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Relationship between Positive Attitude and Job Satisfaction: Evidence from the US Data

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Abstract

Using data from the 1979 National Longitudinal Survey of Youth (NLSY79), a longitudinal data set from the United States, and following different cross-sectional and panel data estimation procedures, the study demonstrates that the worker’s job satisfaction is related positively to his/her positive attitude. This conclusion remains valid regardless of whether the worker’s wage income is treated as an exogenous variable or as an endogenous variable. The study thus claims that the worker’s satisfaction at workplace is related to not only the external job-related factors, but also his/her inner psychological attitude.

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Notes

  1. The author thanks two anonymous reviewers for helping make this clarification.

  2. According to marginal productivity theory, with everything else held constant, rise in job satisfaction by increasing productivity may shift the labor demand curve upwards, and thus, with the given labor supply curve, may result in a higher equilibrium wage.

  3. The author thanks a reviewer for raising and clarifying this issue.

  4. A detailed explanation of how these responses are labeled is presented in the section “Data”.

  5. The author thanks a reviewer for suggesting this important test.

  6. See Wooldridge [2006, pp. 532–533] for a detailed explanation of this test procedure.

  7. Note that the binary variable Positive generated using the response “strongly agree,” is a more reliable indicator of one’s true positive attitude than the binary variable generated using the weaker response “agree” [Mohanty 2009a].

  8. Some of the occupational variables, such as managerial, professional, and technical, are difficult to distinguish in different cross-sectional samples, and consequently different specifications of these variables are used in different samples.

  9. In different contexts, Taubman [1976] and Behrman and Taubman [1989] have shown that parental education and occupation genetically and financially affect the schooling and earnings of the children. In another context, Mohanty and Ullah [2012] have shown that an intact family upbringing during childhood leads to higher earnings during adulthood.

  10. Note that y 2 * and y 1 appear in the right-hand-sides of equations (7) and (8), respectively. Since in equations (10) and (11), they are linear functions of all independent variables, their inclusion in equations (7) and (8) in Stage 2 leads to collinearity when X 1=X 2 because in that case, . In fact, in our empirical section, we could not estimate wage equations with as an explanatory variable when all other independent variables were included in both equations. The variable Hour/week and a number of benefit variables except “promotion” therefore were excluded from the wage equation to make the estimation of both wage and job satisfaction equations feasible. It was necessary to exclude a few benefit variables from the wage regression because exclusion of Hour/week, which indirectly represents the fulltime variable in the wage regression, did not really help in ensuring a meaningful identification of both equations. Consequently, following the traditional wage regression literature, which does not usually treat benefit variables as standard covariates of wage, we excluded a few of these variables from the wage equation with a view to addressing the more urgent problem of identification associated with the estimation of our simultaneous equations model.

  11. For a robustness check, we also estimated the job satisfaction equations by binary probit with the dependent variable that assumes the value 1 when Jobsat=3 and is 0, otherwise. These results are not reported to save space, but can be obtained from the author on request. It is interesting, however, to note that both ordered probit and binary probit in a given sample yield very comparable estimates, indicating robustness of our results to changes in estimation techniques.

  12. With a t-ratio of 1.598, the importance of “years of schooling” in the 1987 job satisfaction equation cannot be completely ignored. Several earlier studies also find the evidence of a negative coefficient of the education variable in their job satisfaction equations. See Clark [1996], Clark and Oswald [1996], Gazioglu and Tansel [2006], and Carleton and Clain [2012] for more on why such a negative relationship may arise.

  13. Freeman [1978, Table 3] also finds a negative relationship between tenure and job satisfaction. He attributes this relationship to either greater aspirations of those already receiving increased benefits due to longer tenure or greater willingness to voice discontent due to job protection associated with seniority.

  14. With a t-ratio above 1.6, the importance of this variable in both 2006 matured adult and 1987 young-adult samples cannot be ignored completely.

  15. The χ 2 statistic associated with this likelihood ratio test is −2(log L R −log L UR ) =χ 1 2. Table 1 reports log likelihoods with and without positive attitude (Positive) as an explanatory variable. Using these values, χ 2 statistics from 2006, 1987, and 1980 samples are computed, respectively, as 76.13, 49.53, and 28.20. With a critical χ 1 2=3.841 (at 5 percent level of significance), the restricted specification without Positive is rejected in favor of the unrestricted specification that includes this variable among other explanatory variables.

  16. Coefficient estimates of the full set of variables can be obtained from the author on request.

  17. The lack of significance of this variable in 1980 ordered probit may indicate that for teenagers, whose earned incomes are not as stable as those of adults, their annual earnings from the previous year may be an underestimate of their desired wage incomes, and consequently this variable does not assume a significant coefficient in their current job satisfaction equation.

  18. The first-stage reduced form estimates of both job satisfaction and wage equations and the full set of coefficient estimates of structural wage equations can be obtained from the author on request.

  19. For example, more leisure is likely to increase the young adult’s job satisfaction. However, it is also expected to lower the wage rate (part-time wages are lower than fulltime wages). Thus there is a negative relationship between more leisure and wage rate. If we mistakenly confuse this leisure-wage relation with the relationship between job satisfaction and wage, we may end up getting a negative coefficient of the job satisfaction variable in the wage equation. Moreover, error in the variable resulting from inclusion or exclusion of variables in X 2 may also lead to this unexpected negative sign. In fact, our unreported results indicate that the sign of the coefficient of the predicted latent job satisfaction variable is highly sensitive to the variable specification in X 2.

  20. To the knowledge of this author, no earlier study has derived the asymptotic variance-covariance matrices of two-stage estimators when the simultaneous equations system involves a mixture of one continuous (wage) and two binary (job satisfaction and positive attitude) dependent variables.

  21. We thank a reviewer for providing this excellent example that shows the need for future research in this direction.

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Acknowledgements

The author wishes to thank Colleen Chrisinger, Miles Finney, participants of the Western Economic Association’s Pacific Rim Conference at Tokyo, the editor and two anonymous reviewers for helpful comments. The usual disclaimer applies.

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Table A1

Table A1 Variable definitions, their means, and standard deviationsa

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Mohanty, M. Relationship between Positive Attitude and Job Satisfaction: Evidence from the US Data. Eastern Econ J 42, 349–372 (2016). https://doi.org/10.1057/eej.2014.76

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