Norms and Conformity I

Human beings are social animals (Aronson, 2003). We constantly interact with others on a daily basis. Social norms establish the boundaries or parameters of acceptable behavior in a group, and they are indispensable for the functioning of a group. Early research in social psychology suggests that in the absence of a social norm, people have the tendency to create one. Sherif (1937) conducted an experiment showing that after repeated public pronouncements, people establish a group norm using information provided by other people’s judgment.

Bicchieri (2005) argues that social norms and conventions are mechanisms to coordinate people’s actions in a group. She maintains that there is a subtle difference between social norms and conventions. Although both social norms and conventions are coordinating device, conventions (which are stable descriptive norms according to Bicchieri) are used to solve a coordination game, while social norms transform social dilemmas into coordination games. In other words, it is consistent with an individual’s interest to follow a convention, while following a social norm may be contradicting with a person’s immediate interest. Traffic rules are a good example of conventions. As long as you know that other people drive on the right according to the convention, you will also want to drive on the right because it fits your personal interest. But being generous and acting fairly toward others, which is prescribed by the social norms, does not square with your personal interest as it incurs material cost or the renunciation of benefit.

Bicchieri’s categorization in my view is not a good one, as it does not really correspond to the essence of social norms. Social norms have a social component in them: you conform to social norms because you want to be liked by others and have a good standing in the group. Whenever an individual thinks about whether her behavior is acceptable by others, she is probably thinking of a social norm. Foot-binding of women in ancient China, as Bicchieri argued, is a convention. No matter how this practice came into existence, it quickly spread to all but the lowest classes, and was accepted as a sign of gentility and an important condition for marriage (Mackie, 1996). It is obvious, however, a woman decides to go through the pains to bind her feet is mainly concerned about the acceptance of others, and for this reason, it is better to treat it as a social norm. Conformity to social norms has a signaling function: it sends signals to others who you are.

Studies on Culture and Social Norms VI

Economists believe that sanctioning is an inherent part of social norms. Fehr and Fischbacher (2004) use third-party punishment to study the functioning and content of social norms. A third party who can take costly action to punish selfish behavior is introduced into the dictator game and the prisoners’ dilemma game. About two thirds of the third parties punished selfish offers of the dictators in a third-party dictator game, and about 60% punished defectors in the prisoners’ dilemma game. Their experiment indicates that third-party sanctions are important for the functioning of social norms. Indeed, it may well be that this third-party punishment itself is prescribed by a social norm.

Social norms can be used to explain some interesting experimental results. Xiao and Houser (2009) found that rejection of unfair offers is significantly less frequent in an ultimatum game when receivers can express their feelings to the proposer. The authors conclude that costly punishment may be just a way to express negative emotions. Using the social norms theory, we can speculate the social norm prescribe to show antipathy when the proposer acts unfairly. When it is possible to use negative emotions to demonstrate an aversion to the unfair behavior, the receiver does not feel necessary to engage in costly punishment. The possibility for the receivers to express negative feelings also makes the proposers more likely to give fair offer, possibly because it exerts focusing influence and draws the attention of the proposers to the social norm.

Xiao and Houser (2005) studied the effect of emotional expression in a one-shot dictator game. Their results confirm that avoiding written expression of disapproval, or negative emotion, plays an important role in promoting fair decision making. Proposers act more generously when the receivers can respond with written messages, although monetary sanctions are more effective when we compare the results from the dictator game and the ultimatum game. These results imply that others' pinion may serve the same function as sanctions in the operation of social norms.

Studies on Culture and Social Norms V

Some experiments have shown that people’s preferences on conformity to social norms may depend on context. Psychologists maintain that norms only influence behavior when one’s attention is drawn to them (focusing influence) and a norm has greater impact on one’s behavior when one observes others behaving in alignment with that norm (informational influence). Krupka and Weber (2009) use a simple one-shot binary dictator game to test this theory. In the experiment, the dictator can choose either X, which leads to a fair allocation ($5, $5), or Y, which gives rise to an unfair allocation ($7, $1). In descriptive focusing treatment, before assigning roles and making decisions, each subject was asked to guess the percentages of subjects who chose X and Y in a previous session. In Bicchieri (2005)’s terms, the subjects were focused on empirical expectations. In the informational focusing treatment, the subjects were asked to guess what percentages of subjects in a previous session had stated X and Y should be chosen, and hence focused on normative expectations. In informational treatment, each subject observed the choices made by four previous participants. Compared to the baseline treatment, subjects exhibited significantly more prosocial behavior in the focusing treatments. The informational treatment shows that individuals are more likely to engage in prosocial behavior when they observe others doing so, which is consistent with the results from Bicchieri and Xiao (2008). This experiment shows that contextual cues that remind the individual the existence of social norms greatly increase pro-social behavior. Hence norm compliance exhibits a focusing effect, and norms only impact behavior when an individual’s attention is drawn to them.

Cason and Mui (1998) also demonstrate the functioning of social influence similar to Krupka and Weber (2009)’s informational treatment in the setting of a sequential dictator game. In their experimental design, 4 subjects, denoted as subject 1 to 4, form a group. In stage 1, all subjects first indicate the amount of money P1 they want to take from the $40 pie in a dictator game with market setting. The information on the decision is then exchanged between subject 1 and 3, and between subject 2 and 4 in the Relevant Information treatment, while the information about the subject’s date of birth is exchanged in the Irrelevant Information
treatment between odd-numbered subjects and between even-numbered subjects. In stage 2, all subjects indicate the amount of money P1 they want to take from the $40 pie. The role of proposer and the receiver is then assigned randomly, either odd-numbered players or even-number players would be the sellers, and each seller can be the seller of only one stage, which are also randomly determined. Their results show that receiving relevant information makes subjects less likely to move to more self-regarding choices. They also find that subjects who exhibit more self-regarding behavior on their first decisions are less likely to change choices in the second stage, implying the sensitivity to social norms is heterogeneous among subjects.

Studies on Culture and Social Norms IV

Rabin (1993) notices reciprocity is a common phenomenon in human interactions. In order to model reciprocity, he uses psychological game theory, incorporate beliefs and intentions into people’s preferences. In this model, people’s intentions are reflected by the action they take. But for situations like the dictator games where there is only one decision maker, the model is inadequate as we don’t have a measure on the other person’s intentions. A more generalized model proposed by Charness and Rabin (2002), even though incorporates social welfare concern, inequality aversion and reciprocity, is still not capable of explaining the above experimental results, because none of these components can explain the fact that people exploit moral wiggle room to behave selfishly.

The inadequacy of current theories on other-regarding preferences implies the importance to incorporate social norms into individuals’ preferences. Bicchieri (2005) is among the first that propose a norm-based utility function. The utility function she proposed has two parts: the monetary payoff and the norm-related utility. A social norm in her model is defined as one prescribed action in a situation, and disutility results when the agent violates the norm. The disutility is related to the reduction in others’ payoffs caused by this violation.

While this model provides better explanation for the above experiments compared to other models with prosocial preferences, it does not consider the case where multiple actions are equally appropriate, and its definition of norms makes it incapable in explaining results from experiments such as the multi-player dictator game studied by Dana et al. (2007).

Krupka and Weber (2013) propose a more refined definition of norms. Instead of defining a single appropriate action prescribed by the norm in a situation, they consider each action has some level of social appropriateness. They design a coordination game to elicit and quantify social norms. In their norm elicitation game, the subjects are asked to rate "social appropriateness” of an action in a specific situation on a scale of 4. The subjects get rewarded if their ratings are the same as the modal rating of the group. Using the elicited norms, Krupka and Weber analyzed the experimental results of various games, including famous games studies by other authors (List, 2007; Dana et al., 2006, 2007; Lazer and Pentland, 2009). Their experimental results show that social norms can provide a good prediction of individual decision making, and
subjects have a stable willingness to sacrifice money and take socially appropriate actions. Their research contributes to the endeavor to develop a theoretical model of social norms.

Studies on Culture and Social Norms III

Croson and Buchan (1999) use a trust game to investigate how gender interacts with cultures to influence individual behavior. The results are similar across cultures: no significant effect of gender is found on the amount send by the proposers, but women reciprocate significantly more than men. Beside cross-cultural studies, many other experimental studies emerging in recent years have shed light on how social norms function. Bicchieri and Xiao (2008) designed an experiment to test Bicchieri (2005)’s theory and test the relative importance of normative expectations and empirical expectations. In their dictator game, they tried to manipulate the subjects’ empirical expectations and normative expectations by showing them dictators’ decision or/and expectations in previous sessions. The expectation elicitation was made incentive compatible by rewarding the dictators based on the accuracy of the expectations they reported. The results show that subjects’ choice are influenced by both normative and empirical expectations, and when there is a conflict between the two expectations, empirical expectations are more important in predicting individual behavior.

Dana et al. (2006) show that people take into account other’s expectations while making decisions. They added one twist to the standard dictator game: the dictators can pay \$1 to exit the game with the advantage that the receiver will never know that the game has been played. About one third of dictators decided to quietly exit the game. In another private game setting where the receiver never knows about the game or where the money received is from, almost not exit was chosen. This implies that some dictators choose to give not because they care for fairness or others’ well-being, but simply because they don’t want to violate others’ expectations.

In another experiment, Dana et al. (2007) used binary dictator game to lend evidence to a similar argument: people behave generously mainly because they dislike appearing unfair to others. In their baseline treatment, the dictator can either choose A, which results in a self-interested allocation (\$6, \$1), or choose B, which leads to a fair allocation B (\$5, \$5). For the other three treatments, the level of transparency is reduced using different designs. In the hidden information treatment, the dictator can again choose A to receive $6 or B to receive $5, but the payoffs for the receiver are uncertain ex ante: with probability 0.5 the payoffs are the same as the baseline treatment (\$1 and \$5 respectively), and with probability 0.5 the payoffs for the receiver flip (\$5 and \$1 respectively). The dictator can choose to reveal the real payoffs or not. Many subjects chose not to reveal the really payoffs and took the selfish action. In their multiple dictator treatment, two subjects play the role of dictator and only when both of them choose A the allocation is inequitable (\$6, \$6, \$1), otherwise it will be a fair allocation (\$5,\$5,\$5). In their final treatment, the plausible deniability treatment, a "cutoff” feature is added to the baseline game: the computer will randomly choose an allocation if the dictator does not choose an action by the end of the cutoff period. Hence the receiver in this game would never know whether the allocation is chosen by the dictator or by the computer. The proportion implementing fair allocation in the three treatments that relax transparency, as the experimental results show, drops significantly compared to the baseline treatment. It seems that when the subjects have the moral wiggle room, they are more likely to behave according to self interest. People are willing shun away from a social norm, even at a cost, in order to make “justified” self-interested decision.

The above experiments have clearly shown that social norms play an important role in individual decision making and the expectations of other people matter. Current theories on prosocial preferences, such as inequality aversion, are inadequate in explaining these results (Fehr and Schmidt, 1999; Bolton and Ockenfels, 2000). In the game studied by (Dana et al., 2006), it is clear that the allocation (\$9,\$1) is preferred to (\$9,\$0) according to the inequality aversion model, but many subjects chose (\$9,\$0) given that the receiver would not know the proposed ever made any decision on the allocation. Similarly, the binary dictator game with hidden information in (Dana et al., 2007), revealing the payoff information for the receiver will aid the proposed in making decisions and increase her expected utility according to the inequality aversion model, but subjects chose to be ignorant on the other party’s potential payoff. Inequality aversion is outcome-based preferences. For the same reason, social welfare concerns, which drive people to maximize the total welfare of all the players, are not enough to explain the above experimental regularities.

Studies on Culture and Social Norms II

One way to understand the functioning of social norms is to study behavioral differences across cultures. In the area of experimental economics, several cross-culture experiments have been conducted, the results of which show how culture can make a difference in individual behavior. Roth et al. (1991) conducted a cross-cultural experiment, comparing related two person bargaining game and multi-person market environment in Japan, Yugoslavia, Israel, and the United States. While the market outcomes converged to equilibrium, the outcomes in the bargaining game varied greatly across culture: the Japanese and Israeli offers are lower, but rejection rates are not higher in these two countries. The similar rejection rates indicate that countries have different sharing norms as to what constitutes a reasonable offer and those norms are well accepted in each country.

Henrich et al. (2001) ran an experiment using ultimatum game in 15 small societies. Their results reveal dramatic differences across cultures: in some societies, people show rational behavior predicted by traditional game theory, and in other societies “hyper-fair” offers are common, which can be interpreted as competitive gift-giving insults. Average offers in each society are strongly correlated with the degree of market integration. Contrary to most people would expect, in cultures with the most market integration, people exhibit more prosocial behavior. This implies that either market experience gives rise to norms of equal division or the norms of fairness promote the development of markets. Henrich et al. (2006) uses ultimatum game and third-party punishment game to study punishing behavior in the same 15 societies. They find that although people from all societies show some willingness to take costly action to punish unfair behavior, the magnitude of the punishment varies a lot across cultures. They also find that costly punishment is positively correlated with altruistic behavior across societies. The study of Henrich et al. (2010) shows that community size positively correlated with punishment and participation in religion is also likely to be associated with fairness. Using third-party dictator game played in 12 societies, Marlowe et al. (2008) also show that people from a larger and more complex society are more likely to engage in “altruistic” punishment. These results suggest that prosocial behavior is not just the product of an inherent psychology but also shaped by norms and institutions that have evolved over the human history.

Buchan et al. (2006) examine cultural difference using a trust game. The game was played in China, Japan, Korea and the United States. They asked the participants to fill out a questionnaire in order to get a measure of their cultural orientation (collectivist or individualist) in the context of the trust game. Their data show that people from different countries exhibit different level of other-regarding behavior: Chinese are most trusting and trustworthy while the Japanese are least so 7. Also, the influence of social distance on a person’s other-regarding behavior varies across country. This implies that individual’s cultural orientation may interact with other factors to influence behavior.

Studies on Culture and Social Norms I

Culture can be defined as the shared values among a group. It consists of unwritten rules of social interactions. In a social group, people depend on these shared cultural values to interact with others. Hofstede et al. (2010) consider culture as “the software of mind”, the collective mental programming that distinguishes one group from another. They characterize seven different cultural dimensions that capture the values of different cultures: power distance, individualism, masculinity, uncertainty avoidance, long term orientation, indulgence versus restraint, and monumentalism. Social norms are an intrinsic part of culture (Elster, 1989, 2009; Bicchieri and Xiao, 2008). They are shaped by culture and serve an important channel through which culture operates. There are an increasing number of academic researchers coming to realize that culture and norms are important in explaining the behavior of individuals, the functioning of organizations, and the difference in economic growth rates across countries (Hofstede et al., 2010; Harrison and Huntington, 2001).

Social norms are a manifestation of cultural values and attitudes in a specific context. Even in the same culture, norms can vary across different group. Cultural values define what is good versus bad; social norms describe which actions are appropriate and which are not in a specific situation. Cultural values are the core of culture, and social norms are the applications of cultural values to different situations. If cultural values are the root of the tree, then social norms are the trunk and leaves. They both belong to the same tree of culture. Loosely speaking, culture is just a collection of social norms in a society. We do need to notice that norms are context dependent, and even in the same culture, different groups may have different norms (Akerlof and Kranton, 2000).

Economists have proposed theories on social norms, and they generally agree that norms are values and beliefs shared among people in a group. Ostrom (2000) defines social norms as shared understandings about actions that are obligatory, permitted, or forbidden. Elster (1989) maintains that social norms are just product of shared expectations and serve no particular purpose. He emphasizes that social norms prescribe actions, rather than outcomes. The functioning of social norms depends critically on the individuals being observed by others, and sanction mechanisms play an important role in the operation of social norms. Fehr and Fischbacher (2004) also emphasizes the role of informal social sanction in the enforcement of social norms. Young (1998) claims that social norms are coordinating device for social games, and define social norms as equilibria of coordination games. Applying stochastic process theory, he illustrates social norms welfare-improving are more likely to appear in the long run. Bicchieri (2005) points out that social norms transform social dilemma game to a coordination game by modifying people’s preferences. She emphasizes the importance of expectations in the working of social norms, and identified two distinct types of expectations. Empirical expectations refer to what one observed or know about the behavior of others in similar situations. Normative expectations are second-order expectations, referring to what we believe others think we ought to do in a situation.

External Incentives vs. Intrinsic Motivation

In economics, we have the rational choice model, which assumes that people are rational, self-interested, and they respond to incentives. The incentives are usually a monetary reward or punishment. If you want to change people’s behavior, just change the incentives.

But reality is not always that simple. Steve Levitt, the author of the book Freakonomics, once mentioned an interesting story about how he potty trained his toddler daughter Amanda. After Amanda's mom got frustrated at the results even after she had tried all the methods she read from the books, Steve decided to take over and handle this as an economist: let incentives to work its way. He promised Amanda that every time she went to pee in the potty, she got a bag of M&M's. It worked perfectly! Well, for the first couple of days. Eventually, this incentive scheme backfired: Amanda would go to the potty, trickle several drops, ask for a bag of M&M's, and go to the potty again, trickle several drops and ask for another bag of M&M's, and more potty, and more M&M's. Incentives can backfire, even in the case of a toddler.

Most social scientists agree that people have the intrinsic motivation to abide by norms, to care for others and to behave virtuously. Even economists agree that intrinsic motivation plays a role. But given intrinsic motivation, incentives should also work, right? Adding external incentives can only reinforce the behaviors driven by existing intrinsic motivation stronger, isn't it obvious? Well, the obvious may not always be true. Behavioral economists start to find more and more evidence that monetary incentives can crowd-out people’s intrinsic motivation (Frey and Jegen, 2001; Bohnet et al., 2001; Fehr and Gächter, 2001). Proposing pecuniary payment, for instance, reduced Swiss citizens’ willingness to host a nuclear waste facility (Frey and Oberholzer-Gee, 1997). Imposing a fine on parents who arrive late to pick up their children from a day care center actually increased the number of parents arriving late, and removal of the fine did not decrease the number (Gneezy and Rustichini, 2000b). Offering a monetary compensation for blood donors reduces the supply of prospective blood donors, especially among women (Mellström and Johannesson, 2008).

The assumption of self-interested individuals may be a self-fulfilling prophecy. Everyone who is in a marriage knows that the best way to kill a  marriage is to consider the other person as lazy, unhelpful, and ungrateful. Doing that will make the your partner act that way. When you consider others as selfish egoists, it is very likely that they would start to act like one. Imposing incentives in situations like this can impair people’s intrinsic motivation.

If you want to change people’s behavior, you should not rely exclusively on rewards, regulations or punishment. People, like water, can always find cracks in any set of regulations. Sometimes, and more than often, the most effective way to change people's behaviors is to resort to their intrinsic motivation like ethics and norms.

When you write a research paper

at least keep these three things in mind:

1. Identify your key idea

2. Make your contributions explicit

3. Use examples

Flu shots! They help a lot?!

Nowadays, many people are taking flu shots each year to prevent themselves from getting flu. Indeed, as I am going on the job market, one piece of advice I got is taking a flu shot. Well, the reasoning is looking for a job as an economist takes a lot of time and energy, and endurance -- It is a marathon, not a sprint -- and the least thing you want is getting sick during that long demanding process. Sure, I'll take a flu shot.

However, have you ever wondered, is it really effective to take a flu shot? I haven't thought about this question, as I have taken it for granted, a flu shot, of course, should prevent you from getting a flue, isn't it clear enough? Until the other day, I talked to a labor economist, and he started to mention how people usually take a correlational relationship as a causal relationship, and then he brought up the issue of the flu shot.

Economists (well, at least some of them) are known for being suspicious. They cast doubt on a lot of seemingly obvious conclusions: going to college brings you a higher salary? Not necessarily. People who go to college tend to earn a higher salary anyway, even without going to college. Did better economy or better policy cause the crime rates to fall by 50% in the early 1990s? No. It was because of Roe vs. Wade in 1973 and birth control (if you don't believe this, check the  book Freakonomics for more information. And still, you don't have to buy this. It is just something shown by analyzing the data).

How about flu shot? Well, what we observe from the data is that people taking flu shots are on average healthier. Is this better health, however, caused by the flu shot? It is really hard to say. Data also shows that people taking the flu shot tend to have fewer car accident, tend to have better marriages, tend to have better salary... If we just look at the data, you would claim that flu shots are the best invention ever -- it is the panacea for all troubles!

The main issue is, the relation shown by the data is just correlational. We cannot conclude that A causes B simply because A happened before B, or A and B happened together. People taking flu shots are those who take better care of themselves, and probably more responsible, and hence have better marriages, and fewer car accident. The labor economist claim that, without an experiment or a good instrumental variable*, we can never prove the effectiveness of flu shots. 

Again, several days later, I talked with an instructor from the department of health and kinesiology about flu shots, and asked what he thought about them. He then mentioned how experiments in the lab show the strain of viruses can be killed by the vaccine contained in the flu shots. I didn't ask for the details about how the experiments are conducted, and whether that results can be carried over from the lab to the human body, but it dawned on me that medical (natural) scientists do have a different approach in tackling a problem. Instead of straining their brains looking for a good instrumental variable or randomly assigned people to take the flu shots and check the effectiveness, they go directly to controlled experiments to study the virus. Nice and easy.

Well, that said, I will continue taking flu shots, if not for their effectiveness, then at least to prove that I am a responsible person.

BTW,  the health and kinesiology instructor did tell me the most effective way to prevent disease: wash your hand, thoroughly. He also mentioned a statistic that you may not be comfortable to hear: about one third of the people do not wash their hands after using the bathroom.

I will not continue to argue whether people who wash their hands are more responsible people, but I do have  the gut feeling that washing your hands might be more effective to prevent disease than taking a flu shot. 

*An instrumental variable  is something used to establish a causal relationship from a correlational relationship. Come and take my econometrics class if you want to know more (https://sites.google.com/site/huanrenzhang/teaching/econ360sp13)

Research Heroes: Richard Thaler

Repost from http://indecisionblog.com/2013/01/15/research-heroes-richard-thaler/

I wish someone had told me at the beginning of my career how to go about combining economics and psychology. There was no road map. Mostly trial and error with lots of errors.

I most admire academically… My greatest inspiration came from Kahneman and Tversky, my mentors who became my friends and collaborators. Danny is still a source of inspiration, going full speed at 78. They were a fantastic team because of their complementary skills, but they were both perfectionists in their different ways. I owe my career to them directly, but in some ways so does the entire field.

The best research project I have worked on during my career… This is like asking a parent to name his favorite child. Not fair. So I will fudge and name more than one. I think mental accounting is probably my best “idea”. Save More Tomorrow is my most important practical application. The book Nudge has reached the widest audience and had the most impact. But truth be told my favorite single paper is the one with Cade Massey on the NFL draft called the “Loser’s Curse”. The great thing about being an academic is that you can write a paper on anything. Certainly the paper that was the most fun to work on (or not work on) was the one with Eldar Shafir called “Invest Now, Drink Later, Spend Never”. Eldar and I didn’t work on it for a solid week in Venice one year. It is about the mental accounting of wine consumption. I still devote a lot of time to that problem!

The worst research project I have worked on during my career… There is nothing in print that I would want to take back. I have abandoned lots of projects. I believe in ignoring sunk costs. As I tell my MBA students: “Ignore sunk costs. Assume everyone else doesn’t.”

The most amazing or memorable experience when I was doing research… I gave a talk on the idea for Save More Tomorrow to a large (>500) group of 401(k) plan administrators in 1996 or so, thinking that at least one one of them would think enough of the idea to try it. Then nothing happened for several years. Very frustrating. Then out of the blue Shlomo Benartzi told me that someone he knew had tried it without even telling us, and the results were fantastic. That was exciting because once we could show people that the idea worked, it was (relatively) easy to get others to try it. Now it is used by millions of people, but we had to get the first employer to try it or we would still be wondering if it would really work.

The one story I always wanted to tell but never had a chance… No such thing. But I am putting all those stories into a book I am working on, so stay tuned. The working subtitle is “The Stories of Behavioral Economics”.

A research project I wish I had done… My phd thesis was on the value of saving a life. The idea was to estimate how much you had to pay people to get them to accept a small increase in risk. So, I did an econometrics exercise regressing wages on occupational mortality rates. But the really clean study to do, as suggested by my buddy Richard Zeckhauser, would be to get people to play Russian Roulette, with a machine gun with many, many chambers (say 10,000). Then tell people there are 5 bullets in the gun, how much would you pay to remove one, or accept to add one. For some reason, no human subject committee has ever been willing to approve this project. Can’t imagine why! (Before I get into trouble, this was intended as a joke.)

If I wasn’t doing this, I would be… Less happy. I feel lucky to have found a way to make a living that is so much fun to do. Who knows what else, but I did think about going to law school instead of economics graduate school. I don’t think I would have been a great lawyer though. I suffer from a diplomacy deficiency.

The biggest challenge for our field in the next 10 years is… I see two. First, JDMers need to learn to get out of the lab some of the time (and journal editors need to encourage such risky activity by applying appropriately different standards to field experiments). The stuff we study is too important and useful for it to be limited to the lab.

Second, I fear that the science-lab model in which increasing numbers of grad students are added to shrinkingly important papers in order to supply graduate students with enough publications to go on the job market. I think this trend stifles creativity and does not encourage students to do enough thinking on their own. More generally, I think psychologists are just publishing too many small papers. Look at the number of papers Kahneman and Tversky wrote that created and defined the field we now call judgment and decision making. The judgment stuff was really 3 papers plus the Science recapitulation. Then came prospect theory. Four blockbusters that led to a Nobel Prize. Not enough for tenure these days! Amos had a line about people that he felt wrote too many papers: “he publishes his waste basket”. I don’t think he would approve of the current state of affairs.

My advice for young researchers at the start of their career is… Work on your own ideas, not your advisor’s ideas (or at least in addition to her ideas). And spend more time thinking and less time reading. Too much reading leads people to think of small variations on existing studies. Admittedly my strategy of writing the paper first and only then reading the literature (or, more likely, letting the referees tell me what they think I should have read) is an extreme one, but it is better than trying to read everything. Try writing the first paper on some topic, not the tenth, and never the 50th.

David Romer's Rules for Making It Through Graduate School and Finishing Your Dissertation

"Out in Five"

---

• Don't clutter up your life with other activities; just write.
• Don't carry out a thorough and comprehensive search of the literature; just write.
• Don't attempt to make sure that every page you write shows the full extent of your professional skills; just write.
• Don't write a well-organized, well-integrated, unified dissertation; just write.
• Don't think profound thoughts that shake the intellectual foundations of the discipline; just write.
• If you don't have a paper started by the spring of your third year, be alarmed.
• If you don't have a paper largely drafted by the fall of your fourth year, panic.
• Have three new ideas a week while you are getting started.
• Don't try to game the profession, work on what interests you.
• Good papers in economics have three characteristics:
  • A viewpoint.
  • A lever.
  • A result.

Panel Data Analysis -- Random Effects vs. Fixed Effects

y_it=beta x_it + a_i + u_it

The unobserved factors affect the dependent variable consist of two types: a_i (constant over time), and u_it (varying over time). a_i is called an unobserved effect or a fixed effect. u_it is called the idiosyncratic error or time varying error.

Fixed effects estimation uses a transformation (time-demeaned data) to remove the unobserved effect a_i. Fixed-effects transformation is also called the within transformation. A pooled OLS estimator uses the time variation in y and x within each cross-sectional observation. It is based on the time-demeaned variables and is called the fixed effects estimator or the within estimator.

In the fixed effects model, any explanatory variable that is constant over time for all i gets swept away by the fixed effect transformation, therefore, we cannot include variables such as gender or a city's distance from a river. The fixed effects estimation is adequate if we want to draw inferences only about the examined individuals.

When we assume that the unobserved effect a_i in the above model is uncorrelated with each explanatory variable in all periods Cov(x_it,a_i)=0 , we have a random effects model. This model is adequate, if we want to draw inferences about the whole population, not only the examined sample.

The fixed effects estimator subtracts the time averages from the corresponding variable. The random effects transformation subtracts a fraction of that time average, where the fraction depends on sigma_u^2, sigma_a^2, and the number of time periods, T.

In practice, it is usually informative to compute the pooled OLS estimates. Comparing the three sets of estimates can help us determine the nature of the biases caused by leaving the unobserved effect a_i, entirely in the error term (as does pooled OLS) or partially in the error term (as does the RE transformation.) Remember, however, the pooled OLS standard errors and test statistics are generally invalid: they ignore the often substantial serial correlation in the composite errors, v_it=a_i + u_it.

One can used Hausman test. A failure to reject means either that the RE and FE estimates are sufficiently close so that it does not mater which is used, or the sample variation is so large in the FE estimates that one cannot conclude practically significant differences are statistically significant.

Using FE is mechanically the same as allowing a different intercept for each cross-sectional unit. FE is almost always much more convincing than RE for policy analysis using aggregated data.


Panel data analysis can also be used to analyze clustered data. Depending on the nature of the clustered data, FE or RE can be used.

xttobit -- Linear Regression Model with Panel-level Random Effects for censored data

xttobit fits random-effects tobit models. There is no command for a parametric conditional fixedeffects model, as there does not exist a sufficient statistic allowing the fixed effects to be conditioned out of the likelihood.

Panel data analysis has three more-or-less independent approches:

1. Independently pooled panels: there are no unique attributes of individuals within the measurement set, and no universal effects across time

2. Random effects model: there are unique, time constant attributes of individuals that are the results of random variation and do not correlate with the individual regressors. This model is adequate if we want to draw inferences about the whole population, not only the examined sample

3. Fixed effects model (or first differenced models): there are unique attributes of individuals that are not the results of random variation and that do not vary across time. Adequate if we want to draw inferences only about the examined individuals. Also known as "least squares dummy variable model"

y_{it}=x_{it}beta + u_i + e_it

u_i is the random effects

e_it is the individual effect

. xi: xttobit Contribution Period Belief AmountInvested Cutoff RandomDraw realized econm

> ajor econyear if Treatment==4, ll(0) ul(20)

Random-effects tobit regression                 Number of obs      =       320

Group variable: Subject                         Number of groups   =        16

Random effects u_i ~ Gaussian                   Obs per group: min =        20

                                                               avg =      20.0

                                                               max =        20

                                                Wald chi2(8)       =    372.07

Log likelihood  = -643.86478                    Prob > chi2        =    0.0000

------------------------------------------------------------------------------

Contribution |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]

-------------+----------------------------------------------------------------

      Period |  -.3714078   .0859276    -4.32   0.000    -.5398229   -.2029927

      Belief |   .8871966   .1082666     8.19   0.000      .674998    1.099395

AmountInve~d |   .4972489   1.099413     0.45   0.651    -1.657561    2.652059

      Cutoff |  -.1031474   .0144602    -7.13   0.000    -.1314888   -.0748059

  RandomDraw |   .0298226   .0128297     2.32   0.020     .0046769    .0549683

    realized |    1.19617   1.115385     1.07   0.284    -.9899439    3.382284

   econmajor |  -.1037135   4.025793    -0.03   0.979    -7.994123    7.786696

    econyear |   -1.11204   1.565532    -0.71   0.478    -4.180426    1.956345

       _cons |   4.846538   4.027966     1.20   0.229    -3.048129    12.74121

-------------+----------------------------------------------------------------

    /sigma_u |   4.153189   .9255023     4.49   0.000     2.339238     5.96714

    /sigma_e |     4.6622   .2652641    17.58   0.000     4.142292    5.182109

-------------+----------------------------------------------------------------

         rho |   .4424506   .1130577                      .2400784    .6614899

------------------------------------------------------------------------------

  Observation summary:        94  left-censored observations

                             185     uncensored observations

                              41 right-censored observations

The output includes the overall and panel-level variance components (labeled sigma e and sigma u, respectively) together with rho, which is the percent contribution to the total variance of the panel-level variance component.

When rho is zero, the panel-level variance component is unimportant, and the panel estimator is not different from the pooled estimator. A likelihood-ratio test of this is included at the bottom of the output. This test formally compares the pooled estimator (tobit) with the panel estimator.

The danger of high blood sugur

Diabetes, is a group of metabolic diseases in which a person has high blood sugar.

Diabetes is due to either the pancreas not produce enough insulin, or because cells of the body do not respond properly to the insulin that is produced. There are three main types of diabetes mellitus:

1. Type 1 DM results from the body's failure to produce insulin.

2. Type 2 DM results from insulin resistance, a condition in which cells fail to use insulin properly, sometimes also with an absolute insulin deficiency.

Consumption of sugar-sweetened drinks in excess is associated with an increased risk. The type of fats in the diet are also important, with saturated fats and trans fatty acids increasing the risk, and polyunsaturated and monounsaturated fat decreasing the risk. Eating lots of white rice appears to also play a role in increasing risk. A lack of exercise is believed to cause 7% of cases.

Why are high blood sugar levels bad for you? Glucose is precious fuel for all the cells in your body -- when it's present at normal levels. But persistently high sugar levels behave like a slow-acting poison.

High sugar levels slowly erode the ability of cells in the pancreas to make insulin. The pancreas overcompensates, though, and insulin levels remain overly high. Gradually, the pancreas is permanently damaged.

All the excess sugar is modified in the blood. The elevated sugar in the blood causes changes that lead to atherosclerosis, a hardening of the blood vessels.

Because high sugar levels are everywhere, the body can be damaged anywhere. Damage to blood vessels, in particular, means no area is safe from too much sugar. High sugar levels and damaged blood vessels cause the multitude of complications that can come with diabetes

3. Gestational diabetes, is the third main form and occurs when pregnant women without a previous diagnosis of diabetes develop a high blood glucose level.

Artificial sweetener increases the likelihood of gaining weight

Experiments have found that sweet taste, regardless of its caloric content, enhances your appetite. Aspartame has been found to have the most pronounced effect, but the same applies for other artificial sweeteners, such as acesulfame potassium and saccharin.

The reason why glucose or sucrose (table sugar) tends to lead to lower food consumption compared to non-caloric artificial sweeteners is because the calories contained in natural sweeteners trigger biological responses to keep your overall energy consumption constant. This was again evidenced in a study

In essence, real sugar allows your body to accurately determine that it has received enough calories, thereby activating satiety signaling. Without the calories, your appetite is activated by the sweet taste, but as your body keeps waiting for the calories to come, sensations of hunger remain.

http://articles.mercola.com/sites/articles/archive/2012/12/04/saccharin-aspartame-dangers.aspx#!

The Key for a Good Experimental Paper

Have a strong alternative hypothesis. Write about this alternative hypothesis in the introduction. An experimental design is interesting only when multiple outcomes can happen. Get the readers interested in the multiple outcomes.

Irwin–Hall distribution

In probability and statistics, the Irwin–Hall distribution, named after Joseph Oscar Irwin and Philip Hall, is probability distribution for a random variable defined as sum of a number of independent random variables, each having a uniform distribution.[1] For this reason it is also known as the uniform sum distribution.

What statistical analysis should I use?

Number of Dependent Variables	Nature of Independent Variables	Nature of Dependent Variable(s)	Test(s)
1	0 IVs (1 population)	interval & normal	one-sample t-test	Stata
		ordinal or interval	one-sample median	Stata
		categorical  (2 categories)	binomial test	Stata
		categorical	Chi-square goodness-of-fit	Stata
	1 IV with 2 levels (independent groups)	interval & normal	2 independent sample t-test	Stata
		ordinal or interval
			Wilcoxon-Mann Whitney test	Stata
		categorical	Chi- square test	Stata
			Fisher's exact test	Stata
	1 IV with 2 or more levels (independent groups)	interval & normal	one-way ANOVA	Stata
		ordinal or interval	Kruskal Wallis	Stata
		categorical	Chi- square test	Stata
	1 IV with 2 levels (dependent/matched groups)	interval & normal	paired t-test	Stata
		ordinal or interval	Wilcoxon signed ranks test	Stata
		categorical	McNemar	Stata
	1 IV with 2 or more levels (dependent/matched groups)	interval & normal	one-way repeated measures ANOVA	Stata
		ordinal or interval	Friedman test	Stata
		categorical	repeated measures logistic regression	Stata
	2 or more IVs (independent groups)	interval & normal	factorial ANOVA	Stata
		ordinal or interval	ordered logistic regression	Stata
		categorical	factorial logistic regression	Stata
	1 interval IV	interval & normal	correlation	Stata
			simple linear regression	Stata
		ordinal or interval	non-parametric correlation	Stata
		categorical	simple logistic regression	Stata
	1 or more interval IVs and/or 1 or more categorical IVs	interval & normal	multiple regression	Stata
			analysis of covariance	Stata
		categorical	multiple logistic regression	Stata
			discriminant analysis	Stata
2 or more	1 IV with 2 or more levels (independent groups)	interval & normal	one-way MANOVA	Stata
2 or more	2 or more	interval & normal	multivariate multiple linear regression	Stata
2 sets of 2 or more	0	interval & normal	canonical correlation	Stata
2 or more	0	interval & normal	factor analysis	Stata
Number of Dependent Variables	Nature of Independent Variables	Nature of Dependent Variable(s)	Test(s)

Source: http://www.ats.ucla.edu/stat/stata/whatstat/
http://www.ats.ucla.edu/stat/stata/whatstat/whatstat.htm#1sampt


In statistics, Spearman's rank correlation coefficient or Spearman's rho, named after Charles Spearman and often denoted by the Greek letter  (rho) or as , is a nonparametric measure of statistical dependence between two variables. It assesses how well the relationship between two variables can be described using a monotonic function. If there are no repeated data values, a perfect Spearman correlation of +1 or −1 occurs when each of the variables is a perfect monotone function of the other.