Table 3.
Significant Predictors of Sick Pay Coverage and Presenteeism
| Sick Pay Coverage | Presenteeism | |||
|---|---|---|---|---|
| (1) | (2) | (3) | (4) | |
| Female | −0.0139 (0.0119) | 0.0054 (0.0119) | 0.0162*** (0.0036) | 0.0150*** (0.0057) |
| No children in HH | 0.1592*** (0.0399) | 0.0842** (0.0362) | −0.0349*** (0.0119) | −0.0647*** (0.0175) |
| 1–3 children in HH | 0.1405*** (0.0402) | 0.0908** (0.0364) | −0.0309** (0.0120) | −0.0564*** (0.0176) |
| Age <25 | −0.4207*** (0.0171) | −0.1217*** (0.0190) | 0.0033 (0.0051) | −0.0152* (0.0092) |
| Age 25–34 | −0.0383*** (0.0147) | −0.0057 (0.0136) | 0.0090** (0.0044) | 0.0218*** (0.0065) |
| Age 65+ | −0.2736*** (0.0340) | −0.1082*** (0.0313) | 0.0053 (0.0102) | −0.0119 (0.0151) |
| Hourly wage <$10 | −0.2947*** (0.0199) | 0.0345*** (0.0096) | ||
| Hourly wage $10–$20 | −0.1143*** (0.0152) | 0.0291*** (0.0073) | ||
| Hourly wage $20–$30 | 0.0164 (0.0172) | 0.0040 (0.0083) | ||
| Full‐time employee | 0.2878*** (0.0155) | 0.0020 (0.0075) | ||
| College education | −0.0007 (0.0204) | 0.0104 (0.0098) | ||
| Mining | 0.0243 (0.0851) | 0.0097 (0.0411) | ||
| Construction | −0.0582 (0.0621) | 0.0210 (0.0300) | ||
| Manufacturing | 0.1480** (0.0605) | 0.0335 (0.0292) | ||
| Wholesale and retail trade | 0.1825*** (0.0598) | 0.0341 (0.0289) | ||
| Transportation and utilities | 0.2277*** (0.0626) | 0.0123 (0.0302) | ||
| Information | 0.1472** (0.0673) | 0.0561* (0.0325) | ||
| Financial activities | 0.3348*** (0.0625) | 0.0403 (0.0302) | ||
| Prof. and business services | 0.1743*** (0.0604) | 0.0274 (0.0291) | ||
| Education and health | 0.2490*** (0.0593) | 0.0364 (0.0286) | ||
| Leisure and hospitality | 0.0684 (0.0608) | 0.0199 (0.0293) | ||
| Other services | 0.0674 (0.0644) | 0.0323 (0.0311) | ||
| Public administration | 0.4104*** (0.0629) | 0.0073 (0.0304) | ||
| Constant | 0.4859*** (0.0440) | 0.1779** (0.0722) | 0.0416*** (0.0131) | 0.0432 (0.0348) |
| Observations | 6,354 | 6,354 | 6,354 | 6,354 |
| R 2 | 0.0981 | 0.2695 | 0.0070 | 0.0178 |
Notes. Standard errors are in parentheses. Each column stands for one Linear Probability Model. The results are robust to estimating probit models and calculating marginal effects. All regression models are weighted using the ATUS‐provided leave module weights. The binary dependent variable in the first two columns indicates whether interviewed employees have access to paid sick leave. The binary dependent variable in the last two columns indicates whether interviewed employees needed to take sick leave but did not do it in the week prior to the interview. All regressions include controls for the calendar month of the interview. Reference groups are as follows: age 35–65, male, more than three children in household, hourly wage above $30, part‐time work. The findings are robust to excluding those 114 respondents with an hourly wage of more than $200.
*p < .1, **p < .05, ***p < .01.
Source.
ATUS 2011, Leave Module, own illustration.