We elicit and compare risk preferences from student subjects and subjects drawn from the general population, using the multiple price list method devised by Holt and and Laury (2002). We find evidence suggesting that students have lower relative risk aversion than others.Risk aversion, CRRA, expo-power, multiple price list

Drichoutis, Andreas

Koundouri, Phoebe

English

Research Papers in Economics

1 
 
Estimating risk attitudes in conventional and artefactual lab experiments 
 
Andreas C. Drichoutis
1 and Phoebe Koundouri
2 
1Dept. of Economics, University of Ioannina, University campus, Ioannina, Greece, email: 
adrihout@cc.uoi.gr, tel: +30-26510-05954, corresponding author 
2 Dept. of International and European Economic Studies, Athens University of Economics and 
Business, 76 Patision st., 10434, Greece, email: pkoundouri@aueb.gr 
 
Abstract 
  We elicit and compare risk preferences from student subjects and subjects drawn from 
the general population, using the multiple price list method devised by Holt and and Laury 
(2002). We find evidence suggesting that students have lower relative risk aversion than others. 
Keywords: Risk aversion, CRRA, expo-power, multiple price list 
JEL codes: C91, D01, D81 
 
1. Introduction 
Economic lab experiments have been mainly performed in academic environments and 
students have therefore posed as the natural standard subject pool. Whether student samples 
provide a reliable sample for extrapolating results to the general population is an issue that is 
heavily criticized. Concerns on the use of students as research surrogates for consumers or adults 
in general, is rather old  (Enis et al., 1972; McNemar, 1946). Reasons are attributed to the fact 
that students exhibit psychological, social and demographical differences from other segments of 
the population but also to the fact that students are not yet complete personalities.  
In addition, most decisions in life and in the lab are made under conditions of uncertainty, 
rendering risk behavior as a fundamental concept in the economic decision making process. Risk 
preferences are important to decisions varying from career choice to stock picking (Barsky et al., 
1997) as well as production decisions (Koundouri et al., 2009; 2006). If risk-neutrality is not a 
general characterization of the sample under investigation, it is important to know the subject’s 
pool preferences over risk. Previous results in the literature regarding risk behavior and the 
nature of the subject pool have not been uniform (Andersen et al., 2010a; 2010b). This study 
sheds more light to risk preference elicitation in a conventional lab experiment (i.e., using a 2 
 
student subject pool) and an artefactual lab experiment (i.e, using a general population subject 
pool). 
 
2. Experimental data 
We compiled data from two previous experiments that involved risk preference 
elicitations tasks. These two experiments were part of a larger project on choice under risk which 
also involved some standard experimental auction tasks. Experimental instructions for the 
experiments are available at https://sites.google.com/site/riskprefs/ . The first experiment used a 
student subject pool while the second experiment used a subject pool drawn from the general 
population. General population subjects were recruited by a professional company. The same 
proctor was used in both experiments.  
In the student subject pool experiment, the purpose was to explore whether risk 
preferences can be manipulated by some treatment variables, so we only used data from the 
control treatment sessions. In the consumer subject pool experiment, risk preferences were not 
part of the experimental manipulation. In all, we used elicited risk preferences from 34 general 
population subjects and 23 student subjects. In the student subject pool experiment, in one 
session the auction task was placed after risk elicitation. For all other subjects, risk elicitation 
followed the auction. We use a dummy variable in our econometric estimation to control for this 
session-specific characteristic. 
To elicit risk preferences we used the multiple price list (MPL) design devised by Holt 
and Laury (2002). In this design each subject is presented with a choice between two lotteries, A 
or B as illustrated in Table 1. In the first row the subject is asked to make a choice between 
lottery A, which offers a 10% chance of receiving €2 and a 90% chance of receiving €1.6, and 
lottery B, which offers a 10% chance of receiving €3.85 and a 90% chance of receiving €0.1. 
The expected value of lottery A is €1.64 while for lottery B it is €0.475, which results in a 
difference of €1.17 between the expected values of the lotteries. Proceeding down the table to the 
last row, the expected values of the lotteries increase but increases much faster for lottery B. 
For each row, a subject chooses A or B and one row is then randomly selected as binding 
for the payout. The last row is a simple test of whether subjects understood the instructions 
correctly. In our experiments subjects undertook three risk aversion tasks: they made choices 
from Table 1 (the 1x table), a table where payoffs were scaled up by 10 (the 10x table) and a 
table similar to Table 1 but without the last three rows (the 1x-framed table). The order of 
appearance of the tables for each subject was completely randomized to avoid order effects 
(Harrison et al., 2005). One of these tables was chosen at the end as binding for the payout. Thus, 
to infer risk preferences, subjects were asked to provide 27 binary choices from the risk 
preference task.  
 3 
 
3. Estimation and Results 
To estimate risk attitudes and assess the importance of the sample type as well as the 
demographics on risk preferences, we follow similar procedures to Holt and Laury (2002) and 
Harrison, et al. (2007).  
  Let the utility function be the constant relative risk aversion (CRRA) specification: 

1
1
r M
UM
r



          ( 1 )  
for r≠1, where r is the CRRA coefficient. In (1), r=0 denotes risk neutral behavior, r>0 denotes 
risk aversion behavior and r<0 denotes risk loving behavior. 
  The binary choices of the subjects in the risk preference tasks can be explained by different 
CRRA coefficients (as reported in Table 1).  
If we assume that Expected Utility Theory holds for the choices over risky alternatives, 
the likelihood function for the choices that subjects make can be written for each lottery i as: 
 
1,2
ij j
j
EU p M U M

   (2) 
where    j p M  are the probabilities for each outcome  j M  that are induced by the experimenter. To 
specify the likelihoods conditional on the model, the Luce stochastic specification is used. The 
expected utility (EU) for each lottery pair is calculated for candidate estimate of r, and the ratio: 
1
11
B
AB
EU
EU
EUE U

  

 (3) 
is then calculated where  A EU  and  B EU  refer to options A and B respectively, and  is a 
structural noise parameter. The index in (3) is linked to observed choices by specifying that the 
option B is chosen when  1
2 EU . 
The conditional log-likelihood can then be written as: 
         ln , ; , ln | 1 ln 1 | 1
RA
ii
i
Lr y E Uy E Uy        X  (4) 
where   11 i y  denotes the choice of the option B (A) lottery in the risk preference task i. Each 
parameter in equation (4) is allowed to be a linear function of demographic and treatment 
variables as exhibited in Table 2. A portion of subject’s fees was stochastic since this have been 
demonstrated to be very important for recruitment (Harrison et al., 2009). In addition, 
recruitment practices necessitated a higher show-up fee for consumer subjects. Thus, a total fee 
endowment variable is included in the econometric model. Equation (4) is maximized using 
standard numerical methods. 
Table 3 (Panel A) shows estimates when assuming a CRRA function with a Luce 
stochastic error. It is obvious that even after controlling for all possible demographic effects 4 
 
general population subjects appear more risk averse than the student sample and the difference is 
highly significant. 
The CRRA characterization of risk preferences, while popular, restricts relative risk 
aversion to be constant over the prize domain. To allow for the possibility that the relative risk 
aversion is not constant we adopt a more flexible functional form; the hybrid expo-power 
function of Saha (1993). The expo-power function can be defined as    
1 1e x p
r uM a M a
   , 
where M is income and a  and r are parameters to be estimated. Relative risk aversion (RRA) is 
then   
1 1
r ra r M
  . Results assuming the expo-power form are presented in Table 4 (panel A). 
It is obvious that by allowing a more flexible functional form the coefficient of the relevant 
sample type dummy is no longer statistically significant for neither a  or r. The magnitude of 
the r coefficient is reduced as well. 
The Luce error popularized by Holt and Laury (2002), however, implicitly imposes a 
stochastic identifying restriction that the true stochastic model is CRRA-neutral (Wilcox, 2008). 
Different stochastic models should then be evaluated. Therefore, we also test the Fechner 
specification (as in Harrison and Rutstrom, 2008), which posits  BA EUE U
EU


  instead of 
(3). Results for the CRRA and expo-power functions are exhibited in panels B in Table 3 and 4 
respectively. The estimates for the Consumer dummy for r are statistically significant, positive 
and remarkably close to the expo-power with Luce error specification estimate. The estimate for 
a  turns negative and is statistically significant. 
So which specification should we trust? A non-nested hypothesis test like the Vuong 
(1989) test is appropriate in this context. The expo-power with Fechner error specification is 
favored in all cases [Vuong statistic: 2.07 (vs. expo-power Luce), 3.69 (vs. CRRA-Luce), 5.00 
(vs. CRRA-Fechner)]. In the expo-power with Fechner error specification the negative 
coefficient of the relevant dummy for a  (-6.142) implies lower RRA for general population 
subjects as compared to the student sample, ceteris paribus. In addition, the average prediction 
for  a  is positive for both subsamples, indicating increasing RRA. Given a positive a , the 
coefficient of the Consumers dummy for r (0.331) implies a lower RRA for subjects of the 
general population as compared to students, ceteris paribus. 
Table 5 exhibits RRA predictions for the two subject pools for M=1 and M=5. The 
predictions indicate higher RRA for general population subjects and lower RRA for students. At 
a first glance this may seem like contrasting with the previous paragraph but should be of no 
surprise given the sign of the Age coefficients and the age difference between subject pools.  
 
 
4. Conclusions 
In this article we tested whether risk preferences of subjects drawn from the general 
population differ with respect to a standard student subject pool. We found evidence suggesting 
that students have lower relative risk aversion than others. Our finding is important given that 5 
 
previous studies have either found no difference in risk aversion between students and the 
general adult population (Andersen et al., 2010a) or that students are more risk averse (Andersen 
et al., 2010b). This finding has significant implications for conventional laboratory experiments 
practice given the importance of risk preferences in everyday economic decision making. More 
studies that will examine differences in risk preferences between students and the general 
population are indeed warranted. 
 
 
5. References 
Andersen, S., G.W. Harrison, M.I. Lau, E.E. Rutström, 2010a. Discounting behavior: A 
reconsideration. working paper. 
Andersen, S., G.W. Harrison, M.I. Lau, E.E. Rutström, 2010b. Preference heterogeneity in 
experiments: Comparing the field and laboratory. Journal of Economic Behavior and 
Organization 73, 209-224. 
Barsky, R.B., F.T. Juster, M.S. Kimball, M.D. Shapiro, 1997. Preference parameters and 
behavioral heterogeneity: An experimental approach in the health and retirement study. 
Quarterly Journal of Economics 112, 537-579. 
Enis, B.M., K.K. Cox, J.E. Stafford, 1972. Students as subjects in consumer behavior 
experiments. Journal of Marketing Research 9, 72-74. 
Harrison, G.W., E. Johnson, M.M. McInnes, E.E. Rutström, 2005. Risk aversion and incentive 
effects: Comment. The American Economic Review 95, 897-901. 
Harrison, G.W., M.I. Lau, E.E. Rutstrom, 2007. Estimating risk attitudes in Denmark: A field 
experiment. Scandinavian Journal of Economics 109, 341–368. 
Harrison, G.W., M.I. Lau, E.E. Rutström, 2009. Risk attitudes, randomization to treatment, and 
self-selection into experiments. Journal of Economic Behavior & Organization 70, 498-507. 
Harrison, G.W., E.E. Rutstrom, 2008. Risk aversion in the laboratory, in: Cox, J.C., Harrison, 
G.W. (Eds.), Research in Experimental Economics Vol 12: Risk Aversion in Experiments. 
(Emerald Group Publishing Limited, Bingley, UK), pp. 41-196. 
Holt, C.A., S.K. Laury, 2002. Risk aversion and incentive effects. The American Economic 
Review 92, 1644-1655. 
Koundouri, P., M. Laukkanen, S. Myyrä, C. Nauges, 2009. The effects of EU agricultural policy 
changes on farmers' risk attitudes European Review of Agricultural Economics 36, 53-77. 
Koundouri, P., C. Nauges, V. Tzouvelekas, 2006. Technology Adoption under Production 
Uncertainty: Theory and Application to Irrigation Technology. American Journal of Agricultural 
Economics 88, 657-670. 
McNemar, Q., 1946. Opinion-attitude methodology. Psychological Bulletin 43, 289-374. 
Saha, A., 1993. Expo-power utility: A flexible form for absolute and relative risk aversion. 
American Journal of Agricultural Economics 75, 905-913. 
Vuong, Q.H., 1989. Likelihood ratio tests for model selection and non-nested hypotheses. 
Econometrica 57, 307-333. 
Wilcox, N., 2008. Stochastic models for binary discrete choice under risk: A critical primer and 
econometric comparison, in: Cox, J.C., Harrison, G.W. (Eds.), Research in Experimental 6 
 
Economics Vol 12: Risk Aversion in Experiments. (Emerald Group Publishing Limited, Bingley, 
UK), pp. 197-292. 
 
  7 
 
Table 1. Sample payoff matrix for the risk preferences tasks 
Lottery A 
 
Lottery B 
Open CRRA interval if subject 
switches to Lottery B and 
background consumption=0  p  €  p  €  p  €  p  € 
0.1  2 0.9  1.6    0.1  3.85  0.9 0.1  -∞ -1.71 
0.2  2 0.8  1.6    0.2  3.85  0.8 0.1  -1.71  -0.95 
0.3 2  0.7  1.6    0.3 3.85 0.7  0.1  -0.95  -0.49 
0.4  2 0.6  1.6    0.4  3.85  0.6 0.1  -0.49  -0.15 
0.5  2 0.5  1.6    0.5  3.85  0.5 0.1  -0.15  0.14 
0.6  2 0.4  1.6    0.6  3.85  0.4 0.1  0.14  0.41 
0.7  2 0.3  1.6    0.7  3.85  0.3 0.1  0.41  0.68 
0.8  2 0.2  1.6    0.8  3.85  0.2 0.1  0.68  0.97 
0.9  2 0.1  1.6    0.9  3.85  0.1 0.1  0.97  1.37 
1  2 0  1.6    1  3.85  0 0.1  1.37  +∞ 
Note: Last two columns showing implied CRRA intervals were not shown to subjects. 
 
Table 2. Variable description 
Variable Description 
General population 
subject pool  Student subject pool 
Mean Std.dev.  Mean  Std.dev. 
Age  Subject’s age  41.176 10.376  20.739  1.322 
Gender  Dummy, 1=males, 0=females  0.324 0.475  0.391  0.499 
Income 
Dummy, household’s 
economic position is above 
average=1, else=0 
0.471 0.507  0.435  0.507 
Education  Dummy, university graduate 
or higher=1, else=0  0.676 0.475  0  0 
TotFee  Total fee endowment  23.794 6.594  16.717  1.146 
ExpCharact 
Dummy, risk preference task 
was conducted after an 
auction, else=0 
1 0  0.522  0.511 
 
 8 
 
Table 3. Estimates of risk preferences (CRRA function) 
  r coefficient 
  Estimate Std.Err
or 
Lower 
95% CI
Upper 
95% CI
  Estimate Std.Err
or 
Lower 
95% CI 
Upper 
95% CI 
  A. CRRA with Luce error    B. CRRA with Fechner error 
Consumers  0.878 0.268 0.352 1.404  0.345 0.084 0.180  0.511 
Age  -0.038 0.015 -0.067 -0.008  -0.017 0.004 -0.025 -0.009 
Gender  -0.011 0.262 -0.525 0.503  -0.081 0.057 -0.193  0.032 
Income  0.070 0.188 -0.299 0.439  0.062 0.043 -0.022 0.146 
Education  -0.311 0.217 -0.735 0.114  -0.099 0.063 -0.222  0.024 
TotFee  -0.138 0.262 -0.651 0.375  -0.149 0.052 -0.251 -0.047 
ExpCharact  -0.041 0.011 -0.063 -0.019  -0.009 0.005 -0.018  0.000 
Constant  1.831 0.278 1.286 2.376  1.259 0.119 1.026  1.491 
  
0.328 0.053 0.224 0.431  0.140 0.051 0.040  0.240 
 
Table 4. Estimates of risk preferences (expo-power function) 
  r coefficient      coefficient 
  Estimate Std.Err
or 
Lower 
95% CI
Upper 
95% CI
  Estimate  Std.Erro
r 
Lower 
95% CI 
Upper 
95% CI 
  A. Expo-power with Luce error 
Consumers  0.351 0.507 -0.643 1.345   27.962 231.477  -425.724  481.647
Age  -0.044 0.014 -0.072 -0.017   3.320 5.899  -8.243  14.882 
Gender  0.198 0.245 -0.282 0.677   -18.323 20.585  -58.668 22.022 
Income  -0.117 0.139 -0.390 0.157   13.488 18.856  -23.469  50.446 
Education  -0.027 0.429 -0.868 0.815   -89.374 220.908  -522.345  343.597
TotFee  0.101 0.535 -0.948 1.149   -4.330 11.894  -27.641  18.982 
ExpCharact  -0.029 0.017 -0.063 0.005   4.898 6.765  -8.361  18.158 
Constant  1.725 0.305 1.127 2.322   -140.857 208.494 -549.497  267.7839 
 
  
0.317 0.056 0.207 0.427        
 
B. Expo-power with Fechner error 
Consumers  0.331 0.115 0.106 0.556   -6.142 2.219  -10.491  -1.792 
Age  -0.023 0.007 -0.036 -0.010   0.042 0.111  -0.176  0.260 
Gender  0.233 0.081 0.075 0.392   -1.297 1.153  -3.558 0.964 
Income  -0.064 0.052 -0.166 0.038   1.378 1.031  -0.643  3.399 
Education  -0.187 0.084 -0.352 -0.022   -6.534 1.890  -10.238  -2.830 
TotFee  -0.013 0.009 -0.031 0.004   1.637 1.023  -0.368  3.642 
ExpCharact   -0.026 0.010 -0.045 -0.006   1.276 0.303  0.682 1.871 
Constant  1.647 0.178 1.298 1.997   -22.392 5.823  -33.804  -10.980 
  
0.031 0.018 -0.005 0.066          
 
Table 5.  Relative risk aversion predictions based on expo-power function with Fechner error 
  Consumers Students 
M=1  2.71 1.06 
M=5  47.10 1.27 
 
 
 
 
 

Discounting behavior: A reconsideration. working paper.

Estimating risk attitudes in Denmark: A field experiment.

Expo-power utility: A flexible form for absolute and relative risk aversion.

Likelihood ratio tests for model selection and non-nested hypotheses.

Opinion-attitude methodology.

Preference heterogeneity in experiments: Comparing the field and laboratory.

Preference parameters and behavioral heterogeneity: An experimental approach in the health and retirement study.

Risk attitudes, randomization to treatment, and self-selection into experiments.

Risk aversion and incentive effects:

Risk aversion and incentive effects.

Risk aversion in the laboratory, in:

Stochastic models for binary discrete choice under risk: A critical primer and econometric comparison,

Students as subjects in consumer behavior experiments.

Technology Adoption under Production Uncertainty: Theory and Application to Irrigation Technology.

The effects of EU agricultural policy changes on farmers' risk attitudes European Review of

Estimating risk attitudes in conventional and artefactual lab experiments

Estimating risk attitudes in conventional and artefactual lab experiments

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