RUHR - UNIVERSITÄT BOCHUM

Transcription

Karsten Hank
Household Labor Demand And Household Labor Supply 1
An empirical analysis of the employment of domestic help in German private
households and its effect on female labor force participation
1.
Introduction
Although there is already a substantial - mainly "gray" - market for household services in
Germany, the role of the private household as place of work has so far been underestimated
by politicians as well as economists.
Only in recent years, the German government has begun to legislate tax policies designed to
encourage the use of domestic servants as a way to stimulate employment in the household
sector. At the same time, already existing jobs in private households shall be transformed into
such, liable to social security contributions.
Just so, economists have not paid much attention yet to the impact of market-procured domestic work on the female labor force participation decision. So far, this has often been conceptualized in terms of the choice between market work and housework only. Domestic duties,
however, need not be performed by the woman herself: the spread of automated household
appliances could be mentioned here as one possible factor behind the growth in the female
labor force 2. Similarly, the use of a domestic servant can be regarded as a readily available
market substitute for self-produced domestic work.
In this paper, data from the German Socio-Economic Panel (SOEP) will be used to examine
the hypothesis that the demand for domestic servants and women’s supply of market work are
joint decisions.
1
This paper is based on work done during a stay at the Center for Policy Research at Syracuse University (USA) in 1997. The author would like to thank Prof. Thomas A. Dunn, who, among many
others, gave lots of helpful comments. Remaining shortcomings are mine.
2
An empirical investigation of the relation between female labor force participation and the household‘s demand for service goods can be found in Wenke, 1991.
1
2.
Overview of the Market for Domestic Work in Germany and Recent Tax
Policies
In the past couple of years, a significant increase in the number of workers who are either not
subject to social security contributions or have a marginal second job 3, could be observed in
the German labor market. At the same time, the share of employment in the service sector
grew larger (see Schupp, Schwarze & Wagner, 1995; Schwarze, 1997).
Research, done by the Institute for Social Research and Social Policy (Institut für Sozialforschung und Gesellschaftspolitik, ISG) for the German Federal Department of Labor and Social Affairs, shows that particularly domestic workers account for a large fraction of these
marginal jobs (see Table 1 and Table 2) 4.
According to the ISG survey, in 1992 more than 25% of all working persons who were not
subject to social security contributions (about 760,000 persons) worked in private households.
An additional 285,000 persons worked there, having a marginal second job, which is 20% of
all marginal second job holders (see ISG, 1993).
The growth of the number of marginally employed domestic helpers in West Germany can be
explained by numerous factors. Among these are the growing labor force participation of
women, deficits in the compatibility of market work and family (see Klenner & Stolz-Willig,
1996), a growing number of single parents as well as senior citizens with a relatively high
income (see Muntz, 1996). These are, however, rather long term factors. As can be seen from
the ISG figures, there has even been a remarkable growth in the market for domestic work
within only five years, between 1987 and 1992 (see Table 1 and Table 2). This may be due to
the migration of ”cheap” labor from Eastern Europe to Germany during the observation period. The wage differential between Germans and Eastern European women allows mutually
beneficial division of labor. This means that a German woman may have an absolute advantage in managing her own household, but she may have a comparative disadvantage compared
to a, say, Polish maid 5.
Altogether, there are about 1 million marginally employed domestic workers in Germany,
3
Neither the worker nor the employer pays social security contributions, if the job is regarded as
marginal, i.e. less than 15 hours per week and with a monthly wage below 620 DM in West Germany
or 520 DM in East Germany (see VDR, 1997).
4
The results of a survey conducted by the ISG in 1997 are not completely published yet. Preliminary
figures, however, underline the dominant role of domestic work in this labor market sector (see Daniels, 1998).
5
This is how Suen (1994) explains the development in the market for domestic labor in Hong Kong,
where foreign helpers - especially from the Philippines - have been imported systematically. For examples of foreigners working in German households, see Hanke & Rudzio, 1996.
2
compared to only 34,000 employees in private households, who did pay social security contributions 6. Comparing these figures with the 117,000 domestic employees (including 38,000
marginally employed) listed in the 1995 Microcensus (see Emmerich, 1997a), shows that reliable data concerning the use of domestic helpers and the job potential in private households
are hard to come by. Other estimates range from 1.4 up to 2.4 million domestic helpers in
Germany (see Weinkopf, 1997a).
One of the main reasons for the underreporting of the official figures is that the German labor
statistics do not include the private household in their definition of places of work (see Hatzold, 1986) and do not cover marginal employment to its full extent (see Schwarze, 1992). It
can also be assumed that many self-employed or freelance workers who employ domestic
servants, do not register them as domestic employees but as business employees, so their cost
can be deducted as a business expense for tax purposes (see Muntz, 1996).
This leads to the point that many jobs in the domestic sector are illegal and beyond the control
of state regulations. Employees in private households e.g. do not have to carry their social
security card with them and cannot be controlled at their place of work because of the constitutional protection of dwellings. This is one reason, why standard instruments, which are used
to fight illegal employment or misuse of the social security system in the construction industry, for example, cannot be applied here. It cannot be controlled, whether somebody is involved in illicit work or has multiple marginal jobs (see Bungart, 1997, Weinkopf, 1996).
Hatzold (1988) finds 7 that, of all households employing domestic servants, 48% illegally
avoid the payment of social security contributions. The fraction of households that does not
pay the related taxes is even as high as 86%.
Until recently this was tolerated, despite losses in tax payments and social security contributions (see Hanke & Rudzio, 1996). The pressure to create new jobs, put on politicians by a
record number of unemployed, however changed the evaluation of the private household's
role as employer 8.
A practical attempt to set incentives for private households to create jobs subject to social
insurance is found in the 1997 federal tax law. Up to 18,000 DM of the costs for a domestic
servant can be deducted from the household income subject to income taxation 9, eligible for
6
This figure is reported in the 1995 labor statistics of the German Federal Bureau of Labor (see Emmerich, 1997b).
7
The survey was conducted by the Ifo-Institute for Economic Research for the German Federal Department of Economy. Its sample of 848 West-German households is not representative, though. By its
design the survey overestimates high-income households. See also Hatzold, 1986.
8
The state parliament of North Rhine-Westphalia for instance held a hearing on "How to make better
use of the job potential in private households" in January 1997 (see Weinkopf, 1997a).
9
The trade union responsible for domestic workers (NGG) suggested a deduction not from the in-
3
all households. In contrast to this, the 1989 law allowed a maximum of 12,000 DM tax deduction only. Furthermore the eligibility was restricted to households with at least two children
under age ten (for single parents at least one child) or with a person needing care (see BMAS,
1997) 10 . These changes primarily intend to increase the number of jobs, especially for
low-skilled workers (see Muntz, 1996). At the same time, the introduction of so called
"household checks" is designed to integrate already existing domestic help jobs into the German social security system 11. While social security contributions usually are calculated by the
employer, in the case of domestic servants the employer has to fill out the household check
only and send it to the health insurance company, which is responsible for collecting the social insurance payments. On the basis of the information given by the employer (including
duration of the job, payment, and whether the servant is employed elsewhere) the insurance
company calculates the amount of the employers' contributions (see VDR, 1997).
Simulations show that the labor market effect of tax deductions will be relatively weak. Although already existing jobs in the household sector which are subject to social security contributions will be stabilized and a modest growth of their number is predicted, a significant net
job effect is not expected to occur (see Emmerich, 1997a, Muntz, 1996). The advantages of
illicit work or marginal employment still seem to outweigh the utility that could be drawn
from regular employment - for the employing households as well as for the domestic helpers
(see Klenner & Stolz-Willig, 1996). The household e.g. might fear additional costs of regularly employed domestic helpers (like continued payment in case of sickness). The servant, on
the other hand, might not be interested in acquiring own social security benefits, as a majority
of these people is already covered in another way, e.g. by the spouse’s insurance (see Schupp,
Schwarze & Wagner, 1995).
An alternative concept was developed by the Social Democratic Party (see Handelsblatt, 1996,
for example). Its model gives priority to social aspects: only households with at least one
child under age 14 or one person aged 80 or older would be supported. No tax deduction is
planned, but direct transfers in form of vouchers, worth 1,200 DM per month (plus 600 DM
for each additional child or person needing care). The use of domestic services, however,
would only be subsidized, if the servant was engaged via a service agency (see Emmerich,
1997b).
The most discussed form of such service agencies are so called "service pools" (much like
come subject to taxation, but from the tax due (see Klenner & Stolz-Willig, 1996). There are doubts,
however, if tax subsidies in general are compatible with the German taxation system (see Mundorf,
1996).
10
For legal details of employment in private households, see BMAS, 1996.
4
temporary employment agencies or maid agencies in the US) 12. Service pools aim to increase
the number of jobs liable to social security in labor market sectors which are characterized by
a high share of marginally employed workers 13. Several small engagements of only a couple
of work hours each, are bundled to a full- or part-time job subject to social security contributions. In case of the domestic sector, it would be the agency and not the household anymore,
which is the employer of a domestic worker. Supporters of the concept of organizing domestic
services in pools claim that households as well as workers would benefit from it. It is assumed
that a higher quality of the work done and more security for both sides would be guaranteed
(see Weinkopf, 1997b).
Whether such changes would distinctively influence the current structure of employment in
German private households - in terms of hours of work or duties - can be doubted.
Although the work hours of German domestic helpers differ widely, most of the jobs are
part-time, i.e. about 5 hours per week with an hourly wage of ca. 15 DM (Emmerich, 1997a;
Weinkopf, 1997a) 14. Hatzold (1986) finds in his survey that not more than 6% of all households employing domestic help have a full-time servant and only 30% of the part-time jobs
are more than 6 hours per week. The hourly cost of a domestic worker with complete social
security coverage, paid vacation, etc. would be between 30 DM and 35 DM (see Emmerich,
1997b). Assuming a full-time job with standard wage, Drohsel (1996) calculates a monthly
gross income for the servant of 2,340 DM.
Disproportionately high is the fraction of women (often housewives) and foreigners working
in private households (see ISG, 1993). Their main duties are according to Hatzold (1986)
cleaning (64%), washing (25%) and child care (14%) 15. Similar results are shown in the 1992
ISG study on marginal employment (see ISG, 1993).
11
A similar policy can be found in France. For details, see Weinkopf, 1996.
For alternative approaches, see Drohsel, 1996.
13
For more on the discussion about how to limit marginal employment, see Schwarze, 1993.
14
Domestic servants in Hong Kong for instance usually live in their employers household and are
full-time employed (see Suen, 1994). This used to be common practice earlier this century in Germany,
too, but is not affordable anymore for most households (see Hatzold, 1988).
15
In comparison, the share of households hiring a nanny in Great Britain, for example, is much bigger.
This can be explained by the relatively low provision of non-school child care in the UK (see The
Economist, 1996).
12
5
3.
The Structure of Households Employing Domestic Help 16
3.1
Data Source
The data used in this paper are drawn from the German Socio-Economic Panel (SOEP), a
longitudinal micro-database, covering socio-economic information on households and individuals in Germany 17.
The survey was started 1984 in the western states of Germany, where about 6,000 households
(including an oversample of foreign headed households) and more than 12,000 individuals
were interviewed. In 1990, the SOEP was supplemented by 2000 households and 4500 individuals from the eastern states (the former GDR). A sample of recent immigrants has been
added to the SOEP in 1994/95 18.
Households in the original samples and their splitoff households are interviewed every year.
Each interview contains a household questionnaire, which collects information on the residence, number of family members and family income, for example. Additionally, all household members over age 16 report individual information on standard demographic characteristics, labor market activity, sources of income, time devoted to various activities, and so on 19.
Data from the 1991 and 1994 waves of the SOEP will be examined here to explore the structure of households demanding market-procured domestic labor. The following question about
the household’s hiring of domestic workers was asked:
"Do you regularly, or occasionally, have someone come in to help with the cleaning or the
household?"
In 1991, questions concerning the employment of baby-sitters and nurses were asked additionally. However, the number of households that reported paying for one of these services is
very small and will therefore not be discussed here.
Neither the information on how many hours a domestic servant works, nor whether more than
one servant (if any) works in the household, is provided. Thus the demand for domestic help
16
Unfortunately, the number of persons who can be identified as working in a private household is even if several waves are pooled - too small for an analysis of the labor supply side with the SOEP
data.
17
For details see Projektgruppe Panel (1995) and Wagner, Burkhauser & Behringer (1993).
18
As data from this sample are not fully available for the 1994 wave of the SOEP, it is excluded from
the analysis done here.
19
Information on children under age 16 are collected in the household questionnaire.
6
will be modeled as a discrete binary choice.
3.2
Descriptive Results 20
The SOEP shows that in 1994 as much as 7.0% of all German households regularly employed
a domestic helper, which equals 2.3 million households (1991: 6.1%; 2.0 million). An additional 4.1% (1.4 million) occasionally had someone come in to help with the housework
(1991: 4.5%; 1.5 million). Altogether, the share of households buying domestic labor on the
market was 11.1%, i.e. 3.7 million (1991: 10.6%; 3.5 million) (see Table 3 and Table 4) 21.
In comparison, estimates by Mediamark Research show that in 1996 a fraction of 9% of all
US households (9.4 million) employed professional housekeepers 22.
Looking for differences in the regional distribution of the use of domestic help shows that the
western states of the Federal Republic barely differ from each other 23. In East Germany however, the fraction of households with domestic help was only 2.7% (1991: 3.7%). Considering
this and socio-economic differences between West and East in general, only households with
a West German head will be analyzed in the following 24. This increases the share of households hiring a servant to 13.9% (1991: 12.7%) (see Table 5 and Table 6).
Especially households with a monthly net income of 5000 DM or more account for a large
fraction of those households, which employ domestic servants. In 24.3% (1991: 25.3%) of all
households belonging to this income group, at least some of the housework is done by a professional.
The demand for waged domestic labor in low income households (up to 2000 DM monthly
net income) turns out to be bigger than one might have expected. With 13.1% (1991: 10.7%),
the share of employing households here is nearly as high as in the average.
This last finding can be explained in part by a large number of retirees, whose pensions are
high enough to afford a housekeeper and who cannot maintain their household without help 25.
20
For more detailed tables, see Hank, 1998. Some information on the extend and structure of employment in German private households can also be found in Hatzold, 1986. These results have to be
handled with care, though (see Footnote 7).
21
Information on both, household and individuals are required here. If households are included, for
which no information on the individual members is available, the number of households employing
domestic help increases to 4.1 million in 1994 and 3.7 million in 1991 respectively (without changing
their relative share, however).
22
Other estimates – using broader definitions - however assume that in 1994 as much as 17% of US
householders employed servants. For both see Dortch, 1996.
23
Unfortunately it was not possible to disaggregate the data and get information on possible differences depending on the community size.
24
This means that only sample A of the SOEP will be used. Foreigners (sample B) and East Germans
(sample C) are dropped.
25
47.8% (1991: 37.3%) of all households, in which a person needing care lives, hire domestic help.
7
The large fraction of single-person-households is another indicator for this assumption. As
20.0% (1991: 19.2%) of these households hire domestic help, it is worth taking a closer look
at this group.
Single households are defined here as households inhabited by one person or a single parent
with his/her child(ren). Using this definition, 18.7% of all West German single households
employed a domestic helper in 1994 (1991: 17.6%) (see Table 5 and Table 6). Most single
households have a monthly net income below 3500 DM, but still a share of about 14% of
these households buys domestic labor on the market. While only 11.4% (1991: 9.4%) of the
working singles employ someone to help in the home, as much as 29.6% (1991: 27.3%) of the
retirees 26 demand domestic services.
The fraction of two-person-households hiring domestic help, is 12.2% (1991: 11.7%). This
share is much smaller in households with three or more members. Thus it can be assumed,
that in families with children the intra-household division of labor still follows traditional
patterns. This means that one adult works in the market, while the other one (usually the female partner) stays home, taking care of the household and the children 27. Also, households
with young children are typically headed by younger, lower-paid workers, who might not afford paying someone to clean the place, although they might need help worse than anyone
else (see Dortch, 1996).
It seems plausible that especially Double Income No Kids - households (DINKs) should be
frequent users of professional domestic services 28. They could afford to pay the cost of a
housekeeper and would be comparatively better off, if they let somebody else do the necessary housework and spend the saved time elsewhere.
If focussing on partner households 29 only, it turns out that the fraction of those which employ
domestic helpers decreases to 10.1% (1991: 9.2%) (see Table 5 and Table 6). Most of the employing households have a monthly net income of more than 3500 DM. Equally high shares
of households hiring domestic servants, however, can be found where no household member
works (1994: 11.5%; 1991: 12.5%) and where both partners perform market work (1994:
These households, however, account for a very small share of the total population only.
26
A person is defined here as retired, if he/she reports not to be employed, not to be registered as
unemployed and is over age 60.
27
The share of households that report having children and employing domestic help is 7.9% (1991:
7.4%), i.e. clearly below the average.
28
For a detailed sociological discussion of the influence of waged domestic labor on the domestic
division of labor within dual career households, see Gregson & Lowe, 1994. The authors analyze
qualitative data from Great Britain.
29
The category ”partner households” excludes multigenerational households, i.e. households with
8
11.1%; 1991: 11.2%). This means that in partner households, too, retirees account for much of
the observed demand for professional household help.
Restricting the analysis to those partner households, in which the woman is in the prime age
for labor force participation (age 25 to 55), causes another drop in the fraction of households
employing domestic help (1994: 8.9%; 1991: 7.7%) (see Table 5 and Table 6). Here, it is indeed the dual career households that, with a share of 11.8% in 1994 (1991: 11.1%), use domestic services the most. This indicates a connection between the woman’s allocation of time
and the household’s labor demand. A clear pattern of the female’s hours worked at home and
on the market and the fraction of households employing a housekeeper supports an assumption like this. Only 7.0% (1991: 5.9%) of the households in which the woman spends more
than 3 hours per day on housework employ a domestic helper. On the other hand, as much as
17.7% (1991: 19.0%) of the households where the woman’s weekly hours of market work
exceed 40 hours use market-procured domestic labor.
In a crossectional analysis, the use of domestic help in West German households appears to be
quite stable, if different subpopulations, income groups, etc. are compared in 1991 and 1994.
A longitudinal analysis however shows that it is not necessarily the same households which
use professional housekeepers (see Table 7) 30. 25.4% of all households which employed domestic help in 1991 did not do so anymore in 1994. On the other hand, as much as 35.5% of
those which demanded domestic services in 1994 were new users, i.e. households that did not
hire in 1991. A total of 1.9 million households (8.8%) in the western states of Germany employed somebody in both years.
In the following, a theoretical framework for an analysis of the connection between the
household’s labor demand and the female partner’s labor supply will be developed (Section 4)
and tested afterwards (Section 5).
more than one adult couple. Children however are included.
30
Unfortunately the sample of households which change their demand for domestic help is too small
for an analysis of their socio-economic characteristics.
9
4.
The Theoretical Model
In this paper, the household’s demand for domestic labor shall not be analyzed on basis of the
neoclassical production theory, but based on the neoclassical household theory, which is also
used for the analysis of labor supply.
The core of the model developed here will be in the tradition of ”the theory of the allocation
of time” models of Becker and Gronau (see Becker, 1965, Gronau, 1977), though it is extended to allow for the hiring of domestic help.
In traditional neoclassical models, labor supply decisions are the result of utility maximization
subject to constraints 31.
In the simple labor-leisure-model, an individual’s utility is assumed to be depending on the
amount of market goods and services C (purchased with labor earnings and nonlabor income)
and hours of leisure L that are consumed per period32. If the individual looses successively
equal amounts of L, he would require successively larger amounts of C to maintain the same
level of utility (law of negatively sloped demand curves). In maximizing his utility function
(1)
U = U(C,L) ,
the individual faces two constraints 33. First, the budget constraint
(2)
PC = WM + V ,
where P is the price of a unit of C, W is the fixed wage per hour of work, M is the number of
work hours and V is income derived from sources unrelated to work. Second, the time constraint
(3)
T=M+L,
where T is the total amount of time available, which can be allocated to M hours of market
work and L hours of leisure time.
Both constraints can be summarized by a budget line. Its slope equals W / P, the real wage. If
the individual decides to allocate all his time to leisure, the amount of C that can be consumed
equals V / P, the real property income. In the case that all available time is devoted to work,
the full income, i.e. (W / P)T + (V / P), can be spent on the consumption of C.
To maximize his utility, the individual spends his full income on leisure and on consumer
goods, i.e.,
(4)
WT + V = WL + PC.
31
For a more detailed discussion of the simple static model of labor supply as it is given here, see
Killingsworth, 1983, Chapter 1; Ehrenberg & Smith, 1996, Chapter 6.
32
This formulation implies that work itself does not carry any utility.
33
Constraints beyond the individual’s choice - like discrimination - are not denied, but will not be
10
The left-hand side expresses the maximum income attainable, if all time is allocated to work,
while the right-hand side represents the expenditures on leisure and consumer goods.
The optimal (C,L) combination is the one lying on the highest possible indifference curve 34
consistent with the requirement that the individual remains on or below the budget line.
According to the approach of Becker (1965), however, goods and time do not yield utility
themselves, but are better regarded as inputs to the production of various commodities. It is
these commodities that are ultimately consumed and which are the direct source of utility.
They are produced via household production functions
(5)
Z= Z(L,C) ,
where Z is a commodity (or activity), L the necessary input of leisure (or nonmarket time) and
C the amount of consumer goods devoted to the production of the activity 35.
Thus, in contrast to the simple model, nonmarket time cannot be regarded as leisure only, but
as a different kind of work, performed at home rather than for an employer (three-way allocation of time). The new time constraint that the individual faces now, can be written as
(6)
T=M+L+H,
where H represents the hours of work in the home 36.
As Gronau (1977) points out, goods and services can be purchased on the market or can be
produced at home. They are assumed to be perfect substitutes for each other 37, which leaves Z
unaffected by the composition of C 38. The value of home goods will be measured in terms of
their market equivalents 39.
The production of home goods or services, according to the function
(7)
CH = f(H) ,
discussed here.
34
The individual's preferences can be symbolized by indifference curves, which show different combinations of C and L, each of them giving the individual the same level of utility. At any given (C,L)
combination, the slope of the curve equals the negative of the marginal rate of substitution of consumer goods for leisure.
35
It takes food and energy combined with preparation time to produce meals, for example.
36
For more on the extension of the labor-leisure-model to a three-way allocation of time to market
work, home work and leisure, see Kaufman, 1991, Chapter 3. - Gronau (1997) summarizes recent
work on the theory of home production and provides an extensive bibliography.
37
This is of course not true, if the individual attaches intrinsic utility (or disutility) to the time spent
on housework, e.g. when it comes to child care (see Pollak & Wachter, 1975). As the focus here,
however, lies on domestic work in the sense of cleaning, washing, etc., the assumption of perfect substitution between market and home production seems to be justified.
38
Assumptions that Z is influenced by the composition of C (like Z = Z(CM, CH, L)), turned out to
have only very limited predictive power (see Gronau, 1973).
39
The cost of time used for the production of home goods equals the wage rate, if the individual participates in the labor force. As the value of time for nonworkers exceeds their potential wage rate, it
has to be measured by a shadow price, equal to the marginal product of time in the household sector.
For more on the value of the housewife’s time, see Gronau, 1973.
11
is, however, characterized by decreasing returns to effort. This means that, if the capital stock
and technology of production in the household are assumed constant, additional hours of
homework increase the amount of home goods produced, but at a diminishing rate (i.e., the
factor input H is subject to declining marginal productivity). This cannot be explained by fatigue or changes in input proportions only. It is also due to a change in the composition of CH:
as H increases, it changes towards activities that have a cheaper market substitute.
Adopting the assumption that there is only one consumption good X 40, one can roughly distinguish the following situations: For low desired values of X, household production alone is
sufficient and the individual does not participate in the paid labor market. At some point,
home production becomes inefficient in the sense that an hour devoted to work at home produces less X than could be purchased with the income from one hour's work in the market.
Given sufficient demand for X, the individual decides to allocate some time to home production and the remainder of work time to the market, where a constant hourly wage is paid 41.
While in the neoclassical theory ”household” is regarded as synonymous with ”individual”,
various empirical findings (see Juster & Stafford, 1991, for example) show that the members
of a family take different roles in the production of utility: traditionally husbands specialize in
work in the market, while the wife specializes in the production of home goods 42. Even
households in which both spouses are employed, show large disparities between husbands and
wives regarding the time they spend on housework (see Hersch & Stratton, 1994; Beblo,
1998).
Following the male chauvinist model of labor supply of family members 43, it is assumed here
that the husband decides on his labor supply without reference to his wife’s labor supply decisions, i.e. solely on the basis of his own wage and the family’s actual property income.
Therefore, the husband’s allocation of time as well as his earnings can be regarded as fixed
parameters of the wife’s labor supply decision 44.
At this point two new features shall be added to the model: a minimum amount of housework
40
According to Hicks’ composite commodity theorem, it makes no difference for the analysis of labor
supply, how many consumer goods there are, as long as the prices of these goods stay in the same
relation to each other (see Killingsworth, 1983).
If necessary, the model can, however, easily be extended to the case of two commodities. Then, a
welfare function U(Z1, Z2) would be maximized subject to the constraints imposed by the transformation curve between the two commodities (see Gronau, 1977).
41
When it comes to the initial decision to enter the labor force, it should not be forgotten though, that
there are some fixed (time and money) costs related to work in the market.
42
For theoretical remarks on the intra-household division of labor, see Becker, 1981, Chapter 2, for
example.
43
For details on this and other models of the relation between household membership and labor supply, see Killingsworth, 1983, Chapter 2.1, Ehrenberg & Smith, 1996, Chapter 7.
44
In effect, the husband becomes a kind of income-producing asset.
12
that has to be done to maintain the household (Xmin), and the availability of market-purchased
labor to perform this work.
Xmin depends on household characteristics, like the size of the household, size of the dwelling,
age and number of children, etc. It can be produced by the male or female partner or it can be
bought on the market (by employing domestic help). If the husband’s labor supply for the
market is regarded as fixed, the same can be assumed to be true for his share of work in the
household sector. Therefore, the remaining time needed for the production of Xmin has to be
done either by the wife or by hired domestic help. This makes market-procured domestic labor an explicit component of the female’s time constraint, which is relevant for her labor supply decision.
Suen (1994) develops a model, in which all housework is done by a domestic servant (if a
servant is hired at all). This model however ignores that home production is the most efficient
way of producing X, as long as the marginal productivity in the household sector exceeds the
productivity of the individual in the market 45.
No hiring and no change in the allocation of time to household and market work occur, if the
minimum amount of necessary production is less than the efficient amount. This means that at
any given point in the production of Xmin the marginal productivity at home is higher than the
woman’s wage rate.
If Xmin is greater than the quantity voluntarily produced and if the wage of the hired worker is
greater than the householder's wage, then the householder will devote more of her time to
home production and less time to the market.
In the final case, where Xmin exceeds the efficient amount produced at home and the householder's wage is greater than the wage of a domestic worker, then hiring occurs and the
householder's hours of home work remain unchanged, since her efficiency in this type of
production has not been altered. However, the direction of the change in the number of hours
she devotes to the paid labor market is ambiguous.
Thus, the householder makes several decisions: how many hours to spend in home work, in
market work, and whether to hire a domestic worker. This set of decisions depends on the
market wage opportunities of the householder, the cost of hiring a domestic worker, the
minimum standard of housework, and the efficiency of the householder in home production.
45
Only if the marginal productivity of work at home falls short of the real wage rate at every point of
time, there is no home production. In this case we would face the traditional labor-leisure-dichotomy
again (see Kaufman, 1991).
13
5.
Empirical Results
5.1
Method
The empirical strategy followed here, is to model the household’s hiring decision and the female partner’s labor supply decision simultaneously 46.
In a first step, however, two separate equations will be estimated. As both decisions are regarded as a discrete choice with a binary dependent variable, a probit model is used for the
regression 47:
(1)
F ( xi′β ) = ∫
xi′ β
−∞
⎧ t2 ⎫
exp⎨− ⎬dt ,
2π
⎩ 2⎭
1
where the function F is the distribution function of the standard normal distribution.
As for many nonlinear regression models, the parameters here are not the marginal effects.
For the normal distribution, the density function that corresponds to the cumulative function is
(2)
∂E [ y ]
= φ ( β ′x) β ,
∂x
where φ(t) is the standard normal density.
To control for a possible interdependence between the hiring and the labor supply decisions, a
bivariate system of equations will be estimated. Along with the simultaneously estimated coefficients for both equations, the correlation among their error terms will be calculated. In a
bivariate probit regression, these correlations can indicate the interdependence of the two decisions, holding other factors fixed. The general specification for a two-equation model would
be:
(3)
y1* = β1x1 + ε1,
y1 = 1 if y1* > 0, 0 otherwise,
y2* = β2x2 + ε2,
y2 = 1 if y2* >0 , 0 otherwise,
E[ε1] = E[ε2] = 0,
Var[ε1] = Var[ε2] = 1,
Cov[ε1, ε2] = ρ
(see Greene, 1993).
46
In a more sophisticated model, it would be possible to estimate the decisions on how many hours to
spend in home work, in market work and whether to hire a domestic worker, altogether. In such a
model, the first two equations were tobit equations, which allow for a concentration of observations at
zero hours of work. The third equation would be a probit for the dichotomous hiring outcome. This is
not done here, however, and must be left to future research.
47
For details and a comparison between the probit model and other regression models for binary outcomes, see Greene, 1993, Chapter 21; Hamerle & Ronning, 1995.
14
For the analysis, the software package STATA is used.
5.2
Variable Description
The dependent variables in the regressions are:
HIRE - If the household reports to employ domestic help either regularly or occasionally, HIRE=1, 0 otherwise.
PARTICIPATE - If the female partner reports to be either full-time or part-time employed or to be in vocational training or being marginally employed and not to be registered
as unemployed, PARTICIPATE=1, 0 otherwise.
These variables are assumed to depend on the female partner’s market wage opportunities, the
cost of hiring a domestic worker, the minimum standard of housework, the efficiency of the
householder in home production and her non-wage income. These factors are operationalized
as follows:
•
Market wage opportunities of the householder
For women who choose not to work, the market wage rate is unobservable. The woman‘s
wage opportunities, however, can be regarded as a function of her education and age. Thus, it
would be possible to use a selectivity-bias corrected regression to construct a predicted wage
(see Heckman, 1979). Findings by Suen (1994), however, suggest that education variables can
be substituted directly into the probit regression without remarkable changes in the outcome
of the other variables’ coefficients. Instead of entering years of education (and their square)
into the regression, a set of dummy variables will be used to measure the householder’s education by the highest schooling or vocational degree she ever received. This has two advantages: 1) Repeated years of schooling may even have a negative effect on wages. 2) It is believed that particularly the German labor market rewards formal qualifications (see
Kreyenfeld, 1996, for example). The reference category for this hierarchy of degrees is having
a low or no schooling / vocational degree. Additionally the female partner’s age and its
square will be used.
UNI
highest schooling / vocational degree is a university degree
VOCDEG
highest schooling / vocational degree is a vocational degree
HIGH
highest schooling / vocational degree is “Abitur” or “Fach-
15
hochschulreife” 48
•
MID
highest schooling / vocational degree is “Mittlere Reife” 49
AGE
woman’s age
AGE-SQ
square of woman‘s age
Cost of hiring a domestic worker
The data do not provide this information. However, if price information in the market for domestic helpers is assumed to be reasonably good, one can treat the price of domestics as constant (see Suen, 1994) 50.
•
Minimum standard of housework
The minimum standard of housework which is necessary to maintain the household is not
observable, but may be determined in part by the size of the residence, the number of children
in the family and whether there is a person needing care in the household. Therefore, the following variables are entered into the regression:
NROOMS
number of rooms bigger than 6 sqm.
NKIDS3
number of children between age 0 and 3
NKIDS6
NKIDS12
NKIDS16
NKIDSBIG
number of children older than age 16
CARE1
person needing care in household (0/1),
who is not the female partner (1991 only)
CARE2
person needing care in household (0/1) (1994 only)
The variable CARE1 is constructed in a way that it equals 1 only, if the female partner does
not report to have suffered from a certain illness or disability for at least a year or chronically.
It is assumed that in this case it is not the woman herself, who is the person needing care in
the household. As the information on the woman’s health is not available for 1994, the variable CARE2 is used in the regression for this year (where CARE2=1, if any person in the
household needs care).
48
“Abitur” usually takes 13 years of schooling, “Fachhochschulreife” 12 years of schooling.
“Mittlere Reife” usually takes 10 years of schooling.
50
The attempt to measure the cost of domestic helpers by calculating the hourly wage of marginally
employed women in West Germany turned out to be not very useful.
49
16
•
Efficiency of the householder in home production
This is not observable and must be dealt with in the empirical model. However, the variable
ILLNESS
suffering for at least a year or chronically
from a certain illness or disability
is used, as it can be assumed, that this affects the woman‘s ability to maintain the household.
This variable is available for 1991 only 51.
Finally, two variables are entered into the equations, which measure possible household income which is unrelated to the female partner’s labor earnings. These are:
OWNER
owner of residence 52
NOLABINC woman’s monthly non-labor income
The variable NOLABINC was constructed by subtracting the woman’s net wage from the
monthly household net income. The result was divided by 100. This increases the otherwise
very small coefficient by 100. To interpret the coefficient, it has to be divided by 100 again.
5.3
Regression Results
Only West German partner households 53, where the female partner is between 25 and 55
years old, will be considered. The age restriction is due to the particular interest in the relation
between the hiring and the labor supply decision. Table 10 and Table 11 display the results of
the regressions.
In the hiring equation as well as in the labor force participation equation, the education dummies show the expected signs. As this set of variables represents a hierarchy of degrees, it is
not surprising that especially having a university degree has a strong and highly significant
impact on both decisions. The probability of employing a domestic servant is 21 percentage
51
Another question in the SOEP asks, whether a person is officially registered as having a reduced
capacity for work or being severely disabled. Since this question is directly related to the woman‘s
ability to work, it was decided not to use it for the probit estimation.
52
The variable OWNER could be influenced by former female labor force participation, which would
make the woman more likely to work on the paid labor market today. However, as this variable can be
regarded as another indicator for the amount of housework that has to be done, it was decided to keep
it in the equation.
53
Regressions were run, using the whole SOEP sample. When it was controlled for household type
(SINGLE / PARTNER) and subsample (WEST / EAST / FOREIGN), the coefficients turned out to be
significant with the expected signs. It was decided, however, to drop single households, East German
and foreign headed households to get a rather homogenous sample, which appears to be more appropriate for the theoretical framework applied here.
17
points higher for households, where the female partner received a university degree, than in
the case of having no or a low vocational / schooling degree (the result is the same for 1991
and 1994). Other degrees, however, turn out to be insignificant for the hiring decision, while
having a vocational degree or “Mittlere Reife” does have a positive effect on the woman’s
labor supply decision. The influence of having a university degree on a woman’s probability
to join the labor force is very high in 1994, where her probability to work in the market is 23
percentage points higher compared to a woman in the reference category. In 1991 however,
the effect of a university degree is not higher than that of having “Mittlere Reife” only (14
percentage points).
Growing older raises the probability for hiring domestic help by about 1.5 percentage points
for each additional year (the marginal effect is about the same in 1991 and 1994). The age
function has a concave shape, peaking at 58 (1991: 45). This means that the maximum probability to employ a servant would be reached, if the female partner was 58 years old (45 years,
respectively). The peak age for joining the labor force is 32 years (1991: 27). As young workers tend to be lower-paid and middle agers usually enjoy the highest earnings of their lifetime 54, the time gap between both peaks could be expected.
Most of the variables used to estimate the minimum standard of housework come out with the
expected signs in both equations and are statistically significant. The number of rooms positively affects the household’s hiring decision (the marginal effect in 1991 is, however, extremely small) as well as the existence of young children in the household. In 1994, additional
children up to the age of 16 raise the probability of employing help between 3 and 4 percentage points each (here, too, the effects are weaker in 1991). If the children become older,
however, the coefficients get a negative sign: each child in the household, being older than 16
years, lowers the probability to hire by about 3 percentage points (in both years). Turning to
the equation for PARTICIPATE, in 1991 and 1994, a decreasing negative influence of children on the female partner’s labor force participation can be observed, as the children age 55.
The outcome of the CARE variables56 used in the regressions differs a lot between both years.
While CARE2 (in 1994) is insignificant for the hiring decision, but shows the expected strong
and negative impact on the labor supply decision, the opposite is true for CARE1 (in 1991). A
person needing care in the household, who is not the female partner herself, raises the probability to hire domestic help by 20 percentage points, but is not significant in the participation
54
For details on life-cycle aspects of labor supply, see Ehrenberg & Smith, 1996, Chapter 7.
For an analysis of women’s labor force transitions triggered by child births, see Gustafsson et. al.,
1996.
56
Note that multigenerational households are excluded from the analysis. This means that, if the exis55
18
equation. The effect of ILLNESS (as an indicator of the woman’s health and her capability to
maintain the household - in 1991 only) has the same direction: a small, but positively significant impact on the household’s hiring decision, but no significance for the woman’s labor
supply.
Being owner of the residence is insignificant for the decision to hire domestic help as well as
for the female partner’s labor force participation decision in 1994. In 1991, however, it turns
out to be at least significant for the hiring decision: compared to households which do not
own their residence, the probability to employ somebody is raised by 4 percentage points.
The woman’s non-labor income shows the expected signs in both equations, although the effect is very small. For a 1 percentage point increase in the probability to hire, the monthly
non-labor income would have to increase by 1000 DM in 1994, by roughly 700 DM in 1991.
The impact on the female’s labor supply is stronger, but still small: a growth of her non-wage
income of 300 DM (1991: 200 DM) lowers the probability to join the labor force by 1 percentage point.
Turning to the bivariate probit regression, we find that the coefficients barely differ from
those discussed above, when the household’s hiring decision and the woman’s labor supply
decision are estimated simultaneously. The variable RHO denotes the correlation coefficient
between the error terms of the bivariate probit regression. The correlation coefficient is estimated to be 0.30 (1991: 0.34) and statistically significant. This indicates a complementary
relation between the household’s labor demand and the female partner’s time allocation decision.
tence of a person needing care is reported, this person is not one of the couple’s parents.
19
6. Concluding Remarks
The bivariate probit model in this paper estimates that the presence of a domestic helper in the
family and the female partner’s propensity to engage in market work are closely connected57.
It is necessary, however, to keep a proper perspective on the importance of market-procured
housework for female labor supply as a whole (and vice versa).
In the analyzed sample, the fraction of households employing domestic help in 1994, was
only 8.1% (1991: 7.2%). Comparing this with the 61.8% (1991: 60.8%) of working women,
shows that the effect of the household’s labor demand on the overall labor force participation
rate need not be large.
Conversely, the total positive effect of women’s labor force participation on the use of domestic help can be regarded as extremely small. A major share of households employing domestic servants does not participate in market work at all. While in partner households the
share of those employing a servant is equally high, whether none or both of the partners work
(about 11.5% in 1991 and 1994), almost every third retired single hires a housekeeper. This is
a share about three times higher than that of singles participating in the labor market.
Considering this, it has to be questioned, if the legislated tax policies are designed appropriately. As retirees usually do not pay taxes, they do not benefit from the possibility to deduct
the cost of a domestic worker from their income subject to taxation. Those households, on the
other hand, which could benefit the most from today’s regulations, do not seem to have a very
high demand for domestic help. If there was an actual need, sufficient to cause a significant
increase in the number of regular jobs in private households, it can be assumed that they
would already buy domestic work on the informal labor market more often than they do.
57
Suen (1994) gets a similar result using data from Hong Kong.
20
7.
Appendix
Table 1: Workers not Liable to Social Insurance in Germany 1987 - 1992
Total
- in private
households
1987
(West Germany)
1992
(West Germany)
1992
(East Germany)
2,284,000
2,616,000
363,000
570,000
(25%)
732,000
(28%)
29,000
(8%)
Source: ISG, 1993
Table 2: Marginal Second Job Holders in Germany 1987 - 1992
1987
(West Germany)
1992
(West Germany)
1992
(East Germany)
Total
539,000
1,217,000
257,000
- in private
households
97,000
(18%)
256,000
(21%)
29,000
(11%)
Source: ISG, 1993
Table 3: Employment of Domestic Help in German Private Households 1991
Fraction of households
employing domestic help
Number of households
(million) 58
10.6%
3.5
- regularly
6.1%
2.0
- occasionally
4.5%
1.5
89.4%
29.8
YES
NO
Source: German Socio-Economic Panel, wave 8 (1991), weighted by HHHRF
58
If households are included in the analysis, for which no information on the individual members is
available, the number of households employing domestic help increases to 3.7 million (without
changing their relative share, however).
21
Table 4: Employment of Domestic Help in German Private Households 1994
(million) 59
11.1%
3.7
- regularly
7.0%
2.3
- occasionally
4.1%
1.4
88.9%
29.4
Yes
No
Source: German Socio-Economic Panel, wave 11 (1994), weighted by KHHRF
Table 5: Employment of Domestic Help in Different Subgroups 1991
Employing domestic help
(million)
All households (n = 6348) *
10.6%
3.5
West German households
(n = 3392) **
12.7%
3.2
West German single households
(n = 1083) ***
17.6%
1.9
West German partner households
(n = 2181) ****
9.2%
1.3
(woman 25-55 years old)
(n = 1496)
7.7%
0.7
Source: German Socio-Economic Panel, wave 8 (1991), weighted by HHHRF
* Information on both, household and individuals required.
** Only sample A of the GSOEP is used. Foreigners (sample B) and East Germans (sample C) are dropped.
*** Includes single parents and their child(ren).
**** This does not include multigenerational households, i.e. households with more than one adult couple.
59
If households are included in the analysis, for which no information on the individual members is
available, the number of households employing domestic help increases to 4.1 million (without
changing their relative share, however).
22
Table 6: Employment of Domestic Help in Different Subgroups 1994
Employing domestic help
(million)
All households (n = 6055) *
11.1%
3.7
West German households
(n = 3226) **
13.9%
3.4
West German single households
(n = 1067) ***
18.7%
2.1
(n = 2032) ****
10.1%
1.3
(woman 25-55 years old)
(n = 1386)
8.9%
0.7
Source: German Socio-Economic Panel, wave 11 (1994), weighted by KHHRF
* Information on both, household and individuals required.
** Only sample A of the SOEP is used. Foreigners (sample B) and East Germans (sample C) are dropped.
*** Includes single parents and their child(ren).
**** This does not include multigenerational households, i.e. households with more than one adult couple.
Table 7: West German Households - Employment of Domestic Help 1991 - 1994 (longitudinal) (n=2700)
Did households which
... still do so in 1994?
employed in 1991 ...
YES 60
NO
74.6%
Did households which
employed in 1994 ...
25.4%
... already do so in 1991?
YES
NO
64.5%
35.5%
Source: German Socio-Economic Panel, wave 8 (1991) and wave 11 (1994), weighted by
HKHHRF (longitudinal weight)
60
The share of West German households employing domestic help in both years is 8.8% (1.9 million).
23
Table 8: West German Partner Households (Woman 25-55 Years Old) – Descriptive Sample
Statistics (unweighted) 1991
Variable 61
Mean
Standard Deviation 62
HIRE
.07
-
PARTICIPATE
.61
-
UNI
.08
-
VOCDEG
.70
-
HIGH
.01
-
MID
.03
-
AGE
39.00
8.81
1598.39
703.66
NROOMS
4.18
1.94
NKIDS3
.20
.47
NKIDS6
.18
.44
NKIDS12
.34
.63
NKIDS16
.18
.42
NKIDSBIG
1.29
1.07
CARE1
.01
-
ILLNESS
.24
-
OWNER
.53
-
34.93
17.27
AGE-SQ
NOLABINC
Source: German Socio-Economic Panel, wave 9 (1991)
61
62
For a detailed variable description see section 5.2 of this paper.
For binary variables standard deviations are not displayed.
24
Table 9: West German Partner Households (Woman 25-55 Years Old) - Descriptive Sample
Statistics (unweighted) 1994
Variable 63
Mean
Standard Deviation 64
HIRE
.08
-
PARTICIPATE
.62
-
UNI
.09
-
VOCDEG
.71
-
HIGH
.02
-
MID
.03
-
AGE
37.34
8.92
1480.38
702.12
NROOMS
4.07
1.50
NKIDS3
.22
.46
NKIDS6
.20
.45
NKIDS12
.34
.63
NKIDS16
.21
.47
NKIDSBIG
1.24
1.08
CARE2
.01
-
OWNER
.52
-
38.38
19.92
AGE-SQ
NOLABINC
63
64
For a detailed variable description see section 5.2 of this paper.
For binary variables standard deviations are not displayed.
25
Table 10: West German Partner Households (Woman 25-55 Years Old) - Probit Estimates for
HIRE and PARTICIPATE 1991
PROBITS
BIVARIATE PROBIT 65
marginal effect
probit coefficient
probability
probit coefficient
probability
Variable
HIRE
PARTICIPATE
HIRE
PARTICIPATE
UNI
.2079
1.1672
.000
.1420
.4001
.015
1.1860
.000
.3963
.016
.0203
.2789
.131
.0751
.1953
.036
.2882
.113
.1938
.037
*
- .0040
- .0104
.975
- 3.9243
.994
- .0113
.973
.2066
.2659
.519
.1450
.4134
.062
.2614
.525
.4156
.061
.0159
.1982
.029
.0231
.0606
.203
.1735
.051
.0621
.191
- .0002
- .0022
.044
- .0004
- .0011
.058
- .0019
.078
- .0011
.054
.0033
.0411
.050
.0056
.0146
.531
.0419
.043
.0115
.587
.0195
.2428
.143
- .4384
- 1.1506
.000
.1838
.261
- 1.1493
.000
.0281
.3499
.031
- .1629
- .4275
.000
.3208
.046
- .4177
.000
VOCDEG
HIGH
MID
AGE
AGE-SQ
NROOMS
NKIDS3
NKIDS6
* as HIGH = 0 predicts the failure perfectly, STATA dropped the 19 observations of this category
65
Note that STATA does not provide marginal effects for bivariate probit regressions.
26
HIRE and PARTICIPATE 1991 (continued)
PROBITS
BIVARIATE PROBIT
marginal effect
probit coefficient
probability
probit coefficient
probability
Variable
HIRE
PARTICIPATE
HIRE
NKIDS12
.0316
.3943
.001
- .0941
- .2470
.001
.3883
.001
- .2476
.001
.0093
.1162
.470
- .0431
- .1131
.256
.0830
.605
- .1084
.275
- .0298
- .3716
.000
.0048
.0125
.832
- .3443
.000
.0130
.825
.1973
1.0724
.009
- .1929
- .4893
.183
1.0594
.010
- .4921
.182
.0193
.2176
.093
- .0101
- .0266
.752
.2270
.077
- .0256
.761
.0390
.4882
.000
.0246
.0646
.423
.4697
.001
.0699
.383
.0015
.0193
.000
- .0054
- .0141
.000
.0184
.000
- .0141
.000
_
_
.3397
.000
N
1477
1496
1496
P(SAMPLE)
.0724
.6076
_
NKIDS16
NKIDBIG
CARE1
ILLNESS
OWNER
NOLABINC
RHO
PARTICIPATE
27
HIRE and PARTICIPATE 1994
PROBITS
BIVARIATE PROBIT 66
marginal effect
probit coefficient
probability
Variable
UNI
VOCDEG
HIGH
MID
AGE
AGE-SQ
NROOMS
NKIDS3
NKIDS6
probit coefficient
probability
HIRE
PARTICIPATE
HIRE
PARTICIPATE
.2143
1.1098
.000
.2285
.7091
.000
1.1089
.000
.7100
.000
.0117
.1261
.493
.1080
.2826
.007
.1253
.493
.2721
.010
*
-.1985
-.5051
.120
-4.1537
.991
-.5168
.111
.0197
.1758
.653
.1308
.3767
.097
.1689
.665
.3782
.096
.0119
.1216
.143
.0537
.1426
.004
.1122
.172
.1430
.004
-.0001
-.0010
.294
-.0008
-.0022
.000
-.0009
.338
-.0022
.000
.0139
.1426
.000
.0251
.0667
.027
.1436
.000
.0683
.023
.0458
.4699
.005
-.4956
-1.3172
.000
.4342
.000
-1.3120
.000
.0445
.4560
.003
-.1780
-.4730
.000
.4448
.009
-.4684
.000
* as HIGH = 0 predicts the failure perfectly, STATA dropped the 20 observations of this category
66
Note that STATA does not provide marginal effects for bivariate probit regressions.
28
HIRE and PARTICIPATE 1994 (continued)
PROBITS
BIVARIATE PROBIT
marginal effect
probit coefficient
probability
probit coefficient
probability
Variable
HIRE
PARTICIPATE
HIRE
PARTICIPATE
NKIDS12
.0315
.3230
.008
-.1627
-.4324
.000
.3048
.012
-.4337
.000
.0300
.3078
.027
-.0913
-.2426
.014
.2975
.031
-.2412
.014
-.0356
-.3652
.000
.0053
.0141
.832
-.3510
.000
.0115
.862
.0247
.2135
.630
-.2950
-.7568
.014
.2079
.632
-.7435
.014
.0147
.1525
.257
.0221
.0588
.495
.1572
.241
.0574
.506
.0010
.0107
.000
-.0035
-.0094
.000
.0100
.000
-.0091
.000
-
-
.2976
.000
N
1365
1385
1385
P(SAMPLE)
.0820
.6180
-
NKIDS16
NKIDBIG
CARE2
OWNER
NOLABINC
RHO
29
8.
References
Beblo, M. (1998). Intrafamily Time Allocation: A Panel-Econometric Analysis of the Division of Housework Time among German Couples. Paper presented at the Third SOEP User’s
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