Parameters
==========
[](https://www.wifo.ac.at/jart/prj3/wifo/resources/person_dokument/person_dokument.jart?publikationsid=66473&mime_type=application/pdf)
This chapter is organized as a self-contained paper:
> Spielauer, Martin, Thomas Horvath, Walter Hyll, Marian Fink (2020) **microWELT: Socio-Demographic Parameters and Projections for Austria, Spain, Finland, and the UK** WIFO Working Paper 6011/2020 [pdf](https://www.wifo.ac.at/jart/prj3/wifo/resources/person_dokument/person_dokument.jart?publikationsid=66473&mime_type=application/pdf)
## Abstract
The aim of this chapter is twofold. First, it provides an overview of the
socio-demographic core modules of the dynamic microsimulation model microWELT.
Second, it describes the essential socio-demographic characteristics of four
European countries - Austria, Spain, Finland, and Great Britain as
representatives of four welfare state regimes (conservative, Mediterranean,
universalistic, and liberal) - and the processes that drive socio-demographic
change which we aim at capturing with the model. MicroWELT is developed as a
tool for the comparative study of the distributional effects of four welfare
state regimes, represented by the four studied countries. Processes with
potential links to welfare state types include (1) the intergenerational
transmission of education, (2) childlessness and fertility by education, (3)
partnership behaviours and lone parenthood, (4) age at leaving home, and (5)
mortality differentials by sex and education. Through microWELT projections, we
identify the impact of these processes on the future population composition by
age, sex, education, and family characteristics of the studied countries.
## Introduction
This chapter identifies essential socio-demographic characteristics of four
European countries – Austria, Spain, Finland, and the UK - and the processes
driving socio-demographic change which we aim at capturing in the microWELT
model. microWELT is a comparative dynamic microsimulation model developed at the
Austrian Institute of Economic Research (WIFO) alongside the research program
WELTRANSIM (Welfare Transfer Simulation). It allows studying the distributional
effects of four welfare state regimes represented by the four studied countries.
microWELT reproduces Eurostat population projections concerning age-specific
fertility, mortality, as well as net migration. While meeting these aggregate
targets, the model also produces detailed family-demographic and education
projections. Modelled processes have potential links to welfare state types,
including the intergenerational transmission of education, childlessness and
fertility by education, partnership behaviours and lone parenthood, age at
leaving home, and mortality differentials by sex and education. Through
microWELT projections, we identify the impact of these processes on the future
population composition by age, sex, education, and family characteristics of the
studied countries.
This chapter is organized as follows. The first part gives an overview of the
microWELT model and its rational and application from the perspective of the
presented research. This part is kept very short, as detailed documentation of
the model and its implementation are available at the project website
(microWELT.eu). In the following – departing from Eurostat population
projections of fertility, mortality, and migration - we discuss how demographic
processes are modelled and parameterized in microWELT. We continue with a
discussion of the inter-generational transmission of education, partnership
patterns, and leaving home. The final part presents detailed simulation results
and how differences in the various behaviours across the studied countries
impact these results.
All parameter tables and the simulation results of the base scenario (organized
in around 60 tables) for the four studied countries are available as Excel
workbooks at the project website. Parameters are estimated and generated by a
set of documented Stata scripts that are also available for download. Besides
transparent model documentation and reproducibility, the scripts also facilitate
porting the model to new countries.
## The microWELT model
microWELT is a dynamic microsimulation platform developed for the study of the
interactions between welfare state regimes, private and public welfare
transfers, and population ageing accounting for educational change, life
expectancy differentials by education, and changing family patterns.
microWELT is a portable continuous-time interacting population model based on
data readily available for many countries, most importantly, the EUROMOD
database. While reproducing existing (Eurostat) demographic projections in
aggregate outcomes, microWELT also produces detailed family-demographic and
education projections integrated with a longitudinal accounting framework based
on the National Transfer Account (NTA) approach. microWELT is fully documented
and designed as a modular platform refinable and extendable for a wide range of
applications going beyond the WELTRANSIM project.
The starting population is generated from 2010 EUROMOD (Sutherland and Figari, 2013) input data, and various parameters are estimated directly from this data
set. Other data sources are the harmonized European Labour Force Survey,
Eurostat projections, NTA variables by age and sex, developed in the Agenta
Project (agenta-project.eu), and new disaggregated NTA data by age, sex,
education, and family type developed within the WELTRANSIM project
(weltransim.eu).
microWELT reproduces Eurostat population projections concerning age-specific
fertility, mortality rates by age and sex, and net migration numbers by age and
sex. While meeting these aggregate targets due to alignment, the model accounts
for the relative differences in rates by education. In particular, first, birth
rates (and thereby childlessness) are modelled by education. Also, the model
accounts for the relative differences in mortality by education, resulting in
observed life expectancy differentials, while overall results are aligned to
mortality by age and sex. microWELT models the intergenerational transmission of
education, which improves the longitudinal consistency of economic accounting
over the life-course. For example, a person achieving higher education has a
higher likelihood of having grown up with higher educated parents, thus having
received higher family transfers as a child. Education (besides age, the
presence of children in the family, and the age of the youngest child) also
impacts the likelihood of a woman living in a partnership. In the simulation,
partners are matched by observed distributions by age and education. Persons are
linked to nuclear families. These links are maintained, responding to events
like union formation, new partnerships, leaving home, and death.
### Population Projections
One of the key features of microWELT is its inbuilt ability to reproduce
existing population projections in aggregate outcomes such as age-specific
fertility, mortality by age and sex, and net migration by age and sex. Figure 1
depicts the very different demographic patterns concerning the changes in the
number of births, deaths, net migration, and the resulting total population
across the studied countries. Except for the UK, births surpass the number of
deaths starting around 2020. While migration stabilizes the total population in
Austria and Spain, the Finish population starts declining from this point in
time. In contrast, the UK has projected numbers of births above the number of
deaths leading to a steady increase in the total population.
Figure 1: Births, deaths, net migration, and total population
Source: microWELT projection results of the base scenario closely reproducing
Eurostat population projections.
~~~
Basic population projection parameters
- Fertility rates by age and year
- Gender ratio
- Mortality rates by year, age, and sex
- Net migration rates by year, age, and sex
~~~
### Fertility
In its base scenario, microWELT reproduces age-specific fertility rates by
calendar year as published and projected by Eurostat. Figure 2 illustrates the
different levels and age patterns observed in 2010 and as projected for 2030.
Besides a slight move to higher ages, fertility is projected to stay at around
2010 levels for Austria and Spain (resulting in a Total Fertility Rate TFR
around 1.4 and 1.3). The Finish rates drop from a comparably high level
(resulting in a TFR of 1.9) to levels comparable to Austria. Rates for the UK
stay comparably high (resulting in a TFR of 1.8, a drop from 1.9). The slight
drop of fertility in the UK is caused primarily by a projected reduction of
fertility younger women, which is currently comparably high.
Figure 2: Historical (2010) and projected (2030) age-specific fertility rates
Source: Eurostat.
In contrast to macro projection models, microWELT does not apply the same
fertility rates to all women regardless of education and parity. In order to
simultaneously meeting overall rates and accounting for individual differences,
first births are modelled separately, parameterized by age-specific cohort first
birth rates by education. As a model option, these rates can be automatically
calibrated to meet an additional parameter for cohort childlessness by
education. In the simulation, it is ensured that first births meet these rates.
In contrast, the remaining (higher-order) births required to meet overall
age-specific fertility are distributed randomly to women of the respective age
who are already mothers.
The rationale of this approach is to meet two critical targets of fertility –
the age distribution at first birth and childlessness by education - while being
able to reproduce overall age-specific fertility rates. Childlessness (versus
ever being a parent) is a key distinguishing characteristic of the model. It is
also applicable for distributional analysis from a longitudinal perspective
(accounting transfers over the whole life course by population group).
Childlessness differs by education following patterns also linked to welfare
state regimes. For example, high childlessness of higher educated women is seen
as a typical phenomenon of "specialization" in conservative countries (Spielauer
2005). Historically, most European countries followed a transition of very high
childlessness at the beginning of the 20th century to very low childlessness
during the baby boom, followed by an increase. As a measure of the concentration
of reproduction – the distribution of family sizes – it impacts the distribution
of family obligations, which are an essential dimension of distributional
analysis (Shkolnikov et al. 2004, Spielauer 2005).
microWELT reproduces these patterns and allows scenarios for the future. In the
base scenario, we maintain the cohort childlessness of the 1960 birth cohort.
Parameters stem from the Cohort Fertility and Education database
() for Austria, Finland, and Spain. Data
for the UK are taken from Berrington et al. (2015). While total levels of
childlessness are comparable across the four studied countries, childlessness by
education follows different patterns. For Austria, Spain, and the UK
childlessness is highest for women with high education. For Finland, cohort
trends have reversed, childlessness now being highest among women with low
education. This reversal is unique, also in the context of Nordic countries,
whereas in all Nordic countries women with high education do have lower levels
of childlessness compared to the rest of Europe (Rotkirch & Miettinen 2017).
Figure 3: Cohort childlessness
Source: Cohort fertility and education database (Austria, Finland, and Spain)
and Berrington et al. (2015) for the UK.
First birth rates by education were estimated from EUROMOD SILC data and
calibrated to reproduce the target cohort childlessness.
Figure 4: Childlessness by education and age 2060 (i.e., women born in the
simulation)
Source: microWELT simulation results – base scenario of constant fertility
patterns.
~~~
Fertility Parameters (other than the basic population projection parameters)
Model selection:
(1) Basic model: Fertility according to age-specific fertility rates (AFR)
(2) Refined model: First births by education, with alignment to AFR
- First birth rates by age, education, and year of birth
- Target childlessness by education and year of birth
- Y/N Selection switch to align first birth rates to the
cohort childlessness parameter
~~~
### Mortality
The modelling of mortality is based on mortality tables available from Eurostat
population projections. In addition to age- and gender-specific mortality, the
model considers the different life expectancies according to education. For
example, in Austria, men with university degrees live on average about six years
longer than men with compulsory schooling as the highest level of education
(Klotz, 2007; Leoni et al. 2020). The simulation uses parameters for the average
remaining life expectancy at 25 and 65 by education (Murtin et al. 2017) to
calculate relative mortality risks. It applies them in such a way that overall
consistency with the mortality tables is maintained. The ability to model life
expectancy differences by education is critical for projections of transfers,
particularly pension transfers, which are higher for higher educated people.
Figure 5: Remaining life expectancy at 25 and 65
Source: Model parameter based on Murtin et al. (2017). Data for Spain are based
on Requena (2017).
Mortality improvements captured in the projected period life tables lead to a
cohort life expectancy higher than today's period estimates. Figure 6 shows the
expected cohort life expectancy at birth by country, sex, and education of
people born 2010-2014. In general, life expectancy differences by education are
more pronounced for men.
Figure 6: Simulated life expectancy of 2010-2014 birth cohorts accounting for
mortality improvements according to Eurostat projections
Source: Eurostat. Own calculation.
~~~
Mortality Parameters (other than the basic population projection parameters)
- Model selection:
(1) Life table: projected mortality rates by year, age, and sex
(2) Life table calibrated to remaining life expectancy at 25 and 65 for each year
(3) Same as (2), with overall mortality aligned to the mortality table as in (1)
- Remaining life expectancy at 25 and 65 by education, year, and sex
~~~
### School Enrolment, Education Outcome, and the Intergenerational Transmission of Education
microWELT distinguishes three levels of education, low corresponding to ISCED
0-2, medium to 3-4, and high to 5+. The model aims at modelling (1) school
enrolment, (2) education attainment, and (3) the intergenerational transmission
of education, i.e., accounting for the influence of parents' education (the
highest level of education if living with both parents) on the education of
their children. The model also allows for easy scenario creation by setting a
target distribution of education outcomes by sex and year of birth. Users are
given a choice to produce these target outcomes, or – from a chosen year onwards
– let education change entirely be driven by the intergenerational transmission
of education, i.e., the changing composition of parents' education. For all
years for which the model is set to reproduce given targets, the selection of
children to progress in the education system is based on the relative
differences by parents' education expressed in odds ratios as well; however,
results are aligned to the overall targets. The odds ratios were estimated for
the transition (1) from lower education to medium education, and (2) from medium
to high.
Table 1: Odds Ratios of education progressions by parent's education
Source: Parameters estimated from the 2009 ad-hoc module of the European Labour
force survey
The impact of parents' education on education transitions is of comparable
magnitude for Austria, Spain, and the UK, while in Finland, odds ratios are
smaller by a factor of 3 for the transition from low to medium, and a factor of
2 for the transition from medium to high education. These diverging results for
Finland are consistent with its universalistic welfare state regime. The
findings are also consistent with the rankings of indices on the
intergenerational educational mobility developed by OECD (2018, p248). These
rankings place Austria and Spain on the low end of European countries and
singling out Finland as the country with the highest mobility. This finding is
also statistically significant (see Table A 5 in the Appendix).
Figure 7 depicts the target education composition of the 2010 birth cohort. Most
noticeable is the comparatively high proportion of low education in Spain, while
the proportion of high is comparable in magnitude – and higher for women -
across all countries.
Figure 7: The target education composition of the 2010 birth cohort
Source: Parameter estimated from European labour force survey data 2014.
For the given distribution of parents' education and given relative differences
in education attainments by parents' education, the model automatically
calculates transition rates that meet the target education composition of each
birth cohort. All following years apply the same transition probabilities by sex
and parents' education as in the last year for which these rates were calculated
within the simulation. In the base scenario, all education changes for cohorts
born after 2010 are entirely driven by the changing composition of parents'
education; thus, we assume transition rates to stay constant for given sex and
parents' education.
The education expansion observed in the past, and the intergenerational
transmission process leads to education considerable improvements in the
education composition of the potential workforce. Figure 8 depicts the resulting
projected education composition of the population 25-59 from 2010 – 2060.
Figure 8: Education composition of the population 25-59 by calendar year
Source: microWELT simulation results – base scenario.
School enrolment is modelled by combining two mechanisms. First, based on
observed current patterns of school attendance, we identified a collection of
typical school trajectories (years of school attendance by school level) by
education outcome together with a probability distribution of these patterns.
While this approach allows a very detailed depiction of observed trajectories
and their distribution in principle, the base scenario only includes
trajectories up to the first high attainment. In order to also include school
enrolment beyond the first high graduation, a second mechanism allows alignment
of school attendance by target rates by age and sex. These rates are based on
current observations, and in the simulation constitute a minimum enrolment rate.
~~~
Education Parameters
Model selection:
1. Use target outcomes without accounting for parents' education
2. Use target outcomes with accounting for parents' education
3. Same as (2), but from a selected year only model the intergenerational
transmission
Overall outcomes:
- Overall education progression probabilities low to medium by
year of birth and sex
- Overall education progression probabilities medium to high by
year of birth and sex
Intergenerational transmission:
- Odds ratios by parents' education for the first education progression
- Odds ratios by parents' education for the second education progression
- First year from which on only the intergenerational transmission is modelled
Education patterns:
- School entry age
- Start of the school year (e.g., September)
- Education patterns: a collection of possible trajectories by the outcome
- Education pattern distribution: likeliness of the various patterns
- School enrolment rates by age and sex for optional alignment
- School enrolment alignment on/off
~~~
### Female Partnership Status and Partner Matching
The female partnership status is modelled accounting for age, education, the
presence of children as well as the age of the youngest child in the family. We
do not distinguish between married versus unmarried cohabitation. Once a woman
enters a partnership, an appropriate male partner is searched in the population,
criteria being age and education. The basic assumption of microWELT for
modelling partnerships is that partnership patterns stay the same for women with
given characteristics. Future changes on the aggregate level arise entirely from
composition effects, for example, due to an increase in childlessness, the
education expansion, or an increase in the age at first birth.
In the same way, the distributions of age differences between partners are
assumed to be time-invariant. The distributions are age-dependent, their spread
increasing with age. For example, while a 50-year-old woman can have a
30-year-old spouse, this age difference is not possible for a 25-year-old.
Observed age patters result from past union formations with usually no data
being available on the individual union durations. In consequence, the age
distributions of spouses by the age of the woman observed in cross-sectional
data cannot be used directly in the simulation. Instead, at each union formation
event, we compare the target age distribution of spouses for women of this age
(targets stemming from current cross-sectional data) with the observed age
distribution within the simulation. Based on this comparison, we identify the
age with the largest negative gap between the distributions and, by filling this
gap, aim at keeping the distributions as close together as possible during the
simulation.
For the modelling of education differences between spouses, we combine two
approaches. The first uses the current distribution of partners' education by
the education of the female partner as observed for the age group 25-35. While
this approach was found to clear the spouse market – virtually all women find a
spouse with the characteristics based on today's distribution in the simulation
– the projected improvements in education were found to lead to implausible
rates of single men by education. Given the changes in the education composition
in the spouse market – and very different to today's observations - very few men
with low education would be single, while the proportion of men of high
education being single would increase considerably. To address this issue, we
limit the number of men available for entering a partnership for each education
group based on the age profile of the share of male singles today. For example,
if a 30-year-old woman with high education based on today's distribution would
randomly pick a 32-year-old spouse with medium education, but the minimum
threshold of 32-year-old male singles with medium education is reached already,
she would try to find a partner of another education group. As a result, the
share of male singles by age and education is kept close to the proportion
observed today. A presentation of the current and projected population
distribution by age group, sex, family type, and education is given in Chapter 4
(Figure 13).
Except for the event of the death of a partner, the partnership status is
updated in yearly steps. The death of a partner leads to an immediate update of
the partnership status. Union dissolutions are modelled only until age 80; after
this age, partnerships are only dissolved due to widowhood.
The probability of women to live in a partnership are estimated from the
starting population file (EUROMOD SILC) using logistic regression. We estimated
the models separately for women not living with dependent children and mothers.
Figure 9 depicts the partnership patterns for mothers. Single motherhood is
highest for very young mothers. In the case of the UK and Finland, there is an
education effect, higher educated mothers being more likely to live in a
partnership. In the first years of the youngest child, lone motherhood is
highest in the UK. In contrast, we observe the steepest decline of the
probability of living with a partner by the age of the youngest child in
Finland. These country-specific peculiarities of the UK and Finland are
statistically significant (see Appendix Table A 1 to Table A 4).
Figure 9: Proportion of mothers living in a partnership by age group, education
and the age of the youngest dependent child
Source: Model parameters based on logistic regression results estimated from
EUROMOD SILC.
While the yearly updates maintain the cross-sectional consistency of the model,
individual partnership careers are not longitudinally consistent at the
individual level. Union formations and dissolutions are performed only to meet
the target proportions of women with given characteristics to stay in a union.
If, for example, there are too many 30-year-old mothers with children below age
2 in a partnership, women of this group whose partnerships are dissolved are
picked randomly until the target proportion is reached. This way, longitudinal
consistency is achieved only on the cohort level. Cohorts can be distinguished
by sex, year of birth, education, and childlessness which are essential
dimension for the generational accounting routines implemented in microWELT.
Figure 10 displays simulation results of the lifetime family experience by
education and by ever having been a mother (versus childlessness) of women born
at the start of the simulation. Regardless of country, women on average spend
about 40 years of their life in a partnership. Higher educated mothers in
average spend less time as lone mothers, the education gradient being highest in
the UK. For women who stayed childless over life, the average time spent in
partnerships diminishes wit education in Austria and Spain. In contrast, no
educational differences can be found for the UK and Finland.
Figure 10: Lifetime family experience (in years) by education and motherhood of
women born in 2010
Source: Simulation results.
~~~
Partnership Parameters
Female partnership status
- The probability of living in a partnership of women not living
with dependent children by age and education
- The probability of living in a partnership of women living with
dependent children by age group, education, and age group of the
youngest child.
Partner matching
- Distribution of the partner's education by the education of
the female partner
- Distribution of the partner's age by the age of the female partner
~~~
### Family linkages and leaving Home
One process accompanying the transition to adulthood is moving out of the
parental home. Across Europe, there are considerable differences in age when
young adults leave their parental home. In southern European countries, young
adults on average stay at home until about 30. In contrast, in northern European
countries, moving out of the parental home on average takes place ten years
earlier in life. The four countries considered - Spain, Austria, Finland and the
UK – cover the entire spectrum. The latest figures from Eurostat show that
within the European Union the average age is about 26 years. Austria with 25.4
and the UK with 24.6 years are close to this average, while in Finland, the
average age at leaving home is 21.8.
Figure 11: Average age leaving home
Source: Eurostat.
There is abundant literature on this topic, linking the differences to housing
costs (Haurin et al., 1993; Ermisch and Di Salvo, 1997), cultural differences
and social norms (Giuliano, 2007; Alesina and Giuliano, 2010), entrance in the
labour market or marriage (Di Stefano, 2017). Several scholars also suggest that
parental resources play an essential role, at which age children leave home
(Avery et al., 1992).
Eurostat calculations of children living at home are based on household data. In
contrast, microWELT models nuclear families. According to our definition,
nuclear families consist of one or two adults and dependent children, if present
in the household. As we do not simulate employment and income, we define
dependency by age and school enrolment. Children reach independence at age 18 if
not enrolled in education. Students staying with their parents are assumed to be
dependent up to their 26th birthday. Birth and partnership events also lead to
the creation of a new nuclear family. This definition is consistent with
disaggregated NTA variables produced alongside the WELTRANSIM project.
Figure 12: Proportion of students living with parents
Source: Own estimations based on EUROMOD SILC.
The proportion of students living with parents depicted in Figure 12 shows the
significant variation across countries as could be expected from the different
ages of leaving home based on Eurostat estimates (Figure 11). Taking the 22nd
birthday as a reference when a first high degree can typically be attained, at
this point in life more than 96% of students still live with parents in Spain,
compared with only 37% in Finland.
When leaving home, children form their own nuclear family. microWELT implements
two types of links to parents. The first refers to biological parents and is
established at birth. If a mother does not have a partner when giving birth but
enters a partnership within the first year after giving birth, this partner is
considered a biological parent as well. Links to biological parents are kept
over the whole life and only dissolved due to death. The second type of
child-parent links refers to social parents. In the case of union dissolution of
parents, children choose with whom to stay and dissolve the other link. If the
remaining parent enters a new partnership, a link to the new partner is
established.
~~~
Family linkages and leaving home parameters
- Probability of students leaving home by age
- Probability of children to stay with the mother in the case
of a union dissolution of parents
~~~
## Socio-demographic Projections
As described in detail in Spielauer et al. (2020a), microWELT is being developed
as a tool integrating dynamic microsimulation with the National Transfer Account
(NTA) approach for the comparative analysis of the effect of demographic change
on transfer flows in four welfare state regimes. NTA data are cross-sectional
age profiles breaking down national accounting variables on consumption, income,
saving, and transfers (United Nations, 2013). NTA data by sex are available for
more than 50 countries, including most European countries (agenta-project.eu).
Alongside the WELTRANSIM Project, we further disaggregated NTA data by education
and family type (Abio et al. 2020). microWELT produces the required detailed
socio-demographic projections for using these disaggregated NTA data. In
particular, simulations cover the following key dimensions used for
disaggregating NTAs:
- Age, sex
- Own education, education enrolment, and education of parents
- Partnership status, ever having had children versus childlessness, and
current cohabitation with dependent children
Figure 13 depicts the population compositions by sex, age, education and family
type as observed at the start of the simulation in 2010 compared to microWELT
projections for the year 2060. In order to synthesize these four dimensions into
a single graph inspired by age pyramids, we collapse age to 4 age groups:
- Children 0-16 by sex and education of parents.
- Young adults 17-25 distinguished in students and non-students. Students are
classified by parent's education if living with their parents, or a group
"independent" if having left home. Non-students are classified by their own
education and by family type (like adults 26-59).
- Adults 26-59 are classified by education and family type. Family types are
singles not living with dependent children, singles living with dependent
children, couples not living with dependent children, and couples living
with dependent children.
- For adults 60+, family types are defined differently as for younger age
groups: we distinguish childless persons from persons ever having had
children.
The surface of each square in Figure 13 represents the share of the respective
group on the total population. For each age group (and sex), we horizontally
divide the population into three education groups (red low, yellow medium, green
high). The population within each education group is vertically divided into
four family types.
These population composition graphs give a quick impression on the population
dynamics of each country but also how countries differ concerning their age and
education structure and how the population composition changes over time.
Comparing the population composition graphs in 2010 and 2060 shows how
population ageing affects the age structure of the population: while the size of
the square of the oldest age-group increases in all countries, it decreases for
the middle age group. At the same time, the red-coloured areas (low education)
decrease within all age-groups while especially the green areas (high education)
increase markedly, showing the consequences of education expansion of the
population. When comparing the population graphs, e.g. of Austria and Spain in
2010, we also get an impression of how the education composition of the
population differs between these countries.
Figure 13: Population composition by age group, education and family type 2010
and 2060
## Outlook and Conclusions
With this paper, we followed two objectives. First, we provided an overview of
the socio-demographic core modules of microWELT model. Second, we described
essential characteristics and processes driving the socio-demographic changes we
capture with the model. microWELT is being developed for the comparative
analysis of four welfare state regimes represented by Austria, Finland, the UK,
and Spain. We found considerable differences in the socio-demographic
characteristics and trends of the four countries.
This paper is part of a series of related papers and other resources which
together build comprehensive documentation and presentation of the research
performed developing and using microWELT. All materials are available at the
project website www.microWELT.eu. One of the objectives of microWELT is the
provision of a modelling platform available for applications beyond the
WELTRANSIM project. A collection of project descriptions and links to these
projects is provided on the project website. microWELT is an open-source
project: the application, the model code, a step-by-step implementation guide as
well as analysis scripts for parameter generation are available for download.
## Appendix
**The proportion of mothers living in a partnership by age group, education and
the age of the youngest dependent child**
In Table A 1, we assess country-specific differences in partnership status by
logistic regression models for each country in our sample. For all countries, we
find that the proportion of mothers living in a partnership decreases with the
age of the youngest child. We also find that the probability of living in a
partnership increases with the age of mothers. Higher educated mothers are more
likely to live in a partnership as compared to mothers with low education,
especially in Finland and the UK.
Table A 1: Proportion of mothers living in a partnership, logistic regression,
Odds Ratio reported
Source: Own calculations based on 2010 EUROMOD data. Exponentiated coefficients;
Robust standard errors in parentheses; frequency weights applied \* *p* \< 0.1,
\*\* *p* \< 0.05, \*\*\* *p* \< 0.01.
In what follows, we test whether there are statistically significant
country-specific differences in partnership status of mothers and how their
partnership status varies by mothers age and the age of the youngest child in
the family. In the following regressions we apply probability weights. Point estimates are almost the same compared to frequency weights. Yet frequency weights due to the generation of a large sample - yield very tiny p-values.
In all tables, we present results from logistic regression models based on 2010
EUROMOD data. In Table A 2 we depict a simple model where we regress the
likelihood of living in a partnership on the country dummies. The base category
is the UK. As the table shows, compared to the UK, mothers are more likely
living in a partnership in all the other three countries, a result that proves
statistically highly significant.
Table A 2: Proportion of mothers living in a partnership, logistic regression,
Odds Ratio reported
Source: Own calculations based on 2010 EUROMOD data. Exponentiated coefficients;
Robust standard errors in parentheses \* *p* \< 0.1, \*\* *p* \< 0.05, \*\*\*
*p* \< 0.01.
In Table A 3 we depict age differences of lone motherhood by regressing the
likelihood of mothers living in a partnership on their age interacted with
country dummies. In the UK, young mothers are less likely to live in a
partnership than mothers aged 40 and older, our base category (which can be seen
by the highly significant mother age group effects in the table). On the other
hand, we do not find any significant age differences for Austria, except for
women in the age of 35-39, who are more likely to be in a partnership compared
to their UK counterpart. In Spain, we only find a significant negative age
effect compared to the UK for women aged 20-24. In comparison, we find that all
interaction coefficients are highly significant, and odds ratios are larger than
one for mothers from Finland.
Table A 3: Proportion of mothers living in a partnership, logistic regression,
Odds Ratio reported
Source: Own calculations based on 2010 EUROMOD data. Exponentiated coefficients;
Standard errors in parentheses \* *p* \< 0.1, \*\* *p* \< 0.05, \*\*\* *p* \<
0.01.
In Table A 4 we assess the potential correlation of partnership status and the
age of the youngest child in the family by regressing the likelihood of living
in a partnership on the age of the youngest child and interact this effect with
the country dummies. The base country here is Finland, and the base age category
of the youngest child is 0-2 years of age.
For Finland, we find that mothers are significantly less likely to be in a
partnership the older the youngest child is. We do not find any significant
different effect for Austrian mothers. However, for Spain and the UK, we find
that compared to Finland, mothers are significantly more likely to live in a
partnership the older the youngest child.
Table A 4: Proportion of mothers living in a partnership, logistic regression,
Odds Ratio reported
Source: Own calculations based on 2010 EUROMOD data. Exponentiated coefficients;
Standard errors in parentheses, \* *p* \< 0.1, \*\* *p* \< 0.05, \*\*\* *p* \<
0.01.
**Education progressions by parent's education**
In a next step, we test for country differences in education outcomes by
parents' highest level of education (i.e. differences in the intergenerational
transmission of education) based on Eurostat's 2009 ad-hoc module of the Labour
Force Survey microdata. For analysing these potential country-specific
differences, we construct two education dummies for a descendant's highest level
of education ("medium" corresponding to ISCED 3-4 or "high" corresponding to
ISCED 5+).
In Table A 5 we estimate logistic regression models for the probability of
having a medium (high) education level on parents' highest level of education
(again distinguishing between low, medium and high education). The country base
category is Finland, and parents' education base category is high.
For Finland, we find that a descendant (irrespective of gender) is less likely
to achieve a medium education level when parents are highly educated but much
more likely to achieve higher education themselves. While the latter effect can
be observed for all countries in our sample, for Austria and Spain, descendants
from parents with intermediate education also show statistically significantly
higher probabilities to achieve intermediate education as their highest
education level. These results confirm that intergenerational transmission of
education is very pronounced in Austria, Spain and the UK while being markedly
lower in Finland.
Table A 5: Probability of having a medium or high education level, logistic
regression, Odds Ratio reported
Source: Own calculations based on Eurostat 2009 ad-hoc module of the European
Labour Force Survey. Exponentiated coefficients; Standard errors in parentheses
\* *p* \< 0.1, \*\* *p* \< 0.05, \*\*\* *p* \< 0.01.
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