uniond               package:DPpackage               R Documentation

_U_n_i_o_n _M_e_m_b_e_r_s_h_i_p

_D_e_s_c_r_i_p_t_i_o_n:

     This data set consider growth information on wages and union
     membership  for 534 workers. The datafile contains 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.

_U_s_a_g_e:

     data(uniond)

_F_o_r_m_a_t:

     A data frame with 534 observations on the following 11 variables.

     '_e_d_u_c_a_t_i_o_n' a numeric vector giving the number of  years of
          education.

     '_s_o_u_t_h' a numeric vector gving an indicator variable for  Southern
          Region (1=Person lives in South,  0=Person lives elsewhere).

     '_s_e_x' a numeric vector giving an indicator variable  for sex
          (1=Female, 0=Male).

     '_e_x_p_e_r_i_e_n_c_e' a numeric vector giving the number of years of  work
          experience.

     '_u_n_i_o_n_v' a numeric vector giving an indicator variable for union 
          membership (1=Union member, 0=Not union member).

     '_w_a_g_e' a numeric vector giving the Wage (dollars per hour).

     '_a_g_e' a numeric vector giving the Age in years.

     '_r_a_c_e' a numeric vector giving the race (1=Other, 2=Hispanic,
          3=White).

     '_o_c_c_u_p_a_t_i_o_n' a numeric vector giving the occupational  category
          (1=Management, 2=Sales, 3=Clerical,  4=Service,
          5=Professional, 6=Other).    

     '_s_e_c_t_o_r' a numeric vector giving the Sector (0=Other, 
          1=Manufacturing, 2=Construction).

     '_m_a_r_r' a numeric vector giving the Marital Status  (0=Unmarried, 
          1=Married).

_D_e_t_a_i_l_s:

     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
     the wages of males (p < .0001). Wages increased 41 additional
     years of education (p < .0001). They increased by 11 for every
     additional 10 years of experience (p < .0001). Union members were 
     paid 23 paid 11 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.

_S_o_u_r_c_e:

_R_e_f_e_r_e_n_c_e_s:

_E_x_a_m_p_l_e_s:

     data(uniond)
     ## maybe str(uniond) ; plot(uniond) ...

