How Behavioral Economics Can Help When You Think You Don't Have Enough Money: A Glimpse into the Romanian Healthcare System

How Behavioral Economics Can Help When You Think You Don't Have Enough Money: A Glimpse into the Romanian Healthcare System

Elena Druică (University of Bucharest, Romania) and Rodica Ianole (University of Bucharest, Romania)
Copyright: © 2017 |Pages: 16
DOI: 10.4018/978-1-5225-1826-6.ch001


This work is a position paper discussing alternative viewpoints on factors that may influence the values of certain crucial healthcare efficiency indicators. We draw attention to the latest research in the area of behavioral economics, and make obvious certain inefficiencies in base healthcare packages, which are in close connection with human behavior. We begin with an analysis of healthcare expenses, first in absolute terms and then compare them with the Euro zone as well as former communist countries from Eastern Europe. The purpose is to offer multiple perspectives in relation to the widespread idea that medical care in Romania does not have the financial backing enjoyed in other places. We continue with the presentation of several healthcare variables and the attempt to find possible alternative explanations for their values and dynamics. Finally, we sum up with an emphasis on certain human behaviors that might underlie inefficiencies in healthcare packages and examine the corresponding experimental results, which offer some simple solutions to correct them.
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The year 1981 brought to the forefront of scientific research one of the most important results in behavioral economics (Tversky & Kahneman, 1981), specifically that our decisions are influenced by many different subjective elements. The way in which information is framed plays a crucial role in the decision making process. The famous example of the Asian disease, discussed in the aforementioned paper, presents the choice between two alternatives, formulated in slightly different ways, as seen below.

We are presented with the (obviously hypothetical) situation in which the US is preparing to deal with an Asian disease that will, most likely, kill 600 people. There are two alternatives that may be chosen, programs A and B. The two options are as follows:

Option 1:

  • If Program A is adopted, 200 people will be saved.

  • If Program B is adopted, there is 1/3 probability that 600 people will be saved, and 2/3 probability that no people will be saved.

Option 2:

  • If Program A is adopted 400 people will die.

  • If Program B is adopted there is 1/3 probability that nobody will die, and 2/3 probability that 600 people will die.

It is easy to notice that, objectively, the two options are equivalent: to say that 200 people would be saved by Program A is the same as saying that 400 would die as a result of the same decision. Furthermore, a 1/3 chance that all 600 would survive is the same as saying that there is a 1/3 probability that nobody would die. Finally, a 2/3 chance that nobody would be saved, as per option 1, is identical to the 2/3 likelihood that all 600 would die. Therefore, the only difference between the two options is, what the authors call, the framing of the decision: positive (in terms of “survival”) in the first case and negative (in terms of “number of dead”) in the second one. The results obtained by Amos Tversky and Daniel Kahneman indicate that people choose differently when the information is presented to them with another framing: option 1 shows that 72% prefer Program A, as opposed to only 28% for Program B, manifesting what is known in the literature as certainty bias when it comes to gaining something: the positive framing in option 1 triggered a preference for saving 200 people. The second options shows that only 22% would choose Program A, the majority of 78% would rather negotiate the loss of 600, instead of coming to terms with 400 people dying.

In the summary of the paper, the two authors tell us how “the psychological principles that govern the perception of decision problems and the evaluation of probabilities and outcomes produce predictable shifts of preference when the same problem is framed in different ways” (Tversky & Kahneman, 1981). In other words, this is the birth statement of the powerful framing effect, with such a simple mechanics and such a great impact on decision-making and different types of behaviors, economic and non-economic ones. Changing preferences for certain alternatives goes beyond the hypothetical and is responsible for different choices which are made in real life, a fact which is explored in further detail in the fifth section.

A second stream of arguments standing for analyzing health care in light of behavioral economics can be generously traced to the more methodological camp of causal relationships and subjective, hidden relationships. The Freakonomics example of Steven D. Levitt and Stephen J. Dubner (Levitt & Dubner, 2005) is still among the most convincing one. The beginning of the 1990s was, in the United States of America, an era in which the state was fighting a losing battle with a decades long growing crime rate. The period before 1995 was characterized by inefficient efforts on the part of the state in order to lower crime rates by: increasing the number of police, improving street lights, gun control and other similar policies. At the time, there were experts predicting that the crime rate would be expected to rise by 15% in the coming decade.

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