As part of our research focusing on maternity services, we will be investigating the drivers of increasing Caesarean section rates in England. In the first of a series of reserach extracts, we describe our findings using crude unadjusted Health Episode Statistics, before setting out our plans for a longitudinal study, investigating the links between incidence of c-section rates, maternal characteristics and maternity staffing levels.
The rising rates of caesarean sections have become a hot topic in the news. Efforts are being made in order to reduce them and promote normal births instead, as the latter are believed to result in improved outcomes for patients and lower costs for the health provider. For the UK, a prominent example is the “Focus on Normal Birth and Reducing Caesarean Section Rates Rapid Improvement Programme" which began in April 2008. Yet, as in many other countries, caesarean section is becoming an increasingly popular method for delivering a baby. According to calculations using the maternity tail of the Hospital Episode Statistics (HES) data, the total caesarean section rate has increased from 21.8% in 2000 to 25.1% in 2013 (first quarter) in English NHS trusts, while it was lying around 12% in 1990 for the UK as a whole.
Delivering Better For Less Through The Lab: How Can Behavioural Economics Help Us Design Better Policies?
One of the main goals of a social scientist is to help how to best advise organizations and policymakers to build better institutions and policies. Traditionally social scientists are divided into two groups: theoreticians and empiricists. Social theoreticians build models around the assumptions that aim at simulating an artificial world. The mathematical relationships between the variables and comparative static analysis help to derive predictions on how certain variables affect how individuals, society and organizations function. Empiricists on the other hand, take the complex real world data and either test the predictions of the theory on the data or try to identify a certain systematic relationship in the data. This can explain the dynamics of the observed outcomes which in turn may feed in to develop and build new and better theoretical models.
The third type of a social scientist that emerged quite recently is a behaviouralist (or some call them experimentalists). Behaviouralists take an intermediary step between theoreticians and empiricists and design experiments - artificial worlds which are bridges between a world of even more artificial theoretical models and a more complex real world where the empirical data is gathered from. Behaviouralists put human psychology (or in some cases even human physiology) in the centre of analysis and designs experiments to test theoretical predictions and assumptions or to identify behavioural regularities that existing theories cannot explain. Experiments are usually built in the laboratories through creating computerized institutions that have specific instructions for participants to understand “the rules of the game”. By keeping as many variables constant as possible, and just altering one variable, behaviouralists can exactly pinpoint what causes the behavioural change from one institution to the other. We call these institutions experimental treatments – controlled environments where only one variable is treated and its effect on the behaviour is compared to the one of the untreated institutional variable. Collecting data via such experiments, a behaviouralist breathes life into a theoretical model and yet easily avoids the complexities and endogeneity problems that most real world circumstances entail.
At the core of their research strategies, behaviouralists explicitly take into account that humans are not perfectly rational decision makers and are prone to biases and heuristics. The research of the last 50 years has identified a number of biases and heuristics that are crucial to take into account when designing policies. One of those is that people are loss averse: the losses loom larger than gains when entering our utility functions (Kahneman, Knetsch & Thaler 1991). Loss aversion explains a number of anomalies and inconsistencies in decision making, when outcomes are framed as losses or gains. By manipulating the description of an outcome, a policy designer can subliminally manipulate the decisions taken (also known as framing effects). In one of the recent experiments by Nosenzo, Offerman, Sefton & van der Veen (2014), experimental subjects played inspection games in the laboratory where they were given a role of either an employee or an employer. An employee could choose an effort level which was either low (shirking) or high (hard-working). An employer could choose whether to inspect the employee and pay an inspection cost or not to inspect. Upon inspection an employer could choose whether to reward (bonus) or punish (fine) an employee. The findings show that low effort could be deterred by fines targeted at shirking individuals much more effectively than encouraged by bonuses awarded to hardworking individuals. Exhibiting how loss aversion influences employer – employee relationships, this initial laboratory finding can help to advise organizations on how to maximize the exerted effort from workers if other mechanisms such as reputational concerns or career ladder opportunities are weak.
Another manifestation of loss aversion is the status-quo bias first found in the laboratory studies by Samuelson & Zeckhauser (1988). Status-quo bias is a strong tendency to remain at the current state because disadvantages loom larger than advantages of leaving or losing the current state. Numerous studies found that when given a role at the start of the experiment, people were more reluctant to switch their roles even though it was to their own disadvantage. This finding has since been heavily used by policy makers to adjust the “defaults” in the direction that is most beneficial for the society. Johnson and Goldstein (2003) have investigated and found that adjusting policies so that people have to opt-out of being an organ donor tremendously increases organ donation rates. More recently, status quo bias has advised a new design of the pension scheme in the UK. Now once employed, an employee is automatically registered to contribute to the pension savings account unless she opts-out from the scheme. It is aimed at increasing pension savings and is an example of a clever design using one bias (status-quo) to avoid undersaving caused by another bias (present biased hyperbolic discounting).
Economists have invested a substantial amount of work on the design of incentives to motivate people to perform better and work harder. The standard economic theory states that financial incentives are the most effective way to improve productivity. However, behavioural economists such as Frey (1994), Benabou and Tirole (2003) and Gneezy, Meier & Rey-Biel (2011) have shown that paying a higher wage in a task which already bears high social value and intrinsic motivation will not prove useful. Putting a price on such activity and increasing financial incentives will not make the task more motivating and may crowd-out the intrinsic motivation to engage in the task and eventually decrease productivity. Given this finding, many recent policy debates such as changing incentives of teachers from fixed rate to piece rates and introducing competitive payment schemes in public services could be criticized. On the other hand, competitive performance-dependent payment schemes have been shown to have productivity boosting effects both in the lab (Eriksson, Teyssier & Villeval, 2009; Dohmen & Falk 2010,) and in the field (Erev, Bornstein, & Galili 1993; Augenblick, & Cunha 2015). More research is needed to disentangle the effects of financial incentives on intrinsic motivation and how it interacts with productivity and motivation boosting competitive payment schemes.
The world of economics and social sciences have now taken a direction of realizing how experiments can be a useful tool to complement theoretical and empirical research to build better policies. Dan Ariely in his book Predictably Irrational said:
“If we all make systematic mistakes in our decisions, then why not develop new strategies, tools, and methods to help us make better decisions and improve our overall well-being? That's exactly the meaning of free lunches- the idea that there are tools, methods, and policies that can help all of us make better decisions and as a consequence achieve what we desire”.
To study the behavioural side of productivity and motivation in organizations is a crucial point to understand how to build better policies to improve productivity in public services. On the other hand, we should be very aware of the possible negative consequences of overly paternalistic policies and manipulative exploitations such as those used by marketing and consumer psychologists to modify behaviour in directions that profit corporations. As social scientists and guardians of ethical conduct, we have to bear in mind that our ultimate goals are to serve humanity and make the world a better place to live in. I believe, that behavioural economics offers tools that will help to advise cleverly designed policies delivering a more productive but even more importantly a more motivated and happier workforce for less than we once imagined.
Augenblick, N., & Cunha, J. M. (2015). Competition and Cooperation in a Public Goods Game: A Field Experiment. Economic Inquiry, 53(1), 574-588.
Benabou, R., & Tirole, J. (2003). Intrinsic and extrinsic motivation. The Review of Economic Studies, 70(3), 489-520.
Dohmen, T., & Falk, A. (2010). You get what you pay for: Incentives and selection in the education system. The Economic Journal, 120(546), 256-271.
Erev, I., Bornstein, G., & Galili, R. (1993). Constructive intergroup competition as a solution to the free rider problem: A field experiment. Journal of Experimental Social Psychology, 29(6), 463-478.
Eriksson, T., Teyssier, S., & Villeval, M. C. (2009). Self‐selection and the efficiency of tournaments. Economic Inquiry, 47(3), 530-548.
Frey, B. S. (1994). How intrinsic motivation is crowded out and in. Rationality and Society, 6(3), 334-352.
Gneezy, U., Meier, S., & Rey-Biel, P. (2011). When and why incentives (don't) work to modify behavior. The Journal of Economic Perspectives, 191-209.
Johnson, E. J., & Goldstein, D. G. (2003). Do defaults save lives? Science, 302, 1338-1339.
Kahneman, D., Knetsch, J. L., & Thaler, R. H. (1991). Anomalies: The endowment effect, loss aversion, and status quo bias. The Journal of Economic Perspectives, 193-206.
Nosenzo, D., Offerman, T., Sefton, M., & van der Veen, A. (2014). Encouraging compliance: bonuses versus fines in inspection games. Journal of Law, Economics, and Organization, 30(3), 623-648.
Samuelson, W., & Zeckhauser, R. (1988). Status quo bias in decision making. Journal of Risk and Uncertainty, 1(1), 7-59.
Announcing the first annual report from the Delivering Better for Less Research project.
This reports features updates from the four work packages as well a comprehensive bibliography of the latest publications and presentations from the project.
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