| Literature DB >> 35055752 |
Yuna Ma1, Jiafeng Gu2, Ruixi Lv3.
Abstract
Despite growing attention to job satisfaction as a social determinant of alcohol-related behaviors, few studies focus on its diverse impacts on alcohol consumption. Using data from the China Family Panel Study in 2018, this study uses logistic regression analysis to examine how job satisfaction affects alcohol consumption in China, finding that people who were satisfied with their jobs were more likely to be regularly drinking. Employed people who were satisfied with their working environment and working hours were more likely to regularly drink, but those who were satisfied with their wages and working security were less likely to be regularly drinking. Findings suggest that the link between job satisfaction and alcohol consumption is dynamic. Employment policies, working wellbeing improvement programs, and alcohol policy improvement should, therefore, be designed on the basis of a comprehensive account of entire job-related attitudes.Entities:
Keywords: China; alcohol consumption; job satisfaction; logistic regression; working
Mesh:
Year: 2022 PMID: 35055752 PMCID: PMC8775457 DOI: 10.3390/ijerph19020933
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 3.390
Figure 1The theoretical framework and related hypotheses.
Distribution of general characteristics.
| Number of Participants | Drinking | ||||
|---|---|---|---|---|---|
| Classification | N | Yes | No | ||
| Overall job satisfaction | Satisfied | 7923 | 1296 | 6627 | 1.33 |
| Others | 3624 | 624 | 3000 | ||
| Wage satisfaction | Satisfied | 6795 | 1106 | 5689 | 1.40 |
| Others | 4752 | 813 | 3939 | ||
| Working-security satisfaction | Satisfied | 8783 | 1376 | 7407 | 24.02 *** |
| Others | 2764 | 543 | 2221 | ||
| Working-environment satisfaction | Satisfied | 8062 | 1282 | 6780 | 9.92 ** |
| Others | 3485 | 637 | 2848 | ||
| Working-hour satisfaction | Satisfied | 8055 | 1336 | 6719 | 0.02 |
| Others | 3492 | 583 | 2909 | ||
| District | Eastern region | 5441 | 909 | 4452 | 27.76 *** |
| Central region | 3356 | 605 | 2717 | ||
| Western region | 2750 | 377 | 2331 | ||
| Gender | Male | 6514 | 1725 | 4694 | 1100.00 *** |
| Female | 5033 | 166 | 4806 | ||
| Hukou | Agriculture | 7628 | 1321 | 6187 | 15.71 *** |
| Nonagriculture | 3919 | 570 | 3313 | ||
| Age | 16–35 | 4916 | 562 | 4279 | 199.77 *** |
| 36–50 | 3831 | 663 | 3114 | ||
| >50 | 2800 | 666 | 2107 | ||
| Highest level of education | Primary school and below | 2867 | 579 | 2288 | 152.63 *** |
| High school | 5740 | 1064 | 4676 | ||
| 3-year college and above | 2940 | 276 | 2664 | ||
| Income | CNY 0–50,000 | 6003 | 902 | 5043 | 18.60 *** |
| CNY 50–150,000 | 2220 | 406 | 1791 | ||
| >CNY 150,000 | 3324 | 583 | 2666 | ||
| Marital status | Married | 8721 | 1583 | 7031 | 80.52 *** |
| Other | 2826 | 308 | 2469 | ||
| Regularly drink | Yes | 1919 | |||
| No | 9628 | ||||
Note. ***, p < 0.01. **, p < 0.05.
Multivariable logistic regression analysis.
| Characteristics | Odds Ratio | Drink_Y_18 | |
|---|---|---|---|
| Work satisfaction | |||
| Satisfied vs. others | 1.025 | 1.006–1.116 | 0.010 |
| District | |||
| Western vs. eastern | 0.719 | 0.647–0.898 | 0.001 |
| Central vs. eastern | 1.063 | 1.042–1.085 | 0.000 |
| Gender: male vs. female | 10.574 | 10.285–10.870 | 0.000 |
| Hukou: agricultural vs. nonagricultural | 1.093 | 1.070–1.116 | 0.000 |
| Age group | |||
| 36–50 vs. 16–35 years | 1.290 | 1.259–1.322 | 0.000 |
| 50+ vs. 16–35 years | 1.557 | 1.517–1.598 | 0.000 |
| Education | |||
| High vs. low | 0.489 | 0.473–0.506 | 0.000 |
| Medium vs. low | 0.854 | 0.836–0.873 | 0.000 |
| Personal income | |||
| CNY 50,000–149,999 vs. CNY 0–49,999 | 1.171 | 1.143–1.199 | 0.000 |
| CNY 150,000+ vs. CNY 0–49,999 | 1.036 | 1.015–1.058 | 0.001 |
| Marital status: married vs. other | 1.411 | 1.375–1.447 | 0.000 |
| Constant | 0.025 | 0.024–0.026 | 0.000 |
| LR chi-squared | 57,909.12 (0.000) | ||
| −2Log likelihood | 315,358.08 | ||
| Cox and Snell R square | 0.130 | ||
| MacFadden square | 0.155 | ||
| Nagelkerke square | 0.220 | ||
Multivariable logistic regression analysis.
| Characteristics | Odds Ratio | Drink_Y_18 | |
|---|---|---|---|
| Wage satisfaction | |||
| Satisfied vs. others | 0.923 | 0.903–0.945 | 0.000 |
| Working-environment satisfaction | |||
| Satisfied vs. others | 1.034 | 1.006–1.062 | 0.016 |
| Working-safety satisfaction | |||
| Satisfied vs. others | 0.909 | 0.884–0.934 | 0.000 |
| Working-hour satisfaction | |||
| Satisfied vs. others | 1.081 | 1.054–1.108 | 0.000 |
| District | |||
| Western vs. eastern | 0.723 | 0.704–0.743 | 0.000 |
| Central vs. eastern | 1.068 | 1.043–1.094 | 0.000 |
| Sex: male vs. female | 10.477 | 10.141–10.825 | 0.000 |
| Hukou: agricultural vs. nonagricultural | 1.098 | 1.071–1.126 | 0.000 |
| Age group | |||
| 36–50 vs. 16–35 years | 1.287 | 1.250–1.324 | 0.000 |
| 50+ vs. 16–35 years | 1.570 | 1.522–1.619 | 0.000 |
| Education | |||
| High vs. low | 0.498 | 0.479–0.518 | 0.000 |
| Medium vs. low | 0.862 | 0.840–0.884 | 0.000 |
| Personal income | |||
| CNY 50,000–149,999 vs. CNY 0–49,999 | 1.184 | 1.151–1.218 | 0.000 |
| CNY 150,000+ vs. CNY 0–49,999 | 1.040 | 1.015–1.066 | 0.000 |
| Marital status: married vs. other | 1.412 | 1.370–1.455 | 0.000 |
| Constant | 0.026 | 0.025–0.028 | 0.000 |
| LR chi-squared | 41,756.03 (0.000) | ||
| −2Log likelihood | 227,400.94 | ||
| Cox and Snell R square | 0.130 | ||
| MacFadden square | 0.155 | ||
| Nagelkerke square | 0.220 | ||