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International Journal of Health Economics and Management

Open Access 15-03-2024 | Research article

Strategic behaviour and decision making in competitive hospital markets: an experimental investigation

Authors: Johann Han, Nadja Kairies-Schwarz, Markus Vomhof

Published in: International Journal of Health Economics and Management

Abstract

We investigate quality provision and the occurrence of strategic behaviour in competitive hospital markets where providers are assumed to be semi-altruistic towards patients. For this, we employ a laboratory experiment with a hospital market framing. Subjects decide on the quality levels for one of three competing hospitals respectively. We vary the organizational aspect of whether quality decisions within hospitals are made by individuals or teams. Realized monetary patient benefits go to real patients outside the lab. In both settings, we find that degrees of cooperation quickly converge towards negative values, implying absence of collusion and patient centred or competitive quality choices. Moreover, hospitals treat quality as a strategic complement and adjust their quality choice in the same direction as their competitors. The response magnitude for team markets is weaker; this is driven by non-cooperative or altruistic teams, which tend to set levels of quality that are strategically independent.

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Note that Bloom (2015) finds that competition has a positive impact on outcome as well as management quality in the English public hospital sector, yet management quality is measured by a survey tool covering things apart from the decision-making processes, including incentives, monitoring, or target setting.

Economists have used laboratory experiments to study competition in markets for quite some time (see Holt, 1995 and Potters & Suetens, 2013 for a more recent example). The majority of these experiments is either set in a quantity or price competition environment. An important distinction between games of quantity and games of price setting is that the former are considered as strategic substitutes and the latter as strategic complements in competition. Generally, for strategic substitutes, the optimal response to an aggressive strategy by the competitor is to become less aggressive – i.e., move into the opposite direction – while for strategic complements the optimal response is to become more aggressive and follow the direction of the move (Tirole, 1988).

Contributions by Pauly and Redisch (1973) and Harris (1977) address the issue of stakeholders with different objectives in hospitals, while Phelps (2002) investigates different preferences in the ruling body of hospitals when it comes to quality decisions. For a detailed discussion of team decisions in hospitals, see Barros and Olivella (2011).

Brosig-Koch et al. (2017a) consider collusive behaviour, but only for two physicians. Han et al. (2017) investigate the effects on quality of a merger due in hospital markets with quality competition and do not investigate coordination behaviour.

The evidence has a tendency, but it is ambiguous to some degree. For example, Bornstein and Gneezy (2002) find that teams converge quicker on the competitive solution, while Raab and Schipper (2009) find no differences between individual and team decisions for three firm Cournot oligopolies.

To reduce complexity for participants, they were provided with a discrete set of quality levels to choose from, even if the theoretical framework was based on continuous functions.

For a comparison of a monopoly and a competitive market, see Han et al. (2017).

The assumption of linear demand is a strong one. Changing the demand function possibly changes the prediction of the model. This is a clear limitation of the model and the corresponding experiment.

In our model approach, hospitals cannot endogenously choose locations since their locations are fixed.

We assume in the following that disutility from traveling is sufficiently small, i.e., \(t<3 p/c\).

A quality level of zero is excluded in the design as this would mean non-treatment. Treating a patient should at least lead to the lowest quality level possible.

Taler is our experimental currency unit. The exchange rates differ for the two conditions: 1 Taler = 0.07€ in the individual market conditions and 1 Taler = 0.21€ in the team market conditions. We do so in order to make individual earnings comparable, although we increase the number of participants per market from one to three in the team conditions. For more detail, see Section "Experimental procedure".

Due to the discrete parametrization for our experiment, the best response is ambiguous.

For simplicity, we assume homogeneity in the degree of altruism.

While it would be interesting to be able to differentiate between the drivers of strategic behaviour, the primary aim of this paper is to identify the latter and to compare the outcomes of markets with an individual decision process to those with a team decision process (see below).

An extension of the experimental model framework to teams is beyond the scope of this paper, which focusses on the experiment.

Most surveys indicate that teams are typically closer to the standard game theory predictions than individuals are (see, e.g., Bornstein, 2008; Cooper & Kagel, 2005; Kugler et al., 2012). However, there is also a small number of studies reporting team decisions to be less selfish and rational compared to individual decisions (see, e.g., Kocher & Sutter, 2007; Müller & Tran, 2013).

Of the participants, 286 were female and 269 were male. Our sample included 27 medical students and 199 business and economics students.

Individual and team conditions had different durations, so sessions were held separately. Also, we only used the observations from the first part for this study.

In the comprehension questions, we first checked for whether participants understood how to read the profit and patient benefit tables. For this, they were given an example part of profit and patient benefit tables with different numbers than in the experiment. They had to indicate the profits of their own hospital and the other hospitals, as well as the respective patient benefit levels for a given quality of care. The other questions were true and false questions to check for the understanding of their role as decision maker. For an example, see the translated screenshots of the comprehension questions for the individual condition in Appendix A.2.4.

The remaining participants were from other fields of study, including healthcare management (1.08%), educational sciences (11.17%), humanities (18.74%), natural sciences (13.33%).

The mean duration of majority voting process in period 15 would be 1.566 (SD 0.990) without the outlier.

It is always two-sided unless indicated otherwise.

Standard deviations are continuously higher in the individual markets than in the team markets. There seems to be a larger variety of different markets in terms of degree of cooperation when individuals are in charge. We will have a closer look at this variety in Appendix A.6. The lower standard deviation also suggests that the voting process in team decisions aligns the voting behaviour.

While we cannot reject an equality in distributions with the MWU, there are still some relevant differences between conditions. The majority of markets in both conditions have negative degrees of cooperation (69 percent of individual and 82 percent of team markets) with almost identical average levels of -0.17 for individuals and -0.18 for teams. However, among markets with positive degrees of cooperation, the average degree of cooperation for individuals is much higher at 0.21 compared to teams with 0.07. In other words, uncooperative markets are very similar between conditions, but cooperative markets are much more cooperative in the individual conditions – but there are not too many. So, if we focused on the means and take a two-sided t-test, we would reject the hypothesis of equality of means with p = 0.049. This will also be addressed in Appendix A.6 when we look at cooperative markets and collusion.

Again, taking means into account changes the test results slightly (two-sided t-test, p = 0.042).

The relatively high quality and vote spreads in the respective individual and team markets in the first period indicate that we do not have an anchoring effect. In case of anchoring on, e.g. the Nash equilibrium or patient optimal quality levels, we should see much lower spreads.

A random effects model on changes in choices, which clusters on market level, yields very similar results.

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Title: Strategic behaviour and decision making in competitive hospital markets: an experimental investigation
Authors: Johann Han
Nadja Kairies-Schwarz
Markus Vomhof
Publication date: 15-03-2024
Publisher: Springer US
Published in: International Journal of Health Economics and Management
Print ISSN: 2199-9023
Electronic ISSN: 2199-9031
DOI: https://doi.org/10.1007/s10754-024-09366-3

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Strategic behaviour and decision making in competitive hospital markets: an experimental investigation

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