Aging, Changes, and Quality of Working Life

Abstract

The aim of this chapter is to examine whether company-level changes affect differentially the quality of working life according to employees’ age. We use the data from a French linked employer-employee survey. The quality of working life is captured through three dimensions: the feeling of fair work recognition, the opportunity to learn new things at work, and the feeling of work overload. We find that the changes in the use of ICTs and management tools have a less negative impact than expected on the quality of working life of older workers. Our indicator of fair work recognition is the only one that is found sensitive to changes, with a lower frequency in changing firms compared with inert ones. This difference is not explained by differences in observable characteristics of older workers employed in the two types of firm. It is rather due to a differing contribution of employment relations and work organization characteristics in shaping the balance between effort and reward in both contexts.

Appendices

Appendix A.1: Variables Included in the Analysis

10.1.1 A.1.1 Dependent Variables: Employee Level

• Feeling of fair work recognition: When the employee makes a balance of what she brings to the company and the benefits she gets back, she thinks that she is fairly recognized.

• Opportunities to learn new things at work: The employee’s job allows her to learn new things at work.

• Feeling of work overload: There are moments at work every day or at least once a week when the employee feels unable to cope or overloaded.

10.1.2 A.1.2 Independent Variables: Employer Level

• Employer-level changes: Categorical variable deriving from two composite indicators measuring, respectively, the cumulative implementation of management tools and of computer tools – ICT changes only, managerial changes only, ICT and managerial changes, and inertia.

• Company size: from 10 to 249, from 250 to 999, and more than 1,000 employees.

10.1.3 A.1.3 Independent Variables: Employee Level

10.1.3.1 A.1.3.1 Personal Characteristics

• Sex: male, female.

• Number of years in education: number of years in education starting from primary schools.

10.1.3.2 A.1.3.2 Employment Characteristics

• Fixed-term contract: The employee is on a fixed-term contract.

• Permanent contract: The employee is on a permanent contract.

• Part-time work: The employee works part time.

• Log of hourly wage: Log of the net daily wage divided by the usual number of daily hours.

• Job seniority and seniority squared: Computed from the declared year when the employee started to hold her current job.

10.1.3.3 A.1.3.3 Work Organization Characteristics

• Training: The employee has taken training courses in the company in 2003, 2004, or 2005.

• High work pace: The work pace of the employee is set by demands needing immediate response.

• Strict work targets: The employee has to achieve set work targets and has no latitude to change them.

• Strict quality procedures: The employee has to follow strict quality procedures.

• Technical support on the job: Since 2003, the employee’s colleagues or her boss showed her or gave her explanations about the operation of a slightly complex piece of machinery or the course of a slightly complex procedure or about how to deal with customers.

• Operational support: When facing a temporary excess workload or when having trouble doing a complicated task, the employee receives support either internal or external to the company.

• Continuous improvement: The employee or his colleagues have made, in the last 12 months, suggestions to improve operations, procedures, or machines, and they have been taken into account.

• Informal discussion: The employee is able to discuss informally what happens in the company with his colleagues.

• Change in colleagues: Over the past 12 months, some or most of the employee’s colleagues have changed.

• Change in used ICTs: Over the past 12 months, the employee’s computer equipment or software has changed.

• Unchanged use of ICTs: The employee uses ICTs, but equipment and software have not changed over the past 12 months.

• Nonuser of ICT: The employee does not use ICTs at work.

Appendix A.2: The Decomposition Method of Yun (2005)

In this chapter, we perform the nonlinear decomposition method proposed by Yun (2005) in order to decompose the observed fair work recognition gap between older workers in changing firms and their counterparts in inert firms into “explained” and “unexplained” components. This decomposition can be done at aggregate and detailed levels.

10.2.1 A.2.1 Aggregate Decomposition

The aggregate decomposition presents the same form as that of the traditional Oaxaca (1973) and Blinder (1973) decomposition in wage discrimination research. In fact, the difference in average probability of feeling fair work recognition ${\overline{I}}_j$ between older workers in changing firms (j = C) and their counterparts in inert firms (j = NC) can be expressed as

$$ {\overline{I}}_c-{\overline{I}}_{NC}=\left[\overline{\Phi \left({X}_C{\widehat{\beta}}_{NC}\right)}-\overline{\Phi \left({X}_{NC}{\widehat{\beta}}_{NC}\right)}\right]+\left[\overline{\Phi \left({X}_C{\widehat{\beta}}_C\right)}-\overline{\Phi \left({X}_C{\widehat{\beta}}_{NC}\right)}\right] $$

(A.2.1)

where X _j is a raw vector of individual, job, and employer characteristics for an older worker belonging to a firm of type j (j = C, NC). ${\widehat{\beta}}_j$ is the corresponding vector of coefficient estimates. Φ is the standard normal cumulative distribution function and “over bar” represents the value of sample’s average. The first term in square brackets corresponds to the part of the fair work recognition gap due to differences in the observed characteristics of older workers between the two types of firms (the aggregate characteristics effect or the “explained” component). It can be seen as the gap in the feeling of fair work recognition that would be observed if the impact of the observed characteristics was homogeneous depending on whether or not firms have experienced changes. The second term in square brackets represents the part due to differences in coefficients, i.e., differences in the behavioral responses of older workers to the observed characteristics depending on whether or not their firms have implemented changes (the aggregate coefficients effect or the “unexplained” component). This can be seen as the gap in the feeling of fair work recognition that would be observed if older workers had not differed in their observed characteristics according to the type of firms. The decomposition described in (A.2.1) is done at the aggregate level. We note that the explained part is computed with logit estimates obtained using the sample of older workers in inert firms. In this study, we are also concerned with decomposing the fair work recognition gap at a detailed level.

10.2.2 A.2.2 Detailed Decomposition

To evaluate the individual contribution of each characteristic included in the vector X to the overall gap, we use the following detailed decomposition equation suggested by Yun:

$$ \begin{array}{c}{\overline{I}}_c-{\overline{I}}_{NC}={\displaystyle \sum_{i=1}^K{W}_{\Delta X}^i}\left[\overline{\Phi \left({X}_C{\widehat{\beta}}_{NC}\right)}-\overline{\Phi \left({X}_{NC}{\widehat{\beta}}_{NC}\right)}\right]\\ {}+{\displaystyle \sum_{i=1}^K{W}_{\Delta \widehat{\beta}}^i}\left[\overline{\Phi \left({X}_C{\widehat{\beta}}_C\right)}-\overline{\Phi \left({X}_C{\widehat{\beta}}_{NC}\right)}\right]\end{array} $$

(A.2.2)

with ${W}_{\Delta X}^i=\frac{\left({\overline{X}}_C^i-{\overline{X}}_{NC}^i\right){\widehat{\beta}}_{NC}^i}{\left({\overline{X}}_C-{\overline{X}}_{NC}\right)\widehat{\beta}_{NC}}$, ${W}_{\Delta \widehat{\beta}}^i=\frac{{\overline{X}}_C^i\left({\widehat{\beta}}_C^i-{\widehat{\beta}}_{NC}^i\right)}{{\overline{X}}_C\left({\widehat{\beta}}_C-{\widehat{\beta}}_{NC}\right)}$, and ${\displaystyle \sum_{i=1}^K{W}_{\Delta X}^i={\displaystyle \sum_{i=1}^K{W}_{\Delta \widehat{\beta}}^i=1}}$.

The weights W ⁱ_ΔX and ${W}_{\Delta \widehat{\beta}}^i$ are, respectively, the individual relative contributions of characteristic i (i = 1,…, K) to the aggregate characteristics and coefficient effects.

Yun uses normalized regressions in computing weights in order to tackle the identification problem that occurs when the detailed decomposition of the aggregate coefficient effect is undertaken. In fact, normalized regressions have the advantage of being invariant to the “left-out” reference category in computing the contribution of dummy variables to the detailed coefficient effect. Moreover, the method of Yun overcomes the “path dependence” problem implying that in nonlinear decomposition, the independent contribution of one variable to the overall difference depends on the order in which the other variables are entered into the decomposition.