LABORsim: An Agent-Based Microsimulation of Labour Supply – An Application to Italy

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LABORsim: An Agent-Based Microsimulation of Labour Supply.
An application to Italy

– draft, november 2005 –


Roberto Leombruni, LABORatorio R. Revelli – Centre for employment studies
Matteo Richiardi, LABORatorio R. Revelli – Centre for employment studies






Abstract

Most Oecd Countries are experiencing a rapid population ageing. Italy adds to this picture a very low labour
market participation of the elders, so that most projections of the impact of ageing on the labour market are rather
pessimistic. However, there are other long run modifications currently underway that will presumably have a sizeable
impact on the labour market, above all changes in the retirement legislation, in educational choices and participation
behaviour. In this paper we present LABORsim, an agent based microsimulation model of labour supply, which offers
new insights on the likely evolution of the labour force in the next decades in Italy. LABORsim integrates the current
demographic projections with simulation modules modelling retirement rules, retirement behaviours, migrations,
education and participation choices, plus a consolle to implement various policy scenario analyses. When all these
factors are taken into account, projections for next decades are not that pessimistic. In most scenarios, the overall
participation rate is expected to increase steadily for the next two decades, while shortages in the labour force supply
and an unfavourable dynamics for the economic dependency rate are expected to show up only after 2020, when the
baby boom generations will arrive at their retirement ages. This is not enough, however, to allow Italy to meet the EU
Stockolm and Lisbon targets for male and female employment rates for many decades to come. The sharp increase in
the participation rates for the elderly (aged 55-64), mainly driven by the recent changes in the retirement eligibility
criteria, will make it possible to meet the Stockholm target of 50% employment rate in this age group by 2015, i.e. with
only 5 years of delay.

JEL Classification: E24, H3, H55, I2, J1, J2, J6, N4, O52, C63
Keywords: microsimulation, participation, employment, retirement, education, policy evaluation

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1. Introduction
In the next decades most Oecd Countries will experience a rapid population ageing, because
of the increase in life expectancy occurred in the second half of last century, and to a concurrent
sharp decrease in fertility rates. This will have strong consequences both on the sustainability of
social security systems – financed in most cases through a pay-as-you-go mechanism – and on the
labour market, with possible labour supply shortages. Within this general picture Italy is no
exception, having one of the oldest populations among Oecd countries. What is worse, Italy also has
one of the lowest participation to the labour market is recorded, particularly for women and the
elderly. Ageing and low participation, taken together, justify the very pessimistic projections that
are currently made about the evolution of economic dependency ratios in the next decades.
Most projections, however, are based on a very simplistic extrapolation of cross-sectional
participation rates as measured today. Oecd [2004], for instance, applies the participation rates
measured in 2000 to the demographic projections, and estimates a dramatic reduction in the
consistency of the labour force – dropping from 24 to 17 millions by 2050 – and an increase in the
economic dependency ratio that is the second worst in all Oecd countries forecasts.
This kind of projections are unsatisfactory under many respects. First, participation choices
should be viewed in a life cycle perspective. To extrapolate cross-sectional data observed today can
produce paradoxical results for the pseudo-individuals belonging to the new cohorts simulated: they
would participate little when they are young – since young people today generally go to school –
and they will participate little when they get old – since today’s elders have generally started
working very early, and are willing to retire.
Second, existing low employment rates for people aged 50 and over, in Italy, are probably
below their long run equilibrium. As regards women, many of those who are not participating to the
labour market, actually never worked during their lives – again, in a life cycle perspective, they
once and for all decided to “offer” their labour services within the family. In the data, however, a
trend towards a higher participation is clearly detectable for new cohorts. Moreover, both men and
women during the last two decades took advantage of the very generous early-retirement schemes
available up to the Eighties. Such a “filtering out” of the labour force has a negative impact on the
participation rates observable today for the elders, but will vanish in the future due to the major
reforms of the pension system delivered in the last twelve years.
In this paper we present an agent based microsimulation model of labour supply, which offers
new insights on the likely evolution of the labour force in the next decades in Italy. The main
focuses of the model are on demography, migration, retirement rules, retirement behaviours,
education and participation choices. All behavioural rules implemented are cohort specific, and
have been estimated on Italian Central Statistical Office (Istat) and Eurostat data over the years
1993-2003. The eligibility criteria implemented for the pension system carefully mimic the actual
three-layer retirement rules in force in Italy. When all these factors are taken into account,
projections for next decades are not that pessimistic. Even in the less favourable scenario –
assuming that the positive trend in higher participation in education and in higher labour force
participation for women will dry out – the overall participation rate is expected to increase steadily
for the next two decades.
The paper is structured as follows. Section 2 deals with some background issues on
participation. Section 3 presents the model. Section 4 discusses the simulation results in the
standard scenario. Section 5 concludes.

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2. Some background issues on participation in Italy
The process of population ageing currently underway in Italy is more pronounced than in most
other Oecd countries. In the last decades the total fertility rate has declined steeply, going below the
replacement rate of 2.1 as soon as at the beginning of the 80s, reaching 1.24 in 20001. At the same
time, life expectancy is among the highest among Oecd countries. Even though a slight recovery in
fertility rates is expected for next years, the transition process to the new demographic regime will
have a deep impact on the age structure. In the next two decades, the baby boom generations of the
60s and early 70s will enter in their retirement ages, and will be replaced by new labour force
cohorts roughly half in size2. By 2050, more than one in three Italians will be over the age of 65.
An additional source of concern about Italy is the fact that the participation rates of the elders
are peculiarly low. Figure 1 compares participation rates for people aged 55-64 in a selection of EU
countries. Italy is 14th out of 16, and contrary to most countries has experienced a decrease in the
participation rates during the Nineties.

Figure 1. Participation rates in 1990 and 2002, people aged 55-64, various EU countries.
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
Sweden
Norway
Denmark
United
Kingdom
Portugal
Ireland
Finland
Netherlands
Greece
Spain
Germany
France
Austria
Italy
Luxembourg
Belgium
19902002

Source: Eurostat LFS 2002.

Putting together ageing and low participation of the elders it is straightforward to build future
scenarios in which the economic dependency rate – the ratio of people out of the labour force on
those who partecipate – will become hardly sustainable. Oecd launched in 2001 a tematic rewiew
on this issue, and built projections about the future evolution of old age dependency rates in Oecd
member countries (see for instance Oecd [2004]). They applied the participation rates measured in
2000 by gender and five-years age groups to the best available demographic projections, and
compared the evolution of a demographic and an economic old age dependency rate (see Figure 1).


1 Here and in what follows we refer to the “central” scenario of the population projections produced by the Italian
Statistical Office (Istat), see http://demo.istat.it/index.html.
2 New births averaged 950 thousands during 1960-69, and 890 thousands during 1970-1974. In recent years (1998-
2002) they averaged 530 thousands per year (see Marano and Sestito [2005]).

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Figure 1. Old age demographic and economic dependency rates, Oecd countries.

Panel A, left: People aged 65 and over to people in the age bracket 20-64.
Panel B, right: People out of the labour force to those in the labour force, excluding under 20.
Source: Oecd [2004].

The most revealing comparison is about Italy and Japan. Both countries are facing the most
rapid ageing in the population (left panel). In Japan, however, workers stay longer in the labour
market, so that the share of those who are not active remains below the Oecd average (right panel).
On top of ageing, Italy adds one of the lowest participation rates for people aged 50 and over, so
that the economic dependency rate is expected to reach a record level of almost 130% by 2050.
These “gloom” estimates do not stand alone. Figure 2 shows projections by the Italian Welfare
Ministry for the European Commission: the economic dependency rate is expected to follow closely
the trend of the demographic dependency rate.

Figure 2. Old age demographic and economic dependency rates, Italy.











Source: EC [2003]. Year 2000 = 100

How reliable are these projections? While the future evolution of the population age structure
reflects a secular change that can hardly be questioned, the use of cross-sectional participation rates
by age and gender is unsatisfactory under many respects. The general argument is that participation
choices should be viewed in a life cycle perspective, since the choices made by young people as
regards education and the entry in the labour market have an impact on all their subsequent working
career. The use of cross-sectional evidence mixes behaviours of young individuals with the
demographic
dep.
economic
dep.

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behaviour of their contemporary elders, that as a rule are not coherent each other. For what concern
the Italian case, this general argument goes together with the following points.
First, the legislation has changed. Actual low participation of the elders in Italy (both males
and females) has to be linked with a retirement legislation that is no more in force. The old system
was particularly biased towards early withdrawing from the labour market, both because it allowed
seniority pensions, and because of the defined benefit rule used for the computation of pension
entitlements, that most of the time provided a financial incentive to retire as soon as one became
eligible. The retirement legislation in force today has higher age requirements, and – because of the
introduction of a defined contribution rule – does not embed incentives towards an as-soon-as-
possible retirement behaviour.
Second, the working careers of individuals has shifted forward in the life cycle. Actual low
participation of the elders in Italy reflects working careers that on average started well before what
can be observed for new cohorts today. In Figure 3 we considered two cohorts: those that in 2001
where aged 60-65, and those that will be in the same age bracket in thirty years. Plotting the
declared age at which they began their working life, as comes out from the European Community
Household Panel (Echp), the shift toward later entries in the labour market is clear. This means that
individuals of actual cohorts will arrive to the retirement ages with shorter seniority in comparison
with those who retired during the last decades3, and this will contribute to postpone their retirement
because seniority requirements (aside age requirements) in the eligibility criteria will be met later.

Figure 3. Reported age at first job/business, two cohorts, 2001.
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
10-11 12-13 14-15 16-17 18-19 20-21 22-23 24-25 26-27 28-29
30-35 60-65

Source: Our elaborations on Eurostat data, ECHP, wave 6.
Note: Ages are grouped in 2-years classes in order to avoid heaping at even ages.

Strictly connected with the last point is the fact that newer generations have on average, in
Italy, a higher level of education (see section on education below). Actually, later entries in the
labour market can be explained by and large by higher participation to education. This will
presumably have an additional, direct impact on the participation to the labour market of future
elders, both because higher education has a positive effect on the employability of workers – that is,
on their capability of finding and retaining a job – and on their willingness to do it, since high skill
jobs usually bear a lower disutility of work.

3 In the same direction goes the fact that youngsters today experience on average more and longer unemployment spells
with respects to what happened to older cohorts.

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Starting later to work may also affect retirement choices via wages, and consequently
pensions. On the one side, shorter contribution spells mean lower pensions, given the same wage
profile. Individuals will then have the incentive or the need to withdraw later from the labor market,
in order to achieve the same standard of living when retired. On the other side, better educated
people should have higher wage profiles. They could then be willing to retire earlier. However, (i)
as the supply of skilled labor force increase, the returns to education may go down, and (ii) better
educated people may revise upwards their standard of living. Moreover, the Italian pension system
pays little attention to the amount of contributions, in determining eligibility. 4 Age and seniority
are much more important. It is thus quite implausible that this consideration could reverse the causal
link between higher education and later exit from the labour market.
Finally, actual low participation of elder women has to be linked with life cycle choices made
many decades ago, when a large share of Italian women decided once and for all to “offer” their
labour services within the family instead of within the labour market. A simple analysis of
unconditional participation profiles of women by cohort reveal a trend towards a lower share of
those who work within the family (see Figure 4). This is coherent with a decreasing average
household sizes that goes together with decreasing fertility rates, and with a long run reduction in
the gender differences as regards the attitudes towards the labour market.

Figure 4. Participation profiles by cohorts, males (left panel) and females (right panel)

.2
.4
.6
.8
1
participation rate
203040
age
5060
1930s
1950s
1970s
1940s
1960s
1980s
men
0
.2
.4
.6
.8
participation rate
203040
age
5060
1930s
1950s
1970s
1940s
1960s
1980s
women

Source: Our elaborations on Istat data, RTFL, 1993-2003.

To sum up, whilst the demographic trends will point in the future to scenarios where the elders
will be a very large share of the population, this will not necessarily turn into a high economic
dependency upon people in the labour force. There have been in the last decades many changes – in
the retirement legislation, in educational choices, in the working careers, in the participation
behaviours of women – that will have a long run impact on future participation to the labour force,
and will probably countervail the demographic ageing of the population.
These issues point to the importance of a micro-foundation of the projections of participation
rates, in order to take dynamically into account all the socio-demographic and economic trends at
work, and correctly simulate the effects of the changes in the legislation. Applying cross-sectional
evidence to demographic projections, or even assuming convergence of gender and age-group
cross-sectional participation rates to some Oecd average, as also done by Oecd [2004], looks no
more than providing scenario analysis, and says very little about the likelihood of each scenario.

4 A minimum level is required, in order to guarantee a minimum pension (equal to 1.2 times the amount of the benefits
for poor people).

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3. A micro-foundation of participation rates
A natural way to overcome the problems outlined above is to use a dynamic microsimulation.
Microsimulation modelling has been an active field of research since the seminal work of Orcutt in
the 1950s [Orcutt, 1957]. Although for many years it has been confined to a small niche of
practitioners, it is now being revitalized by a new wave of interest, partly due to the rise in computer
power that has made it widely accessible. 5 Microsimulation modelling involves the generation of
data on individual units, rather than the analysis of pre-determined, “representative” groups or cells,
or of the aggregate system as a whole (i.e. general equilibrium models), and are particularly suited
for policy evaluation and scenario analysis. When microsimulations do not consider behavioural
changes to a change in the environment (e.g. policy), they are defined as ‘static’ (e.g. tax-benefit
models). Dynamic microsimulations on the other hand incorporate behavioural responses. 6
In general, microsimulations consider many dimensions of socio-economic systems. However,
we are interested here mainly in (i) education, (ii) participation, (iii) retirement, aside the effects of
demographic change. Among the dynamic microsimulation models surveyed in Zaidi and Rake,
(2002) DYNASIM2 (USA, [Wertheimer, 1986]), CORSIM (USA, [Favreault and Caldwell, 1999]),
MOSART (Norway, [Fredriksen, 1998]), DESTINIE (France, [Bonnet and Mahieu, 2000]) and
SAGE (UK, [SAGE 2001-2004]) explicitly consider all the three issues above; DYNAMOD-2
(Australia, [King et al., 1999]) considers (i) and (ii) alone; PENSIM (UK, [Curry, 1996]) only (ii)
and (iii).
A few microsimulation models are also available for Italy. Among those who have a similar
approach to ours, the Bank of Italy’s DYNAMITE model [Ando et al., 2004] focuses on household
formation and income dynamics, while MIND [Vagliasindi et al., 2004] and DYNASIM
7[Mazzaferro and Marciano, 2005] focus on the distributional impact of the demographic evolution
and social security provisions.
LABORsim is a dynamic aging, discrete-event, probabilistic agent-based microsimulation
model of labour supply. Setting the focus of LABORsim we gave precedence to the phenomena
pointed out in previous section, namely to the retirement legislation reforms, the process of
seniority accumulation, the changes in the educational choices of young people and the participation
behaviours of women. All these factors have been integrated with the best demographic projections
available, with an additional focus on migrations dynamics. In the balance between the desire to
include all relevant dimensions and endogenous feedbacks of the phenomenon under investigation,
and the need to keep the model manageable and easy-to-use for policy and scenario evaluations, we
pushed towards the latter. Thus there are things that remain out of the model, at this stage.
In particular, monetary variables (wages and pensions) are not considered, although future
extensions of the model could easily include them. Also, family structure is not considered.
However, in estimating an upward trend in the participation rates of women, we implicitly take the
evolution of family structure into account. Moreover, it should be remembered that, as most
microsimulations, LABORsim is not a general equilibrium model. In LABORsim the demand for
labour is not considered but for the analysis of unemployment differentials, while the overall
unemployment rate is a scenario parameter.
In what follows, we briefly discuss the technical implementation, the data used, and then
proceed describing the overall structure of the model, and the modules it is composed of.
The technical implementation
From a technical point of view, the main novelty of LABORsim is the choice of an agent-
based object-oriented framework. Object-oriented programming allows to define almost stand-alone
software objects to represent individuals, institutions, etc., each with own variables and methods.

5 Mitton et al.[(2000] witnesses this renewed interest.
6 For a review of the microsimulation literature, see Zaidi and Rake [2002], O’Donoghue [2001] and Anderson [1997,
ch.2]
7 which has nothing to do with the original DYNASIM model cited above

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This increases the modularity of the microsimulation, and the transparency of the code. Agent-
based models share a common architecture, and often an implementation based on specifically-built
software platforms. 8 The use of such platforms brings four main advantages: (i) standardization
(many technical issues are solved using the software routines); (ii) efficiency (these routines have
been designed by professional programmers, rather than by economists that had to learn
programming later on in their careers); (iii) brevity (there is less code to write and the code is easier
to interpret; hence easier to debug and exchange); (iv) availability of external tools and resources
(like graphical interfaces, statistical libraries, etc.).
LABORsim is written in Java, and object-oriented programming language, using JAS (Java
Agent-based Simulation Library)9, an open source platform for discrete-event agent-based
modelling. JAS provides a number of libraries for the management of time, the collection of on-line
statistics, on-line graphical widgets and database storage capabilities. Adopting Swarm original
philosophy [Luna and Stefansson, 2000], in addition to providing objects and utilities that make
code writing easier and faster, JAS provides templates for the construction of models. The
description formalism and the specific semantic used follow indeed the same Swarm protocol,
characterized by a clear-cut distinction between the model (a class that provides the environment
and executes the simulation) and the observer (a set of routines to look into the model and collect
statistics, which are then displayed through a graphical user interface).
Two databases are used to support the simulation, accessed through the standard JDBC-ODBC
driver. One contains all technical data, i.e. the initial population, the demographic projections and
the estimated parameters, which are not supposed to be modified by the user unless when an update
is required. The other one (the scenario database) contains the console to manage all scenario
parameters. The console is organized in different forms:
• a demography form, to choose which demographic projections are to be used, both for natives
and for immigrants;
• a participation-to-education form, to set the cohort after which the estimated trend towards
higher participation to the schooling system is supposed to halt;
• a probability-of-graduation form, where the estimated probabilities for increasing one’s own
educational attainment can be varied, for instance in order to mimic the effects of a reform of
the schooling system;
• a participation-to-labor market form, to manage the expected trend towards higher participation
rates;
• an unemployment differentials form, for changing the estimated coefficients for the
unemployment differentials among different groups of the population; this again can be used to
mimic the effects of specific policies (e.g. aimed at reducing the negative effect on
employability of previous unemployment spells);
• three forms for modifying the parameters governing pension eligibility requirements, one for
each scheme (defined benefit, defined contribution and mixed scheme); the default values being
those currently considered by the legislation;
• three forms for setting the values of the parameters affecting the probability of retirement, given
eligibility (one for each scheme).

The output of the simulation consists of online graphics of the most relevant statistics
disaggregated by gender and area. Moreover, whenever specified by the user all personal variables
of all artificial individuals are recorded in each simulated year, and stored in a panel data structure
in the same scenario database. LABORsim also supports a multi-run feature, to allow automatic
multiple simulation runs with the same values of the parameters. The results of all simulation runs
are stored with a specific run id in the same scenario database.

8 For a review of the literature on agent-based modelling in economics, see Tesfatsion [2001].
9 JAS is available from http://jaslibrary.sourceforge.net. See Sonnessa [2004, 2006] for details.

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On a standard Mobile Intel Pentium 4 laptop with 1.8 GHz CPU and 360 MB RAM,
simulating an initial population of 50,000 individuals up to 2050 takes 3.5 minutes with the online
statistics only, and about 1 hour if the panel data creation option is specified (the resulting panel
data has more than 2 million records).
The data
All behavioral parameters have been estimated on waves 1993-2003 of the Rilevazione Trimestrale
delle Forze Lavoro (RTFL), the Quarterly Labour Force Survey delivered by the Italian Central
Statistical Office (Istat).
In particular, the initial population has been derived from the April 2003 RTFL wave,
resampling it in order to have a database of about 50,000 individuals all with constant inflating
factor. From this, at each run of the simulation a sample of variable size is extracted10.
Unfortunately, RTFL data do not include information on seniority, which is relevant for
pension eligibility. We recovered this variable with a two step process. First, we imputed the age
when individuals started their work career by means of a standard regression imputation, using the
European Community Household Panel as a donor (Echp, various waves). The variables common to
the two datasets on which age at first job was estimated are age, education, area of residence, sector
of activity, marital status and family dimension11. The difference between age and age at first job
gives a sort of potential seniority.
In a second step, seniority was finally imputed assuming (i) an uninterrupted employment
spell for those still at work, and (ii) a continuing spell of participation between start of first job and
end of last job, which we have then discounted it by average yearly unemployment rates,
conditional on individual characteristics. It should be noted that (i) implies some degree of
overestimation, while (ii) leads to some degree of underestimation, since conditioning on being
unemployed now the likelihood of past unemployment is probably higher than the average,
unconditioned unemployment rate. The two errors thus (partly) elicit each other. Moreover, this
procedure allows to recover a sufficient amount of variability in individual seniority, including a
fair amount of uninterrupted (full seniority) careers.
The model structure
The simulation is made up of four modules: Demography, Education, Retirement and Employment
(see Figure 5)12. The Demography module takes care of population ageing, determining the number
and characteristics of newborn individuals and of individuals that leave the population, either
because they migrate out of Italy or because they die. As regards individuals aged 14 and below and
those aged 65 and over no other steps are needed, since they are out of the labour force.
For those who are in the labour force age, we distinguish between three moments in their
lifetime: youth (15-30 years), prime age (31-54 years) and old age (55-64). Young individuals
decide whether to attend formal education, and – given enrolment – the event whether they get a
degree is determined. This is accomplished by the Education module. The next module is concerned
only with the elderly, and regards retirement choices (Retirement module). First, eligibility is
checked. Eligible individuals then decide whether they want to retire or not.
Young people after 15, prime age individuals and elderly people who are not eligible enter the
Employment module (thus, we explicitly model the case of working students). The first decision is
whether to participate to the labour market. Conditional on participation, then, their employment
status is determined. As regards eligible people who do not retire, we assume that they are active
and employed.

10 Actual simulation runs are generally based on 5,000 to 50,000 artificial individuals.
11 Pure regression imputation tends to underestimates the variance. Hence, as is standard, we added a noise term to the
predicted values in order to avoid this problem.
12 For more details on the data, model specification and estimation, and scenario parameters, see Leombruni and
Richiardi [2005].

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Figure 5. The four modules of the simulation.

employment
modulemodule
education
modulemodule
retirement
modulemodule
at school? at school?
get degree? get degree?
YY
demographic
module module
ageing, birth, death, migrationsageing, birth, death, migrations
age?age?
active?active?
employed?employed?
eligible?eligible?
retire?retire?
NN
NN
YY
YY
YY
employedemployed unemployed unemployedout of the labour force out of the labour force
YY
NN
NN
NN
15-3015-30
31-5431-54
55-64 55-64
Y/N Y/N
employment
education retirement
demographic


The Demographic module
The demographic evolution of the simulated population is aligned with Istat projections by sex, age
and geographic location. However, official forecasts only consider three scenarios – namely a low,
central and high scenario, where all relevant variables (fertility and mortality rates, plus migrations)
are changed. In order to allow for a richer scenario analysis, we extrapolated from the official
projections the separate evolution of natives and immigrants under the three hypotheses. We can
thus run our experiments under nine different demographic scenarios. Our spatial analysis focuses
on three macro-regions: North, Centre and South of Italy. New cohorts aged 0 identify newborn
individuals. The consistency of the simulated population of natives in any sex, age and geographic
class is aligned with the projections of the selected scenario by randomly removing or cloning
individuals. This is equivalent to the assumption that people who move are ex-ante similar to those
who do not move but ex-post similar to the people in the destination area13, and allows neglecting
the problem of internal migration. Since official statistics only provide projections on the flow of
migrants form abroad, the same approach cannot be used for immigration from abroad. The flow of
migrants is then evolved according to the mortality rates used by Istat. Immigrants are supposed to
have the same average level of education as in the corresponding sex, age and geographical cell,
and – given the level of education – to have the same probability to still attend school. We suppose
that all immigrants older than 15 who are not students enter in the simulation as employed, and with
a seniority of zero. Of course these assumptions reduce the heterogeneity of their career paths.
However, immigration in Italy is not as massive a phenomenon as in other Oecd countries: Istat
central demographic projections assume a constant flow of 156,000 new immigrants each year,

13 The alternative assumption that migrants keep their pre-migration behaviour is equally arbitrary. Note that there are
extremely little data concerning the behaviour of individuals before and after migration.

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joining a population of about 57 millions in the base year. Hence, the reduction in overall
heterogeneity turns out to be negligible.

The Education module
This module is formed by two sub-modules, one for determining the status of student and the other
for determining whether the individual gets his/her degree. We consider three level of education:
basic, diploma and university degree. The first level is compulsory, and we model it as driven only
by age. As Figure 6 shows, we detect an increasing trend towards higher participation rates, both for
high school and for university. We model this trend linearly, and estimate the probability of being a
student conditional on sex, age, geographical location, lagged status as student and cohort (Table 1
and 2). The reference group is composed by non-student males living in the South.

Figure 6. Cohort-effect in schooling participation rates, high school (left panel) and university (right panel).
Coefficients of year-of-birth dummies in a logit regression reported.

-1.6
-1.4
-1.2
-1
-0.8
-0.6
-0.4
-0.2
0
1970197219741976197819801982198419861988

-1.4
-1.2
-1
-0.8
-0.6
-0.4
-0.2
1961196619711976 19811986

Source: Our elaboration on Istat data, RTFL, 1993-2003

Table 1: Logit estimates for high school participation. Data: Istat, Rtfl, 1993-2003
Number of obs. 106,022
Parameter Estimate Error Chi-Square Pr > ChiSq
woman 0.2950 0.0297 98.9079 <.0001
north -0.1575 0.0323 23.7897 <.0001
center 0.2466 0.0428 33.1466 <.0001
lagged student 5.7608 0.0330 30411.2442 <.0001
age 3.1394 0.2329 181.6358 <.0001
age squared -0.0919 0.00637 207.9202 <.0001
cohort 0.1137 0.00480 562.3682 <.0001
_const -254.6 9.8287 671.1519 <.0001

Table 2: Logit estimates for university participation. Data: Istat, Rtfl, 1993-2003
Number of obs. 167,564
Parameter Estimate Error Chi-Square Pr > ChiSq
woman 0.2152 0.0198 118.4854 <.0001
north -0.4721 0.0223 448.2335 <.0001
center -0.1580 0.0272 33.7918 <.0001
lagged student 5.6032 0.0252 49629.8288 <.0001
age 1.8367 0.0475 1495.9943 <.0001
age squared -0.0373 0.000995 1409.3989 <.0001
cohort 0.0336 0.00317 112.5205 <.0001
_const -92.0507 6.3470 210.3386 <.0001


In the scenarios consolle we allow the user to choose a cohort after which the linear trend
towards increasing participation comes to a stop.

Page 12

The probability of graduating is also estimated on Rtfl data, in the period 1998-2003,
conditioning only on age. Figure 7 shows the estimated probability and the approximation
implemented in Labor Sim, which maintains the overall probability of getting the degree, over the
age bracket considered, equal to the empirical one. We assume that everybody leave high school
before 22, and that no one attends university after 30, an assumption not too far from what we
observe in the data (only 1.2% of people aged 22 are still enrolled at high school, while only 2.9%
of people aged 31 are still attending university; both figures decrease for later ages).

Figure 7. Probability of graduating, high school (left panel) and university (right panel).

0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
15
16
17
18
19
20
21
22
estimated
simulated
age
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
21232527
29
31
estimated
simulated
age
Source: Our elaboration on Istat data, RTFL, 1993-2003

Thus, educational paths are completely modeled. Of any individual below 30 we know whether s/he
is still in education, and what degree s/he has. We may have students who work, students that drop
out before completing the degree, students that had dropped out and return to education.

The Retirement module
This module is based on the distinction between eligibility and retirement choice. Given the Italian
legislation, time, age and simulated seniority, eligibility is deterministically determined. In short,
the legislation identifies three different schemes, according to seniority in 1995: the old defined
benefit, pay-as-you-go scheme for workers who had more than 18 years of seniority in 1995, the
new defined contributions, funded scheme for workers who started working after 1995, and a mixed
scheme for workers who had less than 18 years of seniority in 1995. The detailed eligibility criteria
up to the Maroni reform of August 2004 for these three schemes have been implemented. 14
On the contrary, the choice of postponing retirement can be affected by a number of issues. In
the past, with an extremely generous welfare scheme for pension holders, many workers decided to
retire as early as possible. This has in particular been true during the reform period, as workers were
afraid to postpone retirement fearing that the rules could change against them. With the purpose of
improving the balance of the system some proposals have been recently discussed, aiming at
creating incentives to workers for postpone retirements. In order to allow the creation of flexible
scenarios, and given that there are only poor data on which to estimate the retirement propensity, we
have modelled the retirement choice in an entirely parametric way: the user must specify the
probability of retiring as early as possible (i.e. as soon as the individual becomes eligible) and the
probability of postponing retirement until the age of 65. For These two parameters are sex,
education and scheme-specific. For each parameter, two values must be imputed: one for the base
year, and one for the final year of the simulation. In each simulated year a linear interpolation of
these two extremes is then used. The residual probability of retiring is distributed in the interval

14 For a detailed account of the Maroni reform, together with its evaluation within the LABORsim framework, see
Leombruni and Richiardi [2006]

Page 13

between age of eligibility and 65 according to a simple algorithm specifying that, given a cohort of
individuals becoming eligible at the same age, the flow of retirement must remain constant.15 16
Employment module
The employment status of individuals is simulated in two steps: First, their participation to the
labour market is decided; conditional on participating, then, the employment/unemployment status
is simulated.
As already mentioned, labour market participation choices seem to entail a relevant cohort-
effect, especially for women. We modelled participation as a function of lagged participation, a
polynomial of age, year of birth, the status of student and the educational level, by conditioning on
not being retired. The choice of a linear specification of the cohort-effect has been made after a
preliminary analysis of the coefficients of specific year-of-birth dummies17.

Figure 8. Cohort-effect in labour market participation rates, males (left panel) and females (right panel).
Coefficients of year-of-birth dummies in a logit regression reported.

Men
-1
-0.5
0
0.5
1
1.5
2
2.5
1930193319361939194219451948195119541957
cohort
1960196319661969197219751978198119841987
coeff.
North CenterSouth
Women
-1
-0.5
0
0.5
1
1.5
2
19301933193619391942194519481951195419571960196319661969197219751978198119841987
cohort
coeff.
North CenterSouth
Source: Our elaboration on Istat, RTFL, 1993-2003

Not factoring in life-cycle factors such as family formation processes could weaken the ability
of the model to capture sufficient heterogeneity in employment paths. However conditioning
participation rates on lagged activity status, in addition to other individual characteristics, brings
enough heterogeneity for the purpose of the model (forecasting aggregate participation rates, rather
than obtaining realistic individual career paths).
We estimated this model separately by sex and geographical area, and found significant
cohort-effects in most subgroups (see Figure 8). The only exception is for the South of Italy, where
the cohort-effect is less significant, and reversed. For the sake of brevity we omit to report all six
sets of regression results. However, in order to give an intuition of the estimated dynamics we
report the results of two additional logit regressions for the participation probability, for males and
females, pooling together the three macro-areas (Table 3 and Table 4 below). The reference group
is composed by non-students, low educated previously inactive individuals.

Table 3: Logit estimates for male labour market participation. Data: Istat, Rtfl, 1993-2003
Logit estimates Number of obs = 637423

15 A more detailed description of the algorithm can be found in Leombruni and Richiardi [2005].
16 Mazzaferro and Marciano [2005] adopt a similar approach and model retirement decisions parametrically. They
suppose that individuals retire immediately if they become eligible due to the age requirement, while they have a
retirement propensity of .5 in each year if they become eligible due to the seniority requirement. In Vagliasindi et al.
[2004] individuals choose the timing of retirement by looking at the differential benefit of postponing retirement to the
following year. The empirical evidence on retirement choices has been investigated by Spataro [2005], Brugiavini and
Peracchi [2004, 2003], Marano and Sestito [2004], , Colombino et al. [2002], Colombino [2000] and Miniaci [1998].
17 Note that the interpretation of the dummy coefficients in Figure 8 as full cohort-effect is not straightforward, since we
included the lagged participation status (which is correlated with the cohort-effect) as an explanatory variable.

Page 14

Wald chi2(8) = 108843.36
Prob > chi2 = 0.0000
Log pseudo-likelihood = -123089.77 Pseudo R2 = 0.5846

------------------------------------------------------------------------------
| Robust
part. | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lagged part. | 2.968351 .0138102 214.94 0.000 2.941284 2.995419
age | .1215354 .0132734 9.16 0.000 .09552 .1475507
age squared | .0006595 .0003399 1.94 0.052 -6.58e-06 .0013257
age cubed | -.0000314 2.76e-06 -11.39 0.000 -.0000368 -.000026
student | -3.165786 .0284844 -111.14 0.000 -3.221615 -3.109958
diploma | .5887948 .0147846 39.82 0.000 .5598176 .617772
univ. degree | .9996766 .0280698 35.61 0.000 .9446608 1.054692
cohort | .0040348 .0018856 2.14 0.032 .0003392 .0077305
_const | -11.34523 3.765515 -3.01 0.003 -18.7255 -3.96495
------------------------------------------------------------------------------

Table 4: Logit estimates for female labour market participation. Data: Istat, Rtfl, 1993-2003
Logit estimates Number of obs = 684238
Wald chi2(8) = 230110.25
Prob > chi2 = 0.0000
Log pseudo-likelihood = -184363.12 Pseudo R2 = 0.6111

------------------------------------------------------------------------------
| Robust
part. | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lagged part. | 4.354981 .0098822 440.69 0.000 4.335612 4.37435
age | -.2488898 .0134446 -18.51 0.000 -.2752407 -.2225389
age squared | .0071877 .0003326 21.61 0.000 .0065358 .0078395
age cubed | -.0000665 2.64e-06 -25.14 0.000 -.0000717 -.0000613
student | -2.109739 .0324601 -64.99 0.000 -2.173359 -2.046118
diploma | .8268708 .0111389 74.23 0.000 .8050389 .8487026
univ. degree | 1.586245 .0240933 65.84 0.000 1.539023 1.633467
cohort | .0139369 .0015227 9.15 0.000 .0109525 .0169213
_const | -26.74826 3.044314 -8.79 0.000 -32.71501 -20.78152
------------------------------------------------------------------------------

In the scenarios consolle we let the user specify the cohort after which the trend comes to a
stop. However, since it is hard to believe that the weak but negative cohort-effect in the South will
continue in the future, we removed the year-of-birth variable in the related regressions (both for
males and females). We also added two additional convergence effects, one by gender and the other
by area, that by default are set to zero. Setting them to a positive value one can choose to let
participation rates in the South of Italy become closer to participation rates in the North,
participation rates for women become closer to participation rates for men, or both. The user has to
specify the fraction of the gap that has to be filled in each year of the simulation. This option
becomes valuable for scenario analysis and policy evaluation, since it can be used to mimic the
effects of specific policies aimed at increasing participation rates for subgroups of the population
where they are particularly low.
Finally, we come to the employment status. As already mentioned, the microsimulation model
does not model the demand side of the economy. Consequently, the employment module simply
aims at reproducing the observed heterogeneity in unemployment rates across subgroups. The
average unemployment rate in each simulated year is an exogenous parameter to be set by the user.
To estimate the unemployment differentials we modelled the probability of becoming unemployed
as a function of lagged unemployment, sex, age class (below 20, 20-50, over 50), educational level,
geographical area and the average overall unemployment rate with a logit regression. We thus
replaced the usual value of 1 for the constant with the average overall unemployment rate. The
reference group is composed by prime age employed men living in the North with high school
diploma.

Page 15

Table 5: Logit estimates for unemployment. Data: Istat, Rtfl, 1993-2003
Number of obs. 860,172

Parameter Estimate Error Chi-Square Pr > ChiSq
unempl. rate -36.4554 0.1095 110834.681 <.0001
female 0.7660 0.00872 7718.4177 <.0001
low education 0.1398 0.00912 235.2739 <.0001
univ. degree -0.5715 0.0176 1053.8429 <.0001
center 0.2625 0.0125 444.2999 <.0001
south 1.0572 0.00962 12083.8515 <.0001
lagged unempl. 3.0731 0.00924 110652.437 <.0001
young 0.9247 0.00904 10466.5191 <.0001
old -0.5818 0.0159 1331.7270 <.0001

4. Results
The standard scenario
Our standard scenario 18 exactly replicates Istat central demographic projections up to 2050, i.e. we
choose the central forecast both for natives and immigrants. We assume conservatively that the
linear trend towards increasing participation to education ends for individuals entering high school
and university in the base year, i.e. respectively born after 1990 and 1985. Analogously, we assume
that the trend towards increasing labour market participation stops for individuals born after 1980.
The additional convergence effects by gender and area are set to zero. The average unemployment
rate is set to a constant value of 8% of the work force for all simulated years. We assume that the
probability of postponing retirement, given eligibility:
(i) depends on the pension scheme, the defined benefit scheme being not actuarially fair and
thus providing stronger incentives for early retirement 19;
(ii) depends on education, higher education being generally associated with higher utility
from work;
(iii) does not depend on gender, given that eligibility criteria for men are already stricter than
those for women.


Table 6. Standard scenario parameters.


Education
Basic High-school University
Defined Benefits & Mixed
% early retirement
% late retirement
0.7
0
0.6
0.1
0.5
0.2
Defined Contributions
% early retirement
% late retirement
.4
.2
.3
.3
.2
.4


The Maroni reform also allows women in the defined benefit and mixed scheme to retire
earlier by switching to the defined contribution scheme, that is by accepting a lower pension. We
suppose that only a few women will actually choose this opportunity, and that the majority of
female workers (60%) will wait for the other eligibility criteria to be met.

18 For a detailed account of the results of the standard scenario, see Leombruni and Richiardi [2005]
19 the Maroni reform (august 2004) tried to counterbalance this bias by introducing monetary incentives in order to
induce workers to postpone retirement.