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cps_85_wages

cps_85_wages

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Author: Source: Unknown - Date unknown Please cite: Determinants of Wages from the 1985 Current Population Survey Summary: The Current Population Survey (CPS) is used to supplement census information between census years. These data consist of a random sample of 534 persons from the CPS, with information on wages and other characteristics of the workers, including sex, number of years of education, years of work experience, occupational status, region of residence and union membership. We wish to determine (i) whether wages are related to these characteristics and (ii) whether there is a gender gap in wages. Based on residual plots, wages were log-transformed to stabilize the variance. Age and work experience were almost perfectly correlated (r=.98). Multiple regression of log wages against sex, age, years of education, work experience, union membership, southern residence, and occupational status showed that these covariates were related to wages (pooled F test, p < .0001). The effect of age was not significant after controlling for experience. Standardized residual plots showed no patterns, except for one large outlier with lower wages than expected. This was a male, with 22 years of experience and 12 years of education, in a management position, who lived in the north and was not a union member. Removing this person from the analysis did not substantially change the results, so that the final model included the entire sample. Adjusting for all other variables in the model, females earned 81% (75%, 88%) the wages of males (p < .0001). Wages increased 41% (28%, 56%) for every 5 additional years of education (p < .0001). They increased by 11% (7%, 14%) for every additional 10 years of experience (p < .0001). Union members were paid 23% (12%, 36%) more than non-union members (p < .0001). Northerns were paid 11% (2%, 20%) more than southerns (p =.016). Management and professional positions were paid most, and service and clerical positions were paid least (pooled F-test, p < .0001). Overall variance explained was R2 = .35. In summary, many factors describe the variations in wages: occupational status, years of experience, years of education, sex, union membership and region of residence. However, despite adjustment for all factors that were available, there still appeared to be a gender gap in wages. There is no readily available explanation for this gender gap. Authorization: Public Domain Reference: Berndt, ER. The Practice of Econometrics. 1991. NY: Addison-Wesley. Description: The datafile contains 534 observations on 11 variables sampled from the Current Population Survey of 1985. This data set demonstrates multiple regression, confounding, transformations, multicollinearity, categorical variables, ANOVA, pooled tests of significance, interactions and model building strategies. Variable names in order from left to right: EDUCATION: Number of years of education. SOUTH: Indicator variable for Southern Region (1=Person lives in South, 0=Person lives elsewhere). SEX: Indicator variable for sex (1=Female, 0=Male). EXPERIENCE: Number of years of work experience. UNION: Indicator variable for union membership (1=Union member, 0=Not union member). WAGE: Wage (dollars per hour). AGE: Age (years). RACE: Race (1=Other, 2=Hispanic, 3=White). OCCUPATION: Occupational category (1=Management, 2=Sales, 3=Clerical, 4=Service, 5=Professional, 6=Other). SECTOR: Sector (0=Other, 1=Manufacturing, 2=Construction). MARR: Marital Status (0=Unmarried, 1=Married) Therese Stukel Dartmouth Hitchcock Medical Center One Medical Center Dr. Lebanon, NH 03756 e-mail: stukel@dartmouth.edu Information about the dataset CLASSTYPE: numeric CLASSINDEX: none specific

11 features

WAGE (target)numeric238 unique values
0 missing
EDUCATIONnumeric17 unique values
0 missing
SOUTHnominal2 unique values
0 missing
SEXnominal2 unique values
0 missing
EXPERIENCEnumeric52 unique values
0 missing
UNIONnominal2 unique values
0 missing
AGEnumeric47 unique values
0 missing
RACEnominal3 unique values
0 missing
OCCUPATIONnominal6 unique values
0 missing
SECTORnominal3 unique values
0 missing
MARRnominal2 unique values
0 missing
Show all 11 features

107 properties

NumberOfInstances
534
Number of instances (rows) of the dataset.
NumberOfFeatures
11
Number of attributes (columns) of the dataset.
NumberOfClasses
0
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
4
Number of numeric attributes.
NumberOfSymbolicFeatures
7
Number of nominal attributes.
AutoCorrelation
-3.8
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_DecisionStumpErrRate
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_DecisionStumpKappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_NaiveBayesAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_NaiveBayesErrRate
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_NaiveBayesKappa
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_kNN1NAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_kNN1NErrRate
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_kNN1NKappa
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
ClassEntropy
Entropy of the target attribute values.
DecisionStumpAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.02
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
Percentage of instances belonging to the most frequent class.
MajorityClassSize
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
4.99
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
36.83
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
6
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
1.7
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
12.38
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
1.22
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
19.17
Mean of means among attributes of the numeric type.
MeanMutualInformation
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
2.86
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
0.68
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
7.97
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-0.58
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
9.02
Minimum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-0.2
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
2.62
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
Percentage of instances belonging to the least frequent class.
MinorityClassSize
Number of instances belonging to the least frequent class.
NaiveBayesAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
4
Number of binary attributes.
PercentageOfBinaryFeatures
36.36
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
36.36
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
63.64
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-0.53
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
10.02
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
-0.02
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
3.25
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
0.23
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
15.42
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
0.62
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
8.43
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
3.95
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
32.08
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
1.44
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
12.22
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
1.46
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

14 tasks

Supervised Regression on cps_85_wages
2 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: WAGE
Supervised Regression on cps_85_wages
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: WAGE
Supervised Regression on cps_85_wages
0 runs - estimation_procedure: 33% Holdout set - target_feature: WAGE
Clustering on cps_85_wages
0 runs
Clustering on cps_85_wages
0 runs - estimation_procedure: 50 times Clustering
Clustering on cps_85_wages
0 runs - estimation_procedure: 50 times Clustering
Clustering on cps_85_wages
0 runs - estimation_procedure: 50 times Clustering
Clustering on cps_85_wages
0 runs - estimation_procedure: 50 times Clustering
Clustering on cps_85_wages
0 runs - estimation_procedure: 50 times Clustering
Clustering on cps_85_wages
0 runs - estimation_procedure: 50 times Clustering
Clustering on cps_85_wages
0 runs - estimation_procedure: 50 times Clustering
Clustering on cps_85_wages
0 runs - estimation_procedure: 50 times Clustering
Clustering on cps_85_wages
0 runs - estimation_procedure: 50 times Clustering
Clustering on cps_85_wages
0 runs - estimation_procedure: 50 times Clustering
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