In this section we present evidence on whether and how additional factors, like individual characteristics and learning affect investment in safety.
Although our theoretical model assumes risk neutrality, decision makers may not be risk neutral.27 The majority of our subjects exhibit risk aversion when asked about
27We acknowledge that the risk attitude of an individual cannot directly be translated as the risk attitude of a company. It is debatable whether firms are risk averse, like individuals, or not. The attitude towards risk of companies is certainly related to their size and to their financial constraints.
their attitude towards risk at the end of the experiment. In fact, the median reported risk attitude in our experiment is 4 – on a scale from 0 (risk averse) to 10 (risk loving) – which is significantly less than the risk neutral value of 5 (one sample median test). Also, subjects may have a preference for helping others that can interfere with liability rules. Further, subjects may learn. Learning may be due to subjects getting used to the decision situation and also to own experience (e.g. having been hit by an accident in phase 1) and feedback about the experience of others (accidents in the ‘feedback group’). In the regressions in Table 5 we control for these additional factors to show that the increase in investment in safety with respect to treatments NoLis indeed due to liability rules.
The variables that enter the regressions in Table 5 can be grouped into treatment variables (the first four), individual characteristics (the next four), and learning (the last five). Now, we will describe them in detail. The data fromNoL-lowandNoL-high are pooled under theNoLdummy. The same applies to theSLdummy and the Ne dummy. This is done to measure the effect of the type of liability rule only. By con-struction, these dummies ignore the size of the damage. Therefore, we introduced the High damage dummyto measure the effect of the size of the damage, indepen-dently of the liability rule. It takes the value of 1 forNoL-high,SL-high, andNe-high, and 0 otherwise. While regressionsaonly measure the effect of liability rules, regres-sionsb also measure the effect of insolvency (i.e. the size of the damage caused to third parties). The next three variables were elicited in the post-experimental ques-tionnaire: Risk attitudeon a scale from 0 (very risk-averse) to 10 (very risk-loving),
The framework of our experiment is by far too simple to take into account such parameters. To sustain the argument that firms are not necessarily risk neutral and that the resulting decisions can look like the ones of individuals, we refer to Leland and Pyle (1977). These authors show that the assumption of risk aversion has some meaning for small companies that suffer from restricted access to financial markets. However, in order to convince investors that their project is worthwhile, these risk averse small firms accept to bear some risk and, finally, seem to behave like risk neutral big companies.
Dep.var.: INVEST Ia Ib Ic IIa IIb
NoLdummy .69***(.13) .69***(.14) .84***(.15) .74***(.14) .74***(.15) SLdummy .86***(.12) .86***(.13) .75***(.13) .88***(.14) .88***(.14) Nedummy .93***(.12) .93***(.13) .80***(.14) .98***(.14) .98***(.14)
Highdamage dummy – –.00(.07) .01(.06) – –.01(.07)
Risk attitude –.08***(.01) –.08***(.01) –.08***(.01) –.09***(.01) –.09***(.01) Others–selfish –.06**(.03) –.06**(.03) –.06**(.03) –.07**(.03) –.07**(.03)
Me–selfish –.02(.03) –.02(.03) .02(.03) –.01(.03) –.01(.03)
Me–selfish *NoLdummy – – -.12**(.05) – –
Phase 2 dummy –.03(.10) –.03(.10) –.03(.10) – –
Phase 1 dummy * Period –.00(.01) –.00(.01) –.00(.01) – –
Phase 2 dummy * Period .00(.01) .00(.01) .00(.01) –.00(.01) –.00(.01) N. of accidents int−1 –.03(.02) –.03(.02) –.03(.02) .00(.03) .00(.03)
Accident in phase 1 dummy – – – -.12(.14) -.12(.14)
N of observations 1549 1549 1549 856 856
Table 5: Marginal effects from logit regressions explaining investment in safety. Ran-dom effects at the individual level control for the fact that individuals decide repeat-edly. Standard errors (computed with the delta method) in parentheses, ***p < 0.01,
**p <0.05, *p < 0.1. Regressions Ia, Ib, Ic use the whole data set. Regressions IIa, IIb use data from phase 2 only.
Others-selfish28 and Me-selfish29 on a scale from 0 (help others) to 6 (follow own in-terests). The interactionMe–selfish * NoL dummyshows how the opinion of subjects about being selfish or pro-social influences their behavior in treatment NoL only.
Regression Ic differs from Ib only in this variable. Phase 2 dummytakes the value of 1 for phase 1 and 0 for phase 2. It accounts for learning from phase 1 to phase 2.
28Recall that the question was “Would you say that most of the time people try to help others or only follow their own interests?”.
29Recall that the question was “Would you say that most of the time you try to help others or only follow your own interests?”.
The interaction between thePhasedummies andPeriod(going from 1 to 5) accounts for learning within each phase.30 N. of accidents int−1is the number of accidents that occurred in the subject’s feedback group in the previous period. Here we as-sume that accidents from at most the previous period may affect decisions in the current period. Accident in phase 1 dummytakes the value 1 if a subjects was hit by an accident in phase 1.31
We run logit regressions (since the dependent variable investment–in–safety is bi-nary) and report marginal effects. Because individuals make decisions repeatedly, decisions made by the same individual are correlated. Individual-specific random effects correct for this. The significant coefficients are marked with stars. We use a Wald post-estimation test to pairwise compare significant coefficients. If coefficients are different given this post-estimation test, we can conclude that the variable with the larger coefficient has a larger effect.
While regressions “I” make use of the whole data set, regressions “II” use only data from phase 2. The purpose of regressions IIa, IIb is to check whether having experi-enced an accident in phase 1 affects behavior in phase 2. Table 5 shows this is not the case. The following results hold in all regressions. All variables related to learning are insignificant.
30E.g.Phase 1 dummy * periodtakes the value of 1 if we are in phase 1, period 1. The same variable takes the value of 2 if we are in phase 1, period 2, and so on until phase 1, period 5.Phase 1 dummy * periodtakes the value 0 if we are in phase 2.
31Both the number of accidents that occurred to others in the previous period and whether a sub-ject was hit by an accident in the previous phase should not influence investment behavior since accidents occur independently. However, it is well-known that people fall prey to fallacies when faced with a random sequence of events. The two fallacies that may apply here are thegambler’s fallacyand thehot hand fallacy. Given a fair coin, after a sequence of heads, people suffering from the former would expect tails while people suffering from the latter would expect heads (see, e.g.
Sundali and Croson, 2006). For our experiment this would mean respectively that a person who was hit by an accident in phase 1, would not expect to be hit in phase 2 or indeed expect to be hit again in phase 2.
Among the variables that deal with individual characteristics,risk attitudeis always negatively correlated with investment behavior, meaning that independently from the treatment, the more risk-loving an individual is, the less likely she is to invest in safety. Furthermore, the probability to invest in safety decreases with the indi-vidual’s perception of others being selfish. Whether an individual considers herself selfish or not does not influence behavior. Among the treatment variables, sub-jects are not sensitive to the size of the harm caused to third parties (Highdamage dummy is not significant). In regressions Ia, Ib and IIa, IIb, investment in safety is more likely under bothSLandNethan underNoL.32
Regression Ic differs from Ia and Ib in only one variable: Me–selfish * NoL dummy, which is significant, meaning that the more selfish an individual rated herself, the less likely she was to invest in safety in treatmentsNoL. The difference in the coeffi-cients between theNoL dummyand the liability dummies vanishes in regression Ic.
I.e., when there is a control for selfishness in treatmentNoL, behavior in the absence of liability rules does not differ from behavior in the presence of liability rules. This means that liability rules induce the selfish subjects to invest in prevention, such that in the end, prosocial subjects under no liability invest in safety as much as the pool of prosocial and selfish subjects under liability. In other words, the investment in safety of pro-social subjects under no liability is the same like the investment in safety of selfish subjects under liability.
To sum up, risk aversion increases investment in safety, learning (from own and others’ experience) does not change investment behavior, and the opinion about others being selfish decreases investment in safety. Furthermore, controlling for risk aversion, learning, and social preferences does not change our previous conclusions:
SLandNerules induce more investment in safety thanNoLand insolvency does not change investment behavior of subjects. Regression Ic shows that the difference between treatments without liability and with liability is driven by the increased
32SLvs.NoL:p= 0.03,Nevs.NoL:p= 0.00, Wald test.
investment in safety of the selfish subjects in the liability treatments.
5 Conclusions
In this paper, we compare the performance of three liability rules (No Liability, Strict Liabilityand Negligence) enforced against a firm that can potentially cause a disas-ter and thereby harm third parties. We model the firm’s investment in safety as a moral hazard variable. The predictions of our theoretical model are tested in an ex-periment. In line with theory,Strict Liability andNegligence perform better thanNo Liability: agents increase their level of care when they can be held liable for the harm caused. Furthermore, there isnosignificant difference in the effectiveness ofStrict LiabilityandNegligencerule. Last, for a given size of own wealth, agents do not in-vest more when losses to third parties increase (i.e. when the insolvency problem is more stringent). In contrast with theory that predicts zero prevention underNo Lia-bilityand 100% prevention under liability (for risk neutral and risk averse subjects), prevention rates are as high as 50% in the former and significantly below 100% in the latter case. Investment in safety remains below 100% even when excluding risk loving subjects from the analysis.
Our work can be extended in the following directions.
Most of the theoretical predictions were confirmed by a subject pool of German undergraduates. However, the substantial level of investment that appeared under No liabilitywill have to be further explored. Other-regarding preferences, as subjects caring for the well-being of third parties may be responsible for this outcome. This conjecture would be in line with Brennan et al. (2007), who show that once the own outcome is not at risk, subjects care for the risk borne by others. More research will also be needed to provide explanations for the relatively low investment in prevention in the presence of liability rules.
In our setting, the size of the harm is given, and the only way of reducing expected losses is to reduce the probability of an accident. However, one could consider a more general model where both the probability of an accident, and the size of the harm can be influenced by prevention. Then, the size of the harm can be linked to the firm’s scale of activity, and the probability of an accident can be linked to the intensity of safety effort. From the Law and Economics literature33 we know that Strict Liability is effective in providing incentives both for activity and probability reduction, since the responsible firm is held liable for the entire loss whatever her behavior was in the conduct of the operations that have led to damages. The firm has thus incentives to use all the available means to reduce expected losses. On the contrary, Negligence rule is only effective for probability reduction: since the injurer is not held liable if she complied with a standard of due care, only her level of prevention matters. Her level of activity has no influence on the Court’s decision to hold her liable or not. Thus, it would be worth developing an experiment to test such differences in firm’s incentives in managing potential damages to third parties.
An adequate adaptation of the present experiment could also provide empirical ar-guments for a number of long lasting theoretical debates in the Law and Economics and Incentive Regulation literature. For instance, one could test the effectiveness of extended liability,34and also whether the risk of an accident is better controlled with ex-anteinstruments (standard regulation implemented by agencies) or withex-post instruments (liability rules, enforced by Courts of Law).35
33See Segerson (2002) for informal arguments and Shavell (1980) for formal ones.
34See Pitchford (1995) or Hiriart and Martimort (2006a) and the references therein.
35See Shavell (1984a), Kolstad, Ulen and Johnson (1990), or Hiriart, Martimort and Pouyet (2008, 2010).
References
Alberini, A., and Austin, D. H. (1997). “On and off the Liability Bandwagon:
Explaining State Adoptions of Strict Liability in Hazardous Waste Programs”, Re-sources for the Future, Discussion paper 98–08.
Alberini, A., and Austin, D. H. (1998).“Strict Liability as a Deterrent in Toxic Waste Management: Empirical Evidence from Accident and Spill Data”, Resources for the Future, Discussion paper 98–16.
Alberini, A., and Austin, D. H. (1999). “Accidents Waiting to Happen: Liability Policy and Toxic Pollution Releases”,Resources for the Future, Discussion Paper 99–29.
Alberini, A., and Austin, D. H., 2001. “An Analysis of the Preventive Effect of Environmental Liability.” Study commissioned by DG ENV of the European Com-mission.
Brennan, G., Gonzales, L. G., G ¨uth, W., Levati, M. V. (2008). “Attitudes toward private and collective risk in individual and strategic choice situations”, Journal of Economic Behavior and Organization67: 253–262.
Dohmen, T., Falk, A., Huffman, D., Sunde, U., Schupp, J., and G., W.G. (2005). “In-dividual risk attitudes: New evidence from a large, representative, experimentally-validated survey.”Discussion paper 511, DIW Berlin.
Dopuch, N. and King, R. R. (1992). “Negligence versus strict liability regimes in auditing: an experimental investigation”,The Accounting Review, 67: 97–120.
Dopuch, N., Ingberman, D., and King, R. R. (1997). “An experimental investiga-tion of multi-defendant bargaining in ’joint and several’ and proporinvestiga-tionate liability regimes”,Journal of Accounting and Economics, 23: 189–221.
Faure, M. and Skogh, G. (2003). “The Economic analysis of Environmental Policy and Law”, Edward Elgar Publishing.
Fischbacher, U. (2007). “Z-Tree: Zurich toolbox for ready-made economic experi-ments”,Experimental Economics, 10: 171–178.
Greiner, B. (2004). “An Online Recruitment System for Economic Experiments”, Forschung und wissenschaftliches Rechnen 2003, Kurt Kremer, Volker Macho (eds), GWDG Bericht 63, Goettingen: Ges. fuer Wiss. Datenverarbeitung, 79–93.
Hiriart, Y., and Martimort, D. (2006a). “The Benefits of Extended Liability”, Rand Journal of Economics37: 562–582.
Hiriart, Y., and Martimort, D. (2006b).“Liability Rules and Regulation for Environ-mentally Risky Ventures”, inFrontiers in the Economics of Environmental Regulation and Liability, Boyer, Hiriart, Martimort (eds), Ashgate.
Hiriart, Y., Martimort, D., and Pouyet, J. (2008).“The Regulator and the Judge: The Optimal Mix in the Control of Environmental Risk”,Revue d’´economie Politique, 119:
941–967.
Hiriart, Y., Martimort, D., and Pouyet, J. (2010). “The Public Management of En-vironmental Risk: Separating Ex-Ante and Ex-Post Monitors”,Journal of Public Eco-nomics, 94: 1008–1019.
King, R. and Schwartz, R. (1999).“Legal penalties and audit quality: an experimen-tal investigation”,Contemporary Accounting Research16(4).
King, R. and Schwartz, R. (2000). “An experimental investigation of auditors’ lia-bility: implications for social welfare and exploration of deviations from theoretical predictions”,The Accounting Review, 75: 429–451.
Kolstad, C., Ulen, T. and Johnson, G. (1990). “post Liability for Harm vs. Ex-Ante Safety Regulation: Substitutes or Complements”, American Economic Review, 80: 888–901.
Kornhauser, L. and Schotter, A. (1990). “An experimental study of single-actor
accidents”,Journal of Legal Studies, 19(1): 203–233.
Leland, H. and Pyle, D. J. (1977). “Informational Asymmetries, Financial Structure and Financial Intermediation”,The Journal of Finance, 32: 381–387.
Newman, H. and Wright, D. (1990). “Strict Liability in a Principal-Agent Model”, International Review of Law and Economics, 10: 219–231.
Niederle, M. and Vesterlund, L. (2007). “Do women shy away from competition?
Do men compete too much?”,The Quaterly Journal of Economics, 122: 1067–1101.
Pitchford, R. (1995).“How Liable Should a Lender Be? The Case of Judgment-Proof Firms and Environmental Risks”,American Economic Review, 85: 1171–1186.
Posner, R.A. (1992). “Economic Analysis of Law”, Little, Brown and Company, Fourth edition.
Segerson, K., (2002). “Economics and Liability for Environmental Problems”, Ash-gate.
Shavell, S. (1980). “Strict Liability versus Negligence”, Journal of Legal Studies, 9:
1–25.
Shavell, S. (1984a). “A Model of the Optimal Use of Liability and Safety Regula-tion”,RAND Journal of Economics, 15: 271–280.
Steven Shavell (1984b).“Liability for Harm versus Regulation of Safety”,Journal of Legal Studies, 13, 357–374.
Shavell, S. (1986). “The Judgment Proof Problem”,International Review of Law and Economics, June, 6: 45–58.
Shavell, S., (2004).“Foundations of Economic Analysis of Law”. Cambridge, Mass.:
Harvard University Press.
Sundali, J., and Croson, R., (2006). “Biases in Casino Betting: The Hot Hand and
the Gambler’s Fallacy”,Judgment and Decision Making, 1: 1–12.
Viscusi, K., (2007). “Regulation of health, safety, and environmental risks”, Hand-book of Law and Economics, volume I, edited by A. Mitchell Polinsky and Steven Shavell, Elsevier.
Wittman, D., Friedman, D., Crevier, S., and Braskin, A. (1997). “Learning liability rules”,Journal of Legal Studies, 26: 145–164.
Appendix A
Risk-aversion. Let us now assume that the firm is risk-averse and her preferences are reflected by a CARA utility function: u(x) = 1−er−rx, where the parameterr > 0 measures the absolute risk aversion andxis a monetary payoff.
Social optimum. Prevention is socially optimal as long as:
p1u(wt−h−c) + (1−p1)u(wt−c)≥p0u(wt−h) + (1−p0)u(wt),
a condition that can be rewritten as:
c≤ 1 r ∗ln
1−p0+p0erh 1−p1+p1erh
. (4)
No Liability. The firm chooses to invest in prevention as long as:
p1u(wt−c) + (1−p1)u(wt−c)≥p0u(wt) + (1−p0)u(wt),
a condition that boils down to u(wt−c) ≥ u(wt) and that, obviously, never holds true. Hence, the firm never invests in safety in the absence of liability.
Strict Liability. The firm chooses to invest in prevention as long as:
p1u(wt−min{h, wt} −c) + (1−p1)u(wt−c)≥p0u(wt−min{h, wt}) + (1−p0)u(wt),
a condition that can be rewritten as:
c≤ 1 r ∗ln
1−p0+p0ermin{h,wt} 1−p1+p1ermin{h,wt}
. (5)
Comparing (4) and (5), it is straight to see that the firm will take the socially optimal decision if she is wealthy enough, i.e. if her wealthwtis sufficient to cover harmh. Negligence. The firm chooses to invest in prevention as long as:
p1u(wt−c) + (1−p1)u(wt−c)≥p0u(wt−h) + (1−p0)u(wt),
a condition that can be rewritten as:
c≤ 1
r ∗ln 1−p0+p0ermin{h,wt}
. (6)
Comparing (5) and (6), we can show easily that the former is more demanding than the latter: the firm is induced to exercise care for a larger set of parameters when submitted to Negligence rather than Strict Liability.
Hence, the qualitative theoretical results obtained with a risk-neutral firm do not change when moving to the risk-aversion case.
Appendix B
Instructions – for the convenience of the referee, not for publication
Instructions Part I36
These instructions are identical to all 32 participants in the experiment.
Welcome and thank you for participating in this experiment. Please turn off your cell phones and stop communicating with other participants. Please raise your hand if you have any question. We will come to your cubicle and answer your questions in private.
This is the first part of the instructions. The second part will be distributed to you after you finish the following task.
You can earn your endowment by adding up two-digit numbers for five minutes.
When you are done with one mathematical task, please click the “Continue”-button and a new mathematic task will appear on your screen. Only paper and pencil are allowed during Part I of the experiment.
The thirty participants who solve correctly the highest number of mathematical tasks will earn an endowment and hence the right to participate in Part II of the experiment. The remaining two participants will have to leave the laboratory and will receive 3 euros each.
If you have any questions, you may now raise your hand. If everything is clear, please click the “Continue”-button.
36Part I is identical for all treatments.
Instructions Part II
These instructions are identical to all 30 participants in the experiment.
These instructions are identical to all 30 participants in the experiment.