genderinequalityingermany

Christian Arndt; Juergen Volkert

Gender Inequality in Germany –

Quantitative Analyses from a Capability Perspective

Key words: Gender Inequality, Lone Mothers and Lone Fathers, Poverty,

Capabilities, Amartya Sen

Paper to be presented at the 6th Human Development and Capability Association

(HDCA) Conference at the University of Groningen, NL, August 29 – September 1, 2006

Preliminary version, August 2006.

Please do not quote without authors’ permission. Comments are very welcome.

Contact:

Christian Arndt, Research fellow, Institute for Applied Economic Research Tuebingen (IAW), Ob dem

Himmelreich 1, D-72074 Tuebingen, Germany, Tel: 0049-7071-9896-34; Fax: 0049-7071-9896-99; e-mail:

[email protected]

Professor Dr. Juergen Volkert, Professor of Economics and Ethics, Pforzheim University, Tiefenbronner Strasse

65, D-75175 Pforzheim, Germany, Tel.: 0049-7071-255113; Fax: 0049-7071-255114; e-mail:

[email protected]

Christian Arndt, Juergen Volkert - 2 - Capability gender inequality in Germany

1. Introduction and outline

Amartya Sen (1999, p. 109) has argued, that “we cannot analyze gender inequality primarily in terms

of income differences.” He stressed that there is a need for more information to specify inequalities

that exist within economic affluence. He requested information concerning other types of deprivation

to directly assess inequality and poverty and to relate “the extent of relative deprivation of women to

the existing inequalities in opportunities (in earning outside income, in being enrolled in schools and

so on)”.

Sen’s thesis has been addressed in a study by Arndt and Volkert (2006a) on capabilities, poverty and

wealth on behalf of the German Federal Ministry of Labor and Social Affairs that has adopted Sen’s

approach as a conceptual framework for realising subsequent poverty and wealth reports. The authors

have presented a first operational framework of indicators for different dimensions of poverty (and

wealth) that is based on the German Socio-Economic Panel Study (GSOEP), a wide-ranging

representative longitudinal micro-data panel (see the Appendix). Among other things, they highlight

Sen’s argument by showing that women are confronted with slightly higher income poverty rates

compared to men, but are much worse off with respect to lacks of education and with respect to other

capability determinants, especially access to labor markets (Arndt and Volkert, 2006b).

This result raises the following two questions: First, in how far is it possible to assess the ,,real

degree’’ of financial poverty of women? Aggregated gender-specific results may hide intra-household

inequalities as well as possible differences among various types of households, e.g. lone mothers, or

single women. In order to answer this first question we will have a closer look on some specific

household types. This will allow us to distinguish poverty outcomes of male and female persons, at

least to quite some policy-relevant extent.

Second, are gender inequalities that can be measured in non-financial dimensions of welfare be traced

back to a general gender-effect? Or can these differences be attributed to characteristics which are

specific for women that live alone, with or without children, insufficient external child-care, etc.

Our objective is to examine the differences among the determinants of the capabilities of women,

(single-) mothers and men (Agarwal, Humphries and Robeyns, 2005) in financial and non-financial

dimensions, and to show that “descriptive and policy issues can be addressed through this broader

picture on inequality and poverty in terms of capability deprivation” (Sen 1999, p. 109). In contrast to

some part of the gender-specific literature we do not stress the importance of regarding gender-specific

utility-functions nor the necessity to analyze intra-household distributions that lies beyond the scope of

this paper. Instead, for sake of comparison between gender-specific situations, we apply a variety of

identical financial and non-financial indicators for men and women in a symmetric way, and show,


that still remarkable differences between gender and household specific situations can be brought to

the surface. Among them we analyze the situation of lone mothers and fathers, which commonly are

regarded as key issues of gender-based poverty analyses (Abelda et al., 2005).

Having this in mind, we pick up the mentioned indicator set that is sketched in the appendix and

enables the measurement of various dichotomous outcomes of financial and non-financial poverty

which are interpreted as determinants of human capabilities. We depart from one and two-way

descriptive statistics of family-specific determinants and poverty outcomes. Furthermore, we are able

control for various possibly important right-hand side variables, e.g. age, employment situation,

income etc., all of them based on the GSOEP. We allow these determinants to be correlated with

sexes, and thus amplify the analysis by estimating multivariate nonlinear probit models in order to

explain poverty outcomes and to elaborate and test for underlying partial effects for household types

as well as various control variables. Finally we test for gender-specific differences within certain

household-types.

2. Adequacy of Sens capability approach for gender analyses with quantitative data

A general capability framework will perceive poverty as ‘capability or functioning deprivation’ or as

the inability to realize a set of basic functionings or capabilities. A ‘functioning’ is an achievement

that a person manages to do or be. These ‘beings and doings’ can vary from ‘being adequately

nourished’, ‘being in good health’ over to complex achievements like ‘having self-respect’ and

‘appear in public without shame’, ‘taking part in the life of the community’ etc. The various

combinations of functionings (beings and doings) that a person can achieve are called ‘capabilities’.

The freedom that is needed to achieve such well-being is central for the CA, for example in ethical and

political analysis (Sen, 1992: 39-40).

The capability approach acknowledges human diversity, such as race, age, ethnicity, gender, sexuality,

and geographical location as well as whether people are handicapped, pregnant, or have caring

responsibilities (Robeyns 2003). So one possibility of this approach could be to address possible

gender-specific differences in utility functions and allow gender-specific influences of personal,

social, and environmental characteristics. By conceptualizing gender inequality in the space of

functionings and capabilities, there is more scope to account for human diversity, including the

diversity stemming from people’s gender.

Another important feature of the CA for gender analyses – of which we will take advantage – is that it

focuses not only on the financial dimension of poverty (or well-being): a supposedly big part of


unequal endowments with regard to capability determinants may be hidden when only equivalence

scales are used to scrutinize individual human opportunities, because in most cases these scales

attribute the same share of income to all household members. But even if this distribution hypothesis

should hold, income as only variable cannot adequately explain the full range of opportunities.

How can the CA thus enhance a feasible and gender-specific empirical analysis of gender differences?

Firstly, better results with regard to the – also gender-specific – driving forces of capabilities can be

achieved, if not only income, but also further dimensions of the CA are analyzed: the so called

personal conversion factors (e.g. individual qualification, the level of professional education) and the

instrumental freedoms (e.g. political freedoms and participation, social opportunities and economic

facilities). International studies have shown, that inequality between men and women increases, if not

only income but also other determinants of capabilities are regarded (Sen, 1999, p. 109). Secondly, it

will be necessary to disaggregate each of these results in order to highlight group-specific problems to

convert financial means like income into capabilities (e.g. age, gender, handicaps, migration).

Thus, it would be eligible to shed some more light to questions like, if, on the one hand, improvements

in the field of education for women are more urgent for policy than household-oriented financial

redistribution. On the other, we would want to know if possible female deprivation within economic

facilities signals that only improving education will not be enough, but public policy will at the same

time have to ensure other determinants of capabilities like better labor market access and working

conditions for women.

3. Choice of GSOEP data and indicators for capability determinants

The GSOEP is a wide-ranging representative longitudinal micro-data panel that includes 12,000

private households and about 22,000 persons in Germany (total population in Germany: about 80

mio). In principal, it covers almost all relevant CA-dimensions since more than 20 years. Nevertheless,

there remain some specific limitations of GSOEP with respect to a full coverage of the CA in the case

of some important dimensions of capabilities, first of all with respect to the measurement of political

freedoms and political participation (see Arndt/Volkert 2006 for a brief discussion). The GSOEP

micro-data thus allow identifying determinants of capabilities from a microeconomic perspective for

households and individuals. In general, the GSOEP seems to be a suitable database for our purpose as

long as it is combined with an adequate indicator system. In the following we will present some

poverty dimensions and corresponding feasible indicators that can be measured in a satisfactory way

and are important with respect to the CA at the same time.


In order to identify and analyse poverty as ‘capability deprivation’ or ‘capability failure’ and to draw

conclusions for public policies, it would be desirable to address and measure a person’s capability set

directly. However, because of the difficulty to measure the full bundle of capabilities a person can

choose from (irrespective whether he or she actually does or does not) we have to base the

measurement on financial means, personal conversion factors and instrumental freedoms, mostly by

the assessment of functionings. Further relevant indicators that intend to catch the main aspects of CA

with respect to the specific German social policy issues are still being developed. For the subsequent

analysis we have selected indicators from the list in appendix that seem to be important for a

discussion of gender inequalities, cover the main aspects of the CA, and are available in GSOEP.

However, not all determinants of capabilities can already be assessed in a complete and satisfying

way. Quite a number of further elaboration and extensions of GSOEP are desirable. We are aware that

further research will be necessary to try to narrow the gap between measurable functionings and the

finally desirable capabilities from theory.

One feature of our concept is to distinguish ‘individual financial potentials’, personal conversion

factors’ and ‘instrumental freedoms’ (Robeyns 2005; Sen 1999). Together, individual financial

potentials and personal conversion factors make up a group of capability determinants that we call

‘individual potentials’. A characteristic of these individual potentials, like income, health or education,

is that they can be transferred to other countries and societies. Instrumental freedoms are important,

because they comprise the social end environmental conversion factors that are more directly

influenced by society and state.

Not withstanding the broad scope of the CA, we start with indicators with regard to the financial

dimension. We have selected very low current income (at-risk-of poverty threshold ),1 persistent low

income2 as well as very high debts as important indicators of financial poverty.3

Most important with regard to personal conversion factors, also in the case of a developed country in

the 21st century are abilities to be in good health and to be well educated – which in turn are very

valuable capabilities as such. However, they are also of instrumental importance for other

(determinants of) capabilities that are used in our analysis. We consider a person as being confronted

with a bad health if he or she reports a very bad or bad health status along with impairments of

everyday life (that are caused by the bad health status). We categorize people as not sufficiently

educated if they are early school leavers or may have left secondary education without further

1 A person is identified as income poor if its net equivalent household income (new OECD-scale) is smaller

than 60 % of respective median household equivalence incomes (= E.U.’s official “at-risk-of poverty threshold”).

2 Currently income poor and income poor in at least two of the three preceding years. 3 A persons suffers from severe debts if it is living in a household that has to serve debts and has a disposable

income (after debt service) smaller than the official socio-economic financial minimum. This official socio-economic financial minimum is 930 €, for 1. Person, + 350 €, for a 2. Person, + 195 € per each further person


occupational training or apprenticeship. Furthermore we report ,very restricted personal conversion

factors’, which are composed by various sub-indicators (again, see the appendix).

Instrumental freedoms, inter alia, comprise of economic facilities, social opportunities, protective

security, ecological security, political freedoms and participation and transparency guarantees (Sen

1999; UNEP/iisdn 2004). A comprehensive gender-oriented analysis of all instrumental freedoms lies

beyond the scope of this paper. Instead, we focus on deprived economic facilities (e.g. unemployment,

working poor, low-wage jobs) and in particular on low wages. We have decided in favour of this focus

as economic facilities and low wages are reported to be among the most problematic issues among

instrumental freedoms with respect to gender inequalities (Weber 2006).

To the group that suffers from a low monthly wage we count every person that has a regular monthly

net income (in the year before the interview) which is below the mentioned at-risk-of poverty

threshold. Such a definition of low wages also recognizes the lower levels of monthly incomes and

seems to be suitable to identify the dependency (of women) from the income of a partner as well as the

risks associated to these dependencies in case of unemployment of the partner or separation and

divorce. Thus, we take account of the (gender-) specific dependencies and risks assiociated with part-

time jobs. This seems to be adequate because it would be too heroic to assume that full-time jobs and

adequate child-care will be immediately available for mothers as soon as they are in need of.

4. First descriptive gender-specific results

First we want to explore the relevance of Sen’s argument in the case of Germany, using the CA-

framework. Figure 1 shows from a general aggregated perspective that, on the one hand, women are

confronted with only slightly higher income poverty rates (13.2 % versus 10.9 %) compared to men as

well as confronted with almost similar shares of households with extreme debt burdens (6.6 % to 6.3

%). To a large extent this is due to the fact that income is measured at household levels and is

weighted by a function of the number of household members. This weighted net income is attributed

equally to each household member and hence obscures intra-household inequalities in couple-

households. On the other hand aggregated figures show, that women are much worse off with respect

to lacks of education (women 16.7% vs. 8.1%), political participation (women: 16.8% vs. men:

10.1%) and particularly economic facilities (women: 27.9 % vs. men: 16.6 %).


Figure 1: Lack of individual potentials and instrumental freedoms among men and women in Germany

0%

5%

10%

15%

20%

25%

30%Income poverty

Extreme Debts

Bad Health

Disabilities

Lack of Education

Low political interest

Economic opportunities

Social opportunities

Social protection

Environmental protection

Men

Women

Data: SOEP 2004, authors´ calculations.

These results indicate shortcomings of poverty assessments, that only focus on income from an

aggregate perspective. Hence Sen’s CA should play a significant role also for a developed country like

Germany, in order to help to take into account the full scope of poverty in various dimensions. A

similar result has been reported for Ireland by Cantillon and Nolan (2001).

Furthermore, Sen’s thesis that non-financial inequalities may be more pronounced than financial is

already confirmed with respect to very extensive individual potentials and instrumental freedoms in

Germany on the aggregate level – given the definition of our poverty bounds. An analog analysis with

respect to well-being shows that the shares of men in Germany who benefit from extensive individual

potentials and instrumental freedoms in Germany are almost for all dimensions – except disabilities4 –

higher than the female shares.5 This indicates that, at least at an aggregate level, inequality between

women and men is more pronounced within a CA-perspective (for poverty and wealth) than a focus on

income inequality alone might have suggested.

4 Slightly more women (87,8 %) than men (85 %) in Germany report that they do not suffer from any severe

disability in the same data. See for example Arndt/Volkert (2006a). 5 Differences between female and male high income and financial wealth are less pronounced than other

determinants of capabilities, notably in the fields of health status, education, social opportunities (particularly access to privileged health care) and economic facilities.


Finally we want to remark that significantly more women show low political interest and – not

depicted in figure 1 – less women are intensively participating in politics.6 We will not further

intensify the analysis of this dimension because of the already mentioned shortcomings of the GSOEP

data, but stress at the same time the challenge for public policy to strengthen female political interest

and participation in order to increase the probability to overcome existing gender inequalities within

the political competition.

5. Descriptive gender differences in financial individual potentials

In this section we will try to deepen our initial descriptive analysis and ask, whether we may find

gender-specific differences when we disaggregate poverty figures according to gender, household type

and number of children. First, we regard individual financial potentials. For non-financial individual

potentials and instrumental freedoms we refer to the next section.

First we split up couple and single households. When we look at single households we are able to

attribute the financial situation of the entire household to the sex of the single adult head of the

household. We will look more closely at single women (without children), lone mothers with one

child, and lone mothers with at least two children and finally also at such households where single

women live together with their children and their own parents (various generations). We compare

these to the same types of household that are headed by a single man. Further we examine households

that are headed by couples and differentiate these by the number of children (see table 1). For an

alternative typology which emphasizes the marital status of lone mothers in the case of Russia see

Kanji (2005).

By doing this we can analyze the situation of lone mothers, lone fathers as well determine possible

gender influences within these groups. Further we can capture effects of the number of children, both

for single parents and couples. Table 1 shows the number of reports we can refer to in the data and the

shares of these subpopulations with regard to the total population aged 16 and more, that we have

projected to the German population by taking into account the stratification of GSOEP. Even in the

case of the smallest group (lone fathers with more than one child) we still find far more than 100

persons and conclude that we will have reliable results.

6 A look at suitable cross-section date (so called ALLBUS) confirms that also differences can be found with

regard to the degree of political participation of men and women in Germany.


Table 1: Combinations of sex and household type – deprivation in financial dimensions

Number of persons in GSOEP 04

Total Income poor (<60 % median)

Persistently income poor Highly indebted

as weighted shares among all … older than 16 years

women 13.1 % 5.7 % 7.3 % men 11.1 % 4.8 % 7.1 % single mothers + child 457 2.5 % 25.7 % 12.8 % 14.1 % single mothers + children 297 1.4 % 35.8 % 15.7 % 25.5 % single women 1,666 12.8 % 16.0 % 9.3 % 4.4 % women with other generations 358 1.6 % 23.6 % 7.8 % 12.4 %

single fathers + child 216 1.0 % 15.2 % 9.2 % 10.5 % single fathers + children 132 0.6 % 25.4 % 19.2 % 10.6 % single men 1,225 8.8 % 18.4 % 8.7 % 7.3 % man with other generations 270 1.1 % 19.3 % 8.4 % 13.3 %

couples with child 4,012 16.0 % 8.6 % 2.9 % 8.8 % couples with children 5,902 21.2 % 13.3 % 4.9 % 10.8 % couples without children 7,355 33.0 % 6.8 % 2.6 % 3.3 % Total 21,890 100.0 %

Data: SOEP 2004, authors´ calculations.

For the indicators of low income and extreme indebtedness we find the highest shares in the case of

households that are headed by women.7 Worst off among them result single mothers with at least two

children, followed by single mothers with one child. But also households that are headed by single

men show only slightly lower degrees of deprivation in the case of most financial sub-dimensions.

Furthermore, in the case of single households with children we find in general a more severe situation

for single households when the number of children is higher. Finally, comparing male and female

headed households we suppose substantial genders differences that we will want to test later on.

We suppose an influence that is caused by the number of children also in the case of couples: couples

with at least two children show the highest shares among all couples (see table 1). Vice versa, with

exception of high indebtedness, couples without children are best off with regard to each of our

selected financial dimensions.

To summ up, we suppose that financial problems are much more frequent in households headed by

single persons and increase with the number of children, too, and are finally higher in the case of

households with children that are headed by a single mother in relation to those, that are headed by a

single father. Comparing the financial situation of single women and men we cannot find clear

implications.

7 Table 1 takes up our first findings and states, that also for persistent poorness (currently poor and poor in at

least three of the four foregoing years) and indebtedness no big differences can be found between the two sexes.


6. Descriptive gender differences in personal conversion factors and instrumental freedoms

Like for financial means we will have a closer look at various combinations of household-type, sex

and number of children for personal conversion factors and instrumental freedoms, notably economic

facilities. Here, we can measure gender specific differences even in the case of couple households.

In the case of the synthetic indicator ‘personal conversion factors’ (bad health or low education) the

highest shares can be found in the case of single women without children and women living in

households that consist of several generations (each with shares higher than one third of the respective

subpopulation). Single women with one child as well as women in couples without children follow

with shares between 25% and 30%. Woman in couples with children and lone mothers with more than

one child show slightly lower levels than women on average (24.7%). So we find no clear hypothesis

of influences of household-type and number of children. Especially in the case of single women

without children we must suppose, that results, up to now, might be biased by the age of the

interviewed.

In contrast, men in general show lower degrees of individual deprivation, but some groups also suffer

in an especially severe way: as already in the case of women, single men with at most one child and

men living in couples without children or together with various generations suffer from levels that are

well above the average for males (19.0%).

In all household types women show more frequently low educational levels. The descriptive analysis

of individual health presents a mixed picture. Shares for men who suffer from health problems are

similar or higher in the majority of household categories except single persons and persons living with

various generations.

The aggregated differences between women and men (24.7 % - 19.0 % = 5.7%) seem to be mainly

driven by remarkable differences in the case of single persons (17,1 %) and persons living together

with various generations (14,9 %). With regard to the other household types we find lower differences.

With regard to the subindicator lack of professional education we also find considerable differences

between women and men in general. But in this case, the gender-specific differences seem to be more

equally distributed among the different household types (with exception of couples with more than one

child).


Table 2: Combinations of sex and household type – deprivation in selected non-financial dimensions

Personal conversion factors Instrumental freedoms Low non-

financial individual potentials

Lack of professional education

Bad health Restricted economic facilities

Low wage8

as weighted shares among all … older than 16 years women 24.7 % 16.1 % 3.4 % 28.6 % 37.7 % men 19.0 % 8.1 % 2.9 % 16.2 % 11.6 % single mothers + child 28.6 % 20.2 % 2.7 % 39.3 % 30.1 % single mothers + children 15.7 % 14.5 % 1.2 % 58.4 % 46.3 % single women 36.5 % 21.4 % 6.3 % 13.7 % 20.0 % women in Couple + Child 15.9 % 11.1 % 1.0 % 35.9 % 45.4 % women in Couple + Children 10.8 % 9.0 % 0.3 % 44.7 % 60.5 % women in couple, no children 27.8 % 17.2 % 4.0 % 21.6 % 24.1 % women with other generations 37.6 % 28.0 % 8.0 % 34.8 % 46.7 %

single fathers + child 24.1 % 13.2 % 2.9 % 36.6 % 34.8 % Single fathers+ children 9.8 % 9.4 % 0.0 % 38.9 % 45.1 % single men 19.4 % 8.0 % 3.3 % 23.1 % 12.4 % men in Couple + Child 14.9 % 6.9 % 2.5 % 14.9 % 9.5 % men in Couple + Children 11.5 % 8.3 % 0.8 % 18.6 % 11.0 % men in couple, no children 25.4 % 8.1 % 4.4 % 8.6 % 9.6 % men with various gen. 22.7 % 11.6 % 3.6 % 28.1 % 16.8 %

Differences between the sexes in corresponding sub-groups

Single parent + child 4,50% 7,00% -0,20% 2,70% -4,70% Single parent + children 5,90% 5,10% 1,20% 19,50% 1,20% Singles persons 17,10% 13,40% 3,00% -9,40% 7,60% persons in Couple + Child 1,00% 4,20% -1,50% 21,00% 35,90% persons in Couple + Children -0,70% 0,70% -0,50% 26,10% 49,50% persons in couple, no children 2,40% 9,10% -0,40% 13,00% 14,50% persons with various gen. 14,90% 16,40% 4,40% 6,70% 29,90% Data: SOEP 2004, authors´ calculations.

With respect to a bad health status only small differences exist on the aggregate level. But looking at

the different household types shows again, that single persons, couples without children and once

more persons living in households together with various generations are more severely affected than

the respective gender-specific average. Furthermore, for two of the three subgroups we find low

absolute differences but considerable relative gender-specific differences (single persons and persons

in various generations). But these differences might be overlaid by sampling error.

Differences between men and woman on an aggregate level have been very high in the case of

economic facilities as well as in the case of the selected sub-indicator low wages. Analyzing the

subgroups we also find relatively high shares for the case of single mothers and mothers in couples

8 Low wage: as % of all persons older than 16 years that belong to the labor force.


with more than one child. In contrast, men in couples with more than one child seem to be not so

severely affected.

The highest gender-specific differences in low wage reception appear in the case of couples, especially

with children, but also in the case of households consisting of various generations. But, regardless of

the household type, women are confronted with the worst economic opportunities in almost any

household type according to our indicators. The only exception is single women without children, and,

at least to some degree, lone mothers. For lone fathers the situation seems equally severe as for lone

mothers. In all other household types considerable differences exist among men and women.

Summarizing the descriptive part of the analysis, very substantial gender-specific differences appear in

the case of financial means as well as in the case of personal conversion factors economic facilities if

we compare combinations of household situations, sex and number of children. In the next chapter we

undertake a multivariate analysis in order to control for factors that may influence the outcomes of our

indicators, like age, income, professional status and education. Additionally, we will test for gender-

specific differences.

7. What effects can be attributed to gender, household type, and number of children

7.1 Financial individual potentials

The (differences in) shares that we have analyzed so far may be influenced by further (third) variables.

Hence, we wish to find out more about partial effects of these variables. We use a probit model to

regress the probability of being poor on various right-hand side variables and to estimate such partial

effects.

Table 3 shows the marginal effects for the selected indicators of individual financial potentials. In bold

figures we have depicted the main estimation results for the selected household criteria. These effects

have to be interpreted as partial absolute differences in the probability to be ,,poor’’ for the subgroup –

compared c. p. in relation to couples without children. We have selected ,couples without children’ as

our reference group, because this group has been less affected by low financial capabilities as has been

shown in the bivariate analysis in part 4 and, additionally, both sexes are quite evenly represented in

this group. Hence, we can switch off possible gender effects in doing this comparison to a large

extend. Table 3 also shows further results with regard to the numerous controls that we have

considered. Unfortunately, we will not be able to discuss them in greater detail and hence write them

in normal figures.


Table 3: Lack of determinants of financial individual potentials in Germany (2004). Maximum likelihood probit estimates, marginal effects.

COEFFICIENT Income poor

(<60 % median) Persistently income

poor Highly indebted East-Germany 0.04*** 0.02*** 0.05*** (0.00) (0.00) (0.01) Single mother + child 0.13*** 0.07*** 0.08***

[vs.couples, no children] (0.02) (0.02) (0.02) Single mother + children 0.20*** 0.10*** 0.11*** (0.04) (0.03) (0.03) Single woman 0.09*** 0.05*** 0.03*** (0.01) (0.01) (0.01) Woman with other generations 0.07*** 0.04*** 0.05**

(0.02) (0.01) (0.02) Single father + child 0.01 0.02 0.01 (0.02) (0.01) (0.02) Single father+ children 0.01 0.03 0.02 (0.03) (0.02) (0.03) Single man 0.07*** 0.04*** 0.03*** (0.01) (0.01) (0.01) Man with various gen. 0.06** 0.04** 0.04** (0.02) (0.02) (0.02) Couple + Child -0.00 0.00 0.02*** (0.01) (0.00) (0.01) Couple + Children 0.02*** 0.01*** 0.03*** (0.01) (0.00) (0.01) Age 16-29 0.02*** 0.00 -0.00

[vs. Age 30-44] (0.01) (0.00) (0.00) Age 45-64 -0.02*** -0.01** -0.03*** (0.00) (0.00) (0.00) Age 65+ -0.02*** -0.00* -0.05*** (0.01) (0.00) (0.00) Secondary School Degree 0.06*** 0.03*** 0.04***

[vs. technical/upper] (0.01) (0.00) (0.01) Intermediate School Degree 0.03*** 0.01*** 0.02*** (0.01) (0.00) (0.01) Other Degree 0.12*** 0.07*** 0.03*** (0.02) (0.01) (0.01) No School Degree Yet 0.01 -0.00 (0.04) (0.03) Dropout, No School Degree 0.23*** 0.13*** 0.03** (0.03) (0.03) (0.02) Regular Part-Time Employment 0.04*** 0.02*** 0.01**

[vs. Full-Time] (0.01) (0.01) (0.01) Other gainfully empl. stat. 0.11*** 0.05*** 0.02** (0.01) (0.01) (0.01) Not Employed 0.08*** 0.03** 0.09 (0.03) (0.01) (0.06) Civil servant -0.05*** -0.01*** -0.04***

[vs. self employed] (0.00) (0.00) (0.00) Employee -0.05*** -0.01*** -0.03*** (0.01) (0.00) (0.00) Worker -0.03*** -0.00 -0.01 (0.00) (0.00) (0.01) Pensioner -0.05*** -0.01 -0.06*** (0.01) (0.01) (0.02) Unemployed (not employer) 0.03 0.03 -0.04** (0.03) (0.02) (0.02) Nationalized 0.00 -0.00 0.02**

[vs. German born] (0.01) (0.00) (0.01) Foreign 0.04*** 0.01*** 0.02*** (0.01) (0.01) (0.01) Observations 16825 16733 16653 Robust standard errors in parentheses. ***Significant at the 1 % level; **significant at the 5 % level; *significant at the 10 % level. Data: GSOEP 2004, authors ́calculations.


First of all, we find quite similar results for all three different financial indicators: All types of

households that are headed by a single woman show significantly higher probabilities to be poor

relative to the reference group. This is the case for all three indicators. Single mothers with one and

more children show the highest probabilities to be income poor (+13 % and +20 %) and to be

permanently poor (+7 % and +10 %) relative to couples without children.9 This is a result that

confirms the hypotheses established by Strantz (2006) based on descriptive analyses of data stemming

from the German ,Mikrozensus’.

In the case of single fathers we cannot find such significant differences compared to couples without

children. Significantly worse off relative to couples without children are only single men and men

living together with various generations.

In the case of couples, we find that having only one child still not makes a difference in income

poverty, compared to having no child. But with more than one child we find strong effects in the case

of all three financial indicators: couples with more than one child result to suffer from a significantly

higher risk to be poor and highly indebted than couples without children. This might to some degree

be due to inconsistencies in the German tax and transfer system with respect to families – a topic that

we cannot analyse sufficiently within the scope of this paper.

We also have tested the hypothesis that there exist no gender-specific differences within the various

household types. These differences have to be interpreted as differences in percentage-points, relative

to the probability, that couples without children face.10

Table 4: Test of eifference in marginal effects on determinants of financial individual potentials between women and men within different household-types in Germany (2004).

Test of gender-specific differences in corresponding sub-groups

Income poor (<60 % median)

Persistently income poor Highly indebted

Single parent + child *** ** ** Single parent + children *** * ** Singles persons * - - Persons with various gen. - - - Robust standard errors in parentheses. ***Significant at the 1 % level; **significant at the 5 % level; *significant at the 10 % level. Data: GSOEP 2004, authors ́calculations.

With respect to income poverty we find significant differences between men and women in the case of

single parenthood (see table 4). In the case of single persons and contemporaneous income poverty

9 For example, we estimate that, a single mother with one child faces a probability to be poor, that is 13 %

points higher compared to a person (male or female) that is living in a couple without children. 10 This means, these differences are similar to interaction terms between household type and sex, but not

exactly the same. An explicit analysis of interaction terms has to be postponed to further research.


gender also make a difference in probabilities, at least significant at the 10% level. Gender-differences

with respect to persons living together with other generations are not significant.

Gender-specific differences with regard to persistent poverty are in general less significant. Even in

the case of single persons we cannot find significant differences between men and women. This result

also applies to the case of highly indebted persons, where we can find significant differences only in

the case of single parents.

This means, that we can find weak evidence of gender-specific differences in single households and

hence no strong overall gender effect with respect to every case of the selected financial dimensions in

each type of households. But it has become clear, that as soon as children have to be taken into

account, significant differences show up between lone mothers and lone fathers, not only with regard

to income, but also with regard to persistent low income situations and high debts.

In line with our first descriptive results we have found, that the risk of financial problems is

significantly severe in households that are headed only by single persons, or such, where a third

generation also is there. The number of children affects the financial situation, but in the case of

couples, only if at least two children live in the household. Further we have shown, that in the case of

lone parents the probability to be affected by financial problems in Germany is significantly higher in

the case of households, that are headed by lone mothers than in households that are headed by lone

fathers. For single women these differences compared to men is only weak as single men also bear

significantly higher financial risks.

Further it is very important to control for further determinants, that are supposed to be correlated with

sexes and hence can overlay results of the gender-analysis. These controls shall only be discussed in

brevity: Relative to the age-group between 30 and 44 years, older people have a significantly lower

probability to be poor. Younger people (aged 16-29) are more often income poor. Also, our schooling

variable is highly significant in most cases: especially school drop outs face much higher probabilities

to suffer from low capabilities in each of the selected financial sub dimensions. Further, part-time

work has a significant influence on our results. These are supposed to be highly correlated with sex,

and hence have to be controlled for in an multivariate analysis. Also, the employment status has

significant influences on the economic situation. Finally, foreigners face higher financial risks in the

case of all three selected estimators.


7.2 Personal Conversion Factors

In the case of personal conversion factors we find structural differences between the two

subindicators, lack of professional education and severely bad health (see table 5). With exception of

single mothers with at least two children, women in general suffer significantly from higher shares of

insufficient educational background than men or women living as couple without children, although

educational differences have been reported to become less pronounced in recent years in Germany

(Ammermüller/Weber 2005). For women that are living together with other generations as (+8%) and

that are single mothers with one child (+6%) we find the highest differences in comparison to the

reference group, couples without children.

For men, in contrast, we find far fewer significant results: only single men, and men in couples with

children have at least slightly significant lower probabilities to suffer from lacks of education, than

couples without children.

Significant differences between men and women within the household groups (see table 6) only show

up for single persons and couples with children. In the case of single parents we cannot find significant

differences.11

In the case of insufficient health status we only find two significant, but especially interesting, results:

firstly, women in households living with various generations show significant higher probabilities for

suffering from severe health conditions. But, secondly, women in couples with at least two children

are significantly less confronted to severe health conditions compared to couples without children.

Similarly, McMunn et al. (2006) have recently found that women who occupied multiple roles

(working and children) report relatively good health at an age of 54. Nevertheless, compared to the

relative situation of men we cannot find any significant differences (see table 6).

With regard to the combined indicator ,Lack of Personal Conversion Factors’ we find significant

higher probabilities in the case of single women without children, lone mothers and mothers living in

couples, respectively with only one child as also in the case of women living in households together

with various generations. But compared to men in the same situation (see table 6) only single women,

mothers in couples and women living in households together with various generations are significantly

worse off.

Summarizing, we find a strong probability difference to be deprived from low non-financial individual

potentials for lone parents in general (men and women) without gender-differences in this group. But

11 Especially in the case of lone fathers a large 95-% confidence interval [-0,02; 0,06] may be also a problem

because of small degrees of freedom, at least in tendency.


considerable gender-specific differences, that are not influenced by further (gender-specific) variables,

can be found for single persons and couples with only one child.

Table: 5: Lack of Determinants of Personal Conversion Factors and Instrumental Freedoms in Germany (2004). Maximum Likelihood Probit Estimates, Marginal Effects.

Personal conversion factors Instrumental Freedoms

COEFFICIENT Low non-financial individual potentials

Lack of professional education Bad health Restricted economic

facilities Low wage12

East-Germany -0.09*** -0.05*** -0.00 -0.00 0.01 (0.01) (0.00) (0.00) (0.00) (0.01) Single mother + child 0.05** 0.06*** -0.00 -0.00 -0.02

[vs.couples, no children] (0.02) (0.02) (0.00) (0.00) (0.02) Single mother + children -0.01 0.02 -0.00 -0.00 -0.02 (0.02) (0.02) (0.01) (0.00) (0.01) Single woman 0.02** 0.03*** 0.00 -0.01*** -0.02* (0.01) (0.01) (0.00) (0.00) (0.01) Woman in Couple + Child 0.03** 0.03*** -0.00 0.01** 0.06*** (0.01) (0.01) (0.00) (0.00) (0.01) Woman in Couple + Children -0.03** 0.00 -0.01** 0.02*** 0.10*** (0.01) (0.01) (0.00) (0.00) (0.02) Woman with other generations 0.07*** 0.08*** 0.02** 0.04** 0.08** (0.02) (0.02) (0.01) (0.02) (0.04) Single father + child 0.06* 0.02 -0.00 0.00 0.00 (0.04) (0.02) (0.01) (0.01) (0.03) Single father + children -0.00 0.02 -0.01* -0.04 (0.05) (0.03) (0.00) (0.04) Single man -0.01 -0.02*** 0.00 -0.01*** -0.05*** (0.01) (0.01) (0.00) (0.00) (0.01) Man in Couple + Child -0.02* -0.01* 0.00 -0.01*** -0.05*** (0.01) (0.01) (0.00) (0.00) (0.01) Man in Couple + Children -0.03*** -0.01* -0.00 -0.01*** -0.05*** (0.01) (0.01) (0.00) (0.00) (0.01) Man with various gen. -0.00 -0.00 0.01 -0.00 -0.05*** (0.02) (0.01) (0.01) (0.01) (0.01) Age 16-29 -0.02** 0.01 -0.01*** 0.01*** 0.05***

[vs. Age 30-44] (0.01) (0.01) (0.00) (0.00) (0.01) Age 45-64 0.07*** 0.02*** 0.01** 0.00* 0.01 (0.01) (0.01) (0.00) (0.00) (0.01) Age 65+ 0.10*** 0.07*** 0.01*** -0.01*** 0.03 (0.02) (0.01) (0.01) (0.00) (0.04) Regular Part-Time Employment 0.02* 0.01 -0.00 0.26*** 0.46***

[vs. Full-Time] (0.01) (0.01) (0.00) (0.03) (0.02) Other gainfully empl. stat. 0.06*** 0.01 0.01* 0.68*** 0.90*** (0.02) (0.01) (0.01) (0.03) (0.01) Not Employed 0.10*** -0.01 0.05** -0.02* - (0.03) (0.02) (0.02) (0.01) - Civil servant -0.01 -0.05*** 0.01 -0.02*** -0.10*** [vs. self employed] (0.02) (0.01) (0.01) (0.00) (0.00) Employee -0.03* -0.03*** -0.01 -0.01*** -0.09*** (0.01) (0.01) (0.00) (0.00) (0.01) Worker 0.13*** 0.09*** 0.00 -0.00 -0.04*** (0.02) (0.02) (0.00) (0.00) (0.01) Pensioner 0.17*** 0.09*** -0.01 -0.10*** -0.07*** (0.04) (0.03) (0.01) (0.03) (0.00) Unemployed (not employer) 0.06 0.11*** -0.01* 0.28 - (0.04) (0.04) (0.00) (0.18) - Nationalized 0.01 -0.00 0.00 -0.00 -0.01

[vs. German born] (0.01) (0.01) (0.00) (0.00) (0.01) Foreign 0.08*** 0.06*** 0.00 -0.00 -0.01 (0.01) (0.01) (0.00) (0.00) (0.01) Secondary School Degree - - 0.00* 0.01*** 0.06***

[Technical/Upper] - - (0.00) (0.00) (0.01) Intermediate School Degree - - 0.00 0.01** 0.02** - - (0.00) (0.00) (0.01) Other Degree - - 0.01 0.02** 0.05** - - (0.01) (0.01) (0.02) No School Degree Yet - - - 0.05 - - - - (0.05) - Dropout, No School Degree - - 0.01 0.03** 0.10** - - (0.01) (0.01) (0.04) Income poor 0.04*** 0.02** -0.00 0.97*** 0.16***

[vs.non-poor] (0.01) (0.01) (0.00) (0.02) (0.03) OECD2-wght. Inc.(in thousand EURO) -0.06*** -0.05*** -0.00** -0.00** -0.02** (0.01) (0.01) (0.00) (0.00) (0.01) Observations 16766 16811 16686 16825 10387

Robust standard errors in parentheses. ***Significant at the 1 % level; **significant at the 5 % level; *significant at the 10 % level. Data: GSOEP 2004, authors´ calculations.


Table 6: Test of Difference in Marginal Effects on Determinants of Personal Conversion Factors and Instrumental Freedoms between women and men within different household-types in Germany (2004).

Test of gender-specific differences in corresponding sub-groups

Low non-financial

individual potentials

Lack of professional education

Bad health Low economic facilities Low wage13

Single parent + child - - - - - Single parent + children - - - - - Singles persons ** *** - - *** Parent in Couple + Child *** *** - *** *** Parent in Couple + Children - * - *** *** persons with various gen. ** - - *** *** Robust standard errors in parentheses. ***Significant at the 1 % level; **significant at the 5 % level; *significant at the 10 % level. Data: GSOEP 2004, authors´ calculations.

With respect to ,economic facilities’ and ,low wages’ we find quite different results in comparison to

our first descriptive results:

Firstly, in contrast to our first hypotheses, we do not find significant differences compared to the

reference group in the case of single mothers. Secondly, as already hypothesised, the most serious

problems are not found in the case of lone mothers but in the case of mothers that are living in couples

with children or women together in one household with other generations. As already mentioned in

section 3, by construction of our indicator, we especially take account of the (gender-) specific

dependencies and risks associated with part-time jobs, because we consciously did not correct for the

amount of monthly working hours. But, in accordance to the notion of capabilities as opposed to

funcionings, this indicator takes into account that full-time jobs and adequate child-care will not be

immediately available for mothers as soon as they should be in need of.14 Thirdly, we find, that men

are significantly less confronted with insufficient economic facilities in all categories exept lone

fathers with one child and those living together with other generations.

Similar to the analysis of the financial situation, it is also very important to control for further

determinants in the case of personal conversion factors and instrumental freedoms. Their partial

influences again shall be discussed only in brevity: compared to persons aged between 30 and 44

younger people have significant smaller risks to be confronted with bad non-financial personal

potentials. In contrast, older persons are more likely to be influenced by a lack of personal education

and bad health. But in the case of economic facilities younger persons suffer significantly more often

from low wages – older persons generally less.

12 Low wage: as % of all persons older than 16 years that belong to the labor force. 13 Low wage: as % of all persons older than 16 years that belong to the labor force. 14 However, even if we would assume an immediate availability of full-time jobs and perfect corresponding

child-care in Germany, we would have to define low wages on an hourly base. Even then, 18.9 % of women and only 12.8 % of men would be depending on these low wages.


With regard to the employment status it is necessary to comment, that persons that do not belong to

the labour force, suffer in a significantly more severe way from a bad health status and from low non-

financial individual potentials than full-time employees. Further, part-time employment induces low

wages. This result is not astonishing, because it is partly due to the construction of our low wage

indicator. But part-time work is supposed to be highly correlated with sex and hence needs to be

controlled for in our analysis.

Furthermore, we have also controlled for possible correlations between gender and professional

careers. For example workers and pensioners systematically have higher probabilities of suffering

from low levels education, civil servants and employees show significant lower probabilities.

Foreigners are much more often confronted with a lack of professional education, which, in turn,

supposedly is also highly correlated with gender issues. Finally, dropouts show significantly higher

probabilities to suffer from low economic opportunities as well as from low wages.

In the case of these non-financial indicators we have also controlled for the financial situation: people

who are income poor (which is correlated with sexes, as we have already shown) have significantly

higher probabilities to suffer from a lack of non-financial potentials (with exception of health) and low

economic opportunities. Furthermore, when the weighted household net income grows these

probabilities decrease significantly.

8. Conclusion and outlook

First, we tried to assess the ,,real degree’’ of financial poverty of women, because aggregated gender-

specific results may hide intra-household inequalities as well as possible differences among various

types of households, e.g. lone mothers, or single women and had a closer look on some specific

household types. By doing this we were able to analyze the situation of lone mothers, lone fathers as

well determine possible gender influences within these groups. Further we could capture effects of the

number of children, both for single parents and couples. Second, we tried to find out, if gender

inequalities that can be measured in non-financial dimensions of welfare can be traced back to a

general gender-effect or if some of these differences can be attributed to characteristics which are

specific for women that live alone, with or without children, insufficient external child-care, etc.

We applied a selection of identical financial and non-financial indicators for men and women in a

symmetric way, and showed that remarkable differences between gender and household specific

situations can be brought to the surface. The first descriptive results as well as the multivariate

analysis indicate that shortcomings and limitations of income focused poverty assessments highlighted

by Sen’s CA play a significant role in affluent countries like Germany. Compared to men, women are


even worse off with respect to personal conversion factors like education and instrumental freedoms

like economic facilities than only a financial poverty analysis on aggregated level might have

suggested.

Moreover, descriptive analyses have shown that we need to distinguish the circumstances in which

women tend to have more problems than men. We underlined these results by estimating multivariate

nonlinear probit models in which we allowed various right-hand side variables, e.g. age, employment

situation, income etc., to be correlated with sexes, and tested for underlying partial effects for

household types as well as various control variables.

For example, single mothers in Germany have significantly higher financial poverty risks than single

fathers – who do not bear significantly higher risks than our reference group, couples without children.

This may be due to traditional patterns according to which the father can “choose” to care for the child

or not and will “opt for this alternative” only when it is financially viable. On the other side, women

may be expected to stay with children irrespective of their financial situation. Clearly, these

hypotheses deserve further research.

We found a strong probability difference to be deprived from low non-financial individual potentials

for lone parents in general (men and women). Although we found no significant gender-differences in

this group – single mothers with only one child deserve special attention. But considerable and

significant gender-specific differences – that are not caused by further (gender-related) variables – can

be found in the case of single persons and couples with only one child.

However, not single mothers, but women in couples with one or more children bear a significantly

higher risk of deprived from economic facilities and of working in low wage jobs. This causes a high

dependency on the partner’s income which may turn into the already mentioned financial risks in the

case of separation or divorce.

Moreover, we have seen that not only mothers with children but also single women and women and

women living in households with various generations deserve special attention (even controlling for

age) as their overall risks tend to be – sometimes extremely – higher.

Regarding the necessity to redesign public policies, it is obvious that improvements in the field of

education for women in general still are at least as urgent as household-oriented financial

redistribution. However, the even more pronounced female deprivation within economic facilities

signals that improving education will not be enough. Instead, public policy will have to ensure better

labor market access and working conditions not only for lone mothers but also for mothers living in

couples, because these groups are especially affected from low opportunities in the labor markets –

even with the same educational level.


Our research at this point of time can be extended in various ways: Our hitherto attempts to dissecting

gender-differences from influences that stem from other gender-related situations can be further

intensified by regarding gender-specific interactions for various control variables. Further, based on

our indicator system, we similarly can analyze gender-specifics of very high degrees of capabilities.

Moreover, from a more data oriented point of view, we could benefit still much more from the time-

dimension of this panel data in order to widen the amount of underlying data as well as to take a more

closely look at the dynamics of poverty than we did already up to now.

Finally we hope, that using our indicator system for this analysis as well as for further research can

help to stimulate a public discussion on the choice of main indicators for determinants of capabilities

which is necessary, in order to implement CA indicator sets into official wealth reports.

References: Agarwal, Bina; Humphries, Jane; Robeyns, Ingrid (ed.) (2005): Amartya Sens´s work and ideas. A

Gender Perspective, Routledge, London. Albelda, Randy; Himmelweit, Susan, Humphries, Jane (2005): Dilemmas of lone motherhood,

Routledge, New York. Ammermüller, Andreas; Weber, Andrea Maria (2005): „Educational Attainment and Returns to

Education in Germany - An Analysis by Subject of Degree, Gender and Region” ZEW, Discussion Paper No. 06-17.

Arndt, Christian; Volkert, Jürgen (2006a): Amartya Sens Capability-Approach – ein neues Konzept

der deutschen Armuts- und Reichtumsberichterstattung, (Amartya Sen’s Capability Approach – a new concept for official German poverty and wealth reports), in: Deutsches Institut für Wirtschaftsforschung, Vierteljahrshefte zur Wirtschaftsforschung, No. 1 / 2006, 7-29.

Arndt, Christian; Volkert, Jürgen (2006b): Assessing Capability Determinants in Germany. Concept

and First Empirical Results. Paper to be preseted at the 6th Human Development and Capability Association (HDCA) Conference at the University of Groningen, NL, August 29 – September 1, 2006.

Cantillon, Sara; Nolan, Brian (2001): Poverty within Households: Measuring Gender Differences

Using Nonmonetary Indicators”, in: Feminist Economics, 7(1), 5-23. Kanji, Shireen (2005): The Route Matters: Poverty and Inequality among Lone-Mother Households in

Russia, in: Albelda, Randy; Himmelweit, Susan, Humphries, Jane (ed.): Dilemmas of lone motherhood, 207-225.

Robeyns, Ingrid (2003): Sen´s capability approach and gender inequality: selecting relevant

capabilities, in: Feminist economics, 9(2-3), 61-92. Robeyns, Ingrid (2005): The Capability Approach. A Theoretical Survey’, in: Journal of Human

Development. 6 (1), 93-114. Sen, Amartya (1999): Development as Freedom, Oxford, Oxford University Press.


Strantz, Cosima (2006): Zur Armutssituation von Familien in Deutschland, (On the poverty situation

of families in Germany), in: Statistisches Monatsheft Baden-Württemberg 3/2006, 14-16. Volkert, Jürgen (2005). Das Capability-Konzept als Basis der deutschen Armuts- und

Reichtumsberichterstattung, in: J. Volkert (ed.)(2005): Armut und Reichtum an Verwirklichungschancen, (The Capability Approach as a basis for German poverty and wealth reporting, in: J. Volkert (ed.): Poverty and wealth – a capability analysis), Wiesbaden: VS Verlag, 119 - 147.

Weber, Diana 2006. Work-Family Balance: ‘The Effects of Organizational Initiatives on Creating a

Family-Supportive Work Environment’. IAW-Report 34 (1), 103-154.


Appendix : Table of Indicators Indicators for lack of determinants of capabilities

(SOEP 2002, exceptions indicated) (% shares of all respondents > 16 years, unless otherwise specified)

Financial Poverty Income Poverty OR Extreme Debts Income Poverty Net equivalent income of households (new OECD-scale) < 60 % of respective median

household equivalence incomes (= E.U.’s official “at-risk-of poverty threshold”). Extreme Debts Persons in housholds, having to serve debts with a disposable income (after debt

service) < official socio-economic financial minimum (= 930 €, for 1. Person, + 350 €, for a 2. Person, + 195 € per further persons.

Health Impairments Current personal health status AND resulting in impairments of everyday life Current health status Health status subjectively reported as „bad“ or „very bad“

Impairments of everyday life

Severe, frequent or permanent impairments related to at least three of the folowing five activities: - going a staircase up- or downstairs, - exhausting activities - on the workplace or everyday activities (quantitatively OR qualitatively) impaired by physical health conditions - on the workplace or everyday activities (quantitatively OR qualitatively) impaired by mental health conditions - reduced social contacts due to physical or mental health problems

Disability Disability with an officially confirmed „grade” of 50 (maximum: 100) Lack of Education Early school leaver or secondary education without further occupational training or

apprenticeship Lack of Social Opportunities

„Insufficient access to education“ OR „insufficient access to health care” OR „insufficient access to decent housing“

Insufficient access to education

Share of young (16 to 24 year old) early school leavers as % of all young, 16 to 24 year old people.

Insufficient access to health care

Persons, who have not consulted a doctor although they have suffered health impairments in the last three months.

Insufficient access to decent housing

Persons, whose housing is subjectively characterized as ‘in urgent need of complete renovation’ or ‘being in danger of breaking down’ OR ‘overcrowded’ OR ‘lacking socially necessary amenities’.

Lack of economic facilities

Persons, living in jobless households OR being long-term unempoyed OR “Working Poor” OR working for low wage.

Persons living in jobless households

Persons (excluding pensioners and students) in households without any member in the labor force (% of all persons excluding students and pensioners)

Long-term unemploeyed Persons, having been unemployed for at least 12 months on December 2003 (% of all persons).

Low wages Regular monthly net income before the interview below the at-risk-of poverty threshold (60% of median equivalence income) as % of all persons > 16 years in the labor force.

Working Poor Persons, living in a household with at least one person in the labor force with a disposable net equivalence income below the at-risk-of poverty threshold (60% of median equivalence income) as % of all persons in households with at least one member in the labor force.

Protective security ‘Dependence on social assistance’ OR „dangerous environment“ Dependence on social

assistance Share of all Persons depending on minimum social and unemployment assistance (‚Sozialhilfe’‚ Sozialgeld’, Arbeitslosengeld II’)

Dangerous environment (Share of all) persons, who subjectively classify their neighborhood as „very insecure“ Lack of ecological Security

Problems with „Polluted Air“ OR „Noise“

Polluted Air Persons, who subjectively feel to be strongly impaired by air pollution in their housing environment

Noise Persons, who subjectively feel to be strongly impaired by noise in their neighborhood Lack of Political Participation

Absolutely no political interest

genderinequalityingermany

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