A Sequence-Analysis Approach to the Study of the Transition to Adulthood in Low- and Middle-Income Countries

(2021) - Luca M Pesando, Nicola Barban, Maria Sironi, Frank F. Furstenberg

Journal:
Link::
DOI:: 10.1111/padr.12425
Links::
Tags:: #paper #Methods #OptimalMatchingAnalysis #Transition #school-to-work
Cite Key:: [@pesandoSequenceAnalysisApproachStudy2021]

Abstract

This study investigates whether young people in low- and middle-income countries (LMICs) have experienced processes of destandardization of the life course similar to those observed in high-income societies. We provide two contributions to the relevant literature. First, we use data from 263 Demographic and Health Surveys (DHS) across 69 LMICs, offering the richest comparative account to date of women’s transition to adulthood (TTA) patterns in the developing world. In so doing, we adopt sequence analysis and shift the focus from individual life-course events—namely first sexual intercourse, first union, and first birth—to a visually appealing approach that allows us to describe interrelations among events. By focusing on the analysis of trajectories rather than the occurrence of single events, the study provides an in-depth focus on the timing of events, time intervals between events, and how experiencing (or not) one event might have consequences for subsequent markers in the TTA in cross-national comparative perspective. Second, we identify clusters of TTA and explore their changes across cohorts by region and household location of residence (rural vs. urban). We document significant differences by macro-regions, yet relative stability across cohorts. We interpret the latter as suggestive of cultural specificities that make the TTA resistant to change and slow to converge across regions, if converging at all. Also, we find that much of the difference across cluster typologies ensues from variation related to when the transition begins (early vs. late), rather than from the duration between events, which tends to be uniformly quick across three out of four clusters

Notes

“By focusing on the analysis of trajectories rather than the occurrence of single events, the study provides an in-depth focus on the timing of events, time intervals between events, and how experiencing (or not) one event might have consequences for subsequent markers in the TTA in cross-national comparative perspective.” (Pesando et al., 2021, p. 719)

“we find that much of the difference across cluster typologies ensues from variation related to when the transition begins (early vs. late), rather than from the duration between events, which tends to be uniformly quick across three out of four clusters” (Pesando et al., 2021, p. 719)

“Over the past century, there have been significant changes in the prevalence, timing, and complexity of transitions to adulthood (TTA) in the United States and Europe.” (Pesando et al., 2021, p. 720)

“Conceptually, our choice of sequence analysis rests on the idea that increases in women’s mean or median ages at key life-course events (e.g., median age at first marriage) are by now well-documented in LMICs (see, for instance, Bongaarts et al. 2017)” (Pesando et al., 2021, p. 722)

“Sequence analysis is associated with a family of algorithms used to quantify dissimilarities between life-course trajectories. Optimal Matching” (Pesando et al., 2021, p. 726)

“algorithm (OM) is the most known technique that has been applied to social science.3 OM expresses distances between sequences in terms of the minimal amount of “effort,” measured in terms of edit operations (insertion, deletion, and substitution), that is required to change two sequences such that they become identical. Sequence analysis algorithms identify differences in trajectories due to changes in timing (when events happen), quantum (what and how many transitions), and ordering (in what order) of lifecourse events (Billari and Piccarreta 2005; Billari, Fürnkranz, and Prskawetz 2006)” (Pesando et al., 2021, p. 727)

“Upon a deeper interpretation, however, our results also suggest that these cluster typologies share important commonalities. For instance, in three out of the four clusters, transitions are very quick for most groups (hence the adjective “rapid” in the labels), suggesting that the observed heterogeneity very much owes to when the transition begins (early vs. late) rather than to the speed of events or duration between events” (Pesando et al., 2021, p. 741)