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An S factor among census tracts of Boston

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Emil O. W. Kirkegaard at Ulster Institute for Social Research
Emil O. W. Kirkegaard
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A factor analysis was carried out on 6 socioeconomic variables for 506 census tracts of Boston. An S factor was found with positive loadings for median value of owner-occupied homes and average number of rooms in these; negative loadings for crime rate, pupil-teacher ratio, NOx pollution, and the proportion of the population of ‘lower status’. The S factor scores were negatively correlated with the estimated proportion of African Americans in the tracts r = -.36 [CI95 -0.43; -0.28]. This estimate was biased downwards due to data error that could not be corrected for.
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An S factor among census tracts of Boston
Abstract
A factor analysis was carried out on 6 socioeconomic variables for 506 census tracts of Boston. An S
factor was found with positive loadings for median value of owner-occupied homes and average
number of rooms in these; negative loadings for crime rate, pupil-teacher ratio, NOx pollution, and the
proportion of the population of 'lower status'. The S factor scores were negatively correlated with the
estimated proportion of African Americans in the tracts r = -.36 [CI95 -0.43; -0.28]. This estimate was
biased downwards due to data error that could not be corrected for.
Introduction
The general socioeconomic factor (s/S
1
) is a similar construct to that of general cognitive ability (GCA;
g factor, intelligence, etc., (Gottfredson, 2002; Jensen, 1998). For ability data, it has been repeatedly
found that performance on any cognitive test is positively related to performance on any other test, no
matter which format (pen pencil, read aloud, computerized), and type (verbal, spatial, mathematical,
figural, or reaction time-based) has been tried. The S factor is similar. It has been repeatedly found that
desirable socioeconomic outcomes tend are positively related to other desirable socioeconomic
outcomes, and undesirable outcomes positively related to other undesirable outcomes. When this
pattern is found, one can extract a general factor such that the desirable outcomes have positive
loadings and then undesirable outcomes have negative loadings. In a sense, this is the latent factor that
underlies the frequently used term “socioeconomic status” except that it is broader and not just
restricted to income, occupation and educational attainment, but also includes e.g. crime and health.
So far, S factors have been found for country-level (Kirkegaard, 2014b), state/regional-level (e.g.
Kirkegaard, 2015), country of origin-level for immigrant groups (Kirkegaard, 2014a) and first name-
level data (Kirkegaard & Tranberg, In preparation). The S factors found have not always been strictly
general in the sense that sometimes an indicator loads in the 'wrong direction', meaning that either an
undesirable variable loads positively (typically crime rates), or a desirable outcome loads negatively.
These findings should not be seen as outliers to be explained away, but rather to be explained in some
coherent fashion. For instance, crime rates may load positively despite crime being undesirable because
the justice system may be better in the higher S states, or because of urbanicity tends to create crime
and urbanicity usually has a positive loading. To understand why some indicators sometimes load in the
wrong direction, it is important to examine data at many levels. This paper extends the S factor to a
new level, that of census tracts in the US.
Data source
While taking a video course on statistical learning based on James, Witten, Hastie, & Tibshirani (2013),
I noted that a dataset used as an example would be useful for an S factor analysis. The dataset concerns
506 census tracts of Boston and includes the following variables (Harrison & Rubinfeld, 1978):
• Median value of owner-occupied homes
• Average number of rooms in owner units.
• Proportion of owner units built before 1940.
• Proportion of the population that is 'lower status'. “Proportion of adults without, some high
school education and proportion of male workers classified as laborers)”.
• Crime rate.
• Proportion of residential land zoned for lots greater than 25k square feet.
• Proportion of nonretail business acres.
• Full value property tax rate.
• Pupil-teacher ratios for schools.
• Whether the tract bounds the Charles River.
• Weighted distance to five employment centers in the Boston region.
• Index of accessibility to radial highways.
• Nitrogen oxide concentration. A measure of air pollution.
• Proportion of African Americans.
See the original paper for a more detailed description of the variables.
This dataset has become very popular as a demonstration dataset in machine learning and statistics
which shows the benefits of data sharing (Wicherts & Bakker, 2012). As Gilley & Pace (1996) note
“Essentially, a cottage industry has sprung up around using these data to examine alternative statistical
techniques.”. However, as they re-checked the data, they found a number of errors. The corrected data
can be downloaded here, which is the dataset used for this analysis.
The proportion of African Americans
The variable concerning African Americans have been transformed by the following formula: 1000(x
- .63)
2
. Because one has to take the square root to reverse the effect of taking the square, some
information is lost. For example, if we begin with the dataset {2, -2, 2, 2, -2, -2} and take the square of
these and get {4, 4, 4, 4, 4, 4}, it is impossible someone to reverse this transformation and get the
original because they cannot tell whether 4 results from -2 or 2 being squared.
In case of the actual data, the distribution is shown in Figure 1.
Due to the transformation, the values around 400 actually mean that the proportion of blacks is around
0. The function for back-transforming the values is shown in Figure 2.
We can now see the problem of back-transforming the data. If the transformed data contains a value
between 0 and about 140, then we cannot tell which original value was with certainty. For instance, a
transformed value of 100 might correspond to an original proportion of .31 or .95.
To get a feel for the data, one can use the Racial Dot Map explorer and look at Boston. Figure 3 shows
the Boston area color-coded by racial groups.
As can be seen, the races tend to live rather separate with large areas dominated by one group. From
looking at it, it seems that Whites and Asians mix more with each other than with the other groups, and
that African Americans and Hispanics do the same. One might expect this result based on the groups'
relative differences in S factor and GCA (Fuerst, 2014). Still, this should be examined by numerical
analysis, a task which is left for another investigation.
Still, we are left with the problem of how to back-transform the data. The conservative choice is to use
only the left side of the function. This is conservative because any proportion above .63 will get back-
transformed to a lower value. E.g. .80 will become .46, a serious error. This is the method used for this
analysis.
Factor analysis
Of the variables in the dataset, there is the question of which to use for S factor analysis. In general
when doing these analyses, I have sought to include variables that measure something
socioeconomically important and which is not strongly influenced by the local natural environment.
For instance, the dummy variable concerning the River Charles fails on both counts. I chose the
following subset:
• Median value of owner-occupied homes
• Average number of rooms in owner units.
• Proportion of the population that is 'lower status'.
• Crime rate.
• Pupil-teacher ratios for schools.
• Nitrogen oxide concentration. A measure of air pollution.
Which concern important but different things. Figure 4 shows the loadings plot for the factor analysis
(reversed).
2
The S factor was confirmed for this data without exceptions, in that all indicator variables loaded in the
expected direction. The factor was moderately strong, accounting for 47% of the variance.
Relationship between S factor and proportions of African Americans
Figure 5 shows a scatter plot of the relationship between the back-transformed proportion of African
Americans and the S factor.
We see that there is a wide variation in S factor even among tracts with no or very few African
Americans. These low S scores may be due to Hispanics or simply reflect the wide variation within
Whites (there few Asians back then). The correlation between proportion of African Americans and S is
-.36 [CI95 -0.43; -0.28].
We see that many very low S points lie around S [-3 to -1.5]. Some of these points may actually be
census tracts with very high proportions of African Americans that were back-transformed incorrectly.
Discussion
The value of r = -.36 should not be interpreted as an estimate of effect size of ancestry on S factor for
census tracts in Boston because the proportions of the other sociological races were not used. A
multiple regression or similar method with all sociological races as the predictors is necessary to
answer this question. Still, the result above is in the expected direction based on known data concerning
the mean GCA of African Americans, and the relationship between GCA and socioeconomic outcomes
(Gottfredson, 1997).
Limitations
The back-transformation process likely introduced substantial error in the results.
Data are relatively old and may not reflect reality in Boston as it is now.
Supplementary material
Data, high quality figures and R source code is available at the Open Science Framework repository.
References
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Exploratory Meta-analysis. Open Differential Psychology. Retrieved from
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25–46. http://doi.org/10.1080/08959285.2002.9668082
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of Environmental Economics and Management, 5(1), 81–102.
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learning: with applications in R. New York: Springer.
Jensen, A. R. (1998). The g factor: the science of mental ability. Westport, Conn.: Praeger.
Kirkegaard, E. O. W. (2014a). Crime, income, educational attainment and employment among
immigrant groups in Norway and Finland. Open Differential Psychology. Retrieved from
http://openpsych.net/ODP/2014/10/crime-income-educational-attainment-and-employment-
among-immigrant-groups-in-norway-and-finland/
Kirkegaard, E. O. W. (2014b). The international general socioeconomic factor: Factor analyzing
international rankings. Open Differential Psychology. Retrieved from
http://openpsych.net/ODP/2014/09/the-international-general-socioeconomic-factor-factor-
analyzing-international-rankings/
Kirkegaard, E. O. W. (2015). Examining the S factor in Mexican states. The Winnower. Retrieved from
https://thewinnower.com/papers/examining-the-s-factor-in-mexican-states
Kirkegaard, E. O. W., & Tranberg, B. (In preparation). What is a good name? The S factor in Denmark
at the name-level. Open Differential Psychology. Retrieved from https://osf.io/t2h9c/
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1 Capital S is used when the data are aggregated, and small s is used when it is individual level data. This follows the
nomenclature of (Rindermann, 2007).
2 To say that it is reversed is because the analysis gave positive loadings for undesirable outcomes and negative for
desirable outcomes. This is because the analysis includes more indicators of undesirable outcomes and the factor
analysis will choose the direction to which most indicators point as the positive one. This can easily be reversed by
multiplying with -1.
Figure 1: Transformed data for the proportion of blacks by census
tract.
Figure 2: The transformation function.
Figure 3: Racial dot map of Boston area.
Figure 4: Loadings plot for the S factor.
Figure 5: Scatter plot of S scores and the back-transformed
proportion of African Americans by census tract in Boston.
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... Overall results were similar to previous S factor studies. Only one prior study has examined administrative divisions of a major city before and it too found similar results (6). The odd finding of a negative loading for female annual pay in the rank-order analysis deserves more attention in the future, as does the general case where gendered versions of a variable give markedly different results. ...
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... When such socioeconomic variables are factor analyzed, though, a general socioeconomic factor (S factor) emerges such that, most of the time, desirable outcomes load positively and undesirable outcomes load negatively on it. Previous research has found S factors at the national level (Kirkegaard, 2014b), the state/region/department level (Carl, 2015;Kirkegaard, 2015bKirkegaard, , 2015d and the city district level (Kirkegaard, 2015a). Analyses of national and state level data showed that Human Development Index (HDI) scores correlated strongly with S factor scores at typically >.9. ...
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  • Jan 2013
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.
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Article
  • Apr 2002
g is a highly general capability for processing complex information of any type. This explains its great value in predicting job performance. Complexity is the ma-jor distinction among jobs, which explains why g is more important further up the occupational hierarchy. The predictive validities of g are moderated by the criteria and other predictors considered in selection research, but the resulting gradients of g's effects are systematic. The pattern provides personnel psychologists a road map for how to design better selection batteries. Despite much literature on the meaning and impact of g, there nonetheless remains an aura of mystery about where and why g cognitive tests might be useful in selection. The aura of mystery encourages false beliefs and false hopes about how we might reduce disparate impact in em-ployee selection. It is also used to justify new testing techniques whose major effect, witting or not, is to reduce the validity of selection in the service of racial goals. The general mental ability factor—g—is the best single predictor of job perfor-mance. It is probably the best measured and most studied human trait in all of psy-chology. Much is known about its meaning, distribution, and origins thanks to re-search across a wide variety of disciplines (Jensen, 1998). Many questions about g remain unanswered, including its exact nature, but g is hardly the mystery that some people suggest. The totality—the pattern—of evidence on g tells us a lot about where and why it is important in the real world. Theoretical obtuseness about g is too often used to justify so–called technical advances in personnel selection that minimize, for sociopolitical purposes, the use of g in hiring.