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European Journal of Ageing logoLink to European Journal of Ageing
. 2017 Jan 24;14(1):49–61. doi: 10.1007/s10433-016-0407-y

Hard and soft age discrimination: the dual nature of workplace discrimination

Justyna Stypinska 1,✉, Konrad Turek 2
PMCID: PMC5550623  PMID: 28804394

Abstract

The paper concentrates on the problem of age discrimination in the labour market and the way it can be conceptualised and measured in a multi-disciplinary way. The approach proposed here combines two understandings of age discrimination—a sociological and legal one, what allows for a fuller and expanded understanding of ageism in the workplace. At the heart of the study is a survey carried out in Poland with a sample of 1000 men and women aged 45–65 years. The study takes a deeper and innovative look into the issue of age discrimination in employment. Confirmatory factor analysis with WLSMV estimation and logistic regressions were used to test the hypotheses. The study shows that age discrimination in labour market can take on different forms: hard and soft, where the hard type of age discrimination mirrors the legally prohibited types of behaviours and those which relate to the actual decisions of employers which can impact on the employee’s career development. The soft discrimination corresponds with those occurrences, which are not inscribed in the legal system per se, are occurring predominantly in the interpersonal sphere, but can nevertheless have negative consequences. Soft discrimination was experienced more often (28.6% of respondents) than hard discrimination (15.7%) with higher occurrences among women, persons in precarious job situation or residents of urban areas. The role of education was not confirmed to influence the levels of perceived age discrimination.

Keywords: Age discrimination, Labour market, Poland, Older workers, Ageism, Employment

Introduction

Ageism is said to be the ultimate type of prejudice (Palmore 2001) and a threat to “ageing well” in the twenty-first century (Angus and Reeve 2006). Research shows that almost 24% of older Europeans experience discrimination because of their age sometimes or frequently (Abrams et al. 2011). Despite the ubiquitous nature of age discrimination and the solid evidence for its adverse social consequences, European policy confines responses for tackling ageism mostly to discrimination in the workplace. This is done to enhance the productivity and employability of older workers and to deter their dependence on the welfare state (Macnicol 2005). A milestone in age discrimination policies in the European Union was the Council Directive 2000/78/EC of 27 November 2000, which sets out a general framework to ensure equal treatment of individuals at the workplace regardless of their religion or belief, disability, age or sexual orientation. Even though all Member States introduced this legislation, its effects are difficult to assess (Duncan and Loretto 2004; Herring 2011; Neumark 2009).

Despite the progress in legislation, the discrepancy between social and legal understandings of age discrimination remains problematic (Lahey 2010; Macnicol 2006). As Amiraux and Guiraudon emphasise: “the social fluidity of discrimination contrasts with the boundaries of its legal existence” (2010: 1703), what calls for more interaction between the two spheres to enhance the efficiency of the legislation and to increase its social impact (Green 2001). Expanding the social understanding of age discrimination with a legal dimension has two strengths. First, the analysis can be carried out around the axis of legality and illegality of certain behaviours, which is largely missing from the discussions on ageism within social sciences (Doron 2006). The legal provisions existing in European and national legal systems are binding, meaning that the employer can be held accountable for discriminatory practices, as already proven in the ever growing case law of European courts (Rothermund and Temming 2010; Umhauser-Enning 2013). Second, the legal perspective delivers an analytical tool to study age discrimination in a more meticulous way by defining different types of discriminatory occurrences.

The aim of this article is twofold. First, to explain the social and legal understandings of age discrimination by incorporating a perspective of legality into a sociological study of perceived age discrimination in the labour market. Second, the article seeks to explain the empirical manifestations of age discrimination in the labour market with several socio-demographic and occupational factors, such as gender, age, education, place of residence, occupational status and sector of economy, which account for the variations in perceived age discrimination. The structure of this article proceeds as follows. The literature review presents theoretical and empirical findings about age discrimination and provides a country background for this study. This overview is summarised by three hypotheses for the research. The hypotheses are tested based on survey data of 1000 older workers in Poland. The empirical part presents descriptive statistics, confirmatory factor analysis and logit regressions and is followed by discussion and conclusions.

Legal and social understanding of age discrimination

The concept of ageism within social sciences has evolved remarkably over time. Butler’s first definition of ageism as “a process of systematic stereotyping and discrimination against people because they are old” (Butler 1975: 35) was refined in many ways. Several decades later, research on ageism expanded the understanding of the concept and encompassed different types (Iversen et al. 2009). Among the most noteworthy are the distinctions between positive and negative ageism (Palmore 1999), implicit and explicate ageism (Blackburn 2006; Levy and Banaji 2004; Malinen and Johnston 2013) and personal and institutional ageism (Bytheway 1995; Palmore 1999; Palmore et al. 2005). Moreover, social researchers have distinguished such subtypes as interactive ageism (Minichiello et al. 2000), compassionate ageism (Furunes and Mykletun 2010) and gendered ageism (Handy and Davy 2007). The use of a tripartite model of definition of ageism introduced an important distinction (Cuddy and Fiske 2002; Levy and Banaji 2004), which stems from understanding attitudes as being constituted of three mechanisms: prejudice (affective component), discrimination (behavioural component) and stereotyping (cognitive component). This partition enhances the understanding of the distinction between ageism and age discrimination. Hence, McMullin and Marshall state that “there are two interconnected dimensions of ageism: an ageist ideology, which includes negative stereotypes, beliefs, and attitudes and age discrimination, which is behaviour that excludes certain people and places them in a disadvantaged situation relative to others on the basis of their chronological age” (McMullin and Marshall 2001: 112).

With reference to the labour market, Palmore defines age discrimination as the “refusal to hire or promote older workers, or forcing retirement at a fixed age regardless of the worker’s ability to keep working” (Palmore 1999: 119). Macnicol understands it simply as “the use of crude ‘age proxies’ in personnel decisions” (2006: 6). It has also been conceptualised as “prejudice (biased attitudes), discriminatory practice and institutional habits. Biased attitudes may, but do not necessarily, lead to discrimination” (Furunes and Mykletun 2010: 23). The behavioural component of ageism is present in these conceptualisations, but its manifestations lack a thorough examination. Here, a legal perspective can enrich further analyses of age discrimination.

Principally, discrimination in the labour market can take many forms, and the European legislation makes a distinction between direct discrimination (when a person is treated less favourably than another in a comparable situation due to their age, e.g. receiving lower pay, being barred from training or being fired), indirect discrimination (when the effects of apparently neutral action disadvantage people on the grounds of age, e.g. policies affecting older workers more strongly than their younger counterparts), and harassment (violation of the dignity of a person and creation of an intimidating, hostile, degrading, humiliating or offensive environment) (Adamson 2006; EC 2009; Furunes and Mykletun 2010; Fribergh and Kjaerum 2011). The long-term effects of functioning of anti-discrimination legislation are still difficult to assess. On the one hand, experts emphasise that such regulations are important for raising awareness to age discrimination among older workers and employers (Amiraux and Guiraudon 2010; Breda and Schoenmaekers 2006). On the other hand, it is assumed that only a small number of age discrimination practices find their way to court, and even if they do, proving guilt is very problematic (Rothenberg and Gardner 2011). Moreover, severe labour market distractions, such as the economic crisis, make it difficult to recognise discrimination and can weaken the effects of legislation (Neumark and Button 2014). It might also be expected that stronger regulations regarding age discrimination can have a positive effect for stability of employment in older age, yet at the same time, they may negatively affect the chance of older unemployed individuals to find employment because employers realise it is more difficult to terminate their contracts (Neumark 2009).

However, one effect of the anti-discrimination legislation found in research suggests that in modern globalised workplaces, where adherence to formal legal rules is praised, alongside discrimination that is relatively easily interpreted within a legal framework, another type is gaining in importance: “a fluid process of social interaction, perception, evaluation and disbursement of opportunity” (Green 2001: 91). Legislation is not always able to address this type of age discrimination (Cheung et al. 2010), where social factors, such as stereotypes, interpersonal relationships or work atmosphere can play a significant role. This suggests that the discrimination in employment might occur in two forms: one that aligns with the strict legal definitions, and second form that occurs in social interactions and is linked to workplace dynamics (Green 2001). In the analysis that follow, we intend to call these two types soft and hard discrimination.

Soft discrimination as work-related basis for hard discrimination

There are reasons to consider soft discrimination as reflecting an ageist work environment that provides foundations for hard discrimination practices. Soft discrimination is strongly based on age stereotypes, which are common and universal (Taylor and Walker 1994; CIPD 2005; Henkens 2005; Kite et al. 2005; Harper et al. 2006; Posthuma and Campion 2009). In the meta-analysis of opinions about older workers, age negatively correlated with the assessment of many skills, productive potential and productivity (Bal et al. 2011). Negative age stereotypes can significantly influence employers’ decisions that are unfavourable for older workers and set a barrier for increasing employability and retention in older groups (Perry and Finkelstein 1999; Bytheway 2005; Loretto and White 2006; OECD 2006). Positive opinions about older workers, meanwhile, do not often translate into supportive practices (Taylor and Walker 1998). Research shows that “age stereotypes have been shown to influence the outcomes of employment-related decisions in a variety of settings, for example, lower ratings in interviews and performance appraisals” (Posthuma and Campion 2009:165), suggesting that ageist attitudes (expressed for instance, in language used in the workplace) can influence actual employment decisions, thus putting older workers in a disadvantaged position. Economists consider discrimination as an element of the imperfect labour market, resulting from uncertainty about the future performance of workers, prejudices, ignorance or unjustified generalisations (Becker 1957; Phelps 1972; Aigner and Cain 1977; Oswick and Rosenthal 2001). Employers aiming to maximise profits make their decisions according to expectations about their workers. These expectations are always based on limited knowledge, simplifications and pre-conceptualisations and can be affected by social norms, culture and social environment (Arrow 1998; Turek 2015). Therefore, soft discrimination towards older people can lower their expected value for employers, contributing to more harmful practices of hard discrimination. Workplaces where certain ageist behaviours, which are still legal (such as ageist jokes or comments), are tolerated can be more prone to also exhibit illegal types of discrimination (e.g. not hiring someone due to age).

Explaining age discrimination: socio-demographic and occupational factors

Aside from the influence of formal regulations also several socio-demographic and occupational factors cause variation in perception and experiences of age discrimination. As plethora of studies showed age discrimination more often affects women (Duncan and Loretto 2004; Krekula 2007; Lincoln and Allen 2004) and persons of lower educational attainment (van den Heuvel and van Santvoort 2011). Ageism operates also differently across the life course (Bytheway 2005). For instance, Australian studies indicate a higher incidence in the 55–59 group than in the 60+ one (AHRC 2015).

Age discrimination reports differ with respect to employment status and work-related characteristics (Johnson and Neumark 1997). It occurs more often in the group of job seekers than those in employment or the self-employed (AHRC 2015). A precarious situation on the labour market (working as a part-time or seasonal worker, being unemployed) has also been identified as a situation whereby the individual is more likely to be exposed to discrimination, which is often related to their low bargaining power in their relationship with employers (Standing 2011). Further on different rates of age discrimination were reported in different professions and sectors of economy, particularly is those with a small proportion of older workers, fast developing and using new technologies. Such branches as information technology (IT), the banking sector, the catering trade or the hotel industry were more discriminatory than production, craft and traditional services (Johnson and Neumark 1997; Perry and Finkelstein 1999; Gaster et al. 2002; Posthuma and Campion 2009). Some studies showed that service sectors are more discriminatory towards older workers as an effect of “customer-driven discrimination” or employers’ tastes for younger service workers (Chiu et al. 2001; Riach and Rich 2006; Adler and Hilber 2009). Additionally, private sector was more discriminatory that the public one (Johnson and Neumark 1997). Company size and management style also are differentially associated with ageist behaviours: larger companies usually provide better age management and broader opportunities for older workers, while management based on closer relations with workers decreases age stereotypes (Perry and Finkelstein 1999; Posthuma and Campion 2009; Parry and Tyson 2011).

Some studies found higher rates of discrimination in urban areas than in rural areas (Johnson and Neumark 1997; McGuire et al. 2008). Urban and rural labour markets can differ significantly regarding the occupational structure and types of companies, culture and social norms of work and work relations, as well as the age composition of workforce. However, Johnson and Neumark (1997) warned about potential misinterpretation of the place of residence and place of work. The occurrence of age discrimination also differs based on the sociocultural context (Tesch-Roemer and von Kondratowitz 2006). Research has found that older citizens in East and South-East European countries report age discrimination more frequently than those in Western and Northern parts of Europe (van den Heuvel and van Santvoort 2011).

Country background: The situation in Poland

The employment rate for the working population aged 55–64 in Poland is one of the lowest in Europe. In 2015 it equalled 54.2% for men and 35.5% for women (with an EU-28 average of 60.1 and 46.9%, respectively) (Eurobarometer 2015). The lower labour market activity of older people in Poland is a result of system transition and public policies in the 1990s and early 2000s. After 1989, the free market system introduced new rules for the labour market. For the ageing generations, adaptation to these changes was difficult and their skills did not always match employers’ new expectations (Turek et al. 2015). Pushing older people out and extensive early retirement options served for policy makers as a remedy for high unemployment among young and middle-aged people (Ruzik-Sierdzinska et al. 2013). Given the broad supply of young employees from the baby-boom generation, employers had little incentive for any profound concern about older workers, and this have facilitated the development of negative stereotypes about older workers and age discrimination (Perek-Białas and Turek 2012).

According to Polish employers, when compared to younger workers, older ones are characterised by much lower productivity, creativity, willingness to learn or technological skills. They also assessed on average that the age at which a person is too old to work 20+ hours per week is roughly 64, compared to 70 in a study in Denmark or 67 in Germany (Turek and Perek-Białas 2013). In 2014, as many as 75% of Polish employers had precise age preferences for work candidates, and only 38% of them said they would have accepted a 50-year old and 11% a 60-year old for the work they were offering (Turek 2015).

Older people’s problems on the labour market were addressed by policymakers in Poland relatively late, with development of a real ageing policy commencing only after 2009 (Ruzik-Sierdzinska et al. 2013). Among the main goals of the most recent policy strategy for increasing the labour market activity of older people in Poland—“Solidarity between generations” from 2014—is the development of an organisational culture and an environment that is friendly to workers aged 50+. One of its priorities is “improving the image and fighting the stereotypes about workers aged 50+, as well as counteracting age discrimination in firms and institutions” (MPiPS 2014). The document puts forward several practical proposals to reduce age discrimination, which, however, are reduced to soft measures (such as social campaigns fighting the negative stereotypes of older workers), and none of these relates to the already existing legal prohibition on discrimination. Anti-discrimination legislation was adopted in Poland as one of the requirements for joining the European Union in 2004. Relevant amendments1 were made to the labour code, including a prohibition on discrimination on the grounds of age. Nevertheless, according to experts’ assessments, protection against age discrimination is still lower than required. Implementation of the law is additionally hindered by a lack of adequate training for the judiciary, and general low legal awareness in Polish society (Wieczorek and Bogatko 2013).

Hypotheses

Based on the reviewed literature we pose three hypotheses. The first hypothesis (H1) expects the age discrimination in the workplace occurs in two distinct forms, namely discrimination prohibited by law (hard discrimination) and discrimination in social interactions (soft discrimination). In H2 we expect the two forms of discrimination to be interrelated—occurrence of one type of discrimination should coexist the other type. Based on the results of studies H3 states that both socio-demographic characteristics (such as age, gender and education level), as well as work-related characteristics (such as occupational status, characteristics of labour market, type sector and branch) should affect the level of perceived age discrimination. There is, however, no data informing whether a different pattern of predictors occurs for soft and hard age discrimination.

Methods

Data

The research was carried out on a random sample of 1000 inhabitants of Małopolska region in Poland, aged 45–65, who were economically active (in the last 12 months were employed, self-employed or active job seekers).2 A computer-assisted telephone interview (CATI)3 was used based on the sampling frame of a telephone directory (thus non-owners of telephones were not included). The sample characteristics are presented in Table 1.

Table 1.

Sample characteristics by covariates (in  %)

Characteristics Categories % N
Gender Female 58.1 581
Male 41.9 419
Age 45–49 29.3 293
50–54 31.7 317
55–59 25.7 257
60–64 13.3 133
Education level Elementary 5.3 53
Vocational 27.1 271
Secondary 42.5 425
Tertiary 25.1 251
Place of residence Big city and suburbs 30.8 308
Medium, small city 27.5 275
Rural area 41.7 417
Occupational status Working 73.0 730
Unemployed 10.4 104
Retired but still working 6.1 61
Non-active 10.5 105
Occupational group Professionals, managers 31.4 309
Services, technicians, office clerks 35.6 350
Manual and agriculture workers 32.9 324
Type of sector Public 38.6 386
Private 51.7 517
Non-profit or mixed 9.7 97
Sector of economy Production 40.8 398
Services 27.8 271
Public services 31.5 307
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Total N = 1000

Measures

For the purpose of the study an original questionnaire was developed, with a series of 13 questions (see Table 2) about discriminatory behaviours that could have been experienced by older workers and job seekers.4 The questions reflected various types and levels of discrimination. On the one hand, they include those which are directly prohibited by the existing legislation (hard discrimination: items 1–6) and were developed using the Polish labour code and its interpretations (Gonera 2004) in the realm of workplace discrimination. On the other hand, the list also contains behaviours which are not legally prohibited (or at least not directly), but nevertheless may contribute to creating a hostile working environment or even harassment, and have adverse effects on the worker (soft discrimination: items 7–13). Importantly, it is not only employers who are the agents of these types of behaviours, as often framed in the discourse or legislation—the discriminators can be work colleagues, managers, business partners or clients. The presented question set was inspired by Palmore’s “Ageism Survey” (2001), but the questions were modified to accommodate to the context of employment discrimination.5

Table 2.

Occurrences of discrimination in workplace (% of “yes” answers)

Have you in the last 12 months Female Male Total
v1 Not been hired to a new job because of your age 11.4 8.6 10.2
v2 Not been promoted due to your age* 4.0 2.4 3.5
v3 Been refused taking part in professional training 3.6 3.1 3.3
v4 Received lower salary due to your age 3.3 1.7 2.6
v5 Been fired because of you age 2.6 1.6 2.2
v6 Been demoted because of your age* 2.4 0.5 1.6
v1–v6 Sub-total: any type of hard age discrimination* 18.4 11.9 15.7
v7 Encountered stereotypes and negative remarks about older workers* 22.4 16.5 19.9
v8 Heard you are “too old for something”* 15.7 11.2 13.8
v9 Been treated without respect by employer or co-workers due to your age* 9.8 6.0 8.2
v10 Been an object of impolite remarks or jokes about your age 7.6 6.0 6.9
v11 Been treated without respect by clients or business partners due to your age 5.3 3.3 4.5
v12 The outcomes of your work been valued less/worse by your employer 4.5 3.6 4.1
v13 Been intimidated or humiliated because of your age 4.5 2.1 3.4
v7–v13 Sub-total: any type of soft age discrimination* 31.0 25.3 28.6
v1–v13 Total: any type of age discrimination* 35.5 28.6 32.6
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* Chi-square test for gender differences p < 0.05; N = 1000

Covariates

In the regression models, basic socio-demographic characteristics, such as gender (male = 0, female = 1), education level (elementary = ISCED 0–2; vocational = ISCED 3C; secondary = ISCED 3A; Tertiary = ISCED 4–6) and age (grouped into categories: 45–49, 50–54, 55–59, 60–64) were included. Further independent variables represent work-related factors that were available in the dataset and can be expected to affect the perceived discrimination as H3 suggests. Occupational status included people who at the moment of the interview were: working, unemployed (not working and actively looking for job), retired but still working, and non-active (not working and not looking for job, but having done so in the last 12 months). Place of residence (large cities and suburbs over 100,000; medium and small cities below 100,000; and rural areas) represents the type of labour market. A potential misinterpretation of the place of residence and place of work (Johnson and Neumark 1997) should not be a problem in Poland, where the average distance from home to work is small (10 min on average, HCS 2014) and most people work near their area of residence. Further on different rates of age discrimination were reported in different professions and sectors of economy (Johnson and Neumark 1997; Gaster et al. 2002; Adler and Hilber 2009). In the research occupational group was based on ISCO classification of occupations and recoded into three general categories: professionals, managers (including professionals, managers, teachers, health and care workers, higher administration); services, technicians, office clerks (including service and sales workers, technicians and associate professionals, clerical support workers); and manual and agriculture workers (including agricultural, forestry and fishery workers, craft and trades workers, plant and machine operators, elementary occupations). Type of sector differed between three categories: public, private, and non-profit or mixed. Sector of economy was based on the Polish Classification of Economic Activities (PKD),6 regrouped into three general categories: production (construction, manufacturing, mining, agriculture, forestry and fishing); services (wholesale and retail trade, accommodation and food, transportation and storage, information and communication, finances and insurance, real estate, professional, scientific and technical activities); and public services (public administration, education, health and social work, culture, art, entertainment and recreation).

Statistical analysis

The analyses aim to recognise the structure and main factors affecting age discrimination. The first step was to verify whether the 13 items compose a two-factorial structure of hard and soft discrimination (H1) and whether the two factors are correlated (H2). For this purpose, confirmatory factor analysis (CFA) with WLSMV estimation (weighted least squares with mean and variance correction) designed for binary indicators was performed in Mplus 7. In the second step, to assess which socio-demographic factors and occupational characteristics relate to age discrimination (H3), we used two separate logistic regression analyses, in which hard and soft discrimination were dependent variables.

Results

Descriptive results

As many as 32.6% of respondents had experienced at least one type of age discrimination, with higher rates among women (35.5%) than men (28.6%) (Table 2). Out of 13 questions about discriminatory practices, only three were answered positively by more than 10% of the respondents. Most frequent were negative and stereotypical remarks about older workers (19.9%) and remarks of being “too old” for something (13.8%). A further 8.2% reported not having been hired for a new job because of age. While the first two types of behaviours are not directly covered by the European anti-discrimination directives and thus are not legally prohibited, the third type of ageist behaviour can already be characterised as direct age discrimination—it is illegal and could be punished by law. Other types of ageist experiences were reported less frequently. In total 15.7% respondents reported any type of hard discrimination and 28.6% any type of soft one, while 11.7% reported both types (Table 3).

Table 3.

Co-occurrence of soft and hard discrimination (% of each cell from total)

Soft discrimination (any type) Total
No Yes
Hard discrimination (any type)
No 67.4 16.9 84.3
Yes 4.0 11.7 15.7
Total 71.4 28.6 100.0
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N = 1000

Confirmatory factor analysis of age discrimination practices

The initial model of CFA with WLSMV estimation included two correlated factors: the first measured by items 1–6, and the second by items 7–13. It has a very good fit: Chi2(27): 114.2, p < 0.001, CFI = 0.982, RMSEA = 0.028 (90% confidence interval 0.019–0.036). The RMSEA is lower than the standard maximum acceptable value of 0.05, and CFI is higher than the minimum acceptable 0.95 (O’Boyle and Williams 2011). All standardised item loadings are significant at p < 0.001; they range from 0.71 to 0.88. Few modifications were tested,7 but the initial solution was chosen as the most appropriate. The final standardised solution is presented in Fig. 1.

Fig. 1.

Fig. 1

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Confirmatory factor analysis model for hard and soft age discrimination. Standardised results. Notes All coefficients significant at p < 0.001; CFA with WLSMV estimator for categorical variables used (Mplus 7); Model fit: CFI = 0.982, TLI = 0.978, RMSEA = 0.028; N = 1000. No missing values

Socio-demographic and work-related factors associated with age discrimination

Further analyses are based on binary representations of the two types of age discrimination discovered in the previous step, where 0 means no discrimination, and 1 any type of hard or soft discrimination. Such binary variables simplify interpretation and can be used as dependent variables in logit regression models. Table 4 presents descriptive results of how these two types of discrimination occur in particular categories of independent variables.

Table 4.

Soft and hard discrimination according to socio-demographic characteristics and work-related characteristics (% of respondents indicating some type of soft or hard discrimination)

Characteristics Categories Soft discrimination Hard discrimination N
Gender Female 31.0* 18.4** 581
Male 25.3* 11.9** 419
Age 45–49 27.3 13.0 293
50–54 30.0 16.4 317
55–59 28.4 16.7 257
60–64 28.6 18.0 133
Education level Elementary 26.4 18.9 53
Vocational 25.8 14.4 271
Secondary 31.5 17.4 425
Tertiary 27.1 13.6 251
Place of residence Big city and suburbs 29.6 17.5* 308
Medium, small city 31.6 18.9* 275
Rural area 25.9 12.2* 417
Occupational status Working 24.0*** 8.0*** 730
Unemployed 50.0*** 53.9*** 104
Retired but still working 26.2*** 9.8*** 61
Non-active 41.0*** 35.2*** 105
Occupational group Professionals, managers 25.6 10.7** 309
Services, technicians, office clerks 31.7 20.0** 350
Manual and agriculture workers 26.9 13.0** 324
Type of sector Public 28.2* 15.3** 386
Private 26.9* 13.7** 517
Non-profit or mixed 39.2* 27.8** 97
Sector of economy Production 25.9 14.8 398
Services 29.5 15.1 271
Public services 30.3 14.3 307
Total 28.6 15.7 1000
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Chi-square test for differences between categories within variables ** p < 0.01; * p < 0.05

Table 5 presents the results of logit regressions. The explanatory power of Model 1 (soft discrimination) is low (Nagelkerke R 2 = 0.074), with occupational status as the only significant variable—unemployed and non-active people experienced this type of discrimination between 2.46 and 3.30 times more often.

Table 5.

Models of logistic regression for variable “hard discrimination”

Variables Categories Model 1: soft discrimination Model 2: hard discrimination Model 3: hard discrimination (+ soft discr. as predictor)
Gender (R = men) 1.00 1.00 1.00
Women 1.19 1.63* 1.64*
Age (R = 45–49) 1.00 1.00 1.00
50–54 1.12 1.28 1.19
55–59 0.97 1.09 0.99
60–64 0.81 0.80 0.76
Education level (R = primary) 1.00 1.00
Vocational 1.07 0.93 0.79
Secondary 1.48 1.27 0.98
Tertiary 1.36 1.34 1.15
Place of residence (R = rural area) 1.00 1.00 1.00
Big city, suburbs 1.25 1.86* 2.24**
Small and medium sized city 1.24 1.61 1.65
Occupational status (R = working) 1.00 1.00 1.00
Unemployed 3.30*** 14.32*** 13.86***
Retired but still working 1.19 1.35 1.31
Non-active 2.46*** 7.29*** 6.90***
Occupational group (R = professionals, managers) 1.00 1.00 1.00
Services, technicians, office clerks 1.21 1.57 1.65
Manual and agriculture workers 1.34 1.14 1.06
Type of sector (R = non-profit or mixed) 1.00 1.00 1.00
Public 0.59* 0.58 0.64
Private 0.65 0.55 0.60
Sector of economy (R = production) 1.00 1.00 1.00
Services 1.06 0.64 0.56*
Public services 1.36 0.80 0.63
Soft discrimination (0 = no discrimination) – – 1.00
Occurs (1) – – 11.53***
Constant 0.22*** 0.06*** 0.02***
Model fit Nagelkerke R 2 0.074 0.274 0.455
Cox & Snell R 2 0.052 0.159 0.264
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*** p < 0.001; ** p < 0.01; * p < 0.05; N = 1000

In contrast, the explanatory power of Model 2 (hard discrimination) is rather high (Nagelkerke R 2 = 0.274). The probability of experiencing any type of these practices was 63% higher for women than for men, and for inhabitants of big cities it was 86% higher than for inhabitants of rural areas. Age and education had no statistical significance. The highest differences in the odds ratio were observed within occupational status—compared to the working group, unemployed people were almost 14 times more likely to experience hard discrimination. In the case of non-active persons this risk was over seven times higher.

In Model 3, based on theoretical assumptions, soft discrimination was included in the predictors as a work-related factor. Given the high correlation with the dependent variable, not surprisingly soft discrimination boosted the model fit (up to Nagelkerke R 2 = 0.455). When it occurred it increased the chances of hard discrimination by 11.53 times, with other variables at a similar level to Model 2 (except occurrence of the significant OR for the services sector, indicating a 44% lower probability than in production).

Discussion

Hard and soft discrimination

The empirical analyses confirm that there are two separate, but nevertheless linked types of age discrimination in the workplace, which relate to the legal status of the included items (which supports H1). The first type—hard discrimination—is measured by such practices as: not being hired because of age, receiving lower salary, and being demoted, fired or refused participation in training. It has two main characteristics. Firstly, it is a practice of employers (sometimes other co-workers), which is prohibited by the existing legislation, as it constitutes direct discrimination in a legal sense, that is “less favourable treatment” of individuals because of their age. Secondly, hard discrimination is observable in the personnel decisions of supervisors, which have a direct impact on older workers’ career opportunities and can significantly lower their occupational position and development.

The second type—soft discrimination—is measured by: being treated without respect by employers, co-workers or clients, being the subject of impolite remarks or jokes about age, being commented upon as “too old for something”, encountering stereotypes and negative remarks about older workers, experiencing lower evaluation of outcomes, or being intimidated or humiliated because of age. It refers to those discriminatory events which take place in interpersonal relations and are not directly prohibited by law. This type of discrimination includes the use of ageist language, remarks, jokes, as well as manifestations of lack of respect towards older workers. It reflects the general biased attitudes and the functioning of negative stereotypes, which might negatively influence the overall workplace atmosphere and be harmful to older workers. It also includes lower evaluation of outcomes. Such occurrences, when happening only once, do not constitute discrimination in the legal sense. However, if their frequency is higher they can meet the requirements of being “long-lasting and persistent” and constitute mobbing or harassment (Kędziora and Śmiszek 2008; O’Cinneide 2006). An important, but problematic characteristic of this type of discrimination is the fact that it is relatively difficult to prove due to its subjective and sometimes subtle nature, as well as the difficulty of providing evidence if legal action were to be taken.

In the surveyed sample, soft discrimination was reported more often (28.6%) than hard discrimination (15.7%). As expected in H2, there is a strong positive correlation between them. This means that occurrence of one of the forms of discrimination is followed by the other one. As presented in theoretical review soft discrimination can be considered as a work-related basis for hard discrimination (as in Model 3). Since the data do not allow analysis of the causal relationship, the nature of this relation between these two constructs remains a hypothesis for further research which would include investigation of soft and hard discrimination in the perspective of age stereotypes and other work-related characteristics.

The gender dimension, the role of other socio-demographic characteristics and work-related factors

The higher prevalence of experiences of age discrimination among women (35.5% in total) than men (28.2%) is in line with previous research (Handy and Davy 2007; van den Heuvel and van Santvoort 2011) and H3. This might be due to the higher awareness and sensitivity to social inequality issues sometimes attributed to women (Palmore 2001). At the same time, these results confirm the vast literature on “double jeopardy” or “gendered ageism” experienced by women, where two types of discrimination, based on age and gender, can be intertwined and strengthen each other (Krekula 2007; Macnicol 2006; Sontag 1972; Walker et al. 2007). However, when checked for other variables, gender differences remained significant only in the case of hard discrimination.

Hard discrimination was also reported more frequently by respondents living in urban areas (large and medium cities) than in rural areas, as confirmed in other research (Johnson and Neumark 1997; McGuire et al. 2008). Differences in age composition between urban and rural labour markets have been increased in Poland by the outflow of younger and skilled people from village to cities (Szymańska and Biegańska 2014). This might have resulted in intergenerational competitiveness and pressure on older workers, which has stirred age discrimination in urban labour markets. On the other hand, agriculture, located in rural areas, is a sector with a higher share of older workers and lower levels of age discrimination.

Contrary to other studies (van den Heuvel and van Santvoort 2011) and H3, education played no role in perceived discrimination. Perhaps this should come as no surprise, as the level of formal education decreases in importance for employers in the case of middle-aged and older workers, replaced by the requirement for experience and particular skills (Turek 2015).

Although differences regarding the occupational group and type of sector were significant in the descriptive analysis, they were non-significant in the logit models. Based on some studies we might have expected that service sectors are more discriminatory towards older workers as an effect of “customer-driven discrimination”. These findings were not confirmed in this study. As expected in H3, however, large differences between categories occurred for occupational status, where unemployed and inactive people reported higher scores in both types of discrimination. This can be explained in two ways. On the one hand, joblessness can result—at least partly—from discriminatory practices. These are the groups that are most exposed to differential treatment in the labour market due to their weaker bargaining position in comparison to those who are employed. This can particularly refer to hard discrimination. On the other hand, according to the attribution theory, people are more likely to attribute their disadvantaged position to external factors (Weiner 1974). Several studies have found that disadvantaged individuals were more willing to interpret unfavourable practices as unfair and discriminatory, what served them as an explanation for their failures (McElroy and Morrow 1983; Ruggiero and Taylor 1995; Kaiser and Miller 2001; Schmitt et al. 2014). Therefore, unemployed and inactive people could have been more sensitive for reporting age discrimination practices, especially of soft nature, than people in advantaged position.

Conclusions

This study aimed to contribute to the existing literature by focusing on perceived age discrimination in employment in Poland. Two types of age discrimination were specified based on legal regulations in this field—soft and hard discrimination—and were empirically verified on a sample of Polish older workers. The findings support theoretical reflection on the issue of the legality of discrimination (Fribergh and Kjaerum 2011; Macnicol 2006; Mcmullin and Marshall 2001), as the two recognised factors proved to be conceptually and theoretically sound. Soft discrimination reflects the types of ageist behaviours which are not directly enshrined in legislation, such as ageist jokes or comments. On the other hand, the hard type of age discrimination mirrors those events which are directly prohibited in the legal statutes and can be legally challenged in courts. These two types are interrelated, as the fact of experiencing soft discrimination increased the probability of an individual experiencing hard discrimination in the workplace. This suggests that workplaces which tolerate ageist behaviours, even if they are not directly unlawful, are also more prone to exhibit patterns of prohibited discriminatory behaviours.

Furthermore, the results indicate that age discrimination has unequal distribution among populations of older workers. This might direct the attention of future research on age discrimination in employment to these areas in order to expand the understanding of how ageism operates under those particular circumstances. Higher prevalence of age discrimination was reported among women and by respondents living in urban areas, confirming the conclusions from other studies. Also precarious job situations (job seekers, part-time workers, persons with breaks in employment) strongly influenced the feeling of being discriminated against. The efficiency of the anti-discrimination laws on the actual situation of older workers is difficult to assess. However, this study shows that fruitful incorporation of the legal framework into measuring the prevalence of age discrimination in employment is possible and suggests that further research should be carried out in a systematic way as to observe the changes over time and the possible impact of the anti-discrimination legislation as a precondition for successful policies to support older workers.

Footnotes

1

Prohibition on discrimination was added to the labour code by way of an amendment dated 24 August, 2001 and then altered due to the subsequent code amendments dated 14 November, 2003 and 21 November, 2008. In October 2010 the Act on the Implementation of Certain Provisions of the European Union in the Field of Equal Treatment was passed by the Parliament.

2

2241 people were sampled and contacted. 1000 respondents who met the inclusion criteria (age 45-65, active in the labour market) were interviewed. Individuals who were retired or received sickness or disability benefits but declared that they were still active in the labour market were included in the research.

3

The survey was financed by the Polish Ministry of Science and Higher Education within a doctoral research grant.

4

Previous measures of ageism included: the Attitudes Towards Old People Scale (Kogan 1961); the Fraboni Scale of Ageism (Fraboni et al. 1990) measuring the affective component of ageism; the Palmore Ageism Survey (Palmore 2001); a prescriptive ageism scale (North and Fiske 2013) focusing on prescriptive beliefs concerning potential intergenerational conflicts. Most of these tools measure the cognitive and affective dimensions of ageism and are not designed to study workplace discrimination. Yet this is the aim of the Nordic Age Discrimination Scale (Furunes and Mykletun 2010), which incorporates six items which are supposed to measure the perception of age discrimination among employees. However, it does not assess the experiences of age discrimination among older workers, which might still be disparate from the general view of employees about age discrimination in their workplace.

5

One of the difficulties faced by researchers when trying to measure age discrimination is the multiple meanings and interpretations of the term “discrimination” in the population. The term has been used extensively in various European Union social campaigns in recent years, and certainly raised awareness of the problem of discrimination, but it has nevertheless also contributed to many false conceptions of what discrimination is (Stypińska 2015). Hence, usage of this word in survey questionnaires poses several analytical and later interpretative problems, as respondents might hold very different ideas and convictions of what discrimination means.

6

PKD is related to the international Statistical Classification of Economic Activities in the European Community (NACE).

7

We tested: (1) leaving out item 1 (not hired because of age) slightly improves the model (CFI = 0.989, RMSEA = 0.023) as it correlated only weakly with other variables; item 1 was, however, included in the model because it performed relatively well in CFA (with R 2 = 0.51) and was important for theoretical reasons; (2) connecting item 7 (lower value of outcomes due to age) with both factors was suggested by modification indices and slightly improved the model (CFI = 0.985, RMSEA = 0.026), it also had theoretical premises (in this case the legal interpretation depends on other aspects, like repeatability, intense or consequences—it might be, but does not have to be illegal), but due to complexity in interpretation and further analysis it remained as a measure of the second factor only.

Responsible editor: Liat Ayalon and Clemens Tesch-Römer (guest editors) and Howard Litwin.

References

  1. Abrams D, Russel PS, Vauclair P, Swift H. Ageism in Europe. Findings from the European Social Survey. London: Age UK; 2011. [Google Scholar]
  2. Adamson L. Age discrimination—the new regime. Legal Inf Manag. 2006;6(04):302–305. doi: 10.1017/S1472669606000946. [DOI] [Google Scholar]
  3. Adler G, Hilber D. Industry hiring patterns of older workers. Res Aging. 2009;31(1):69–88. doi: 10.1177/0164027508324635. [DOI] [Google Scholar]
  4. AHRC (2015) National prevalence survey of age discrimination in the workplace. Australian Human Rights Commission, Sydney. https://www.humanrights.gov.au/sites/default/files/document/publication/AgePrevalenceReport2015.pdf. Accessed 3 Dec 2016
  5. Aigner D, Cain G. Statistical theories of discrimination in labor markets. Ind Labor Relat Rev. 1977;30(2):175–187. doi: 10.2307/2522871. [DOI] [Google Scholar]
  6. Amiraux V, Guiraudon V. Discrimination in comparative perspective: policies and practices. Am Behav Sci. 2010;53(12):1691–1714. doi: 10.1177/0002764210368092. [DOI] [Google Scholar]
  7. Angus J, Reeve P. Ageism: a threat to “aging well” in the 21st century. J Appl Gerontol. 2006;25(2):137–152. doi: 10.1177/0733464805285745. [DOI] [Google Scholar]
  8. Arrow K. What has economics to say about racial discrimination? J Econ Perspect. 1998;12(2):91–100. doi: 10.1257/jep.12.2.91. [DOI] [Google Scholar]
  9. Bal A, Reiss A, Rudolph C, Baltes B. Examining positive and negative perceptions of older workers: a meta-analysis. J Gerontol B Psychol Sci Soc Sci. 2011;66(6):687–698. doi: 10.1093/geronb/gbr056. [DOI] [PubMed] [Google Scholar]
  10. Becker GS. The economics of discrimination. Chicago: University of Chicago Press; 1957. [Google Scholar]
  11. Blackburn R. The global pension crisis: from gray capitalism to responsible accumulation. Polit Soc. 2006;34(2):135–186. doi: 10.1177/0032329206288150. [DOI] [Google Scholar]
  12. Breda J, Schoenmaekers D. Age: a dubious criterion in legislation. Ageing Soc. 2006;26(04):529–547. doi: 10.1017/S0144686X06004946. [DOI] [Google Scholar]
  13. Butler R. Why survive? Being old in America. New York: Harper & Row; 1975. [Google Scholar]
  14. Bytheway B. Ageism. Buckingham: Open University Press; 1995. [Google Scholar]
  15. Bytheway B. Ageism and age categorization. J Soc Issues. 2005;61(2):361–374. doi: 10.1111/j.1540-4560.2005.00410.x. [DOI] [Google Scholar]
  16. Cheung C, Kam P, Ngan R. Age discrimination in the labour market from the perspectives of employers and older workers. Intern Soc Work. 2010;1:1–20. doi: 10.1177/0020872810372368. [DOI] [Google Scholar]
  17. Chiu WCK, Chan A, Snape E, Redman T. Age stereotypes and discriminatory attitudes towards older workers: an East–West comparison. Hum Relat. 2001;50(6):629–661. doi: 10.1177/0018726701545004. [DOI] [Google Scholar]
  18. CIPD . Tackling age discrimination in the workplace. Creating a new age for all. London: Chartered Institute of Personnel and Development; 2005. [Google Scholar]
  19. Cuddy A, Fiske S. Doddering but dear: process, content, and function in stereotyping of older persons. In: Nelson T, editor. Ageism: stereotyping and prejudice against older persons. 2. Cambridge: MIT Press; 2002. pp. 3–26. [Google Scholar]
  20. Doron I. Elder law: current issues and future frontiers. Eur J Ageing. 2006;3(1):60–66. doi: 10.1007/s10433-006-0019-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Duncan C, Loretto W. Never the right age? Gender and age-based discrimination in employment. Gend Work Organ. 2004;11(1):95–115. doi: 10.1111/j.1468-0432.2004.00222.x. [DOI] [Google Scholar]
  22. EC (2009) Developing anti-discrimination law in Europe. The 27 EU Member States compared. European Commission, Brussles
  23. Eurobarometer (2015) Discrimination in the EU in 2015. Report. European Commission, Brussles. doi:10.2838/499763
  24. Fraboni M, Saltstone R, Hughes S. The Fraboni scale of ageism (FSA): an attempt at a more precise measure of ageism. Can J Aging. 1990;9(01):56–66. doi: 10.1017/S0714980800016093. [DOI] [Google Scholar]
  25. Fribergh E, Kjaerum M. Handbook on European non-discrimination law. Publications Office of the European Union. Luxembourg. 2011 doi: 10.2811/11978. [DOI] [Google Scholar]
  26. Furunes T, Mykletun RJ. Age discrimination in the workplace: validation of the Nordic age discrimination scale (NADS) Scand J Psychol. 2010;51(1):23–30. doi: 10.1111/j.1467-9450.2009.00738.x. [DOI] [PubMed] [Google Scholar]
  27. Gaster L, Ashon K, Bass M. Past it at 40? A grassroots view of ageism and discrimination in employment. Bristol: The Policy Press; 2002. [Google Scholar]
  28. Gonera K (2004) Counteracting discrimination in Poland—information publication (Przeciwdziałanie Dyskryminacji W Polsce—Informator). Publication of Plenipotentiary of Government for Equal Treatment, Warsaw
  29. Green TK. Discrimination in workplace dynamics: toward a structural account of disparate treatment theory. Harv Civ Rights Civil Lib Law Rev. 2001;38:91–157. doi: 10.2139/ssrn.367701. [DOI] [Google Scholar]
  30. Handy J, Davy D. Gendered ageism: older women’s experiences of employment agency practices. Asia Pac J Hum Res. 2007;45(1):85–99. doi: 10.1177/1038411107073606. [DOI] [Google Scholar]
  31. Harper S, Khan HTA, Saxena A, Leeson G. Attitudes and practices of employers towards ageing workers: evidence from a global survey on the future of retirement. Age Horiz. 2006;5:31–41. [Google Scholar]
  32. HCS (2014) Human Capital Study, Polish representative research of working age population. https://bkl.parp.gov.pl/dane.html
  33. Henkens K. Stereotyping older workers and retirement: the managers’ point of view. Can J Aging. 2005;24(4):353–366. doi: 10.1353/cja.2006.0011. [DOI] [Google Scholar]
  34. Herring J. Age discrimination and the law: forging the way ahead. In: Parry E, Tyson S, editors. Managing age diverse workforce. Basingstoke: Palgrave Macmillan; 2011. pp. 24–43. [Google Scholar]
  35. Iversen TL, Larsen L, Solem PE. A conceptual analysis of ageism. Nord Psychol. 2009;61(3):4–22. doi: 10.1027/1901-2276.61.3.4. [DOI] [Google Scholar]
  36. Johnson RW, Neumark D. Age discrimination, job separations, and employment status of older workers: evidence from self-reports. J Hum Resour. 1997;32(4):779. doi: 10.2307/146428. [DOI] [Google Scholar]
  37. Kaiser CR, Miller CT. Stop complaining! The social costs of making attributions to discrimination. Pers Soc Psychol B. 2001;27(2):254–263. doi: 10.1177/0146167201272010. [DOI] [Google Scholar]
  38. Kędziora K, Śmiszek K. Discrimination and mobbing in employment (Dyskryminacja i mobbing w zatrudnieniu) Walnut Creek: C.H. Beck; 2008. [Google Scholar]
  39. Kite ME, Stockdale GD, Whitley BE, Johnson BT. Attitudes toward younger and older adults: an updated meta-analytic review. J Soc Issues. 2005;61(2):241–266. doi: 10.1111/j.1540-4560.2005.00404.x. [DOI] [Google Scholar]
  40. Kogan N. Attitudes toward old people: the development of a scale and an examination of correlates. J Abnorm Soc Psychol. 1961;62:44–54. doi: 10.1037/h0048053. [DOI] [PubMed] [Google Scholar]
  41. Krekula C. The Intersection of age and gender: reworking gender theory and social gerontology. Curr Sociol. 2007;55(2):155–171. doi: 10.1177/0011392107073299. [DOI] [Google Scholar]
  42. Lahey JN. International comparison of age discrimination laws. Res Aging. 2010;32(6):679–697. doi: 10.1177/0164027510379348. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Levy BR, Banaji MR. Implicit ageism. In: Nelson TD, editor. Ageism: stereotyping and prejudice against older persons. 2. Cambridge: MIT Press; 2004. pp. 49–75. [Google Scholar]
  44. Lincoln AE, Allen MP. Double jeopardy in Hollywood: age and gender in the careers of film actors, 1926–1999. Sociol Forum. 2004;19(4):611–631. doi: 10.1007/s11206-004-0698-1. [DOI] [Google Scholar]
  45. Loretto W, White P. Employers’ attitudes, practices and policies towards older workers. Hum Res Manag J. 2006;16(3):313–330. doi: 10.1111/j.1748-8583.2006.00013.x. [DOI] [Google Scholar]
  46. Macnicol J. The age discrimination debate in Britain: from the 1930s to the present. Soc Pol Soc. 2005;4(3):295–302. doi: 10.1017/S1474746405002447. [DOI] [Google Scholar]
  47. Macnicol J. Age discrimination: an historical and contemporary analysis. Cambridge: Cambridge University Press; 2006. [Google Scholar]
  48. Malinen S, Johnston L. Workplace ageism: discovering hidden bias. Exp Aging Res. 2013;39(4):445–465. doi: 10.1080/0361073X.2013.808111. [DOI] [PubMed] [Google Scholar]
  49. McElroy JC, Morrow PC. An attribution theory of sex discrimination. Pers Rev. 1983;12(4):11–13. doi: 10.1108/eb055485. [DOI] [Google Scholar]
  50. McGuire SL, Klein DA, Chen SL. Ageism revisited: a study measuring ageism in East Tennessee, USA. Nurs Health Sci. 2008;10(1):11–16. doi: 10.1111/j.1442-2018.2007.00336.x. [DOI] [PubMed] [Google Scholar]
  51. Mcmullin JA, Marshall VW. Ageism, age relations, and garment industry work in Montreal. Gerontologist. 2001;41:111–122. doi: 10.1093/geront/41.1.111. [DOI] [PubMed] [Google Scholar]
  52. Minichiello V, Browne J, Kendig H. Perceptions and consequences of ageism: views of older people. Ageing Soc. 2000;20(3):253–278. doi: 10.1017/S0144686X99007710. [DOI] [Google Scholar]
  53. MPiPS (2014) Solidarity between generations. Policies towards activation of persons 50 + on the labour market (Program Solidarność pokoleń. Działania dla zwiększenia aktywności zawodowej osób w wieku 50 +). Ministry of Labour and Social Policy in Poland (Ministerstwo Pracy i Polityki Społecznej), Warszawa
  54. Neumark D. The age discrimination in employment act and the challenge of population aging. Res Aging. 2009;31(1):41–68. doi: 10.1177/0164027508324640. [DOI] [Google Scholar]
  55. Neumark D, Button P. Did age discrimination protections help older workers weather the great recession? J Pol Anal Manag. 2014;33(4):566–601. doi: 10.1002/pam. [DOI] [Google Scholar]
  56. North MS, Fiske ST. A prescriptive intergenerational-tension ageism scale: succession, identity, and consumption (SIC) Psychol Assess. 2013;25(3):706–713. doi: 10.1037/a0032367. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. O’Cinneide C (2006) Fumbling towards coherence: the slow evolution of equality and anti-discrimination law in Britain. Soc Sci Res Netw. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=875408. Accessed 10 June 2014
  58. O’Boyle EH, Williams LJ. Decomposing model fit: measurement vs. theory in organizational research using latent variables. J Appl Psychol. 2011;96(1):1–12. doi: 10.1037/a0020539. [DOI] [PubMed] [Google Scholar]
  59. OECD (2006) Live longer, work longer. Organisation for Economic Co-operation and Development, Paris. doi:10.1787/9789264035881-en
  60. Oswick C, Rosenthal P. Towards a relevant theory of age discrimination in employment. In: Noon M, Ogbonna E, editors. Equality, diversity and disadvantage in employment. New York: Palgrave; 2001. pp. 156–171. [Google Scholar]
  61. Palmore E. Ageism, negative and positive. 2. New York: Springer; 1999. [Google Scholar]
  62. Palmore E. The ageism survey: first findings. Gerontologist. 2001;41:572–575. doi: 10.1093/geront/41.5.572. [DOI] [PubMed] [Google Scholar]
  63. Palmore E, Branch L, Harris DK, editors. Encyclopedia of ageism. New York: The Haworth Press; 2005. [Google Scholar]
  64. Parry E, Tyson S, editors. Managing age diverse workforce. London: Palgrave Macmillan; 2011. [Google Scholar]
  65. Perek-Białas J, Turek K. Organisation-level policy toward older workers in Poland. Int J Soc Welf. 2012;21:101–116. doi: 10.1111/j.1468-2397.2012.00878.x. [DOI] [Google Scholar]
  66. Perry EL, Finkelstein LM. Toward a broader view of age discrimination in employment-related decisions: a joint consideration of organizational factors and cognitive processes. Hum Resour Manag Rev. 1999;9(1):21–49. doi: 10.1016/S1053-4822(99),00010-8. [DOI] [Google Scholar]
  67. Phelps E. The statistical theory of racism and sexism. Am Econ Rev. 1972;62(4):659–661. [Google Scholar]
  68. Posthuma R, Campion M. Age stereotypes in the workplace: common stereotypes, moderators, and future research directions. J Manag. 2009;35(1):158–188. [Google Scholar]
  69. Riach PA, Rich J (2006) An experimental investigation of age discrimination in the French labour market. IZA Discussion Paper (2522). www.papers.ssrn.com/sol3/papers.cfm?abstract_id=956389. Accessed 12 July 2015
  70. Rothenberg JZ, Gardner DS. Protecting older workers: the failure of the Age Discrimination in Employment Act of 1967. J Sociol Soc Welf. 2011;38(1):9–30. [Google Scholar]
  71. Rothermund K, Temming F. Age discrimination (Diskriminierung aufgrund des Alters) Berlin: Antidiskriemienierung Stelle des Bundes; 2010. [Google Scholar]
  72. Ruggiero KM, Taylor DM. Coping with discrimination: how disadvantaged group members perceive the discrimination that confronts them. J Pers Soc Psychol. 1995;68:826–838. doi: 10.1037/0022-3514.68.5.826. [DOI] [PubMed] [Google Scholar]
  73. Ruzik-Sierdzinska A, Perek-Bialas J, Turek K. Did transition to market economy and the EU membership have an impact on active ageing policy in Poland? In: Ervik R, Lindén TS, editors. The making of aging policy: theory and practice in Europe. Cheltenham: Edward Elgar; 2013. pp. 124–147. [Google Scholar]
  74. Schmitt M, Branscombe NR, Postmes T, Garcia A. The consequences of perceived discrimination for psychological well-being: a meta-analytic review. Psychol Bull. 2014;140(4):921–948. doi: 10.1037/a0035754. [DOI] [PubMed] [Google Scholar]
  75. Sontag S. The double standard of aging. Saturday Rev Lit. 1972;39:29–38. [Google Scholar]
  76. Standing G. The precariat: the new dangerous class. London: Bloomsbury Academic; 2011. [Google Scholar]
  77. Stypińska J. Older worker in the Polish labour market: 40 + ? 50 + ? Or only “plus”? (Starszy pracownik na rynku pracy w Polsce: 40 + ? 50 + ? Czy tylko “plus”?) Stud Socjol. 2015;2(217):143–165. [Google Scholar]
  78. Szymańska D, Biegańska J. Characteristics of rural areas in Poland in the context of population ageing (Charakterystyka obszarów wiejskich w Polsce w kontekście starzenia się ludności) Studia Obszarów Wiejskich. 2014;XXXV:89–108. [Google Scholar]
  79. Taylor PE, Walker A. The ageing workforce: employers’ attitudes towards older people. Work Employ Soc. 1994;8(4):569–591. doi: 10.1177/095001709484005. [DOI] [Google Scholar]
  80. Taylor PE, Walker A. Employers and older workers: attitudes and employment practices. Ageing Soc. 1998;18(6):641–658. doi: 10.1017/S0144686X98007119. [DOI] [Google Scholar]
  81. Tesch-Roemer C, von Kondratowitz HJ. Comparative ageing research: a flourishing field in need of theoretical cultivation. Eur J Ageing. 2006;3(3):155–167. doi: 10.1007/s10433-006-0034-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  82. Turek K. Meaning of age in labour market—model of relationship between employer and employee (Znaczenie wieku na rynku pracy—model relacji pomiędzy pracownikiem i pracodawcą) Stud Socjol. 2015;2(217):167–194. [Google Scholar]
  83. Turek K, Perek-Białas J. The role of employers opinions about skills and productivity of older workers: example of Poland. Employee Relat. 2013;35(6):648–664. doi: 10.1108/ER-04-2013-0039. [DOI] [Google Scholar]
  84. Turek K, Perek-Białas J, Stypińska J. Socio-economic status in ageing Poland: a question of cumulative advantages and disadvantages. In: Komp K, Johansson S, editors. Lifecourse perspective on ageing populations: critical and international approaches. Bristol: Policy Press; 2015. pp. 85–107. [Google Scholar]
  85. Umhauser-Enning A. The EU ban on age-discrimination and older workers: potentials and pitfalls. Int J Comp Labour Law Ind Relat. 2013;29(4):391–414. [Google Scholar]
  86. Van den Heuvel WJ, van Santvoort MM. Experienced discrimination amongst European old citizens. Eur J Ageing. 2011;8(4):291–299. doi: 10.1007/s10433-011-0206-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  87. Walker H, Grant D, Meadows M, Cook I. Women’s experiences and perceptions of age discrimination in employment: implications for research and policy. Soc Policy Soc. 2007;6(01):37–48. doi: 10.1017/S1474746406003320. [DOI] [Google Scholar]
  88. Weiner B. Achievement motivation and attribution theory. Morristown: General Learning Press; 1974. [Google Scholar]
  89. Wieczorek M, Bogatko K (2013) Anti-discrimination law in praxis of Polish judiciary. Report from monitoring (Prawo Antydyskryminacyjne w Praktyce Polskich Sądów Powszechnych. Raport z Monitoringu). Polskie Towarzystwo Prawa Antydyskryminacyjnego, Warszawa

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