The Impact of Age on the Reservation Wage: The Role of Employment Efficacy and Work Intention

Abstract

This study explores the relationship between age and reservation wage. The authors investigate whether individuals’ attitudes toward employment, that is, their “employment efficacy” and “work intention,” mediate this relationship. The authors examine this in the Belgian labor market, where substantial differences exist between blue-collar workers, white-collar workers, and civil servants regarding payment systems, employment protection, and pension benefits. Path analysis on a sample of 22,796 Belgian workers aged 18 to 60 years reveals a reverse U-shaped relationship between age and the reservation wage via employment efficacy and a U-shaped relationship via work intention. In addition, study analyses also show a direct relationship between age and the reservation wage. The effects vary with employment status. The authors discuss implications for theory, practice, and future research.

Keywords

reservation wage aging employment efficacy work intention labor supply

In most Western economies, wage costs rise with age (Organisation for Economic Cooperation and Development [OECD], 2006, 2009) and may restrict older individuals’ employment prospects, especially if higher wages do not reflect higher productivity (Adler & Hilber, 2009; Skirbekk, 2003). In this study, we investigate how individuals’ age affects their wage-setting behavior. If wage demands increase with age, older individuals may maneuver themselves into an adverse labor market position. More specifically, we examine how the reservation wage, that is, an individual’s minimum wage that would have to be offered in order for him or her to accept a job (McFadyen & Thomas, 1997; Nattrass & Walker, 2005), evolves with age among workers aged 18 to 60 years. Despite the relevance for late career issues, the reservation wage concept receives little attention in the literature on the aging workforce. The few studies that investigate the reservation wage include age as a control variable. They find divergent results and do not add additional variables explaining the relationship with age. In this study, we include two factors that may mediate the relationship between age and reservation wage, namely employment efficacy and work intention.

Whereas most studies on reservation wages examine primarily the impact of alternative financial incomes, like unemployment benefits (e.g., Addison, Centeno, & Portugal, 2008) or financial wealth (e.g., Bloemen & Stancanelli, 2001), we include two factors put forward by labor supply theory and job search theory (Killingsworth, 1983; Mortensen, 1986). Employment efficacy relates to individuals’ perceived capability to find a job (Wanberg, Zhu, & van Hooft, 2010). Work intention refers to a person’s readiness to (continue to) work (see Ajzen, 2006). Although labor supply theory and job search theory (Killingsworth, 1983; Mortensen, 1986) stress the importance of employment efficacy and work intention in determining the reservation wage, empirical studies hardly include these factors. Moreover, research concludes that both factors relate to age (Hurd, 1996; Wittekind, Raeder, & Grote, 2010). Therefore, it is useful to study employment efficacy and work intention as mediators in the relationship between age and reservation wage.

Studying age-related changes in the reservation wage is important among both employed and unemployed (Nattrass & Walker, 2005). Both groups may need to position themselves in the labor market and determine the wage at which they want to accept a (new) job offer. Workers may, for instance, face job loss or want to change jobs. In this article, we focus on the employed. We concentrate on the Belgian labor market for two reasons. First, in Belgium earnings rise more steeply with age compared to most OECD countries (OECD, 2006), thereby strengthening the perception that older workers are relatively expensive. Second, structural differences exist between blue-collar workers, white-collar workers, and civil servants concerning payment systems, employment protection, and public pension benefits. Thus, workers’ employment status may influence their position in the labor market. We will investigate how age influences wage claims among workers with a different employment status, using path analysis on a sample of 22,796 Belgian workers aged 18 to 60 years.

The article starts with a discussion of the Belgian context. Next we discuss the reservation wage concept and its relationship with age. We hypothesize how work intention and employment efficacy may evolve with age and affect the reservation wage. After presenting the methodology and results, we discuss the main findings and the key implications of the research.

Belgian Context

Belgium is one of the countries experiencing substantial fiscal pressure due to its population aging. The employment rate among individuals aged 50 or older is far below the European average. Consequently, it is considered important to increase their labor market participation and discourage early exit. Therefore, it is highly relevant to study how individuals’ reservation wage evolves with age in the Belgian labor market.

The Belgian wage-bargaining system is highly centralized, resulting in a relatively homogeneous wage structure that is not directly related to worker’s productivity (European Foundation for the Improvement of Living and Working Conditions [Eurofound], 2010; International Monetary Fund [IMF], 2012). The wage structure mainly aims to preserve external competitiveness compared to wage developments in the three main trading partners (France, Netherlands, and Germany). The national agreement between social partners sets the framework for subsequent bargaining at industry and company level. Agreements at lower level may only improve on what has been negotiated at a higher level.

In Belgium the seniority premium is among the highest in Western Europe, making older workers relatively expensive as earnings rise steeply with age (Mastrobuoni & Taddei, 2011; OECD, 2006). Yet important structural differences exist between different types of workers regarding payment systems, employment protection, and public pension benefits.

In the private sector, Belgian law differentiates blue-collar workers from white-collar workers based on the manual or intellectual nature of the job, respectively (Erkes, 2011). Research on delayed payment contracts states that the nature of a job may affect the wage structure (Hutchens, 1986, 1989; Lazear, 1979). When it is difficult for employers to monitor employees’ effort, for example, in intellectual jobs, employers and employees may have implicit contracts whereby workers are underpaid initially yet overpaid at the end of the contract to discourage shirking and malfeasance. Consequently, delayed payment systems, like seniority wages, are more common among white-collar workers than among blue-collar workers (OECD, 2006). Blue collar workers are mostly paid on an hourly basis, whereas white collar workers typically receive a monthly salary. In the public sector, civil servants’ wages are set according to fixed pay scales determined by legislation and pay increases are largely based on tenure (Van Guys, 2008). Thus, seniority wages are more common among white-collar workers and civil servants yet rarely used among blue-collar workers.

Another important difference concerns employment protection, namely, the length of notice periods (Erkes, 2011). In the private sector, blue-collar workers generally get a 28-day notice period if they served the company for less than 20 years and 58 days if they served for more than 20 years. White-collar workers are (at least) entitled to a notice period of 3 months for every 5 years of tenure. As blue-collar workers are also entitled to lower severance pay than white-collar workers, the difference between both statuses is substantial. For civil servants, employment protection is based on the traditional principle of “lifetime employment.” They can only be dismissed in defined cases, like poor performance. So whereas blue-collar workers are far less protected than white-collar workers, civil servants have the highest protection.

The Belgian pension system is mainly based on public pension benefits. Pension benefits are generally available from the age of 65. Yet while pension benefits are 60% of the average wage over the entire career for the private sector, they are based on the average salary of the last 5 years in the public sector (IMF, 2012). Consequently, public pension benefits are most generous for civil servants. Differences between white-collar and blue-collar workers are less pronounced as private sector arrangements apply to both categories.

The Belgian context is highly relevant to study the relationship between age and the reservation wage. The payment system, employment protection, and pension benefits may influence the relationship between age and individual wage claims considerably. These factors differ substantially among blue-collar workers, white-collar workers, and civil servants. Therefore, we study the impact of age on the reservation wage separately for these three groups.

Theoretical Background and Hypotheses

Reservation Wage

The reservation wage is a key concept in labor economics. In labor supply theory, the reservation wage is a cutoff. Individuals are assumed to only participate in the labor market at wages above their reservation wage (Killingsworth, 1983). According to Mortensen’s job search theory, the probability of finding employment is a product of the probability of getting a job offer and the probability of accepting an offer. The reservation wage is the main factor determining whether a job offer is accepted or not (Mortensen, 1986). The reservation wage property states that it is optimal for an individual to accept an offer and stop searching when the highest offered wage exceeds the reservation wage (Bloemen & Stancanelli, 2001; McFadyen & Thomas, 1997). The concept is widely applicable and is used to study the labor market behavior of employed, unemployed, and inactive persons (Hui, 1991).

Some authors argue that the reservation utility rather than the reservation wage should be used. Blau (1991) for instance found that the number of working hours matters when evaluating job offers. This may particularly hold for older individuals (Maestas & Li, 2006) since they are more likely to have health issues or stronger preferences to spend time with their family (Gauthier & Smeeding, 2003; Greller & Simpson, 1999). The reservation wage decision rule can be extended to other nonpecuniary job characteristics like job status, working conditions, job security, and distance to work (Bradley & Taylor, 1992; Devine & Kiefer, 1993; McFadyen & Thomas, 1997). However, the reservation utility property is rarely used in empirical research since it is very complex to study. Since wage costs play a key role in late career issues, we strictly focus on the reservation wage in the remainder of this article.

The Relationship Between Age and the Reservation Wage

Several studies found a relationship between age and the reservation wage. Yet results are ambiguous. Most studies found a positive effect of age on the reservation wage (Addison, Centeno, & Portugal, 2004; Christensen, 2001; Gorter & Gorter, 1993; Walker, 2003) among both employed and unemployed persons, although workers set higher reservation wages than unemployed (Nattrass & Walker, 2005). Some studies however, found a reverse U-shaped relationship: The reservation wage increases until individuals are in their 30s and then declines with age (Bloemen & Stancanelli, 2001; Prasad, 2003). However, another study of Prasad (2000) revealed a U-shaped relationship: The reservation wage declined until the age of 53 and then increased with age.

Overall, most studies include age as a control variable and do not explain its effect on the reservation wage. Furthermore, results are inconsistent and empirical studies hardly investigate the nature of the relationship. Therefore, we add to the research by examining whether age relates to the reservation wage in a curvilinear way and by studying two factors that may act as mediators.

Employment Efficacy and Work Intention

The number of empirical studies on determinants of the reservation wage is limited (Addison et al., 2008). Existing literature usually studies the impact of alternative sources of income, like unemployment benefits (Addison et al., 2008) or financial wealth (Bloemen & Stancanelli, 2001). However, labor supply theory also points to the importance of individuals’ preference for working over nonworking (i.e., leisure; Killingsworth, 1983). Individuals choose not to participate in the labor market if their value of leisure time exceeds the offered wage rate. This attitude toward working versus nonworking determines the minimum wage at which someone is willing to supply labor (i.e., reservation wage). Furthermore, labor supply theory assumes that perceived work opportunities also influence a person’s reservation wage (Killingsworth, 1983). This reasoning matches the principles of value-based pricing (Hinterhuber, 2004) stating that individuals may set their price in the labor market according to the perceived value of their labor to future employers.

This simultaneous importance of the attitude toward working and the perceived opportunities available also exists in research on turnover (March & Simon, 1958), job search (Wanberg et al., 2010), employability (Forrier & Sels, 2003), and work role transitions (Forrier, Sels, & Stynen, 2009). On the basis of these distinctions, we include two variables in our model. The first variable reflects the preference for working over nonworking. We call it “work intention,” that is, individuals’ general readiness to (continue to) work in case of job loss (see Ajzen, 2006). Work intention reflects individuals’ perceived desirability to (continue to) supply their labor. The second variable concerns the perceived opportunities in the labor market. Following Wanberg et al. (2010), we call it “employment efficacy,” that is, individuals’ confidence in their ability to find an acceptable job. Employment efficacy refers to the demand they perceive for their labor given their profile. Individuals scoring high on work intention do not necessarily score high on employment efficacy. People may be very willing to supply labor yet have limited or very specialized knowledge, skills, and competencies and, therefore, perceive few opportunities. Or vice versa, individuals perceiving a lot of possibilities in the labor market may still prefer nonworking over working. Both concepts influence individuals’ decision to supply labor.

We use a two-step approach to argue why work intention and employment efficacy may help explain how age influences the reservation wage in a curvilinear way. After discussing how they may evolve with age, we argue how both factors may relate to the reservation wage. Figure 1 shows our conceptual model.

Figure 1.

The research model.

Age Affects Employment Efficacy and Work Intention

Employment efficacy

Research among workers (Rothwell & Arnold, 2007; Wittekind et al., 2010) and the entire labor force (Berntson, Sverke, & Marklund, 2006) indicates that people’s confidence to find new employment decreases with age due to individual as well as structural factors (Berntson et al., 2006).

Individuals may perceive fewer chances to find a job as they age. Greller and Stroh (2004) found that persons aged 50 and older are often not aware of their career opportunities. In addition, health issues may become more prevalent. For instance, declining physical abilities may keep people from doing jobs requiring physical strength (Greller & Simpson, 1999; Nuñez, 2010).

Structural factors also explain why employment efficacy is likely to decline with age. Age norms, that is, consistent perceptions within organizations concerning the career stage one should have reached at a particular age (Lawrence, 1996), can narrow down the options people consider available as they age. Furthermore, age-related stereotypes and discrimination may create barriers to employment opportunities as people age (Lahey, 2010; Okoye & Obikeze, 2005; Posthuma & Campion, 2009; Taylor & Walker, 1998). If job seekers expect negative attitudes of future employers toward older applicants, their perceived alternatives in the labor market may decrease with age.

Although previous studies concluded that employment efficacy decreases with age, they did not verify whether age affects employment efficacy in a nonlinear way. Still, individuals extend their human capital throughout their career by accumulating experience. So, their labor market value increases according to human capital theory (Becker, 1964). Although this suggests a positive relationship between age and employment efficacy, experience concentration theory (Thijssen, 1992; Thijssen & Rocco, 2010) stresses that it is necessary to distinguish the quantity from the diversity of experience. Although the quantity of experience most likely rises with age, its diversity is likely to decrease as experience may become concentrated in specific work domains. Consequently, due to experience concentration, employment efficacy may decline as workers get older.

This reasoning suggests that employment efficacy initially increases with age as individuals accumulate experience but then stagnates or decreases because of experience concentration, individual factors like health issues, or structural factors like age norms or age-related stereotypes. Therefore, we assume a reverse U-shaped relationship between age and employment efficacy.

Hypothesis 1: The relationship between age and employment efficacy is reverse U-shaped.

Work intention

Levinson’s model of life development (Levinson, 1986; Levinson, Darrow, Klein, Levinson, & McKee, 1978) stipulates that individuals go through a sequence of age-related stages, each of which is characterized by specific activities and psychological adjustments. People in the early adult stages are expected to explore both life and career preferences and to keep their options open. As individuals reach the settling-down period of the mid-late 30s, they are assumed to perceive pressure to advance and strive for professional accomplishment. After this stage of high employment commitment, individuals are hypothesized to reconsider the importance of work and focus more on their family. Likewise, Super’s (1957) model of career stages stipulates that individuals initially explore their options. In doing so, they may not only consider employment but also other positions like education. After this exploratory phase, people are expected to experience a higher need for career achievement until they reach the decline stage, in which they are more likely to withdraw from their career. These theories thus suggest a reverse U-shaped relationship between age and work intention.

As work environments have become more dynamic, more recent models no longer assume a single set of career stages over the lifecycle (Hall & Mirvis, 1995) but expect people to be mobile and develop boundary-less careers (Arthur & Rousseau, 1996). However, Copin and Vandenbrande (2007) found that multiple European labor markets are still characterized by low job mobility. They concluded that Belgian workers have among the lowest job mobility and among the highest organizational tenure. They also concluded that younger individuals are most inclined to change jobs voluntarily supporting the supposition that individuals initially explore their options. Moreover, several factors suggest withdrawal behavior as people approach the retirement age. First, the value attributed to leisure time increases with age (Kooij, de Lange, Jansen, & Dikkers, 2008; Soidre, 2005). As people age and face a shorter time horizon, they may prefer to use the “time left” for hobbies (Carstensen, 1998; Higgs, Mein, Ferrie, Hyde, & Nazroo, 2003) or to spend it with their family, especially if the partner is retired (Gauthier & Smeeding, 2003; Pienta, 2003). Second, older individuals may perceive less financial pressure to work as they may experience a lower economic need to work (Bloemen & Stancanelli, 2001; Loi & Shultz, 2007; Wanberg, Hough, & Song, 2002). This may cause working to lose part of its appeal. Finally, work may become harder as individuals age due to declining functional abilities (Hurd, 1996). Since people become more likely to avoid situations harming their self-concept as they age (Kanfer & Ackerman, 2004), their work intention may decrease. Consequently, the age-related career stages of Super’s model still seem to be useful to understand career dynamics. Hence, we assume a reverse U-shaped relationship between age and work intention.

Hypothesis 2: The relationship between age and work intention is reverse U-shaped.

Work Intention and Employment Efficacy Affect the Reservation Wage

Figure 1 shows that work intention and employment efficacy are both hypothesized to influence the reservation wage yet in opposite way.

Employment efficacy

A few studies have examined how individuals’ perceived reemployment chances influence their reservation wage. Although Walker (2003) found that difficulties job searchers expect when looking for work do not affect the reservation wage, Christensen (2001) and Pannenberg (2007) concluded that unemployed job searchers set a higher reservation wage when expecting good reemployment chances.

We hypothesize that reservation wages relate positively to a person’s employment efficacy. Consistent with labor supply theory (Killingsworth, 1983) and value-based pricing (Hinterhuber, 2004), the idea of having a high “market value” may make one believe it is justified to demand a higher wage, that is, to set a higher reservation wage. Since employment efficacy relates to the perception of moving easily in the labor market, a higher employment efficacy is likely to strengthen the belief of having a strong negotiating position with future employers. This may result in higher wage claims. In addition, if people are convinced they have multiple alternatives, they will target the higher-paying jobs (Mortensen, 1986).

In contrast, people perceiving difficulties to find employment may be more inclined to lower their reservation wage to increase their chance of finding a job. Being aware of their weak labor market situation, they may moreover expect few job offers and intend to accept the first offer they get, even if this implies settling with a lower-paying job.

Hypothesis 3: Employment efficacy relates positively to the reservation wage.

Work intention

Empirical studies on reservation wages have not yet studied work intention as a determinant. We assume a negative relationship between work intention and the reservation wage. Based on job search theory, people having a low willingness to work may be more selective in their job search and choose to price themselves out of the labor market by setting an unrealistically high reservation wage (Christensen, 2001; Hogan, 2004; Nattrass & Walker, 2005). Conversely, individuals with a higher work intention may set a lower reservation wage, allowing them to broaden the range of acceptable jobs and increase the probability of an acceptable offer.

Hypothesis 4: Work intention relates negatively to the reservation wage.

Differences Among Blue-Collar Workers, White-Collar Workers, and Civil Servants

As indicated in the discussion of the Belgian context, seniority wages are common among civil servants and white-collar workers but not among blue-collar workers. Civil servants’ pay increases are largely based on tenure, which is transferable between functions in the public sector. Consequently, they may perceive pay increases as being age-related since they accumulate throughout the career. For white-collar workers, increasing tenure is also one of the reasons for pay increase. Yet tenure is not necessarily accumulated across employers. Hence, a similar mechanism may be in order but to a smaller extent. In the absence of seniority wages, blue-collar workers may be less likely to have this mindset. Consequently, we expect the strongest relationship between age and reservation wage among civil servants, and the weakest among blue-collar workers.

When employment protection is high, employers may not only be less inclined to fire employees but also less triggered to hire new employees (OECD, 2006). Since more employment protection increases the gap between insiders and outsiders in the labor market (Lindbeck & Snower, 1989), people may expect more difficulties to find a new job with higher protection. Since employment protection increases with tenure for blue-collar and white-collar workers, and the latter have more protection, we expect a stronger relationship between age and employment efficacy for white-collar workers. Civil servants work in a culture of lifelong employment where career progression is strongly related to tenure and, in this case, age. Therefore, age norms are predominant in the public sector and may narrow down the options people consider available to them when aging. This, together with the strong insider–outsider gap due to the very high employment protection, leads us to expect the strongest relationship between age and employment efficacy for civil servants.

Status may also moderate age-related changes in work intention. Since civil servants work in a culture of lifelong employment, their career is most likely to follow the states set by Super (1957). Consequently, we expect the strongest relationship between age and work intention among this group. Moreover, the more generous the public pension benefits, the less financial pressure people may experience to continue to work as they age. Consequently, when losing their job, civil servants may feel less need than blue-collar and white-collar workers to find employment, since their financial situation once retired is less likely to be problematic due to the generous pension benefits. The longer notice periods and more generous severance pay of white-collar workers compared to blue-collar workers may decrease their pressure to find employment. Moreover, these entitlements increase with tenure. Therefore, we expect the relationship between age and work intention to be stronger among white-collar workers than among blue-collar workers.

Taking the above assumptions together, we formulate the following hypothesis:

Hypothesis 5: Age effects are stronger among white-collar workers than among blue-collar workers yet most pronounced among civil servants.

Methods

Sample and Data

We use data from Belgium’s largest cross-sectional wage survey among employees. In May and June 2008 we invited all Belgian wage earners to fill in an online questionnaire through two widespread weekly job magazines, one published in Dutch and targeting the Flemish population, the other primarily serving French-speaking Belgians, and their websites. The two job magazines organize a similar cross-sectional wage survey every 2 years. The 2006 data were used in a study by Theunissen et al. (Theunissen, Verbruggen, Forrier, & Sels, 2009).

Participation in the study was voluntary. Presurvey instructions made it clear that the research was aimed at active employees, excluding anyone who was not working for an employer in April 2008 (such as students, unemployed, self-employed, and retired people). To boost participation, two substantial cash prizes (equivalent to the winner’s monthly wage) were randomly awarded after the data collection. The Research Centre of Organisation Studies (KU Leuven, Belgium) collected the data, which are not publicly available. The survey contained multiple modules, of which one was dealing with reservation wages. In sum, 29,155 respondents aged 18 to 60 years completed this module. We excluded workers older than 60 from the sample since (a) 59.5 is the average age to leave the labor market in Belgium (see data for 2010 from the National Institute of Statistics), and (b) (re)employment opportunities of Belgian workers aged 60 or more are minimal (European Commission, 2010; OECD, 2006). We excluded part-time workers (3,334 respondents) since their wage cannot be compared directly with that of full-time workers. Only studying full-time workers allows us to keep the reported wages pure. After removing outliers on the reservation wage question (see Hair, Anderson, Tatham, & Black, 1998), 24,262 respondents remained in the sample. Another 1,466 observations were omitted due to missing information on at least one of the control variables (list-wise deletion in structural equation modeling [SEM]). The final sample consists of 22,796 observations.

The sample consists of 2,363 blue-collar workers (10%), 17,360 white-collar workers (77%), and 3,073 civil servants (13%; Table 1). The average age of the participants is 39 years; 51% are aged between 18 and 34 years, 39% are aged between 35 and 49, 10% are aged 50 years or older. The sample consisted predominantly male participants (62%); 8% of the respondents did not have a high school degree, 23% had a high school degree, and 69% had a bachelor’s or master’s degree. The composition of the sample of white-collar workers is similar to that of the total sample. The sample of the blue-collar workers has more male respondents and respondents with low education levels, whereas a larger proportion of the civil servants are aged 50 or above. These proportions do not fully reflect the actual composition of the 18- to 60-year-old Belgian workforce (see data for 2007 from the National Institute of Statistics). Both the youngest age group and the highly educated are overrepresented in the sample. Women, blue-collar workers, and civil servants are underrepresented. Since the sample’s composition deviates from the composition of the Belgian labor force, we used weighted data in the reported analyses.

Table 1.

Sample Characteristics.

	Full sample	Blue-collar workers	White-collar workers	Civil servants
	N = 22,796	n = 2,363	n = 17,360	n = 3,073
Age
18-34 (%)	51	37	51	40
35-49 (%)	39	51	39	40
50+ (%)	10	12	10	20
Gender (% male)	62	85	62	58
Level of education
No high school degree (%)	8	39	8	8
High school degree (%)	23	51	23	25
Higher education (%)	69	10	69	67

Measures

Reservation wage

Consistent with studies looking into self-reported reservation wages among employed individuals (Nattrass & Walker, 2005), we measured the reservation wage as follows:

“Suppose you would lose your job and you would receive an offer for a job with the same number of working hours and the same benefits as your current job, how high should the net monthly salary at least have to be for you to accept a concrete job offer?”

This question is also consistent with measures used in studies examining reservation wages of unemployed individuals (Bloemen & Stancanelli, 2001; Gorter & Gorter, 1993; Haurin & Sridhar, 2003).

First, we asked respondents to indicate whether this net monthly wage (a) could be below their current net monthly wage, (b) had to be equal to their current net monthly wage, or (c) had to exceed their current net monthly wage. This gave an impression of the extent to which people hold on to their current net monthly wage. It moreover gave respondents a framework for reflecting on the level of their reservation wage. Then, people indicating the second option were automatically given their current net monthly wage—reported earlier in the questionnaire—as their reservation wage. The others were asked to report a wage in Euros.

In the analyses, we used the logarithm of the reservation wage (in Euros) to control for scale effects resulting from the wide variation in the reservation wage variable compared to other variables.

Age

Age was measured as a continuous variable (in years) ranging from 18 to 60. Since the linear and the quadratic age variables are highly correlated, we followed Aiken and West (1991) by centering the age variable around its mean and using the centered age and age-square variables in the analyses to solve the problem of multicollinearity. As a result, in the analyses, ages below the age of 39 have a negative sign, whereas ages above age 39 are positive.

Work intention

Work intention was assessed using an index. We developed two items and added the scores. The respondents had to indicate on a 5-point scale (1 = strongly disagree to 5 = strongly agree) what they would do if they lost their job: “I would immediately look for another job,” and “I would stop working.” The second item was reverse scored to obtain a score that increases with the general readiness to (continue to) work.

Employment efficacy

To measure employment efficacy we used a 5-item scale relating to one those developed by Wanberg et al. (2010, p. 794). Respondents had to specify on a 5-point scale (1 = strongly disagree to 5 = strongly agree) how easy they thought it would be for them to find a job if they lost their current job. The items measured the expected difficulty to (a) find another job; (b) find a job corresponding to their knowledge, skills, and expertise; (c) matching their interests, (d) with the same wage as the current job, and (e) at the same functional level as the current job.

Control variables

We controlled for the current net monthly earnings, current functional level, gender, the partner’s labor market position, the number of children one is financially responsible for, the level of education, and the perceived financial hardship (reflecting the number of months one can bridge financially when becoming unemployed). These factors have known effects on the reservation wage. Research indicates that a job searcher’s previous wage relates positively to the reservation wage (Haurin & Sridhar, 2003) and that men set higher reservation wages than women (Prasad, 2003). It also indicates that the reservation wage is lower for job searchers with a working partner (Walker, 2003) and with children they are financially responsible for (Bloemen & Stancanelli, 2001). The reservation wage moreover tends to increase with the level of education (Gorter & Gorter, 1993) and financial wealth (Bloemen & Stancanelli, 2001).

The current wage was included as the logarithm of the net monthly earnings. We distinguished four functional levels: senior management, middle management, professional staff, and clerical staff. The gender variable was dichotomous with 0 = female and 1 = male. The partner variable comprised three categories: (a) not having a partner, (b) having a working partner, and (c) having a partner who is currently unemployed, retired, or not active in the labor market. A dummy variable indicated whether the respondent was financially responsible for minimum one child (1 = yes). We coded three educational levels: (a) low: no high school degree, (b) average: high school degree, and (c) high: bachelor’s or master’s degree. We measured financial hardship asking respondents to indicate how many months they could bridge (financially) without being employed. After removing outliers, the number of months ranged between 0 and 120. We used reversed scores.

Results

Table 2 shows basic statistics and correlations between the continuous variables for the full sample. The average ratio of the reservation wage to the current wage is 1.03. This ratio is similar to the ratios reported by Jones (1989), Pannenberg (2010), and Eriksson and Lagerström (in press). Table 3 indicates that age correlates differently to work intention, employment efficacy, and the reservation wage among blue-collar workers, white-collar workers, and civil servants.

Table 2.

Means, Standard Deviations, and Correlations.

Variables	M	SD	1	2	3	4	5	6
1. Log (reservation wage)	7.47	0.26	—	—	—	—	—	—
2. Age	38.94	9.79	.39***	—	—	—	—	—
3. Age²;	1617.00	779.63	−.03***	.12***	—	—	—	—
4. Work intention	9.32	1.17	−.12***	−.27***	−.19***	—	—	—
5. Employment efficacy	3.06	0.80	−.00***	−.29***	−.13***	.16***	—	—
6. Log (current wage)	7.44	0.27	.89***	.37***	−.06***	−.10***	−.04***	—
7. Financial hardship	111.54	12.39	−.22***	−.20***	−.06***	.23***	.02***	−.25***

Note: N = 22,796. For informational purposes, M and SD of the age and age²; variables concern M and SD for the noncentered values. The correlations are calculated using the mean-centered variables as these variables are used in the path analysis.

***

p < .001.

Table 3.

Means, Standard Deviations, and Correlations for Three Categories of Workers.

Blue-collar workers, n = 2,363	M	SD	1	2	3	4	5	6
1. Log (reservation wage)	7.38	0.38	—	—	—	—	—	—
2. Age	38.02	9.26	.21***	—	—	—	—	—
3. Age²;	1530.81	717.79	−.10***	−.08***	—	—	—	—
4. Work intention	9.37	1.96	−.00	−.20***	−.16***	—	—	—
5. Employment efficacy	3.14	1.41	.12***	−.21***	−.05***	.17***	—	—
6. Log current wage	7.33	0.38	.84***	.17***	−.13***	.05*	.10***	—
7. Financial hardship	113.08	18.81	−.11***	−.16***	−.05***	.19***	−.02**	−.14***
White-collar workers, n = 17,360	M	SD	1	2	3	4	5	6
1. Log (reservation wage)	7.51	0.20	—	—	—	—	—	—
2. Age	34.98	8.76	.50***	—	—	—	—	—
3. Age²;	1300.56	671.10	−.10***	.07***	—	—	—	—
4. Work intention	9.38	0.80	−.16***	−.26***	−.15***	—	—	—
5. Employment efficacy	3.13	0.56	−.01	−.32***	−.13***	.13***	—	—
6. Log (current wage)	7.49	0.21	.90***	.46***	−.12***	−.14***	−.06***	—
7. Financial hardship	110.82	9.45	−.26***	−.23***	−.04***	.25***	.02*	−.30***
Civil servants, n = 3,073	M	SD	1	2	3	4	5	6
1. Log (reservation wage)	7.51	0.38	—	—	—	—	—	—
2. Age	39.17	10.22	.47***	—	—	—	—	—
3. Age²;	1638.75	830.02	.11***	.37***	—	—	—	—
4. Work intention	9.19	1.86	−.16***	−.33***	−.29***	—	—	—
5. Employment efficacy	2.86	1.13	−.05**	−.29***	−.19***	.16***	—	—
6. Log (current wage)	7.48	0.38	.87***	.44***	.08***	−.15***	−.09***	—
7. Financial hardship	110.84	18.86	−.21***	−.18***	−.11***	.24***	.04*	−.23***

Note: For informational purposes, M and SD of the age and age²; variables concern M and SD for the noncentered values. The correlations are calculated using the mean-centered variables as these variables are used in the path analysis.

p < .05. **p < .01. ***p < .001.

We tested the research model using SEM. The measurement model, containing the five items of the employment efficacy scale, fits the underlying factor structure well, χ²;(4) = 311,85, p < .001 (root mean square error of approximation [RMSEA] = 0.06, comparative fit index [CFI] = 0.99, normed fit index [NFI] = 0.99, nonnormed fit index [NNFI] = 0.99). All factor loadings are statistically significant at .001 level and range from 0.57 to 0.90. We moreover find a decent fit between the overall hypothesized model, containing the measurement and the structural model and the observed data, χ²;(87) = 8427.24, p < .001 (RMSEA = 0.06, CFI = 0.951, NFI = 0.95, NNFI = 0.90).Table 4 shows the results of the path analysis.

Table 4.

Results of the Path Analysis (Standardized Coefficients).

Variables	Employment efficacy	Work intention	Log (reservation wage)
Age (mean centered)	−.25***	−.25***	.07***
Age²; (mean centered)	−.11***	−.17***	.01**
Employment efficacy			.05***
Work intention			−.03***
Log (wage)			.84***
Senior management^A			.02***
Middle management^A			.02***
Professional staff^A			.00
Male^B			.00
High school degree^C			.02***
Bachelor’s or master’s degree^C			.03***
No partner^D			−.01*
Working partner^D			−.03***
Financial responsibility for child(ren)^E			.01***
Financial hardship			.01***
R²;	.08	.10	.79

Notes: The reference categories are A = clerical staff; B = female; C = no high school degree; D = having a partner who is unemployed, retired, or not active in the labor market; E = not having children for whom one is financially responsible.

p < .05. **p < .01. ***p < .001.

The first two hypotheses are confirmed. Hypothesis 1 (β_age = −.25, p < .001; β_age²; = −.11, p < .001) and Hypothesis 2 (β_age = −.25, p < .001; β_age²; = −.17, p < .001) both stated reverse U-shaped relationships between age and employment efficacy and work intention, respectively. Both employment efficacy and work intention increase until the age of 38 and then decrease.

Hypotheses 3 and 4 concern the impact of employment efficacy and work intention on the reservation wage. The results confirm that employment efficacy positively relates to the reservation wage (β = .05, p < .001) (Hypothesis 3) and the reservation wage decreases with work intention (β = −.03, p < .001; Hypothesis 4). Generally, the data support our supposition that age relates to the reservation wage via two paths. While we find a reverse U-shaped relationship between age and the reservation wage via employment efficacy, we find a U-shaped relationship via work intention (see Figures 2 and 3). Given the small coefficients these effects are limited.

Figure 2.

Indirect relationship between age and the reservation wage via employment efficacy.

Figure 3.

Indirect relationship between age and the reservation wage via work intention.

Table 4 shows that age also directly relates to the reservation wage irrespective of workers’ work intention or employment efficacy. The explained variance of the reservation wage (R²; = .79, Table 4) is mainly accounted for by the (current) wage variable. However, when testing the model without controlling for the current wage, we find similar relationships and still an R²; of .43.

To test Hypothesis 5 studying the moderation of status, we conducted multigroup SEM. The analyses show an acceptable fit between the research model and the observed data (standardized root mean square residual [SRMR] = 0.05, RMSEA = 0.06, CFI = 0.91). Table 5 shows standardized path estimates for the three groups.

Table 5.

Results of the Multigroup Structural Equation Modeling (SEM; Standardized Coefficients).

	Employment efficacy			Work intention			Log (RW)
Dependent variable	Blue-collar workers	White-collar workers	Civil servants	Blue-collar workers	White-collar workers	Civil servants	Blue-collar workers	White-collar workers	Civil servants
Age (mean centered)	−.18****	−.26****	−.28****	−.19****	−.22****	−.35****	.07****	.06****	.11****
Age²; (mean centered)	−.09****	−.13****	−.11****	−.17****	−.13****	−.18****	.01	.01**	−.00
Employment efficacy							.04****	.06****	.04****
Work intention							−.03***	−.03****	−.01
Log (wage)							.81****	.86****	.82****
Senior management^B							.03***	.02****	.01
Middle management^B							.02	.01****	.01
Professional staff^B							.03***	−.00	−.01
Male^C							.02	.00	.01
High school degree^D							.01	.00	.03**
Bachelor’s or master’s degree^D							.02*	.02**	.05****
No partner^E							−.03**	.01	−.01
Working partner^E							−.06****	−.01***	−.04****
Financial responsibility for child(ren)^F							.03**	.01***	.00
Financial hardship							.02**	.02****	. 00
R²;	.04	.08	.08	.07	.10	.14	.72	.79	.80

Notes: The reference categories are: A = no seniority wage; B = clerical staff; C = female; D = no high school degree; E = having a partner who is unemployed, retired, or not active in the labor market; F = not having children for whom one is financially responsible.

p < .10. **p < .05. ***p < .01. ****p < .001.

For all predictor–outcome relationships, we conducted a series of chi-square difference tests to investigate whether path estimates differed between the groups. The shapes of the quadratic relationships between age and employment efficacy and the relationships between age and work intention are similar for all three categories (see Table 5). The strength of the age effects differs according to Hypothesis 5. Age-related changes in employment efficacy and work intention are weakest for blue-collar workers and strongest for civil servants (χ²; differences > 3.84, p < .05). Furthermore, we find some additional differences between the three groups. Employment efficacy relates more strongly to the reservation wage among white-collar workers (p < .05). Civil servants differ from blue-collar and white-collar workers as work intention does not relate to the reservation wage (p < .05).

Table 5 shows that the direct relationship between age and reservation wage also differs according to workers’ status. It is strongest among civil servants (χ²; differences > 10.83, p < .001). For blue-collar workers (β_age = .07, p < .001) and civil servants (β_age = .11, p < .001), age relates directly to the reservation wage in a linear way. For white-collar workers, we find a U-shaped relationship with a minimum reservation wage at the age of 32 (β_age = .06, p < .001; β_age²; = .01, p < .01).

Overall, for blue-collar workers, we find a U-shaped relationship via work intention, a reverse U-shaped relationship via employment efficacy, and a linear direct relationship. In sum, this gives a rather linear positive relationship between age and the reservation wage (Figure 4). For white collar-workers, we find a U-shaped direct relationship on top of the U-shaped relationship via work intention and the reverse U-shaped relationship via employment efficacy resulting in an overall U-shaped relationship (Figure 5). For civil servants, we find a reverse U-shaped relationship via employment efficacy and a linear direct relationship resulting in an overall reverse U-shaped relationship (Figure 6). The figures show for each status the margin between which the logarithm of the reservation wage fluctuates, that is, Δ log (RW).

Figure 4.

Overall relationship between age and the reservation wage among blue-collar workers.

Figure 5.

Overall relationship between age and the reservation wage among white-collar workers.

Figure 6.

Overall relationship between age and the reservation wage among civil servants.

Discussion

This article examines the mediating role of employment efficacy and work intention in the relationship between age and the reservation wage. We investigate differences between blue-collar workers, white-collar workers, and civil servants in the Belgian labor market. These statuses differ considerably in payment system, employment protection, and public pension benefits.

Generally, individuals’ age affects their reservation wage through both employment efficacy and work intention, yet in an opposite way. First, the analyses show that workers’ employment efficacy increases until the age of 38 and then decreases. As employment efficacy relates positively to the reservation wage, the relationship between age and the reservation wage via employment efficacy is reverse U-shaped. Second, we find that individuals’ work intention also increases until the age of 38 and then decreases. Since work intention relates negatively to the reservation wage, the relationship between age and the reservation wage via work intention is U-shaped. Although the coefficients are quite small, employment efficacy and work intention influence the reservation wage to a larger extent than factors that are traditionally included in studies aimed at identifying the determinants, that is, gender, level of education, and the financial responsibility for the partner or children (Bloemen & Stancanelli, 2001; Christensen, 2001; Prasad, 2003). Furthermore, we find a direct relationship between age and the reservation wage, indicating that factors other than work intention and employment efficacy also play a role.

Multigroup SEM confirmed our supposition that statuses and their related payment systems, employment protection, and public pension benefits influence the age effect on employment efficacy, work intention, and reservation wage. Age effects are stronger among white-collar workers than among blue-collar workers yet most pronounced among civil servants. Moreover, the shapes of the overall relationship are different. Civil servants have the most tenure-related, in their case often age-related, entitlements. For this group, we find a strong overall reverse U-shaped relationship between age and the reservation wage, determined by the strongest indirect relationship via employment efficacy and the strong direct linear relationship. The direct relationship may be caused by the common use of seniority wages creating the impression that it is self-evident to receive higher wages when aging. We did not find any effect of work intention on the reservation wage, possibly because wages are set according to fixed pay scales in the public sector. Furthermore, delayed payment contracts, like seniority wages, are more likely to be used in large established firms, like governmental institutions (Hutchens, 1989). Hence, this category of older workers might well be earning more than their alternative wage in the outside/private labor market. Therefore, the prospect of a worse match may drive up the reservation wage, particularly among civil servants.

Blue-collar workers have the least tenure-related entitlements. For them, we find the weakest overall relationship. As the indirect effects via employment efficacy and work intention cancel each other out, the direct relationship determines the overall age effect.

For white-collar workers, having more tenure-related entitlements than blue-collar workers, the overall relationship between age and reservation wage is U-shaped. We find a reverse U-shaped relationship via employment efficacy and a U-shaped relationship via work intention. The direct relationship is also U-shaped. In early career stages, white-collar workers may look for the right match and therefore lower their wage demands to find employment. They may initially make these wage concessions knowing that compensation follows after gaining tenure.

Implications for Theory and Practice

We contribute to the literature in different ways. First, we introduce individuals’ wage-setting behavior through the reservation wage concept in research on the aging workforce. Although older workers’ reservation wage may affect their labor market position; so far it has received little attention in research on aging. Second, we provide insights in people’s mindset toward acceptable jobs whereas existing research mainly elaborates on (early) retirement issues.

This study also contributes to the reservation wage literature. It is the first to explore which factors mediate the relationship between age and the reservation wage. Guided by labor supply and job search theory, we introduce work intention and employment efficacy in empirical research on the reservation wage. Although the two factors do not fully explain the relationship between age and the reservation wage, they play a role. This study stresses the need to focus on “subjective” factors when studying the reservation wage. It also shows that institutional factors and tenure-related entitlements influence the reservation wage. Moreover, it adds significantly to the field of labor economics since empirical research on reservation wages is very limited.

From a practical point of view, our results show that wage demands increase with age and underline the relevance of studying individuals’ reservation wage for late career issues in the labor market. Moreover, this study shows that both work intention and employment efficacy affect people’s wage-setting behavior. Since increasing wage costs may restrict workers’ employment prospects, it is highly relevant that both work intention and employment efficacy can be interfered with to increase workers’ wage flexibility. The results also show that institutional factors, like payment systems, employment benefits, and public pension benefits, influence the age effect on employment efficacy, work intention, and reservation wage. The stronger workers’ status is determined by age-related factors, the stronger age will affect their positioning in the labor market and their wage claims. To increase older workers’ labor market participation, policy makers should therefore consider rethinking age- and tenure-related entitlements.

Limitations and Future Research

This study has some limitations that future research can take into account. First, employment efficacy and work intention do not fully explain the relationship between age and the reservation wage. Future research should further clarify age-related changes in wage claims. In addition, future research may include nonpecuniary job characteristics to investigate reservation utility and check whether older workers are more demanding when other job characteristics are considered.

Second, we studied the relationship between age and reservation wage in the Belgian labor market. Although this particular context, and more specifically status-related differences, proved useful to study the impact of some institutional factors, future research could examine the effect of different institutional settings and age cultures on the relationship between age and the reservation wage.

Although it is highly relevant to study the reservation wage among employed individuals, future research could verify whether the results of this study apply to the population of unemployed job seekers.

Furthermore, cross-sectional data do not allow for studying the dynamic nature of the relationship between the reservation wage and its determinants. It moreover does not allow for a comparison between the reported reservation wage and the actual accepted wage. Future research may use longitudinal data to investigate these matters and verify whether the reservation wage is indeed a lower limit of the wage a person is willing to accept.

Finally, this study focuses exclusively on the supply side of the labor market. Future research could elaborate on the demand side as wage structures and employers’ wage-setting policies are also relevant in the discussion on why the transition from unemployment to employment gets harder as people age.

Footnotes

Authors’ note

This study is based on confidential survey data collected via voluntary participation. It complies with the ethical code of the KU Leuven and the Belgian privacy law ().

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received the following financial support for the research, authorship, and/or publication of this article: This study was supported by a grant from the Research Foundation Flanders (Fonds Wetenschappelijk Onderzoek – Vlaanderen), G.0086.08.

Author Biographies

An De Coen, PhD, is research fellow at the Faculty of Economics and Business, campus Thomas More of the KU Leuven. Her research interests focus on the labor market participation of older workers, job search, and employability.

Anneleen Forrier, PhD, is associate professor at the Faculty of Economics and Business, campus Thomas More of the KU Leuven. Her main research interests are late careers, employability, and labor market trajectories.

Luc Sels, PhD, is dean of the Faculty of Economics and Business of the KU Leuven. His primary research interests center around active aging, career management, psychological contracts, the relationship between investments in HR management and firm performance (ROI), and features of strong HRM systems.

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