Pension Wealth and the Gender Wealth Gap
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Cordova, Karla; Grabka, Markus M.; Sierminska, Eva Article — Published Version Pension Wealth and the Gender Wealth Gap European Journal of Population Provided in Cooperation with: Springer Nature Suggested Citation: Cordova, Karla; Grabka, Markus M.; Sierminska, Eva (2022) : Pension Wealth and the Gender Wealth Gap, European Journal of Population, ISSN 1572-9885, Springer Netherlands, Dordrecht, Vol. 38, Iss. 4, pp. 755-810, https://doi.org/10.1007/s10680-022-09631-6 This Version is available at: https://hdl.handle.net/10419/308175 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/
Pension Wealth and the Gender Wealth Gap Karla Cordova 1 ·Markus M. Grabka 2 ·Eva Sierminska 3 Received: 7 July 2021 / Accepted: 13 June 2022 / Published online: 22 August 2022 ©The Author(s) 2022 Abstract We examine the gender wealth gap with a focus on pension wealth and statutory pension rights. By taking into account employment characteristics of women and men, we are able to identify the extent to which the redistributive effect of pension rights reduces the gender wealth gap. The data for our analysis come from the German Socio-Economic Panel (SOEP), one of the few surveys that collects information on wealth and pension entitlements at the individual level. Pension wealth data are available in the SOEP for 2012 only. While the relative raw gender wealth gap is about 35% (or 31,000 euros) when analysing the standard measure of net worth, it shrinks to 28% when pension wealth is added. This reduction is due to redistributive elements such as caregiver credits provided through the statutory pension scheme. Results of a recentred influence functions (RIF) decomposition show that pension wealth reduces the gap substantially in the lower half of the distribution. At the 90th percentile, the gender wealth gap in net worth and in augmented wealth remains more stable at roughly 27–30%. Keywords Gender wealth gap · Pension entitlements · Germany · Redistribution · SOEP Socio-Economic Panel (SOEP) JEL: H55 · D31 · J16 &Markus M. Grabka [email protected] Karla Cordova [email protected] Eva Sierminska [email protected] 1 Pomona College, Claremont, USA 2 DIW Berlin, Berlin, Germany 3 Luxembourg Institute of Socio-Economic Research (LISER), DIW, IZA and GLO, Esch/Alzette, Luxembourg 123 European Journal of Population (2022) 38:755–810 https://doi.org/10.1007/s10680-022-09631-6(0123456789().,-volV)(0123456789().,-volV)
1 Introduction Employment plays an important role in determining private wealth accumulation. Not only does it provide income that can be saved to build wealth, but it also enables the accrual of pension rights, as the majority of pension systems are earnings-related (Frey, 2021, p. 123). Given that women still earn less than men on average, it comes as no surprise that the gender wealth gap widens when taking pension assets (the present value of all pension entitlements from statutory and occupational pension schemes) into account. The average gap in net wealth between working-age men and women in Germany was 31,000 euros in 2012 and widens to around 45,000 euros when pension assets are added. 1 In this paper, we examine the gender wealth gap with a focus on pension wealth and statutory pension rights. By taking into account the employment histories of women and men, we are able to measure the extent to which gender-specific disadvantages of women in the labour market—for instance, the pay gap (Blau & Kahn, 2017), the glass ceiling effect (Biagetti &Scicchitano, 2011), and the motherhood penalty (Anderson et al., 2002)—reduce the accumulation of net worth. Moreover, we can determine whether these negative effects are further reinforced when pension wealth is considered. Since the old-age pension system in Germany is based strictly on the equivalence principle, pension entitlements de facto directly reflect women’s employment and career trajectories and thus their disadvantageous situation in the labour market. This situation is countered by redistributive elements of the statutory pension system that are intended to compensate women for employment interruptions, for instance, by granting caregiver credits for periods of child-rearing (Bonnet &Rapoport, 2020). The question arises to what extent these redistributive elements are able to compensate for the disadvantages women experience. The case of Germany is also of interest from another perspective. Since the end of the Second World War up to German reunification in 1989, East and West Germany differed significantly in their development. Even today, there are relevant cultural, normative, and economic differences between the two formerly separate parts of Germany. When it comes to gender differences, the male-breadwinner model was and in some cases still is predominant in the former West, whereas a dual-earner model predominates in the former East (Trappe et al., 2015). One important continuing difference is in women’s labour market participation. In 1989, women in the German Democratic Republic (GDR) had one of the highest rates of labour market participation in the world, at 91.3%, compared to just 51% in the Federal Republic of Germany (FRG) (Wippermann, 2015). 2 These general structural differences persist and are expressed in the wage gap: In 2018, this gap was 22% in the former West compared to 7% in the former East (Destatis, 2020). Labour market differences are also reflected in the opportunities conducive to accumulating 1 Private pension plans play a relatively small role in the German pension system and are a standard component of net worth. Public pension wealth has not been considered in previous research on the gender wealth gap. 2 However, there was also a wage gap in the GDR, which in 1989 amounted to 24% among full-time employees (Nickel, 1995). 123 756 K. Cordova et al.
pension assets. Occupational pensions are overall much less prevalent in the East, yet in 2018, the gender pension gap among retired people was 55% in the West and 23% in the East (BMAS, 2020). The empirical data for the present study come from the Socio-Economic Panel (SOEP), one of the few datasets containing information on wealth as well as pension entitlements at the individual level. Pension wealth data is available in the SOEP for 2012 only. Individual-level wealth data allow us to analyse the gender wealth gap between women and men across all households. Thanks to the longitudinal character of the SOEP data, we are also able to consider detailed information on employment trajectories and family-related events (such as childbirth, marriage, divorce, and widowhood) that can have an effect on (public) pension entitlements. We assume a simple model of wealth accumulation in which assets in each period are the result of the stock of assets in the previous period augmented by savings (income minus consumption) and the gross rate of return (depicted in (Davies &Shorrocks, 2000), (Sierminska et al., 2010)). Individuals differ in wealth accumulation for several reasons. First, different individuals start off with different stocks of assets, in some cases due to intergenerational transfers (inheritances and bequests), and they also differ in their ability and willingness to save. Such differences are often substantial between women and men and are discussed further in Sect. 5. Previous research focused primarily on the standard measure of net worth when analysing the gender wealth gap. This paper is—as far as we know—the first to consider pension wealth to analyse this gap. Pension wealth consists of three components: statutory public pensions, occupational pensions, and private pensions. Although private pensions are often taken into account in the standard measure of net worth, there is often no survey data available on statutory public pension entitlements or occupational pensions, leading to their neglect in the analysis of the gender wealth gap. The distinction between private pensions on the one hand and statutory public and occupational pensions on the other is necessary for several reasons. Whereas private pensions are based solely on a voluntary investment decision by an individual, statutory pension insurance is tied to employment and is compulsory for employees who are subject to social security contributions. Occupational pensions are also tied to employment, but they are not always provided by employers. Furthermore, both statutory and occupational pensions cannot be sold or used as collateral, meaning that the usual functions of wealth, except for the security function, are not fulfilled. Despite this, as Bönke et al. (2019) show in the case of Germany, employees accumulate comparable amounts of statutory public and occupational pensions to what they accumulate in net worth. This underscores the importance of considering the former two pension types when analysing differences in wealth between groups. In the empirical part of this paper, we characterise gender differences in net wealth and then present an augmented measure of private wealth that includes pension wealth. We then decompose the gender wealth gap using the Oaxaca–Blinder decomposition (Oaxaca, 1973; Blinder, 1973) at the mean and throughout parts of the distribution following (Firpo et al., 2009). Then, we examine the distribution of pension wealth by type and test the robustness of our results, conducting the estimations for several subsamples. The structure of the paper is as follows. Section 2reviews the literature on the gender pension wealth gap, and Sect. 3describes the characteristics of the German pension system. Section 4 123 Pension Wealth and the Gender Wealth Gap 757
discusses the data and is followed by Sect. 5on the analytical framework and empirical strategy. In Sect. 6, we provide a descriptive analysis of the wealth data and individual characteristics. The results for the mean and detailed decomposition are found in Sect. 7for each of the wealth aggregates, as well as for different pension types and sub-samples. Finally, we conclude in Sect. 9and discuss possible policy implications and future steps. 2 Literature Review Research on gender differences in private or pension wealth is usually confronted with a lack of individual-level wealth data, which has meant that only a few papers to date have been able to analyse the gender gap in private wealth or pension wealth. One of the few representative population surveys to collect wealth information at the individual level is the German SOEP. Several papers make use of SOEP data to describe gender differences in wealth levels or wealth changes, including Frick et al. (2007), Sierminska et al. (2010), Grabka et al. (2015), Lersch (2017a,b), Boertien and Lersch (2021), and Kapelle and Baxter (2021). These authors show that there is a significant gender gap in private wealth in Germany, not only between single men and women, but even within married couples. The main driver of the gender gap in private wealth are differences in labour market outcomes such as participation in the labour market and earnings levels. Individuals who work in stable, full-time, higherprestige occupations will consistently earn more (and have higher permanent income), which will improve their ability to save (Ruel &Hauser, 2013). Women’s lower labour market participation rate, their lower working hours, the glass ceiling effect, and the still existing gender pay gap all hinder women’s wealth accumulation (Warren et al., 2001). In addition, there is vertical and horizontal segregation between men and women in the labour market that contributes to the gender pay gap 3 and consequently to the wealth gap. Women face a motherhood penalty in wages (Anderson et al., 2002) due to gender stereotypes and assumptions about traditional roles in the family (e.g. (Lewis, 1992)). However, women’s labour force participation is also significantly influenced by the availability and quality of childcare facilities (Kreyenfeld &Hank, 2000). Finally, the fiscal regime matters. In many OECD countries, couples’incomes are pooled for tax purposes, which implies that the tax rate on second earners remains significantly higher than on single individuals, which has a negative impact on labour force participation of the lower earner (Jaumotte, 2004). Besides labour market differences, intergenerational transfers play an important role in wealth accumulation. However, several papers show that there are no systematic gender differences in the amount of inheritance received (e.g. (Ruel &Hauser, 2013)). Women and men also show different levels of returns from their investments due to diverging risk preferences, which results in different wealth portfolios (Sunden &Surette, 1998; Chang, 2010; Lersch, 2017b). For example, women are significantly less likely to own business assets (e.g. Austen et al. 2014) 3 Horizontal segregation exists when, for example, a particular industry is composed mainly of one gender, whereas vertical segregation exists when employees are not given a position above a certain threshold because of their gender. 123 758 K. Cordova et al.
and are more likely to own property. As Goldsmith-Pinkham and Shue (2020) argue, the gender gap in housing returns can explain 30% of the gender gap in wealth accumulation at retirement. Access to credit (Alesina et al., 2013) and mortgages (e. g. Goldsmith-Pinkham and Shue 2020) may differ between women and men, which affects their ability to accumulate additional wealth. Additionally, financial literacy influences investment decisions (Huston, 2010; Lusardi &Mitchell, 2008), and it has been shown that women have lower financial knowledge than men, which leads them to have more conservative investment patterns and thus lower (that is, safer) returns than men (Almenberg &Dreber, 2015). Another aspect that could affect wealth levels are marital status transitions. The dissolution of marriage is negatively related to the accumulation of wealth over time, and the side effects are similar for both genders. However, the dissolution of cohabiting unions is accompanied by wealth losses for women but not for men (Boertien &Lersch, 2021). In addition, parenthood, within or outside of marriage, has a negative effect on women’s employment and wages and thus impairs their individual wealth accumulation (Yamokoski &Keister, 2006; Lersch, 2017b). 4 The presentation so far relates to drivers of gender differences in private wealth. When it comes to gender differences in pension wealth, similar but also additional aspects come to light. First, a large number of papers investigate membership in occupational or private pension plans. Rõõm and Soosaar (2021) show that in the euro area, more men than women have pension wealth from defined contribution (DC) pension plans. The raw gap in the value of pension wealth is 65% of the mean value of women’s pension wealth, which is considerably larger than the average gender wage gap in Europe. When the authors control for observable characteristics, this gap shrinks to 9%. Gender differences in private pension wealth are more pronounced. Not only do women contribute less to private pension schemes (e.g. (Foster &Smetherham, 2013), (Gardiner et al., 2016)), the gender gap in mean values is also significantly larger for private pensions. Johnson et al. (1999), for example, find that full-time workers’median pension wealth for their current job is 76% greater for men than for women. Differences in age, occupational position, earning levels, working hours, and having dependent children in the household account for most of the gender gap in pension wealth. For statutory pensions, the picture is quite different. Although public pension systems are often earnings-related, which means that the gender pay gap translates into a gender pension gap, there are several redistributive elements in favour of women that dampen the effect. In the case of Switzerland, Kuhn (2020) notes that women tend to have higher pension wealth from statutory pensions, which is due to a weak relationship between earnings and statutory pension levels there. In other countries, the contribution ceiling favours women, as fewer women earn above the threshold. In addition, statutory pension schemes usually have strong redistributive elements to benefit women. This is true in particular for caregiver credits. In the Norwegian pension scheme, for instance, these have the strongest effect on reducing the gender pension wealth gap (Halvorsen &Pedersen, 2019). In France, caregiver credits almost completely offset the differences in pension entitlements between 4 This can be seen, for example, in the fact that single women achieve a higher retirement income in Germany and Britain than married women (Fasang et al., 2013). 123 Pension Wealth and the Gender Wealth Gap 759
mothers and non-mothers, but not those between genders (Bonnet &Rapoport, 2020). However, in the majority of European countries, caregiver credits are not able to compensate for the motherhood penalty that is accrued over the course of working life (Möhring, 2018). Looking at the differences between East and West Germany, net wealth is significantly higher in the West than in the East (Grabka, 2014). This is due, on the one hand, to historical conditions: In the East prior to German reunification, there was no opportunity to invest in companies or shares and little opportunity to buy real estate, which hampered wealth accumulation. On the other hand, this difference is due to demographic developments. Large parts of East Germany still face population decline, which has a negative effect on real estate prices. With respect to pension wealth, the picture is more mixed. While for statutory pensions, male (female) pensioners in East Germany receive about 6% (44%) higher gross pensions than their peers in West Germany, the respective figure for occupational pensions is −54% (−45%) (BMAS, 2020). Not only are occupational pensions significantly lower in East Germany, they are also much less prevalent. Although women in the East have a higher rate of labour force participation and work longer hours on average than women in the West, wage levels in the East are still lower. Thus, ultimately, what happens to the gender wealth gap in these two regions when pension entitlements are taken into account is an empirical question. 3 The German Pension System The German pension system consists of three pillars. The first pillar is the statutory public pension scheme, consisting of statutory pension insurance, civil servant, and liberal profession pension insurance. The second pillar is the occupational pension scheme. In these two pillars, insured individuals acquire pension entitlements throughout their working careers. Following the principle of equivalence, pension entitlements from the first and second pillars are proportionate to overall life-cycle earnings during the active phase of working life. The third pillar consists of private voluntary insurance plans (for an overview of old-age security policy in Germany, see (Schmähl, 2018)). First Pillar: The Statutory Public Pension Scheme Statutory pension insurance About three-quarters of the German working-age population (20–65 years) 5 are insured through the statutory pension insurance (GRV: Gesetzliche Rentenversicherung), which at retirement provides a monthly pension that closely relates to the sum of earnings subject to compulsory insurance from contribution periods. For example, if earnings in a given year coincide with the average earnings of all insured individuals in the same year (50 % of the national average), 1.0 (0.5) remuneration points are credited. An individual is vested in their pension plan after having contributed for 60 months. Pension credits can also be earned during non-contribution periods for a limited time period for the following reasons (i) sickness, rehabilitation, higher education; (ii) military service or detention for political reasons; (iii) parenting or caring for family members, if this care required 5 The retirement age has been raised gradually from 65 to 67. The phase-in started with individuals born in 1947 and has been increased by one month per birth cohort and reached 67 for individuals born after 1963. 123 760 K. Cordova et al.
the individual to withdraw from the labour market; and (iv) during spells of unemployment while receiving unemployment benefits. The statutory pension insurance has different redistributive elements that explicitly and implicitly favour women during non-contributory periods (e.g. pregnancy, maternity, or parental leave). The most relevant one is parental leave. The person who takes responsibility for child-rearing (this defaults to mothers unless registered otherwise) gains 3 (2) earning points in the statutory pension insurance for children born after (before) 1992, independent of the person’s previous labour income. As women typically earn less than men, they usually profit more from these periods than men. For women who did not participate in the labour market before pregnancy, this benefit alone amounts to 95.67 (287.01) euros a month for one (three) child (children) in 2019 compared to an average of 890 euros gross pension for all retirees in Germany in the statutory pension insurance. In addition, pension entitlements of mothers with low earnings, for instance, from part-time work, can be topped up during periods of child-rearing (Frericks et al., 2008). Civil servant pension insurance Roughly, 5% of working people in Germany are civil servants. The pension provided through civil servant pension insurance depends on the overall tenure and average salaries in the last position the individual held as a civil servant for at least two years. Each year of full time-service awards 0.01793375 replacement points up to a 0.7175 maximum. It is possible to receive both a statuary pension and a civil servant pension, although deductions apply. For child-rearing (parenting) periods, a supplement is granted comparable to the one in the statutory pension insurance. Pension insurance for the liberal professions Liberal professions have a separate but compulsory pension scheme according to the laws of the Laender for about 85 liberal professions, including architects, chartered accountants, dentists, lawyers, notaries, pharmacists, physicians, and psychotherapists. These schemes provide oldage pensions, disability benefits, and survivors’benefits. Entitlements are highly individual and are difficult to determine by simple rules. Liberal professions comprise roughly 3.5% of the workforce. Members of the liberal professions pension scheme can also apply for a child-rearing supplement from the statutory pension insurance and thus profit from this redistributive element. Second Pillar: Occupational Pension Schemes Occupational pension insurance is provided by companies to their employees on a voluntary basis. There are at least five different pensions plans in Germany. They comprise defined benefit (DB) plans, defined contribution (DC) plans, and also contributions with a minimum benefit. In 2019, about 54% of all employees subject to social security contributions had entitlements from occupational pension schemes (BMAS, 2021). Caregiver credits were only granted to employees in the public sector. 6 In 2019, among retired individuals aged 65 and older, almost 90% received statutory pensions, 26% occupational pensions, only 5% civil servant pensions, and roughly 1% liberal profession pensions. In all pension schemes, gross rents for men are significantly higher than for women (see Table 1). 6 The details of these different pension plans are discussed in detail in Bönke et al. (2019). 123 Pension Wealth and the Gender Wealth Gap 761
4 Data We use the 2012 and 2013 waves of the Socio-Economic Panel (SOEP) (Goebel et al., 2019), which is an ongoing longitudinal survey of individuals living in private households in Germany. The 2012 wave includes the wealth module, which provides information on ten different asset and debt components for each adult in the household separately. These include property wealth (and associated debt), building loan contracts, financial assets (e.g. savings accounts, bonds, shares, or investments), 7 private insurance policies, collectibles (in the form of gold, jewellery, coins, or valuable collections, etc.), net business assets (gross business assets minus debts) and on the debt side, consumer credits and mortgages. For wealth components that are held jointly, respondents are asked to state their individual share. In 2013, SOEP respondents were asked for the first time to report current pension entitlements based on the official annual information provided by their insurer for the year 2012. Using this information, pension wealth can be calculated based on the so-called “accrual method”(see (Wolff, 2015)) as the expected capitalised value of entitlements. Our primary dependent variable is augmented wealth, the sum of pension wealth and net wealth, which is the sum of assets minus total debts. Besides wealth and pension information, we use individual characteristics and information about the employment history, which is described in Appendix A. The focus of our sample is the working-age non-retired population aged 25 to 60. 8 Following (Sierminska et al., 2019), we topand bottom-code wealth variables at 99.9% and 0.1%, respectively. Missing values are corrected with multiple imputation techniques (see (Grabka &Westermeier, 2015)). Table 1 Average pensions per month and share of persons with own pensions aged 65 and over (2019, amounts in Euros) All Women Men Gross Share Gross Share Gross Share (in %) (in %) (in %) Statutory pensions 1.082 89 833 90 1.409 87 Civil servant 3.127 5 2.701 3 3.283 10 Liberal profession 2.163 1 1.659 1 2.378 2 Occupational pension 503 26 290 20 663 34 Occupational pension (public) 352 12 280 13 461 11 Source: BMAS (2020) Table B.2.1 7 Note that the survey does not ask explicitly about assets in checking accounts or cash, but these may be included in financial assets. Credit card debt—although relevant, for example, in the USA—does not play a major role in Germany. 8 This makes a total of 16,385 observations. Excluding 271 early pensioners and individuals younger than 25 and older than 60 leaves 8,894 observations, including 1,135 not employed or jobless. 123 762 K. Cordova et al.
Table 5 Oaxaca–Blinder decomposition at means of the gender wealth gap, pension wealth, and augmented wealth for the whole sample and youngest age cohort All Ages 25–60 Age cohort 25–36 Net wealth Augmented wealth Pension wealth Net wealth Augmented wealth Pension wealth Gap(%) 36.2% 30.1% 21.3% 43.3% 29.4% 4.56% Male 114,118.9*** 194,278.4*** 79,994.0*** 30,205.1*** 46,695.3*** 16,490.2*** (4928.5) (5344.0) (1486.0) (5781.1) (5834.6) (761.5) Female 72,699.3*** 135,788.2*** 62,932.0*** 17,104.9*** 32,926.5*** 15,737.3*** (2273.8) (2711.0) (1061.6) (1237.5) (1439.7) (615.9) Difference 41,419.6*** 58,490.2*** 17,062.0*** 13,100.3* 13,768.8* 752.9 (5427.7) (5992.4) (1826.3) (5912.0) (6009.7) (979.4) Explained 9272.1* 27,391.1*** 18,248.5*** −567.7 −741.0 −190.7 (3790.7) (4470.3) (1701.9) (3671.1) (3829.1) (915.8) Unexplained 32,147.5*** 31,099.1*** −1,186.5 13,667.9 14,509.7 943.6 (6202.8) (6618.3) (1756.4) (7850.9) (7904.7) (1004.8) Observations 8894 8894 8894 2205 2205 2205 Estimated using the SOEPv30 2012 and 2013 sample including non-retired individuals ages between 25 and 60 and sample weights. Percentages in terms of males. Decomposition estimated using the full set of controls described in Appendix A. The wealth gap is calculated in terms of males. 123 Pension Wealth and the Gender Wealth Gap 769
Table 6 Oaxaca–Blinder decomposition at means of the gender wealth gap, pension wealth, and augmented wealth by age cohort Age cohort 37–48 Age cohort 49–60 Net wealth Augmented wealth Pension wealth Net wealth Augmented wealth Pension wealth Gap(%) 40.3% 33.7% 20.1% 32.3% 27.4% 21.7% Male 114,229.5*** 171,563.4*** 57,078.0*** 163,943.8*** 300,855.0*** 136,722.1*** (8598.0) (8853.0) (1511.9) (8724.7) (9331.9) (2750.3) Female 68,152.2*** 113,716.0*** 45,558.9*** 110,958.8*** 218,269.4*** 106,978.7*** (3861.9) (4033.6) (998.7) (4290.6) (5025.9) (2049.3) Difference 46,077.3*** 57,847.4*** 11,519.1*** 52,985.0*** 82,585.6*** 29,743.4*** (9425.5) (9728.6) (1812.0) (9722.7) (10,599.3) (3429.9) Explained 14,725.9* 29,150.7*** 14,474.6*** 11,736.4 43,373.5*** 31,762.2*** (6638.7) (6932.0) (1672.1) (7691.9) (8704.7) (3392.8) Unexplained 31,351.4** 28,696.8* −2955.4 41,248.6*** 39,212.1** −2,018.8 (11,689.2) (12,036.8) (2270.4) (12,263.0) (13,260.4) (3942.8) Observations 3059 3059 3059 3630 3630 3630 Estimated using the SOEPv30 2012 and 2013 sample including non-retired individuals ages between 25 and 60 and sample weights. Percentages in terms of males. Decomposition estimated using the full set of controls described in Appendix A. The wealth gap is calculated in terms of males. 123 770 K. Cordova et al.
variables play a major role in explaining the gap in pension wealth, the gap is expected to be smaller for the younger cohort. 16 The results in Tables 5and 6by age cohort indicate that the gap in net wealth is the largest, at 43.3%, for 25–36-year-old individuals, while the gap in pension wealth is the smallest for this group (4.6%). This small gap in pension wealth points to the relatively similar labour force participation rates of men and women in younger cohorts. The relatively large absolute gap in net worth among older individuals stems, among other things, from the fact that gender differences in labour market outcomes have accumulated and been magnified up to this later point in the life course. Since the majority of wealth is held by the older population, the gaps for those over 48 coincide largely with those for the whole population. For the sample as a whole, differences in characteristics explain around one quarter of the gender difference in net wealth, while the unexplained component— the returns to those characteristics—account for around three quarters of the difference. Appendix Table 16 indicates that the most important components of the explained portion of the gap are differences in self-employment, work experience, having a white-collar occupation, company size, being divorced, and not being employed. The differences in working part-time, not being employed, having a white collar occupation and the size of a company favour women and help close the gap. In contrast, differences in being self-employed, being divorced, years worked full-time, and being unemployed favour men. The characteristics that contribute positively to the unexplained component (the returns) are: having attended university and being self-employed. The characteristic that contributes negatively to the unexplained component is being from East Germany, which reflects the historically poorer opportunities for wealth accumulation before reunification as well as the generally poorer labour market situation and the associated lower wage level in this region. In the augmented wealth decomposition, the share of the explained portion increases to almost half, given that pension wealth is highly correlated to lifetime earnings. The main differences from the net wealth decomposition resulting from characteristics are differences in industries, which favour men. The OB decomposition of the pension wealth gap shows that differences are almost entirely due to differences in characteristics. Differences in having children, self-employment, being a white-collar worker, and years of experience as a part-time employee help close the gap in pension wealth for women. The coefficient on the size of the company becomes positive and contributes to the increase in the pension wealth gap. The second approach we utilise to estimate the gender wealth gap is the detailed decomposition for the whole distribution. We estimate Eq. (5) using the FFL recentred influence function decomposition method (RIF) for the 25th, 50th, and 90th percentiles. The results from these are summarised in Table 7. The complete results can be found in Appendix Tables 17,18, and 19. These estimations also include the full set of control variables. The largest gap is at the bottom of the net wealth distribution, at the 25th percentile (90%), narrowing down to 37.8% at the median and 26.5% at the 90th percentile. In contrast to net wealth, the pension wealth 16 See also Westermeier et al. (2017) on the trend of a declining gender pension gap by cohorts. 123 Pension Wealth and the Gender Wealth Gap 771
distribution has a significantly smaller wealth gap at the bottom of the distribution of 10.1%, increasing to a somewhat wider gap of 21.2% at the median and 20.8% at the 90th percentile of the distribution. Thus, including pension wealth decreases the augmented wealth gap at the bottom of the distribution to 21.5% and 21.6% at the bottom 25th percentile and 50th percentile, respectively. But at the 90th percentile, the gender wealth gap remains at almost the same level as in net worth at 30%. This is where the smallest share of pension wealth constitutes augmented wealth (as per Table 4). This is partly due to an assessment ceiling in statutory pension insurance, which limits the influence of this component. The differences in net wealth between men and women vary across the distribution, and so do the contributions of the explained and unexplained components of the decomposition. The overall differences in characteristics contribute positively to the gap across the distribution and explain over half of the differences in the net wealth distributions. The differences in returns, or the Table 7 RIF-OAXACA decomposition of gender gap, population 25–60 (1) (2) (3) Q25 Q50 Q90 Net wealth Gap(%) 90.3% 37.8% 26.5% Male 1,755.9232,985.83 261,051.92 Female 171.11 20,514.79 191,978.74 Difference 1584.81 12,471.04 69,073.18 Explained 9,974.06 25,674.91 117,993.01 Unexplained −8,389.25 −13,203.87 −48,919.83 Augmented wealth Gap(%) 21.5% 21.6% 30% Male 35,606.59 104,686.24 441,057.15 Female 27,941.02 82,124.06 308,558.83 Difference 7,665.57 22,562.18 132,498.33 Explained 23,249.64 58,872.41 163,094.04 Unexplained −15,584.07 −36,310.23 −30,595.72 Pension wealth Gap(%) 10.1% 21.2% 20.8% Male 18,591.39 52,141.44 180,664.76 Female 16,701.36 41,046.49 143,067.56 Difference 1890.0211,094.95 37,597.20 Explained 7942.37 19,013.44 46,576.87 Unexplained −6,052.35 −7918.48 −8979.67 Estimated using the SOEPv30 2012 and 2013 sample with 8894 observations including non-retired individuals ages between 25 and 60 and sample weights, 8894 observations. Percentages in terms of males p\0:05, p\0:01, p\0:001 123 772 K. Cordova et al.
unexplained component, favour women with a negative contribution to the difference throughout the whole distribution. At the bottom of the distribution, the unexplained components—which favour women substantially—contribute to reducing the gap in both net worth and augmented wealth. At the top of the distribution, however, the differences in characteristics account for most of the gap. The statistically significant returns that contribute negatively include not being employed and industry type. At the top, only the returns to being widowed and experience in part-time employment help to close the gap. Differences in characteristics that contribute to the gap include: selfemployment (?), white collar occupations (-), industry (?25th, ?50th), company size (-90th), being divorced (?25th, ?50th), being widowed (?90th), experience working full-time (?), experience working part-time (?90th), and being unemployed (?20th, ?50th). Thus, to further decrease the gap in characteristics, women would need to be self-employed, in more similar industries to men, not lose as a result of divorce or widowhood, and have similar experience working full-time and part-time. 7.2 Decomposition by Pension Entitlements Next, we study each of the pension entitlements distributions separately. Table 8 includes the decomposition estimates for Eq. (4) for each pension type. The mean decomposition includes the full set of control variables. Around 87% of our sample has some type of statutory pension wealth. There is an estimated 13.1% gender gap in statutory pensions, 32.7% in civil pensions, and 41.8% in occupational pensions. The relatively small gap in statutory pensions can be explained in large part by two aspects: first, the contribution ceiling, which limits the accumulation of earning points in the public scheme for high earners, and second, the aforementioned redistributive elements. Only, 8% of the individuals in our sample have civil servant pensions. Within the civil service, men often hold higher positions than women, so differences in characteristics explain almost all of the gap in this pension type. The Table 8 Oaxaca–Blinder decomposition at means of the statutory pension wealth gap, civil pension wealth, and occupational pension wealth (1) (2) (3) Statutory Civil Occupational Gap(%) 13.1% 32.7% 41.8% Male 53,253.9 12,177.6 14,562.5 Female 46,268.0 8190.0 8474.0 Difference 6,985.8 3987.6 6088.6 Explained 11,126.8 4002.3 3119.4 Unexplained −4141.0 −14.7 2969.1 Observations 8894 8894 8894 Estimated using the SOEPv30 2012 and 2013 sample including nonretired individuals ages between 25 and 60. The statutory pension wealth decomposition includes the full set of control variables and can be found in Appendix A p\0:05, p\0:01, p\0:001 123 Pension Wealth and the Gender Wealth Gap 773
biggest gender pension gap is in occupational pensions. Occupational pension wealth is positive for 30% of our sample. The gap of 41.8% has an explained and unexplained component that accounts for about half of the gap, both favouring men. Occupational pensions are typically provided by larger companies with higher earnings levels and in industries with a higher share of male workers. Additionally, there is no upper contribution ceiling that might reduce pension entitlements (and thus the gap). Table 9includes estimates of the RIF decomposition for statutory pension at the 25th, 50th and 90th percentile of its distribution. We focus on this pension, as it not only has the highest prevalence, but also quantitatively constitutes the largest component of pension wealth. At the 25th percentile, the gender wealth gap is in favour of women with a value of −7.4%. This result corresponds to the one by age group, as the wealth gap is generally smaller for younger people than for older ones due to similar employment histories of women and men at this stage and the impact of the redistributive element of pensions, which contributes more to women’s pension value at this stage. At the median, there is a gap of 16.5% due to emerging differences in characteristics favouring men. Here, the gender pay gap becomes more relevant in explaining the gap—and so do the returns to characteristics favouring women in statutory pension accumulation. At the 90th percentile, the gap is 14.3% and almost triples in absolute terms. The difference in characteristics in this case plays a large role. The returns, which reduce the difference, are smaller in percentage terms than at other points of the distribution. 7.3 East and West Germany As pointed out in the introduction, there are still pronounced economic, cultural, and normative differences between East and West Germany. These are also reflected in different wealth levels in Table 10. For example, the net worth for men is only about 58,810 euros in East Germany, while it is more than twice that in West Germany (132,416 euros). Average augmented wealth for men is also almost 100,000 euros Table 9 RIF-OAXACA decomposition of statutory pension wealth gap Statutory Q25 Statutory Q50 Statutory Q90 Gap(%) −7.4% 16.5% 14.3% Male 10,649.6 38,144.4 128,505.5 Female 11,440.5 31,846.0 110,066.9 Difference −790.9 6298.5 18,438.6 Explained 2548.214,344.3 28,070.7 Unexplained −3339.1 −8045.8 −9632.0 Estimated using the SOEPv30 2012 and 2013 sample of 8894 observations including non-retired individuals ages between 25 and 60. The estimation includes the full set of controls and can be found in Appendix A p\0:05, p\0:01, p\0:001 123 774 K. Cordova et al.
Table 10 Oaxaca–Blinder decomposition at means of the gender wealth gap, augmented wealth, and pension wealth by region East Germany West Germany Net wealth Augmented wealth Pension wealth Net wealth Augmented wealth Pension wealth Gap 29.8% 10.8% −7.1% 37.6% 33.8% 27.9% Male 58,810.2 119,633.0 60,882.8 132,415.7 218,971.9 86,316.2 (4474.7) (5291.9) (2243.6) (6355.1) (6834.6) (1818.7) Female 41,277.7 106,701.8 65,219.7 82,596.3 144,949.7 62,211.4 (2733.6) (4304.7) (2345.8) (2843.9) (3282.7) (1184.4) Difference 17,532.5 12,931.2 −4336.9 49,819.4 74,022.2 24,104.8 (5243.6) (6821.7) (3246.0) (6962.4) (7582.1) (2170.4) Explained 1701.1 6653.7 4810.6 24,192.0 51,720.1 27,564.1 (3110.6) (4671.9) (2581.7) (4447.9) (5135.6) (1851.6) Unexplained 15,831.4 6277.5 −9147.5 25,627.4 22,302.1 −3459.3 (5524.4) (6876.3) (2690.6) (6245.5) (6667.1) (1828.4) Observations 2167 2167 2167 6727 6727 6727 Estimated using the SOEPv30 2012 and 2013 sample including non-retired individuals ages between 25 and 60 and sample weights. Percentages in terms of males. Decomposition estimated using the full set of controls described in Appendix A. The wealth gap is calculated in terms of males. 123 Pension Wealth and the Gender Wealth Gap 775
higher in the western part of the country. For pension wealth, however, the advantage is much smaller for people living in the West. As discussed in sect. 2, the disadvantages faced by women in the West, due to the still prevailing norms of the male breadwinner model, result in a gender private wealth gap of 37.6% compared to only 29.8% in the East. While the gender gap decreases only slightly to 33.8% for augmented wealth in West Germany, it decreases to 10.8% in East Germany due to the much smaller gap in pension wealth in that part of the country. In East Germany, women hold 65,220 euros in pension wealth and men hold 60,883 euros. In West Germany, in contrast, the pension wealth gap is about 27.9%, with men holding 24,105 euros more in pension wealth, due to the more pronounced gender wage gap in that region and the higher prevalence of occupational pensions among men. Although women in the East have succeeded in narrowing the augmented wealth gap more than women in the West, East German women hold the lowest augmented wealth levels of all four groups under consideration. The relevance of the explained component also differs between the two regions. While observed characteristics contribute little to explaining the differences in East Germany, they explain almost 50% or more of the differences in West Germany, which means that if women had more similar characteristics to men in the West, the gaps would be much smaller. Table 11 Oaxaca–Blinder mean decomposition RIF-OAXACA median wealth gaps, individuals without children OB (Mean) Net wealth Augmented wealth Pension wealth Gap(%) 24.1% 11.9% −5.6% Male 79,746.0 133,077.4 53,374.3 Female 60,560.3 117,208.3 56,387.4 Difference 19,185.715,869.2 −3013.1 Explained −4718.0 −9461.7 −4490.1 Unexplained 23,903.725,330.91477.1 RIF-OB (Q50) Net wealth Augmented wealth Pension wealth Gap(%) 0.7% −10.1% −8.2% Male 10,527.2 48,398.8 26,550.0 Female 10,448.2 53,276.3 28,726.3 Difference 78.9 −4877.5 −2176.4 Explained 18.3 525.6 −595.9 Unexplained 60.6 −5403.1 −1580.4 Observations 2225 2225 2225 Estimated using the SOEPv30 2012 and 2013 sample including non-retired individuals without children ages between 25 and 60 and sample weights. Percentages in terms of males. Decomposition estimated using the full set of controls described in Appendix A *p<0.05, ** p<0.01, ***p<0.001 123 776 K. Cordova et al.
7.4 Robustness Checks To check the robustness of our results, we estimate the gender wealth gap decompositions in restricted samples. First, we exclude self-employed individuals from our analysis because contributions to statutory and occupational pensions are not compulsory for the self-employed (only for certain occupations). Thus, we expect the relative gap to be smaller. We estimate the FFL recentred influence function decomposition method for the 25th, 50th, and 90th percentile for this sub-sample. Table 22 shows that excluding the self-employed from the sample results in slightly smaller net wealth and augmented wealth gaps. These reductions are consistent with the explained portion in the case of the whole sample, where self-employment makes a significant positive contribution to the gap. For pension wealth, the gap is now stable at 16 to 19% over the whole distribution. Excluding the self-employed leads to a more homogeneous population, where differences in earning levels between sexes play the main role in the gap. The unexplained portion helps reduce the gap. Next, we restrict the sub-sample to adults without children in order to focus on individuals who have no career interruptions due to child-rearing. Table 11 shows the estimation results for the mean and median decomposition for all wealth variables. In this case, the differences are only statistically significant for the net wealth mean decomposition. Net worth is significantly lower for individuals without children. This is partly because some individuals are single, who do not profit from the economies of scale that arise from cohabitation and also do not profit from joint taxation of married couples. Additionally, individuals without children are often younger than the sample as a whole and are thus still at the beginning of their working life. All this leads to a significantly lower gap in net wealth. At the mean, the relative gap is only about 24% compared to 36% for the total population including those with children. For pension wealth, the gap is negative—although not statistically significant—that is, men show lower levels of pension wealth than women. This is in line with previous research showing that women without children perform better in the German labour market, while motherhood entails significant risks for both a career and pension entitlements (Schrenker &Zucco, 2020). As a result, the gap in augmented wealth is strongly reduced for those without children and is even negative at the median at −10% compared to 21.6% for the total population. 17 8 Limitations The analyses presented here face some limitations. First, these concern the availability of information on pension entitlements. So far, this information has been collected in SOEP in 2013 for the 2012 reference year. This means that the 17 As an additional robustness check, we also present an alternative specification in Table 23 that includes inheritances. The results do not change significantly in terms of the size of the gap. The net wealth and augmented wealth gap increase slightly at the median in absolute terms. Inheritances are significant in explaining the gap at the top of the distribution favouring men. Yet the returns to inheritances are in favour of women at the 25th and 50th percentiles. 123 Pension Wealth and the Gender Wealth Gap 777
effect of the maternity pension introduced in 2014 is not taken into account. It stipulates that mothers or fathers are credited with an additional year of child-raising time for children born before 1992. However, one additional pension point only corresponds to an additional monthly pension entitlement of 34.19 euros in 2020 in West Germany and therefore has a limited effect. Besides this, the continued rise in women’s labour force participation in Germany is likely to have had a positive impact on the gender gap in pension wealth and will be visible in more recent data. Between 2012 and 2019 alone, women’s labour force participation increased by 4.8 percentage points according to the Federal Statistical Office. 18 Another data-related limitation concerns the under-representation of multimillionaires and billionaires in SOEP (see (Westermeier &Grabka, 2015) and their potential under-reporting of assets (Davies, 2009)). The fact that there are very few women among the top 1,000 richest individuals in Germany, as shown by the “rich list”of the German Manager-Magazin, indicates that our estimates of the wealth gap can be treated as a lower bound of the real gap at the top. This affects the OB decomposition but not our preferred RIF decomposition, where we look at the 90th percentile and not at the very top of the distribution. 19 Moreover, our measure of net worth consisting of ten different asset components does not include, for instance, the value of vehicles or student loans (both collected in SOEP for the first time in 2017). Although there are no gender-specific differences in the spread of student loans, men have an almost 20% higher probability of owning vehicles compared to women and thus slightly underestimating the gap for private wealth. One final limitation concerns pension entitlements for liberal professions, as they are not collected for the working-age population in SOEP but only for retirees. Although the level of entitlements among those who are eligible is above average compared to the statutory pension scheme, the share of recipients is low at around one percent. Thus, the overall effect of this omission should be negligible. It should also be noted that the gender pension gap for beneficiaries is around ten percentage points lower than in the statutory pension insurance (see Table 1). 9 Conclusion We extend the study of the gender wealth gap by including pension wealth in the standard measure of net worth. For this purpose, we use detailed individual data on personal wealth and pension entitlements of the working-age population from the 2012 and 2013 waves of the German SOEP. The unconditional gender wealth gap increases in levels from an average of 31,000 euros to 45,000 euros when pension wealth is included, while the relative gap decreases from 35 to 28%. We take two 18 Since 2013, new marginal jobs below 450 euros per month are generally subject to compulsory insurance in the statutory pension scheme, but marginal workers can also opt out. In addition, it is now possible to make special payments to compensate for pension reductions starting at the age of 50. Prior to 2017, such payments were only possible starting at the age of 55. However, both reforms are unlikely to have any significant impact on the level of pension entitlements overall. 19 This data gap has now been filled by the SOEP group with a new sample of the very wealthy that was launched in 2019. Data on pension entitlements are not collected in this sample, however. 123 778 K. Cordova et al.
Table 14 continued Net wealth Augmented wealth Pension wealth Male Female Male Female Male Female Financial intermediation −14,329.6 27,482.8 −7122.6 39,991.29508.2 13,482.0 (31,536.3) (14,957.5) (32,909.8) (16,350.2) (7216.9) (5124.5) Real estate 8,143.7 11,979.5 6874.9 12,143.2 734.7 1203.6 (23,465.8) (11,545.5) (24,487.8) (12,620.5) (5370.0) (3955.5) Public adm. and defence −33,559.1 −16,819.1 −59,131.0 −28,413.7−23,432.4 −9894.9 (29,628.4) (12,872.2) (30,918.8) (14,070.8) (6780.3) (4410.1) Education −44,289.0 −13,541.9 −59,525.8 −12,574.7 −12,505.7 1520.9 (31,481.7) (11,351.8) (32,852.8) (12,408.8) (7204.4) (3889.2) Health and social work −48,569.1 6,517.2 −59,362.92009.5 −8358.1 −3434.5 (27,592.1) (10,044.2) (28,793.9) (10,979.4) (6314.3) (3441.2) Other ser. act. −90,201.7 −11,378.9 −98,597.2 −18,103.6 −6647.5 −5657.7 (30,426.5) (13,617.6) (31,751.7) (14,885.6) (6963.0) (4665.5) Activities of households −127,822.3 978.9 −136,304.6 1268.8 −6376.4 1002.6 (290,462.7) (27,055.3) (303,113.7) (29,574.5) (66,471.1) (9269.3) Extra-territorial org. 33,404.6 −66,503.6 28,076.6 −76,349.7 −3906.7 −8602.4 (169,038.5) (74,080.7) (176,400.9) (80,978.7) (38,683.7) (25,380.5) No coworkers −222,898.1 −17,075.5 −225,739.6 −19,513.6 −4633.7 −2551.2 (29,114.2) (15,422.8) (30,382.2) (16,858.8) (6662.6) (5283.9) Small company −9127.1 24,264.5 −11,260.3 19,283.6−1912.2 −5011.7 (15,814.0) (6931.7) (16,502.8) (7577.1) (3619.0) (2374.8) Large company −26,240.46355.9 −15,634.4 13,679.210,687.9 6935.6 (12,469.9) (6207.0) (13,013.0) (6784.9) (2853.7) (2126.5) Cohabiting 11,890.3 −23,016.8 8854.2 −25,169.3 −2861.2 −1887.4 123 Pension Wealth and the Gender Wealth Gap 785
Table 14 continued Net wealth Augmented wealth Pension wealth Male Female Male Female Male Female (16,522.2) (8031.5) (17,241.8) (8779.4) (3781.0) (2751.6) Single 32,121.7 −21,475.1 33,272.9 −23,106.7 1442.5 −1234.8 (17,083.6) (7,818.7) (17,827.7) (8546.7) (3909.5) (2678.7) Divorced/separated −3415.6 −45,187.9 −10,456.2 −40,941.2 −7416.94591.6 (15,973.1) (6382.5) (16,668.8) (6976.8) (3655.4) (2186.7) Widowed −32,453.8 1538.9 −61,455.8 13,813.4 −28,794.5 12,648.9 (88,012.4) (14,448.5) (91,845.7) (15,793.8) (20,141.2) (4950.1) Exp, full-time 3399.3 741.1 7176.5 4342.8 3872.3 3561.9 (1035.8) (378.8) (1080.9) (414.0) (237.0) (129.8) Exp, part-time −3040.8 1081.7−970.2 2799.0 2064.9 1712.8 (2183.3) (464.8) (2278.4) (508.1) (499.6) (159.3) Exp, unemployed −4561.5 −4709.7 −3959.7 −4665.6 777.2 42.1 (2363.9) (869.0) (2466.9) (949.9) (541.0) (297.7) Has statutory pensions 23,094.7 −5744.8 21,151.6 −5684.9 −1701.8 324.4 (19,316.5) (9779.9) (20,157.8) (10,690.6) (4420.5) (3350.7) Has civil servant pension −50,218.0 −5824.4 12,243.6 38,328.4 62,662.9 43,804.6 (40,305.7) (18,822.6) (42,061.2) (20,575.3) (9223.8) (6448.7) Has occupational pension 3816.1 4480.7 47,337.8 34,724.5 43,157.9 30,423.2 (11,321.6) (5473.2) (11,814.7) (5982.9) (2590.9) (1875.2) Educ. flag −3320.1 17,253.5 11,501.3 16,440.2 15,053.4 −892.2 (41,675.5) (19,333.1) (43,490.7) (21,133.3) (9537.3) (6623.6) Marst. flag 57,544.0 −97,676.4 62,305.8 −142,312.9 5844.2 −43,691.9 (129,983.6) (85,165.7) (135,645.0) (93,095.8) (29,746.2) (29,178.2) 123 786 K. Cordova et al.
Table 14 continued Net wealth Augmented wealth Pension wealth Male Female Male Female Male Female miss_expft12 −1854.6 −35,072.6 −81,569.9 −25,095.0 −77,514.4 9694.8 (293,679.9) (104,414.7) (306,471.0) (114,137.3) (67,207.3) (35,773.1) Comp. size flag −56,890.912,298.2 −49,612.4 14,706.5 7080.1 3114.5 (24,919.5) (10,451.5) (26,004.9) (11,424.6) (5702.7) (3580.7) Occup. flag 82,073.0 44,671.4 166,476.4 56,086.0 69,735.5 11,711.2 (60,646.7) (27,269.3) (63,288.2) (29,808.5) (13,878.7) (9342.6) Constant 7372.9 51,299.6 −47.4 63,356.9 −12,153.7 11,011.2 (46,769.3) (18,841.0) (48,806.3) (20,595.3) (10,702.9) (6455.0) Observations 4047 4847 4047 4847 4047 4847 OLS regression estimates utilising the SOEPv30 2012 and 2013. The sample includes non-retired individuals between 25 and 60 years old and sample weights. Each column includes the estimates for indicated wealth variable as the dependent variable and the indicated gender. OLS regression estimated using the full set of controls described in Appendix A. Standard errors in parenthesis p\0:05, p\0:01, p\0:001 123 Pension Wealth and the Gender Wealth Gap 787
Table 15 Determinants of accumulated pension wealth, by gender Statutory Occupational Civil Male Female Male Female Male Female Number of children 922.4 2565.7 7769.5 496.7 3659.4 576.8 (661.5) (509.7) (4985.0) (5357.5) (2283.0) (1538.5) Children in household −3699.6−1325.3 −11,277.9 6068.4 3080.7 −4470.6 (1668.1) (1224.0) (11,911.4) (11,218.1) (5441.8) (3400.6) Immigrant −9679.5 −7541.4 −13,806.6 −8180.0 −11,985.9 −5728.8 (2901.6) (2002.2) (51,006.9) (73,163.5) (12,115.9) (8716.9) East −10,310.9 −5456.4 −9342.6 −21,909.6 −19,333.3 −4299.1 (1483.4) (1208.2) (12,414.7) (12,007.4) (5565.4) (3453.5) Age cohort 25-36 −19,110.6 -23,880.6 −61,940.3-16,720.6 3552.5 1622.5 (3445.9) (2015.2) (26,740.8) (21,858.3) (12,396.3) (6156.7) Age cohort 37-48 −23,079.2 -22,598.5 −67,735.6 −55,977.4 −8216.1 −1518.0 (2083.2) (1437.5) (16,543.6) (15,137.8) (7159.9) (4057.1) Lower vocational 47.5 −905.4 37,553.1 −27,321.6 168.1 1876.4 (2570.4) (1691.9) (29,110.0) (75,139.7) (12,482.8) (6537.3) Upper vocational −499.7 −479.0 59,126.6 −24,709.4 14,059.6 1795.0 (2952.1) (1978.6) (30,902.3) (75,125.1) (13,129.2) (6975.0) University 18,790.3 4524.347,681.8 −14,394.3 47,395.1 13,090.7 (3000.3) (1957.5) (30,853.3) (74,286.4) (13,213.8) (6917.5) Not employed −3758.5 −2783.2 72,114.6 −57,256.7 −1745.7 4,402.1 (3049.9) (1964.7) (61,391.3) (67,924.0) (18,727.1) (7856.6) Trainee 5189.1 2690.0 −17,597.2 −31,402.2 31,132.8 2500.9 (6216.7) (4794.1) (67,207.1) (98,266.3) (27,034.9) (17,884.1) Self-employed −21,993.9 −10,706.1 168,455.5 −50,627.2 14,436.6 27,997.6 123 788 K. Cordova et al.
Table 15 continued Statutory Occupational Civil Male Female Male Female Male Female (2988.6) (2790.2) (48,514.2) (97,344.2) (13,482.0) (11,003.3) White collar 6427.2 3328.438,866.4 −102,588.2 11,524.1 8185.0 (1,672.1) (1,527.3) (35,190.6) (66,643.4) (6,086.4) (5,404.8) Civil servant low 18,871.6 −7960.9 83,113.1−33,341.2 22,179.4 16,298.6 (20,783.0) (21,541.9) (34,952.2) (66,907.4) (24,487.9) (15,467.8) Civil servant high 1924.3 −5525.3 126,263.5 −4624.6 −5070.5 20,985.5 (12,997.4) (8967.5) (36,103.6) (66,351.0) (17,866.7) (10,762.0) Fishing 11,286.7 6568.2 37,221.9 (36,352.4) (30,395.2) (76,279.1) Mining and quarrying 11,100.6 2339.3 −5866.5 −23,081.1 (10,701.9) (21,537.3) (31,411.6) (47,981.7) Manufacturing 2834.6 2098.8 176,411.8 4,332.4 8,570.5 (2698.9) (2447.4) (53,850.6) (10,377.6) (7,181.6) Electricity, gas and water −5139.6 5955.2 −7,832.0 −10,284.6 (5562.2) (6502.9) (16,225.1) (13,608.6) Construction 2629.9 8153.2−13,590.2 −15,658.6 −7846.2 (3102.4) (4044.8) (50,369.6) (13,310.6) (13,296.1) Wholesale and retail −196.3 −4529.9 86,374.6 216,009.1 10,065.2 −5802.2 (3375.2) (2331.8) (66,033.2) (64,105.4) (14,046.4) (7698.2) Hotels and restaurants 1059.4 1000.3 55,239.6 679,686.2 27,178.7 −21,984.1 (5670.7) (3592.6) (87,178.2) (76,258.3) (29,496.5) (15,017.1) Transp., storage and com. 373.0 3911.2 −21,927.6 −14,124.2 −5700.1 571.3 (3482.1) (3,420.5) (34,542.5) (34,594.8) (12,958.0) (9,226.1) 123 Pension Wealth and the Gender Wealth Gap 789
Table 15 continued Statutory Occupational Civil Male Female Male Female Male Female Financial intermediation 7534.8 3385.2 6543.1 49,788.4 −4717.4 13,012.7 (4097.3) (3216.3) (58,185.6) (76,832.3) (12,478.9) (7520.6) Real estate 318.5 1587.9 −11,250.8 −6215.2 6235.0 1051.8 (3186.5) (2552.7) (38,757.4) (41,810.0) (12,007.0) (8348.0) Public adm. and defence −1893.1 875.0 −31,052.2 818.3 −24,828.5 −12,367.5 (4363.7) (2964.8) (28,151.2) (18,754.0) (12,802.4) (6965.1) Education −4,768.9 −488.3 −14,885.7 34,316.4 −21,890.3 −9,053.8 (4653.2) (2592.8) (29,926.9) (17,875.4) (14,116.2) (6751.8) Health and social work 81.8 −2093.8 45,157.2 −8297.8 −7451.7 −5862.5 (3687.0) (2230.6) (55,772.2) (30,937.5) (12,367.1) (6401.5) Other ser. act. −2214.2 −3944.0 −86,375.1 −1786.3 −12,887.4 −5972.6 (4065.1) (3003.7) (44,979.1) (40,503.9) (13,906.8) (8449.0) Activities of households −10,147.4 −1991.3 −25,850.1 (36,008.2) (5,705.2) (48,061.2) Extra-territorial org. 619.6 −9732.2 57,107.2 −39,576.8 −261.2 (20,904.2) (15,313.6) (101,130.0) (52,872.7) (34,490.4) No coworkers −298.3 −544.2 −106,927.9 −15,315.9 −3279.7 9914.5 (4165.0) (3627.2) (86,547.7) (73,479.3) (21,466.9) (15,544.0) Small company −2570.0 −,236.5 −19,634.4 19,198.6 10,135.4 −7297.4 (2036.4) (1488.8) (31,356.5) (19,154.0) (9099.2) (4701.4) Large company 7793.5 5892.4 −26,321.8−4181.5 13,098.82478.6 (1620.8) (1359.6) (13,095.1) (10,717.1) (5449.4) (3305.4) Cohabiting −3217.3 −208.7 −20,805.9 −8232.5 7105.6 −3812.3 123 790 K. Cordova et al.
Table 15 continued Statutory Occupational Civil Male Female Male Female Male Female (2198.0) (1795.6) (18,413.0) (15,533.7) (7537.6) (4662.2) Single −563.2 −1475.8 −11,814.0 −4793.6 −248.2 −3710.6 (2283.1) (1771.5) (19,677.9) (14,120.1) (8222.2) (4899.2) Divorced/separated −4256.63489.9−18,607.4 −30,113.8−1686.4 6169.8 (2106.0) (1368.3) (15,090.5) (13,906.6) (7188.6) (3854.1) Widowed −7773.8 6001.9110,036.2 801.6 19,434.6 (11,437.5) (3054.3) (41,671.0) (42,638.1) (9314.0) Exp, full-time 2926.4 2790.9 6772.2 10,088.2 2633.5 1299.0 (137.5) (81.3) (1,098.4) (859.9) (486.4) (249.0) Exp, part-time 1494.9 1365.9 7827.0 4675.3 453.5 986.6 (288.8) (99.2) (2658.1) (1040.4) (995.3) (302.9) Exp, unemployed 266.1 103.4 −6602.0 847.5 824.2 −424.5 (304.5) (183.0) (3510.6) (7976.5) (2153.6) (1300.8) Educ. flag 3768.5 360.2 167,610.6 −14,368.0 17,114.7 1359.0 (6267.5) (4561.6) (55,013.1) (90,561.2) (29,044.3) (16,545.1) Marst. flag −6006.8 −13,408.6 38,702.1 (17,993.0) (21,461.7) (42,561.8) Comp. size flag 7270.41523.3 34,269.3 9010.4 −10,272.2 −1145.2 (3384.2) (2343.0) (36,872.7) (17,882.5) (14,327.9) (7102.2) Occup. flag 46,604.0 11,800.3−125,883.8 31,135.9 299.2 (7683.9) (5817.4) (87,917.4) (22,371.4) (15,395.7) miss_expft12 2125.2 −116,816.8 (30,644.8) (97,317.2) 123 Pension Wealth and the Gender Wealth Gap 791
Table 15 continued Statutory Occupational Civil Male Female Male Female Male Female Constant 8134.8 19,823.4 −71,676.9 23,282.1 −47,135.6−5030.7 (6020.9) (3717.9) (60,176.2) (102,755.6) (23,587.9) (12,636.4) Observations 3427 4261 336 332 1259 1413 Estimated using the SOEPv30 2012 and 2013 sample including non-retired individuals between 25 and 60 years old. Each column includes the estimates for indicated wealth variable as the dependent variable. Decomposition estimated using the full set of controls described in Appendix A p\0:05, p\0:01, p\0:001 123 792 K. Cordova et al.
Table 16 Oaxaca–Blinder decomposition at means of the gender wealth gap, pension wealth and augmented Wealth (1) (2) (3) Net wealth Augmented wealth Pension wealth Male 114,118.9 194,278.4 79,994.0 Female 72,699.3 135,788.2 62,932.0 Difference 41,419.6 58,490.2 17,062.0 Explained 9,272.127,391.1 18,248.5 Unexplained 32,147.5 31,099.1 −1186.5 Explained Number of children −1,717.5−2476.9 −758.4 Children in household −265.7 −171.4 87.6 Immigrant 475.7561.384.0 East −548.3 −667.9 −117.2 East/female interaction −4644.8−6365.6 −1631.3 Age cohort 25–36 306.7 432.0 124.3 Age cohort 37–48 157.9 301.0 143.2 Lower vocational 38.9 28.9 −10.2 Upper vocational 67.6 56.3 −11.6 University 1260.9 1538.7269.0 Not employed −4,163.8 −4531.1 −428.6 Trainee 165.7 240.8 77.7 Self-employed 11,907.6 10,811.7 −1018.2 White collar −3593.6 −4827.7 −1245.5 Civil servant low 542.4629.892.0 Civil servant high 305.8 526.8 220.3 Industry 2014.8 3265.8 1431.5 Company −3,171.2 −2,166.3967.2 Cohabiting −133.8 −180.0 −42.5 Single −153.8 −185.0 −16.4 Divorced/separated 1215.1 1215.9 −0.2 Widowed 32.4 −126.8 −164.1 Exp, full-time 12,106.5 40,049.7 27,941.2 Exp, part-time −5807.9−14,265.6 −8510.7 Exp, unemployed 1027.9 1002.0 −32.5 Has statutory pensions −186.7 −198.1 −21.3 Has civil servant pension −343.1 386.4 726.8 Has occupational pension 95.7 804.5 706.7 Educ. flag 12.1 18.5 6.4 Marst. flag −19.7 −27.2 −7.3 Exp. FT flag 5.9 8.5 2.5 Exp. PT flag 0.0 0.0 0.0 Exp. UE flag 0.0 0.0 0.0 Comp. size flag 2,313.3 1,755.2 −596.4 123 Pension Wealth and the Gender Wealth Gap 793
Table 16 continued (1) (2) (3) Net wealth Augmented wealth Pension wealth Occup. flag −30.9 −53.1 −19.4 Unexplained Number of children 11,818.1 12,056.7 −183.5 Children in household 1016.4 290.1 −558.6 Immigrant −292.3 −521.6 −226.1 East −6234.1 −7636.8 −1352.0 East/female interaction 4644.86365.6 1631.3 Age cohort 25 −36 11,684.512,226.71121.1 Age cohort 37 −48 8630.7 7050.4 −1413.4 Lower vocational 1366.4 2381.2 857.0 Upper vocational 651.7 1280.9 612.6 University 13,891.5 20,331.3 6370.3 Not employed −430.1 −201.7 376.0 Trainee 183.8 356.0 203.7 Self-employed 19,464.3 18,528.3 −760.7 White Collar −1055.9 3625.7 4729.4 Civil servant low 819.1 731.2 −100.9 Civil servant high −39.2 14.1 100.6 Industry −32,129.3 −35,309.7 −2520.3 Company −27,014.4 −25,250.21,937.7 Cohabiting 3870.53779.2−101.3 Single 8962.7 9425.3 448.3 Divorced/separated 4,951.43596.2 −1443.6 Widowed −155.5 −353.7 −201.2 Exp, full-time 44,990.9 48,948.2 6244.5 Exp, part-time −3335.9 −2895.3 514.3 Exp, unemployed −52.8 534.7 775.6 Has statutory pensions 24,793.6 23,106.9 −1705.0 Has civil servant pension −3427.3 −1995.2 1475.4 Has occupational pension −214.8 3799.1 3850.5 Educ. flag −309.4 −73.5 240.6 Marst. flag 151.2 192.3 41.5 Exp. FT flag 8.1 −18.3 −25.6 Exp. PT flag 0.0 0.0 0.0 Exp. UE flag 0.0 0.0 0.0 Comp. size flag −11,366.6 −10,542.3682.3 Occup. flag 231.9 681.7 358.0 Observations 8894 8894 8894 123 794 K. Cordova et al.
Table 19 continued (1) (2) (3) Q25 Q50 Q90 Single −281.10 95.10 1906.80 Divorced/separated 342.56 −560.38 −4681.36 Widowed −279.28 −110.24 −2,589.71 Exp, full-time ,783.77 6530.25 4615.59 Exp, part-time 43.01 −3,012.77 8,899.83 Exp, unemployed −51.80 −1,723.52 4,507.00 Has statutory pensions 3645.65 −1750.00 −17,020.78 Has civil servant pension 382.08 −92.02 1276.29 Has occupational pension 307.71 1,301.11 7195.99 Educ. flag −150.66 47.48 576.12 Marst. flag 4.13 −2.58 117.26 Exp. FT flag 44.64 −11.78 −139.52 Exp. PT flag 0.00 0.00 0.00 Exp. UE flag 0.00 0.00 0.00 Comp. size flag −154.10 602.76 549.35 Occup. flag −13.99 88.01 1180.05 Observations 8894 8894 8894 Estimated using the SOEPv30 2012 and 2013 sample including non-retired individuals between 25 and 60 years old. Each column includes the estimates for indicated wealth variable as the dependent variable. Decomposition estimated using the full set of controls described in Appendix A p\0:05, p\0:01, p\0:001 Table 20 Oaxaca–Blinder decomposition at means of the statutory pension wealth gap, civil pension wealth, and occupational pension wealth Statutory Occupational Civil overall Male 53,253.9 12,177.6 14,562.5 Female 46,268.0 8190.0 8474.0 Difference 6985.8 3987.6 6088.6 Explained 11,126.8 4002.3 3119.4 Unexplained −4141.0 −14.7 2,969.1 Explained Number of children −407.4 −109.9 −241.1 Children in household 123.5 −2.1 −33.8 Immigrant 82.6−8.8 10.1 East −72.5 −7.5 −37.2 East/female interaction −942.5−32.3 −656.5 Age cohort 25–36 89.5 22.5 12.3 123 Pension Wealth and the Gender Wealth Gap 801
Table 20 continued Statutory Occupational Civil Age cohort 37–48 97.5 24.3 21.4 Lower vocational −1.1 −4.7 −4.4 Upper vocational −2.7 −4.9 −4.0 University 158.9 −31.0 141.1 Not employed 414.9 −397.9 −445.5 Trainee 39.2 4.6 34.0 Self-employed −924.8 −190.096.6 White collar −557.7 −194.0−493.7 Civil servant low −284.7 309.5 67.3 Civil servant high −136.4 350.2 6.5 Industry 1287.8 −710.4 854.1 Company 668.6 −63.2 361.8 Cohabiting −31.8 −7.6 −3.1 Single 55.3 −105.1 33.4 Divorced/separated −35.4 32.1 3.1 Widowed −91.3 −28.3 −44.5 Exp, full-time 18,646.6 5498.0 3796.6 Exp, part-time −5846.3 −1709.2 −955.2 Exp, unemployed 3.7 −11.8 −24.4 Has statutory pensions −744.0 644.5 78.2 Has civil servant pension −107.9 860.0−25.3 Has occupational pension 141.7 −84.2 649.1 Educ. flag 2.6 1.5 2.3 Marst. flag −9.0 −7.0 8.8 Exp. FT flag −3.7 6.5 −0.4 Exp. PT flag 0.0 0.0 0.0 Exp. UE flag 0.0 0.0 0.0 Comp. size flag −472.2−43.0 −81.2 Occup. flag −13.9 1.6 −7.0 Unexplained Number of children −1359.0 −511.4 1686.9 Children in household −505.7 −1329.51276.6 Immigrant −61.4 −29.5 −135.2 East −956.8346.4 −741.6 East/female interaction 942.532.3 656.5 Age cohort 25–36 967.0 −263.7 417.8 Age cohort 37–48 −144.7 57.5 −1,326.2 Lower vocational 463.2 384.2 9.7 Upper vocational 81.2 84.0 447.4 University 3,101.5 −115.9 3384.7 Not employed 88.9 −144.4 431.5 Trainee 102.0 −101.6 203.4 123 802 K. Cordova et al.
Table 20 continued Statutory Occupational Civil Self-employed −858.8 256.3 −158.2 White collar 2,087.9 572.5 2069.0 Civil servant low −11.6 −161.5 72.3 Civil servant high −534.7 663.1 −27.7 Industry −1801.5 1555.5 −2274.3 Company 1208.3 −1597.7 2327.0 Cohabiting −332.8 −163.6 395.0 Single −215.4 64.1 599.6 Divorced/separated −850.3 −183.9 −409.5 Widowed −74.2 −41.6 −85.5 Exp, full-time 1605.0 −2651.0 7290.5 Exp, part-time 423.8 −122.2 212.7 Exp, unemployed −184.0 60.3 899.3 Has statutory pensions 2911.2 −3305.9 −1,310.2 Has civil servant pension −159.3 1815.5 −180.9 Has occupational pension −765.3 241.8 4374.1 Educ. flag 68.8 76.1 95.6 Marst. flag −0.7 11.3 30.9 Exp. FT flag 16.9 −43.1 0.5 Exp. PT flag 0.0 0.0 0.0 Exp. UE flag 0.0 0.0 0.0 Comp. Size flag 704.0 40.6 −62.3 Occup. flag 225.6 −22.0 154.4 Observations 8894 8894 8894 Omitted high ed, high civil servant, married Estimated using the SOEPv30 2012 and 2013 sample including non-retired individuals between 25 and 60 years old. Each column includes the estimates for indicated wealth variable as the dependent variable. Decomposition estimated using the full set of controls described in Appendix A p\0:05, p\0:01, p\0:001 123 Pension Wealth and the Gender Wealth Gap 803
Table 21 RIF-OAXACA decomposition of statutory pension wealth gap Statutory Q25 Statutory Q50 Statutory Q90 Gap (%) 7.4% 16.5% 14.3% Male 10,649.6 38,144.4 128,505.5 Female 11,440.5 31,846.0 110,066.9 Difference −790.9 6298.5 18,438.6 Explained 2548.214,344.3 28,070.7 Unexplained −3339.1 −8045.8 −9632.0 Explained Number of children −27.5 −38.7 −746.0 Children in household −22.8 −21.0 537.8 Immigrant 17.1 85.5 185.3 East −26.6 −46.3 −143.8 East/female interaction 0.0 0.0 0.0 Age cohort 25–36 52.7 198.2 −92.1 Age cohort 37–48 −6.8 88.4 175.2 Lower vocational 4.5 4.3 6.5 Upper vocational 15.2 6.7 11.0 University 99.1 183.4 803.8 Not employed 154.8 560.5 52.1 Trainee 10.3 54.0 129.6 Self-employed −558.7 −1341.6 −1701.8 White collar 71.9 −545.4−2645.1 Civil servant low −190.6 −285.9 −283.4 Civil servant high −86.3 −155.9 −223.5 Industry 737.0960.8 3239.1 Company 242.5 757.0 2,315.5 Cohabiting −5.7 −12.2 −98.1 Single −192.6242.7 71.6 Divorced/separated −62.0 −92.1 332.2 Widowed −17.3 −156.5 1,301.6 Exp, full-time 4876.3 18,562.2 48,170.3 Exp, part-time −683.4 −2,675.9 −21,591.5 Exp, unemployed 1.0 134.3 −107.0 Has statutory pensions −1813.3 −926.9 107.2 Has civil servant pension −46.3 −320.7−334.1 Has occupational pension 11.5 81.0 212.0 Educ. flag −15.8 9.5 23.4 Marst. flag −1.0 5.2 −62.6 Exp. FT flag −6.6 −13.3 −16.1 Exp. PT flag 0.0 0.0 0.0 Exp. UE flag 0.0 0.0 0.0 Comp. Size flag 15.2 −956.0−1,452.6 Occup. flag 2.4 −1.2 −106.0 123 804 K. Cordova et al.
Table 21 continued Statutory Q25 Statutory Q50 Statutory Q90 Unexplained Number of children −400.1 −3505.6−1999.8 Children in household 1702.1 2901.5 −6951.8 Immigrant 114.9 367.5 −926.6 East 145.6 46.3 −2,042.2 East/female interaction 0.0 0.0 0.0 Age cohort 25–36 2771.4 −2049.3 6,236.3 Age cohort 37–48 761.3 −1737.9 2368.4 Lower vocational −845.1 −2233.5 4685.2 Upper vocational −106.1 −575.2 2187.8 University −1.1 564.6 10,287.8 Not employed 642.5 24.4 1,232.2 Trainee 132.1 102.4 300.9 Self-employed −386.3−1012.9 −124.0 White collar 18.7 1483.4 10,056.0 Civil servant low 13.8 64.0 436.2 Civil servant high 7.1 −98.1 682.3 Industry −196.4 −2,564.9 −3403.6 Company 537.7 1,073.6 10,450.2 Cohabiting −74.1 92.9 −433.0 Single −404.2 570.2 −580.0 Divorced/separated 88.5 −118.2 −2803.6 Widowed −18.8 94.1 −1821.8 Exp, full-time 2910.3 ,810.2 −6773.4 Exp, part-time −1078.8 −4534.812,081.7 Exp, unemployed −209.6 −1607.2 1851.2 Has statutory pensions 8230.6 7064.4−913.2 Has civil servant pension −250.1 −1131.7−2005.5 Has occupational pension −174.6 −728.5 −1937.1 Educ. flag −202.663.6 207.1 Marst. flag 4.4 15.2 −67.3 Exp. FT flag 28.5 36.4 23.4 Exp. PT flag 0.0 0.0 0.0 Exp. UE flag 0.0 0.0 0.0 Comp. size flag −30.5 1303.7 2325.0 Occup. flag −26.5 −10.4 1073.2 Observations 8894 Estimated using the SOEPv30 2012 and 2013 sample including nonretired individuals between 25 and 60 years old. Each column includes the estimates for indicated wealth variable as the dependent variable. Decomposition estimated using the full set of controls described in Appendix A. p\0:05, p\0:01, p\0:001 123 Pension Wealth and the Gender Wealth Gap 805
Table 22 RIF-OAXACA decomposition of gender gap, population 25-60 excluding self-employed (1) (2) (3) Q25 Q50 Q90 Net wealth Gap(%) 42.4% 30.2% 16.5% Male 3906.44 35,900.00 222,585.64 Female 2247.44 25,037.41 185,805.23 Difference 1659.00 10,862.59 36,780.41 Explained 8547.47 20,194.23 78,481.46 Unexplained −6888.47 −9331.63−41,701.05 Augmented wealth Gap(%) 12.7% 15.3% 23.9% Male 43,429.01 113,628.38 415,665.01 Female 37,903.74 96,143.40 316,214.74 Difference 5525.2717,484.98 99,450.28 Explained 18,436.02 45,143.32 122,621.53 Unexplained −12,910.76 −27,658.34 −23,171.26 Pension wealth Gap(%) 15.9% 16.0% 18.9% Male 26,157.32 62,572.56 196,332.49 Female 21,991.29 52,556.08 159,214.21 Difference 4166.03 10,016.48 37,118.28 Explained 8,325.13 23,592.94 45,219.42 Unexplained −4,159.09 −13,576.45 −8101.14 Observations 8138 Estimated using the SOEPv30 2012 and 2013 sample including non-retired individuals ages between 25 and 60 and sample weights and excluding self-employed. Sample includes 3600 males and 4538 females. Percentages in terms of males. Decomposition estimated using the full set of controls described in Appendix A p\0:05, p\0:01, p\0:001 123 806 K. Cordova et al.
Table 23 RIF-OAXACA decomposition of gender gap, population 25-60 including inheritance values (1) (2) (3) Q25 Q50 Q90 Net Wealth Gap(%) 38.6% 36.8% 27.2% Male 6232.7 48,755.2 281,551.6 Female 3,826.6 30,832.6 205,089.1 Difference 2406.1 17,922.6 76,462.5 Explained 12,698.3 34,567.1 120,017.7 Unexplained −10,292.2 −16,644.5 −43,555.2 Explained Inheritance 32.7 201.5 3277.8 Unexplained Inheritance −377.1 −659.7 −3,404.9 Augmented wealth Gap(%) 14.0% 20.3% 28.4% Male 49,714.3 129,923.7 470,177.9 Female 42,746.1 103,533.9 336,292.0 Difference 6968.226,389.8 133,885.9 Explained 33,897.8 50,863.1 150,136.3 Unexplained −26,929.7 −24,473.3 −16,250.4 Explained Inheritance 95.8 283.8 4444.7 Unexplained Inheritance −595.2 −1256.8 −1830.1 Pension wealth Gap(%) 8.3% 11.4% 17.9% Male 24,387.4 61,029.5 196,981.7 Female 22,351.5 54,083.8 161,756.0 Difference 2035.9 6945.7 35,225.7 Explained 8987.0 18,343.3 41,800.2 Unexplained −6951.1 −11,397.6 −6,574.6 Explained Inheritance −15.1 −8.6 31.6 Unexplained Inheritance 75.9 −108.7 −151.0 Observations 8894 Estimated using the SOEPv30 2012 and 2013 sample including non-retired individuals between 25 and 60 years old. Each column includes the estimates for indicated wealth variable as the dependent variable. Decomposition estimated using the full set of controls described in Appendix A p\0:05, p\0:01, p\0:001 123 Pension Wealth and the Gender Wealth Gap 807
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