Introducing New Instruments into Science: An Example from Experimental Economics George Mengov,...

Introducing New Instruments into Science: An Example from Experimental Economics

George Mengov, Ph.D.,

FEBA

Outline

• Does experimental economics need new instruments

• An experiment from economic psychology

• Analysis of the experimental data

• Unresolved issues

Experimental Economics

• Experimental economics is the use of experimental methods to evaluate theoretical predictions of economic behaviour. It uses controlled, scientifically-designed experiments to test economic theories under laboratory conditions.

• Economic theory depends on assumptions about the preferences of economic agents. Whether these assumptions are correct is not observable from economic activity. Preferences can only be inferred from choices. In laboratory conditions preferences are identified and their influence on economic choices is established.

New instruments in experimental economics

• Experimental economists study market performance (rationality) given individual valuations.

• In market experiments…the temptation is to ignore reality because it is poorly understood, and does not yield to our familiar but inadequate modeling tools…

(Vernon Smith, 2002 Nobel Lecture)


• If people in certain context [economic experiment] make choices that contradict our formal theory of rationality, rather than conclude that they are irrational, we:– ask why [do they look irrational], – reexamine maintained hypothesis, including

procedures, payoffs, context, instructions – inquire what new concepts and experimental designs

can help us to better understand the behaviour.

(Vernon Smith)


• Psychologists and “behavioral economists” who study decision behaviour almost uniformly report results contrary to rational choice theory. Psychologists, to their credit, have maintained an intensive programme examining the behavioral nature of these contradictions.

(Vernon Smith)


• Therefore better understanding of economic agents’ behaviour might be achieved in synergy with behavioral economics / economic psychology

• Nowadays psychological instruments and methods are implemented side by side with economic research approaches.

An experiment from economic psychology

• The rationality tenet in economic decision making is vulnerable when the economic agents assume the role of consumers. Confronted with choices, they consider not only utility maximization, but also emotional factors.

• Understanding such complicated situations from the stand point of a purely economic theory is difficult.


• In this study we analyze people’s choices in a controlled computer experiment which bears some resemblance to the market of mobile communications with two suppliers.


• In each round a participant (a customer) receives an offer from one supplier. The offer is simple – an advertised price (Pa) that the consumer would probably pay at the round end. A few seconds later a final price (Pr) is announced. It may be higher or lower than the advertised price - to the disappointment or satisfaction of the customer. The latter is free to abandon this supplier at no cost, or continue with it.

A Possible Experimental Setting 1

Supplier in this round is company А



Advertised price: 37



Advertised price: 37Final price: 51


Would you like to change your supplier?



No Yes


Supplier in this round is company B



Advertised price: 42





No Yes


• In Setting 1 the decision to retain or abandon the current supplier depends on these factors:– Advertised price (Pa)– Final price (Pr)– Price difference (Pa – Pr)


• In Setting 1 the decision to retain or abandon the current supplier depends on these factors:– Advertised price (Pa)– Final price (Pr)– Price difference (Pa – Pr)– Immediately previous choice of supplier

(ChangeS)


• In Setting 1 the decision to retain or abandon the current supplier depends on these factors:– Advertised price (Pa)– Final price (Pr)– Price difference (Pa – Pr)– Immediately previous choice of supplier

(ChangeS)– All previous Pa, Pr, Pa – Pr, ChangeS


• Data


• Data• Analysis Objective:

determine the factors influencing the decision to change supplier


• Data• Analysis Objective:

determine the factors influencing the decision to change supplier

• Tool – Logistic Regression


• What logistic regression does


• What logistic regression does

It estimates models of this type. ChSi →{0;1}.

That is, based on

estimates of β0, β1, β2, and β3 are produced which are optimal in some mathematical sense. These betas are measures of the influence of each factor

NiChSPa iii ,...,1,,Pr,

13210 )Pr(exp1

1

iiiii ChSPaPa

ChS


• How adequate is this model– R2 and Radj

2 are useless for logistic regression (Gujarati, 1995)

– Counting coincidences of ‘1’ and’0’ is insufficient

– Use Rank Correlations. Dependent variable is formally dummy, however contains information about preferences between two suppliers, i.e., the scale is ordinal.


• Result for the model explaining ChS with (Pa – Pr) and previous ChS

Both Kendall and Spearman

Rank Correlations give 42.34%


• Result for the model explaining ChS with (Pa – Pr) and previous ChS

Both Kendall and Spearman

Rank Correlations give 42.34%

• Tossing a coin – a lot less than 1%

Experimental Setting 2

• Take into account the positive or negative emotion due to the difference (Pa – Pr)

• Develop a psychometric scale to measure this emotion


• Take into account the positive or negative emotion due to the difference (Pa – Pr)

• Develop a psychometric scale to measure this emotion

Extremely disappointed

Very disappointed

Disappointed More disappointed than satisfied

As much satisfied as

disappointed

More satisfied than

disappointed

Satisfied Very satisfied Extremely satisfied

-4 -3 - 2 - 1 0 1 2 3 4

_______________________________

Thanks are due to prof. E. Gerganov for developing the DS scale


A Detour in psycholinguistics

• Adverbs are multipliers to adjectives

(Norman Cliff, 1959)

• Example from the English language:

Adverb Value

Slightly 0.538

Rather 0.843

Very 1.254

Extremely 1.446



Please indicate your satisfaction or disappointment due to this particular outcome according to the following scale:


Very disappointed



disappointed

More satisfied than

disappointed


-4 -3 - 2 - 1 0 1 2 3 4


No Yes






Very disappointed



disappointed

More satisfied than

disappointed


-4 -3 - 2 - 1 0 1 2 3 4


No Yes



Significant Factors: DSi, ChSi-1. Rank Correlation: 55.12%


Insignificant Factor PrDiff = (Pa – Pr).


Significant Factors: DSi, DSi-1, ChSj-1. Rank Correlation: 55.53%


• Intermediate conclusions– The emotion is very important determinant of

consumer behaviour. DS improved model adequacy - rank correlations increased from 42.34% to 55.53%.

– The decision to retain or abandon a supplier depended on DS, not directly on Pa, Pr, or PrDiff.

– DSi and DSi-1 participated with opposite signs


• Four Experimental Conditions

• Pa was determined by interviewing students about their typical phone bills

• Pa was 40 ± 5 BGN• Each participant did

17 rounds

Distribution of Phone Bills

0

10

20

30

40

50

60

70

Fre

qu

en

cy

Treatment A

• A market with minor variability in price differences (Pa – Pr). Typical for saturated markets.

• 31 participants2.5 5 7.5 10 12.5 15

-15

-10

-5

5

Round #

Pa - Pr

Treatment B

• A market with suppliers aggressively attracting consumers with favourable prices. Pa – Pr > 0

• 34 participants

Round #

Pa - Pr

2.5 5 7.5 10 12.5 15

2.5

5

7.5

10

12.5

15

17.5

Treatment C

• Suppliers act to the disadvantage of their customers. Typical for state monopolies or cartels.

• Pa – Pr < 0 mostly

• 36 participants

Round #

Pa - Pr

2.5 5 7.5 10 12.5 15

-12.5

-10

-7.5

-5

-2.5

2.5

Treatment D

• A market with substantial price fluctuations. Typical for emerging markets.

• 28 participants

Round #

Pa - Pr

2.5 5 7.5 10 12.5 15

-15

-10

-5

5

10

15


• Next conclusions

Case Factors Rank Correlations

Treatment A(saturated)

DSi, ChSi-1 0.5586

Treatment B(favourable)

DSi, DSi-1, PrDiffi, ChSi-1 0.5903

Treatment C(unfavourable)

PrDiffi, DSi 0.5724

Treatment D(emerging)

DSi 0.5336


• What affects the positive or negative emotion

• Tool: Linear regression, model adequacy measured by R2 and Radj

2

• Example: Treatment A, the saturated market

Experimental Setting 2• Linear regression results on all treatments

Case Factors explaining DSi R2adj

Treatment A(saturated)

Pai, Pai-1, PrDiffi, PrDiffi-1, DSi-1 0.7035

Treatment B(favourable)

Pa, PrDiffi, PrDiffi-1, PrDiffi-2, DSi-1, DSi-2 0.5984

Treatment C(unfavourable)

PrDiffi, PrDiffi-1, DSi-1, DSi-2 0.6447

Treatment D(emerging)

PrDiffi, PrDiffi-1, DSi-1 0.7300

All 129 Pai, Pai-2, PrDiffi, PrDiffi-1, PrDiffi-2, DSi-1, DSi-2 0.6256

• Observation: Variables from current and previous rounds are significant for DSi

Conclusions… but not the end!

• By introducing a psychometric scale (DS) we understood consumer decisions better

• By regression analysis we conclude that choice of supplier is governed not by financial figures directly, but by emotion due to financial figures

• Paradox: (1) On many occasions participants expressed disappointment when they had to pay actually less than advertised. (2) Also people were occasionally satisfied even though they had to pay slightly more than announced.

The Paradox: disappointed when saving, satisfied when paying more

Total Number of Choices

Paradoxical Choices

Paradoxical in Total

Treatment A(saturated market)

527 36 6.83%

Treatment B(favourable market)

579 102 17.62%

Treatment C(unfavourable

market)

612 159 25.98%

Treatment D(emerging market)

476 46 9.66%

Unresolved Issues

• Generally, those regression models (linear and logistic) were not very good

• The need for a separate model for each treatment is a methodological issue

• Paradoxical behaviour intensified in the homogeneous treatments B and C

Introducing New Instruments into Science: An Example from Experimental Economics George Mengov,...

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