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The Interaction of Job Satisfaction, Job Search, and Job Changes. An Empirical Investigation with German Panel Data

Abstract

Using the rich data set of the German Socio-Economic Panel (GSOEP) this article analyzes the effects of job characteristics on job satisfaction as well as the conditions under which low job satisfaction leads to job search, and under which job search leads to job changes. Individual fixed effects are included into the analysis in order to hold unobserved heterogeneity constant. According to the empirical results, the strongest determinants of job satisfaction are relations with colleagues and supervisors, task diversity and job security. Furthermore, job satisfaction is an important determinant of the self-reported probability of job search, which in turn effectively predicts actual job changes. The effect of job search on the probability of changing jobs varies with job satisfaction and is strongest at low levels of job satisfaction. The effects of job dissatisfaction on job search and of job search on quits are stronger for workers with lower tenure, better educated workers, workers in the private sector and when the economy and labor market are in a good condition.

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Notes

  1. 1.

    Job satisfaction has also been analyzed as a determinant of job changes within the same organization. Delfgaauw (2007) analyzed a Dutch data set and found that dissatisfaction of public sector employees with certain organization-specific job domains increased the probability of leaving the current employer, while dissatisfaction with job aspects that vary sufficiently within an organization can lead to job changes within the organization. However, the present analysis remains confined to job changes to a new employer.

  2. 2.

    An exception is D’Addio et al. (2007), who find that including fixed-effects into the estimation affects the results importantly, and that the random-effects specification is rejected in favor of the fixed effects specification.

  3. 3.

    Simultaneity arises when an explanatory variable is jointly determined with the dependent variable. Simultaneity biases the estimates of the structural coefficients (Wooldridge 2006, pp. 552–555).

  4. 4.

    Consequently, quits that lead to unemployment or withdrawals from the labor force are not considered. The reason is that they are not natural outcomes of job search, and job search is central to the analysis. Considering job changes initiated by the employee does not imply that these are voluntary separations. In particular, job changes occurring in anticipation of future job loss can be viewed as involuntary (Manski and Straub 2000).

  5. 5.

    Since 1999, respondents are asked to indicate the probability in percent, choosing between 11 options ranging from 0%, 10%, 20% etc. up to 100%. I harmonize the reply options by recoding 0% as unlikely, 10%–50% as probably not, 60%–90% as probable, and 100% as certain. The recoding is chosen in such a way that in the years before and after the change of the reply options similar fractions of respondents are found in the four categories.

  6. 6.

    In the fixed-effects specification, the coefficient of good relations with colleagues is 0.203, while that on the log wage is 0.275. This implies that the log wage would need to rise by 0.203/0.275 = 0.74 log points in order to compensate (hold job satisfaction constant) when relations with colleagues are bad instead of good. A rise of the log wage by 0.74 point is equal to a wage raise of about 110%, as exp(0.74) − 1 = 1.1

  7. 7.

    This result is in contrast to Clark’s (2003) finding, with British panel data, that higher unemployment reduces the well-being of employed (and increases the well-being of unemployed) individuals.

  8. 8.

    As the previous analysis has shown, there is a strong association between job search and job satisfaction. Including both variables in the job change regression might potentially cause multicollinearity. I therefore also repeated the estimation excluding job satisfaction as a regressor from the job change equation (results available upon request). The magnitudes of coefficients, significance levels and the variance inflation factors computed after the linear regression were very similar. Multicollinearity due to the job satisfaction variable therefore does not seem to be harmful to the present regression.

  9. 9.

    If the job change equation is estimated by a fixed-effects logit model instead of the linear fixed-effects model, the sample size shrinks from 47,175 to 7,050 and individuals in the restricted sample differ systematically from those in the complete sample. For example, the sample quit rate rises from 0.035 to 0.21 and mean work experience in the sample falls from 21 to 17 years.

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Acknowledgements

I would like to thank Olaf Hübler, Uwe Jirjahn, the participants of the labor economics seminar of the Economics Department at Leibniz Universität Hannover, and three anonymous reviewers for helpful comments. Financial support by the DFG (project no. HU 368/4) is gratefully acknowledged.

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Correspondence to Thomas Cornelißen.

Appendix A1: Details of the Estimation

Appendix A1: Details of the Estimation

Equations (1)–(3) form a recursive system of equations that can be estimated by treating each of the equations separately. The inclusion of fixed effects is not so straightforward in models for binary or more general ordinal dependent variables such as the (ordered) probit and the (ordered) logit model. The fixed-effects probit model leads to inconsistent parameter estimates (see for example Baltagi 2001, p. 206; Hsiao 2003, p. 194), and the fixed-effects logit model can only be estimated on the subsample of individuals that have longitudinal variation in the dependent variable, which leads to small sample sizes and selective samples.Footnote 9 To circumvent these problems, I applied linear fixed-effects models to the binary and to the ordinal dependent variables. In the case of multinomial ordered variables with more than two classes (job satisfaction and job search), I rescaled the dependent variable before applying the linear regression model as proposed by van Praag and Ferrer-i-Carbonel (2004). The rescaling makes the coefficients of the linear model comparable with the coefficients of the ordered probit model. Van Praag and Ferrer-i-Carbonel (2004) call this probit-adapted OLS (POLS). The rescaling consists of deriving those Z-values of a standard normal distribution that correspond to the cumulative frequencies of the different categories of the ordinal dependent variable. Suppose an ordinal variable x coded from 1 to 4 has the following distribution: P(x = 1) = 0.1, P(x = 2) = 0.3, P(x = 3) = 0.5, and P(x = 4) = 0.1. The cumulated frequencies are then P(x ≤ 1) = 0.1, P(x ≤ 2) = 0.4, P(x ≤ 3) = 0.9, and P(x ≤ 4) = 1, and the corresponding Z-values of the standard normal distribution are: Z0.1 = −1.28, Z0.4 = −.25, Z0.9 = 1.28, and Z1 = ∞. For a given value of the original ordinal variable, the value of the “cardinalized” dependent variable is constructed by considering the expectation of a standard normally distributed variable under the condition that it is in the interval between those two Z-values that correspond to the class of the value of the original variable. In the above example, this means that cardinalized variable x c takes on the values:

$$ x_{c} = \left\{ {\begin{array}{*{20}l} {{E(Z|Z < - 1.28) = - \phi ( - 1.28)/\Upphi ( - 1.28)} \hfill} & {{{\text{if}}} \hfill} & {{{\text{x}} = 1} \hfill} \\ {{E(Z| - 1.28 < Z < - 0.25) = [\phi ( - 1.28) - \phi ( - 0.25)]/[\Upphi ( - 0.25) - \Upphi ( - 1.28)]} \hfill} & {{{\text{if}}} \hfill} & {{{\text{x}} = 2} \hfill} \\ {{E(Z| - 0.25 < Z < 1.28) = [\phi ( - 0.25) - \phi (1.28)]/[\Upphi (1.28) - \Upphi ( - 0.25)]} \hfill} & {{{\text{if}}} \hfill} & {{{\text{x}} = 3} \hfill} \\ {{E(Z|1.28 < Z) = \phi (1.28)/[1 - \Upphi (1.28)]} \hfill} & {{{\text{if}}} \hfill} & {{{\text{x}} = 4} \hfill} \\ \end{array} } \right., $$

where Z is a standard normal random variable, φ being the standard normal probability density function, and Φ being the standard normal cumulative density function, which leads to:

$$ x_{c} = \left\{ {\begin{array}{*{20}l} {{ - {\text{1}}{\text{.75}}} \hfill} & {{{\text{if}}} \hfill} & {{{\text{x = 1}}} \hfill} \\ {{ - {\text{.70}}} \hfill} & {{{\text{if}}} \hfill} & {{{\text{x = 2}}} \hfill} \\ {{{\text{.42}}} \hfill} & {{{\text{if}}} \hfill} & {{{\text{x = 3}}} \hfill} \\ {{{\text{1}}{\text{.75}}} \hfill} & {{{\text{if}}} \hfill} & {{{\text{x = 4}}} \hfill} \\ \end{array} } \right. $$

In principle, I follow this approach but I replace the Z-values from the standard normal distribution by the cutoff points from the ordered probit regression instead. I prefer this approach because it uses the information of the whole model and not only the frequency distribution of the dependent variable for the re-scaling.

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Cornelißen, T. The Interaction of Job Satisfaction, Job Search, and Job Changes. An Empirical Investigation with German Panel Data. J Happiness Stud 10, 367–384 (2009). https://doi.org/10.1007/s10902-008-9094-5

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Keywords

  • Job satisfaction
  • Job mobility
  • Job changes
  • Job search
  • Fixed effects