Life course analysis: two (complementary) cultures? Reflections on how to analyze the transition to adulthood Francesco C. Billari Institute of Quantitative

Life course analysis: two (complementary) cultures? Reflections on how to analyze the transition to adulthood

Francesco C. BillariInstitute of Quantitative Methods

Bocconi University and

IGIER

Structure

Life course analysisTwo cultures? The debate in

statisticsThe event-based, or “causality”

cultureThe algorithmic, or “holistic” cultureConclusions and perspectives

Life course analysis

The life course approach as an interdisciplinary program of study has been under development since the mid-1970s

The idea of studying the unfolding of individual lives has unavoidably brought life course scholars to emphasize complexity rather than simplicity

The four chief elements shaping life courses (Giele & Elder, 1998)


The study of the transition to adulthood has been a primary field for life course scholars, and it has greatly benefited from advances in the life course approach and in life course analysis

For the Encyclopedia of Population I defined life course analysis as the statistical analysis of data on the timing of events (when do events happen?), their sequencing (in which order do events happen?), and their quantum (how many events happen?)


Ideally, life course analysis includes the possibility to analyze the timing, sequencing, and quantum of events as depending on the elements mentioned by Giele and Elder: individual-level human development, social relations, location in time and place.

I shall argue that there are two main approaches to life course analysis, serving complementary aims


The “event-based” approach focuses on events (mostly their timing and quantum) as explananda, and looks for causality event-history analysis program evaluation

The “holistic” approach focuses on (parts) of the life course as a whole sequence analysis

Two “cultures”? The debate in statistics

The two approaches are somehow related to the idea of two “culture” in statistics, which has “caused” heated debate among statisticians

Breiman (2001) on Statistical Science, with several discussants


The data modeling culture, which is mainstream in statistics, assumes that “data are generated by a given stochastic data model” (discussion by David Cox)

The algorithmic modeling culture that treats the data generation mechanism as unknown (paper by L. Breiman)


Breiman: the focus on data models has “led to

irrelevant theory and questionable scientific conclusions”, “kept statisticians from using more suitable algorithmic models”, and “prevented statisticians from working on exciting new problems”


Breiman: the algorithmic culture points to: Rashomon or “the multiplicity of good

models” conflict between simplicity and

predictive accuracy (Occam’s razor) dimensionality problem


Cox the starting point is not “data” but “an

issue, a question or a scientific hypothesis” and real scientific applications are targeted at unraveling causal links using statistics

also data collection designs count model-based statistical techniques provide

the best opportunity to illuminate causality

The event-based or “causality” culture

In life course analysis, a series of techniques has been developed to illuminate the determinants (if possible, the causes) of the timing and quantum of events event-history analysis program evaluation

Event-history analysis

Event history analysis generalizes life-table and standardization techniques that have been extensively used in twentieth-century demography, it usually aims at modeling individual-

level data collected from sample surveys or population register

focuses on the time-to-event as the dependent variable


The regression models of event history analysis have contributed to the explanation of life course dynamics by linking time-to-event with explanatory variables (covariates)

Time-varying covariates (events) provide a key link to causes (what triggers what)


The so-called “causal approach” (Blossfeld & Rohwer, 1995, 2002) assumes that all factors that are relevant to the simultaneous analysis of several trajectories are observed and included in the past history of the trajectories

I.e. if one looks at whether pregnancy causes marriage among cohabitants, pregnancy is a time-varying covariate in the hazard equation of time-to-marriage for cohabitants


Problem: selectivity (or unobserved heterogeneity)… cohabitants who are more “family-oriented” anticipate pregnancy and marriage. There is spurious dependence

Proposal (Lillard, 1993): use of simultaneous hazard equations for interdependent processes with potentially common determinants

Simultaneous hazard models (Brien et al., 1999)


Other developments: multilevel event-history models, which allow to grasp other parts of Giele & Elder’s frame by controlling for unobserved aggregate-level factors

Example: maps of age at first sexual intercourse in Italy (Billari & Borgoni, 2002)… not causal

Log-odds of age at first intercourse (women, Italy)

Program evaluation

The main task of program evaluation is to estimate the causal impact of a certain program (usually, a labor market program), the treatment, on a specific outcome. This estimate is used as a support for policy decisions

Program evaluation

Two key issues: to illuminate policies, one wants to isolate

the causal impact of a certain program from other factors that link the program with the outcome

the impact has to be estimated with the least bias, because for cost-benefits evaluations the size of the impact, not only its direction or statistical significance, matters

Program evaluation

For the transition to adulthood we may be interested: in evaluating the causal impact of events

(i.e. timing or sequencing) in the transition to adulthood on the subsequent pathways to adulthoodDoes teenage childbearing influence

subsequent educational or labor outcomes during early adulthood? (i.e. Hotz et al., 1997)

Program evaluation

For the transition to adulthood we may be interested: in evaluating the causal impact of events

involving relevant others, in particular youths’ parents, on the transition to adulthooddoes parental divorce have a causal impact

on educational outcomes or family choices in the transition to adulthood? (i.e. Painter and Levine, 2001)

Program evaluation

For the transition to adulthood we may be interested: in studying the causal impact of

pathways to adulthood on relevant others does the leaving home of a child, and in

particular the transition to an “empty nest” have a causal impact on parental outcomes, i.e. happiness? (Mazzuco, 2003)

Program evaluation

The basic “evaluation problem” or the “fundamental problem of causal inference” (Holland, 1986) is that to truly know the effect of a certain event (i.e. the participation into a program), we must compare the observed outcome of an individual who has experienced the event of interest with the outcome that would have resulted had that person not experienced the event (counterfactual)

Program evaluation

We want to estimate treatment effects: average effect of treatment: what impact

would the event have on a randomly drawn individual (if the event becomes compulsory);

average effect of treatment on the treated: what impact has the event had on individuals who have experienced the event (impact of the choice, more relevant to us, see Hotz et al. 1997)

Program evaluation

Three main approaches: data on highly related individuals: twin studies

(rare, very good on physiology); instrumental variables approach. Estimation

based on IV (correlated with explanatory variables but not with outcomes)—difficult to find in life course. Most promising approach estimates bounds (Manski bounds)

propensity score matching: matching of treated and untreated individuals according to observed covariates summarized in a “propensity score”. Removes bias due to observed factors

Program evaluation

Propensity score matching. Two steps: “parametric step” : the “propensity score” is

estimated from a set of (possibly abundant) covariates that are supposed to affect the probability that the event of interest (treatment) is experienced and may also influence the outcome

i.e. one can use a probit or logit model with the probability of experiencing a parental divorce as a function of a set of youth and family characteristics

Program evaluation

Propensity score matching. Two steps: “nonparametric step” : individuals who

experienced the event of interest (treated) are matched to individuals who have not experienced the event of interest (untreated), according to the propensity scores estimated in the first step. Different approaches to matching

Program evaluation

Example of PSM with difference-in-differences estimator (controlling for time-constant unobserved factors): Mazzucco (2003) comparison of changes in parental well-being caused by the last child leaving home in France (“early” home leaving) and Italy (“late” home leaving). ECHP data

Program evaluation

France Italy Mothers Fathers Mothers Fathers Satisfaction +0.407** 0.066 -0.568** -0.023 Self-Rated Health

-0.005 0.059 -0.149** -0.013

Note: ** indicates statistically significant differences with p-values <0.005. Satisfaction is measured with a sum of satisfaction items ranging from 4 to 24, self-rated health ranges from 1 to 5.

The holistic or “algorithmic” culture

By focusing mainly on specific events, with what Elder has called the “short-view in analytical scope” researchers may not grasp a unitary, holistic, perspective on life courses


Two reasons to complement event-based analysis with holistic analysis (Billari, 2001 … Canadian Studies in Population ): strong: life courses are seen as subject

to accurate inter-temporal planning, for instance as an outcome of utility maximization


Two reasons to complement event-based analysis with holistic analysis pragmatic: the life course as a

conceptual unit is thought of as being a contingent results of subsequent events. A holistic view is still useful as an “algorithmic” way to describe and to summarize the timing, sequencing, and quantum of life course events

Sequence analysis

In the 1990s Abbott introduced sequence analysis in the social sciences. Origins in information science and computational biology (DNA)

Life courses are represented in terms of sequences of states (time is intrinsically discrete)

Sequence analysis

As a simple example, we shall consider three states: single (S), cohabiting (C), married (M), in a monthly time scale from 20 years to 24 years and 12 months. The sequence representation of an individual life course may thus be:SSSSSSCCCCCCCSSSSSSSSSSSSSSSSSSSSSSCCCS-SSSSSSSSSSMMMMMMMMM

Sequence analysis

Having several sequences (like from a sample survey), it is already difficult to describe them

I.e. using colors (like in genomics…). Sequences of school&work for young men in Monterey, Mexico (Solis & Billari, 2003)

Cluster Occupational Trajectory

Age> 14

9

10

11

12

15

5

6

7

8

1

2

3

4

16 17 18 19 20 21 22 23 24 2925 26 27 28

N. Not yet in labor forceI. Managers & ProfessionalsII. Skilled White Collar WorkersIII. Clerical Workers & Sales AgentsIV. Sales Employees & Control WorkersV. Skilled Manual WorkersVI. Unskilled Manual WorkersVII. Unskilled Service WorkersVIII. Farm WorkersO. Temporarily out of work

Sequence analysis

But description is not at all easy (… working on it)

So let us think “algorithmically”. Clustering and classifying is typical algorithmic thinking

OMA (Optimal Matching Analysis) is a method for the alignment of biosequences, which gives a similarity measure for each pair of sequences

Sequence analysis

OMA operates by transforming a sequence into another one by using three elementary operations: insertion of a state deletion of a state substitution of a state

Each operation has a “cost”. The distance between two sequences is the total cost of transforming a life into another one...

Sequence analysis

E.g. if insertion and deletion have cost 1 and substitution 2, the distance between

SSCCMMMand

SCCMMis 2 (SSCCMMM-->SCCMMM --> SCCMM)

Sequence analysis

The resulting matrix of distance can be directly describe (e.g. average distance, Billari 2001a) or used as input for further multivariate analyses, mostly clustering

Advantages: works with almost every kind of

sequences of states

Sequence analysis

Disadvantages: subjective cost specification (especially

in demography) difficult to identify the determinants

of group formation “subjectivity” of clustering techniques

Sequence analysis

Other approaches: for binary sequences (characterized by

binary states): use of monothetic divisive algorithms (Billari & Piccarreta, 2001)

for sequences coded differently, in classification: machine learning (Billari, Fuernkranz, Prskawetz, 2001)

multiple correspondence analysis of sequence data

Conclusions and perspectives

The weight of the different cultures and the impact they may have on life course research is also connected to the availability of easy-to-use software packages

In general, this requires _real_ flexibility...

Software

For event-history analysis TDA (Rohwer&Poetter, freeware) but

no simultaneous hazard STATA (commercial) but no simultaneous

hazard; other general packages are less specialized (SAS, SPSS)

aML (commercial) specialized for simultaneous equations (normality assumption) available since 2000

Software

For program evaluation Propensity Score Matching. A set of

STATA programs written by Becker and Ichino is freely available

Software

For sequence analysis TDA performs some description and

computes OMA distances distances can be then transferred to

general packages for cluster analysis (we did it with SAS or STATA)

Data

Shortly: we need more and more longitudinal data, especially data measuring factors related to selection (ability, personality, socialization and orientations) at the beginning


Although the “event-based” culture is closer to social- and economic-science theory, the “holistic” culture is useful to try to simplify complexity

Let us do both…


Thanks for your patience

Documents

Life course analysis: two (complementary) cultures? Reflections on how to analyze the transition to adulthood Francesco C. Billari Institute of Quantitative