The sale of non-performing loan portfolios by lenders over the past decade was driven by a need for banks to reduce the negative impact of these portfolios on their balance sheets. However, the sale of such loan portfolios has been rife with borrower dissatisfaction, political pressure, media attention and ultimately led to the Consumer Protection (Regulation of Credit Servicing) Act 2015, which introduced a new regulatory regime for Credit Servicing Firms.
This paper will look at the legislative change that brought Credit Servicing Firms under regulatory remit of the Central Bank of Ireland (the Central Bank), the background as to why this was deemed necessary and what this meant from a regulatory perspective. In his explanatory memorandum, the Minister for Finance
stated that the purpose of this legislative change “is to regulate the activity of ‘credit servicing’ and the ‘credit servicing firms’ engaged in such activity so that the borrowers retain the protections that they have before a loan book is sold”. [i]
Consumer Protection (Regulation of Credit Servicing) Act 2015
In July 2015, the Consumer Protection (Regulation of Credit Servicing) Act 2015 (the CSF Act 2015) was enacted, amending Part V of the Central Bank Act 1997. The CSF Act 2015 introduced a new regulatory regime for persons (
Does the Federal Reserve Bank and FOMC Fall Victim to Decision Biases?
A Fusion of Behavioral Economics and Monetary Policy
Abstract: Does the Federal Reserve Bank and FOMC fall victim to cognitive (decision) biases while conducting monetary policy? While there are many different decision biases, this paper will focus on a select few that are relevant for monetary policy. The paper will discuss scenarios in which the Federal Reserve may have shown a lapse in rational decision making. I will offer potential ways the Federal Reserve Bank can attempt to limit or eliminate these biases. Additionally, ways in which the Federal Reserve is already designed that may eliminate or reduce these decision biases will be presented. The paper will conclude by suggesting that the members of the Federal Reserve Bank and FOMC are, in fact; human. The truth of the matter is that being human comes with a number of limitations, barriers, and cognitive biases which cause a lapse of rationality in judgment and can affect decision making, including the decisions made while conducting monetary policy. While this paper suggests that these decision biases do affect FOMC decision making, it remains open for discussion whether or not these biases have truly affected policy.
Behavioral economics is the blend of psychology and economics that explores what happens when individuals begin to display human limitations and barriers. It is a form of economic study that applies psychological understandings of human behavior in an attempt to explain economic decision-making. It is this decision-making that will be the focus of this paper. Behavioral economics has begun to gain ground in the world of economics by questioning the rationality assumption that is prominent in many standard economic models. Through questioning the rationality of human behavior, the subject of cognitive biases (decision biases or decision errors) has surfaced in relation to individual decision-making. A cognitive bias, or decision bias, is a lapse of rationality in judgment. It is when individuals create their own, subjective, reality based on their own perception of a certain situation.In this paper, I will suggest, and provide examples of, potential decision biases the Federal Reserve Bank may fall victim to while conducting monetary policy. This paper will also offer ways in which the Federal Reserve Bank may be able to protect itself from these biases and explain ways in which the Federal Reserve Bank is already designed that may eliminate or reduce these decision biases.
Potential Decision Biases
With such a vast number of decision biases that the area of psychology has introduced to economics I was forced to concentrate on only a few. For the purpose of this paper I will focus explicitly on status quo bias, availability bias, overconfidence, representativeness bias, and loss aversion. I will provide examples of where each of these biases may occur in monetary policymaking.
Status quo is the idea that doing nothing or not deviating from a previous decision is always an option when making a decision. This remain true even when the situation changes. People often continue the same outlook as before and occasionally completely ignore any new information. Kahneman and Tversky (1982)*cite* suggest that people feel more guilt for unfavorable outcomes that resulted from taking new actions than what they do for unfavorable outcomes that result from maintaining the status quo. A good example of the Federal Reserve Bank experiencing this bias is its “fear of liftoff” after the Great Recession in 2015. The bank was hesitant to normalize policy even when there was evidence suggesting it should do so.
When people are asked to calculate how often they believe a certain outcome of a situation occurs, we often rely on similar situations which we easily remember. When you ask an economist about financial crises, they will probably mention the 2008 crisis. Ask an economist about inflation and they will probably tell you about the inflation of the 1970s. Ask any American about terrorist attacks and you can be sure you’ll hear about September 11, 2001. When answers such as these come to mind so easily, we tend to overestimate how often these events actually occur. When the answers require more thought or research, the frequency tends to be much more accurate (Tversky and Kahneman 1973: 163–65.)*cite*. Members of the Federal Reserve Bank and the FOMC are often faced with decisions that need to be made quickly. Should a company be saved before markets open in the morning? Will making certain large scale purchases be enough to stimulate the economy? If so, how much is needed? Will any sign of deflation be enough to cause an overreaction leading to recession? While some of these situations are often researched more in depth, I do not believe it is uncommon for the Federal Reserve to make spur-of-the-moment decisions, especially in a time of crisis. In fact, some of the examples listed above is exactly what the Federal reserve dealt with during the Great Recession. Ben Bernanke (Chairman of the Federal Reserve Bank during the Great Recession) was a long time student of the Great Depression and often praised for how he handled the financial crisis of 2008. Cite BB Book. Was he acting under the assumption that this was another Great Depression scenario? We may never know, but, based off his study of the subject, it may not be far off to assume the actions he took were taken with a higher implied probability than actual probability of the events leading to another depression. The Great Depression, and the way the Federal Reserve handled it, may be the biggest event in macroeconomic history to create an opportunity for an availability bias. I believe it is safe to assume that, because of only a few major macroeconomic crises like the Great Depression in history, the Federal Reserve Bank is forced to make decisions based off availability biases.
Overconfidence is another bias that is found in behavioral economics. Over confidence is a scenario where an individual portrays a higher subjective confidence level in themselves than the objective confidence level may be. For example, a teacher asks her students on the first day of class who thinks they will be it the top 50 percent when the class concludes. Nearly all of the students would say they believe they will end up in the top 50 percent when it is statistically impossible. We can also see this sort of overconfidence in the decision making of the Federal Reserve and FOMC. Members of the Federal Reserve and FOMC may portray overconfidence in the areas of timing and effectiveness of certain decisions. If the Fed makes decisions that end up being seen as not enough, it may be a result of the bank subjectively believing their policy would have been more effective than objectivity would suggest. Overconfidence may become an issue if we rely on things like the interest rate to measure the opinion of monetary policy. When there is a low interest rate it means a loose monetary policy, but it could also mean the economy is weakening. The Fed may also overestimate their ability to reverse an aggressive decision they made making it tougher to recover from certain scenarios. Overconfidence is a topic that should be taken into consideration as part of the decision making process of monetary policy.
A representative biases takes a look at situations where generalizations are made, often times used with stereotypes. Once we discover that a set of characteristics applies to a group, we assume that every time we see those characteristic, they must be a member of that group. An example given by Tversky and Kahneman (1983) *cite*: “Linda is 31 years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti-nuclear demonstrations. Which is more probable? Linda is a bank teller or Linda is a bank teller and is active in the feminist movement.” Most would guess that Linda is a bank teller and is active in the feminist movement based on a representativeness bias but it is more probable that she is only a bank teller. Representativeness biases can find their way into Fed decision making in a number of ways. If housing prices increase, does that mean all prices are increasing? Does an increase in unemployment in Detroit mean an increase in unemployment everywhere? Meade and Sheets (2005) *cite* found that votes made by governors of the different Federal Reserve Bank branches are influenced by their district. This can be of concern when dealing with representative biases.
Loss aversion is one of the most prevalent findings in behavioral economics. Loss aversion is the concept that individual decision makers tend to weight losses more than gains. Stated differently, individuals hate to lose more than they like to win. An example of this is when you think about the Great Inflation of the 1970s. Trying to fight against the high inflation may cause a bias against any policy that would risk higher inflation, but by implementing contractionary policy during the recovery may put at risk any employment gains that were made during that period. Reference points, often stated numerically, are used in monetary policy to value any gain or loss that had taken place. For example, there is no particular reason why an inflation rate target of two percent is used in the United States. Even with that being said, policymakers consider any deviation, higher or lower, from this two percent target a loss. The same example can be used for the unemployment rate where any deviation higher than the target rate is considered a loss. Debates on whether or not the Fed should use a rule based policy system focuses on the assumption that loss aversion will cause a bias in the decisions of policymakers to follow the rule even in cases where a deviation may be needed.
Is it feasible for the Federal Reserve Bank and the FOMC to overcome these biases? If so, how? The first situation we will look at is if experts can overcome these decision biases or do they also fall victim like the rest of us? Daniel Kahneman (2011) *cite* suggest that experts also fall victim to biases. He says that experts often try too hard to be clever and think outside the box while making their predictions and decisions. Kahneman says that experts are always look for one piece of data that may offer an argument that requires a different plan of attack. This can been seen when the Federal Reserve declares it will be more dependent on data. Kahneman also points out that experts give different and inconsistent answers to the same questions. If true, that can be extremely concerning when the Federal Reserve tries to provide the public their thoughts on the direction of the economy. An argument can be made that these experts have sixth sense much like a police officer can detect unsafe environments even before they become unsafe. Kahneman argues that these skills are learned over time and not learned overnight. This seems to benefit the Federal Reserve since their governors are allowed long terms. On the other hand, according to Woolley and Gardner (2009)*cite* the majority of the governors don’t serve their full time as governor and the average experience of FOMC members is about six years. This is concerning when you take into consideration that most crises and recessions happy about every 10 years. This means that most of these members will only see about one crisis in their entire career. Another problem the Fed faces is the unpredictability of the economy. It seems as though experts are reliable when they have time to learn from experience and can work in a stable environments, monetary policy falls under neither of these.
One big argument that can be made against bringing behavioral decision biases into monetary policy is that behavioral biases are based off of individual decision making and monetary policy is conducted mainly by a committee. Blinder and Morgan (2005) *cite* argue that monetary policy decisions that are made by a committee are better than when they are made individually. The problem with a monetary policy committee is that the committee consist of individuals that are extremely similar. Woolley and Gardner (2009) *cite* say that the overwhelming dominance of economists in central banks may have caused the decline in time spent on decision making. They also state that member of the Federal Reserve and FOMC often come from the same geographical regions. This can cause a scenario where groupthink becomes an issue and decision biases may surface. While there is some evidence that committee-based decisions can reduce biases it is important to note that the structure of those committees is also an important factor.
Tversky, A., and Kahneman, D. (1983). “Extensional Versus Intuitive reasoning: The Conjunction Fallacy in Probability Judgment,” Psychol. Rev. 90, 4.