Differentials Within Industries and Occupations

X. Differentials Within Industries and Occupations

We next examine nonproŽt differentials within speciŽc industries or occupations. Table 8 summarizes the results for four industries— social services, hospitals, other health services, and nursing personal care facilities—that account for 54 percent of nonproŽt employment but also have substantial involvement by for-proŽt companies. The regression models are the same as above except that indus- try controls are excluded and the panel sample is limited to persons in the speciŽed industry in both years one and two. The latter restriction avoids confounding the effect of industry mobility with that of changes in nonproŽt status. The Žndings are again generally consistent with the hypothesis of competitive wage setting without labor donations to nonproŽts. The cross-sectional regressions provide no indication of a nonproŽt penalty. Instead, small premiums between 1.1 and 4.5 percent are predicted in three of the four industries. These higher earnings are mostly due to transferable individual characteristics, however, as evidenced by the statistically insigniŽcant 0 to 1 percent earnings differentials obtained in the Table 8 NonproŽt Earnings Differentials for SpeciŽc Industries Nursing Other Personal Social Health Care SampleProcedure Services Hospitals Services Facilities Wage levels in Year 1 Cross-sectional sample 0.015 0.025 20.003 0.044 0.015 0.008 0.017 0.016 Panel sample 0.011 0.047 20.039 0.034 0.028 0.012 0.028 0.029 Wage changes for industry stayers Symmetric 0.000 0.002 0.010 0.059 0.033 0.012 0.042 0.032 Asymmetric P ® N 0.054 0.009 0.016 0.092 0.052 0.019 0.066 0.049 N ® P 20.045 20.005 0.005 0.031 0.047 0.018 0.058 0.046 Notes: See notes on Tables 5 and 7. The wage levels are calculated for Year 1. Wage changes refer to the panel data set for persons remaining in the same industry in Years 1 and 2. All speciŽcations include controls for age and age squared, marital status, raceethnicity, education, metropolitan residence, the survey year, and levels or changes in weekly work hours and ten occupation categories. Sample sizes are 5, 155, 13,085, 4,469, and 4,231 1,541, 4,685, 1,487, and 1,322 for social services, hospitals, other health services, and nursing personal care facilities in the wage level regressions for the cross-sectional panel sample. Corresponding sample sizes for industry stayers in the wage change equations are 1,051, 4,063, 848, and 971. Table 9 NonproŽt Earnings Differentials for SpeciŽc Occupations Managers Health Not Health Health Technicians Administrative SampleProcedure Education Professionals Services Support Wage levels in Year 1 Cross-sectional sample 20.074 20.004 0.020 0.022 0.014 0.010 0.013 0.010 Panel sample 20.069 0.005 0.055 0.023 0.022 0.016 0.022 0.017 Wage changes for occupation stayers Symmetric 20.047 0.003 0.011 0.007 0.025 0.016 0.024 0.018 Asymmetric P ® N 0.004 0.013 0.013 0.031 0.041 0.025 0.036 0.028 N ® P 20.079 20.006 0.009 20.013 0.032 0.023 0.034 0.026 Notes: See notes on Tables 5 and 7. The wage levels are calculated for Year 1. Wage changes refer to the panel data set for persons remaining in the same occupation in Years 1 and 2. All speciŽcations include controls for age and age squared, marital status, raceethnicity, education, metropolitan residence, the survey year, and levels or changes in weekly work hours and eight industry categories. Sample sizes are 33,273, 10,813, 9,462, and 35,971 11,532, 3,879, 3,015, and 12,158 for managers, health professionals, health technicians service workers, and secretariesadministrative support workers. Corresponding sample sizes for occupation stayers in the wage change equations are 7,896, 3,223, 2,168, and 7,863. symmetric wage change equations for the social service, hospital, and other health service industries. One exception is that nonproŽt workers in nursing personal care facilities receive a pay premium estimated at between 3 and 10 percent, raising the possibility of rent-sharing. The bounds on the nonproŽt differential obtained from the asymmetric Žrst-difference model are also reasonably wide for Social Service workers, although not signiŽcantly different from zero. Table 9 displays results for four occupation groups— managers outside of health and education, health professionals, health techniciansservice workers, and ad- ministrative support workers—that are responsible for 46 percent of nonproŽt em- ployment and have sizeable participation by proŽt-seeking Žrms. 37 The regression speciŽcations are identical to Table 8, except that industry rather than occupation covariates are included and the wage change sample is restricted to those in the speciŽed occupation in the two years. The results suggest small or nonexistent nonproŽt differentials for the three non- managerial occupations, generally ranging between 21 and 3 percent and usually 37. These categories correspond to those in Table 3, except that health technicians and service personnel have been combined into a single group, as have secretaries and other administrative support workers. statistically insigniŽcant, providing further support for competitive wage-setting without labor donations. However, nonproŽt managers earn about 7 percent less than their for-proŽt peers, controlling for observables, with the wage-change regressions bounding the penalty between 0 and 8 percent. 38 This indicates some scope for labor donations by managers.

XI. Discussion