The acceptance of earnings losses after voluntary mobility

Schneck, Stefan Article The acceptance of earnings losses after voluntary mobility Economics: The Open-Access, Open-Assessment E-Journal Provided in Cooperation with: Kiel Institute for the World Economy – Leibniz Center for Research on Global Economic Challenges Suggested Citation: Schneck, Stefan (2011) : The acceptance of earnings losses after voluntary mobility, Economics: The Open-Access, Open-Assessment E-Journal, ISSN 1864-6042, Kiel Institute for the World Economy (IfW), Kiel, Vol. 5, Iss. 2011-2, pp. 1-47, https://doi.org/10.5018/economics-ejournal.ja.2011-2 This Version is available at: https://hdl.handle.net/10419/44436 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by-nc/2.0/de/deed.en Vol. 5, 2011-2|January 26, 2011 | http://dx.doi.org/10.5018/economics-ejournal.ja.2011-2 The Acceptance of Earnings Losses After Voluntary Mobility Stefan Schneck Leibniz Universität Hannover Abstract Because rational individuals know that they cannot always get what they want, they are assumed to make appropriate adjustments. However, little is known about trade-off reasoning in labor market mobility decision making. The objective of this paper is to analyze the effect of job-specific amenities on the decision to voluntarily accept wage cuts. Application of German household data reveals that voluntarily mobile workers are more likely to accept lower wages when strain can be improved. In other words, the considered mobile workers trade off amenities and monetary rewards when changing employers. JEL J24, J30, J62 Keywords Mobility; wage cut; quit Correspondence Stefan Schneck, Institute for Empirical Economics, Leibniz Universität Hannover, Königsworther Platz 1, D-30167 Hannover, email: [email protected] Citation Stefan Schneck (2011). The Acceptance of Earnings Losses After Voluntary Mobility. Economics: The OpenAccess, Open-Assessment E-Journal, Vol. 5, 2011-2. doi:10.5018/economics-ejournal.ja.2011-2. http://dx.doi.org/10.5018/economics-ejournal.ja.2011-2 © Author(s) 2011. Licensed under a Creative Commons License - Attribution-NonCommercial 2.0 Germany conomics: The Open-Access, Open-Assessment E-Journal 1 Introduction Today’s labor markets are characterized by a large degree of flexibility. Among a variety of aspects, labor market mobility contributes to this flexibility (OECD (1997)). In recent times, a growing strand of literature corroborates that a considerable fraction of workers are changing jobs at the cost of wage cuts. In Germany, a large number of workers are shown to be mobile toward lower wages. Fitzenberger and Garloff (2007) refer to establishment-to-establishment transitions during two successive years and show that more than one in five individuals are mobile with wage cuts. Jolivet et al. (2006) apply data from the European Community Household Panel Survey to reveal that 36.3% of job-to-job transitions in Germany are accompanied by wage cuts. The authors define job-to-job mobility as transitions without noticeable unemployment spells of less than one month. Transitions to lower wages are not a typical German phenomenon. In their cross-country analysis, Jolivet et al. (2006) show that almost one in five individuals is mobile to lower wages in Portugal and Belgium. The largest shares of wage cuts are observed in Denmark, France, and Germany. In these countries, more than 34% of mobile individuals suffer wage cuts in the period of mobility. In line with this result, Postel-Vinay and Robin (2002) show that more than one in three workers changing jobs directly did so at the cost of a wage cut. 1 For the United States, Jolivet et al. (2006) indicate that 23% of job-to-job transitions are to lower wages. 2 Nosal and Rupert (2007) utilize the Panel Study of Income Dynamics and show that about two in five individuals (voluntarily) change to lower wages. The results of these studies for different countries indicate that scientists should turn their attention to the reasons for mobility with wage cuts. This paper sets forth an analysis of the reasons for job-to-job mobility to lower wages with a special focus on changes in different (non-pecuniary) job characteristics after the transition. It utilizes the German Socio-Economic 1 Using French data, Postel-Vinay and Robin (2002) refer to direct mobility as job-to-job mobility with a maximum intervening unemployment spell of 15 days. 2 Using the Panel Study of Income Dynamics, the authors refer to job-to-job mobility when intervening unemployment, if any, does not exceed three weeks. www.economics-ejournal.org 1 conomics: The Open-Access, Open-Assessment E-Journal Panel (GSOEP in the following; see Wagner et al. (2007)), which includes questions on the reasons for job termination at the previous employer and surveys comparisons between both jobs. This is a major enhancement to previous papers because it allows one to determine whether workers voluntarily accept wage cuts in order to improve job-specific (non-wage) amenities. The paper is structured as follows. The next section illustrates briefly the basic framework and the research hypotheses. Section 3 describes the data set, main variables, and econometric models. I present the econometric results for the impact of subjective improvements in different job-specific characteristics on the decision to accept wage cuts in section 4. A conclusion is presented in section 5. 2 Framework and Research Questions Recent literature considers wage cuts a result of job termination. In Jolivet (2009), workers are allowed to change jobs directly to lower wages because their only alternative is non-employment. These transitions are referred to as job reallocations and are also mentioned in other studies (e.g., Jolivet et al. (2006)). Other theoretic approaches explain wage cuts as an investment in future wage growth (Connolly and Gottschalk (2008), Postel-Vinay and Robin (2002)). It is also reasonable to change to a new employer offering lower wages if the wage cut at the current employer had been larger (see, e.g., Shi (2009) or Mortensen and Pissarides (1994)). Schneck (2010) empirically suggests the prevalence of investments in future wage growth but also revealed that a substantial fraction of workers are mobile to permanently lower wages. Because workers are shown to accept lower wages on a permanent basis, other determinants are hypothesized to affect mobility decisions. For example, it is suggested that job-specific (non-wage) amenities affect the job choice (Nosal and Rupert (2007)). Economic and psychological literature, however, lack detailed information about the reasons for accepting lower wages. The basic idea of this paper proposes that differences in wages between two jobs might be balwww.economics-ejournal.org 2 conomics: The Open-Access, Open-Assessment E-Journal anced out by differences in (non-wage) job characteristics. Analogously to Rosen (1974, 1987) one could hypothesize that jobs consist of bundles of various characteristics with implicit, or hedonic, prices. Competent and self-supporting individuals, however, know that they cannot always get what they want, and that is the reason why they are expected to make appropriate adjustments. More specifically, individuals are expected to know that it is unlikely to find a better job with a higher wage, more flexible work time arrangements, and more job security right at their front door. It is important to analyze the extent of trade-off reasoning in the context of labor market mobility because ”Trade-off reasoning should be so pervasive and so well rehearsed as to be virtually automatic for the vast majority of the [...] population” (Tetlock (2000), p. 239). Here, I assume that workers only change jobs if the utility U of worker i at employer jin time texceeds the utility at the previous employer:3 Uijt > Ui,j−1,t−1(1) Workers are confronted with job offers which contain information on the wage and a set of various job-specific amenities. Wage offers of employer j are offered to worker i in t independently of the worker’s marginal willingness to avoid disamenities or to pay for amenities. Utility maximization implies that the worker changes employer if: Uijt(wage,amenities)> Ui,j−1,t−1(wage,amenities)(2) This paper concentrates on whether voluntary mobile workers accept a decrease in wages in exchange of an improvement in amenities. For this reason, the article mainly focuses on the theory of trade-off reasoning. The exclusive concentration on voluntary quits in the paper is assumed to assure that the drop in wages is compensated for by improvements in amenities. wageijt −wagei,j−1,t−1 | {z } − =U(amenitiesijt −amenitiesi,j−1,t−1) | {z } + (3) 3 Mobility costs are ignored. In addition, this paper is only responsive to short-term wage cuts which might pay off in the long-run. www.economics-ejournal.org 3 conomics: The Open-Access, Open-Assessment E-Journal P r(W age Cutijt = 1) = Uijt(improvement in amenities | {z } + ,S) P r(W age Cutijt = 1) = Φ(β0+β0improvement in amenitiesijt +δ0Sijt) (4) The hypothesis about trade-off reasoning is summarized in Equation (3). Worker i balances out improvements in job-specific amenities between two jobs and the wage decline when changing employer in period t . The probability to accept wage cuts, then, is expected to be positively affected by certain job-specific amenities. S summarizes further determinants which might affect the decision to accept lower wages. I tested the hypothesis by application of the probability model in which Φis the cumulative density function of the standard normal distribution. Evidence in favor of trade-off reasoning in mobility decisions is provided in case of a positive estimate for β . The following strategy to estimate the willingness to pay for amenities ( β ) exploits the preferences about wages and amenities that are revealed when workers change jobs voluntarily. 4 Precisely, utility-maximizing workers only change employers if job-specific amenities compensate for their loss in wages. In the following, I described the effects of the job-specific amenities, namely, ’flexible work schedules’, ’subjective job security against job loss’, ’promotion possibilities’, and ’strain’ on the probability to accept a wage cut. The paper addresses whether workers trade off improvements in strain and wages. Strain is shown to negatively affect individual satisfaction (see, e.g., Loscocco and Spitze (1990)). Cornelißen (2009) finds a negative effect 4 Note that selection of workers might bias the estimates. In regression analysis, biased estimates are obtained when unobserved determinants of the outcome and unobserved determinants of selection into the the sample are correlated. The correlation between unobservables, however, cannot be directly evaluated. I expect that my estimates rather provide an upper bound for the acceptance for wage cuts since the workers in my sample are indeed compensated for the loss in wages by amenities. Note that some workers might also be compensated for the improvement in job-specific amenities by sacrificing (large) wage markups. www.economics-ejournal.org 4 conomics: The Open-Access, Open-Assessment E-Journal of hard manual labor and stress (which are dimensions of job strain) on job satisfaction. According to Mobley (1977), dissatisfaction with a job is translated into thoughts of leaving the employer, evaluation of alternatives, and mobility because starting a new job is expected to result in a higher satisfaction. In fact, Judge (1993) shows that dissatisfied workers are more likely to quit than other individuals. Literature, however, lacks information on whether mobile individuals are willing to accept wage cuts in order to leave the dissatisfying job. This paper assesses whether individuals who expect decreasing strain when changing jobs are willing to accept lower wages. Analogous argumentation is expected to hold for improved job security by wage cuts because Cornelißen (2009) shows that satisfaction with the job is negatively affected by worries about (perceived) job security. Based on the question of Altonji and Paxson (1988) on whether workers are willing to sacrifice wage gains for better working hours when quitting a job, I ask whether workers are even willing to accept wage cuts for an improvement of work time regulations. The main reason for a special focus on the latter hypothesis is that individuals face a trade-off between time constraints and monetary rewards. To be more precise, if the current employer offers few possibilities for flexible leisure, then, working at a new employer with more flexible working schedules might be preferred despite lower wages. In other words, workers know that it is very problematic (almost impossible) to achieve the highest flexibility without paying a price for it. In addition, the possibilities for promotions at the new employer might affect the decision to accept wage cuts. Pfeifer and Schneck (2010) show that workers who change to higher relative wage positions compared to the previous establishment have, on average, a lower probability to change to lower wages. Workers who change to lower relative wage positions, in turn, likely suffer more wage cuts. For this reason, the authors do not present evidence in favor of trade-off reasoning in relative wage positions and wages. However, it is suggested that workers who change with wage cuts to a lower relative wage position might benefit from better chances for future promotions within the new firm. For this reason, it is argued that workers might pay for future promotion opportunities by wage cuts. www.economics-ejournal.org 5 conomics: The Open-Access, Open-Assessment E-Journal Usually workers evaluate these job-specific amenities before the transition. The data, in turn, refers to realized transitions with completed trade-off reasoning (a more detailed description of the data follows in the next section). For this reason, individual answers on the questions about subjective improvements in the new job might involve problems regarding cognitive dissonance reduction theory (Festinger (1957)). This particular theory describes that unpleasant arousal drives people to resolve the cognitive inconsistency. In other words, if two cognitions are discrepant, individuals simply change one to make it consistent with the other. Here, workers might act contrary to their attitude because of mobility to lower wages. As a consequence, these workers adjust their cognition about the job in a positive way to balance out this effect. In the underlying case, workers might change their attitude toward the new job in a positive way as a consequence from the decision to be mobile to lower wages. As a consequence, workers who accept wage cuts report to be more satisfied with the new job compared to workers changing without wage cuts. If this is true, the estimated coefficients on subjective comparisons (improvements) between the previous and the current job would be upwardly biased. A direct test of this possible critique cannot be conducted by application of the GSOEP. 3 Data and Procedure 3.1 Data This study utilizes the GSOEP household survey to examine the impact of job-specific amenities on the probability of being mobile with wage cuts. The main advantage of this data set stems from the fact that it includes subjective comparisons between the previous and current jobs. I restricted the analysis to German citizens who are employed full-time in two successive years during the period 1994–2007. The sample considers private sector employees with permanent contracts aged between 20 and 60 years. The www.economics-ejournal.org 6 conomics: The Open-Access, Open-Assessment E-Journal lower age boundary is chosen because the school degrees are usually achieved before 20 years of age.5 The data include annual information on the last monthly gross wage of individual i in period t (measured in Euros) which is applied in the consecutive analysis. I apply the consumer price index provided by the Statistisches Bundesamt Deutschland (annual averages, with year 2005 = 100) to deflate the wages. In addition, the questionnaire asks the ”How many hours are stipulated in your contract (excluding overtime)?” The corresponding information is utilized to calculate the hourly wage of individuals. The hourly wage ( wijt ) as well as the real hourly wage ( wreal ijt ) of individual iin period tat employer jare defined as follows: wijt =monthly wageijt 4.33∗contractual weekly working timeijt wreal ijt =deflated monthly wageijt 4.33∗contractual weekly working timeijt (5) Note that the GSOEP also includes information on overtime or the actual hours worked. I decided to concentrate on the contractual working hours because this measure is less affected by (cyclical or employer-specific) fluctuations. As the data are set up as a panel, information about the wage in the previous year is utilized to determine wage cuts and wage improvements. To examine the probability of wage cuts, a binary variable is constructed to illustrate whether individuals are mobile to lower wages or not:6 W age Cutijt =(1 mobility to lower wages (wit −wi,j−1,t−1<0) 0 mobility to higher wages (wit −wi,j−1,t−1≥0) W age Cutreal ijt =(1 mobility to lower wages (wreal it −wreal i,j−1,t−1<0) 0 mobility to higher wages (wreal it −wreal i,j−1,t−1≥0) (6) 5 I consider the years of education which is based on information provided by the GSOEP. 6 Wage information of the year 1993 is utilized to calculate the wage growth of mobile workers in 1994. I drop reported wages of zero. www.economics-ejournal.org 7 conomics: The Open-Access, Open-Assessment E-Journal willingness to pay for different amenities is estimated via application of OLS regression. The dependent variable describes the wage change while the set of control variables is identical to the one in the probit model discussed previously. w(real) ijt w(real) i,j−1,t−1 =a+b0Xijt +d0Sijt +uijt (8) Finally, the corner solution (tobit) estimation approach is applied which combines aspects of the binomial probit for the distinction of w(real) ijt w(real) i,j−1,t−1 ≥ 1and w(real) ijt w(real) i,j−1,t−1 < 1and the regression model for E [ w(real) ijt w(real) i,j−1,t−1 |Xijt,Sijt,w(real) ijt w(real) i,j−1,t−1 < 1]. w(real) ijt w(real) i,j−1,t−1 ∗ =e+f0Xijt +g0Sijt +vijt (9) workers assess their own aspiration levels best (Clark et al. (1996)). More than two in three mobile individuals are renters. The average education in years is between 12 and 13 years. Regional mobility plays a minor role by simple consideration of its frequency, since workers are shown to leave their federal state for a new job rarely. Only 4.75% of individuals perform cross-border transitions between federal states in Germany. A minority of mobile individuals life together with a partner (21.25%). About one in 20 transitions are from a blue-collar job to a white-collar job. It is also necessary to account for the workforce in the previous and the current firm (see, e.g., Brown and Medoff (1989)). 13.25% of individuals are leaving a firm with more than 2,000 employees while 17.13% of mobile workers are employed at a new firm with more than 2,000 employees. The following cross-table illustrates the number of observations by firm-size categories. Number of observations by firm-size Dummy variable for Dummy variable for workforcei,j,t >2,000 workforcei,j−1,t−1>2,000 0 1 Total 0 604 90 694 1 59 47 106 Total 663 137 800 www.economics-ejournal.org 14 conomics: The Open-Access, Open-Assessment E-Journal w(real) ijt w(real) i,j−1,t−1 =         w(real) ijt w(real) i,j−1,t−1 ∗ if w(real) ijt w(real) i,j−1,t−1 <1 0 if w(real) ijt w(real) i,j−1,t−1 ≥1 (10) Estimation of the corner solution model, then, allows to compute the marginal willingness to pay for different amenities by wage cuts, given that the individual changes to lower wages. 11 As the data are set up as a panel, I am able to make effort to control for unobserved individual heterogeneity. All the tests do not reject the null hypothesis of no individual heterogeneity. 12 Note that the analysis of this particular trade-off reasoning might be characterized by simultaneity in the acceptance of wage cuts and improvements in the new job. This problem might introduce problems regarding endogeneity. Note that one single endogenous regressor might seriously affect the results. One way to deal with this type of problem is to utilize a two-stage least square estimator, where I need to identify instrument variables. However, it is hard to find any variable which is partially correlated with subjective improvements between two jobs and exogenous in the decision to accept wage cuts. Given any simultaneity in trade-off reasoning, the following coefficients do not have a causal interpretation. 11 The tobit approach can be viewed as a special case of the so-called Heckman sample selection model (Heckman (1979)) when the selection equation and the regression equation are identical. One reason to refer to the tobit model is that it is problematic to define a reasonable selection equation because of a lack of literature on the acceptance of wage cuts in voluntary mobility decisions. 12 Precisely, different tests were conducted for the entire sample. A likelihood-ratio test was conducted in order to assess whether individual random-effects were evident in the probit model which explains whether a wage cut was accepted or not. The Breusch-Pagan Lagrange multiplier test (Breusch and Pagan (1980)) was applied to test for unobserved individual heterogeneity in the linear model on the wage change. Finally, a likelihood-ratio test was applied for the tobit model. The null hypothesis cannot be rejected in all cases. www.economics-ejournal.org 15 conomics: The Open-Access, Open-Assessment E-Journal 3.4 Specification This section concentrates on the choice of specification. As mentioned above, the data include a large set of dummy variables for subjective comparisons which can be included in Xijt . Note that some of the dummy variables of subjective comparisons between jobs are highly correlated. Table A3 presents the correlation coefficients where Spearman’s correlation and Tetrachoric correlations for binary variables are applied. Obvious problems regarding multicollinearity, however, are not revealed because of a maximum correlation coefficient of 0.6501 for a worsening in fringe benefits and a worsening in job security. Note that I abstract from Tetrachoric correlations of -1.000 between improvements and worsenings in job-specific amenities which are plausible because an improvement can never be associated of the same subjective comparison measure. Regarding the choice of specification, the match-specific component (comparison of use of skills) is included in all specifications because of its importance on the wage determination in economic literature. As discussed in the framework above, individual preferences about trade-off reasoning are also revealed when comparing flexible work schedules, strain, promotion chances, and perceived job security between the previous job and the current job. For this reason, these determinants are subject to the first (”preferred”) specification. In a further step, I extended the preferred specification by inclusion of dummy variables for subjective improvements and worsenings of fringe benefits, of commuting, and of the general job type. This specification, then, might be referred to as the full specification because all subjective comparisons (with exception of the subjective comparison of wages) are considered. Please note that subjective perceptions about the general job type and the use of skills are significantly correlated. 13 This suggests that both variables might describe the subjective change in the match quality when comparing the current job to the previous job. Fringe benefits might be 13 The corresponding Tetrachoric correlation equals 0.6173 for an improvement in the general job type and and better use of skills and is the third highest correlation coefficient in Table A3. For worse jobs in general and less use of skills, the correlation is similar (0.6066) and significant. www.economics-ejournal.org 16 conomics: The Open-Access, Open-Assessment E-Journal monetary amenities which are paid by the firm. For this reason, this measure might reflect some redeployment of wages rather than trade-off reasoning. However, a serious concern emerges in case of endogeneity in the decision to accept wage cuts and the change in commuting expenses. As mentioned above, this pattern seriously might affect the results. Nevertheless, the full specification is expected to provide a valuable robustness check of the results obtained by the preferred specification. In a next step, factor analysis is utilized in order to reduce the dimension from the multitude of dummy variables of subjective comparisons to a lower number of factors. Precisely, principal component factor analysis with orthogonal varimax rotation is conducted. The obtained factors are a set of independent and mutually orthogonal linear combinations of all of the subjective comparisons between the jobs. Because the choice of the number of factors is complex, one can rely on information criteria or one can search for solutions which are to be interpreted in an economically meaningful way. The Bayesian information criterion suggests considering six factors wherein the factor loadings can be meaningfully interpreted. The corresponding results are shown in Table 2. Table 2: Factor analysis with six factors Rotated factor loadings (pattern matrix) and unique variances Variable Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Uniqueness Interpretation of job job the factor working amenities match match amenities conditions improved improved commuting worsened worsened Strain ↓0.7224 0.0181 0.0862 0.0226 0.0176 0.0932 0.4609 Strain ↑-0.6747 0.0571 0.2635 -0.0341 0.3162 -0.0612 0.3671 Work time ↓0.6259 -0.1730 0.2254 0.1308 0.3259 0.0014 0.4042 Work time ↑-0.5616 0.4256 0.1224 -0.1563 0.0467 0.1743 0.4315 Fringe benefits ↑-0.1447 0.7672 0.0955 0.0173 0.0550 0.0103 0.3780 Job security ↑-0.0057 0.7061 0.1845 -0.0010 0.0774 -0.0601 0.4578 General job ↑-0.1250 0.0378 0.7325 -0.0086 -0.1662 0.0269 0.4179 Use of skills ↑0.0808 0.1743 0.6448 0.0061 -0.4248 0.0557 0.3638 Promotion chances ↑0.0718 0.1430 0.6134 0.0895 0.1598 -0.4008 0.4040 Commuting ↑-0.0590 0.0495 0.0760 -0.8640 0.0749 0.0420 0.2345 Commuting ↓0.0506 0.0533 0.1102 0.8457 0.0553 0.0812 0.2576 Use of skills ↓-0.1225 0.0290 -0.1259 -0.0536 0.7662 0.0595 0.3748 General job ↓0.2454 0.2133 -0.2175 0.0325 0.5831 0.2444 0.4462 Promotion chances ↓0.0350 0.1279 -0.1654 0.0354 0.0489 0.7731 0.3537 Job security ↓-0.0256 -0.3762 0.1573 0.0809 0.1791 0.5987 0.4359 Fringe benefits ↓0.2331 -0.4429 0.2563 -0.0766 0.1395 0.4473 0.4584 Method: principal component factors with orthogonal varimax rotation. Number of observations: 800. ↑describes improvements, ↓refers to subjective worsenings. www.economics-ejournal.org 17 conomics: The Open-Access, Open-Assessment E-Journal Table 2 presents the factor loadings which are used for interpretation of the six factors, where the bold numbers describe the highest loadings for the different factors. One can learn from the table that factor 1 is highly affected by subjective comparisons in strain and work time regulations. For this reason, these variables are used to assign the label to factor 1 because workload and work schedules are dimensions of job-specific working conditions. Analogously, factor 2 can be interpreted as an improvement in ’job amenities’, as fringe benefits and the perceived job security against job loss exhibit the highest factor loadings. Note that, for example, the factor loadings for an improvement in work time also loads high on the factor two. For this reason, a more flexible work schedule is suggested to affect factor 2 as well but is not directly included in the following interpretation of this particular factor. The remaining factors are defined in a similar way. The next step calculates the factor scores as proposed by Thomson (1951) which are applied in the last specification. Workers who report improvements in strain and work time are likely to have a negative factor score for ’working condition’, whereas workers who change to jobs with worse strain and worse work schedules are more likely to be associated with positive scores for factor 1. Workers who report an improvement in commuting never obtain a positive factor score for the factor ’commuting’, whereas workers reporting a worsening never obtain a negative value. Interpretation of the remaining factors is straightforward. Note that the determination of a set of factors allows a reduction in the dimensionality of the analysis but it can also hide what is going on at the disaggregated level. Therefore, inclusion of the factor scores into the estimation framework should only be viewed as a further robustness check.14 In sum, the analysis concentrates on three different sets of variables included in Xijt . The first specification refers to the variables mentioned in the framework. Precisely, subjective comparisons of the use of skills, flexible work schedules, strain, commuting, promotion chances, and perceived job security are subject to the first specification. The second specification 14 The following table shows the factor scores which have a mean close to zero and a standard deviation of one. www.economics-ejournal.org 18 conomics: The Open-Access, Open-Assessment E-Journal additionally accounts for comparisons in fringe benefits and the general job type. The set of variables in third specification contains the factor scores which are described above. 4 Results This section presents the results of the multivariate analysis. At first, I accounted for the specifications including the dummy variables for subjective comparisons between jobs. Table 3 presents the results for the probit estimation framework on whether workers accepted a wage cut when changing jobs. Note that the endogenous variable varies over specifications. Precisely, specifications (1) and (2) explain mobility to lower wages when accounting for gross wages, specifications (3) and (4) correspond to deflated gross wage cuts, and specifications (5) and (6) present the probit estimates for the subjective decline in wages. The link test for the corresponding probit models shows that the following specifications are satisfactory because ˆy2 is insignificant in all the test equations (see Ramsey (1969) for a comparable test). Regarding the above hypothesis of trade-off reasoning between subjective improvements in amenities and mobility decisions to lower wages, different specifications in Table 3 provide distinct insights. One can learn from specification (1) that individuals pay for an improvement in strain by lower wages. The coefficient is significant and positive, which implies that an improvement in strain compared to the previous job increases the Descriptive statistics: Factor scores Variable Mean Std. Deviation Minimum Maximum Factor score 1 1.39e-09 1 -2.314253 2.998188 Factor score 2 6.73e-10 1 -2.629714 3.765187 Factor score 3 7.42e-11 1 -1.978934 2.801363 Factor score 4 3.31e-10 1 -1.594935 1.68852 Factor score 5 -1.41e-09 1 -1.771901 4.142451 Factor score 6 -1.38e-10 1 -1.640562 5.140013 Number of observations: 800. www.economics-ejournal.org 19 conomics: The Open-Access, Open-Assessment E-Journal probability for voluntary mobility to lower wages. The estimated coefficient in specification (1) equals 0.0650, which can be interpreted in that an improvement in strain increases the probability for mobility to lower wages by 0.0650 percentage points. As a result, trade-off reasoning is evident. The effect is relatively robust to the inclusion of the remaining dummy variables for subjective comparisons between jobs included in the data (see specification (2)). The subjective evaluation of promotion opportunities also have sizable impact by considering the size as well as significance of the coefficients. Workers changing to a job with subjectively improved opportunities to climb up the hierarchy are less likely to accept wage cuts, whereas workers who change to worse future career prospects are more likely to suffer earnings losses. This result contradicts the ones obtained by Pfeifer and Schneck (2010), who report that a change in relative wage positions is positively correlated to a change in wages. Accordingly, transitions to lower relative wage positions which, in turn, increase future career prospects are accompanied by lower wages. The results obtained here, however, suggest that workers who change to jobs with better promotion opportunities are less likely to change to lower wages. The differences might stem from the definition of the variables in both studies: This study utilizes subjective comparisons between jobs which can be evaluated in a completely different manner when compared to an objective measure (the change in the relative wage position) as utilized in Pfeifer and Schneck (2010). The results for mobility to lower wages are basically comparable when deflated wages are considered. Specifications (5) and (6) in Table 3 show that subjective improvements in strain are also paid for by perceived wage cuts whereas the coefficients are basically comparable to the ones presented in specifications (1) and (2). This result reveals that trade-off reasoning between improvements in strain and wages is evident. Note that some of the coefficients for the subjective wage cut contradict the ones obtained for the objective measures for wage cuts. An interpretation for the different signs of the coefficient for a worsening in strain across specifications is that workers who are less satisfied with the current job have a higher probability to feel to be subjectively worse off in wages. A somewhat surprising result is that the match indicator variable does not www.economics-ejournal.org 20 conomics: The Open-Access, Open-Assessment E-Journal contribute any significant effect on the probability to accept wage cuts. Individuals, however, are less likely to suffer wage cuts when better use of skills is achieved in the new job compared to the previous one. For less use of skills, negative effects are found in specifications (1) to (4), while positive effects are revealed in specifications (5) and (6). An explanation for this result might be that workers who are not able to use all of their skills might feel bored, which possibly introduces dissatisfaction with wages or perceptions of earnings losses. The negative coefficients in specifications (1) to (4) are hardly to explain. It might be hypothesized that workers change to jobs where they are not able to use all of their skills, but instead apply one very special and highly paid skill. Thus, especially for highly qualified specialists, less use of skills also might reduce the probability of wage cuts. The effect of less security against a job loss is not robust across specifications. Interpretation, thus, is hardly to justify. Table 3 shows that improvements in commuting are likely to increase the probability of wage cuts whereas this effect is only statistically significant in specification (2). But the size of the coefficients advert to economic significance and, thus, reveal trade-off reasoning. The effect of a worsening in commuting expenses is not robust across specifications. More fringe benefits in the current job compared to the previous one significantly decrease the probability that workers suffer wage cuts. Note that, however, fringe benefits can also be included in the monthly payments, and thus, might be interpreted as monetary job-specific amenities. Workers who change to less fringe benefits perceive significant wage losses. This might be explained by habit-persistence, where workers get used to different amenities and react with strong negative perceptions in case amenities disappear. For the growth in the unemployment rate, I do not find any significant impact that confirms the considerations above. Cyclical fluctuations only have low impact on the acceptance of voluntary wage cuts. www.economics-ejournal.org 21 conomics: The Open-Access, Open-Assessment E-Journal Table 3: Probit model on whether workers accepted a wage cut (1) (2) (3) (4) (5) (6) Variables mobility to lower wages mobility to lower wagesreal subjective perception of wage loss Strain improved 0.0650* 0.0702* 0.0921** 0.0991** 0.0625** 0.0733*** (0.0373) (0.0375) (0.0405) (0.0405) (0.0245) (0.0240) Strain worsened -0.0310 -0.0312 -0.0601 -0.0624 0.0209 0.0132 (0.0423) (0.0423) (0.0468) (0.0466) (0.0312) (0.0262) Work time improved -0.0150 -0.0102 -0.0598 -0.0514 0.0206 0.0341 (0.0341) (0.0365) (0.0374) (0.0398) (0.0212) (0.0210) Work time worsened 0.0201 0.0251 0.0289 0.0252 0.0300 0.00716 (0.0510) (0.0534) (0.0541) (0.0563) (0.0353) (0.0267) Security against job loss improved 0.0270 0.0489 0.0211 0.0455 -0.00441 0.0138 (0.0350) (0.0377) (0.0384) (0.0409) (0.0194) (0.0186) Security against job loss worsened 0.0566 0.0535 -0.0163 -0.0398 0.0121 -0.0281 (0.0651) (0.0701) (0.0647) (0.0671) (0.0345) (0.0202) Use of skills improved -0.0351 -0.0233 -0.0386 -0.0341 -0.0167 -0.0120 (0.0336) (0.0357) (0.0372) (0.0393) (0.0183) (0.0172) Use of skills worsened -0.0426 -0.0476 -0.0365 -0.0419 0.00217 0.00150 (0.0451) (0.0455) (0.0518) (0.0521) (0.0244) (0.0228) Chances for promotion improved -0.0781** -0.0700** -0.0729** -0.0658* -0.0850*** -0.0751*** (0.0322) (0.0327) (0.0362) (0.0369) (0.0190) (0.0174) Chances for promotion worsened 0.194*** 0.199** 0.252*** 0.254*** 0.163*** 0.141** (0.0754) (0.0775) (0.0770) (0.0777) (0.0603) (0.0582) Commuting improved 0.0659* 0.0563 0.0101 (0.0385) (0.0422) (0.0200) Commuting worsened -0.00415 0.00282 0.0128 (0.0389) (0.0420) (0.0212) Fringe benefits improved -0.0591* -0.0688* -0.0402** (0.0354) (0.0402) (0.0169) Fringe benefits worsened -0.000826 0.0490 0.156*** (0.0546) (0.0624) (0.0530) Job improved -0.0322 -0.0142 -0.0202 (0.0354) (0.0388) (0.0178) Job worsened 0.0209 0.0165 -0.00512 (0.0872) (0.0945) (0.0349) Homeowner 0.0554* 0.0626* 0.0380 0.0437 0.0104 0.0162 (0.0332) (0.0334) (0.0363) (0.0365) (0.0197) (0.0186) Number of previous individual mobility -0.00117 -0.00318 0.0134 0.0124 0.00177 0.00265 (0.0168) (0.0167) (0.0187) (0.0188) (0.00873) (0.00773) Age 0.0339** 0.0352** 0.00842 0.0112 0.00872 0.0107 (0.0153) (0.0153) (0.0166) (0.0166) (0.00803) (0.00719) Age2-0.000344* -0.000358* -1.65e-05 -4.96e-05 -8.35e-05 -0.000115 (0.000203) (0.000203) (0.000220) (0.000220) (0.000104) (9.43e-05) Education in years -0.0174*** -0.0188*** -0.0100 -0.0114 -0.00484 -0.00547 (0.00672) (0.00660) (0.00732) (0.00731) (0.00387) (0.00352) Bluecollar to whitecollar transition 0.00507 0.00814 -0.0207 -0.0223 0.0259 0.0307 (0.0649) (0.0661) (0.0705) (0.0715) (0.0410) (0.0390) Male -0.00945 -0.00172 -0.0289 -0.0257 -0.0114 -0.0133 (0.0340) (0.0335) (0.0378) (0.0379) (0.0182) (0.0164) Partner 0.0118 0.0142 0.00971 0.0142 0.0226 0.0211 (0.0396) (0.0400) (0.0433) (0.0440) (0.0260) (0.0234) Firm more than 2,000 workers 0.0329 0.0516 0.0433 0.0667 -0.0145 -0.00529 (0.0433) (0.0451) (0.0468) (0.0486) (0.0219) (0.0213) Previous firm more than 2,000 workers -0.0170 -0.0226 0.00122 -0.00934 0.0631* 0.0413 (0.0464) (0.0455) (0.0524) (0.0517) (0.0356) (0.0295) Regional mobility -0.0603 -0.0748 -0.111 -0.128* 0.00281 -0.0169 (0.0663) (0.0624) (0.0724) (0.0699) (0.0430) (0.0303) Growth in unemployment rate 0.0174 0.0189 0.00139 0.00409 -0.00503 -0.00195 (0.0191) (0.0190) (0.0217) (0.0217) (0.0107) (0.00928) Number of observations 800 Pseudo R20.0698 0.0784 0.0519 0.0585 0.144 0.1939 Predicted Pr(y = 1 |¯x) 0.2276 0.2253 0.3093 0.3080 0.0663 0.0557 Marginal effects at ¯xare presented. All specifications are satisfactory by consideration of the link test because ˆy2is insignificant in all test equations. Robust standard errors clustered for 670 individuals in parentheses. *** p<0.01, ** p<0.05, * p<0.1 www.economics-ejournal.org 22 conomics: The Open-Access, Open-Assessment E-Journal Before turning the focus on the absolute and relative wage change, the six factors obtained via factor analysis described above are applied to check the robustness of the results. Table 4 shows that a better match quality significantly reduces the probability of the acceptance of earnings losses in all specifications. This might be explained by economic literature where the match quality is a main factor of wage determination. A worsening in job amenities is not suggested to be compensated for by higher wages. In fact, the reverse is true because individuals are significantly more likely to suffer lower wages at the new employer compared to the previous one. An interesting result is that the factors ’working conditions’ and ’improved job amenities’ significantly affect the subjective perception of wage cuts while insignificantly affecting the probability of an objective wage cut. The size of the coefficients, however, is comparable across specifications. It seems plausible that workers with improved job amenities are significantly less likely to perceive worsenings in wages because of general satisfaction with the job which also might result in more satisfaction with wages. Remember that it is not straightforward to interpret the factor score for working conditions because it includes subjective worsenings and improvements of strain and work time regulations. For this reason, I omit interpretation of this factor. In sum, Tables 3 and 4 reveal that workers accept lower wages for improved strain. The remaining coefficients are, to the largest extent, imprecisely measured by consideration of the standard errors or are not consistent with the hypothesis of trade-off reasoning in mobility decisions. Promotion opportunities are shown to have a robust and highly significant effect on the probability of mobility to lower wages. The estimates, however, reveal no evidence in favor of trade-off reasoning as hypothesized above. The results, furthermore, contradict the ones presented in Pfeifer and Schneck (2010), which might be reasoned by different definitions of the measures for future career prospects. Pfeifer and Schneck (2010) use an objective measure for the change in promotion opportunities, whereas this study applies a subjective measure which depends on individual perceptions. Evidence on the basis of the factor scores (which potentially hide the mechanisms on the less aggregated level) do not support the hypothesis of trade-off reasoning between wages and job amenities in mobility decisions as well. In fact, www.economics-ejournal.org 23 conomics: The Open-Access, Open-Assessment E-Journal that worse career prospects in the new job compared to the previous one enforce larger wage cuts in Euros for males than for females. Investigation of the relative wage change reveals the opposite because, on average, females pay for worse career prospects more than males. The dummy variable for homeowners in Table 5 suggests that homeowners are significantly worse off when compared to renters with identical characteristics. Distinction between homeowners and renters reveals that better workload is traded off by renters. For homeowners, the estimated coefficient of this particular measure is reverse. Precisely, homeowners are able to obtain (insignificant) wage markups for improvements in strain. When considering workers who are younger than the median age (younger than 34 years), improvements in strain are paid for by wage cuts. Older workers, in turn, are shown to be almost unaffected for better workload. An interesting result is that the coefficient for less job security in the sample of young workers is positive (and economically as well as statistically significant) while it is negative for older workers. Therefore, for workers below the median age, Table A4 indicates an especially pronounced compensating wage differential for low job security. During booms (∆ u≤ 0), the effect of a subjective worsening of promotion opportunities in the current job compared with the previous one is economically as well as statistically significant. A possible interpretation of the negative coefficient might be that mobility to worse future career prospects during booms signals low own career ambitions, where employers impose a penalty for this type of signal. During recessions, voluntarily mobile workers who are able to improve the match quality achieve considerable wage markups. www.economics-ejournal.org 30 conomics: The Open-Access, Open-Assessment E-Journal Table 6: OLS regression results on wage change (trimmed sample) (1) (2) (3) (4) (5) (6) (7) (8) Variables wijt wi,j−1,t−1 wreal ijt wreal i,j−1,t−1 wijt −wi,j−1,t−1wreal ijt −wreal i,j−1,t−1 Strain improved -0.00537 -0.00753 -0.00774 -0.01000 0.00596 -0.0186 -0.0435 -0.0684 (0.0125) (0.0126) (0.0123) (0.0125) (0.145) (0.149) (0.156) (0.160) Strain worsened 0.00819 0.0102 0.0118 0.0136 0.119 0.144 0.145 0.171 (0.0154) (0.0153) (0.0154) (0.0154) (0.175) (0.175) (0.189) (0.189) Work time improved 0.0146 0.0161 0.0140 0.0148 -0.0471 -0.0337 0.0809 0.0957 (0.0122) (0.0129) (0.0121) (0.0128) (0.146) (0.154) (0.155) (0.164) Work time worsened -0.00464 0.00106 -0.00657 -0.000587 -0.183 -0.126 -0.0929 -0.0314 (0.0157) (0.0166) (0.0157) (0.0166) (0.189) (0.199) (0.199) (0.210) Security against job loss improved 0.00977 0.00640 0.00794 0.00409 0.172 0.132 0.139 0.100 (0.0119) (0.0124) (0.0118) (0.0123) (0.142) (0.149) (0.151) (0.157) Security against job loss worsened 0.0165 0.0322 0.0149 0.0310 0.514* 0.678** 0.417 0.586* (0.0256) (0.0266) (0.0251) (0.0262) (0.293) (0.311) (0.316) (0.338) Use of skills improved 0.0279** 0.0276** 0.0246** 0.0249** 0.317** 0.320** 0.319** 0.318* (0.0118) (0.0125) (0.0116) (0.0123) (0.138) (0.152) (0.149) (0.164) Use of skills worsened 0.00804 0.0156 0.00801 0.0151 0.0249 0.0656 0.0254 0.0713 (0.0172) (0.0183) (0.0170) (0.0180) (0.197) (0.206) (0.212) (0.221) Chances for promotion improved 0.0140 0.0136 0.0165 0.0161 0.145 0.148 0.0820 0.0834 (0.0123) (0.0123) (0.0120) (0.0120) (0.145) (0.146) (0.155) (0.156) Chances for promotion worsened -0.0542** -0.0476** -0.0550** -0.0486** -0.660** -0.615** -0.759** -0.707** (0.0230) (0.0227) (0.0225) (0.0224) (0.307) (0.310) (0.327) (0.331) Commuting improved -0.0331** -0.0296** -0.310* -0.318* (0.0141) (0.0139) (0.161) (0.171) Commuting worsened -0.0186 -0.0164 -0.245 -0.215 (0.0138) (0.0137) (0.160) (0.175) Fringe benefits improved 0.00947 0.0114 0.0907 0.0891 (0.0139) (0.0139) (0.166) (0.176) Fringe benefits worsened -0.0307 -0.0314* -0.292 -0.312 (0.0191) (0.0189) (0.238) (0.259) Job improved 0.00635 0.00455 0.0753 0.0728 (0.0118) (0.0117) (0.150) (0.160) Job worsened -0.0295 -0.0284 -0.0933 -0.131 (0.0335) (0.0330) (0.382) (0.418) Homeowner -0.00680 -0.00691 -0.00522 -0.00542 -0.0934 -0.0994 -0.0558 -0.0615 (0.0119) (0.0118) (0.0117) (0.0117) (0.136) (0.136) (0.149) (0.150) Number of previous individual mobility -0.00213 -0.00189 -0.000310 -0.000297 0.0130 0.0123 -0.00656 -0.00730 (0.00553) (0.00549) (0.00530) (0.00535) (0.0723) (0.0733) (0.0773) (0.0782) Age -0.00733 -0.00792 -0.00766 -0.00840 -0.0438 -0.0476 -0.0484 -0.0522 (0.00552) (0.00545) (0.00546) (0.00539) (0.0643) (0.0651) (0.0690) (0.0695) Age28.17e-05 8.82e-05 8.83e-05 9.69e-05 4.98e-04 5.39e-04 5.18e-04 5.59e-04 (7.46e-05) (7.40e-05) (7.37e-05) (7.31e-05) (8.82e-04) (8.94e-04) (9.48e-04) (9.56e-04) Education in years 0.000233 0.000637 0.000533 0.000920 0.0369 0.0392 0.0274 0.0297 (0.00245) (0.00244) (0.00241) (0.00240) (0.0294) (0.0294) (0.0316) (0.0316) Bluecollar to whitecollar transition 0.00874 0.00418 0.0107 0.00679 0.241 0.203 0.309 0.274 (0.0224) (0.0224) (0.0219) (0.0219) (0.260) (0.265) (0.282) (0.287) Male 0.0182 0.0184 0.0174 0.0180 0.355*** 0.356*** 0.422*** 0.422*** (0.0116) (0.0116) (0.0114) (0.0115) (0.130) (0.132) (0.139) (0.142) Partner -0.00315 -0.00276 -0.00149 -0.00120 -0.0397 -0.0366 -0.0389 -0.0343 (0.0136) (0.0133) (0.0135) (0.0132) (0.156) (0.156) (0.167) (0.167) Firm more than 2,000 workers 0.0159 0.00882 0.0113 0.00423 0.310 0.231 0.323 0.243 (0.0165) (0.0166) (0.0161) (0.0162) (0.202) (0.205) (0.220) (0.224) Previous firm more than 2,000 workers 0.00393 0.00588 0.00379 0.00631 0.0136 0.0357 0.0582 0.0791 (0.0171) (0.0170) (0.0168) (0.0167) (0.200) (0.200) (0.219) (0.219) Regional mobility 0.0401 0.0514* 0.0399 0.0511* 1.073*** 1.141*** 1.097*** 1.170*** (0.0291) (0.0287) (0.0291) (0.0288) (0.375) (0.370) (0.389) (0.385) Growth in unemployment rate -0.00643 -0.00635 -0.00592 -0.00598 -0.0725 -0.0731 -0.0370 -0.0382 (0.00677) (0.00669) (0.00675) (0.00668) (0.0810) (0.0821) (0.0864) (0.0876) Constant 1.229*** 1.249*** 1.210*** 1.232*** 1.246 1.436 1.340 1.525 (0.0960) (0.0948) (0.0951) (0.0937) (1.106) (1.123) (1.175) (1.189) Number of observations 640 Number of individuals 544 545 551 550 R20.0508 0.0678 0.0479 0.0638 0.0781 0.0884 0.0707 0.0802 Robust standard errors clustered for individuals in parentheses. *** p<0.01, ** p<0.05, * p<0.1 www.economics-ejournal.org 31 conomics: The Open-Access, Open-Assessment E-Journal Table 7: OLS regression results on wage change (factor scores) (1) (2) (3) (4) (5) (6) (7) (8) complete sample trimmed sample Variables wijt wi,j−1,t−1 wreal ijt wreal i,j−1,t−1 wijt−wreal ijt −wijt wi,j−1,t−1 wreal ijt wreal i,j−1,t−1 wijt−wreal ijt − wi,j−1,t−1wreal i,j−1,t−1wi,j−1,t−1wreal i,j−1,t−1 Scores for factor 1 0.0155 0.0149 0.175 0.187 -0.00264 -0.00101 0.00347 0.00676 (working conditions) (0.0131) (0.0129) (0.157) (0.165) (0.00545) (0.00539) (0.0657) (0.0696) Scores for factor 2 0.00322 0.00330 0.00497 0.00203 0.0103* 0.0102* 0.0646 0.0762 (job amenities improved) (0.0127) (0.0124) (0.144) (0.154) (0.00607) (0.00599) (0.0714) (0.0766) Scores for factor 3 0.0150 0.0146 0.296* 0.304 0.0157*** 0.0146*** 0.182*** 0.170** (match improved) (0.0147) (0.0144) (0.178) (0.192) (0.00566) (0.00560) (0.0669) (0.0721) Scores for factor 4 0.0275* 0.0269* 0.358** 0.370** 0.00593 0.00556 0.0394 0.0465 (commuting) (0.0141) (0.0138) (0.175) (0.188) (0.00518) (0.00513) (0.0617) (0.0665) Scores for factor 5 -2.35e-05 0.000165 0.0113 0.00915 -0.00693 -0.00655 -0.0734 -0.0812 (match worsened) (0.0118) (0.0116) (0.138) (0.145) (0.00573) (0.00567) (0.0658) (0.0718) Scores for factor 6 -0.00717 -0.00702 -0.136 -0.136 -0.0102* -0.0108* -0.0880 -0.0935 (job amenities worsened) (0.0143) (0.0141) (0.167) (0.178) (0.00564) (0.00555) (0.0706) (0.0759) Homeowner -0.0442** -0.0427* -0.623* -0.699* -0.00551 -0.00371 -0.0716 -0.0322 (0.0224) (0.0220) (0.364) (0.382) (0.0118) (0.0117) (0.137) (0.149) Number of previous -0.0207* -0.0205** -0.104 -0.123 -0.00334 -0.00163 -0.0120 -0.0319 individual mobility (0.0105) (0.0104) (0.160) (0.167) (0.00548) (0.00537) (0.0731) (0.0785) Age -0.0185 -0.0180 -0.110 -0.112 -0.00643 -0.00666 -0.0265 -0.0326 (0.0135) (0.0133) (0.176) (0.184) (0.00549) (0.00545) (0.0657) (0.0710) Age20.000190 0.000183 0.00112 0.00106 6.99e-05 7.53e-05 0.000279 0.000316 (0.000175) (0.000172) (0.00250) (0.00260) (7.43e-05) (7.37e-05) (0.000902) (0.000976) Education in years 0.00877* 0.00860* 0.259*** 0.260*** 0.000618 0.000939 0.0405 0.0301 (0.00501) (0.00493) (0.0708) (0.0737) (0.00240) (0.00237) (0.0293) (0.0315) Bluecollar to whitecollar -0.0311 -0.0294 -0.436 -0.378 0.00554 0.00746 0.204 0.279 transition (0.0374) (0.0369) (0.422) (0.448) (0.0222) (0.0218) (0.263) (0.287) Male -0.000506 -0.000380 0.278 0.255 0.0165 0.0162 0.321** 0.386*** (0.0301) (0.0295) (0.332) (0.359) (0.0115) (0.0114) (0.130) (0.139) Partner -0.00375 -0.00321 -0.00659 0.00266 -0.00163 0.000418 -0.0298 -0.0212 (0.0378) (0.0370) (0.400) (0.433) (0.0136) (0.0135) (0.156) (0.166) Firm more than 2,000 -0.00470 -0.00468 -0.0469 -0.103 0.0123 0.00796 0.304 0.296 workers (0.0311) (0.0307) (0.544) (0.569) (0.0164) (0.0160) (0.202) (0.219) Previous firm more than 0.00208 0.00180 -0.194 -0.283 0.00644 0.00678 0.0151 0.0616 2,000 workers (0.0434) (0.0428) (0.606) (0.647) (0.0171) (0.0168) (0.198) (0.218) Regional mobility 0.0985 0.0975 0.406 0.432 0.0478* 0.0473 1.151*** 1.177*** (0.0749) (0.0733) (0.840) (0.882) (0.0290) (0.0289) (0.372) (0.389) Growth in unemployment -0.0121 -0.0114 -0.237 -0.211 -0.00641 -0.00589 -0.0691 -0.0320 rate (0.0145) (0.0142) (0.189) (0.196) (0.00672) (0.00671) (0.0809) (0.0867) Constant 1.493*** 1.467*** 0.887 0.980 1.236*** 1.213*** 1.162 1.309 (0.245) (0.241) (2.796) (2.936) (0.0958) (0.0950) (1.129) (1.202) Number of observations 800 640 Number of individuals 670 544 545 551 550 R20.0439 0.0439 0.0485 0.0456 0.0476 0.0434 0.0669 0.0615 Robust standard errors clustered for individuals in parentheses. *** p<0.01, ** p<0.05, * p<0.1 www.economics-ejournal.org 32 conomics: The Open-Access, Open-Assessment E-Journal Finally, I conducted tobit (corner solution) estimation in order to explain the willingness to pay for improvements in job-specific amenities, given the probability that the individual changes to lower wages. As a consequence, interpretation of the marginal effects in Table 8 is based on the condition that workers voluntarily changed to lower wages. The table reveals that workers significantly pay for improvements in strain. The corresponding marginal effects suggest that workers with wage cuts pay for better workload by an average of about 1.2% or 30 to 36 Cent, respectively. For worse strain, a statistically insignificant as well as economically small moderating effect on the wage cut is estimated. As above, the tobit model confirms positive effects for both indicators of the match quality. This implies that subjectively better or worse matches do not reduce wages when changing jobs. The effects of promotion opportunities are also robust to the results in the OLS regressions. Better promotion opportunities mitigate wage cuts while worse career prospects in the new job compared to the previous one increase wage cuts. It is also confirmed that workers seem to pay for less commuting expenses by earnings losses whereas the effect is comparable to the one of better workload. Inconsistencies can be found for the effects of subjective perceptions about job security. Table 9 presents the results for the tobit model when considering the factor scores. Workers changing to lower wages, on average, pay for less commuting expenses by lower wages. The factor score, however, reveals an economically small effect because, on average, less than 0.55% or an average maximum of 15 Cent are paid for the improvement in commuting. This effect is considerably smaller compared to the one presented in Table 7 but reveals robustness of this particular coefficient. A further similarity to the results in the OLS regressions is the moderating effect on wage cuts if the match quality in the current job is better than in the previous job. A worsening in job amenities is paid for by lower wages. This result is highly robust when compared to Table 7. Albeit highly statistically significant, the effect is of small economic significance. It might be argued that workers changing to worse job amenities are changing to some sort of low-pay sector with dead-end jobs, low job stability (or low job security), and low (or inexistent) fringe benefits. This result is also consistent with the ”segmented www.economics-ejournal.org 33 conomics: The Open-Access, Open-Assessment E-Journal Table 9: Tobit regression results for wage cut, given that individuals change to lower wages (factor scores) (1) (2) (3) (4) Variables wijt wi,j−1,t−1 wreal ijt wreal i,j−1,t−1 wijt −wi,j−1,t−1wreal ijt −wreal i,j−1,t−1 Scores for factor 1 0.00352 0.00297 0.0575 0.0478 (working conditions) (0.00242) (0.00225) (0.0569) (0.0555) Scores for factor 2 4.03e-05 0.000575 0.0272 0.0446 (job amenities improved) (0.00249) (0.00237) (0.0592) (0.0589) Scores for factor 3 0.00512* 0.00425* 0.151** 0.132* (match improved) (0.00267) (0.00238) (0.0752) (0.0690) Scores for factor 4 0.00533** 0.00402* 0.121* 0.0905 (commuting) (0.00251) (0.00230) (0.0716) (0.0668) Scores for factor 5 -0.000386 -0.000560 -0.0128 -0.0138 (match worsened) (0.00236) (0.00217) (0.0540) (0.0514) Scores for factor 6 -0.00662*** -0.00551*** -0.129** -0.102** (job amenities worsened) (0.00215) (0.00206) (0.0507) (0.0509) Homeowner -0.00843 -0.00536 -0.292* -0.222 (0.00545) (0.00500) (0.158) (0.147) Number of previous individual mobility -0.00139 -0.00261 -0.0248 -0.0629 (0.00286) (0.00267) (0.0714) (0.0682) Age -0.00418 -0.00113 -0.115* -0.0386 (0.00260) (0.00228) (0.0589) (0.0516) Age23.82e-05 1.41e-06 0.00110 0.000176 (3.48e-05) (3.07e-05) (0.000767) (0.000693) Education in years 0.00357*** 0.00245** 0.0606** 0.0350 (0.00115) (0.00102) (0.0280) (0.0262) Blue-collar to white-collar transition -0.00414 -0.00234 -0.0135 0.0505 (0.0117) (0.0111) (0.251) (0.243) Male -0.000965 0.00102 -0.0698 -0.0236 (0.00538) (0.00487) (0.133) (0.125) Partner 0.000133 0.00122 0.00757 0.0351 (0.00643) (0.00572) (0.154) (0.143) Firm more than 2,000 workers -0.00473 -0.00490 -0.179 -0.196 (0.00746) (0.00674) (0.234) (0.228) Previous firm more than 2,000 workers -0.00263 -0.00428 -0.140 -0.205 (0.00891) (0.00802) (0.261) (0.253) Regional mobility -0.00215 0.00176 0.0255 0.138 (0.0157) (0.0148) (0.359) (0.350) Growth in unemployment rate -0.00320 -0.00127 -0.0705 -0.0260 (0.00319) (0.00283) (0.0771) (0.0710) Number of observations 800 Uncensored observations 195 254 195 254 Censored observations 605 546 605 546 Pseudo R20.1071 0.0939 0.0270 0.0169 Log-likelihood -229.8 -208.5 -852.7 -1037.3 Marginal effects after tobit regression. Robust standard errors clustered for 670 individuals in parentheses. *** p<0.01, ** p<0.05, * p<0.1 www.economics-ejournal.org 34 conomics: The Open-Access, Open-Assessment E-Journal Table 8: Tobit regression results for wage cut, given that individuals change to lower wages (1) (2) (3) (4) (5) (6) (7) (8) Variables wijt wi,j−1,t−1 wreal ijt wreal i,j−1,t−1 wijt −wi,j−1,t−1wreal ijt −wreal i,j−1,t−1 Strain improved -0.0122* -0.0128** -0.0128** -0.0136** -0.297* -0.318** -0.327** -0.355** (0.00638) (0.00636) (0.00565) (0.00563) (0.152) (0.153) (0.142) (0.142) Strain worsened 0.00240 0.00261 0.00395 0.00426 0.0178 0.0223 0.0553 0.0624 (0.00742) (0.00734) (0.00684) (0.00676) (0.207) (0.206) (0.204) (0.203) Work time improved 0.00175 0.00154 0.00515 0.00467 0.142 0.146 0.237 0.231 (0.00593) (0.00621) (0.00535) (0.00555) (0.147) (0.164) (0.146) (0.161) Work time worsened 0.00117 0.00109 0.000170 0.00108 0.0893 0.0715 0.0702 0.0787 (0.00770) (0.00783) (0.00669) (0.00671) (0.193) (0.195) (0.175) (0.176) Security against job loss improved -0.00664 -0.00964 -0.00600 -0.00886 -0.113 -0.180 -0.100 -0.170 (0.00580) (0.00631) (0.00526) (0.00569) (0.138) (0.155) (0.132) (0.149) Security against job loss worsened -0.00132 -0.000167 0.00540 0.00808 -0.0284 -0.0213 0.153 0.207 (0.00861) (0.00922) (0.00775) (0.00802) (0.214) (0.224) (0.205) (0.207) Use of skills improved 0.00784 0.00594 0.00734 0.00618 0.279* 0.210 0.278* 0.227 (0.00577) (0.00590) (0.00521) (0.00540) (0.155) (0.150) (0.146) (0.144) Use of skills worsened 0.0119* 0.0132* 0.0109* 0.0123** 0.318* 0.333** 0.316** 0.334** (0.00691) (0.00688) (0.00615) (0.00613) (0.164) (0.166) (0.150) (0.151) Chances for promotion improved 0.0117** 0.0105* 0.0101* 0.00908* 0.185 0.148 0.148 0.117 (0.00571) (0.00573) (0.00521) (0.00526) (0.149) (0.150) (0.143) (0.144) Chances for promotion worsened -0.0303** -0.0291** -0.0315*** -0.0297*** -0.709** -0.696** -0.765*** -0.738*** (0.0118) (0.0119) (0.0112) (0.0113) (0.288) (0.289) (0.279) (0.276) Commuting improved -0.0117* -0.00967* -0.306 -0.264 (0.00634) (0.00568) (0.196) (0.182) Commuting worsened 0.00126 9.43e-05 0.0112 -0.0210 (0.00615) (0.00552) (0.156) (0.147) Fringe benefits improved 0.00880 0.00849 0.178 0.186 (0.00608) (0.00559) (0.155) (0.152) Fringe benefits worsened -0.000998 -0.00540 0.0100 -0.116 (0.00851) (0.00762) (0.202) (0.190) Job improved 0.00412 0.00215 0.184 0.133 (0.00560) (0.00516) (0.150) (0.141) Job worsened -0.00778 -0.00790 -0.0816 -0.0718 (0.0134) (0.0127) (0.293) (0.281) Homeowner -0.00851 -0.00974* -0.00601 -0.00687 -0.294* -0.323** -0.241 -0.262* (0.00544) (0.00543) (0.00499) (0.00499) (0.156) (0.160) (0.147) (0.150) Number of previous -0.00149 -0.00114 -0.00240 -0.00215 -0.0246 -0.0158 -0.0544 -0.0477 individual mobility (0.00284) (0.00281) (0.00265) (0.00265) (0.0704) (0.0709) (0.0673) (0.0683) Age -0.00438* -0.00456* -0.00150 -0.00183 -0.118** -0.118** -0.0446 -0.0500 (0.00258) (0.00257) (0.00228) (0.00228) (0.0584) (0.0576) (0.0519) (0.0522) Age24.00e-05 4.20e-05 5.30e-06 9.31e-06 0.00112 0.00112 0.000234 0.000293 (3.46e-05) (3.44e-05) (3.07e-05) (3.08e-05) (0.000764) (0.000760) (0.000699) (0.000709) Education in years 0.00330*** 0.00354*** 0.00223** 0.00245** 0.0575** 0.0628** 0.0328 0.0377 (0.00114) (0.00111) (0.00100) (0.000988) (0.0277) (0.0272) (0.0257) (0.0254) Bluecollar to whitecollar -0.00360 -0.00388 -0.00177 -0.00177 0.0170 -0.0138 0.0817 0.0589 transition (0.0117) (0.0117) (0.0110) (0.0110) (0.249) (0.253) (0.238) (0.241) Male 0.00104 -0.000409 0.00282 0.00193 -0.0123 -0.0537 0.0306 0.00300 (0.00536) (0.00528) (0.00485) (0.00484) (0.132) (0.131) (0.125) (0.126) Partner -6.26e-05 -0.000431 0.000465 -6.04e-05 0.0106 0.000696 0.0231 0.00743 (0.00636) (0.00636) (0.00565) (0.00568) (0.152) (0.153) (0.141) (0.143) Firm more than 2,000 -0.00409 -0.00682 -0.00427 -0.00701 -0.159 -0.220 -0.171 -0.236 workers (0.00734) (0.00736) (0.00665) (0.00667) (0.226) (0.229) (0.219) (0.221) Previous firm more than -0.00370 -0.00260 -0.00544 -0.00401 -0.159 -0.146 -0.227 -0.204 2,000 workers (0.00888) (0.00861) (0.00799) (0.00780) (0.263) (0.254) (0.254) (0.247) Regional mobility -0.00440 -0.00116 -0.000589 0.00243 -0.0383 0.0388 0.0745 0.149 (0.0163) (0.0156) (0.0154) (0.0148) (0.370) (0.356) (0.363) (0.350) Growth in unemployment rate -0.00297 -0.00322 -0.00111 -0.00147 -0.0704 -0.0746 -0.0262 -0.0337 (0.00320) (0.00316) (0.00285) (0.00284) (0.0775) (0.0770) (0.0714) (0.0718) Number of observations 800 Uncensored observations 195 254 195 254 Censored observations 605 546 605 546 Pseudo R20.1199 0.1354 0.1217 0.1380 0.0305 0.0350 0.0230 0.0262 Log-likelihood -226.5 -222.5 -202.1 -198.4 -849.6 -845.7 -1030.9 -1027.5498 Marginal effects after tobit regression. Robust standard errors clustered for 670 individuals in parentheses. *** p<0.01, ** p<0.05, * p<0.1 www.economics-ejournal.org 35 conomics: The Open-Access, Open-Assessment E-Journal labor market” in Villanueva (2007), where wage penalties are attached to job-specific disamenities. To sum up, trade-off reasoning, as hypothesized above, is a key feature of the acceptance of wage cuts. The results show that subjectively better workload is paid for by lower wages. I am, however, not able to find distinct support for the hypothesis that workers trade off improvements in work time arrangements, better security against job loss, and the acceptance of lower wages. There is weak (mostly statistically insignificant) evidence in favor of compensating wage differentials for worse strain and for less security against a job loss. In addition, the hypothesis that workers pay for better career prospects by wage cuts cannot be supported in this paper. In fact, the reverse is suggested because better promotion opportunities are accompanied by higher wages. For worse promotion opportunities, individuals are not compensated for by higher wages. The findings on some of the job-specific amenities differ when considering subjective perceptions about worsenings in wages instead of using objective measures for wage cuts. This might be driven by cognitive dissonance reduction where workers adjust their perceptions about the job in a positive way to resolve cognitive dissonance introduced by mobility to lower wages. 5 Discussion This paper investigates the relationship between subjective improvements between two jobs and voluntary mobility to lower wages. This allows to assess the impact of trade-off reasoning on individual labor market decisions. The results suggest that job-specific (non-wage) amenities affect the job choice. More specifically, workers are shown to voluntarily accept wage cuts when improvements in strain can be achieved. The loss of utility through decreasing wages is, thus, compensated for by an increase in utility through improvements in job-specific amenities in the new job. Besides, the paper reveals evidence in favor of compensating wage differentials for less security against job loss. www.economics-ejournal.org 36 conomics: The Open-Access, Open-Assessment E-Journal The results also have important implications for employers. Offering non-wage amenities can attract workers of competitors who pay higher wages. This implies that those employers who offer, for example, activities to decrease job-specific strain are suggested to attract employees of competitors despite lower wages. Since Schneck (2010) showed that transitions to permanently lower wages are common, it might be hypothesized that workers trade off permanent lower wages with subjective improvements in certain job-specific characteristics. This study shows that less strain is a potential candidate for the acceptance of downward mobility. www.economics-ejournal.org 37 conomics: The Open-Access, Open-Assessment E-Journal Appendix Table A1: Frequencies of subjective comparisons between old and new job How would you judge your present position compared to your last one? In what ways has it improved, stayed the same, or worsened Variable improved stayed the same worsened Length of commute to and from work 280 274 246 Work load (strain) 269 367 164 Work schedule regulations (work time) 349 324 127 Security against job loss 276 471 53 Chances for promotion 348 402 50 General Job type 461 306 33 Fringe benefits 280 428 92 Wages 521 203 76 Are you able to use your professional skills and abilities today more, about the same, or less than in your previous position? more about the same less (improved) (stayed the same) (worsened) Use of skills 328 370 102 Number of observations 800 www.economics-ejournal.org 38 conomics: The Open-Access, Open-Assessment E-Journal Table A2: Descriptive statistics of the control variables Mean Standard Deviation Subjective improvement in Work load (strain) 0.3363 0.4727 Work schedule regulations (work time) 0.4363 0.4962 Security against job loss 0.3450 0.4757 Use of skills 0.4100 0.4921 Commuting 0.3500 0.4773 Chances for promotion 0.4350 0.4961 Fringe benefits 0.3500 0.4773 Job type 0.5763 0.4945 Subjective worsening in Work load (strain) 0.2050 0.4040 Work schedule regulations (work time) 0.1588 0.3657 Security against job loss 0.0663 0.2489 Use of skills 0.1275 0.3337 Commuting 0.3075 0.4617 Chances for promotion 0.0625 0.2422 Fringe benefits 0.1150 0.3192 Job type 0.0413 0.1990 Dummy variable for homeowners 0.3063 0.4612 Number of individual quits 1.7500 0.8992 Age 35.0725 7.9551 Age21293.2850 593.8163 Education (in years of schooling) 12.7719 2.5192 Dummy variable for blue-collar to white-collar 0.0575 0.2329 Dummy variable for males 0.6475 0.4780 Dummy variable for partner 0.2125 0.4093 Dummy variable for workforceijt>2,000 0.1713 0.3770 Dummy variable for workforcei,j−1,t−1>2,000 0.1325 0.3392 Dummy variable for regional mobility 0.0475 0.2128 Growth in unemployment rate -0.0571 0.7854 Number of observations 800 Number of individuals 670 www.economics-ejournal.org 39 conomics: The Open-Access, Open-Assessment E-Journal Mortensen, D. 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