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Public goods in the field: Katrina evacuees in Houston.

1. Introduction

Crises and disasters, whether natural or man-made, are defined by conditions of uncertainty, disorder, and stress. This raises the question of whether social cooperation is sustainable in the aftermath of a disaster. In this research, we focus on cooperation with a public goods game played by individuals evacuated from New Orleans to Houston after Hurricane Katrina. We ask whether stress affects how individuals contribute to a public good in the aftermath of a natural disaster.

The study predominantly focuses on African-American evacuees from New Orleans who were relocated to Houston-area shelters in the weeks immediately after Hurricane Katrina. In this study, 352 evacuees participated in small groups across six different Houston evacuation shelters from September 10 through 19, 2005. The study consisted of several standard experiments and a questionnaire. The experiments reported here are adaptations of "dictator" and "public goods" experiments that measure cooperation among Katrina evacuees. We find strong evidence of group cooperation in the Houston area shelters. We also find an independent negative effect of stress on cooperation.

2. Motivation

On the morning of August 29, 2005, after the passage of Hurricane Katrina over the Gulf Coast of Louisiana, Mississippi, and Alabama, levees designed to protect the city of New Orleans from flood surges gave way and parts of New Orleans began to fill rapidly with water. Many of those who stayed behind sought shelter by moving to the Superdome (and later to the New Orleans Convention Center), although many remained trapped on rooftops, attics, and upper-level apartment and office buildings. Although there are many conflicting reports and viewpoints as to the handling of the crisis by the city of New Orleans, the state of Louisiana, and the Federal Emergency Management Agency, as well as regarding the behavior of the citizens of New Orleans in response to the flooding, there was consensus that the city had experienced a major disaster.

The conventional wisdom is that a natural disaster throws victims into a state of anarchy in which social conventions are abandoned and replaced by self-interested survival instincts. Certainly observers of the post-Katrina landfall were not surprised by reports of widespread looting. Media reports of gang behavior, rapes of children, and murders in both the Superdome and Convention Center were readily accepted. It turned out these reports were exaggerated or inaccurate.

Substantial research suggests that cooperative norms are commonplace in the aftermath of a natural disaster. In a synthesis of a multitude of studies, Drabek (1986, p. 179) notes that in the aftermath of a disaster there will be "heightened levels of internal solidarity." In effect, the in-group of victims becomes well defined, and group identification becomes highly salient. This is understandable in that everyone is in the same position and they have shared experiences. This leads us to expect high levels of group cooperation in response to a disaster, especially for people who identify with one another as victims or survivors.

By contrast, many psychologists point to disasters as heightening stress for victims. It is well known that stress is part of an emotional state that leads to a decrement in the quality or speed of performance in most tasks. For example, Lazarus (1999) argues that stress should be considered a subset of emotions, with important physiological and cognitive effects. Much of what is of interest are the coping strategies of humans to stress. Janis (1993) contends that decision making under stress results in "hypervigilance" to some stimuli and disregard of others. This leads to "stereotyped thinking in terms of oversimplified categories and reliance on simpleminded decision rules" (p. 65). Weisaeth (1993) points to "post-traumatic stress disorder" as common following a natural disaster, although the long-term effects for individuals are highly variable.

Evidence for cooperative or noncooperative behavior after a natural disaster is practically nonexistent. Although there are numerous attitudinal studies and anecdotes about behavior, to our knowledge no one has used measurement tools taken from experimental economics and applied them to the aftermath of a disaster. Those working in the field of disasters and hazards agree with Bolin's (1989, p. 71) point that there has been little study of "sheltering populations evacuating a disaster site...." It is well known that victims of lower socioeconomic status are more likely to use such shelters. Staying in a large shelter is also known to be a stressor if people are removed from their family or witness interpersonal violence (Bolin 1989, p. 71). Beyond this, there is little understanding of the behavioral strategies that evacuees from a large-scale disaster might employ.

We use a behavioral experiment to shed light on responses to crisis and disaster, and we raise the question of whether cooperation, as measured by a public goods game, exists among very poor people who were involuntarily evacuated from a natural disaster.

3. Subject Recruitment and Sample Characteristics

We conducted these experiments "in the field" under unusual conditions. We selected subjects recuperating from highly traumatic conditions while housed in evacuation shelters. In the early stages of the Hurricane Katrina evacuation, Houston, Texas, was the primary evacuation point for people from New Orleans and greater Louisiana. Within the course of a week, the evacuee population in the Houston area swelled to an estimated 200,000 people. Many of those people evacuated before the landfall of Hurricane Katrina. However, a significant group of people stranded in New Orleans were bused to Houston and were then temporarily housed in the Houston Astrodome, the neighboring Reliant Center, and the George R. Brown Convention Center in downtown Houston. As those shelters overflowed, other small and mid-sized shelters were opened by a wide range of organizations, including the Salvation Army, the American Red Cross, and church and community organizations. Our focus is on the individuals who were stranded in New Orleans, were bused to Houston, and were housed in the shelters.

The shelters varied in terms of size, living conditions, and security. In the larger shelters, evacuees often slept in large arena-sized areas, whereas in some smaller shelters, they were accommodated in spaces ranging from indoor gymnasiums to small school rooms and even semiprivate "family" rooms in some church community buildings. Living conditions within the shelters varied. Both large and small shelters were cramped and noisy, and many evacuees showed signs of both physical exhaustion and emotional fatigue. Security was heavy within the large mass-accommodation shelters of the Astrodome, Reliant Center, and the Convention Center. Evacuees were given picture IDs and were required to wear them. Relief workers were also required to wear identification badges, and National Guard members and other security officers screened everyone coming into the shelters. Security in the smaller shelters varied considerably, although none were as restrictive as in the mass shelters. These factors played an important role in recruiting subjects and the conditions under which we conducted the experiments.

Our sampling began from the City of Houston's list of all recognized evacuation shelters within the greater Houston area. We selected locations to conduct the study on the basis of size, accessibility, and feasibility of the location for conducting the experiments. Generally, we required a space large enough to accommodate 20 to 25 people at a time. In each shelter that we visited, we obtained prior permission from the shelter administrators, local government officials, or both to conduct our study. In no case were we turned down by a shelter official to conduct the study.

The first author, with the help of an assistant, recruited all subjects for this study. Participants should be regarded as a convenience sample. A systematic random sample of evacuees was deemed impossible given the diversity of conditions and size of each shelter. We often recruited evacuees from common areas within the shelters such as dining halls and TV rooms, but also in the general sleeping quarters. We were concerned that having family members in the same session might bias our results in favor of cooperation; therefore, we tried to restrict participation to only one family member per session. When individuals agreed to participate, the assistant took them to the room or area where we were conducting the study. The size of each group session varied depending on the facilities available to us. If the room was small, we stopped recruiting after 16-18 subjects.

In most cases, we used a room or private area in the shelter to conduct the study. In the best circumstances, we were given a room with chairs and tables and closed doors. In other cases, we ran the experiments in common areas, which were more prone to noise and distractions. Overall, however, distractions were relatively minor once we began the session and usually were caused by small children, a ringing cell phone, or the occasional late-comer wanting to participate. However, general research conditions were satisfactory given the difficulty of conducting experiments in the field and given the overall conditions within the shelters that we visited.

Overview of the Sample

A total of 352 volunteers took part in this study in Houston-area evacuation shelters between September 11 and September 18, 2005. Seventeen group sessions were run, ranging in size from 15 to 26 participants per session. Table 1 provides a summary of the shelter locations visited, the number of sessions completed at each shelter, and the dates of each session. On average, subjects made $65 for their participation.

As noted above, the sample is made up of participants who were stranded in New Orleans after Katrina hit. Before being evacuated to Houston, approximately 30% of the sample stated that they came from the New Orleans Superdome, whereas roughly 18% came from the house of a friend or family member or hotel inside New Orleans. Approximately 12% had been stranded on an upper-story building or rooftop. Among those picking "other," several mentioned they were saved by "Black Hawk choppers" or other means of rescue. Of our sample, 58.2% arrived in Houston by bus, 35.0% indicated they were given a ride in a car, and the remaining 6.8% arrived by train or plane.

Over 98% of the sample is African-American, 52.4% of evacuees are female, and 31.0% had never completed a high school degree (or GED). The average age was 36.1, with participants ranging in age from 18 to 69. Approximately 20% of subjects were married, 10% were divorced or separated, and almost 60% indicated that they were single. Approximately half of the subjects had two or more children, and 51.1% of the sample reported an annual household income of less than $15,000. By and large this group is poor, poorly educated, and African-American. (1)

4. Research Design

When participants entered the experiment area, they were seated and given a consent form to read and sign. Those who remained were given a unique identification number to use in place of their name on all subsequent research forms, ensuring anonymity. To ensure further privacy, we provided each subject with a cardboard screen to prevent others from seeing their work.

After a brief introduction by an experimenter, the study began. (2) Five distinct tasks were run: a dictator game, a dictator game elicitation, an ultimatum game, a public goods game, and a risk instrument. All tasks were conducted with paper and pencil, and the experimenter read from a fixed script. All tasks were performed in the same order. Although the focus is with the public goods game, we also use results from the dictator game in the analysis.

In the dictator game, subjects are given 10 one dollar bills, 10 green slips of paper, an envelope labeled SEND, and an envelope labeled KEEP. Subjects are told that they must place 10 items in each envelope--whatever is put in the KEEP envelope they keep, and whatever is put in the SEND envelope should be sealed and that envelope will go to another evacuee in the Houston area. At this point, subjects became very attentive to the instructions and noise levels dropped considerably. Once everyone made their decision, the sealed SEND envelopes were collected and participants were told to put the KEEP envelope to the side.

Immediately following the dictator game, subjects were handed a sealed envelope marked SEND. This envelope came from a Katrina evacuee from a prior session. Participants were told that the envelope was theirs to keep, but they could not open it until the end of the experiment. At the same time they were given a sheet of paper and asked to guess how much was in the envelope. This task was designed to elicit expectations about what they would receive from others.

In the public goods game, subjects were given 10 one dollar bills, 10 green slips of paper, and two envelopes. Subjects were told that whatever they put into their KEEP envelope they would again keep for themselves. Whatever was put into the SEND envelope would be added to what everyone else in the room put into their SEND envelopes, that amount would be doubled by the experimenter, and everyone in the room would get an equal share of the doubled amount. While we tried to keep the size of the sessions fixed at 20, this was not possible in all experiments. As a consequence, the marginal per capita rate of return fluctuates. However, in the analysis presented below, we control for this by running fixed effects models on the sessions. Subjects in this task knew how much they kept, but did not learn their share of the group pot until the end of the experiment.

After completion of the experiment, subjects were given a self-administered questionnaire. (3) The questions not only tapped a variety of demographic characteristics for the individual subjects, but also queried them about their experiences before the hurricane, their experiences with the evacuation process, their satisfaction with the conditions in the shelters where they had been staying, and their perceptions of the way the evacuation process was handled by various government organizations and public officials.

5. Analysis

The aggregate results are consistent with other findings from single-shot public goods games. If we consider the evacuees to have a strong in-group identity, then these results are similar to those reported by Solow and Kirkwood (2002). Much like the findings by Dawes et al. (1986) and Dawes, Vandekragt, and Orbell (1988), who often used nonstudent populations, evacuees contributed almost 40% of their money to the group pool (the average contribution is $3.99 with a standard deviation of 3.13). Likewise, these results are consistent with those reported by Isaac, Walker, and Williams (1994) for large groups and consistent with the meta survey of public goods by Ledyard (1995).

There is a great deal of heterogeneity among subjects in what is contributed. Figure 1 provides the distribution of dollars contributed to the public good. The modal contribution is half the amount available. Only 20.5% of subjects kept all $10, contributing nothing to the common pot. Almost 74% contributed at least $2 or more to the common pot and 13.6% put all $10 into the group pool.

Did contributing to the public good pay off?. The average amount of money subjects contributed was $3.99. By session, the average group earnings range from a high of $10.80 to a low of $4.80 per person. The average return to subjects was $7.96 (SD $1.87). Combining this with the average amount initially kept out of the pot brings the total payoff close to $14 ($13.98, SD 3.13). Hence, groups benefited through cooperation, increasing their possible earnings by almost $4 in the public goods game.

Can we get any purchase on the observed heterogeneity in contributions by looking at differing demographic and behavioral factors? (4) Although our sample is overwhelmingly African-American (hence no variation on race), the sample is broadly mixed by age, gender, and education. We turn, then, to several multivariate models that could further explain the variation in contributions.

Multivariate Models

Our concern with estimating the individual contribution data is to see whether some of the heterogeneity can be explained by individual factors. Of primary interest is the effect of personal trauma on contributions. Although everyone in the sample had experienced the same trauma of being evacuated from New Orleans, losing their possessions, and living in shelters in Houston, a sizeable proportion still had not located family members. Participants were asked whether there were "members of your immediate family who you have still not been able to locate." Forty-four percent of the sample responded Yes to this item (see Table 2). We construct a dummy variable in which those who responded Yes are given a value of 1 and those responding No are given a value of 0. We expect that those still missing family members were more traumatized than those who knew the whereabouts of their immediate family. It is important to realize that in the weeks after Katrina, evacuees were sent to locations all over the United States. Although the Red Cross and other organizations quickly set up communication services to track lost family members, the setting was chaotic.

Several other variables are included as controls for our models. We use the age of subjects, because of the suggestion that "normal" populations are different from student populations in what they give (e.g., Bahry and Wilson 2006). We also include the educational level of the subjects. This was coded as three categories--those who were not high school graduates, those who graduated from high school or held a GED, and those who had obtained some education beyond high school. This variable is used as a proxy for economic capital. At this point, none of the subjects were employed or drawing any income. None knew the status of their apartments or what remained of their possessions in New Orleans. Consequently, we think of this as a useful proxy for their prospects. Table 2 reports the distribution of these different variables.

Finally, we include the amount that subjects thought would be sent to them by another person. Other subjects, in a prior session, made a decision in the dictator game. Subjects were handed an envelope and asked to predict the amount sent to them by someone else. We take this to be a measure of expectations about the fairness of others.

The first model takes all of the data and the estimates are reported in Table 3. Because we expect that there might be session effects (subjects were playing the public goods game in a large group), we estimate their contributions to the public good using a fixed effects Tobit model for the session. We find that educational attainment is unrelated to contributions. We take this to mean that current and future prospects did not matter in the decision about what to contribute.

Age is weakly related to contributions. The older a subject is, the larger their contribution. A number of alternative specifications were tried, but the effect remains linear with age. We also find a weak positive relationship with the prediction about how much would be received in the dictator game. This appears to be a proxy for optimism about the group. Although the subjects knew that the envelopes they were handed were coming from a different session, they also knew that the envelopes were coming from other evacuees. We speculate that those who thought others were generous were likely to contribute more to the public good, anticipating a return from contributions.

Our primary variable of interest, whether the subject was missing family members, is negative and has a strong effect. Controlling for all of the other parameters, it reduces contributions by a third. We regard this measure as one of heightened stress. Almost all of our subjects had experienced the same trauma of being stranded in New Orleans and then being housed in shelters in Houston. What differentiates these subjects is whether they have been reunited with their immediate family or not. Uncertainty about the whereabouts of other family members was very stressful, especially two weeks out from the event.

It could be that this model misspecifies the behavior of subjects by including egoists with altruists. A standard finding in much of the literature on dictator games and public goods games is that anywhere from 20% to 25% of the population keeps everything. Indeed, we find that 20% of the population sent nothing in the public goods game. We estimate separate models for two groups, those whom we categorize as egoists and those we call altruists. (5)

Rather than estimating a subject's behavior based on how one behaves in the public goods game (which is our dependent variable), we use the subject's behavior in the dictator game. That game is often used as a measure of altruistic or fair behavior.

In the dictator game, we find that almost 70% of the subjects sent $2 or more (out of a total of $10) to another anonymous evacuee. Consistent with the meta results reported by Camerer (2003), just over 25% kept all $10 for themselves. The modal category was to divide the money equally. Finally, 8% behaved as pure "altruists" in the experiment, sending all $10 to the anonymous evacuee counterpart and keeping nothing for themselves.

Although the dictator game and the public goods game looked similar from the standpoint of the subject (both games used two envelopes, both had 10 one dollar bills, and both had 10 blank slips of paper), subjects differed in the way they played both games. Around 40% of the subjects did the same thing in both tasks. Not surprising, those subjects are concentrated among those who sent nothing and those who sent half. Otherwise, there is considerable variation in people's actions across both tasks.

Table 4 estimates two Tobit models: one for the egoists in the dictator game and a second for the altruists. Both models use fixed effects, controlling for session variation. Two things are notable in the egoist model. First, the effect of missing family members remains negative and significant. This implies that stress remains an important component of the model, affecting a subject's willingness to contribute to the public good. Second, the other variables are unimportant in predicting contributions, except for what subjects predict they will receive from dictators. Keep in mind that the subjects in this model sent nothing in the dictator game. However, most thought that other people would send them something. Egoists are contributing in the public goods game, anticipating that others will do the same. Their contribution is tied to predictions that others are generous. Perhaps we have unfairly tagged these individuals as egoists--instead some of them are conditional egoists, anticipating gains based on an assessment of the group.

The story is different for those we have labeled altruists. Here age is significant, with older individuals contributing more to the public good. Education, a proxy for prospects, adds nothing to the model. Two things stand out in this second model. First, the effect of the predicted amount received disappears. It is as if these individuals are likely to contribute regardless of what they think others might do. They do not rely on estimates of what the rest of the group is doing. Second, the negative correlation for missing family members remains strong. Again this reflects the stress and trauma experienced by these individuals.

6. Discussion

These results are remarkable in several senses. First, little is known about the behavioral strategies of groups of individuals in situations of stress. There is every reason to believe that these subjects were under considerable stress. As a group, they had recently been evacuated from their home and thrown together with a large number of strangers. Contrary to popular conception, in which victims of natural disasters are thought to abandon basic social norms, these individuals were highly cooperative. As measured by their contributions to a public good, these subjects were willing to cooperate with one another. It might be that the experiment tapped the fact that they were a highly salient in-group and, as a consequence, contributing to the group was easy.

Second, however, we do find that subjects who are under the greatest stress are the least likely to look outward. In the public goods game, they send less to the group pool. We measure stress in a simple, but salient way: These were subjects who were still missing immediate family members. Stress brought on by such a traumatic event tends to turn individuals inward. It may be that the uncertainty about family members, coupled with a hurried evacuation, leads to worrying less about others. This is consistent with many studies pointing to the aftermath of a disaster and how individuals cope.

Third, these results point out that subjects may have two-part utility functions that account for their own private interests and expectations about others. A number of scholars have argued this possibility in public goods games (see, e.g., Andreoni 1995; Coats and Neilson 2005). In an interesting twist, we find that those who keep everything in the dictator game ("egoists") are the most likely to be sensitive to what they expect others to do. In the meantime, those who are likely to send something in the dictator game are less sensitive to expectations about others. They will simply contribute no matter their views of others.

The authors acknowledge the support of the National Science Foundation (NSF, SES 0552439). NSF is not responsible for any of the findings reported here. The authors are also grateful for the efforts by Gavin Dillingham, Cliff Landry, Jeanette Fabre-Hunter, Loy Glenn, Glen Riggs, and Andrea Ganier, who helped conduct these experiments. Cliff, Jeanette, and Andrea themselves evacuated from New Orleans and worked while not knowing what had happened to their own apartments or houses. We also thank Megan Mullholland, Chris Aresu, Kim Hartsnn, and Laura Krumland for their help in preparing many of the materials for these experiments.

References

Andreoni, James. 1995. Cooperation in public-goods experiments: Kindness or confusion? American Economic Review 85:891 904.

Bahry, Donna P., and Rick K. Wilson. 2006. Confusion or fairness in the field? Rejections in the ultimatum game under the strategy method. Journal of Economic Behavior & Organization 60:37 54.

Bolin, Robert. 1989. Natural disasters. In Psychosocial aspects of disaster, edited by Richard Gist and Bernard Lublin. New York: John Wiley & Sons, pp. 61 85.

Brodie, Mollyann, Erin Weltzlen, Drew Ahman, Robert J. Blendon, and John M. Benson. 2006. Experiences of Hurricane Katrina evacuees in Houston shelters: Implications for future planning. American Journal of Public Health 96:1402-8.

Camerer, Colin F. 2003. Behavioral game theory: Experiments in strategic interaction. New York: Russell Sage Foundation, Princeton University Press.

Coats, Jennifer C., and William S. Neilson. 2005. Beliefs about other--Regarding preferences in a sequential public goods game. Economic lnquiry' 43:614-22.

Dawes, Robyn M., John M. Orbell, Randy T. Simmons, and Alphons J. C. Vandekragt. 1986. Organizing groups for collective action. American Political Science Review 80:1171-85.

Dawes, Robyn M., Alphons J. C. Vandekragt, and John M. Orbell. 1988. Not me or thee but we--The importance of group identity in eliciting cooperation in dilemma situations Experimental manipulations. Acta Psychologica 68:83-97.

Drabek, Thomas E. 1986. Human system responses to disaster: An inventory of sociological findings. New York: SpringerVerlag.

Gachter, Simon, Benedickt Herrmann, and Christian Thoni. 2004. Trust, voluntary cooperation, and socio-economic background: Survey and experimental evidence. Journal of Economic Behavior and Organization 55:505-31.

Isaac, R. Mark, James M. Walker, and Arlington W. Williams. 1994. Group size and the voluntary provision of public goods: Experimental evidence utilizing very large groups. Journal of Public Economics 54:1-36.

Janis, Irving L. 1993. Decision making under stress. In Handbook of stress: Theoretical and clinical aspects, edited by Leo Goldberger and Shlomo Breznitz. New York: The Free Press, pp. 56-74.

Lazarus, Richard S. 1999. Stress and emotion: A new synthesis. New York: Springer Publishing Company.

Ledyard, John O. 1995. Public goods: A survey of experimental research. In Handbook of experimental economics, edited by John Kagel and Alvin E. Roth. Princeton, N J: Princeton University Press, pp. 111-94.

List, John A. 2004. Young, selfish and male: Field evidence of social preferences. Economic Journal 114:121-49. Ma, Leanne, Katerina Sherstyuk, Michael Dowling, and Oliver Hill. 2002. Altruism and voluntary provision of public goods. Economics Bulletin 3:1-8.

Solow, John L., and Nicole Kirkwood. 2002. Group identity and gender in public goods experiments. Journal of Economic Behavior and Organization 48:403-12.

Weisaeth, Lars. 1993. Disasters: Psychological and psychiatric aspects. In

(1) Our sample matches that reported by Brodie et al. (2006). That survey used a random sampling technique within the largest shelters in Houston and questioned 614 subjects around the same date as our experiment.

(2) The script used in the experiment is available at http://brl.rice.edu/katrina.

(3) Approximately 6% of our subjects were functionally illiterate and the questionnaire and other items were read to them.

(4) Recent work trying to do much the same thing on different populations includes Gachter, Herrmann, and Thoni (2004) and List (2004).

(5) Ma et al. (2002) do a variation of this, predicting and sorting types into different groups prior to playing a public goods game.

Sam Whitt * and Rick K. Wilson ([dagger])

* Department of Political Science, University of Tennessee, 1001 McClung Tower, Knoxville, TN 37996-0410, USA; E-mail swhitt3@utk.edu.

([dagger]) Department of Political Science, MS 24, Rice University, Houston, TX 77251-1892, USA; E-mail rkw@rice.edu; corresponding author.
Table 1. Distribution of Subjects by Houston Shelter Sites

Site Sessions Suhjects

Salvation Army shelter 1 16
Baptist Church shelter 1 20
Old Thurgood Marshall School 1 20
George R. Brown Convention Center 7 140
Reliant Center-Astrodome 6 141
Prince's Gym 1 15

Site Shelter Session
 Size Dates

Salvation Army shelter Small 11 Sep
Baptist Church shelter Small 11 Sep
Old Thurgood Marshall School Small 12 Sep
George R. Brown Convention Center Large 13 Sep-14 Sep
Reliant Center-Astrodome Large 15 Sep-17 Sep
Prince's Gym Small 18 Sep

Table 2. Distributions for Variables from the Full Sample

Variable Mean SD Minimum Maximum N

Public good contribution 3.99 3.13 0 10 352
Age 36.10 12.67 18 69 338
Education 1.36 0.60 1 3 345
Missing family members 0.44 0.50 0 1 343
Predicted dictator game 5.13 3.01 0 10 352
Sent in dictator game 3.57 2.89 0 10 352

Table 3. Fixed Effects Tobit Regression Estimating the Amount
Contributed in the Public Goods Experiment

Variable Estimate (SE) (a)

Intercept 1.650
Age 0.036 * (0.019)
Education 0.386
Missing family members -1.640 *** (0.489)
Predicted amount received 0.156 * (0.082)
N 328
Log likelihood -746.51

(a) SE indicates standard error.

* p < 0.10.

** p < 0.05.

*** p < 0.01.

Table 4. Fixed Effects Tobit Regression Estimating the Amount
Contributed in the Public Goods Experiment, by Subject Type

Variable Egoist Model (SE) Altruist Model (SE)

Intercept -2.856 2.678 *** (1.096)
Age -0.033 0.050 *** (0.018)
Education 1.276 0.171
Missing family members -3.999 ** (1.911) -1.359 *** (0.462)
Predicted amount received 0.724 *** (0.262) 0.050
N 83 245
[R.sup.2] -148.00 -565.41

* p < 0.10.

** p < 0.05.

*** p < 0.01.

Figure 1. Percent Contributing Different Dollar Amounts
in the Public Goods Game

Amount %
Sent

0 20.5
1 6.0
2 8.8
3 8.0
4 7.1
5 32.7
6 1.4
7 1.4
8 0.3
9 0.3
10 13.6
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Title Annotation:Symposium
Comment:Public goods in the field: Katrina evacuees in Houston.(Symposium)
Author:Whitt, Sam; Wilson, Rick K.
Publication:Southern Economic Journal
Article Type:Author abstract
Geographic Code:1USA
Date:Oct 1, 2007
Words:5238
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