Reactions to Control with(out) Background Control: Evidence from the Internet and the Laboratory Katrin Schmelz∗ and Anthony Ziegelmeyer† Preliminary version: February 2011
1
Introduction
This paper investigates whether agents’ reactions to a principal’s imposition of control differ in the presence of an independent source of control. Henceforth, we refer to the independent source of control as background control. We compare an internet and a laboratory implementation of an experimental principal-agent game where the principal can control the agent by imposing either a low or a medium effort level before the agent chooses an effort. Our experimental results show that negative reactions to control are stronger in the laboratory experiment, i.e. in the presence of background control, than in the internet experiment, i.e. in the absence of background control. However, negative reactions to control are of similar magnitude at the end of the two experiments which indicates that experienced agents react similarly to the implementation of control in both environments. Internet experiments differ from laboratory experiments since they involve a substantial loss of control over the environment. In the laboratory, the experimenter carefully briefs the participants, privacy is carefully controlled, and the computer interface is rigid which prevents participants from engaging in other activities while making their choices. With the Internet, a participant is free to engage in other activities while making choices for the experiment. Additionally, less social pressure from peers is placed on participants in the internet since the social distance is increased. In a nutshell, participants’ interactions in the laboratory take place in the presence of an exogenous source of control which is (almost) absent on the internet. We take advantage of this difference between the two environments to investigate whether the degree of control aversion in a principal-agent relationship is influenced by the background control. As detailed later, our internet experiment is identical to our laboratory experiment except that it is conducted online. We consider a principal-agent game which is a straightforward extension of the game implemented by Falk and Kosfeld (2006) in their main treatments. This setting assumes an extreme form of control policy where the principal has the possibility to remove the agent’s most opportunistic choices from his choice set.1 There is by now substantial evidence that in such a setting hidden costs of control exist. However, hidden costs of control are usually not substantial enough to undermine the effectiveness of economic incentives (Ploner, Schmelz, and Ziegelmeyer, 2010). In this paper we are merely concerned with the impact of background control on control aversion and we do not pursue the question of whether hidden costs of control outweigh the benefits of control. The remainder of this paper is organized as follows. Section 2 outlines the experimental design and procedures of our two experiments. Section 3 describes our results and section 4 concludes. ∗
Corresponding author: Max Planck Institute of Economics, IMPRS “Uncertainty”, Kahlaische Strasse 10, D-07745 Jena, Germany. Email:
[email protected]. † Max Planck Institute of Economics, Strategic Interaction Group, Kahlaische Strasse 10, D-07745 Jena, Germany. Email:
[email protected]. 1 In most employment relationships, the employer only has the possibility to make shirking unprofitable.
1
2
Experimental design and procedures
We investigate the impact of background control on the motivation crowding-out effect which results from the imposition of a control device. To do so, we compare participants’ behavior in a laboratory and internet experiment. In both experiments, participants repeatedly play the monitoring game which describes an employment relationship between a principal and a single agent. In a given session, each participant is assigned either the role of agent or the role of principal. Participants gain experience with the context and the behavior of other participants during 10 rounds of the game. Roles are kept constant over all rounds. The matching follows a perfect strangers protocol in which no participant is ever matched with a counterpart he has been previously matched with. Participants also complete a survey in which they are asked about their socio-economic characteristics as well as their subjective attitudes to control. Participants who are working answer additional questions about their workplace environment. The monitoring game Consider an agent who chooses a productive activity e which is costly to her but beneficial to the principal. Before the agent decides on e, the principal has the possibility to restrict the agent’s choice set by choosing one out of three enforcement levels: No enforcement (e = 1), low enforcement (e = 2), and medium enforcement (e = 3) (referred to as N E, LE and M E, respectively). The agent then chooses an effort level e ∈ {e, e + 1, . . . , 10}. We employ the strategy method, meaning that the agent makes her choice for each of the three enforcement levels before knowing the principal’s actual decision. Concretely, each agent is asked to choose a triplet of effort levels eN E , eLE , eM E where eN E ∈ {1, 2, . . . , 10} is payoff-relevant in case the principal does not enforce a minimal effort, eLE ∈ {2, 3, . . . , 10} is payoffrelevant in case the principal enforces a low effort and eM E ∈ {3, 4, . . . , 10} is payoff-relevant in case the principal enforces a medium effort. Table 1 shows the payoff functions with convex effort costs for the agent and concave benefits for the principal. Convex effort costs reflect the idea that exerting low effort at work is usually not very costly, but ones the agent is working to capacity marginal effort costs become tremendous. Decreasing marginal benefits from the agent’s effort for the principal are plausible from productivity losses due to physical restrictions. The fair and most efficient effort level locates somewhere in the middle (e = 7). To diminish the confounding effect of opportunism on control aversion, the payoff function is such that expressing the dislike of control bears low costs for selfish agents.
Effort level
Agent’s payoff Principal’s payoff
1
2
3
4
5
6
7
8
9
10
99 1
98 16
96 29
93 41
89 53
83 64
75 75
65 82
51 87
35 90
Table 1: Payoffs in experimental currency units.
For a given agent’s triple of efforts eN E , eLE , eM E , the choice of a low enforcement level has a direct
and indirect effect which are expressed by max 2 − eN E , 0 and eLE − max 2, eN E , and the choice of a
medium enforcement level has a direct and indirect effect which are expressed by max 3 − eN E , 0 and
eM E − max 3, eN E . If the indirect effect is negative, there are hidden costs of control. On the contrary, if the indirect effect is positive, there are hidden benefits of control.
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Belief elicitation Before they interact in the employment relationship, participants are asked to guess the average behavior of their counterpart. In each repetition, participants make three guesses. Principals are asked to guess, for each enforcement level, the average effort that will be chosen by agents (since we employ the strategy method, for each enforcement level all agents choose an effort). Each principal reports his guesses by e keying in a vector b1P , b2P , b3P with e ≤ bP ≤ 10 (each guess has at most one decimal place). Agents are asked to guess, for each enforcement level, the natural frequency of principals that will chose that e enforcement level. Each agent reports his guesses by keying in a vector b1A , b2A , b3A with 0 ≤ bA ≤ 100 and b1A + b2A + b3A = 100 (no decimal place is allowed). We limit the possibility to learn about other participants. Once all guesses and choices have been made in a given round, each participant is only informed about the behavior of his counterpart. Participants do not learn about the correctness of their guess during the experimental session. Earnings Each participant is paid a flat amount of 30 experimental currency units (ECUs) for completing the survey. As far as the interaction part is concerned, only one of the 10 rounds is payoff-relevant. The payoff-relevant round is randomly selected at the end of the experiment. For each participant, depending on the outcomes of random draws, either one of the three guesses is paid or the earnings are determined by the employment relationship. The randomly chosen guess is paid according to the following scheme: If an agent’s (principal’s) guess differs by no more than 5 (0.5) from the true value then the participant earns 70 ECUs. Otherwise the participant earns 20 ECUs.
2.1
Practical procedures
Both experiments were conducted with the help of EMaX, an internet platform hosted by the Max Planck Institute of Economics in Jena, and all 106 subjects were students from the University of Jena who have agreed to participate in economic experiments. Subjects were invited using the ORSEE recruitment system (Greiner, 2004). Students received an invitation email with a link to a registration page. On this page they were informed about our institution, general rules of the study, and about the fact that the other participants are those they usually interact with in the laboratory.2 For registration, students had to enter some information (student number, gender, mother tongue, nationality and email address). Each student could register only once. Registered participants received a survey token via email. Answering the survey questions took on average 10 minutes and participants had a time frame of a few days to do so. In both experiments, participants completed the survey at their place of choice (e.g. at home). Participants who completed the survey could register for an experimental session and received a session token to the experiment via email. To circumvent a potential impact of the survey on choices in the interactive part, experimental sessions were conducted a few days later. Each session took slightly more than one hour. Only participants who completed an experimental session were eligible for payment. In the internet experiment, there was a prearranged start time for each of the two sessions, and each of the 58 participants had to log on not later than that time. Like for the survey, participants made their interactive choices at their place of choice. The two sessions of the laboratory experiment took place at the Experimental Laboratory of the Max Planck Institute of Economics (ELMPIE) with a total of 48 participants. Instructions were not read aloud. In both experiments, participants received their earnings at the ELMPIE. In the internet experiment, we informed participants that they would receive 16 ECUs as a compensation fee for collecting their earnings. In the laboratory experiment, we added a show-up fee of 16 ECUs to participants’ earnings. In both experiments, 15 ECUs were converted to 1 euro. 2
Experiments conducted by members of the Max Planck Institute of Economics strictly adhere to a non-deception policy.
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The two samples of Jena students have very similar characteristics. 60% and 46% of the participants are male in the internet and laboratory experiment respectively. In the laboratory experiment, the sample composition according to educational background is such that 39.6%, 29.2% and 31.2% of the participants belong to the category “business administration & economics”, “other behavioral & social sciences”, and “engineering, life & natural sciences” respectively. In the internet experiment, 36.8%, 28.1% and 35.1% of the participants belong to the category “business administration & economics”, “other behavioral & social sciences”, and “engineering, life & natural sciences” respectively.
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Results
In Section 3.1, we first present some descriptive statistics of the data. We then assess the impact of background control on participants’ behavior with the help of regression models in Section 3.2.
3.1
Descriptive statistics
Table 2 shows agents’ effort levels as a function of the principal’s decision in the two experiments. In each panel, the first row reports the average effort for each enforcement level followed by the indirect impact of low and medium control measured in percentage terms. The second row reports standard deviation followed by 1st quartile followed by median followed by 3rd quartile for each enforcement level. In both experiments, the average effort increases with the degree of control which implies that benefits of control always outweigh hidden costs of control. This is hardly surprising since our experimental environment is less conducive to substantial degrees of control aversion than the one considered by Falk and Kosfeld (2006). More interestingly, indirect impacts of control are larger in the laboratory than on the internet but the difference is reduced over time as shown in Figure 1.
No enforcement
Low enforcement
Medium enforcement
Indirect impact of low enforcement medium enforcement
Internet experiment
2.89 (2.29;1;1;5)
3.20 (1.65;2;2;4)
3.67 (1.34;3;3;4)
-6.25 %
-7.90 %
Laboratory experiment
3.08
3.12
3.27
-16.35 %
-28.75 %
(2.68;1;1;5)
(1.49;2;2;4)
(0.65;3;3;3)
Table 2: Agents’ efforts as a function of the principal’s enforcement level.
Indirect impact of low control
5%
5%
0%
0%
‐5%
‐5%
‐10%
‐10%
‐15%
‐15%
‐20%
‐20%
‐25%
‐25%
‐30% 30%
‐30% 30%
‐35%
‐35%
‐40%
‐40% 1
2
3
4
5
6
7
8
9
1
10
Round
Figure 1:
2
3
4
5
6
7
8
Round
Indirect impacts of control over time.
(Internet=lines with white dots; Laboratory=lines with black dots)
4
9
10
Indirect impact of medium control
The fact that more control aversion is observed in the laboratory than on the internet might be a consequence of a larger degree of background control but the difference could also be partially driven by the principals’ behavior. The latter explanation does not seem to be supported by the data as shown in Table 3. In each panel, the first row reports the frequency of each enforcement level chosen by the principals over all rounds. The second row reports the same frequencies in the first (round 1 to 5) and second half (round 6 to 10) of the experiment. Overall as well as initial frequencies are very similar in the two experiments, and as the session progresses the medium (respectively low) enforcement level is chosen more often (respectively less often). No enforcement
Low enforcement
Medium enforcement
Internet experiment
14 % (12;17)
13 % (19;7)
73 % (69;76)
Laboratory experiment
13 % (16;8)
10 % (13;8)
77 % (71;84)
Table 3: Frequencies of principals’ enforcement levels.
Beliefs Overall, agents’ beliefs are well in line with principals’ chosen enforcement levels. Expected frequencies of no enforcement, low and medium enforcement are 14%, 19% and 67% (respectively 10%, 12% and 78%) in the internet experiment (respectively laboratory experiment). Initially, the accuracy of agents’ beliefs in the laboratory experiment is much larger than the accuracy of agents’ beliefs in the internet experiment. In particular, agents in the internet experiment expect that only half of the principals choose a medium enforcement level in round 1 (almost 70% of them does so). As the session progresses, agents’ beliefs become more accurate in both experiments but the adjustment is twice as large in the internet than in the laboratory experiment. On the contrary, principals’ beliefs do not match well agents’ chosen effort levels. Figure 2 shows for each experiment the temporal patterns of indirect impacts of control as expected by the principals. Except in round 1, principals do not anticipate the observed difference between the degrees of control aversion in the two experiments. Additionally, principals expect lower indirect impacts of control than the observed ones especially in case of medium enforcement. The fact that principals’ beliefs do not adjust in the right direction is hardly surprising since they only observe the agent’s effort level which corresponds to the enforcement level they have chosen. Thus, a principal who always chooses a medium enforcement level will never learn that some agents exert more effort in case of no enforcement.3
3.2
Statistical analysis
With the help of two regression models, we assess the variation in agents’ control aversion due to the extent of background control. Our statistical analysis also sheds light on the relationship between demographics and control aversion as well as on the relationship between beliefs and control aversion. The estimation method is OLS with mixed effects where a random effect for the agent and another random effect for the session are included. Random effects are assumed to be independent and to follow a normal distribution with mean zero. With this specification we allow the behavior of the same agent in different rounds to be correlated as well as the behavior of different agents from the same session to be correlated. Table 4 summarizes our estimation results. 3
The proportion of principals who choose the enforcement level which according to their elicited beliefs maximizes their monetary payoffs is very similar in each round of the two experiments and this proportion increases as the session progresses to reach about 85%. See Appendix 1 for details.
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Expected indirect impact of low control
10%
10%
5%
5%
0%
0%
‐5%
‐5%
‐10%
‐10%
‐15%
‐15%
‐20%
Expected indirect impact of medium control
‐20% 1
2
3
4
5
6
7
8
9
1
10
2
3
4
5
Round
6
7
8
9
10
Round
Figure 2: Expected indirect impacts of control over time. (Internet=lines with white dots; Laboratory=lines with black dots)
Age Other T ech M ale Lab Round Lab ∗ Round b2A − b1A
Dependent variable: Indirect effect of low enforcement
Dependent variable: Indirect effect of medium enforcement
Model 1 0.013 (0.042) 0.371 (0.234) 0.064 (0.250) -0.225 (0.206) -0.608*** (0.238) -0.023* (0.014) 0.045** (0.020) 0.000 (0.002)
Model 3 0.043 (0.079) 0.671 (0.435) -0.151 (0.466) -0.534 (0.383) -0.893** (0.415) -0.032* (0.019) 0.038 (0.027)
Model 4 0.038 (0.078) 0.673 (0.430) -0.129 (0.461) -0.542 (0.378) -0.950** (0.408) -0.040** (0.018) 0.041 (0.027)
-0.002 (0.002) -0.848 (1.869) 530 -762.457
-0.789 (1.850) 530 -763.124
Model 2 0.013 (0.042) 0.373 (0.234) 0.064 (0.251) -0.226 (0.206) -0.611*** (0.237) -0.023* (0.014) 0.045** (0.020)
b3A − b1A Constant
-0.374 -0.373 (0.998) (0.999) Observations 530 530 Log-likelihood -603.568 -603.578 ∗∗∗ (1%); ∗∗ (5%); ∗(10%) significance level.
Table 4: Indirect effect of control.
In model 1 the dependent variable indirect effect of low enforcement, eLE − max 2, eN E , is regressed against an intercept, three demographic controls (academic major,4 age and gender), the experimental condition (the dummy variable Lab identifies data from the laboratory experiment), a time trend (Round ), and a difference in beliefs (b2A − b1A ). Since the temporal patterns of the indirect effect of low enforcement seems to differ between the two experiments (see Figure 1), we also include the interaction effect Lab*Round. The estimated coefficient of Lab is significantly negative whereas the estimated coef4 Other equals 1 if the participant’s educational background belongs to the category “other behavioral & social sciences”, and 0 otherwise. Tech equals 1 if the participant’s educational background belongs to the category “engineering, life & natural sciences”, and 0 otherwise.
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ficient of Lab*Round is significantly positive. Accordingly, the indirect effect of low enforcement is more negative in the laboratory than in the internet experiment but the difference is reduced over time. Given the magnitude of these significant coefficients, indirect effects of low enforcement are estimated to be almost equal in both experiments at the end of the experimental session (the difference is even reduced by the fact that the estimated coefficient of Round is significantly negative, though weakly). Model 2 is the same as model 1 except that we drop the insignificant variable “difference in beliefs”.5 Almost no change is observed in the estimation results. In model 3 the dependent variable indirect effect of medium enforcement, eM E − max 3, eN E , is regressed against the same covariates as in model 1 except that the difference in beliefs is b3A − b1A . Again, the indirect effect of enforcement is more negative in the laboratory than in the internet experiment but the reduction of this difference over time is weaker. Model 4 is the same as model 3 except that we drop the insignificant variable “difference in beliefs”, and the indirect effects of medium enforcement come significantly closer in the two experiments. In conclusion, hidden costs of control are larger in the laboratory than in the internet experiment and this difference is stronger the more effort is enforced. This first observation suggests a positive impact of background control on the degree of control aversion expressed by agents. However, hidden costs of control are of similar magnitude at the end of the two experiments which indicates that experienced agents react similarly to the implementation of control in both environments. This second observation suggests that the Internet is a viable alternative to the laboratory for the experimental investigation of control aversion in employment relationships.6 Principals With the help of a regression model, we assess the variation in principals’ behavior due to the extent of background control. The estimation method is ordered probit with robust standard errors clustered on sessions as the independent units of observation. In a nutshell, we find that (i) the less principals believe in control aversion the more they enforce a low or medium effort; (ii) the medium enforcement level is chosen more often as the session progresses; and (iii) neither the demographic controls nor the experimental condition have a significant impact on principals’ behavior. These estimation results confirm that the observed difference in control aversion between the two experiments is unlikely to be driven by the principals’ behavior.
4
Conclusion
We investigate whether agents’ reactions to a principal’s imposition of control differ in the presence of background control. To do so, we compare an internet and a laboratory implementation of an experimental principal-agent game where the principal can impose control on the agent. Our experimental results show that negative reactions to control are stronger in the laboratory experiment than in the internet experiment. Background control increases the agents’ control aversion which follows the imposition of additional control by the principal. Our results also show that negative reactions to control are of similar magnitude at the end of the two experiments. Accordingly, the degree of control aversion revealed by experienced agents is similar on the internet and in the laboratory. After all, the internet might be a viable alternative to the laboratory for the experimental investigation of control aversion in employment relationships. 5
We interpret the absence of beliefs’ influence on control aversion as a sign of reliable data since agents can condition their effort on the enforcement level. 6 With the help of exact permutation tests for paired observations, we conclude that in round 1 of the internet experiment hidden costs of control are statistically insignificant both in case of low and medium enforcement. In round 1 of the laboratory experiment, hidden costs of control are statistically significant at a 10 percent level in case of low enforcement and statistically significant at a 5 percent level in case of medium enforcement.
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References Falk, A., and M. Kosfeld (2006): “The Hidden Costs of Control,” American Economic Review, 96(5), 1611–30. Greiner, B. (2004): “An Online Recruitment System for Economic Experiments,” in Forschung und wissenschaftliches Rechnen 2003., ed. by K. Kremer, and V. Macho, pp. 79–93. GWDG Bericht 63. Ges. f¨ ur Wiss. Datenverarbeitung, G¨ ottingen. Ploner, M., K. Schmelz, and A. Ziegelmeyer (2010): “Hidden Costs of Control: Three Repetitions and an Extension,” JERP 2010-007.
Appendix 1: Additional figure Figure 3 shows for each experiment and in each round the proportion of principals who choose the enforcement level which according to their elicited beliefs maximizes their monetary payoffs.
100% 90% 80% 70% 60% 50% 40% 30%
Internet Laboratory
20% 10% 0% 1
2
3
4
5
6
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Round
Figure 3: Frequencies of best-replies.
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