The hiring of older workers in Germany has seen an increase in the past two decades, with older workers typically having lower unemployment rates and job separation rates. However, the literature on the hiring behavior of firms in the context of an aging society is limited. This article fills this gap by analyzing panel data from the Establishment History Panel and regional unemployment data. It finds that an aging workforce and an increasing share of older unemployed workers positively influence the hiring of older workers, but the relationship is inelastic. The study also highlights the negative impact of partial retirement schemes on hiring older workers, despite their intended purpose to alleviate employment conditions for older workers. The article provides valuable insights into the factors influencing the hiring of older workers and the challenges faced by firms in an aging society.
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Abstract
This article analyses how hiring older workers adjusts to demographic change in the labour force by using information from more than 500,000 firms in Germany. We find robust evidence that firms faced with an ageing labour market hire relatively more older workers. However, the pace of this adjustment is relatively slow, particularly when ageing happens outside the firm. The tendency to employ older people is more considerable in East Germany, where the demographic change moves forward faster. Furthermore, part-time working models support hiring older workers, but this effect becomes less important in larger firms and East Germany. Finally, while partial retirement regulations enhance flexibility within the firm, they, unfortunately, diminish the employment opportunities for older external job seekers.
Notes
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1 Introduction
The share of older workers in the German labour force has increased continuously in the last two decades. Compared to younger workers, older workers typically have lower unemployment rates, lower job separation rates and lower job-finding rates (see Ochsen (2023), for example). In addition, on average, older workers have longer job tenure, which means they often grow older within the firm. Therefore, the firms’ share of older workers increases when this group is larger (baby boomers). However, less is known about the behaviour of firms in hiring older workers in ageing societies.
The literature provides empirical evidence that a rising share of older personnel is positively related to the average age at hire or the share of hired older workers.1 Concerning Germany, Heywood et al. (2010) analyse management attitudes towards hiring older workers in general. However, they do not measure the hiring behaviour directly. The literature discussed uses cross-sectional data and comparatively small samples. Hence, they cannot capture developments over time and cannot represent changes in the age structure of the labour force and their relationship to the hiring of older workers.
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The literature related to the hiring of older unemployed is somewhat different. Axelrad et al. (2018) conclude that the probability of an unemployed individual finding a new job decreases with age. Age discrimination against older unemployed workers in the hiring process is reported by Neumark et al. (2019) for the USA and Oesch (2020) for Switzerland. In the German context, Dietz and Walwei (2011) and Ochsen (2023) calculate the job-finding rates and conclude that these rates decrease steeply with age. Wright (2015) concludes that German establishments are still reluctant to hire older workers, which fosters long-term unemployment.2 Consequently, it is a substantial problem for older workers to become re-employed after a job separation, especially if they seek full-time employment (Adams and Heywood 2007 and Daniel and Heywood 2007).
None of the above studies analyses the joint implications of the increasing shares of older employed and unemployed in the labour force for the hiring behaviour of firms. For this reason, our research question is how labour market ageing characteristics are related to hiring older workers. In particular, we are interested in the role of the shares of full-time and part-time older employed, the share of partial retirement and the share of older unemployed in the firms’ local area.
We use the share of older workers (50–64 years old) hired on all hires as the variable of primary interest. Figure 1 reveals the evolution of this variable in Germany from 2000 to 2019. Firms hire increasingly relatively more older workers in the considered period. However, how is this related to the ageing of the labour force?
This article contributes to the literature in two ways. Firstly, we analyse panel data using the Establishment History Panel and match this panel with data on unemployment at the administrative district level (Landkreise und kreisfreie Städte). This enables us to analyse the firm’s hiring behaviour over a period of 20 years, which contrasts existing studies that mainly analyse cross-sectional data. Secondly, to consider both the employed and the unemployed part of the labour force, we use variables at different aggregation levels to examine the hiring behaviour of firms. We use the age structure of the employees at the firm level (to control for the effect of an ageing workforce) and the age-specific unemployment shares at the level of the administrative district where the firm is located (to capture the impact of an ageing pool of unemployed). By considering both types of hired older workers, we can compare the hiring behaviour with respect to older employed and older unemployed persons.
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We find that the joint effect of an ageing labour force on hiring older workers is positive but inelastic. A higher share of older unemployed increases the firms’ hiring of older workers, but the elasticity varies by around 0.07. The internal age structure at the firm level is more important because the elasticity for full-time workers is 0.33, and the elasticity for part-time workers is 0.54, on average. Hence, firms’ adjustment to the demographic trend is relatively slow, implying firms react hesitantly to the demographic development. In addition, we show that a rising share of employees in partial retirement schemes is negatively related to the share of hired workers aged 50 and older. This result has to be seen in context with particularities of the German law on partial retirement. Although the law intends to alleviate the employment situation of older workers, we show that the regulation impedes hiring older workers. Lastly, we present the results differentiated by firm size, sector, and East and West Germany.
This paper is organised as follows. The following section provides some stylised facts. Section 3 describes our database and the empirical strategy. Section 4 entails the discussion of the results, and a robustness check is provided in Sect. 5. Section 6 concludes.
2 Stylised facts
Using data for the US labour market and the period 1983–1995, Hirsch et al. (2000) find that the share of hired older workers (50 years and older) on all hires is about 0.1. In addition, they calculate the percentage of older workers in the workforce and receive an average value of 0.19. Following Hutchens (1986), they finally calculate the openness to older hires (the proportion of hired older workers divided by the proportion of older workers in the workforce) and receive 0.53. A value between 0 and 1 can be interpreted as information that hiring opportunities are restricted for older workers. Rearranging this measure additionally allows us to interpret it as the relative hiring rate for older workers (the proportion of hired older workers in all older workers divided by the overall hiring rate). From this, it follows that the average hiring rate is about twice as large as the hiring rate of older workers.
We consider the Establishment History Panel for Germany to calculate the macroeconomic indicators discussed (Table 1).3 Basically, four measures are necessary to examine the general labour market situation for older workers in this context. Measure (1), the share of hires aged 50–64 to all hires (\(H_{old} / H\)), provides information on the general hiring behaviour of firms related to older workers. Measure (2), the share of workers in the workforce who are 50–64 years old (\(L_{old} / L\)), offers the same idea as the first measure, but for the workforce. Measure (3), the ratio of hires to the workforce (the hiring rate), indicates how open firms are to job seekers. Measure (4), a similar proportion, the ratio of hires of older workers to workforce share of older workers (\(H_{old} / L_{old}\)), indicates the deviation for older workers from the mean.4
Table 1
Different labour market measures
Older workers
Rest of the workers
(1)
Hiring share 50–64
\(H_{old}/H\)
0.139
Hiring share 15–49
\(H_{rest}/H\)
0.861
(2)
Share workers 50–64
\(L_{old}/L\)
0.238
Share workers 15–49
\(L_{rest}/L\)
0.762
(3)
Hiring rate
H/L
0.263
Hiring rate
H/L
0.263
(4)
Hiring rate 50–64
\(H_{old}/L_{old}\)
0.153
Hiring rate 15–49
\(H_{rest}/L_{rest}\)
0.297
(5)
Relative hiring ratio
(4)/(3)
0.583
Relative hiring ratio
(4)/(3)
1.130
(6)
Openness
(1)/(2)
0.583
Openness
(1)/(2)
1.130
source: EHP-7520 and own calculations
The relative hiring ratio (5) shows that the considered age group is less likely to be hired than the average if the number is below 1. Openness (6) measures the openness to older hires relative to the proportion of older workers in the firm. As mentioned above, both measures provide the same quotient. The results for (1), (2) and (6) are similar to the findings of Hirsch et al. (2000). Hence, on average, in the last two decades, German firms seem not to be more open to older workers than US firms in the 1980s and 1990s. Furthermore, relative labour market opportunities seem not to have improved for older workers, even in ageing times.
Concerning measures (5) and (6), we also can conclude that when the share of the older workforce increases only due to ageing within the firm, the relative hiring ratio for older workers must decline when the ratio of hires aged 50–64 to all hires remains unchanged. In contrast, when the ratio of hires aged 50–64 to all hires increases, the relative hiring ratio for older workers must increase when the share of the older workforce remains constant. In the first case, the relative hiring ratio for older workers declines due to the ageing of the workforce, while in the second case, the relative hiring ratio for older workers increases due to a higher share of hired older workers. When both causes of ageing happen (what we observe), the relative hiring ratio for older workers may increase, decrease or remain unchanged.
On the right-hand side of Table 1, we report the results for the age group 15 to 49 (rest of the workers). The measures (5) and (6) for the rest of the workers are almost twice as large. This is related to an above-average hiring rate of this age group (4) and a hiring share (1) that is larger than the share of workers of this age group (2).
We argue in the introduction that we prefer to analyse the share of hired older workers (1) to understand the hiring behaviour of firms. In principle, the hiring rate of older workers (4) seems to be an appropriate alternative. We apply a simple approach to motivate that (1) is more suitable as the dependent variable than (4). Given that we have only two age groups (old and rest), there are different ways to write down the hiring rate:
From this, it follows that the hiring share of older workers (1) equals the weighted relative hiring ratio of older workers. We decided to use (1) as our dependent since it is not directly related to the firm’s workforce ageing.
Figure 2 provides the development of the variables discussed. The first value of each series is scaled to one to ease comparability. On the one hand, both hiring rates follow a similar trend; consequently, the ratio of both is almost unchanged. Hence, the overall relative labour market opportunities for older workers do not increase during the period considered. On the other hand, the share of hired older workers and the percentage of older in the workforce have a similar positive pattern. This means that firms hire a larger share of older workers, and ageing within the firm increases the share of the older workforce at the same pace. The data analyses below examine the relationship between the share of hired older workers and the share of older employed at the firm level. In addition, we calculate the relative hiring ratio for each considered subset of data.
Fig. 2
Development of different labour market measures for older Workers
This study uses the weakly anonymous Establishment History Panel (EHP) for Germany.5 The EHP is a 50 per cent sample of all establishments throughout Germany with at least one employee subject to social security at the reference date of June 30 for a given year. Hence, the panel structure is yearly firm-level data.
Our database consists of 9,595,987 observations for 1,893,345 firms distributed across all 401 administrative districts in Germany from 2000 to 2019.6 Our dependent variable is the ratio of hired older workers aged 50–64 to all hires at the firm level. In the baseline specification, we remove all establishments with less than five employees from the panel. Since our dependent variable is a fraction, small firms are more likely to hire fewer people. If a firm hires exactly one person, the share of older workers entering the firm in that respective year is either 0 or 1. Furthermore, we only consider firms in the panel that are included for at least five years. This is due to our general interest in within-firm changes and the relationship between the dependent and independent variables. Hence, for the baseline model, we use 5,879,369 observations for 530,658 firms.7
To capture the age distribution in the workforce, we consider the following variables as controls in our regressions. We include the youth share (15 to 24 years) and the share of older workers (50 to 64 years) and consider the remaining age group (25 to 49 years old) as the reference. We differentiate between full-time and part-time employees to account for flexible working time models. In addition, we include the share of employees working in partial retirement schemes. To proxy the age structure of the labour force further, we consider the youth share and the share of older workers among the unemployed at the administrative district level. The Federal Employment Agency provides these data. Table 2 reports summary statistics for the variables of primary interest. For a data description including control variables, see Appendix A.
Table 2
Summary statistics
Model
Obs
Mean
Std. err.
Min
Max
Firms
Hires 50–64
5,879,369
13.89
23.19
0
100
Full-time 50–64
5,879,369
22.00
23.09
0
100
Full-time 15–24
5,879,369
9.60
16.39
0
100
Part-time 50–64
5,879,369
25.66
25.42
0
100
Part-time 15–24
5,879,369
23.86
27.19
0
100
Partial retirement
5,879,369
0.43
2.14
0
100
Administrative district
Share unemployment 50–64
5,879,369
30.23
5.50
17.70
58.33
Share unemployment 15–24
5,879,369
10.47
2.27
2.70
21.09
We analyse the impact of demographic change on the share of hired older workers by using a panel data model with fixed firm-level effects and year effects.8 The model is specified as follows:
F is a vector comprising \(k=1,...,K\) variables at the firm level, and X describes a vector of \(m=1,...,M\) variables at the administrative district level. Index i represents the firm level, j the administrative district level and t the annual time dimension. Finally, \( \epsilon \) depicts the residuals, \(\alpha \) fixed effects and \(\delta \) time effects.
Concerning the statistical relevance of our estimates, we provide cluster-robust standard errors at the district level to control for heteroskedasticity, serial correlation and cross-sectional dependence in the residuals.9 In addition, we consider the False Positive Risk (FPR) to provide information on the statistical evidence of the estimated coefficients. In contrast to the p-value, the FPR measures the probability of the null hypothesis being true (Colquhoun 2019 and Colquhoun 2017).10 For a discussion of the misinterpretation of p-value, see, for example, Wasserstein and Lazar (2016). For the computation of the FPR, we refer to Appendix B.
4 Results
First, we provide the results for our baseline model and Germany overall. In the following subsection, we differentiate by firm size, sector and East and West Germany.
4.1 Baseline model
Table 3 presents our baseline results. We found a positive correlation between the share of older full-time workers (full-time 50–64) and the share of hired older workers. A 4.8 percentage point increase in the share of older employees corresponds to a one percentage point increase in the share of hired older workers. The corresponding elasticity of about 0.33 suggests a relatively slow adjustment to demographic change. This could be due to firms’ experiences with older employees and adjustments in production processes and services.
Table 3
Hiring older workers: baseline results
Dependent variable: share hires 50–64
Baseline
Firm level
Full-time 50–64
0.2084\(^\ddag \)
(0.0024)
Part-time 50–64
0.2945\(^\ddag \)
(0.0023)
Full-time 15–24
\(-\)0.0394\(^\ddag \)
(0.0008)
Part-time 15–24
\(-\)0.0268\(^\ddag \)
(0.0006)
Partial retirement
\(-\)0.2454\(^\ddag \)
(0.0127)
Administrative district level
Unemployed 50–64
0.0314\(^\ddag \)
(0.0056)
Unemployed 15–24
0.0371
(0.0115)
Observations
5,879,369
Firms
530,658
\(R^2\)
0.1158
Fixed effects regression. For further controls, see the data description. Cluster-robust standard errors at the district level are in parentheses. \(^\dag \): FPR \(\le \) 0.05, \(^\ddag \): FPR \(\le \) 0.01
An increase in the share of older part-time workers (part-time 50 to 64) is also positively related to hiring older workers. The corresponding elasticity is 0.54, indicating a slightly more elastic relationship. This might highlight the particularities of German law. Usually, part-time employees enjoy the same rights as full-time employees regarding vacation time or dismissal protection. However, paragraph 14, Sect. 3 of the Teilzeit- und Befristungsgesetz (TzBfG) allows the employer to causelessly limit the employment contract to a maximum of five years if the employee has been unemployed before re-employment for at least four months and is at least 52 years old. This information is important because it grants the employer more flexibility. Employers might specifically target hiring older workers to exploit this option since they do not have to account for dismissal protection. In this sense, Heywood et al. (2010) and Daniel and Heywood (2007) point out that older workers might be hired into part-time positions to protect the core workforce.
We also found a positive correlation between the share of older unemployed (unemployed 50–64) and the dependent variable. This means that a larger share of older unemployed in a firm’s region coincides with a larger share of older workers among hires. Specifically, a ten percentage point increase in the share of older unemployed corresponds to a 0.314 percentage point increase in the share of hired older workers. This corresponds to an elasticity of 0.07. This extremely inelastic relationship aligns with the lowest job-finding rate of older workers among all age groups in Germany (see, for example, Ochsen 2023).
So far, these findings give rise to the assumption that firms adjust their hiring behaviour towards the ageing process in the labour force. If older workers become a relatively abundant resource in the labour market, firms hire them more frequently. Taking the effects of older full-time, part-time and unemployed together, a 3.8 percentage point rise in the share of older workers in the labour force is related to a one percentage point rise in the share of hired older workers.11 This still reflects an inelastic relationship between an increasing share of older workers in the labour force and this age group’s hiring. As pointed out above, this might reflect that older workers have lower turnover rates and, consequently, are underrepresented among hires. However, firms that experience ageing within the firm hire relatively more older workers than firms that essentially experience ageing outside the firm. In the latter case, the workforce is not ageing due to a targeted hiring policy. This corresponds to the observation that older workers face the highest risk of long-term unemployment compared to all other age groups (see, for example, Ochsen 2023).
A rising share of younger full-time and part-time employees (full-time 15 to 24) is negatively related to the chances for older workers to be hired. One reason is that firms want to establish long-term employer–employee relationships to reduce fluctuation costs. Hiring is costly, and firms might prefer to hire younger workers since that gives them more time to amortise their initial costs (e.g., search costs and on-the-job training). Concerning part-time, firms might prefer to hire young workers into part-time positions to provide these young workers a stepping stone into future full-time jobs (Cai et al. 2014). In addition, young employees will likely be apprenticeship or university graduates who have formed some initial human capital. Rather than hiring more older workers, firms with a growing share of younger employees might invest further in forming firm-specific and industry-specific human capital.
Conversely, the share of the younger unemployed (unemployed 15 to 24) has a positive coefficient, though with low statistical evidence. Keeping the share of the older unemployed constant, an increasing share of younger unemployed implies a decreasing share of prime-age unemployed. Therefore, regions with a smaller proportion of prime-age workers in the unemployment pool tend to favour older unemployed job seekers. This is consistent with the findings at the firm level because here, we also consider firm-specific requirements.
The negative sign of the coefficient for partial retirement poses an interesting example of how the effects of law may undermine legislators’ intentions. The German partial retirement law aims to alleviate older employees’ transition into retirement and improve the working conditions of older workers. However, our robust result shows that hiring older workers decreased due to this legislation. Paragraph 1, Sect. 2 of the Altersteilzeitgesetz (AltTZG) states that the Federal Employment Agency subsidises firms that offer partial retirement schemes if the respective worker has reached an age of at least 55 years and if the firm hires someone new into the then vacant position. Thus, a rising share of employees in partial retirement could indicate that firms use these schemes to ease the transition into retirement for their older part-time personnel. This may render the jobs in the firm more attractive for older workers. However, some additional regulations exist in Paragraph 3, Sect. 2 of the AltTZG. To be entitled to the subsidy, a firm must fill the vacant spot either with a worker who has been unemployed or has successfully completed his/her vocational training. In this sense, the law not only eases the transition from working life into retirement for older employees but also subsidises efforts by the firms to rejuvenate their workforce. In this way, the regulation harms the employment of older workers. However, the elasticity for partial retirement is \(-\)0.01. Although the coefficient in the regressions is statistically relevant, economically, the effect is of minor importance.
4.2 Further analyses: firm size, sector and region
Our database does not include monetary variables, such as investment or profitability. We also have no information on the need for specific human capital, tasks or the hiring process’s general expectations (e.g., search duration or the degree of labour shortage). Therefore, this section provides further data analyses of different subgroups to determine the results’ sensitivity. First, we subdivide the data into three firm size groups. This allows us to analyse the relationship between firm size and hiring older workers in greater detail. We expect large firms to behave differently from small firms concerning the hiring process. Second, we split the data into primary, secondary and tertiary sectors. Here, we expect different dynamics, particularly between the primary and the tertiary sectors. Third, we analyse separated East and West Germany. Demographic change is much more advanced in East Germany; hence, the supply of older workers is relatively larger in the East.
Table 4
Hiring older workers: firm size
Dependent variable: share hires 50–64
Small firms
Medium firms
Large firms
Firm level
Full-time 50–64
0.2737\(^\ddag \)
0.2012\(^\ddag \)
0.2269\(^\ddag \)
(0.0026)
(0.0026)
(0.0036)
Part-time 50–64
0.4790\(^\ddag \)
0.2531\(^\ddag \)
0.1426\(^\ddag \)
(0.0020)
(0.0026)
(0.0026)
Full-time 15–24
\(-\)0.0400\(^\ddag \)
\(-\)0.0419\(^\ddag \)
\(-\)0.0648\(^\ddag \)
(0.0007)
(0.0011)
(0.0026)
Part-time 15–24
\(-\)0.0313\(^\ddag \)
\(-\)0.0295\(^\ddag \)
\(-\)0.0142\(^\ddag \)
(0.0007)
(0.0009)
(0.0017)
Partial retirement
\(-\)0.3851\(^\ddag \)
\(-\)0.2200\(^\ddag \)
0.0125
(0.0212)
(0.0172)
(0.0208)
Administrative district level
Unemployed 50–64
0.0287\(^\ddag \)
0.0420\(^\ddag \)
0.0365\(^\ddag \)
(0.0078)
(0.0069)
(0.0093)
Unemployed 15–24
\(-\)0.0105
0.0577\(^\ddag \)
0.0675\(^\ddag \)
(0.0179)
(0.0152)
(0.0186)
Observations
2,839,260
2,905,010
750,970
Firms
372,899
269,905
60,290
\(R^2\)
0.2282
0.0929
0.1076
Fixed effects regression. For further controls, see the data description. Cluster-robust standard errors at the district level are in parentheses. \(^\dag \): FPR \(\le \) 0.05, \(^\ddag \): FPR \(\le \) 0.01
4.2.1 Firm size
In the baseline regression, we restrict the number of employees in a firm to a minimum of five. In the group small firms, we remove this restriction and consider those firms with at least one and up to nine employees. These enterprises represented around 80 per cent of all firms in Germany in 2016. Subsequently, we run our regression for the group medium firms with at least ten but no more than 49 employees. Together, both groups cover around 96 per cent of all German enterprises. The last group, large firms, comprise those firms with at least 50 employees but no more than 249. These three firm groups covered 99.3 per cent of all German enterprises and employed more than 60 per cent of the German workforce. Thus, analysing the three groups gives us a representative reflection of the German corporate landscape. The results are provided in Table 4.
Overall, the different subgroups confirm the empirical evidence and direction of the estimated effects in the baseline model. However, while the medium firms roughly match the baseline, small and large firms indicate some deviations. The share of older full-time employees is always positively related to the share of hired older workers. While the effects are almost identical to the baseline for medium and large firms, the coefficient for small firms is somewhat larger. However, the share of older full-time workers increases with firm size (see Appendix).12
The positive effect of the share of older employees in part-time declines as the firm size increases. At the same time, this group’s workforce share increases with firm size (see Appendix). Hence, although this share is larger in large firms, it is less important in hiring older workers. Weighing both the full-time and the part-time effect with their respective shares and adding the effect of the unemployed, we find that a one percentage point change in the overall share of the older labour force results in a rise in the share of hired older workers by 0.24–0.36 percentage points. The coefficients of the share of older unemployed remain almost unchanged across firm size (Table t5).
Both shares of the young, full-time and part-time, still have small negative effects, changing only slightly when the firm size changes. For the youth share among the unemployed, we find statistical evidence for medium and large firms. Taken together, we infer that the reaction towards the compositional effect of demographic change of the baseline model is observable in all three firm size groups.
Concerning the effect of a growing share of workers employed in partial retirement schemes, we find that the adverse impact decreases with the firm size. Furthermore, the effects for small and large firms differ substantially from the baseline results. However, on average, only one per cent of the workforce is in partial retirement schemes, regardless of the firm size.
The relative hiring ratio for older workers for all firm size groups is below one and below the average (see Table 7). Small firms are more open to older workers than larger ones, which is driven by the smaller average share of older workers in small firms. We interpret this as a catch-up process for small firms.
The elasticities for older workers show that larger firms are more elastic for full-time workers, and smaller firms are more elastic for part-time workers. The elasticities for older unemployed are similar to the baseline and again lower than for older employed. Hence, firms seem more reluctant when ageing happens outside the firm, independent of the firm size. While the estimated effects of partial retirement are adverse, the elasticities and economic impact are very small.
4.2.2 Economic sectors
Our regression analysis, conducted separately for primary, secondary and tertiary sectors, reveals some key findings (see Table 5). While the results sometimes deviate in magnitude from the baseline model, the overall conclusions remain consistent across all sectors, underscoring the robustness of our analysis.
Concerning full-time 50–64, the coefficient for the primary sector is larger than the baseline result. However, the share of older employed in this sector is also above the average. Concerning the tertiary sector, we observe the opposite for both. From this, we conclude that the tertiary sector is less open to older full-time workers. With regard to the part-time 50–64 effects, the tertiary sector seems more open to flexible working time models.
The coefficients for both youth shares for the tertiary sector are similar to the baseline. In contrast, the primary sector has more negative attitudes towards older workers when the youth share is larger. The negative relationship between partial retirement and the dependent variable seems much more pronounced in the secondary sector. Empirical evidence for the older unemployed shares was only found for the secondary sector.
The relative hiring ratio for older workers for all sectors is below one and very similar to the average (see Table 7). The elasticities for older workers show that the primary sector is more elastic for full-time workers, while the tertiary sector is more elastic for part-time workers. The elasticity for older unemployed is above the average for the secondary sector. For the other sectors, we find no statistically evident result. Hence, firms in these two sectors seem more reluctant when ageing happens outside the firm. Also, the partial retirement elasticities suggest that the economic impact is very small for the different sectors.
Table 5
Hiring older workers: economic sectors
Dependent variable: share hires 50–64
Primary sector
Secondary sector
Tertiary sector
Firm level
Full-time 50–64
0.3489\(^\ddag \)
0.2633\(^\ddag \)
0.1786\(^\ddag \)
(0.0093)
(0.0036)
(0.0029)
Part-time 50–64
0.2156\(^\ddag \)
0.2580\(^\ddag \)
0.3296\(^\ddag \)
(0.0044)
(0.0020)
(0.0042)
Full-time 15–24
\(-\)0.0749\(^\ddag \)
\(-\)0.0514\(^\ddag \)
\(-\)0.0341\(^\ddag \)
(0.0052)
(0.0013)
(0.0008)
Part-time 15–24
\(-\)0.0449\(^\ddag \)
\(-\)0.0290\(^\ddag \)
\(-\)0.0259\(^\ddag \)
(0.0036)
(0.0008)
(0.0008)
Partial retirement
\(-\)0.3318\(^\ddag \)
\(-\)0.5157\(^\ddag \)
\(-\)0.2118\(^\ddag \)
(0.0457)
(0.0266)
(0.0140)
Administrative district level
Unemployed 50–64
\(-\)0.0327
0.0513\(^\ddag \)
0.0049
(0.0309)
(0.0088)
(0.0074)
Unemployed 15–24
0.0385
\(-\)0.0213
0.0523\(^\ddag \)
(0.0705)
(0.0180)
(0.0138)
Observations
141,683
2,058,404
3,663,985
Firms
12,723
185,085
334,329
\(R^2\)
0.1140
0.1196
0.1179
Fixed effects regression. For further controls, see the data description. Cluster-robust standard errors at the district level are in parentheses. \(^\dag \): FPR \(\le \) 0.05, \(^\ddag \): FPR \(\le \) 0.01
4.2.3 East and West Germany
We estimate the model separately for East Germany and West Germany in the final sensitivity analyses because the labour markets are very different. We divided the panel into two subsamples for the former territory of East Germany and West Germany and assigned the city of Berlin to East Germany. Tables 6 and 7 show the results.
Table 6
Hiring older workers: east and west Germany
Dependent variable: share hires 50–64
East Germany
West Germany
Firm level
Full-time 50–64
0.2760\(^\ddag \)
0.1945\(^\ddag \)
(0.0051)
(0.0019)
Part-time 50–64
0.2580\(^\ddag \)
0.3058\(^\ddag \)
(0.0039)
(0.0027)
Full-time 15–24
\(-\)0.0674\(^\ddag \)
\(-\)0.0362\(^\ddag \)
(0.0059)
(0.0007)
Part-time 15–24
\(-\)0.0230\(^\ddag \)
\(-\)0.0274\(^\ddag \)
(0.0011)
(0.0006)
Partial retirement
\(-\)0.1046\(^\ddag \)
\(-\)0.3001\(^\ddag \)
(0.0271)
(0.0119)
Administrative district level
Unemployed 50–64
0.0682\(^\ddag \)
0.0221\(^\ddag \)
(0.0156)
(0.0064)
Unemployed 15–24
\(-\)0.0008
0.0246
(0.0342)
(0.0132)
Observations
1,023,460
4,855,909
Firms
95,117
435,541
\(R^2\)
0.1191
0.1162
Fixed effects regression. For further controls, see the data description. Cluster-robust standard errors at the district level are in parentheses. \(^\dag \): FPR \(\le \) 0.05, \(^\ddag \): FPR \(\le \) 0.01
Table 7
Elasticities and relative hiring ratio
Model
Elasticities
Full-time 50–64
Part-time 50–64
Partial retirement
Unemployed 50–64
Relative hiring ratio
Baseline
0.33
0.54
\(-\)0.01
0.07
0.58
Firms
Small firms
0.28
0.74
\(-\)0.003
0.06
0.68
Medium firms
0.33
0.47
\(-\)0.01
0.09
0.58
Large firms
0.45
0.29
0.001
0.08
0.51
Sectors
Primary sector
0.56
0.40
\(-\)0.02
\({-{} \textit{0.06}}\)
0.59
Secondary sector
0.42
0.43
\(-\)0.01
0.11
0.62
Tertiary sector
0.28
0.64
\(-\)0.01
0.01
0.56
Regions
East Germany
0.42
0.40
\(-\)0.004
0.13
0.64
West Germany
0.31
0.59
\(-\)0.01
0.05
0.57
Cursive values are based upon statistically weak evidence
In general, baseline and West Germany estimates are similar. Concerning the dependent variable, we find that the share of hired older workers is, on average, four percentage points larger in East Germany (see Appendix). This reflects how firms in the East German labour market act as pioneers when confronted with demographic change. The larger effect of the older workers’ full-time share and the fact that the share of older workers in the labour force is larger in East Germany reflects that East German firms react stronger to their demographic situation (faster ageing). Considering the effects of the share of older unemployed, we find a more substantial effect for the East.
Table 8
Hiring older workers: specification tests
Dependent variable: share hires 50–64
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Firm level
Full-time 50–64
0.2083\(^\ddag \)
0.2083\(^\ddag \)
0.2077\(^\ddag \)
0.2077\(^\ddag \)
0.2068\(^\ddag \)
0.2085\(^\ddag \)
0.2064\(^\ddag \)
0.2084\(^\ddag \)
0.1833\(^\ddag \)
(0.0024)
(0.0024)
(0.0024)
(0.0024)
(0.0024)
(0.0024)
(0.0024)
(0.0024)
(0.0026)
Part-time 50–64
0.2965\(^\ddag \)
0.2965\(^\ddag \)
0.2961\(^\ddag \)
0.2961\(^\ddag \)
0.2938\(^\ddag \)
0.2945\(^\ddag \)
0.2934\(^\ddag \)
0.2945\(^\ddag \)
0.2720\(^\ddag \)
(0.0022)
(0.0022)
(0.0022)
(0.0022)
(0.0022)
(0.0023)
(0.0022)
(0.0023)
(0.0019)
Full-time 15–24
\(-\)0.0432\(^\ddag \)
\(-\)0.0432\(^\ddag \)
\(-\)0.0433\(^\ddag \)
\(-\)0.0433\(^\ddag \)
\(-\)0.0389\(^\ddag \)
\(-\)0.0393\(^\ddag \)
\(-\)0.0390\(^\ddag \)
\(-\)0.0394\(^\ddag \)
\(-\)0.0466\(^\ddag \)
(0.0008)
(0.0008)
(0.0008)
(0.0008)
(0.0008)
(0.0008)
(0.0008)
(0.0008)
(0.0007)
Part-time 15–24
\(-\)0.0391\(^\ddag \)
\(-\)0.0391\(^\ddag \)
\(-\)0.0389\(^\ddag \)
\(-\)0.0389\(^\ddag \)
\(-\)0.0265\(^\ddag \)
\(-\)0.0268\(^\ddag \)
\(-\)0.0266\(^\ddag \)
\(-\)0.0268\(^\ddag \)
\(-\)0.0398\(^\ddag \)
(0.0007)
(0.0007)
(0.0006)
(0.0006)
(0.0006)
(0.0006)
(0.0006)
(0.0006)
(0.0005)
Partial retirement
\(-\)0.2641\(^\ddag \)
\(-\)0.2641\(^\ddag \)
\(-\)0.2644\(^\ddag \)
\(-\)0.2645\(^\ddag \)
\(-\)0.2489\(^\ddag \)
\(-\)0.2449\(^\ddag \)
\(-\)0.2449\(^\ddag \)
\(-\)0.2454\(^\ddag \)
\(-\)0.3888\(^\ddag \)
(0.0130)
(0.0130)
(0.0129)
(0.0129)
(0.0128)
(0.0127)
(0.0127)
(0.0127)
(0.0140)
Additional firm variables
\(\checkmark \)
\(\checkmark \)
\(\checkmark \)
\(\checkmark \)
\(\checkmark \)
Administrative district variables
\(\checkmark \)
\(\checkmark \)
Sector dummies
\(\checkmark \)
\(\checkmark \)
\(\checkmark \)
\(\checkmark \)
Year dummies
\(\checkmark \)
\(\checkmark \)
\(\checkmark \)
\(\checkmark \)
District-year dummies
\(\checkmark \)
\(R^2\)
0.1145
0.1145
0.1146
0.1146
0.1157
0.1158
0.1157
0.1158
0.1813
Fixed effects regression. Number of observations 5,879,369. Number of Firms 530,658. For further controls, see the data description. Cluster-robust standard errors at the district level are in parentheses. \(^\dag \): FPR \(\le \) 0.05, \(^\ddag \): FPR \(\le \) 0.01
The part-time and partial retirement shares are similar in East and West Germany. For the estimates, however, the positive effect of the older part-time workers and the negative effect of partial retirement are also somewhat smaller in the East. This might be related to cultural differences in the labour market because, before the German reunification, part-time employment and partial retirement schemes were very uncommon in the East.
The coefficient of the share of the younger full-time workforce is almost twice as large for East Germany. Hence, a rising share of younger employees in East Germany means that firms in this region hire younger workers faster, to the disadvantage of older workers. However, the share of young employees is smaller in East Germany, which limits the substitution of older workers by the youth.
The relative hiring ratio for older workers in both regions is similar to the baseline result (see Table 7). This is interesting because in East Germany, the share of older workers and the share of hired older workers are larger, and the ageing experiences are more comprehensive. The elasticities for older workers show that East German firms react more elastically concerning full-time workers, and West German firms respond more elastically to older part-time workers. These outcomes coincide with the findings that firms in the East are more open to older workers but less to working time flexibility. In addition, firms in both regions seem more reluctant when ageing happens outside the firm, but the eastern part adjusts more to the advanced ageing process. Finally, we also find the adverse effects of partial retirement for both regions. However, the economic impact is minimal.
5 Robustness
In this section, we further analyse how robust our results are. Our specification potentially suffers from a simultaneity bias. Recognising that the share of hired older workers (the flow) and the share of employed older workers (the stock) might influence each other, we provide two approaches to analyse how important this potential problem is to our estimates. In addition, our specification may suffer from a potential omitted variable bias. Therefore, in the next section, we vary the set of control variables to see how our core variables behave. Subsequently, we provide instrumental variable (IV) estimates to detect a potential bias in the parameters for the older worker shares.
5.1 Specification
To see how robust the coefficients for the full-time 50–64 and part-time 50–64 variables are, we take the baseline estimates as a reference and exclude different sets of control variables. Table 8 provides an overview. Overall, we ran nine regressions. While the five core variables remain in each specification, the rest are excluded in various combinations. This is shown in the lower part of the table. For comparison reasons, specification (8) is the baseline specification already shown in Table 3. Across regressions (1) to (8), the full-time 50–64 coefficient varies between 0.2085 and 0.2064 and the part-time 50–64 coefficient is between 0.2965 and 0.2934. Both differences are minimal and indicate a robust statistical relation with the dependent variable. In regression (9), we add more than eight thousand dummies at the regional level. Again, the coefficients of interest only change slightly. From this, we conclude that our estimates and calculated elasticities in the former section are statistically reliable. The remaining three variables also vary only a little.
5.2 IV estimation
A more formal way to handle a potential simultaneity bias is to apply IV estimates. We instrument the share of older full-time and part-time employees to control for a possible simultaneity bias using the following two instruments: First, the one-year lagged labour force shares of older (50–64 years) people in the administrative district (county).13 This variable covers the complete regional labour supply of the old in the former year. This instrument is independent of the firm’s hiring decisions in the current year, not only because of the time lag but also because this is aggregated information unknown to the individual firm.
Table 9
Hiring older workers: baseline IV estimates
Dependent variable: share hires 50–64
(1)
(2)
(3)
First stage
Instrumented variable
Full-time 50–64
Part-time 50–64
Full-time 50–64
Part-time 50–64
Share of the old in the district labour force \({_{t-1}}\)
0.2619\(^\ddag \)
0.3163\(^\ddag \)
0.2655\(^\ddag \)
0.5389\(^\ddag \)
(0.0257)
(0.0300)
(0.0256)
(0.0395)
Share of the old in the neighbour firms
0.3060\(^\ddag \)
0.2277\(^\ddag \)
0.3049\(^\ddag \)
0.1620\(^\ddag \)
(0.0233)
(0.0211)
(0.0233)
(0.0298)
Second stage
Full-time 50–64
0.2710\(^\ddag \)
0.2424\(^\ddag \)
(0.0276)
(0.0340)
Part-time 50–64
0.3517\(^\ddag \)
0.3312\(^\ddag \)
(0.0278)
(0.0362)
Kleibergen–Paap LM
91.1\(^\ddag \)
59.3\(^\ddag \)
73.0\(^\ddag \)
Kleibergen–Paap Wald
261.4\(^\ddag \)
125.0\(^\ddag \)
63.7\(^\ddag \)
Hansen J-statistic \(\chi ^2\)
1.0
1.3
–
Test of endogeneity \(\chi ^2\)
5.1
4.5
6.2
Observations
5,879,369
5,879,369
5,879,369
Firms
530,658
530,658
530,658
\(R^2\)
0.1137
0.1135
0.1142
IV = Instrumental variable regression. For further controls, see the data description. Cluster-robust standard errors at the district level are in parentheses. \(^\dag \): FPR \(\le \) 0.05, \(^\ddag \): FPR \(\le \) 0.01
As a second instrument, we use the average employment share of older workers in other firms in the same county. While the first instrument covers the complete regional labour force, this second instrument covers the average share of older workers in neighbourhood firms. This variable is taken from the same sample data as the potential endogenous variable. This instrument is independent of the firm’s hiring decisions because this information is unknown to the individual firm. Both instruments are correlated with the share of older workers in a specific firm but are not causally related to the dependent variable. However, potentially, our instrumental variables could be correlated with other not-considered county-level variables that, in turn, might influence the hiring behaviour of firms. Consequently, the instruments would not be exogenous, and the results would be biased. In addition, we must keep in mind that the share of older unemployed is larger than that of older workers or older workers hired. Hence, there is no evidence of higher market tightness in this age group compared to others.
We provide three IV regressions (Table 9). In the first, we instrument the variable full-time 50–64; in the second, we instrument the variable part-time 50–64. In the third regression, we instrument both variables. In the table, we report the coefficients of the instruments from the first stage regression and below the second stage coefficient of the instrumented variable. Taking all three results together, we do not see essential differences to the baseline reference in Table 3. The additional test statistics in the lower part of the table give us further information on the regression quality. The Kleibergen–Paap LM test gives information on underidentification, while the Wald test is employed because of potentially weak instruments. The null hypothesis of underidentification is rejected, and there is also no evidence for weak instruments. The Hansen statistic tests the overidentification of all instruments and states that the instruments are coherent with each other in both cases. Finally, the endogeneity test provides no evidence that the potential endogenous regressors are, in fact, endogenous. Overall, we conclude that a simultaneity bias is small or nonexistent. The results in this section support the robustness of the results provided in Sect. 4.
6 Conclusions
This article analyses how the labour force’s internal and external age structure is related to hiring older workers. Unlike previous studies, we combine panel data from the Establishment History Panel and regional unemployment data. Encouragingly, we find a positive relationship between hiring older workers and ageing among both the employed and unemployed. This suggests a gradual adjustment to demographic shifts. However, the pace of this adjustment is disappointingly slow. Our measure of the relative hiring ratio for older workers further substantiates our findings. We find adverse labour market opportunities for different firm sizes, sectors, and East and West Germany. Consequently, our measure shows that the labour market is less open for older workers. The calculated elasticities further support these findings. Moreover, we find that the speed of adjustment is even slower when ageing occurs outside the firm. This coincides with low job-finding rates for older workers in Germany. Surprisingly, a rising share of employees in partial retirement schemes is negatively related to the share of hired older workers. Thus, while this policy enhances flexibility for internal employees, it unfortunately diminishes the employment opportunities for older external job seekers.
Our results should be viewed in the context of the ongoing debate in Germany about the retirement age, particularly considering the policy reform 2008, which gradually increased the mandatory retirement age from 65 to 67 years, starting in 2012. Firms do respond to demographically induced changes in the labour force composition by hiring more older workers. However, the proportion of older people among the unemployed outweighs their share among the hired or within the firm, and the hiring elasticity of older unemployed is very small. These findings have important implications for policymakers and professionals in the labour market, highlighting the need for strategies to improve the employment prospects of older workers in Germany. A promising agenda for future research might be the consideration of gender-related questions. Specifically, the hiring behaviour towards women in general, particularly older women, might yield interesting results since female labour is seen as a potential resource of labour supply.
Acknowledgements
We would like to thank Axel Börsch-Supan, Bernhard Boockmann, Ulrich Walwei, and two anonymous referees for helpful comments.
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The dependent variable is the ratio of hires aged 50–64 to all hires at the firm level.
Concerning the explanatory variables at the firm level, we control for the existing age structure of full-time and part-time employees. For both groups of employees, We include the youth share (15 to 24 years) and the share of older workers (50–64 years) and consider the age group 25 to 49 years old as the reference group. We also include the firm size, measured by the number of employees.
Furthermore, we consider the share of part-time workers in each firm. In addition to the age structure, we consider the share of employees with an academic background as a control. We also add the share of low-skilled employees and take the share of medium-skilled employees as a reference. Furthermore, we consider the share of employees undergoing vocational training. We also include the share of marginal part-time workers whose monthly earnings are at most 450 EUR.
In addition, we include the ratio of older workers who left the firm to all separations and the share of job-to-job movers. The latter variable is the share of hired workers with an employment relationship with another firm in the previous year.
Finally, we control for industry-specific differences in the hiring behaviour of firms by including 13 sector-specific dummy variables.14
Administrative district level variables
To proxy the size and age structure of the labour force, we consider the share of the youth and older workers among the unemployed and the total number of unemployed at the administrative district level. The reference is the share of unemployed aged 25 to 49 years. Also, we control for the degree of urbanisation at the administrative district level. We obtained the data from BBSR (see fn. 5) and followed their classification, which defines four basic categories. The first entails cities with at least 100,000 inhabitants. Category 2 comprises all districts where at least 50 per cent of all inhabitants live in major or medium-sized cities, and the population density is at least 150 inhabitants per \(km^{2}\). Category 3 encloses all districts sharing the attributes from Category 2 but reports a population density between 100 and 150 inhabitants per \(km^{2}\). Finally, Category 4 comprises all districts where under 50 per cent of the population lives in major or medium-sized cities, and the population density is at most 100 inhabitants per \(km^{2}\).
Appendix B: Computation of the false positive risk
The false positive risk (FPR) was introduced by Colquhoun (2019, 2017) and measures the probability that the result occurred by chance \(P(H_0|data)\). The approach is based on the Bayes theorem that we express in odds:
Following Colquhoun, the Bayes factor becomes a likelihood ratio (LR), and the prior odds can be expressed using the probability that there is a real effect,
Among others, Sellke et al. (2001) provide an approach to calculate the LR based on the p-value: \(LR=1/(-eplog(p))\). However, this measure can be considered only as long as \(p<1/e\), with e as Euler’s number. Taking things together and considering \(P(H_0|data)=1-P(H_1|data)\) gives us the FPR:
Applying the FPR approach requires to specify \(P(H_1)\) first. However, specifying the prior probability in regression analysis is (even in replication studies) difficult, and we should always be careful when defining this unknown number (you have to convince the reader). We use
which means that both probabilities have the same weight. This is equal to a 50:50 chance for a real effect specified before the data are analysed. This seems reasonable when we do not know what to choose or are open to the results. Hence, the prior probability of a real effect, \(P(H_1)\), is fixed to 0.5. In this case, the FPR is much larger than the corresponding p-value, and for example, \(p=0.05\) is equal to a FPR of 0.2893.
Appendix C
Table 10
Hiring older workers: unrestricted panel
Dependent variable: share hires 50–64
Baseline
firm level
Full-time 50–64
0.2702\(^\ddag \)
(0.0030)
Part-time 50–64
0.4333\(^\ddag \)
(0.0023)
Full-time 15–24
\(-\)0.0446\(^\ddag \)
(0.0006)
Part-time 15–24
\(-\)0.0252\(^\ddag \)
(0.0004)
Partial retirement
\(-\)0.3988\(^\ddag \)
(0.0128)
Administrative district level
Unemployed 50–64
0.0139
(0.0055)
Unemployed 15–24
0.0098
(0.0116)
Observations
9,595,987
Firms
1,893,345
\(R^2\)
0.2003
Fixed effects regression. For further controls, see the data description. Cluster-robust standard errors at the district level are in parentheses. \(^\dag \): FPR \(\le \) 0.05, \(^\ddag \): FPR \(\le \) 0.01
Concerning the average age at hire, see, for example, Adams and Heywood (2007) for Australia and Medeiros Garcia et al. (2017) for Portugal. Related to the share of hired older workers, see, for example, Scott et al. (1995) and Adler and Hilber (2009) for the USA, Heywood et al. (1999) for Hong Kong, Daniel and Heywood (2007) and Kidd et al. (2012) for the UK.
A closer look at the long-term unemployment statistics reveals that older workers are statistically more prone to be long-term unemployed than all other age groups, which marks a critical indicator of the chances of older people being hired from the labour market. According to data for 2019, 42 per cent of all older (55 to 65 years old) unemployed were long-term unemployed. Moreover, with almost one-third of all long-term unemployed, this age group is the largest among the long-term unemployed.
Data access was provided via on-site use at the Research Data Centre (FDZ) of the German Federal Employment Agency (BA) at the Institute for Employment Research (IAB) and remote data access (Schmucker et al. 2016).
We acknowledge that our restrictions reduce the sample size considerably. For comparison reasons, we also estimate the model on the unrestricted panel. The results are provided in Table 10 in Appendix C.
We also test cluster-robust standard errors at the firm level. However, to capture the correlation pattern at the district level, we decide in favour of this level.
We weigh the coefficients for older full-time and part-time workers by their group weights (full-time = 0.75 and part-time = 0.25) and add the coefficient for older unemployed. Finally, we use the inverse of this value.
For a detailed depiction, see the FDZ data report of the Sample of Integrated Labour Market Biographies Regional File (SIAB-R) 1975–2021, Table A9 on page 74.
