The crew directly responsible for

52


passenger amenities (victualling and a la carte crew) did not have the same

information as the deck and engine crews. Therefore, the deck and engine

crewmembers could use their comparative advantage to increase their chances of

survival. We may also observe a ―closeness effect.‖ The officers directing the loading

of the lifeboats and deciding which crew went with which boat were members of the

deck crew. They would have been somewhat biased towards those of their own work

group.

Hypothesis E3: The deck and engine crewmembers have a higher chance of survival

than other crewmembers.

5.2.2 NATURAL DETERMINANTS

Based on the theoretical framework, we are also able to cover natural (biological)

determinants. In the situation of a large excess demand for places in the lifeboats, a

selfish homo economicus, faced with life or death, would fight to be able to board a

lifeboat. People with greater physical strength, that is, people in their prime, would

have an advantage over older people in the fight for survival. Physical strength is

correlated with higher ability and lower marginal effort costs in the event of such a

disaster. Thus, we can develop the following hypothesis:

Hypothesis N1: People in their prime have a higher chance of survival than older

people.

As a next step, let us assume that some people onboard the Titanic make the effort to

help others survive. For example, let us assume that j is willing to help i and that the

utility function depends on the level of relatedness (r) between individuals, where

0/ ,, jiji ru . Moreover, we assume that j is prepared to make additional efforts to

help i (e.g., due to moral costs). We define individual i‘s fitness to survive without

help as 0

iF and individual j‘s fitness to survive without helping as 0

jF . This model

of helping behaviour is similar to biological studies conducted on helping effort and

fitness in cooperative animal societies (see (Cant & Field, 2001) assuming that

53


individuals have interdependent preferences (see, e.g., Becker (1974); Sobel (2005)).

The fitness level of j due to helping h can be written as:

hjF 0

jF (1 – h) (11)

where h is the level of h and the cost of helping (cost per unit of help extended).

Thus, hjF is a decreasing function of h. The maximum possible level of help would

be 1/ , where hjF 0.

Individual j‘s investment in h increases the survival probability of individual i.

Helping investment, I(h), is subject to diminishing benefits in terms of efficiency so

that I(h) is a positive but decelerating function of h. The level of investment is taken

to be driven by society‘s helping norms, n (e.g., ―women and children first‖). Thus,

the helping investment, I(h), can be written as:

I(h) = n(1 - qhe ) (12)

where q determines how rapidly the marginal investment of help diminishes. This

allows us to define new utility functions for i and j:

Uhj =

hjF + r I(h) (13)

U hi = 0

iF + I(h) + r hjF (14)

The utility function of individual i(j) is positively correlated with a higher survival

rate of j(i), which means that preferences are interdependent. Substituting equations

(11) and (12) with (13) and (14) leads to:

Uhj =

0

jF (1 – h) + r n(1 - qhe ) (15)

54


U hi = 0

iF + n(1 - qhe ) + r 0

jF (1 – h) (16)

The optimal level of help is generated by maximizing equations (15) and (16) with

respect to h. This leads to:

0

* ln1

jj F

nqrq

h (17)

0

* ln1

ji rF

nqq

h (18)

Equation (17) measures the optimal level of help from the perspective of the helper,

j, and equation (18) from the perspective of the person being helped, i. They can be

seen as an upper and lower limit. We observe that individual j‘s optimal level of help

increases with an increase in society‘s norm of helping (n) and the level of

relatedness (r).

An alternative determinant of survival is based on socio-biology. It stresses the

relevance of the ―procreation instinct.‖ As the survival of a species depends on its

offspring, a high value must be placed upon females of reproductive age as a valuable

resource. Social norms may be created to protect the reproductive and child-rearing

role of women (higher n). It is an attempt to protect children rather than the result of

a greater value put on women‘s lives. A potential shortage of women would limit the

number of offspring, while a shortage of men would not (Felson, 2000). In humans,

the period of peak reproduction is between the ages of 16 and 35 (American Society

for Reproductive Medicine, 2003). Females (on average) are not reproductively

functional before age 15, and the reproductive cycle begins to slow down from age

35 to age 50 when the reproductive function is usually lost altogether. It has also

been emphasized that the social norm of helping women may be related to the

relative physical and structural vulnerability of women (Felson, 2000).

Females may also have a strong incentive to ensure the survival of their children in

the event of a disaster like the Titanic (strong r relationship between child and

55


mother). In anthropology, ―parental investment‖ is an important concept. It argues

that the females of most species invest more in ensuring the survival of their

offspring than the males. The females of the species are the ones who are responsible

for their young during gestation and lactation, and they generally protect them from

predators and educate them (Geary, 1998). The male contribution is usually much

smaller. Because of the much larger investment on the part of the females, the

opportunity cost of losing offspring is higher and the drive to ensure offspring

survival is stronger (Campbell, 1999). It has been shown that the mortality rates of

children with a mother are 1.4 times lower than those without a mother (Voland,

1998) and that the survival rates of offspring can be directly linked to maternal

survival (Bjorklund & Shackelford, 1999). Under these conditions, it is to be

expected that females with children would be much more alert to possible danger and

would aggressively fight other females to ensure a safe haven (Cashdan, 1997).

Moreover, it has been emphasized that it is the parent who has the greater investment

in promoting the survival of offspring who is the more valued resource (Eswaran &

Kotwal, 2004; Trivers, 1972). These socio-biological considerations lead to the

following two hypotheses:

Hypothesis N2: Women of reproductive age have a higher probability of survival due

to being subject to a social norm of helping.

Hypothesis N3: Women with children have a higher probability of survival than

women without children.

5.2.3 SOCIAL DETERMINANTS

A key norm under life and death conditions is that women and children are to be

saved first (higher n). This norm may work directly in the sense that men let women

and children board the lifeboats first. The norm may also have been supported

institutionally, thus it could have worked indirectly if the officers in charge of

loading the lifeboats directed the male passengers to let women and children proceed

first. Interestingly, there is no international maritime law that requires that women

and children be rescued first. Similar norms can be found in other areas where people

56


need to be evacuated. Humanitarian agencies often evacuate ―vulnerable‖ and

―innocent‖ civilians, such as women, children, and elderly people first. The Geneva

Convention provides special protection and evacuation priority for pregnant women

and mothers of young children (Carpenter, 2003). The following hypothesis tests

whether this social norm was acted upon when the Titanic sank.

Hypothesis S1: Women and children have a higher probability of survival than men.

Passengers travelling alone may be expected to have a lower chance of survival in

life and death situations because they are less likely to receive information indirectly

and to obtain psychological and physical support from others (lower r). On the other

hand, being alone makes decision making less cumbersome and conflictive (lower

transaction costs), increasing the survival chance of all (lower ). Following the

(crude) homo economicus concept centred on individualistic considerations, the

advantage of being able to act alone and to only have to consider one‘s own best

interests seems to prevail. Moreover, a higher r increases j‘s willingness to help i

(e.g., one‘s partner), but also reduces a partner‘s incentive to request help.

Hypothesis S2: Passengers travelling alone have a higher probability of survival

than those travelling in a group (n 2).

The Titanic was built in Great Britain, operated by British subjects, and manned by a

British crew.26

It is to be expected that national ties were activated during the disaster

and that the crew would give preference to British subjects, easily identified by their

language (higher r). In contrast, passengers from other nationalities, in particular

Americans, Irish, and Scandinavians would be at a disadvantage.

Hypothesis S3: British subjects have a higher chance of survival than people of other

nationalities.

26

Interestingly enough, the Ocean Steam Navigation Company, popularly known as the ―White Star‖

line because of the white star appearing on the company flag, was under the management of the

industrial giant, J.P. Morgan. Nevertheless, the public perceived the Titanic as being a British ship.

57


5.3 THE DATA

The sinking of the RMS Titanic was a tragic event and resulted in a sorrowful loss of

life. However, the event offers economic researchers an exceptional opportunity to

control exogenous factors within a quasi-natural field experiment. The event itself is

completely isolated, making the external shock applicable to every person aboard the

ship and the exogenous factors the same for everyone. The event is such that every

person is impacted by the shock and is unable to defer making a decision. Even if one

chooses not to participate in the scramble for lifeboat seats, the outcome is the same

as that of someone who does strive for a seat and fails. The great advantage of a

natural field experiment is the randomization and realism. The participants do not

know that their fate can be looked at as being the result of an (natural) experiment;

their behaviour is therefore unaffected (Reiley & List, 2007).

We have been able to construct a detailed dataset, despite the facts that the event

occurred almost 100 years ago and the records were not very detailed. Our data

consist of 2,207 persons who were confirmed to be aboard the R.M.S. Titanic. The

data were gathered from the Encyclopaedia Titanica and crosschecked with other

sources.27

Summary statistics of the variables collected are reported in the Appendix

(see Table 5.4). The dependent variable is whether someone survived or not. Out of

2,207 passengers and crewmembers, 1,517 people died. Based on the records, we

were able to gather information about the gender, age, nationality, port where people

boarded the Titanic, ticket price and therefore the passenger-class status (first,

second, or third class). In addition, we were able to generate individual information

related to travel plans and companions. Limited information was available with

regard to the cabin allocation (only 15.2 percent).28

Of the 2,207 persons onboard, the

27

The cross-checked resources include: Beavis (2002); Bryceson (1997); Eaton & Haas (1994); Geller

(1998); Howell (1999); Lord (1955, 1988); Nova Scotia Archives and Records Management (2008);

Quinn (1999); Ruffman (1999); Subcommittee of the Committee on Commerce (1912); U.S. National

Archives (2009); Wreck Commissioner's Court (1912). 28

The data also indicate that this information has been mainly provided by the survivors and is

therefore biased. Moreover, as the iceberg was struck shortly before midnight, some passengers were

not yet in their cabins, but somewhere else on the ship.

58


age of all but 21 individuals is known. Thus, using age in the regression reduces the

number of observations to 2,186 persons.29

Out of the 2,186 people onboard, 1,300

were passengers and 886 crewmembers. Among the passengers, 43 were servants.

Additionally, of the 2,186 aboard, 1,704 were male (78 percent), and 460 of the 1,300

passengers were female (35 percent).

We have complete information on each person‘s country of residence (nationality).

From this, we have been able to generate several variables to investigate the effects

of nationality. We have created dummies for the most populous national groups

aboard the Titanic. These include Great Britain (the largest group), Ireland, Sweden,

the USA, and a group for all other nationalities. Passenger groupings have been

identified by anecdotal evidence taken from family histories and known travel

arrangements, ticket numbers, and cabin allocations.30

Because the impact of age is prominent in this investigation, it is important to use

generally accepted groupings: children, adults, and older people. The United Nations

standard for age, which classifies children as being fifteen years of age or under, is

used. Among the 2,186 people aboard, 124 were children (65 girls and 59 boys).

Adulthood begins post childhood and ends at old age, defined by the British Royal

Commission in 1894 as beginning at age 50.31

In humans, the peak reproductive age,

as defined by the American Society for Reproductive Medicine (2003), is between 15

and 35 years of age. There were 280 women out of the 2,186 people aboard between

16 and 35 years of age. While there is some anecdotal conjecture that there may have

been other people aboard (stowaways), the list of survivors corresponds to the

―official‖ passenger lists, which would indicate that the stowaways did not compete

with other passengers for lifeboat seats.

29

Out of these 21 people, four were crewmembers and 17 passengers. 30

Those passengers for whom there is no clear or known evidence were assumed to be travelling alone

and assigned as single. 31

The British Royal Commission was based upon the payment of benefits from the friendly societies

(see Boyer (1988)) to its members who were too old to work; these benefits began at age 50. The

Commission accepted the reasoning and adopted this for government-aged welfare.

59


5.4 ECONOMETRIC ESTIMATES AND RESULTS

The nine hypotheses developed are empirically tested using probit estimates. The

tables below show the estimated parameter and the significance level (indicated by z-

values). The respective marginal effects are also indicated. Table 5.1 deals with the

economic and natural determinants and Table 5.2 with the social determinants.

5.4.1 TESTING ECONOMIC DETERMINANTS

Table 5.1 presents the results of the first set of hypotheses, those relating to economic

determinants.

The estimates are consistent with the hypotheses. According to estimation (1),

passengers in first class had a higher chance of survival than those in second class,

and second-class passengers had a higher chance of survival than those in third class.

The marginal effects suggest that a passenger in the highest class was 40 percent

more likely to survive the catastrophe than a passenger in third class. A second-class

passenger had a 16 percent higher chance of survival than somebody travelling in

third class. These are large and robust differences. Adding controls for the gender

composition of the various classes (estimation 2) as well as possible effects of the

crew (estimation 3) has practically no impact on these marginal effects. Thus,

hypothesis E1 cannot be rejected.

Estimation (3) indicates that the crew had an 18 percent higher chance of survival

than the passengers, controlling for passenger class and gender. This result is

consistent with the second economic hypothesis (E2).

Consistent with hypothesis E3, the survival rate is higher among deck and engine

crewmembers than among members of the rest of the crew. In particular, the deck

crew were more likely to save themselves than other crewmembers. According to

estimation (4), the deck crew had a much higher (74 percent) chance of survival,

compared to 39 percent for the engine crew and 32 percent for the victualling crew

(always compared to the remaining crew).

60


Table: 5.1 Economic and Natural Determinants

Probit Passenger Passenger All Crew Adult

Passenger

All

Adult

Female

Passenger

Adult

Female

Passenger

[1] [2] [3] [4] [5] [6] [7]

1st CLASS 0.990*** 1.020*** 1.023*** 1.309*** 2.156*** 2.158***

11.24 10.32 10.33 10.76 8.25 8.07 0.378 0.387 0.387 0.484 0.43 0.417

2nd CLASS 0.408*** 0.368*** 0.368*** 0.318** 1.060*** 1.068***

4.46 3.59 3.58 2.79 5.59 5.57 0.158 0.14 0.136 0.119 0.211 0.204

FEMALE 1.485*** 1.509*** 2.097*** 1.641***

17.7 18.59 6.1 17.69 0.536 0.547 0.694 0.581

FEMALE AGE

16–35

0.528**

0.572**

2.83 3.00 0.15 0.159

AGE 16–35

0.512***

4.66 0.177

CREW 0.496***

6.21 0.176

DECK CREW 2.322***

6.47 0.744

ENGINE CREW 1.211***

3.65 0.385

VICTUALLING

CREW

1.091**

3.32 0.319

HAS CHILD

/CHILDREN

0.937*

2.05 0.158

Obs. 1300 1300 2186 886 1178 401 401

Prob.>chi2 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Pseudo R2 0.076 0.276 0.203 0.12 0.328 0.249 0.26

Notes: Dependent variable: Survival (value=1). The symbols *, **, *** represent statistical significance at the 5,

1, and 0.1% levels, respectively. Adult=Age>15. In the reference group: THIRD CLASS, MALE, PASSENGER

(EQ3), A LA CARTE CREW (EQ4), AGE>36 (EQ5), FEMALE AGE>35 (EQ6 & EQ7), NOT HAVING A

CHILD/CHILDREN (EQ7).

61


5.4.2 TESTING NATURAL DETERMINANTS

Table 5.1 also shows the results obtained with respect to the natural (socio-

biological) determinants of surviving the catastrophe. Passengers in their prime (16

to 35 years of age) had an 18 percent higher chance of surviving the disaster

(estimation 5) than older people. These results are consistent with hypothesis N1. In

line with the socio-biological hypotheses N2 and N3, females of childbearing age

(16–35) had a 15 percent higher probability of survival than older women (estimation

8). In addition, if these women had a child, their survival probability was further

increased by 16 percent (estimation 7).

5.4.3 TESTING SOCIAL DETERMINANTS

Estimation (8) suggests that being a female or child had a highly significant positive

effect on being saved. The probability of surviving is 53 percent higher for females

than for males and 15 percent higher for children than for adults (i.e., age 16 and

above). The same effect can be observed for the crew where females even had a 64

percent higher chance of being saved (estimation 9). These results are consistent with

hypothesis S1, suggesting that social norms were to some extent observed even under

conditions of extreme duress.

Being aboard the Titanic as a single person did not increase the chance of survival

(see estimation 10). The advantage of lower transaction costs in the decision-making

process when travelling alone may have been overshadowed by psychological or

even physical disadvantages and a lack of information. Thus, we can reject

hypothesis S2.

Similarly, hypothesis S3 is refuted. As can be seen in estimation (11), British subjects

had a 10 percent lower chance of survival than passengers from other countries. This

may be because the norms of being a ―gentleman,‖ even under extreme duress, were

valid at that time in Britain. Estimation (12) shows that passengers from the USA had

a 12 percent higher probability of survival than British subjects.

62


Table: 5.2 Social Determinants of Survival

Probit Passenger Crew Passenger Passenger Passenger All

[8] [9] [10] [11] [12] [13]

FEMALE 1.468*** 1.858*** 1.456*** 1.444*** 1.447*** 1.475***

17.44 5.50 16.77 16.58 16.41 17.38 0.53 0.64 0.526 0.522 0.523 0.536

AGE SUB 15

(CHILDREN)

0.382** 0.807*** 0.808*** 0.821*** 0.754***

2.83 3.93 3.91 3.96 3.78 0.148 0.313 0.313 0.318 0.289

AGE 16–50 0.470** 0.476** 0.479** 0.422**

2.99 3.01 3.03 2.86 0.161 0.162 0.163 0.132

1st CLASS 1.066*** 1.140*** 1.122*** 1.075*** 1.072***

10.62 10.75 10.55 9.00 9.09 0.403 0.429 0.423 0.406 0.404

2nd CLASS 0.387*** 0.407*** 0.500*** 0.471*** 0.451***

3.74 3.90 4.51 4.10 3.97 0.148 0.155 0.191 0.180 0.168

TRAVELLING ALONE -0.057 -0.070 -0.078 -0.071

-0.62 -0.76 -0.84 -0.77 -0.021 -0.026 -0.029 -0.024

ENGLAND (1,143) -0.268*

-2.56 -0.096

IRELAND (114) 0.238 0.180

1.37 1.10 0.091 0.065

SWEDEN (106) 0.090 0.053

0.52 0.31 0.034 0.019

USA (424) 0.309* 0.258*

2.49 2.39 0.116 0.093

ALL OTHER NATIONS

(399)

0.283* 0.237*

2.37 2.19 0.106 0.085

CREW 0.644***

5.47 0.228

Obs. 1300 886 1300 1300 1300 2186

Prob.>chi2 0.000 0.000 0.000 0.000 0.000 0.000

Pseudo R2 0.280 0.041 0.286 0.290 0.291 0.212

Notes: Dependent variable: Survival (value = 1). The symbols *, **, *** represent statistical significance at the 5, 1, and

0.1% levels, respectively. In the reference group: MALE, AGE>15 (EQ8), AGE >50 (EQ10-EQ13), THIRD CLASS,

GROUP (couples with and without children and/or servants, singles with children and/or servants, extended group also

covering friends), NOT FROM ENGLAND, (EQ11), ENGLAND (EQ12 & EQ13), PASSENGER (EQ13).

63


The last estimation (13) in Table 5.2 includes all the social determinants. It is

presented to indicate that the estimated parameters and marginal effects are quite

robust. They are of similar magnitude, independent of which further determinants are

included in the estimate.

Instead of splitting up the sample of persons aboard the Titanic as in Tables 5.1 and

5.2, Table 5.3 considers the complete sample and then captures the influence of

gender by using interaction effects. As can be seen, the estimates are robust when the

additional determinants relating to the crew, the reproductive age of women, and

children are added. The qualitative results and the statistical significance remain

unchanged when compared to the estimates in Tables 5.1 and 5.2.

The most comprehensive estimate presented in estimation (17) suggests that the

survival probability more than doubles in its magnitude for women travelling in first

class compared to males travelling in third class. Similarly, females travelling in

second class have a 67 percent higher probability of surviving the disaster than our

base group of third-class males. Men travelling in first class had a 30 percent higher

chance of surviving than men travelling in third class, but there is no statistically

significant difference between men travelling in second or third class.

A female member of the crew had a 59 percent higher probability of surviving the

disaster than the male members of the crew and a 77 percent higher probability of

surviving than non-crew male members. Female crewmembers have a 57 percent

higher survival probability than non-crew women. In addition, male crewmembers

had an 18 percent higher chance of survival than male non-crew members. Women of

reproductive age had a higher survival chance than males and females in other age

categories. Female (male) children had a 77 percent (14 percent) higher probability of

surviving than adults. Moreover, female children had a 62 percent higher survival

probability than male children. Finally, those from the USA had a 9 percent higher

chance to save themselves than the British.

64


Table: 5.3 Robustness Tests Including Interaction Terms

Probit All All All All

(14) (15) (16) (17)

FEMALE 1.054*** 0.942*** 0.710*** 0.532**

9.86 8.21 4.61 3.17 0.395 0.354 0.267 0.199

AGE -0.014*** -0.010*

-4.36 -2.60 -0.005 -0.003

1st CLASS 0.640*** 0.603*** 0.790*** 0.777***

4.96 4.65 5.71 5.59 0.243 0.229 0.301 0.296

2nd CLASS -0.008 -0.047 -0.008 -0.015

-0.05 -0.32 -0.06 -0.10 -0.003 -0.017 -0.003 -0.005

CREW 0.443*** 0.377** 0.451*** 0.492***

3.85 3.22 3.76 4.04 0.159 0.135 0.162 0.178

IRELAND 0.268 0.294 0.223 0.245

1.67 1.84 1.36 1.50 0.100 0.110 0.082 0.091

SWEDEN 0.125 0.129 0.114 0.091

0.74 0.77 0.68 0.54 0.045 0.047 0.041 0.033

USA 0.242* 0.237* 0.259* 0.249*

2.19 2.15 2.32 2.22 0.088 0.087 0.095 0.091

ALL OTHER

NATIONS

0.238* 0.236* 0.184 0.175

2.18 2.17 1.67 1.57 0.087 0.086 0.067 0.064

TRAVELLING

ALONE

-0.120 -0.136 -0.082 -0.032

-1.34 -1.52 -0.89 -0.34 -0.042 -0.047 -0.029 -0.011

HAS CHILD /

CHILDREN

0.379*

2.08 0.143

1st CLASS*

FEMALE

1.118*** 1.225*** 1.337*** 1.402***

4.55 4.92 5.14 5.25 0.424 0.459 0.494 0.513

2nd CLASS*

FEMALE

1.088*** 1.197*** 1.260*** 1.284***

4.95 5.35 5.53 5.56 0.414 0.450 0.470 0.477

CREW* FEMALE 0.906* 0.982** 1.034**

2.53 2.77 2.93 0.349 0.376 0.395

REPRODUCTIVE

AGE* FEMALE

0.334* 0.523**

2.20 3.01 0.124 0.199

CHILDREN*

FEMALE

1.118*

2.48 0.423

Obs. 2186 2186 2186 2186

Prob.>chi2 0.000 0.000 0.000 0.000

Pseudo R2 0.221 0.224 0.234 0.238

Notes: Dependent variable: Survival (value = 1). The symbols *, **, *** represent statistical

significance at the 5, 1, and 0.1% levels, respectively. Reference group: Male, 3rd Class, England, Not

Travelling Alone, Not a Child (EQ17).

65


5.5 CONCLUSIONS

The econometric estimates of the factors determining survival during the sinking of

the Titanic produce a coherent story. However, this story is not necessarily in line

with the simple model of selfish homo economicus. While people in their prime were

more likely to be saved, it was women—rather than men—who had a better chance

of being saved. Children also had a higher chance of surviving. At the time of the

disaster, the unwritten social norm of ―saving women and children first‖ seems to

have been enforced.

There is also support for socio-biological explanations of who was saved and who

perished. Women of reproductive age and women with children had a higher

probability of being saved.

However, we do find evidence suggesting that the effects predicted using the

standard homo economicus model are also important. People in their prime drowned

less often than older people. Passengers with high financial means, travelling in first

class, were better able to save themselves as were passengers in second class

(compared to third class). Crewmembers who had access to better informational and

relational resources managed to survive more often than others aboard. This applies

in particular to the deck crew who were partly in charge of the rescue operations. In

contrast, the British passengers who were the same nationality as most of the

crewmembers did not take advantage of this fact. They had a higher probability of

perishing than other nationalities, thus exhibiting behaviour consistent with the

prevailing concept of being a gentleman.

The sinking of the Titanic represents a rare case of a well-documented and most

dramatic life and death situation. However, even under these extreme situations, the

behaviour of human beings is not random or inexplicable, but can be accounted for

by economic analysis.

66


5.6 TABLE

Table: 5.4 Summary Statistics

Variables Mean Std. Dev. Min Max N

SURVIVED 0.319 0.467 0 1 2207

FEMALE 0.220 0.414 0 1 2207

AGE 30.044 11.610 1 74 2186

AGE< 16 (CHILDREN) 0.052 0.221 0 1 2186

AGE 16-50 0.891 0.312 0 1 2186

FEMALE 16-35 0.121 0.324 0 1 2186

1st CLASS 0.146 0.354 0 1 2207

2nd CLASS 0.129 0.335 0 1 2207

TRAVELING ALONE 0.217 0.412 0 1 2207

ENGLAND 0.529 0.499 0 1 2207

IRELAND 0.052 0.221 0 1 2207

SWEDEN 0.048 0.214 0 1 2207

USA 0.191 0.394 0 1 2207

OTHER NATIONALITIES 0.180 0.385 0 1 2207

CREW 0.405 0.491 0 1 2207

Sources: The Encyclopaedia Titanica (2008) has been used as the primary source, which was crosschecked across

the following resources: Beavis (2002), Bryceson (1997), Subcommittee on Commerce (1912), Eaton and Hass

(1994), Geller (1998), Howell (1999), Lord (1955), Lord (1988), NSARM (2008), Quinn (1999), Ruffman (1999),

U.S. National Archives (2008), Wreck Commissioner‘s Court (1912).

Chapter 6: Should I stay or should I go now? and Time and tide: constrained altruism? 67

Chapter 6: Should I stay or should I go

now? and Time and tide:

constrained altruism?














Combined Papers: Should I stay or should I go now? and Time and Tide: Constrained

Altruism? Both April, 2009 -Papers completed and submitted for publication.


David A. Savage

Has equally contributed to all aspects of these papers, including research,

analysis and writing

Date

Bruno S. Frey*



Benno Torgler*





authorship.

Benno Torgler 09/11/2009 _______________________ ____________________ ______________________

Name Signature Date

68

68 Chapter 6: Should I stay or should I go now? and Time and tide: constrained altruism?

6.1 INTRODUCTION

On Friday the 7th

of May 1915 the German u-boat U-20 torpedoed sank the passenger

liner R.M.S Lusitania south of Kinsale, off the Irish coast. In less than 18 minutes32

the liner had slipped beneath the waves taking almost 1200 lives, despite valiant

efforts of crew and fishing boats racing to the rescue. This occurred only three years

after the most famous maritime disaster in history, the sinking of the RMS Titanic

with comparable loss of life. And like the Titanic the sinking of the Lusitania has

been surrounded by myth and mystery, creating an enduring interest that survives to

this day. In the 90 years since the sinking many books, movies and documentaries

have attempted unravel some of the murky innuendo and mystery that surrounds this

vessel.33

Despite such fame and frequent disaster reports in the media, a quantitative

understanding of the survival in life and death situations is still lacking. In this study,

we explore the interaction between natural survival instincts and the materialization

of internalized social norms using data on these two disasters. While it is virtually

impossible for any two natural disasters to be identical there are numerous and

strongly compelling parallels that can be drawn between the Titanic and the

Lusitania. Both of which demonstrate a similar shortage of lifeboats and survival

rates (around 30 percent), a comparable number of crew members in relation to

passengers (around 40 percent) and similarities in passengers‘ socio-demographic

and socio-economic structures (see Table 6.1). As the two maritime disasters

occurred within three years of each other, one can also assume stable historical

norms. In addition, maritime disasters can be seen as quasi-natural experiments.

The disasters occur due to an exogenous event, and the resulting life and death

situation affects all persons aboard equally and nobody is unable to defer making a

decision. Even if one chooses not to participate in the scramble for lifeboat seats, the

outcome is the same as that of someone who does strive for a seat and fails. The great

32

Testimony given by Captain Turner to the British Wreck Commission Inquiry 15th

June, 1915 33

For example Sinking of the Lusitania (2008), Lusitania (2003), Last Voyage of the Lusitania (2005),

The Last Voyage (2006), The Lusitania Murders (2007).

69


advantage of such natural experiments is the randomization and realism. The people

do not know that their fate can be looked at as being the result of an (natural)

experiment; their behaviour is therefore unaffected. In addition, the real life or death

situation guarantees that the reactions and behaviours of all individuals can be taken

as natural. Additionally it is impossible to replicate or even approximate the levels of

fear or stress under any laboratory conditions (Reiley & List, 2007; Levitt & List,

2008). These are strong advantages compared to a laboratory experimental setting.

An important aspect in both disasters is the shortage of lifeboat spaces creating an

excess of demand for survival. It is a combination of time and the excess of demand

that determined the type of competition observed in each event. The Lusitania sank

in under 18 minutes whereas the Titanic slowly sank over a 2 hour 40 minutes. Thus,

we are able to compare these two maritime disasters, similar to an experimental

design consisting of a control and treatment group. Where the essential differences

between both cases is the level of time restriction.

We believe this is the first time that these arguably very well-known shipping

disasters have been analysed in a comparative manner with advanced statistical

(econometric) techniques using individual data of the passengers and crew. The

analysis provides innovative insights into the behaviour of individuals under extreme

conditions. Economics traditionally assumes that human beings behave in a rational

and selfish way, which is shaped by external conditions (Becker, 1976; Frey, 1999).

Recent research has provided evidence that these assumptions do not always hold

(Kahneman et al., 1982; Thaler & Sunstein, 2008; Fehr & Fischbacher, 2003). Even

though the two vessels and the composition of the passengers were quite similar, the

behaviour of the individuals on board was dramatically different. On the Lusitania,

selfish behaviour prevailed (which corresponds to the classical homo oeconomicus),

while on the Titanic the adherence to social norms and social status (class)

dominated. This difference could be attributed to the fact that the Lusitania sank in

only 18 minutes, creating a situation in which the short-run flight impulse dominates

behaviour; while on the slowly sinking Titanic (2 hours, 40 minutes), there was time

for socially determined behavioural patterns to re-emerge. It also could be argued that

the Lusitania was sunk during a time of war, which may provoke different reactions.

70


Table: 6.1 Lusitania vs. Titanic

Disasters Lusitani

a

Titanic

Variables Mean Mean

SURVIVED 0.326 0.32

FEMALE 0.26 0.22

AGE 31.57 30.04

AGE < 16 0.15 0.051

AGE 16 – 35 0.475 0.663

AGE 36 – 50 0.281 0.217

AGE 50 + 0.094 0.069

1st CLASS 0.149 0.147

2nd

CLASS 0.307 0.129

TRAVEL ALONE 0.386 0.217

CREW 0.355 0.403

HAS CHILDREN 0.21 0.0531

For example, we may observe less risk-averse passengers on the Lusitania. Warning

notices had been printed in the leading newspapers reminding transatlantic

passengers that a state of war was in effect and any vessel travelling under the British

flag was liable to destruction, and passengers sailed at their own risk. On the other

hand, there are several reasonable suppositions supporting the idea that the Lusitania

‗should‘ not have been at risk, primarily because it was capable of speeds fast enough

to outrun enemy torpedoes. The Lusitania held the transatlantic Blue Riband award

for speed, and it was a vessel carrying civilian passenger, not a warship. Finally, it

was carrying a number of neutral American civilians. Maritime law states that in

wartime merchant vessels must be given a warning prior to attack, whereas warships

should not expect any warning. The Lusitania was never given such a warning by the

attacking U-boat (Bailey, 1935).

The likelihood that the passengers of the Lusitania knew about the tragic events of

the sinking of the Titanic should not be excluded. For example, whereas many of the

passengers on the Titanic may have (wrongly) believed that they would ultimately be

rescued (Subcommittee of the Committee on Commerce, 1912), those on the

Lusitania may have learned from the experience of the Titanic. This may have led

those passengers to change their behaviour (increase in self-preserving behaviour).

71


Nevertheless, maritime disasters have similarities to quasi-natural experiments whose

great advantage is randomization and realism (Reiley & List, 2007; Levitt & List,

2008; List, 2008).

Many social scientists assume that in a life-and-death situation self-interested

reactions predominate. Social cohesion is expected to disappear, and the desire to act

in accordance with self interests takes over (Mintz, 1951; Kelley et al., 1965). In

states of extreme privatization (Lang & Lang, 1962), ‗the social contract is thrown

away, and each man single-mindedly attempts to save his own life at whatever cost to

others‘ (Brown, 1965). On the other hand, social norms are followed for intrinsic

reasons; people believe them to be ‗right‘ (Elster, 2006), or they fear social sanctions

when violating them (Polinsky & Shavell, 2000). The emerging disaster literature

suggests that pro-social behaviour predominates in such contexts (Quarantelli, 2001).

Laboratory experiments have shown that strategic incentives are important to

understand whether self-regarding or other-regarding preferences dominate (Camerer

& Fehr, 2006).

Our study proposes that context differences matter. Time appears to be a key

parameter for explaining the adoption of either social or self-interested behaviours.

Our results indicate that adherence to social norms and social power requires time to

manifest (evolve) and cannot compete against individual self-interested flight

behaviour in a shorter window of opportunity where competition for survival of the

fittest prevails. The rapid sinking of the Lusitania very likely created a situation in

which simple physical prowess and maybe also good fortune or randomness played a

larger role, while social norms were much more influential in the case of the Titanic.

To have more time at one‘s disposal, as in the case of the Titanic, may also have

eased the restrictions on bargaining for lifeboats and facilitated information

generating advantages, which may have benefited first- and second-class passengers

when compared to third-class passengers (with the crew favouring the rich and

powerful).

The research on fight or flight behaviour may also provide further insights into how

people reacted in these different conditions. Fight or flight behaviour, as the

72


instinctual reaction to a perceived danger, has been discussed in different disciplines

such as biology, psychology and sociology (Cannon, 1929a, 1929b; Gray, 1988;

Mawson, 2007; Cory, 2000). Biologically, fight or flight behaviour has two distinctly

separate stages (Vingerhoets & Perski, 1999). The short-term response triggers a

surge in adrenaline production via the hypothalamus and can last from a few seconds

to a few minutes. This response is limited to a few minutes because adrenaline

degrades rapidly and leaves the body in a state of exhaustion (Henry & Wang, 1998).

The elevated operational state is maintained for a short period after the threat has

passed, then the response mechanism switches off and the system returns to

homeostasis (Everly, 2002). The duration extends beyond the active flight response

time and includes a cool down period. Only after returning to homeostasis do the

higher-order brain functions of the neo-cortex begin to override instinctual responses,

which may lead to a change towards pro-social individual behaviours.

6.2 DATA AND MODEL

We were able to collect unique data sets containing detailed information about

gender, age, ticket price and thus the passenger-class status for both the Titanic and

the Lusitania with which to test these propositions. The dependent variable in the

multivariate analysis is a 0/1 variable that indicates whether an individual survived

the disaster or did not survive (survived = 1). Table 6.2 shows the estimated

parameters, the significance level (indicated by z-values) and the quantitative

(marginal) effects for the Lusitania (L) and Table 6.3 for the Titanic (T). The Titanic

data consist of 2,207 persons confirmed to be aboard the R.M.S. Titanic. The data

was gathered from the Encyclopaedia Titanica and crosschecked with other sources

(Beavis, 2002; Eaton & Haas, 1994; Geller, 1998; Howell, 1999; Lord, 1955, 1988;

Quinn, 1999; Ruffman, 1999).

The dependent variable is whether someone survived or not. Out of 2,207 passengers

and crewmembers, 1,517 people died. Out of the 2,186 people onboard, 1,300 were

passengers and 886 crewmembers. Among the passengers, 43 were servants, of the

2,186 aboard, 1,704 were male (78 percent), and 460 of the 1,300 passengers were

73


female (35 percent). The Lusitania data consist of 1,949 persons confirmed to be

aboard the R.M.S. Lusitania. The data was gathered from numerous sources and

crosschecked with other sources (Butler, 2000; O‘Sullivan, 2000; Wreck

Commissioner‘s Court, 1915; Preston, 2002). The dependent variable is whether

someone survived or not. Out of 1,949 passengers and crewmembers, 1,313 people

died. Out of the 1,949 people onboard, 1,258 were passengers and 691 crewmembers.

Among the passengers, 19 were servants, of 1,949 aboard, 1,441 were male (73

percent), and 483 of the 1,258 passengers were female (38 percent).

Based on the records, we were able to gather information about the gender, age,

nationality, port where people boarded, ticket price and therefore the passenger-class

status (first, second, or third class). In addition, we were able to generate individual

information related to travel plans and companions. We have complete information

on each person‘s country of residence (nationality). From this, we have been able to

generate several variables to investigate the effects of nationality. We have created

dummies for the most populous national groups aboard both the Titanic and

Lusitania. These include Great Britain (the largest group), Ireland, Sweden, the USA,

and a group for all other nationalities. Passenger groupings have been identified by

anecdotal evidence taken from family histories and known travel arrangements, ticket

numbers, and cabin allocations. Because the impact of age is prominent in this

investigation, it is important to use generally accepted groupings: children, adults,

and older people. The United Nations standard for age (American Society for

Reproductive Medicine, 2003) which classifies children as being fifteen years of age

or under is used. Adulthood begins post childhood and ends at old age, defined by a

British Royal Commission in 1894 as beginning at age 50. In humans, the peak

reproductive age, as defined by the American Society for Reproductive Medicine

(1982), is between 15 and 35 years of age.

We use a probit model of the survival probability for a typical passenger:

Pr(y = 1 | x1, x2, …, xk ) = ( + 1x1 + 2x2 + … + kxk). (19)

74


Here y is a dummy variable indicating whether the passenger survived (y = 1) or not

(y = 0); the variables (x1, x2, …, xk) are explanatory variables such as gender, age,

etc; ( , 1, 2, … k) are parameters to estimate; and is the cumulative standard

normal distribution function. The role of , which is increasing in its argument, is to

keep the probability Pr(y = 1) in the zero to one interval. Each passenger contributes

one observation on (y, x1, x2, …, xk). From a sample of such observations, assumed

independent, the parameters can be estimated by maximum likelihood. This is a

standard probit model (see e.g. Baum, 2003; Wooldridge, 2002, 2003). Since the

coefficients are difficult to interpret directly, the marginal effect of a continuous

explanatory variable xj will, as usual, be interpreted through the partial derivative

Pr(y 1 | x1,x2,...,xk )

x jj ( 1x1 2x2 kxk ) , (20)

evaluated at the means, where is the standard normal density function (not the

cumulative density ). Since > 0, the sign of the marginal effect is the same as the

sign of j. For a discrete xj, a difference rather than a derivative will be used in place

of a partial derivative.

6.3 RESULTS

6.3.1 IS SELF-INTEREST DOMINANT IN THE LUSITANIA CASE?

A self-interested reaction in a situation of life and death is predominantly used as a

theoretical foundation. Unstable rewarding structure (survival) in a competitive

environment (shortage of lifeboats) leads to the disappearance of cohesion and the

desire to act in accordance with own individual needs or in other words to pursuit self

interests (Mintz, 1951; Kelley et al., 1965). In a period of 18 minutes individuals also

have problems organizing themselves. It is difficult to secure a place on a lifeboat

through economic means (bargaining power). The danger is eminent and there is a

low distribution of information asymmetry between the passengers on board. Thus,

class status shouldn‘t matter and physical power should dominate social power. Only

crew members are supposed to be better prepared for such a catastrophic event. The

75


battle for lifeboats can be seen as a survival of the fittest competition. In such a flight

period physical attributes (e.g., strength and speed or agility) are expected to be quite

successful in determining the survival success. Therefore, we should observe people

between the ages 16 and 35 to have higher survival probabilities. Not only prime

males should have a relative advantage but also prime females in their main

reproductive age. Procreation instinct places a high value on its progeny as the

survival of a species relies on females in their reproductive age. A potential shortage

of such women would limit the number of off-spring while a shortage of men would

not (Felson, 2000). A self-interested reaction in the Lusitania may also be driven by

instinctual reaction to a perceived danger that includes self preservation functions

(Cannon, 1929a, 1929b; Gray, 1988). The short term response triggers the adrenal

production via the hypothalamus which promotes flight behaviour that can last from

a few seconds to a few minutes (Everly, 2002).

Table 6.2 shows the estimated parameters, the significance level (italics) and the

quantitative (marginal) effects (bold). EQ1 shows that the reference group (prime age

16-35) has the highest probability of surviving and the oldest age group (age>50) the

lowest one (59% lower than the prime age group). Females have ceteris paribus not a

higher probability of surviving than males. Children have even a 26% lower

probability of surviving than the reference group. Moreover, we don‘t observe that

first and second class passengers have a higher probability of surviving. Passengers

in the first class fared even worse than those in third class. Thus, physical power

indeed dominates social power. In EQ2 we recode the prime age category into males

and females. The results show that both, males and females have a higher probability

of surviving (between 7.8 and 10.5%). Thus, not only physical strength but also

procreation instinct seemed to matter. In EQ3 we extend the regression exploring

whether persons accompanying children have a higher survival chance than persons

without children. This seems not be the case. In EQ4 we control whether individuals

travelling alone have a higher probability of surviving. People travelling alone have

lower transaction costs and are more prone to flight behaviour (Mawson 2005). EQ4

shows a positive relationship without being statistically significant. In EQ5 we

76


Table: 6.2 Determinants of Passengers‘ Survival on Lusitania

Probit

Lusitania

[1] [2] [3] [4] [5]

Female -0.030 -0.31

-0.010

Age<16 -0.261**

-1.97 -0.083

Age 35 - 50 -0.170

-1.57

-0.056

Age 50+ -0.589***

-3.21

-0.167

1st Class -0.339*** -0.360*** -0.358*** -0.350*** -0.334***

-2.85 -3.07 -3.02 -2.96 -2.84 -0.109 -0.116 -0.115 -0.112 -0.112

2nd

Class 0.038 0.005 0.002 0.026 0.038

0.36 0.05 0.02 0.25 0.36 0.013 0.002 0.001 0.009 0.013

Male Age 16-35 0.227** 0.231** 0.218** 0.286***

2.16 2.13 2.00 2.91 0.079 0.080 0.075 0.103

Female Age 16-35 0.297** 0.299** 0.314*** 0.292***

2.55 2.55 2.67 2.56 0.105 0.106 0.111 0.107

Has Children 0.016 0.150 0.159

0.15 1.07 1.13 0.005 0.052 0.057

Single 0.190 0.178

1.56 1.46 0.064 0.062

Crew 0.622***

3.73 0.236

Obs. 933 933 933 933 933 Prob > chi2 0.0000 0.0000 0.0001 0.0001 0.0000

Pseudo R2 0.0276 0.0230 0.0231 0.0253 0.0409 Notes: Dependent variable: Probit Model. Survival (value = 1). The symbols *, **, *** represent statistical

significance at the 10, 5, and 1% levels, respectively. Reference groups: MALE and AGE 16-35 [1]; MALE,

ADULTS, and THIRD CLASS [2]; MALE/FEMALE AGE >35, THIRD CLASS and NOT HAVING

CHILDREN [3]; MALE/FEMALE AGE >35, THIRD CLASS, NOT HAVING CHILDREN and NON-

SINGLE [4]; MALE/FEMALE AGE >35, THIRD CLASS, NOT HAVING CHILDREN, NON-SINGLE

and PASSENGERS [5]. Coefficients are in normal text, z-stat in italics and marginal effects in bold.

extend the regression adding also crew members to see whether they are better

prepared for a catastrophic event. The results indeed indicate that crew members are

able to push through their comparative advantage reporting a 34% higher survival

probability.

77


6.3.2 PRO-SOCIAL BEHAVIOUR AND SOCIAL POWER IN THE TITANIC?

There is a growing viewpoint in the disaster literature that pro-social behaviour,

rather than antisocial behaviour, predominates in such contexts (Quarantelli, 2001).

However, empirical evidence is still scarce how and to what extent social norms

survive in a life threatening situation. Social norms may followed for intrinsic

reasons also in life threatening situations, because people take them to be ―right‖

(Elster, 2006) or because they fear the social sanctions when violating them (Polinsky

& Shavell, 2000). Moreover, once the first stage of flight behaviour has run its course

and exhaustion has set in (Henry & Wang, 1998) we may observe a change in

individual behaviours and actions due to self awareness and the consideration of

complex social interactions (Everly, 2002). The slowly sinking of the Titanic over 2

hours and 40 minutes allows for such a cool down period. Social interactions should

become more important allowing for the creation of and adherence to social norms. It

may enhance norms that promote social responsibility such as ―women and children

first‖. Such a norm has been found in other areas where people were evacuated

(Carpenter 2003). Moreover, more time also means fewer restrictions to bargain for

lifeboats and to generate information advantages. Thus, we would expect a

comparative advantage for first and second class passengers. Social power can be

achieved while physical power gets less important. In other words, individuals in the

prime age may a lower comparative advantage in the Titanic compared to the

Lusitania. This may hold in particular for males as we may still observe the

procreation instinct for females in this age group. Moreover, the survival rates of

offspring may be directly linked to maternal survival (Bjorklund and Shackelford,

1999). Table 6.3 presents the results. EQ6 reports that females have 53% higher

probability of surviving than males.

In addition EQ6 shows that children have a 10.6% higher probability of surviving

than the prime age group. Thus, the norm ―women and children first‖ is very

dominant in the Titanic case. Nevertheless, physical power is still relevant. Older age

groups (age 35 to 50 and age>50) have a lower probability of surviving than the

prime age group (between 14 and 24%). According to EQ7, passengers in first class

78


Table: 6.3 Determinants of Passengers‘ Survival on Titanic

Probit

Lusitania

[6] [7] [8] [9] [10]

Female 1.48*** 17.32 0.533

Age<16 0.279**

2.03 0.107

Age 35 - 50 -0.416*** -3.61 -0.145

Age 50+ -0.793*** -4.66 -0.241

1st Class 1.35*** 0.977*** 0.963*** 0.947*** 0.9998***

11.46 9.94 9.74 9.53 10.21 0.501 0.372 0.367 0.361 0.379

2nd

Class 0.455*** 0.394*** 0.378*** 0.374*** 0.389***

4.33 3.99 3.81 3.77 3.93 0.174 0.151 0.145 0.143 0.145

Male Age 16-35 -0.438*** -0.409*** -0.364*** -0.211***

-4.77 -4.42 -3.89 -3.03 -0.159 -0.149 -0.133 -0.074

Female Age 16-35 1.04*** 1.06*** 1.05*** 1.14***

9.62 9.70 9.55 11.07 0.397 0.402 0.398 0.432

Has Children 0.555*** 0.463*** 0.486***

3.13 2.59 2.72 0.217 0.180 0.185

Single -0.284*** -0.301***

-3.33 -3.55 -0.104 -0.101

Crew 0.178**

2.12 0.063

Obs. 1300 1300 1300 1300 1300 Prob > chi2 0.0000 0.0000 0.0000 0.0000 0.0000

Pseudo R2 0.2967 0.1932 0.1990 0.2055 0.1495 Notes: Dependent variable: Probit Model. Survival (value = 1). The symbols *, **, *** represent statistical

significance at the 10, 5, and 1% levels, respectively. Reference groups: MALE and AGE 16-35 [6]; MALE,

ADULTS, and THIRD CLASS [7]; MALE/FEMALE AGE >35, THIRD CLASS and NOT HAVING

CHILDREN [8]; MALE/FEMALE AGE >35, THIRD CLASS, NOT HAVING CHILDREN and NON-

SINGLE [9]; MALE/FEMALE AGE >35, THIRD CLASS, NOT HAVING CHILDREN, NON-SINGLE

and PASSENGERS [10]. Coefficients in normal text, z-stat in italics and marginal effects in bold.

had a higher chance of survival than those in second class, and second-class

passengers had a higher chance of survival than those in third class. The marginal

effects suggest that a passenger in the highest class was 50 percent more likely to

survive the catastrophe than a passenger in third class. Thus, social power is quite

important. The effect remains robust for all the remaining equations in Table 6.3. In

79


EQ8 we are interested to see gender differences in regards to the prime age group. In

contrast to the Lusitania case, on the Titanic only females in the reproductive age

group 16-35 have a (40%) higher probability of surviving supporting the importance

of the procreation instinct. Conversely the male age group 16-35 has a lower survival

chance (16%) despite their physical strength. Not only children have a higher

probability of surviving but also persons accompanying children have an around 20%

higher probability of surviving that persons without children (see EQ8 to 10). In

addition, contrary to the Lusitania case, being aboard the Titanic as a single person

did not increase the chance of survival (see EQ9). The advantage of lower transaction

costs in the decision-making process when travelling alone may have been

overshadowed by psychological or even physical disadvantages and a lack of

information. Finally, also in the Titanic case we observe that the crew members have

a higher probability of surviving (EQ10).

6.4 CONCLUSIONS

In our paper we show that time appears to be a key parameter linked to the adoption

of individualistic self-centred or social behaviour. When time is scarce individual

self-interested flight behaviour becomes more important resulting in a stronger

survival of the fittest competition. On the other hand, altruism and social norms are

still observed in an extreme life or death situation, given sufficient time to evolve.

These results suggest a stronger competition for survival (of the fittest) in the

Lusitania case. In the environment of the Titanic, social norms were enforced more

often, and there was also a higher willingness among males to surrender a seat on a

lifeboat. Moreover, the results indicate that social power also requires time to evolve

and cannot compete against physical strength in a shorter window of opportunity.

Economic class or social power warranted a relative advantage. First-class

passengers, and to some extent also second-class passengers, tried to secure the same

preferential treatment with respect to lifeboat access that they were used to receiving

on the vessel. However, the generation of such a relative advantage takes time.

Indeed, Tables 6.2 and 6.3 shows a higher survival rate for the Titanic, but not the

Lusitania where first-class passengers fared even worse than those in third class. On

the other hand, the results in regards to the crew member seemed to indicate that

80


access in advance to more information and relational resources enhanced the survival

rate. This empirical analysis suggests that the adoption of a specific behaviour can

depend on time as a factor, although one should note that time may not be the only

factor at work. Such a natural environment is less controlled than an experimental

setting. However, it seems that on the more slowly sinking Titanic pro-social

behaviour predominated (in a stronger manner), while a more selfish conduct

prevailed on the rapidly sinking Lusitania.

81

Chapter 7: Concluding Remarks

These three essays are the first steps into the analysis of individual behaviour and

choices made under extreme environmental conditions, such as life and death disaster

events, utilising a behavioural economics approach. This chapter provides a summary

or overview of the previous chapters.

The sinking of the Titanic and Lusitania allows the exploration of the behavioural

actions and decisions of individuals in extreme events but also demonstrates the

choices made in an excess of demand situation with the highest possible stakes,

which is the difference between life and death. As discussed in the introduction

sections, there exists little to no experimental or empirical work pertaining to the

decision making process in extreme situations. To the best of my knowledge these

works form the first attempt to analyse this problem using econometric methodology.

There is little empirical evidence available that illuminates whether interdependent

preferences or pro-social behaviours matter in extreme situations, such as helping

behaviour, altruism, social or moral norms.

While the econometric estimates of the factors determining survival during the

sinking of the Titanic produce a coherent story, it is at times contrary to the myth that

surrounds the event. The story is not necessarily in line with a simple model of

selfish homo economicus, such that the results offer strong support for the

assumption that social norms and altruism matter. The adherence to the unwritten

social norm of ―women and children first‖ was enforced in this life and death

situation, where being female or being a child strongly raised the probability of

survival. Additionally, females of reproductive age and having a child also increased

the probability of survival in relation to the males onboard. These results are in line

with the socio-biological theories of procreation instincts or parental investment.

However, there is some evidence that does support the outcomes that would be

predicted by using of a standard homo economicus model of behaviour on the Titanic

82

82

event. Individuals utilised whatever advantages they possessed to aid in their

survival, such as social power, physical fitness or informational advantage. People in

their prime drowned less often than older people. Social class has a strong effect,

passengers of the first and second class may have utilised their social power to affect

a higher probability of survival. Similarly, crew-members who had access to better

informational and relational resources (deck and engine crew) may have used their

advantage to generate a higher probability of survival. However, this advantage did

not extend to nationality advantages, the British passengers who were the same

nationality as most of the crewmembers were not able to take advantage of this.

These effects were clearly secondary to the ―women and children first‖ social norm,

they remained thus because of the adherence of the male passengers to follow the

norm. Otherwise, it would have been easy for male passengers to revolt against this

norm and enforce a survival of the fittest competition, which would have ensured a

greater rate of males. This demonstrates that the social norms were strong enough to

keep the ―public good‖ problems under control, limiting individual self-interested

behaviour.

In contrast to these findings the Lusitania, can be viewed as a time treatment for the

Titanic. Assuming stable historical norms and given the closeness in the

demographic makeup of the two vessels, the only significant variation in the events is

the amount of time taken for the Lusitania to sink, 18 minutes versus 2 hours and 40

minutes. The results demonstrate that time is a key factor in the adoption of

individualistic self-centred or social behaviour. In time restricted events or where the

event horizon is extremely shortened, individual self-interested flight behaviour

becomes predominant, resulting in a stronger survival of the fittest competition. This

result is observed in the Lusitania event, where individuals with a greater fitness for

survival have a greater probability to do so. Individuals who are physically stronger

or fitter, such as males at peak age (16-35), had better information about the vessel,

such as crew members, both had higher survival rates in relation to others. Moreover,

the results indicate that social power also requires time to evolve and cannot compete

against physical strength in a shorter window of opportunity. Economic class or

social power warranted a relative advantage but such an advantage takes time to

enforce. On the Titanic, 1st Class passengers demonstrated a much higher survival

83

83

rate than 2nd

Class, which in turn had a better survival rate than 3rd

Class passengers,

but not the Lusitania where first-class passengers fared even worse than those in third

class. This was further reflected in the survival rates of the crew, where access in

advance to more information and relational resources enhanced the survival rate.

Even while following the social convention of ―women and children first‖,

individuals will utilise any remaining advantage that increases survival outcomes.

Altruistic and socially normative behaviour were the predominate behaviour on the

Titanic, but underlying this behaviour was still the self-interested homo economicus.

Such that, even though an individual followed the normative behaviour, they would

use any possible advantage if a chance to save them self became available. These

self-interested advantaged could have included, superior fitness (age), improved

information advantage (knowledge of life saving devices), class or some other type of

advantage. These events demonstrate that behaviour of individuals in disaster events

does not follow the traditional mythology of mass panic. Their behaviour is neither

random nor inexplicable and as such it can be accounted for by using economic

analysis. Additionally, it can be observed that simple models of behaviour do not

fully predict survival outcomes. It could be that a mixed strategy approach may

provide better modelling outcomes.

What does the future hold for this research area? I hope to be able to continue

this work: firstly by finishing the initial maritime theme with a paper using the

Estonia as a historical treatment to examine changes over time. Beyond this, I plan on

extending the research into other areas where individuals are forced to make

decisions under extreme pressures. I have begun this by analysing the decision

process of elite athletes, by empirically investigate the relationship between stress

and performance, in an extreme pressure situation (football penalty kicks) in a winner

take all sporting environment (FIFA World Cup and UEFA European Cup

competitions). Specifically, the penalty shootouts between 1976 and 2008 covering in

total 16 events. The results indicate that extreme stressors can have a positive or

negative impact on individuals‘ performance. On the other hand, more commonly

experienced stressors do not affect professionals‘ performances (Savage & Torgler

84

84

2009). Other research areas include mountaineering in the Himalayan Mountains,

natural disasters (such as forest fires, floods, tornados etc.). It will be these topics that

will form the basis of my PhD research and will most likely remain the focus of

investigations for many years to come.

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Appendices 103

Appendices

APPENDIX A: PUBLICATIONS

Frey, B. S., Savage, D. A., & Torgler, B. (2010). Noblesse Oblige? Determinants of

survival in a life and death situation. Journal of Economic Behavior and


Frey, B. S., Savage, D. A., & Torgler, B. (2010). Interaction of natural survival

instincts and internalized social norms exploring the Titanic and Lusitania disasters,

Proceedings of the National Academy of Science (P.N.A.S.), 107(11), 4862-4865

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