This is an update on my previous post, analyses, and data from the Forbes 400 "The Richest People
in the U.S." website. To be on this list one had to have a net worth of at least $1.30 billion (in order to keep the list to "400"; other lists on the website provide info on all billionaires). For more info on the Forbes 400, see: http://www.forbes.com/lists/2008/54/400list08_The-400-Richest-Americans_Rank.html I've attached an SPSS system data file with my hand-entered data from the list and with additional created numerical variables for level of educational achievement and whether the person was a drop out. A few comments on this dataset: (1) In course of entering this data I apparently missed entering 2 cases so that the total number of people is 398 and not 400. This is why in certain places I will refer to this dataset as "400minus2". If anyone can identify who has been left out, please let me know and I'll update the dataset. (2) Although Forbes provide information on the highest educational level achieved, at least on their website this info is either unclear or inconsistent. There are 30 cases where there is no information provided on education. Examination of the biographical info on Forbes and on other websites indicate that there are variety of reasons for this. Some cases appear to be just a matter of privacy though it is likely that person has had some college education. In some cases, it appears that there may have been little formal education, that is, either only grade school or partial high school (there is one person who is identified as a high school dropout). In other cases it is not clear why there is no information about educational level -- given the money, power, and status of such people, such information is likely to be available but for whatever reasons it is not reported. I highlight this point in order to emphasize that it is unlikely that people who do not report educational background would have graduate degree such as a masters or Ph.D. (an issue that may play a role in some analyses of this data). Finally, there are certain problem cases like Jerry Yang who is listed as having a B.A. and completed his studies though some sources say that he was in graduate school at Stanford and dropped out to create Yahoo. I am not entirely sure that this is the correct thing to do but it appears that it will be difficult to identify all grad school dropouts. (3) Given that this dataset represents that richest 400minus2 people in the U.S. in 2008 and under the assumption that is exhaustive, this group is not a sample but a population. Consequently, the usual tests of statistical significance would not apply (e.g., testing whether the correlation between networth in $billions and educational level is zero or not would not be appropriate since we are dealing with the population rho and not the sample r). Bootstrapping and re-sampling techniques can be used to estimate standard errors for various statistics/parameters but one would do so under specific explicit assumptions. Note also that the usual formula for the variance and standard deviation which correct for sample estimates/sampling error would provide overestimates of the true variance and standard deviation This data can be used productively, I believe, in a variety of courses, especially statistics courses. New variables can be added (e.g., the sex/gender of the billionaires is not provided but can be readily added on the basis of a person's name or accessing their description on the Forbes website). There may be other forms of analysis (e.g., which college produced how many billionaires, do different regions of the U.S. produce more billionaires than others, etc.) Let me now provide some basic analyses that I've conducted on this data: (1) For the N=398, the mean age=64.75, with a range from 24 to 94 years of age. This will become relevant shortly. The mean networth of these individuals was $3.94 billion, the median was $2.30 billion, and the mode was $1.50 billion. Not surprisingly, graphical examination of the distribution indicates that it is non-normal, more like a chi-square with 1 degree of freedom or a negative exponential distribution (folks can have fun fitting different population functions to these values). (2) Frequency Tables for Educational Achievement: five categories were created to represent the highest level of educational achievement 00=High School (includes high school dropouts and college dropouts) 10=Associate Degree 20=Bachelors (i.e., BA, BS, LLB) 30=Masters (i.e., MA, MS, MBA) 40=MD or JD 50=Doctorate (Ph.D., Forbes is unclear about other degees, e.g., Ed.D.) 99=No Available information The frequencies for these categories are: Degree.1 Highest Ed Degree Achieved (All M's Comb) Freq Perc VPer CumPerc 00 High Schl 45 11.3 12.2 12.2 10 Associate 2 0.5 0.5 12.8 20 Bachelors 166 41.7 45.1 57.9 30 Masters 100 25.1 27.2 85.1 40 MD or JD 37 9.3 10.1 95.1 50 Doctorate 18 4.5 4.9 100.0 Total 368 92.5 100.0 Missing 30 7.5 Total 398 100.0 However, with respect to Masters' degrees, there were a large number of MBAs relative to the MA/MS and a re-categorization was created. The frequencies for this are: Degree.2 Highest Ed Degree Achieved (MBA Sep) Freq Percent VPerc CumPercent 00 High Sch 45 11.3 12.2 12.2 10 Associate 2 0.5 0.5 12.8 20 Bachelors 166 41.7 45.1 57.9 30 Masters 18 4.5 4.9 62.8 31 MBA 82 20.6 22.3 85.1 40 MD or JD 37 9.3 10.1 95.1 50 Doctorate 18 4.5 4.9 100.0 Total 368 92.5 100.0 Missing 30 7.5 Total 398 100.0 MBA (N=82) represent 22.3% of the group of people reporting education information. In some respects this should not come asa surprise but surprises await. Note that 4.5% of this group have doctorates. In previous posts on this topic, estimates of the percentage in the general population were calculated using the Census’ Community Survey data. In retreospect, this is the wrong calculation to do, that is, one should not take the number of Ph.D. estimated in the population and divide it by the total number of people in the sample. This does give one the percentage of the general population that have Ph.D. but for purposes of comparison, the denominator should the number of people between 24 to 94 years of age, that age range of the richest groups. Children, which would be included in the total sample number will inflate the denominator and not provide the appropriate number for comparison. In other words, to determine whether the 4.5% of Ph.D.s in this richest group is an “overrepresentation” or “underrepresentation” requires one to compare 4.5% to the percentage of Ph.D.s in the age range of 24 to 94 (excluding the richests). (3) Mean Networth in $Billions for each level of education: using the Degree.2 above (separates MA/MS from MBA), here are the descriptive statistics (standard errors are provided but they may not be meaningful): Estimates for NetWorth$Bil Degree.2 Mean Std.Er 00 High School 6.076 0.776 10 Associate 2.600 3.680 20 Bachelors 3.330 0.404 30 Masters 8.817 1.227 31 MBA 3.545 0.575 40 MD or JD 3.389 0.855 50 Doctorate 3.189 1.227 In the 400minus2 group, the educational level with the highest net worth are 18 people with a non-MBA masters' degree, mean=$8.82B, followed by the 45 people with "High School" (contains high school dropouts, people with a HS diploma, and college dropouts), mean=$6.08B. The 18 people with a Doctorate have the second lowest mean networth, mean=$3.19B (Lesson: Mothers, don't let you children grow up to get Associate degrees -- but N=2 for this group). A Pearson r between level of education and networth$bil Provides r=-.093, that is, as level of education increases, networth in billions of dollars decrease. That is, the more education you have, the lower your networth. Strictly speaking, the Pearson r is probably not the best measure of association to use in this situation because as coded here level of educational achievement is only ordinal while networth is either interval or ratio. In summary, what can one say about the richest 400minus2 people in the U.S.? For these people there appears to be no relationship between networth and level of education (there is a suggestion of a negative relationship). This raises a number of questions about the role of education, especially college and post-graduate education, and the acquisition of wealth. Other research has demonstrated that there appears to be a positive monotonic relationship between level of education and either income or networth but this relationship seems to hold for the non-Super Rich. By the way, since the Forbes list have been published some of the Super Rich are no longer on the list (e.g., “Sir” Allen Stanford) and some of the Super Rich may be dropped because they are facing criminal charges for activities ranging from securities fraud to drug dealing. I wonder if someone will try to compare the “Good” Super Rich with the “Bad” Super Rich in order to find which has the greater networth? -Mike Palij New York University m...@nyu.edu --- To make changes to your subscription contact: Bill Southerly (bsouthe...@frostburg.edu)
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