LabFam Individual Biographies harmonised family and employment histories based on panel surveys

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The LabFam Individual Biographies (LIB) project addresses this challenge by providing open, cross-national harmonization of life-course histories from five long-running panels: Australia (HILDA), Germany (SOEP), Switzerland (SHP), the United Kingdom (BHPS/UKHLS), and the United States (PSID). LIB reconstructs spell-based data across three domains—fertility (number and timing of births), partnership (timing of union formation/dissolution), and employment (employment spells and job characteristics)—by integrating panel questionnaires, calendar modules, and retrospective components to recover events between waves and before survey entry. Outputs are organized as dated spells with explicit starts and ends, enabling duration and transition analyses within and across countries. We document variable definitions, harmonization and conflict-resolution procedures, and validation against internal survey indicators and external benchmarks. Implemented in R and released as open code, LIB supports complete reproduction, user customization, and linkage to complementary infrastructures (e.g., CPF/CNEF), thereby reducing setup costs and improving comparability. Sociology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Background & Summary There is a growing concern about reproducibility in the social sciences, as researchers often struggle to obtain similar results using the same databases. This lack of reproducibility reduces the credibility of findings (Freese et al., 2022 ) and slows down the process of doing research, as academics often cannot rely on or build upon the work of others (Eubank, 2016 ). Huntington-Klein et al. ( 2021 ) demonstrated these issues in a field experiment in which fellow economists failed to replicate a study, sometimes obtaining opposite results or even being unable to recreate the sample. This state of affairs calls for action to remedy the situation. Several options are available, some of which rely on individual researchers, such as pre-registering the data analysis and depositing the statistical code in data repositories. Others rely on the provision of public goods to be used by the community, such as building research infrastructure. The LabFam Individual Biographies (LIB) is an initiative that belongs to the latter group. We harmonized individual-level longitudinal data for life-course analysis across five countries (panels): Australia (HILDA), Germany (SOEP), Switzerland (SHP), the United Kingdom (BHPS/UKHLS), and the United States (PSID). These surveys are among the longest-running longitudinal studies worldwide and therefore constitute a natural starting point. LIB reconstructs spell-based histories for individuals along three different life dimensions: (i) fertility, which records the number and timing of births; (ii) partnership, documenting the timing of union formation and union dissolution; and (iii) employment, which collects data on employment spells and characteristics of the jobs held. In this format, each spell represents a continuous episode with a defined start and end date, during which the status remains constant. The data are therefore structured as timelines of episodes rather than point-in-time observations at interview waves, enabling more precise measurement of durations and transitions over the life course. Our project is inspired by existing harmonization initiatives, particularly the Cross-National Equivalent File (CNEF) developed by Frick et al ( 2007 ), the Comparative Panel File (CPF) created by Turek et al. ( 2021 ), and the Harmonized Histories produced by Perelli-Harris et al. ( 2010 ). It addresses some of the shortcomings of the previous efforts, making it a valuable resource for researchers in the social sciences. LIB expands the histories provided by CNEF and CPF, both of which harmonized the same datasets but primarily relied on variables from the main survey. These variables pertain to the timing of the interview and predominantly focus on the respondent’s labor market or family statuses at that specific moment. While building on CPF, our approach goes further by incorporating 1) calendar data to bridge individual employment histories between main waves, and 2) retrospective components to construct histories prior to survey entry. Collectively, these data sources provide detailed information on the timing and order of transitions, whether it is its entry into parenthood, employment, or a new partnership. In its use of retrospective data, LIB resembles the Harmonized Histories in which partnership and fertility biographies are recovered from retrospective questions in the Gender and Generations Survey (GGS). However, our database has two advantages over the Harmonized Histories. First, by providing employment histories, in addition to fertility and partnership histories, it is broader in scope. GGS contains little information on individual labour market status from periods prior to the interview making the reconstruction of employment histories unfeasible. Second, LIB is not solely based on retrospective information, but largely makes use of long-lasting panel data, which reduces recall and survival biases. Unfortunately, these differences also imply that comparisons to the histories harmonized by Perelli-Harris et al. ( 2010 ) should be made with caution, given its shorter panel structure and limited pre-interview employment content. By focusing on transitions and covering information on fertility, partnership, and employment histories in detail, LIB can be used to answer questions that were left orphaned by previous harmonization efforts and allows for adopting a comparative perspective. Researchers can examine, for example, how mounting employment uncertainty over the life course affects fertility and partnership behaviours (union formation or disruption) or how retirement patterns change across generations. Likewise, LIB enables studies on employment transitions around important family-related events (e.g., childbirth or union disruption) or interdependencies between partners’ employment careers at different life course stages (union formation, entry to parenthood, subsequent childbirths, mid-life, and retirement). Many of these issues have been studied in the past, but largely for single countries (Drobnič, 2002 ; Kreyenfeld, 2010 ; Di Nallo & Koeksal, 2023, Osiewalska et al., 2024 ), as the costs of data preparation for multiple countries were often too high. By providing harmonized, spell-based timelines across panels, LIB lowers these barriers, facilitating cross-national, duration-sensitive analyses of family and employment trajectories. The LabFam Individual Biographies consist of two distinct elements. The first is the source data (original longitudinal datasets), which users must obtain independently from third-party data providers (see Section 2.1 ). The second is the open-source code written in the open-source programming language R (version 4.4.0), which allows for reconstructing LIB histories once access to the source data is granted (see Section 2.3 ). As our initiative builds upon the CPF, we also provide code that facilitates the merging of LIB with CPF data, thereby enabling straightforward enrichment with additional respondent and household characteristics. Our open-source code is modular and configurable: users can tailor outputs by country, period, or life-course domain. By distributing the full workflow, we not only facilitate reproducibility and reuse but also enhance transparency, as dependencies across variables become clearer, and all transformations are visible. 2.Methods In this section, we first describe the source data (Section 2.1 ). Next, we discuss briefly how the fertility, partnership, and employment histories were constructed, considering the peculiarities of each source dataset (Section 2.2 ). Finally, we describe the codebase used to produce each of the three individual biographies: fertility, partnership, and employment, and provide guidance for its use (Section 2.3 ). 2.1 Source Data The LIB harmonizes data from five different longitudinal databases: The Australian Household, Income and Labor Dynamics (HILDA), The German Socio-Economic Panel (SOEP), The Swiss Household Panel (SHP), the British Household Panel Survey (BHPS) / The UK Household Longitudinal Study (UKHLS), and the Panel Study of Income Dynamics (PSID). Their basic features, such as sample sizes, time span, and frequency, are listed in Table 1 . These databases need to be obtained independently from the original data providers by researchers who want to reconstruct the LIB, as LIB distributes code, not data. We use the most recent panel releases available to us under our data agreements (see Table 1 ); however, as access conditions vary across providers and users, newer releases may exist and be accessible to other researchers, as these longitudinal surveys are still ongoing. Taken together, the databases encompass a period spanning from year of 1968 to 2024. Table 1 Number of waves and respondents (LIB v1.0) Survey Country Frequency Sample Size(approx.) Time Span Version Panel Study of Income Dynamics (PSID) USA Annual (to 1997), Biennial (since 2003) 18,000 individuals 5,000 households 1968–2021 44 Socio-Economic Panel (SOEP) Germany Annual 30,000 individuals 20,000 households 1984–2021 40 British Household Panel Survey (BHPS) Understanding Society (UKHLS) UK Annual 13,000 individuals 7,000 households 1991–2023 BHPS 18, UKHLS 14 Swiss Household Panel (SHP) Switzerland Annual 7,000 individuals / 5,000 households 1999–2023 24 Household, Income and Labour Dynamics in Australia (HILDA) Australia Annual 17,000 individuals / 12,000 households 2001–2020 20 Note: the table presents the main features of the databases employed in LIB at the time of writing. 2.2. Individual Biographies While the source databases share many features, they also collect relevant variables for the harmonized histories in somewhat different ways. In this section, we provide information on how variables related to fertility, employment, and partnerships were derived from each of the longitudinal databases. General steps in the code across datasets Data harmonization proceeds in three steps. First, for each biography (fertility, partnership, employment) we identified the required source files and extracted the variables pertinent to that biography, we list those files below. Second, we integrated all selected sources within each country and each biography to form a consolidated dataset and we standardized variable types. Third, we organize the output in wide format—one row per individual with consistently ordered fields—to support reproducible downstream processing and inspection 2.2.1 Fertility Biographies The fertility history database collects information on the respondents’ biological and adopted children, including the date of birth of each child (month and year of birth), the child’s biological sex, and birth order during and prior to the respondents’ participation in the survey. The effort required to build fertility histories varied greatly across databases. Some original data providers had already prepared specialized files, which we used. Other databases required browsing through all main surveys and retrospective files (for information on non-resident children or children born prior to entering the survey). Moreover, the data collected was not identical across databases. Broadly speaking, the original data differ on whether resident or also non-resident children are covered (if the latter are missing, the database is biased towards more recent births), on whether adopted children are distinguished from biological children, and on the precision with which dates of birth are recorded (though monthly data appears almost as a common denominator). SOEP Fertility Biography The fertility database provided by SOEP provides monthly information on the timing of births (month and year of birth), as well as the demographic characteristics of children and clear linkages to their parents. In this case, the fertility biography in LIB is an edited version of the biobirth.dta file distributed by DIW (Deutsches Institut für Wirtschaftsforschung; the German Institute for Economic Research) and prepared by Schmitt, C. ( 2020 ). The data used to create the biobirth.dta file come from the main questionnaire and a retrospective survey that respondents complete during their first participation. The biobirth.dta file lists all biological children regardless of their status of residence. BHPS and UKHLS Fertility Biography BHPS and UKHLS provide three different files that enable the construction of fertility biographies. In BHPS, we used files on non-resident children during the period of running BHPS (variables: b_childnt, k_childnt, l_childnt from special licence.SN 6931), which cover non-resident children in waves 1, 11, and 12 of BHPS. Those files provide information on the biological sex and date of birth (month and year) of non-resident children. In UKHLS, we used two sources of data for resident (i) and non-resident (ii) children: For resident children, xhhrel.dta (family matrix) provides the IDs of all biological children who lived in the respondent household at least once during the study period (1991–2022).We merged the children’s IDs with the xwavedat.dta file to add information on each child's biological sex and date of birth (including the month and year of birth). For nonresident children, a_natchild and f_natchild (wave 1 and wave 6 of UKHLS) give information on the biological sex and year of birth of each non-resident child. The final fertility biography of BHPS/UKHLS is a file that combines all the above-mentioned data. To avoid duplication while combining different sources of data on children, we merged files by individual ID, child parity, and year of birth of each child. HILDA Fertility Biography Since HILDA does not distribute a unified file containing all births, we constructed histories from several variables coming from combined files for each wave separately, and eventually, choose the most recent available information. For resident children, HILDA provides the IDs of their biological parents ( hhbmxid for the biological mother and hhbfxid for the biological father). By linking these IDs, we recovered information on each biological (and resident) child, including their biological sex and year of birth. Information on non-resident children was extracted from variables ncage1-16 : age of non-resident child and ncsex1-16 : biological sex of non-resident child, available in each wave. Unfortunately, HILDA does not provide information on children's months of birth. Even though it is possible to compute the quarter of birth for children born since the survey was launched, this information is not available for children born before. For the sake of consistency, we opted to report only the year of birth. SHP Fertility Biography SHP provides information on resident and non-resident children. For resident children, information by parity was extracted from the shp_mp dta file, using the variables idmoth_ and idfath_ (mother’s and father’s identification numbers). The shp_mp.dta file includes each participant’s year and month of birth, gender, and ID, which enable d us to link children with their parents. This allowed us to determine the month and year of birth and biological sex for any child who was a resident during the SHP survey period (1999–2023). Information on non-resident children was extracted from shpWave_p_user.dta , where data on the month of birth was not available. From waves 1 to 15, participants reported only the biological sex and year of birth of non-resident children. Unfortunately, in wave 14, SHP stopped collecting information on non-resident children. In consequence, for individuals who joined SHP after wave 15 (2013), it is not possible to obtain information about non-resident children. In 2013 (wave 15), SHP fielded a special retrospective fertility module (shpii_fa_user.dta ) that includes the year of birth for all biological children, including those who did not survive until 2013. We used this file to supplement the main survey data, particularly for non-resident children. PSID Fertility Biography PSID began in 1968, but a consolidated fertility history is available only from 1985 onward. To incorporate PSID into LIB, we used the 1985–2021 Childbirth and Adoption History File which contains all births (and adoptions) for eligible individuals within a PSID family. The file provides month and year of birth, child’s biological sex, and parity for biological and adopted children, but it does not distinguish resident from non-resident children. It also includes retrospective reports collected in 1985 that reconstruct complete birth histories for the household head and spouse; available only for individuals remaining in the study as of 1985. The file has been updated in subsequent waves and records births up to the most recent year used here (2021). 2.2.2 Partnership Biographies Partnership biographies record the timing of each union formation, the type of union (cohabitation, or marriage), and, if the union has ended, its end date and the reason for termination (separation, divorce, or the partner's death). We also recorded the date when cohabitation was transformed into marriage. Like in the case of fertility histories, some original data providers distribute files containing most of the information required to reconstruct partnership histories. This is the case of BHPS/UKHLS, SOEP, and PSID. In SHP and HILDA, we built partnership histories from the main surveys. Similar to fertility histories, partnership histories are also reported in a wide format, where each row represents one individual, covering all partnerships from age 15 to the last available wave, with spells ordered chronologically by their start date. A spell is defined as a continuous episode in which an individual’s partnership status remains unchanged, with clear start and end dates, making it possible to reconstruct complete partnership trajectories over time. BHPS/UKHLS Partnership Biography Partnership histories in BHPS/UKHLS are collected in the Marital and Cohabitation Histories, 1991–2022 file ( phistory_long.dta licence 8473 ), which also covers the period prior to entering the survey. This file contains spells of cohabitation and marriage, partner ID, and the start and end of each relationship. Couples transitioning from cohabitation to marriage are recorded in two separate spells. We complemented this file by adding periods of singlehood and variables indicating the end of the union. SOEP Partnership Biography In SOEP, partnership histories are assembled from three sources: biocouplm.dta (relationship histories; monthly; prospective), biomarsm.dta (marital histories; monthly; prospective), and biomarsy.dta (marital histories; yearly; retrospective and prospective), following the integration strategy of Hamjediers, Schmelzer, and Geschke ( 2020 ). All three files include spells of singlehood and, in addition, record post-divorce and widowhood periods as separate spells; before integration, we recoded these post-union spells to singlehood for consistency. We then expanded each spell file into person–period panel data, aligned by respondent identifier and calendar time (monthly where available, otherwise yearly). This expansion yields one observation per individual per time unit, with covariates carried across intervals and events flagged at their endpoints, facilitating coherent alignment across sources and enabling longitudinal/event-history analyses. When overlapping spells are present, we prioritize biocouplm.dta and biomarsm.dta because - in contrast to biomarsy.dta - they provide monthly start and end dates. Because none of the three history files includes partner identifiers, we merge the integrated panel with pgen.dta by person ID and year to obtain partner IDs and other harmonized wave variables. Finally, we reconvert the aligned panel back to spell format, producing chronologically ordered partnership states with precise start and end dates. PSID Partnership Biography In PSID, partnership histories are collected in the 1985–2021 Marriage History File , which we used in LIB. Unlike the previous databases, PSID includes only information about marriages, i.e., cohabitation is not included. The information on cohabiting partners was also not available in the main survey and thus is not included in LIB. SHP Partnership Biography We constructed SHP partnership histories by integrating the annual individual surveys ( shpwave_p_user.dta ) with two retrospective modules fielded in 2002 and 2013 ( shp0_bvcs_user.dta and shpiii_cs_user.dta ). The retrospective files report starts and end years of relationships (no months) and do not include partner identifiers; each respondent appears in exactly/at least one of these modules. Both collect marriages, separations, and divorces, but they differ otherwise: the 2002 module does not capture cohabitation spells yet identifies never-married respondents, whereas the 2013 module records cohabitation but lacks a never-married indicator. By contrast, the main survey provides civil status at interview, partner IDs, month/year of current cohabitation onset, and month/year of status changes (marriage, separation, divorce, widowhood). We first expanded the retrospective histories from spell to yearly person-period format, then merged all sources by respondent ID and year. When overlaps occurred, we prioritized the main survey because it offers monthly timing and is closer to the interview date, reducing recall bias. Finally, we recomputed spells and returned the integrated series to wide format. HILDA Partnership Biography We constructed HILDA partnership biographies from the main survey data stored in the letter_Combined.dta files. These files record the current (or most recent) union and up to four prior unions; however, precise start and end dates (month and year) are available only for the most recent union. The survey also captures ongoing cohabitation at interview—recording the relationship start and the partner’s identifier. Some spells begin before sample entry and are therefore partly retrospective. Importantly, HILDA does not provide full pre-entry cohabitation histories, so coverage of cohabitation prior to panel entry is less complete than in other panels. 2.2.3 Employment Biographies across Databases Compared to fertility and partnership, employment trajectories are more complex. Individuals can hold several jobs simultaneously or combine jobs and other duties, such as education. It is also the domain in which we observed greater heterogeneity across databases. Longitudinal surveys differ in terms of what information is collected about spells in-between waves, the availability of retrospective information (available only in SOEP, SHP, and BHPS), the types of questions asked in the main survey, and even the levels available for categorical variables (different occupation or industry codes). Consequently, we adopted a minimalistic approach and constructed histories of the main labor market status, coded at the monthly level. We derived six different categories: [1] working, [2] unemployed, [3] not in the labor force, [4] retired, [5] other.Persons with employment contract even if not working (e.g. maternity leave) were classified as employed. All types of job contracts (e.g. short-term, mini, midi jobs) were also included in this category. To construct employment biographies, we applied two core rules. First, when multiple sources report the same period, we privileged the source temporally closest to the spell, thereby minimizing recall error (Watson, 2009 ). For example, in HILDA the retrospective calendar spans January of the prior year to the interview date, so a given month (e.g., January) may be reported in two consecutive waves; in such cases we used the report from the wave nearest to the month in question. Second, when respondents hold multiple statuses within a month, we resolved conflicts via a hierarchical ordering (e.g., full-time employment > part-time employment > unemployment > out of the labor force), and when multiple jobs are reported we adopted the respondent-designated main job to define the spell. Job spells may change in-between the survey waves. PSID allows for recovering some of the job features, such as industry, occupation, or hourly wages. However, other databases (SOEP, HILDA, SHP, BHPS/UKHLS) provide information only about the characteristics of the current job, performed at the time of the interview. Even if we know that the job characteristics changed between the two waves, we cannot establish when the change took place. As a consequence, we opted not to introduce these job characteristics into the LIB. BHPS and UKHLS Employment Biography To construct the employment history for BHPS and UKHLS, we relied on the work of Wright ( 2020 ), who harmonized the start and end dates of labor market activity spells. However, we adjusted this approach to meet our specific needs, ensuring compatibility with our project. In BHPS, we combined information from the main survey files indresp_protect.dta and retrospective files jobhist_protect.dta files to construct employment spells. In UKHLS, we combined the main survey indresp_protect.dta file and retrospective lifemst_protect.dta files (employment status: leshst ). The main survey indresp_protect.dta files contain information on whether the job has changed since the last interview and employment a status at the time of interview ( jbstat ), and if so, provide the end date of the activity (month and year). We combined information from BHPS and UKHLS together and recalculate spells. For individuals who participated in both BHPS and UKHLS, we treated their entry point into UKHLS as a new spell. SOEP Employment Biography In building a consistent SOEP employment biography, we followed Schmelzer and Hamjediers ( 2020 ), integrating retrospective and calendar files with tenure variables to capture changes in employment status and job transitions within spells. To accomplish this, we utilized three files: pbiospe.dta (retrospective file in years), artkalen.dta (calendar file in months), and pl.dta (survey data). From the last, we obtained the variable tenure indicating the start date with the current employer: plb0035 ( At Current Employer Since-Month ) and plb0036_h ( Employed by current employer since year ); This strategy recovers employment histories covering retrospective and prospective periods with monthly accuracy. Firstly, we cleaned and expanded artkalen.dta to a monthly format, merged it with tenure data to mark smooth employment transitions as separate spells. Secondly, we converted it to a yearly format to be able to merge with pbiospe.dta . In cases of overlap, we prioritized artkalen.dta as it is more precise, contains monthly data and is closer to the interview date. HILDA Employment Biography We constructed HILDA employment biographies from the letter_wave_Combined.dta files, drawing on two components: (i) the employment calendar, which records labor-market activity at the beginning, middle, and end of each month for the period back to the start of the previous financial year (statuses: employed, unemployed, not in the labor force), and (ii) tenure variables (e.g., letter_jbemlwk weeks with current employer; letter_jjbemlyr years with current employer) that capture time with the current employer. The calendar allows respondents to report up to 12 jobs (sequential or concurrent) as well as non-employment states. Because these sources can overlap, we prioritized employment over non-employment in a given month and, for multiple jobs, did not differentiate among concurrent positions since job-level characteristics are unavailable. We then integrated tenure information at the interview date to identify smooth job-to-job transitions (without intervening unemployment) and to initiate new employment spells. Although HILDA lacks fully retrospective employment histories, tenure measures at first interview permit inference of job start dates prior to panel entry, extending the employment biography backward. SHP Employment Biography We constructed SHP employment biographies using three sources. First, the activity calendar in shpwave_p_user.dta provides monthly employment status between interviews for respondents who completed the individual questionnaire. Following the SHP (2013) documentation, calendar coverage spans the interval from wave w–1 to w for continuing respondents, or the last 12 months for those absent in w–1 ; calendars are empty in waves without an interview, and questions are asked only when respondents report a change since the prior wave. We cleaned these monthly indicators (12 months for active and 12 for inactive persons) and added tenure information ( p_w66, p_w69 ) to identify smooth job-to-job transitions without intervening unemployment. Second, we incorporated retrospective modules— SHP0_bvwl_user.dta (collected in 2001–2002) and shpiii_prof_act_user.dta (2013)—which record paid work, unemployment, and inactivity prior to panel entry at yearly resolution. We harmonized status categories and expanded spells to a person–year format. Finally, we merged calendars, tenure, and retrospectives by respondent ID and time. Where overlaps arise, we prioritized the calendar module because it offers monthly timing and is closer to interview, thereby reducing recall bias. PSID Employment Biography To build employment biographies in the PSID, we divided the database into four periods (1968–1976, 1976–1987, 1988–2001, and 2003–2021), reflecting major changes in how employment information was collected. Because of these differences, consistent labor market histories cannot be reconstructed across all periods; complete work trajectories can be traced only from 1988 onward. Across these four periods, the construction of employment spells and the coverage of household members differ substantially. In 1968–1976, we define spells by between-wave status changes for the household head only and excluded job-to-job moves. In 1976–1987, we built spells based on between-wave changes but supplemented with tenure measures; coverage extends to heads and spouses. In 1988–2001, we defined spells using employer-specific start and end dates for heads and spouses and monthly status was inferred from reported job start and end dates (no calendar file existed). From 2003–2021, spells combine a monthly activity calendar with employer start/end dates (variables differ from the 1988–2001 period); we use family files (heads and spouses; detailed job dates) and individual files (status at interview, ever worked, work since prior year, tenure, months searching, retirement timing, and start month/year for up to four jobs). It is important to underline that PSID remains distinct from other panels: data have been collected biennially since 2003 (increasing recall bias), questionnaires are answered by the household reference person (which may lead to misreporting for spouses), and employment information for other household members (non-heads and non-spouses), such as working offspring, is available only at the time of the interview. 2.3 A Note on Codes - How to Work With LIB? The LIB does not distribute data, instead it provides the code required to create the desired biographies. All codes are written in R version 4.4.0, an open-source statistical programming language. The code is stored in 28 different files, which are listed in Table 3 . From the users' perspective, two files are particularly important for customization and adjustment of the code according to individual preferences: 00_setting_the_workspace , which contains all paths and necessary packages required by the subsequent scripts; and 01_data_selector , which specifies which biographies from which databases should be constructed (the default option is to construct all biographies from all databases). 01_data_selector also allows for selecting the format of the file to which the databases should be exported. Three options are available: . csv, .dta, and .Rdata . The remaining scripts developed for each database (PSID, SOEP, BHPS_UKHLS, SHP, and HILDA) include separate code files for each biography (employment, fertility, and partnership), as well as a master file encompassing variables common to all biographies (e.g., personal identifiers). In the case of PSID, we produced additional scripts containing auxiliary functions. These files are self-contained and should not be modified to ensure that the resulting databases are identical. Finally, we also provide a script that allows linking selected variables from the CPF dataset to LIB. Table 3 Summary of scripts Database name of script 0 readme file 000_readme_file LIB.txt 1 directories 00_setting_work_space.R 2 Running LIB 01_data_selector.R 3 CPF 02_CPF_merging.R 4 UK BHPS_UKHLS_master_file.R 5 UK BHPS_UKHLS_employment.R 6 UK BHPS_UKHLS_fertility.R 7 UK BHPS_UKHLS_partnership.R 8 SOEP SOEP_master_file.R 9 SOEP SOEP_employment.R 10 SOEP SOEP_fertility.R 11 SOEP SOEP_partnership.R 12 HILDA HILDA_master_file.R 13 HILDA HILDA_employment.R 14 HILDA HILDA_fertility.R 15 HILDA HILDA_partnership.R 16 PSID PSID_master_file.R 17 PSID PSID_employment.R 18 PSID PSID_fertility.R 19 PSID PSID_partnership.R 20 PSID PSID_variables_needed_21_century.R 21 SHP SHP_master_file.R 22 SHP SHP_employment.R 23 SHP SHP_fertility.R 24 SHP SHP_partnership.R 25 Validation validation_fertility.R 26 Validation validation_employment.R 27 Validation validation_partnership.R 28 Validation validation_employment.R 29 Validation validation_graph_functions.R Notes: List of scripts used to create the LIB database. The first step to create LIB is to specify the folder structure in the file 00_setting_the_workspace . We recommend the structure presented in Figure A1 in the Appendix. This structure divides the root folder into three subfolders: (1) Raw data, which contains the unzipped databases organized by country name, (2) codes, which is where the LIB codes should reside; and (3) output, which is created by the code (if it does not exist), and which contains the database with the selected histories for the selected countries. This recommended folder structure is not binding; users can customize it to suit their specific needs. The second step is to specify the desired biographies and execute 01_data_selector.R . This script writes outputs to the designated folder: (i) a consolidated file, LIB_, containing the selected biographies from the selected panels; (ii) per-panel/per-biography files named _. If the user is interested only in the full database, which contains histories from all countries covered by LIB, these other databases, except for LIB_, can be safely deleted. Lastly, the code creates a dated ‘readme’ file. 3. Data Records Upon executing the codes, the user will have created the LIB_ database in the selected folder stored as an Rdata file, a comma-separated file, or a Stata file. If user selects all biographies, the final dataset contains basic information about respondents and their fertility, partnership, and employment biographies stored in a wide format. This means that each row corresponds to a different individual, whereas different columns indicate either spells or events, depending on the biography. The basic information about respondents includes their IDs, biological sexes, and years of birth as well as the dates of their first and last interviews. The basic information also describes participants' status in the survey. These variables include the date of each interview and the interview status. These variables are indexed with a # (numbers) to indicate different waves. Furthermore, we included the variables used for validation: emp_status_# , ever_married , and anychild . We list these variables in Table 4 . Additionally, we collected variables that enable us to filter individuals who are not representative and should be excluded for external validation purposes. Additional sample-specific variables are included to identify subsample characteristics and respondent eligibility. These include psample (subsample identifier in GSOEP), sample (PSID sample type, e.g., "Immigrant or Latino", "SEO", "SRC"), drop (dropout status in 1997 in PSID), and followable (followability classification in PSID). For SHP, retro_marriage and flag_retro_fertility indicate inclusion in retrospective marriage and fertility files, respectively. The memorig variable marks respondents from an ethnic minority boost sample in BHPS/UKHLS. Table 4 Demographic and Technical Variables variable label Time invariant variables pid ID of the respondent BORN_Y Year of birth 1906–2023 [-1] unknown BORN_M Month of birth 1–12 [-1] unknown SEX Biological sex [1] male [2] female [-1] unknown FIRSTOBS_Y Year of first interview 1968–2023 [-1] unknown LASTOBS_Y Year of last interview 1968–2023 [-1] unknown Time variant variables INTERVIEW_DATE_# date of interview 1968–2023 [NA] unknown INTERVIEW_STATUS_# type of survey response for a given individual [1] fully responsive [2] proxy, which includes the child respondent, proxy respondent Variables needed for validation EMP_STATUS_# Employment status [0] not-working [1] working [-1] unknown EVER_MARRIED Ever married? [0] - no [1] - yes [-1] unknown ANYCHILD Any child at the last possible interview? [1] yes [2] no Note: # corresponds to the year of the wave Tables 5 to 7 lists the variables contained in LIB, as well as the availability across countries. Fertility biography (Table 5 ) contains the respondent’s ID, the date of birth of each child (month and year), the biological sex of each child, and the parity of each child. The “ $ ” suffix in the variables indicates parity. The fertility biography also contains the total number of children born to an individual, i.e., the NR_KIDS variable. The Partnership biography (Table 6 ) covers information on respondents’ partnerships, including the type of the partnership (cohabitation, marriage), start and end dates of each partnership, and the reason for the partnership’s end. When available, we also included the information on partners’ IDs, which allows for an easy recovery of partners’ characteristics from the original datasets. The dollar sign indicates the spell number, which is defined as the intersection of a union type with a given partner. For example, if a cohabiting couple decides to marry, these two states will be coded as separate spells (cohabitation and marriage). We created two further variables to indicate how the spell ended, according to differences in the type of union. Finally, an employment biography (Table 7 ) includes the start and end dates of each labor market activity spell, as well as the employment status during the spell. The dollar signs indicate the spell number. Table 5 List of available variables in LIB fertility biography Fertility biography pid ID of the respondent KID_M$ month of birth [-1] unknown month of birth [NA] no child of order $ KID_Y$ year of birth 1900–2024 [-1] unknown year of birth [NA] no child of order $ KID_S$ Child’s biological sex: [1] male [2] female [-1] unknown [NA] no child of order $ KID_$ Indicator of child order (provides info if child was born, even if birth date unknown) [0]: no child (of order $ ) [1]: child (of order $ ) NR_KIDS Number of children in LIB Table 6 List of available variables in LIB partnership biography Partnership biography PARTNER_ID_$ ID of respondent’s partner [NA] not applicable for partnership spells “no union” PARTNERSHIP_STATUS_$ Type of partnership status: [0] no union [1] cohabitation [2] marriage [3] gap START_Y_$ The year when the partnership status started 1900–2023 [-1] unknown start year [NA] not applicable START_M_$ The month when the partnership status started 1–12 [-1] unknown start month [NA] not applicable END_Y_$ The year when the partnership status ended 1900–2023 [-1] unknown end year [NA] not applicable END_M_$ The month when the partnership status ended 1–12 [-1] unknown end month [NA] not applicable END_SINGLE_$ How the single spell ended: [0] ongoing, not ended spell [1] cohabitation [2] marriage [-1] [unknown end] [NA] not applicable END_UNION_$ How the union spell ended: [0] ongoing, not ended spell [1] marriage [2] separation [3] the death of a partner [-1] unknown end [NA] not applicable DIVORCE_$ [0] divorce did not occur [1] divorce occurred DIVORCE_Y_$ year of divorce 1900–2023 [-1] divorce year unknown [NA] not applicable DIVORCE_M_$ month of divorce 1–12 [-1] divorce month unknown [NA] not applicable Table 7 List of available variables in LIB employment biography Employment biography EMPLOYMENT_STATUS_$ Employment status: [1] working [2] unemployed [3] not in the labor force [4] retired [5] other ENTRY_Y$ Year of entry into employment status 1900–2023 [-1] unknown entry year [NA] not applicable ENTRY_M$ Month of entry into employment status 1–12 [-1] unknown entry month [NA] not applicable ENTRY_D$ 1–31 [NA] not applicable EXIT_Y$ Year of exit of an employment status 1900–2023 [-1] unknown exit year [NA] not applicable EXIT_M$ Month of exit of an employment status 1–12 [-1] unknown exit month [NA] not applicable EXIT_D$ 1–31 [NA] not applicable 4. Technical Validation The LIB results from the harmonization of multiple data sources, making validation essential to ensure that the combined data accurately represent key demographic and labor market phenomena. We conducted both internal and external validation. Internal validation compares statistics derived from the LIB biographies with those obtained at the time of the interview in the main survey, while external validation compares LIB statistics with independent sources. Graphs which compare statistics derived from the LIB biographies with internal and external statistics are displayed in the article. Under each graph we also listed sources for external statistics. Here we only briefly discuss the main findings from this validation. We validated fertility histories by comparing LIB estimates to three statistics: the proportion of childless women at age 40 by cohort (Fig. 1 a–e), the mean age at birth (all parities) (Fig. 2 a–e), and completed cohort fertility at age 40 (Fig. 3 a–e). In the first case, most LIB-based estimates closely match external benchmarks, with larger deviations only in PSID for the oldest (1941–1950) and youngest (1971–1977) cohorts. Similarly, the mean age at birth is closely aligned across surveys, with modest differences again concentrated in PSID for the oldest and youngest cohorts. Finally, for completed cohort fertility at age 40, LIB estimates are also close to the benchmark, and small differences appear only among the youngest cohorts (1971–1977) in SOEP, BHPS&UKHLS, and HILDA, where LIB slightly underestimates fertility compared to external data. To validate partnership biographies, wederived the proportion of never married women by cohort from LIB biographies and compared it to external sources. As shown in Fig. 4 a–e, LIB-based estimates largely replicate external benchmarks across all surveys, with particularly close agreement for the 1951–1960 and 1961–1970 cohorts. Larger deviations occur in PSID and HILDA for the youngest cohort (1971–1977), where LIB slightly underestimates the never-married share, and in SOEP and SHP for the oldest cohort (1941–1950). We further validated our database, by comparing the mean age at first marriage. In Fig. 5 a–e, we observe that LIB reproduces trends in rising ages at first marriage across surveys. Coherence is strongest in SOEP and BHPS&UKHLS, while PSID and SHP show small underestimations in the earliest (2000–2004) and latest (2015–2019) periods, HILDA displays very stable correspondence. The large confidence intervals (CIs) observed for SHP are likely due to the small sample size, as this analysis includes only individuals from the retrospective cohort, which represents a limited number of cases. For employment biographies, we compared male and female labor force participation rates. The comparisons are displayed in Figs. 6 a–e and 7 a–e, for men and women, respectively. LIB-based estimates align closely with both internal and external benchmarks, indicating that reconstructed employment histories capture male and female labor force participation accurately. Minor discrepancies appear in PSID and BHPS&UKHLS for recent cohorts, where LIB slightly underestimates employment rates, while SOEP and SHP show nearly perfect alignment. HILDA exhibits only small, consistent fluctuations. Overall, these checks confirm that the harmonized LIB provides a reliable representation of fertility, partnership, and employment histories across surveys. Deviations are minor and typically confined to specific cohorts or datasets, underscoring the robustness and comparability of the LIB-derived measures. 5. Usage Note Compatibility of LIB with other harmonization projects LIB is compatible with other harmonization efforts, particularly the Cross-National Equivalent File (CNEF) developed by Frick et al. ( 2007 ) and the Comparative Panel File (CPF) created by Turek et al. ( 2021 ). While LIB extends coverage by incorporating between-wave calendars and retrospective modules prior to sample entry, focusing specifically on partnership, fertility, and employment histories, CNEF/CPF provide a broader set of contemporaneous covariates (e.g., education, health, and detailed job characteristics at interview). LIB can be linked to these infrastructures via respondent identifiers and country codes, enabling the enrichment of spell histories with wave-specific attributes. The method we propose to merge the database requires first converting the LIB biographies into the long format, where each row represents a single month. Then, it can be merged with the CPF data using respondent IDs, country level identifiers, and the interview date. Notice that in the resulting database information from CPF will be incorporated only in the interview months, and missing values for the remaining months. The decision on how to proceed further will depend on users’ specific research needs and objectives (e.g., may require using imputation techniques to fill in the missing values in the CPF variables). The code for merging the LIB and CPF datasets in the way discussed above is provided alongside the remaining scripts (see Table 3 ). Declarations Funding This research was supported by the Polish National Agency for Academic Exchange (Polish Returns Programme 2019) (PI: Anna Matysiak) and the European Research Council under the ERC Consolidator Grant “Globalization- and Technology-Driven Labour Market Change and Fertility” (LABFER, grant agreement no 866207) (PI: Anna Matysiak). Code Availability The R code to prepare LabFam harmonized histories is available at: https://github.com/weychert/LabFam-Individual-Biographies Code prepared using R [64-bit] Version 4.4.0 on Windows 11, and RStudio. References Di Nallo, A. and Köksal, S. (2023) Job loss during pregnancy and the risk of miscarriage and stillbirth, Human Reproduction , 38(11), 2259–2266, https://doi.org/10.1093/humrep/dead183 Drobnič, S. (2002). Retirement Timing in Germany: The Impact of Household Characteristics. International Journal of Sociology , 32 (2), 75–102. https://doi.org/10.1080/15579336.2002.11770250 Eubank, N. (2016), Lessons from a Decade of Replications at the Quarterly Journal of Political Science, PS: Political Science & Politics ; 49(2), 273–276. https://doi.org/10.1017/S1049096516000196 Freese, J., Rauf, T., & Voelkel, J. G. (2022). Advances in transparency and reproducibility in the social sciences. Social Science Research, 107, 102770. https://doi.org/https://doi.org/10.1016/j.ssresearch.2022.102770 Frick, J. R., Jenkins, S. P., Lillard, D. R., Lipps, O., & Wooden, M. (2007). The Cross-National Equivalent File (CNEF) and its member country household panel studies. Journal of Contextual Economics–Schmollers Jahrbuch(4), 627–654. Hamjediers, M., Schmelzer, P., & Geschke, S. C. (2020). SOEP-Core v35: The couple history files BIOCOUPLM and BIOCOUPLY, and marital history files BIOMARSM and BIOMARSY (No. 871). SOEP Survey Papers. Huntington-Klein, N., Arenas, A., Beam, E., Bertoni, M., Bloem, J. R., Burli, P., Chen, N., Grieco, P., Ekpe, G., Pugatch, T., Saavedra, M., & Stopnitzky, Y. (2021). The influence of hidden researcher decisions in applied microeconomics. Economic Inquiry, 59(3), 944–960. https://doi.org/https://doi.org/10.1111/ecin.12992 Kreyenfeld, M. (2010), Uncertainties in Female Employment Careers and the Postponement of Parenthood in Germany, European Sociological Review, 26(3), 351–366, https://doi.org/10.1093/esr/jcp026 Osiewalska, B., Matysiak, A., & Kurowska, A. (2024). Home-based work and childbearing. Population Studies, 78(3), 525–545. https://doi.org/10.1080/00324728.2023.2287510 Perelli-Harris, B., Kreyenfeld, M. R., & Kubisch, K. (2010). Harmonized histories: Manual for the preparation of comparative fertility and union histories. Mimeo Schmelzer, P. Hamjediers, M. (2020). SOEP-Core v35 - activity biography in the files PBIOSPE and ARTKALEN, SOEP Survey Papers, No. 877, Deutsches Institut für Wirtschaftsforschung (DIW), Berlin Schmitt, C. (2020). SOEP-Core v35-BIOBIRTH: A data set on the birth biography of male and female respondents (No. 875). SOEP Survey Papers. Turek, K., Kalmijn, M., & Leopold, T. (2021). The Comparative Panel File: Harmonized Household Panel Surveys from Seven Countries. European Sociological Review, 37(3), 505–523. https://doi.org/10.1093/esr/jcab006 Watson, N. (2009, July). Disentangling overlapping seams: the experience of the HILDA survey. In HILDA Survey Research Conference, University of Melbourne (pp. 16-17). Wright, L. (2020). Producing working-life histories in the BHPS and UKHLS 2017-2020. [Data Collection]. Colchester, Essex: UK Data Service. 10.5255/UKDA-SN-854327 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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17:03:48","extension":"html","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":138084,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8376548/v1/e8c3fdb4b10a6b5b38c1deef.html"},{"id":98376101,"identity":"3558161b-48df-4e90-a965-3a3f6f66ccbb","added_by":"auto","created_at":"2025-12-17 07:04:23","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":187091,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValidation of Fertility Biographies: Percent Childless Women by Cohort\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eNote:\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e In all samples, we excluded individuals who were below 40 years old at the year of their last interview, as their fertility history is not yet complete\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e \u003c/em\u003e\u003cem\u003e\u003cstrong\u003ePSID:\u003c/strong\u003e\u003c/em\u003e \u003cem\u003ei) We excluded individuals who are: non-sample and not part of the elderly group, Moved-in sample, Joint inclusion sample, Followable non-sample parent, non-sample elderly.ii) We further restricted the sample to include only individuals from families that were not dropped in 1997. Families dropped in 1997 may lack complete fertility histories, especially for the youngest cohort, due to the suspension of roughly half of the low-income sample at that time. iii) We excluded: Immigrant Sample (1997), Immigrant Sample (1999), Immigrant Sample (2017), Immigrant Sample (2019), Latino Sample (1990), Latino Sample (1992). \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eSOEP:\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e We included individuals that are in: A 1984 Initial Sample (West),B 1984 Migration (until 1983, West),C 1990 Initial Sample (East),D 1994/5 Migration (1984-1994, West),E 1998 Refreshment, F 2000 Refreshment, H 2006 Refreshment,[10] J 2011 Refreshment, [11] K 2012 Refreshment,[27] R 2022 Refreshment. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eBHPS \u0026amp; UKHLS:\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e We excluded the ethnic minority boost. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eSHP:\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e We excluded individuals absent in the retrospective file. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eHILDA:\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e no additional restrictions. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eExternal percent of childless women by cohort\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e: USA - Human Fertility Database (2024), Germany - Statistics Bundesstaat (2024), UK - Office for National Statistics (2024), Switzerland - census (2000), Australia - census 2021\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8376548/v1/fca56eccacf751b281eae57a.jpeg"},{"id":98376105,"identity":"03b54a78-93c1-4b51-a052-742528f5b1d7","added_by":"auto","created_at":"2025-12-17 07:04:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":519301,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFemale cohort mean age at birth for all birth orders\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eNote:\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Sample restrictions as in Figure 4.1. \u003c/em\u003e\u003cstrong\u003eExternal Female cohort mean age at birth for all birth orders\u003c/strong\u003e - Human Fertility Database (2024)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8376548/v1/30d9708bf684ca915bd0d757.png"},{"id":98441105,"identity":"9b5d98d8-e35d-447f-8d84-c3d65c71ad39","added_by":"auto","created_at":"2025-12-17 17:04:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":492839,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValidation of Fertility Biographies: Completed Cohort Fertility at 40 by cohort\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eNote:\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Sample restrictions as in Figure 4.1.\u003c/em\u003e \u003cstrong\u003eExternal Completed Cohort Fertility at 40 by cohort:\u003c/strong\u003e \u003cem\u003eUSA, Germany, United Kingdom, Switzerland - Human Fertility \u003c/em\u003eDatabase (2024), Australia (\u003cem\u003eHuman Collection \u003c/em\u003eDatabase, 2025)\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8376548/v1/6c5525569e8b2a54700f5df0.png"},{"id":98441179,"identity":"98bbd7f2-682a-4efd-b42e-7f15f973e47b","added_by":"auto","created_at":"2025-12-17 17:05:01","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":192663,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValidation of Partnership Biographies: Percent Never Married Women by Cohort\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eNote:\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e In all samples we included only individuals who were not 40 at the last available wave.\u003c/em\u003e \u003cem\u003e\u003cstrong\u003ePSID:\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e i) We excluded individuals who are: non-sample and not part of the elderly group, Moved-in sample, Joint inclusion sample, Followable non-sample parent, non-sample elderly. These groups were excluded because they may not have complete fertility histories. ii) We further restricted the sample to include only individuals from families that were not dropped in 1997. Families dropped in 1997 may lack complete fertility histories, especially for the youngest cohort, due to the suspension of roughly half of the low-income sample at that time. iii) We excluded: Immigrant Sample (1997), Immigrant Sample (1999), Immigrant Sample (2017), Immigrant Sample (2019), Latino Sample (1990), Latino Sample (1992). \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eSOEP:\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e We included individuals that are in: A 1984 Initial Sample (West),B 1984 Migration (until 1983, West),C 1990 Initial Sample (East),D 1994/5 Migration (1984-1994, West),E 1998 Refreshment, F 2000 Refreshment, H 2006 Refreshment, J 2011 Refreshment, K 2012 Refreshment, R 2022 Refreshment. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eBHPS \u0026amp; UKHLS:\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e We excluded the ethnic minority boost and included only individuals who are original sample members and ethnic minority boost.\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e SHP:\u003c/strong\u003e\u003c/em\u003e\u003cem\u003eWe included individuals that are in the retrospective files. . \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eHILDA:\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e no additional restrictions.\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eExternal: Percent Never Married Women by Cohort \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/em\u003e\u003cem\u003eUnited Kingdom - ONS(2024) USA, Germany, Switzerland, Australia - (United Nations [UN], 2019)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8376548/v1/843b9f017c9837dc29274108.jpeg"},{"id":98376099,"identity":"7c7929a8-1de1-416e-a6e3-167a119201c9","added_by":"auto","created_at":"2025-12-17 07:04:23","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":239746,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValidation of Partnership Biographies: Period Female Mean Age at First\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eNote:\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e The datasets were filtered as in Figure 4.4. \u003c/em\u003e\u003cstrong\u003eExternal Period Female Mean Age at First Marriage - \u003c/strong\u003e\u003cem\u003eOECD (2025)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8376548/v1/947bb3e26fb9e4b66f9676c0.jpeg"},{"id":98376107,"identity":"be617433-ba25-46c9-a9b4-16df80d64d1f","added_by":"auto","created_at":"2025-12-17 07:04:24","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":185501,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValidation of Employment Biographies: \u0026nbsp;Male employment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eNote:\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e We included only individuals who are on ages 15 to 64. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003ePSID:\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e i) We excluded individuals who are: non-sample and not part of the elderly group, Moved-in sample, Joint inclusion sample, Followable non-sample parent, non-sample elderly. These groups were excluded because they may not have complete fertility histories. ii) We further restricted the sample to include only individuals from families that were not dropped in 1997. Families dropped in 1997 may lack complete fertility histories, especially for the youngest cohort, due to the suspension of roughly half of the low-income sample at that time. iii) We excluded: Immigrant Sample (1997), Immigrant Sample (1999), Immigrant Sample (2017), Immigrant Sample (2019), Latino Sample (1990), Latino Sample (1992). \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eSOEP:\u003c/strong\u003e\u003c/em\u003e\u003cem\u003eWe included individuals that are in: A 1984 Initial Sample (West),B 1984 Migration (until 1983, West),C 1990 Initial Sample (East),D 1994/5 Migration (1984-1994, West),E 1998 Refreshment, F 2000 Refreshment, H 2006 Refreshment, J 2011 Refreshment, K 2012 Refreshment, R 2022 Refreshment. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eBHPS \u0026amp; UKHLS\u003c/strong\u003e\u003c/em\u003e\u003cem\u003eWe excluded ethnic minority boost. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eSHP:\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e no additional restrictions. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eHILDA:\u003c/strong\u003e\u003c/em\u003e\u003cem\u003eno additional restrictions\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e External \u0026nbsp;male employment \u003c/strong\u003e\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u0026nbsp;- OECD (2025)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8376548/v1/7d15c6c720f23dd0321c7a8a.jpeg"},{"id":98440660,"identity":"71d1eb0e-0c5d-463b-8c37-3ebc29ecd8a5","added_by":"auto","created_at":"2025-12-17 17:04:09","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":179370,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValidation of Employment Biographies: Female employment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eNote:\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e We included only individuals who are on ages 15 to 64. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003ePSID:\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e i) We excluded individuals who are: non-sample and not part of the elderly group, Moved-in sample, Joint inclusion sample, Followable non-sample parent, non-sample elderly. These groups were excluded because they may not have complete fertility histories. ii) We further restricted the sample to include only individuals from families that were not dropped in 1997. Families dropped in 1997 may lack complete fertility histories, especially for the youngest cohort, due to the suspension of roughly half of the low-income sample at that time. iii) We excluded: Immigrant Sample (1997), Immigrant Sample (1999), Immigrant Sample (2017), Immigrant Sample (2019), Latino Sample (1990), Latino Sample (1992). \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eSOEP:\u003c/strong\u003e\u003c/em\u003e\u003cem\u003eWe included individuals that are in: A 1984 Initial Sample (West),B 1984 Migration (until 1983, West), C 1990 Initial Sample (East), D 1994/5 Migration (1984-1994, West),E 1998 Refreshment, F 2000 Refreshment, H 2006 Refreshment, J 2011 Refreshment, K 2012 Refreshment, R 2022 Refreshment. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eBHPS \u0026amp; UKHLS\u003c/strong\u003e\u003c/em\u003e\u003cem\u003eWe excluded ethnic minority boost. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eSHP:\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e no additional restrictions. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eHILDA:\u003c/strong\u003e\u003c/em\u003e\u003cem\u003eno additional restrictions\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e External female employment \u003c/strong\u003e\u003c/em\u003e\u003cem\u003e- OECD (2025)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8376548/v1/1552ed87e495dafb7f6d993d.jpeg"},{"id":98774619,"identity":"d6ec454e-3a5e-428c-b144-1006392610b4","added_by":"auto","created_at":"2025-12-22 12:05:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3644543,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8376548/v1/e671a12b-4807-46ae-829e-23fc04b7d3a7.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eLabFam Individual Biographies harmonised family and employment histories based on panel surveys\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Background \u0026 Summary","content":"\u003cp\u003eThere is a growing concern about reproducibility in the social sciences, as researchers often struggle to obtain similar results using the same databases. This lack of reproducibility reduces the credibility of findings (Freese et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and slows down the process of doing research, as academics often cannot rely on or build upon the work of others (Eubank, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Huntington-Klein et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) demonstrated these issues in a field experiment in which fellow economists failed to replicate a study, sometimes obtaining opposite results or even being unable to recreate the sample. This state of affairs calls for action to remedy the situation. Several options are available, some of which rely on individual researchers, such as pre-registering the data analysis and depositing the statistical code in data repositories. Others rely on the provision of public goods to be used by the community, such as building research infrastructure.\u003c/p\u003e \u003cp\u003eThe LabFam Individual Biographies (LIB) is an initiative that belongs to the latter group. We harmonized individual-level longitudinal data for life-course analysis across five countries (panels): Australia (HILDA), Germany (SOEP), Switzerland (SHP), the United Kingdom (BHPS/UKHLS), and the United States (PSID). These surveys are among the longest-running longitudinal studies worldwide and therefore constitute a natural starting point. LIB reconstructs spell-based histories for individuals along three different life dimensions: (i) fertility, which records the number and timing of births; (ii) partnership, documenting the timing of union formation and union dissolution; and (iii) employment, which collects data on employment spells and characteristics of the jobs held. In this format, each spell represents a continuous episode with a defined start and end date, during which the status remains constant. The data are therefore structured as timelines of episodes rather than point-in-time observations at interview waves, enabling more precise measurement of durations and transitions over the life course.\u003c/p\u003e \u003cp\u003eOur project is inspired by existing harmonization initiatives, particularly the Cross-National Equivalent File (CNEF) developed by Frick et al (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), the Comparative Panel File (CPF) created by Turek et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and the Harmonized Histories produced by Perelli-Harris et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). It addresses some of the shortcomings of the previous efforts, making it a valuable resource for researchers in the social sciences. LIB expands the histories provided by CNEF and CPF, both of which harmonized the same datasets but primarily relied on variables from the main survey. These variables pertain to the timing of the interview and predominantly focus on the respondent\u0026rsquo;s labor market or family statuses at that specific moment. While building on CPF, our approach goes further by incorporating 1) calendar data to bridge individual employment histories between main waves, and 2) retrospective components to construct histories prior to survey entry. Collectively, these data sources provide detailed information on the timing and order of transitions, whether it is its entry into parenthood, employment, or a new partnership. In its use of retrospective data, LIB resembles the Harmonized Histories in which partnership and fertility biographies are recovered from retrospective questions in the Gender and Generations Survey (GGS). However, our database has two advantages over the Harmonized Histories. First, by providing employment histories, in addition to fertility and partnership histories, it is broader in scope. GGS contains little information on individual labour market status from periods prior to the interview making the reconstruction of employment histories unfeasible. Second, LIB is not solely based on retrospective information, but largely makes use of long-lasting panel data, which reduces recall and survival biases. Unfortunately, these differences also imply that comparisons to the histories harmonized by Perelli-Harris et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) should be made with caution, given its shorter panel structure and limited pre-interview employment content.\u003c/p\u003e \u003cp\u003eBy focusing on transitions and covering information on fertility, partnership, and employment histories in detail, LIB can be used to answer questions that were left orphaned by previous harmonization efforts and allows for adopting a comparative perspective. Researchers can examine, for example, how mounting employment uncertainty over the life course affects fertility and partnership behaviours (union formation or disruption) or how retirement patterns change across generations. Likewise, LIB enables studies on employment transitions around important family-related events (e.g., childbirth or union disruption) or interdependencies between partners\u0026rsquo; employment careers at different life course stages (union formation, entry to parenthood, subsequent childbirths, mid-life, and retirement). Many of these issues have been studied in the past, but largely for single countries (Drobnič, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Kreyenfeld, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Di Nallo \u0026amp; Koeksal, 2023, Osiewalska et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), as the costs of data preparation for multiple countries were often too high. By providing harmonized, spell-based timelines across panels, LIB lowers these barriers, facilitating cross-national, duration-sensitive analyses of family and employment trajectories.\u003c/p\u003e \u003cp\u003eThe LabFam Individual Biographies consist of two distinct elements. The first is the source data (original longitudinal datasets), which users must obtain independently from third-party data providers (see Section \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e2.1\u003c/span\u003e). The second is the open-source code written in the open-source programming language R (version 4.4.0), which allows for reconstructing LIB histories once access to the source data is granted (see Section \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e2.3\u003c/span\u003e). As our initiative builds upon the CPF, we also provide code that facilitates the merging of LIB with CPF data, thereby enabling straightforward enrichment with additional respondent and household characteristics. Our open-source code is modular and configurable: users can tailor outputs by country, period, or life-course domain. By distributing the full workflow, we not only facilitate reproducibility and reuse but also enhance transparency, as dependencies across variables become clearer, and all transformations are visible.\u003c/p\u003e"},{"header":"2.Methods","content":"\u003cp\u003eIn this section, we first describe the source data (Section \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e2.1\u003c/span\u003e). Next, we discuss briefly how the fertility, partnership, and employment histories were constructed, considering the peculiarities of each source dataset (Section \u003cspan refid=\"Sec4\" class=\"InternalRef\"\u003e2.2\u003c/span\u003e). Finally, we describe the codebase used to produce each of the three individual biographies: fertility, partnership, and employment, and provide guidance for its use (Section \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e2.3\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Source Data\u003c/h2\u003e \u003cp\u003eThe LIB harmonizes data from five different longitudinal databases: The Australian Household, Income and Labor Dynamics (HILDA), The German Socio-Economic Panel (SOEP), The Swiss Household Panel (SHP), the British Household Panel Survey (BHPS) / The UK Household Longitudinal Study (UKHLS), and the Panel Study of Income Dynamics (PSID). Their basic features, such as sample sizes, time span, and frequency, are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. These databases need to be obtained independently from the original data providers by researchers who want to reconstruct the LIB, as LIB distributes code, not data. We use the most recent panel releases available to us under our data agreements (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e); however, as access conditions vary across providers and users, newer releases may exist and be accessible to other researchers, as these longitudinal surveys are still ongoing. Taken together, the databases encompass a period spanning from year of 1968 to 2024.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumber of waves and respondents (LIB v1.0)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurvey\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSample Size(approx.)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTime Span\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVersion\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePanel Study of Income Dynamics (PSID)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnnual\u003c/p\u003e \u003cp\u003e(to 1997), \u003c/p\u003e \u003cp\u003eBiennial\u003c/p\u003e \u003cp\u003e(since 2003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18,000 individuals\u003c/p\u003e \u003cp\u003e5,000 households\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1968\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSocio-Economic Panel (SOEP)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnnual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30,000 individuals 20,000 households\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1984\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBritish Household Panel Survey (BHPS)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e Understanding Society (UKHLS)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnnual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13,000 individuals 7,000 households\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1991\u0026ndash;2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBHPS 18,\u003c/p\u003e \u003cp\u003eUKHLS 14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSwiss Household Panel (SHP)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSwitzerland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnnual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7,000 individuals / 5,000 households\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1999\u0026ndash;2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold, Income and Labour \u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eDynamics in Australia (HILDA)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnnual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17,000 individuals / 12,000 households\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2001\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: the table presents the main features of the databases employed in LIB at the time of writing.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Individual Biographies\u003c/h2\u003e \u003cp\u003eWhile the source databases share many features, they also collect relevant variables for the harmonized histories in somewhat different ways. In this section, we provide information on how variables related to fertility, employment, and partnerships were derived from each of the longitudinal databases.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGeneral steps in the code across datasets\u003c/b\u003e \u003c/p\u003e \u003cp\u003eData harmonization proceeds in three steps. First, for each biography (fertility, partnership, employment) we identified the required source files and extracted the variables pertinent to that biography, we list those files below. Second, we integrated all selected sources within each country and each biography to form a consolidated dataset and we standardized variable types. Third, we organize the output in wide format\u0026mdash;one row per individual with consistently ordered fields\u0026mdash;to support reproducible downstream processing and inspection\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Fertility Biographies\u003c/h2\u003e \u003cp\u003eThe fertility history database collects information on the respondents\u0026rsquo; biological and adopted children, including the date of birth of each child (month and year of birth), the child\u0026rsquo;s biological sex, and birth order during and prior to the respondents\u0026rsquo; participation in the survey. The effort required to build fertility histories varied greatly across databases. Some original data providers had already prepared specialized files, which we used. Other databases required browsing through all main surveys and retrospective files (for information on non-resident children or children born prior to entering the survey). Moreover, the data collected was not identical across databases. Broadly speaking, the original data differ on whether resident or also non-resident children are covered (if the latter are missing, the database is biased towards more recent births), on whether adopted children are distinguished from biological children, and on the precision with which dates of birth are recorded (though monthly data appears almost as a common denominator).\u003c/p\u003e \u003cp\u003e \u003cb\u003eSOEP Fertility Biography\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe fertility database provided by SOEP provides monthly information on the timing of births (month and year of birth), as well as the demographic characteristics of children and clear linkages to their parents. In this case, the fertility biography in LIB is an edited version of the \u003cem\u003ebiobirth.dta\u003c/em\u003e file distributed by DIW (Deutsches Institut f\u0026uuml;r Wirtschaftsforschung; the German Institute for Economic Research) and prepared by Schmitt, C. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The data used to create the \u003cem\u003ebiobirth.dta\u003c/em\u003e file come from the main questionnaire and a retrospective survey that respondents complete during their first participation. The \u003cem\u003ebiobirth.dta\u003c/em\u003e file lists all biological children regardless of their status of residence.\u003c/p\u003e \u003cp\u003e \u003cb\u003eBHPS and UKHLS Fertility Biography\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBHPS and UKHLS provide three different files that enable the construction of fertility biographies. In BHPS, we used files on non-resident children during the period of running BHPS (variables: \u003cem\u003eb_childnt, k_childnt, l_childnt\u003c/em\u003e from special licence.SN 6931), which cover non-resident children in waves 1, 11, and 12 of BHPS. Those files provide information on the biological sex and date of birth (month and year) of non-resident children.\u003c/p\u003e \u003cp\u003eIn UKHLS, we used two sources of data for resident (i) and non-resident (ii) children: For resident children, \u003cem\u003exhhrel.dta\u003c/em\u003e (family matrix) provides the IDs of all biological children who lived in the respondent household at least once during the study period (1991\u0026ndash;2022).We merged the children\u0026rsquo;s IDs with the \u003cem\u003exwavedat.dta\u003c/em\u003e file to add information on each child's biological sex and date of birth (including the month and year of birth). For nonresident children, \u003cem\u003ea_natchild\u003c/em\u003e and \u003cem\u003ef_natchild\u003c/em\u003e (wave 1 and wave 6 of UKHLS) give information on the biological sex and year of birth of each non-resident child. The final fertility biography of BHPS/UKHLS is a file that combines all the above-mentioned data. To avoid duplication while combining different sources of data on children, we merged files by individual ID, child parity, and year of birth of each child.\u003c/p\u003e \u003cp\u003e \u003cb\u003eHILDA Fertility Biography\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSince HILDA does not distribute a unified file containing all births, we constructed histories from several variables coming from combined files for each wave separately, and eventually, choose the most recent available information. For resident children, HILDA provides the IDs of their biological parents (\u003cem\u003ehhbmxid\u003c/em\u003e for the biological mother and \u003cem\u003ehhbfxid\u003c/em\u003e for the biological father). By linking these IDs, we recovered information on each biological (and resident) child, including their biological sex and year of birth. Information on non-resident children was extracted from variables \u003cem\u003encage1-16\u003c/em\u003e: age of non-resident child and \u003cem\u003encsex1-16\u003c/em\u003e: biological sex of non-resident child, available in each wave. Unfortunately, HILDA does not provide information on children's months of birth. Even though it is possible to compute the quarter of birth for children born since the survey was launched, this information is not available for children born before. For the sake of consistency, we opted to report only the year of birth.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSHP Fertility Biography\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSHP provides information on resident and non-resident children. For resident children, information by parity was extracted from the \u003cem\u003eshp_mp dta\u003c/em\u003e file, using the variables \u003cem\u003eidmoth_\u003c/em\u003e and \u003cem\u003eidfath_\u003c/em\u003e (mother\u0026rsquo;s and father\u0026rsquo;s identification numbers). The \u003cem\u003eshp_mp.dta\u003c/em\u003e file includes each participant\u0026rsquo;s year and month of birth, gender, and ID, which enable d us to link children with their parents. This allowed us to determine the month and year of birth and biological sex for any child who was a resident during the SHP survey period (1999\u0026ndash;2023).\u003c/p\u003e \u003cp\u003eInformation on non-resident children was extracted from \u003cem\u003eshpWave_p_user.dta\u003c/em\u003e, where data on the month of birth was not available. From waves 1 to 15, participants reported only the biological sex and year of birth of non-resident children. Unfortunately, in wave 14, SHP stopped collecting information on non-resident children. In consequence, for individuals who joined SHP after wave 15 (2013), it is not possible to obtain information about non-resident children.\u003c/p\u003e \u003cp\u003eIn 2013 (wave 15), SHP fielded a special retrospective fertility module \u003cem\u003e(shpii_fa_user.dta\u003c/em\u003e) that includes the year of birth for all biological children, including those who did not survive until 2013. We used this file to supplement the main survey data, particularly for non-resident children.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePSID Fertility Biography\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePSID began in 1968, but a consolidated fertility history is available only from 1985 onward. To incorporate PSID into LIB, we used the 1985\u0026ndash;2021 \u003cem\u003eChildbirth and Adoption History File\u003c/em\u003e which contains all births (and adoptions) for eligible individuals within a PSID family. The file provides month and year of birth, child\u0026rsquo;s biological sex, and parity for biological and adopted children, but it does not distinguish resident from non-resident children. It also includes retrospective reports collected in 1985 that reconstruct complete birth histories for the household head and spouse; available only for individuals remaining in the study as of 1985. The file has been updated in subsequent waves and records births up to the most recent year used here (2021).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Partnership Biographies\u003c/h2\u003e \u003cp\u003ePartnership biographies record the timing of each union formation, the type of union (cohabitation, or marriage), and, if the union has ended, its end date and the reason for termination (separation, divorce, or the partner's death). We also recorded the date when cohabitation was transformed into marriage. Like in the case of fertility histories, some original data providers distribute files containing most of the information required to reconstruct partnership histories. This is the case of BHPS/UKHLS, SOEP, and PSID. In SHP and HILDA, we built partnership histories from the main surveys. Similar to fertility histories, partnership histories are also reported in a wide format, where each row represents one individual, covering all partnerships from age 15 to the last available wave, with spells ordered chronologically by their start date. A spell is defined as a continuous episode in which an individual\u0026rsquo;s partnership status remains unchanged, with clear start and end dates, making it possible to reconstruct complete partnership trajectories over time.\u003c/p\u003e \u003cp\u003e \u003cb\u003eBHPS/UKHLS Partnership Biography\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePartnership histories in BHPS/UKHLS are collected in the \u003cem\u003eMarital and Cohabitation Histories, 1991\u0026ndash;2022\u003c/em\u003e file (\u003cem\u003ephistory_long.dta licence 8473\u003c/em\u003e), which also covers the period prior to entering the survey. This file contains spells of cohabitation and marriage, partner ID, and the start and end of each relationship. Couples transitioning from cohabitation to marriage are recorded in two separate spells. We complemented this file by adding periods of singlehood and variables indicating the end of the union.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSOEP Partnership Biography\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn SOEP, partnership histories are assembled from three sources: \u003cem\u003ebiocouplm.dta\u003c/em\u003e (relationship histories; monthly; prospective), \u003cem\u003ebiomarsm.dta\u003c/em\u003e (marital histories; monthly; prospective), and \u003cem\u003ebiomarsy.dta\u003c/em\u003e (marital histories; yearly; retrospective and prospective), following the integration strategy of Hamjediers, Schmelzer, and Geschke (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). All three files include spells of singlehood and, in addition, record post-divorce and widowhood periods as separate spells; before integration, we recoded these post-union spells to singlehood for consistency.\u003c/p\u003e \u003cp\u003eWe then expanded each spell file into person\u0026ndash;period panel data, aligned by respondent identifier and calendar time (monthly where available, otherwise yearly). This expansion yields one observation per individual per time unit, with covariates carried across intervals and events flagged at their endpoints, facilitating coherent alignment across sources and enabling longitudinal/event-history analyses. When overlapping spells are present, we prioritize \u003cem\u003ebiocouplm.dta\u003c/em\u003e and \u003cem\u003ebiomarsm.dta\u003c/em\u003e because - in contrast to \u003cem\u003ebiomarsy.dta\u003c/em\u003e - they provide monthly start and end dates. Because none of the three history files includes partner identifiers, we merge the integrated panel with \u003cem\u003epgen.dta\u003c/em\u003e by person ID and year to obtain partner IDs and other harmonized wave variables. Finally, we reconvert the aligned panel back to spell format, producing chronologically ordered partnership states with precise start and end dates.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePSID Partnership Biography\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn PSID, partnership histories are collected in the 1985\u0026ndash;2021 \u003cem\u003eMarriage History File\u003c/em\u003e, which we used in LIB. Unlike the previous databases, PSID includes only information about marriages, i.e., cohabitation is not included. The information on cohabiting partners was also not available in the main survey and thus is not included in LIB.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSHP Partnership Biography\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe constructed SHP partnership histories by integrating the annual individual surveys (\u003cem\u003eshpwave_p_user.dta\u003c/em\u003e) with two retrospective modules fielded in 2002 and 2013 (\u003cem\u003eshp0_bvcs_user.dta\u003c/em\u003e and \u003cem\u003eshpiii_cs_user.dta\u003c/em\u003e). The retrospective files report starts and end years of relationships (no months) and do not include partner identifiers; each respondent appears in exactly/at least one of these modules. Both collect marriages, separations, and divorces, but they differ otherwise: the 2002 module does not capture cohabitation spells yet identifies never-married respondents, whereas the 2013 module records cohabitation but lacks a never-married indicator. By contrast, the main survey provides civil status at interview, partner IDs, month/year of current cohabitation onset, and month/year of status changes (marriage, separation, divorce, widowhood). We first expanded the retrospective histories from spell to yearly person-period format, then merged all sources by respondent ID and year. When overlaps occurred, we prioritized the main survey because it offers monthly timing and is closer to the interview date, reducing recall bias. Finally, we recomputed spells and returned the integrated series to wide format.\u003c/p\u003e \u003cp\u003e \u003cb\u003eHILDA Partnership Biography\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe constructed HILDA partnership biographies from the main survey data stored in the \u003cem\u003eletter_Combined.dta\u003c/em\u003e files. These files record the current (or most recent) union and up to four prior unions; however, precise start and end dates (month and year) are available only for the most recent union. The survey also captures ongoing cohabitation at interview\u0026mdash;recording the relationship start and the partner\u0026rsquo;s identifier. Some spells begin before sample entry and are therefore partly retrospective. Importantly, HILDA does not provide full pre-entry cohabitation histories, so coverage of cohabitation prior to panel entry is less complete than in other panels.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Employment Biographies across Databases\u003c/h2\u003e \u003cp\u003eCompared to fertility and partnership, employment trajectories are more complex. Individuals can hold several jobs simultaneously or combine jobs and other duties, such as education. It is also the domain in which we observed greater heterogeneity across databases. Longitudinal surveys differ in terms of what information is collected about spells in-between waves, the availability of retrospective information (available only in SOEP, SHP, and BHPS), the types of questions asked in the main survey, and even the levels available for categorical variables (different occupation or industry codes). Consequently, we adopted a minimalistic approach and constructed histories of the main labor market status, coded at the monthly level. We derived six different categories: [1] working, [2] unemployed, [3] not in the labor force, [4] retired, [5] other.Persons with employment contract even if not working (e.g. maternity leave) were classified as employed. All types of job contracts (e.g. short-term, mini, midi jobs) were also included in this category.\u003c/p\u003e \u003cp\u003eTo construct employment biographies, we applied two core rules. First, when multiple sources report the same period, we privileged the source temporally closest to the spell, thereby minimizing recall error (Watson, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). For example, in HILDA the retrospective calendar spans January of the prior year to the interview date, so a given month (e.g., January) may be reported in two consecutive waves; in such cases we used the report from the wave nearest to the month in question. Second, when respondents hold multiple statuses within a month, we resolved conflicts via a hierarchical ordering (e.g., full-time employment\u0026thinsp;\u0026gt;\u0026thinsp;part-time employment\u0026thinsp;\u0026gt;\u0026thinsp;unemployment\u0026thinsp;\u0026gt;\u0026thinsp;out of the labor force), and when multiple jobs are reported we adopted the respondent-designated main job to define the spell.\u003c/p\u003e \u003cp\u003eJob spells may change in-between the survey waves. PSID allows for recovering some of the job features, such as industry, occupation, or hourly wages. However, other databases (SOEP, HILDA, SHP, BHPS/UKHLS) provide information only about the characteristics of the current job, performed at the time of the interview. Even if we know that the job characteristics changed between the two waves, we cannot establish when the change took place. As a consequence, we opted not to introduce these job characteristics into the LIB.\u003c/p\u003e \u003cp\u003e \u003cb\u003eBHPS and UKHLS Employment Biography\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo construct the employment history for BHPS and UKHLS, we relied on the work of Wright (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), who harmonized the start and end dates of labor market activity spells. However, we adjusted this approach to meet our specific needs, ensuring compatibility with our project. In BHPS, we combined information from the main survey files \u003cem\u003eindresp_protect.dta\u003c/em\u003e and retrospective files \u003cem\u003ejobhist_protect.dta\u003c/em\u003e files to construct employment spells. In UKHLS, we combined the main survey \u003cem\u003eindresp_protect.dta\u003c/em\u003e file and retrospective \u003cem\u003elifemst_protect.dta\u003c/em\u003e files (employment status: \u003cem\u003eleshst\u003c/em\u003e). The main survey \u003cem\u003eindresp_protect.dta\u003c/em\u003e files contain information on whether the job has changed since the last interview and employment a status at the time of interview (\u003cem\u003ejbstat\u003c/em\u003e), and if so, provide the end date of the activity (month and year). We combined information from BHPS and UKHLS together and recalculate spells. For individuals who participated in both BHPS and UKHLS, we treated their entry point into UKHLS as a new spell.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSOEP Employment Biography\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn building a consistent SOEP employment biography, we followed Schmelzer and Hamjediers (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), integrating retrospective and calendar files with tenure variables to capture changes in employment status and job transitions within spells. To accomplish this, we utilized three files: \u003cem\u003epbiospe.dta\u003c/em\u003e (retrospective file in years), \u003cem\u003eartkalen.dta\u003c/em\u003e (calendar file in months), and \u003cem\u003epl.dta\u003c/em\u003e (survey data). From the last, we obtained the variable tenure indicating the start date with the current employer: \u003cem\u003eplb0035\u003c/em\u003e (\u003cem\u003eAt Current Employer Since-Month\u003c/em\u003e) and \u003cem\u003eplb0036_h\u003c/em\u003e (\u003cem\u003eEmployed by current employer since year\u003c/em\u003e); This strategy recovers employment histories covering retrospective and prospective periods with monthly accuracy. Firstly, we cleaned and expanded \u003cem\u003eartkalen.dta\u003c/em\u003e to a monthly format, merged it with tenure data to mark smooth employment transitions as separate spells. Secondly, we converted it to a yearly format to be able to merge with \u003cem\u003epbiospe.dta\u003c/em\u003e. In cases of overlap, we prioritized \u003cem\u003eartkalen.dta\u003c/em\u003e as it is more precise, contains monthly data and is closer to the interview date.\u003c/p\u003e \u003cp\u003e \u003cb\u003eHILDA Employment Biography\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe constructed HILDA employment biographies from the \u003cem\u003eletter_wave_Combined.dta\u003c/em\u003e files, drawing on two components: (i) the employment calendar, which records labor-market activity at the beginning, middle, and end of each month for the period back to the start of the previous financial year (statuses: employed, unemployed, not in the labor force), and (ii) tenure variables (e.g., \u003cem\u003eletter_jbemlwk\u003c/em\u003e weeks with current employer; \u003cem\u003eletter_jjbemlyr\u003c/em\u003e years with current employer) that capture time with the current employer.\u003c/p\u003e \u003cp\u003eThe calendar allows respondents to report up to 12 jobs (sequential or concurrent) as well as non-employment states. Because these sources can overlap, we prioritized employment over non-employment in a given month and, for multiple jobs, did not differentiate among concurrent positions since job-level characteristics are unavailable. We then integrated tenure information at the interview date to identify smooth job-to-job transitions (without intervening unemployment) and to initiate new employment spells. Although HILDA lacks fully retrospective employment histories, tenure measures at first interview permit inference of job start dates prior to panel entry, extending the employment biography backward.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSHP Employment Biography\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe constructed SHP employment biographies using three sources. First, the activity calendar in \u003cem\u003eshpwave_p_user.dta\u003c/em\u003e provides monthly employment status between interviews for respondents who completed the individual questionnaire. Following the SHP (2013) documentation, calendar coverage spans the interval from wave \u003cem\u003ew\u0026ndash;1\u003c/em\u003e to \u003cem\u003ew\u003c/em\u003e for continuing respondents, or the last 12 months for those absent in \u003cem\u003ew\u0026ndash;1\u003c/em\u003e; calendars are empty in waves without an interview, and questions are asked only when respondents report a change since the prior wave. We cleaned these monthly indicators (12 months for active and 12 for inactive persons) and added tenure information (\u003cem\u003ep_w66, p_w69\u003c/em\u003e) to identify smooth job-to-job transitions without intervening unemployment.\u003c/p\u003e \u003cp\u003eSecond, we incorporated retrospective modules\u0026mdash;\u003cem\u003eSHP0_bvwl_user.dta\u003c/em\u003e (collected in 2001\u0026ndash;2002) and \u003cem\u003eshpiii_prof_act_user.dta\u003c/em\u003e (2013)\u0026mdash;which record paid work, unemployment, and inactivity prior to panel entry at yearly resolution. We harmonized status categories and expanded spells to a person\u0026ndash;year format. Finally, we merged calendars, tenure, and retrospectives by respondent ID and time. Where overlaps arise, we prioritized the calendar module because it offers monthly timing and is closer to interview, thereby reducing recall bias.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePSID Employment Biography\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo build employment biographies in the PSID, we divided the database into four periods (1968\u0026ndash;1976, 1976\u0026ndash;1987, 1988\u0026ndash;2001, and 2003\u0026ndash;2021), reflecting major changes in how employment information was collected. Because of these differences, consistent labor market histories cannot be reconstructed across all periods; complete work trajectories can be traced only from 1988 onward. Across these four periods, the construction of employment spells and the coverage of household members differ substantially. In 1968\u0026ndash;1976, we define spells by between-wave status changes for the household head only and excluded job-to-job moves. In 1976\u0026ndash;1987, we built spells based on between-wave changes but supplemented with tenure measures; coverage extends to heads and spouses. In 1988\u0026ndash;2001, we defined spells using employer-specific start and end dates for heads and spouses and monthly status was inferred from reported job start and end dates (no calendar file existed). From 2003\u0026ndash;2021, spells combine a monthly activity calendar with employer start/end dates (variables differ from the 1988\u0026ndash;2001 period); we use family files (heads and spouses; detailed job dates) and individual files (status at interview, ever worked, work since prior year, tenure, months searching, retirement timing, and start month/year for up to four jobs).\u003c/p\u003e \u003cp\u003eIt is important to underline that PSID remains distinct from other panels: data have been collected biennially since 2003 (increasing recall bias), questionnaires are answered by the household reference person (which may lead to misreporting for spouses), and employment information for other household members (non-heads and non-spouses), such as working offspring, is available only at the time of the interview.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3 A Note on Codes - How to Work With LIB?\u003c/h2\u003e \u003cp\u003eThe LIB does not distribute data, instead it provides the code required to create the desired biographies. All codes are written in R version 4.4.0, an open-source statistical programming language. The code is stored in 28 different files, which are listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eFrom the users' perspective, two files are particularly important for customization and adjustment of the code according to individual preferences: \u003cem\u003e00_setting_the_workspace\u003c/em\u003e, which contains all paths and necessary packages required by the subsequent scripts; and \u003cem\u003e01_data_selector\u003c/em\u003e, which specifies which biographies from which databases should be constructed (the default option is to construct all biographies from all databases). \u003cem\u003e01_data_selector\u003c/em\u003e also allows for selecting the format of the file to which the databases should be exported. Three options are available: .\u003cem\u003ecsv, .dta, and .Rdata\u003c/em\u003e. The remaining scripts developed for each database (PSID, SOEP, BHPS_UKHLS, SHP, and HILDA) include separate code files for each biography (employment, fertility, and partnership), as well as a master file encompassing variables common to all biographies (e.g., personal identifiers). In the case of PSID, we produced additional scripts containing auxiliary functions. These files are self-contained and should not be modified to ensure that the resulting databases are identical. Finally, we also provide a script that allows linking selected variables from the CPF dataset to LIB.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of scripts\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDatabase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ename of script\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ereadme file\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e000_readme_file LIB.txt\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003edirectories\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e00_setting_work_space.R\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eRunning LIB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e01_data_selector.R\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCPF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e02_CPF_merging.R\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUK\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBHPS_UKHLS_master_file.R\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUK\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBHPS_UKHLS_employment.R\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUK\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBHPS_UKHLS_fertility.R\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUK\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBHPS_UKHLS_partnership.R\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSOEP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSOEP_master_file.R\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSOEP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSOEP_employment.R\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSOEP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSOEP_fertility.R\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSOEP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSOEP_partnership.R\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHILDA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHILDA_master_file.R\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHILDA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHILDA_employment.R\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHILDA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHILDA_fertility.R\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHILDA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHILDA_partnership.R\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePSID\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePSID_master_file.R\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePSID\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePSID_employment.R\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePSID\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePSID_fertility.R\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e19\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePSID\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePSID_partnership.R\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePSID\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePSID_variables_needed_21_century.R\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e21\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSHP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSHP_master_file.R\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e22\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSHP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSHP_employment.R\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e23\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSHP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSHP_fertility.R\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e24\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSHP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSHP_partnership.R\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eValidation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003evalidation_fertility.R\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eValidation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003evalidation_employment.R\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e27\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eValidation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003evalidation_partnership.R\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e28\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eValidation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003evalidation_employment.R\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e29\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eValidation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003evalidation_graph_functions.R\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNotes: List of scripts used to create the LIB database.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe first step to create LIB is to specify the folder structure in the file \u003cem\u003e00_setting_the_workspace\u003c/em\u003e. We recommend the structure presented in Figure A1 in the Appendix. This structure divides the root folder into three subfolders: (1) Raw data, which contains the unzipped databases organized by country name, (2) codes, which is where the LIB codes should reside; and (3) output, which is created by the code (if it does not exist), and which contains the database with the selected histories for the selected countries. This recommended folder structure is not binding; users can customize it to suit their specific needs.\u003c/p\u003e \u003cp\u003eThe second step is to specify the desired biographies and execute \u003cem\u003e01_data_selector.R\u003c/em\u003e. This script writes outputs to the designated folder: (i) a consolidated file, LIB_\u0026lt;currentdate\u0026gt;, containing the selected biographies from the selected panels; (ii) per-panel/per-biography files named\u0026thinsp;\u0026lt;\u0026thinsp;database\u0026gt;_\u0026lt;biography\u0026gt;. If the user is interested only in the full database, which contains histories from all countries covered by LIB, these other databases, except for LIB_\u0026lt;currentdate\u0026gt;, can be safely deleted. Lastly, the code creates a dated \u0026lsquo;readme\u0026rsquo; file.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Data Records","content":"\u003cp\u003eUpon executing the codes, the user will have created the LIB_\u0026lt;currentdate\u0026thinsp;\u0026gt;\u0026thinsp;database in the selected folder stored as an Rdata file, a comma-separated file, or a Stata file. If user selects all biographies, the final dataset contains basic information about respondents and their fertility, partnership, and employment biographies stored in a wide format. This means that each row corresponds to a different individual, whereas different columns indicate either spells or events, depending on the biography.\u003c/p\u003e \u003cp\u003eThe basic information about respondents includes their IDs, biological sexes, and years of birth as well as the dates of their first and last interviews. The basic information also describes participants' status in the survey. These variables include the date of each interview and the interview status. These variables are indexed with a # (numbers) to indicate different waves. Furthermore, we included the variables used for validation: \u003cem\u003eemp_status_#\u003c/em\u003e, \u003cem\u003eever_married\u003c/em\u003e, and \u003cem\u003eanychild\u003c/em\u003e. We list these variables in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAdditionally, we collected variables that enable us to filter individuals who are not representative and should be excluded for external validation purposes. Additional sample-specific variables are included to identify subsample characteristics and respondent eligibility. These include \u003cem\u003epsample\u003c/em\u003e (subsample identifier in GSOEP), \u003cem\u003esample\u003c/em\u003e (PSID sample type, e.g., \"Immigrant or Latino\", \"SEO\", \"SRC\"), \u003cem\u003edrop\u003c/em\u003e (dropout status in 1997 in PSID), and \u003cem\u003efollowable\u003c/em\u003e (followability classification in PSID). For SHP, \u003cem\u003eretro_marriage\u003c/em\u003e and \u003cem\u003eflag_retro_fertility\u003c/em\u003e indicate inclusion in retrospective marriage and fertility files, respectively. The \u003cem\u003ememorig\u003c/em\u003e variable marks respondents from an ethnic minority boost sample in BHPS/UKHLS.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and Technical Variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003elabel\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTime invariant variables\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003epid\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eID of the respondent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBORN_Y\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear of birth\u003c/p\u003e \u003cp\u003e1906\u0026ndash;2023\u003c/p\u003e \u003cp\u003e[-1] unknown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBORN_M\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonth of birth\u003c/p\u003e \u003cp\u003e1\u0026ndash;12\u003c/p\u003e \u003cp\u003e[-1] unknown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSEX\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBiological sex\u003c/p\u003e \u003cp\u003e[1] male\u003c/p\u003e \u003cp\u003e[2] female\u003c/p\u003e \u003cp\u003e[-1] unknown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFIRSTOBS_Y\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear of first interview\u003c/p\u003e \u003cp\u003e1968\u0026ndash;2023\u003c/p\u003e \u003cp\u003e[-1] unknown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLASTOBS_Y\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear of last interview\u003c/p\u003e \u003cp\u003e1968\u0026ndash;2023\u003c/p\u003e \u003cp\u003e[-1] unknown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTime variant variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eINTERVIEW_DATE_#\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edate of interview\u003c/p\u003e \u003cp\u003e1968\u0026ndash;2023\u003c/p\u003e \u003cp\u003e[NA] unknown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eINTERVIEW_STATUS_#\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etype of survey response for a given individual\u003c/p\u003e \u003cp\u003e[1] fully responsive\u003c/p\u003e \u003cp\u003e[2] proxy, which includes the child respondent, proxy respondent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVariables needed for validation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEMP_STATUS_#\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmployment status\u003c/p\u003e \u003cp\u003e[0] not-working\u003c/p\u003e \u003cp\u003e[1] working\u003c/p\u003e \u003cp\u003e[-1] unknown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEVER_MARRIED\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEver married?\u003c/p\u003e \u003cp\u003e[0] - no\u003c/p\u003e \u003cp\u003e[1] - yes\u003c/p\u003e \u003cp\u003e[-1] unknown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eANYCHILD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAny child at the last possible interview?\u003c/p\u003e \u003cp\u003e[1] yes\u003c/p\u003e \u003cp\u003e[2] no\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eNote: # corresponds to the year of the wave\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e to \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e7\u003c/span\u003e lists the variables contained in LIB, as well as the availability across countries.\u003c/p\u003e \u003cp\u003eFertility biography (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e) contains the respondent\u0026rsquo;s ID, the date of birth of each child (month and year), the biological sex of each child, and the parity of each child. The \u0026ldquo;\u003cspan\u003e$\u003c/span\u003e\u0026rdquo; suffix in the variables indicates parity. The fertility biography also contains the total number of children born to an individual, i.e., the \u003cem\u003eNR_KIDS\u003c/em\u003e variable.\u003c/p\u003e \u003cp\u003eThe Partnership biography (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e6\u003c/span\u003e) covers information on respondents\u0026rsquo; partnerships, including the type of the partnership (cohabitation, marriage), start and end dates of each partnership, and the reason for the partnership\u0026rsquo;s end. When available, we also included the information on partners\u0026rsquo; IDs, which allows for an easy recovery of partners\u0026rsquo; characteristics from the original datasets. The dollar sign indicates the spell number, which is defined as the intersection of a union type with a given partner. For example, if a cohabiting couple decides to marry, these two states will be coded as separate spells (cohabitation and marriage). We created two further variables to indicate how the spell ended, according to differences in the type of union. Finally, an employment biography (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e7\u003c/span\u003e) includes the start and end dates of each labor market activity spell, as well as the employment status during the spell. The dollar signs indicate the spell number.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of available variables in LIB fertility biography\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFertility biography\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003epid\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eID of the respondent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKID_M$\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emonth of birth\u003c/p\u003e \u003cp\u003e[-1] unknown month of birth\u003c/p\u003e \u003cp\u003e[NA] no child of order \u003cspan\u003e$\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKID_Y$\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyear of birth\u003c/p\u003e \u003cp\u003e1900\u0026ndash;2024\u003c/p\u003e \u003cp\u003e[-1] unknown year of birth\u003c/p\u003e \u003cp\u003e[NA] no child of order \u003cspan\u003e$\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKID_S$\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChild\u0026rsquo;s biological sex:\u003c/p\u003e \u003cp\u003e[1] male\u003c/p\u003e \u003cp\u003e[2] female \u003c/p\u003e \u003cp\u003e[-1] unknown\u003c/p\u003e \u003cp\u003e[NA] no child of order \u003cspan\u003e$\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKID_$\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndicator of child order (provides info if child was born, even if birth date unknown)\u003c/p\u003e \u003cp\u003e[0]: no child (of order \u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e[1]: child (of order \u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNR_KIDS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of children in LIB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of available variables in LIB partnership biography\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePartnership biography\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePARTNER_ID_$\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eID of respondent\u0026rsquo;s partner\u003c/p\u003e \u003cp\u003e[NA] not applicable for partnership spells \u0026ldquo;no union\u0026rdquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePARTNERSHIP_STATUS_$\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType of partnership status:\u003c/p\u003e \u003cp\u003e[0] no union\u003c/p\u003e \u003cp\u003e[1] cohabitation\u003c/p\u003e \u003cp\u003e[2] marriage\u003c/p\u003e \u003cp\u003e[3] gap\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSTART_Y_$\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe year when the partnership status started\u003c/p\u003e \u003cp\u003e1900\u0026ndash;2023\u003c/p\u003e \u003cp\u003e[-1] unknown start year\u003c/p\u003e \u003cp\u003e[NA] not applicable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSTART_M_$\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe month when the partnership status started\u003c/p\u003e \u003cp\u003e1\u0026ndash;12\u003c/p\u003e \u003cp\u003e[-1] unknown start month\u003c/p\u003e \u003cp\u003e[NA] not applicable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEND_Y_$\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe year when the partnership status ended\u003c/p\u003e \u003cp\u003e1900\u0026ndash;2023\u003c/p\u003e \u003cp\u003e[-1] unknown end year\u003c/p\u003e \u003cp\u003e[NA] not applicable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEND_M_$\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe month when the partnership status ended\u003c/p\u003e \u003cp\u003e1\u0026ndash;12\u003c/p\u003e \u003cp\u003e[-1] unknown end month\u003c/p\u003e \u003cp\u003e[NA] not applicable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEND_SINGLE_$\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHow the single spell ended:\u003c/p\u003e \u003cp\u003e[0] ongoing, not ended spell\u003c/p\u003e \u003cp\u003e[1] cohabitation\u003c/p\u003e \u003cp\u003e[2] marriage\u003c/p\u003e \u003cp\u003e[-1] [unknown end]\u003c/p\u003e \u003cp\u003e[NA] not applicable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEND_UNION_$\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHow the union spell ended:\u003c/p\u003e \u003cp\u003e[0] ongoing, not ended spell\u003c/p\u003e \u003cp\u003e[1] marriage\u003c/p\u003e \u003cp\u003e[2] separation\u003c/p\u003e \u003cp\u003e[3] the death of a partner\u003c/p\u003e \u003cp\u003e[-1] unknown end\u003c/p\u003e \u003cp\u003e[NA] not applicable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDIVORCE_$\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[0] divorce did not occur\u003c/p\u003e \u003cp\u003e[1] divorce occurred\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDIVORCE_Y_$\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyear of divorce\u003c/p\u003e \u003cp\u003e1900\u0026ndash;2023\u003c/p\u003e \u003cp\u003e[-1] divorce year unknown\u003c/p\u003e \u003cp\u003e[NA] not applicable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDIVORCE_M_$\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emonth of divorce\u003c/p\u003e \u003cp\u003e1\u0026ndash;12\u003c/p\u003e \u003cp\u003e[-1] divorce month unknown\u003c/p\u003e \u003cp\u003e[NA] not applicable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of available variables in LIB employment biography\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEmployment biography\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEMPLOYMENT_STATUS_$\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmployment status:\u003c/p\u003e \u003cp\u003e[1] working \u003c/p\u003e \u003cp\u003e[2] unemployed\u003c/p\u003e \u003cp\u003e[3] not in the labor force\u003c/p\u003e \u003cp\u003e[4] retired\u003c/p\u003e \u003cp\u003e[5] other\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eENTRY_Y$\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear of entry into employment status\u003c/p\u003e \u003cp\u003e1900\u0026ndash;2023\u003c/p\u003e \u003cp\u003e[-1] unknown entry year\u003c/p\u003e \u003cp\u003e[NA] not applicable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eENTRY_M$\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonth of entry into employment status\u003c/p\u003e \u003cp\u003e1\u0026ndash;12\u003c/p\u003e \u003cp\u003e[-1] unknown entry month\u003c/p\u003e \u003cp\u003e[NA] not applicable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eENTRY_D$\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;31\u003c/p\u003e \u003cp\u003e[NA] not applicable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEXIT_Y$\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear of exit of an employment status\u003c/p\u003e \u003cp\u003e1900\u0026ndash;2023\u003c/p\u003e \u003cp\u003e[-1] unknown exit year\u003c/p\u003e \u003cp\u003e[NA] not applicable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEXIT_M$\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonth of exit of an employment status\u003c/p\u003e \u003cp\u003e1\u0026ndash;12\u003c/p\u003e \u003cp\u003e[-1] unknown exit month\u003c/p\u003e \u003cp\u003e[NA] not applicable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEXIT_D$\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;31\u003c/p\u003e \u003cp\u003e[NA] not applicable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"4. Technical Validation","content":"\u003cp\u003eThe LIB results from the harmonization of multiple data sources, making validation essential to ensure that the combined data accurately represent key demographic and labor market phenomena. We conducted both internal and external validation. Internal validation compares statistics derived from the LIB biographies with those obtained at the time of the interview in the main survey, while external validation compares LIB statistics with independent sources. Graphs which compare statistics derived from the LIB biographies with internal and external statistics are displayed in the article. Under each graph we also listed sources for external statistics. Here we only briefly discuss the main findings from this validation.\u003c/p\u003e\n\u003cp\u003eWe validated fertility histories by comparing LIB estimates to three statistics: the proportion of childless women at age 40 by cohort (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea\u0026ndash;e), the mean age at birth (all parities) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea\u0026ndash;e), and completed cohort fertility at age 40 (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea\u0026ndash;e). In the first case, most LIB-based estimates closely match external benchmarks, with larger deviations only in PSID for the oldest (1941\u0026ndash;1950) and youngest (1971\u0026ndash;1977) cohorts. Similarly, the mean age at birth is closely aligned across surveys, with modest differences again concentrated in PSID for the oldest and youngest cohorts. Finally, for completed cohort fertility at age 40, LIB estimates are also close to the benchmark, and small differences appear only among the youngest cohorts (1971\u0026ndash;1977) in SOEP, BHPS\u0026amp;UKHLS, and HILDA, where LIB slightly underestimates fertility compared to external data.\u003c/p\u003e\n\u003cp\u003eTo validate partnership biographies, wederived the proportion of never married women by cohort from LIB biographies and compared it to external sources. As shown in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea\u0026ndash;e, LIB-based estimates largely replicate external benchmarks across all surveys, with particularly close agreement for the 1951\u0026ndash;1960 and 1961\u0026ndash;1970 cohorts. Larger deviations occur in PSID and HILDA for the youngest cohort (1971\u0026ndash;1977), where LIB slightly underestimates the never-married share, and in SOEP and SHP for the oldest cohort (1941\u0026ndash;1950). We further validated our database, by comparing the mean age at first marriage. In Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea\u0026ndash;e, we observe that LIB reproduces trends in rising ages at first marriage across surveys. Coherence is strongest in SOEP and BHPS\u0026amp;UKHLS, while PSID and SHP show small underestimations in the earliest (2000\u0026ndash;2004) and latest (2015\u0026ndash;2019) periods, HILDA displays very stable correspondence. The large confidence intervals (CIs) observed for SHP are likely due to the small sample size, as this analysis includes only individuals from the retrospective cohort, which represents a limited number of cases.\u003c/p\u003e\n\u003cp\u003eFor employment biographies, we compared male and female labor force participation rates. The comparisons are displayed in Figs. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea\u0026ndash;e and \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ea\u0026ndash;e, for men and women, respectively. LIB-based estimates align closely with both internal and external benchmarks, indicating that reconstructed employment histories capture male and female labor force participation accurately. Minor discrepancies appear in PSID and BHPS\u0026amp;UKHLS for recent cohorts, where LIB slightly underestimates employment rates, while SOEP and SHP show nearly perfect alignment. HILDA exhibits only small, consistent fluctuations.\u003c/p\u003e\n\u003cp\u003eOverall, these checks confirm that the harmonized LIB provides a reliable representation of fertility, partnership, and employment histories across surveys. Deviations are minor and typically confined to specific cohorts or datasets, underscoring the robustness and comparability of the LIB-derived measures.\u003c/p\u003e"},{"header":"5. Usage Note","content":"\u003cp\u003e \u003cb\u003eCompatibility of LIB with other harmonization projects\u003c/b\u003e \u003c/p\u003e \u003cp\u003eLIB is compatible with other harmonization efforts, particularly the Cross-National Equivalent File (CNEF) developed by Frick et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and the Comparative Panel File (CPF) created by Turek et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). While LIB extends coverage by incorporating between-wave calendars and retrospective modules prior to sample entry, focusing specifically on partnership, fertility, and employment histories, CNEF/CPF provide a broader set of contemporaneous covariates (e.g., education, health, and detailed job characteristics at interview). LIB can be linked to these infrastructures via respondent identifiers and country codes, enabling the enrichment of spell histories with wave-specific attributes.\u003c/p\u003e \u003cp\u003eThe method we propose to merge the database requires first converting the LIB biographies into the long format, where each row represents a single month. Then, it can be merged with the CPF data using respondent IDs, country level identifiers, and the interview date. Notice that in the resulting database information from CPF will be incorporated \u003cem\u003eonly\u003c/em\u003e in the interview months, and missing values for the remaining months. The decision on how to proceed further will depend on users\u0026rsquo; specific research needs and objectives (e.g., may require using imputation techniques to fill in the missing values in the CPF variables).\u003c/p\u003e \u003cp\u003eThe code for merging the LIB and CPF datasets in the way discussed above is provided alongside the remaining scripts (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was supported by the Polish National Agency for Academic Exchange (Polish Returns Programme 2019) (PI: Anna Matysiak) and the European Research Council under the ERC Consolidator Grant \u003cem\u003e\u0026ldquo;Globalization- and Technology-Driven Labour Market Change and Fertility\u0026rdquo;\u003c/em\u003e (LABFER, grant agreement no 866207) (PI: Anna Matysiak).\u003c/p\u003e\u003ch2\u003eCode Availability\u003c/h2\u003e \u003cp\u003eThe R code to prepare LabFam harmonized histories is available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/weychert/LabFam-Individual-Biographies\u003c/span\u003e\u003cspan address=\"https://github.com/weychert/LabFam-Individual-Biographies\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Code prepared using R [64-bit] Version 4.4.0 on Windows 11, and RStudio.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDi Nallo, A. and K\u0026ouml;ksal, S. (2023) Job loss during pregnancy and the risk of miscarriage and stillbirth, \u003cem\u003eHuman Reproduction\u003c/em\u003e, 38(11), 2259\u0026ndash;2266, https://doi.org/10.1093/humrep/dead183\u003c/li\u003e\n\u003cli\u003eDrobnič, S. (2002). Retirement Timing in Germany: The Impact of Household Characteristics. \u003cem\u003eInternational Journal of Sociology\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(2), 75\u0026ndash;102. https://doi.org/10.1080/15579336.2002.11770250\u003c/li\u003e\n\u003cli\u003eEubank, N. (2016), Lessons from a Decade of Replications at the Quarterly Journal of Political Science,\u003cem\u003e PS: Political Science \u0026amp; Politics\u003c/em\u003e; 49(2), 273\u0026ndash;276. https://doi.org/10.1017/S1049096516000196 \u003c/li\u003e\n\u003cli\u003eFreese, J., Rauf, T., \u0026amp; Voelkel, J. G. (2022). Advances in transparency and reproducibility in the social sciences. Social Science Research, 107, 102770. https://doi.org/https://doi.org/10.1016/j.ssresearch.2022.102770 \u003c/li\u003e\n\u003cli\u003eFrick, J. R., Jenkins, S. P., Lillard, D. R., Lipps, O., \u0026amp; Wooden, M. (2007). The Cross-National Equivalent File (CNEF) and its member country household panel studies. Journal of Contextual Economics\u0026ndash;Schmollers Jahrbuch(4), 627\u0026ndash;654. \u003c/li\u003e\n\u003cli\u003eHamjediers, M., Schmelzer, P., \u0026amp; Geschke, S. C. (2020). SOEP-Core v35: The couple history files BIOCOUPLM and BIOCOUPLY, and marital history files BIOMARSM and BIOMARSY (No. 871). SOEP Survey Papers.\u003c/li\u003e\n\u003cli\u003eHuntington-Klein, N., Arenas, A., Beam, E., Bertoni, M., Bloem, J. R., Burli, P., Chen, N., Grieco, P., Ekpe, G., Pugatch, T., Saavedra, M., \u0026amp; Stopnitzky, Y. (2021). The influence of hidden researcher decisions in applied microeconomics. Economic Inquiry, 59(3), 944\u0026ndash;960. https://doi.org/https://doi.org/10.1111/ecin.12992 \u003c/li\u003e\n\u003cli\u003eKreyenfeld, M. (2010), Uncertainties in Female Employment Careers and the Postponement of Parenthood in Germany, European Sociological Review, 26(3), 351\u0026ndash;366, https://doi.org/10.1093/esr/jcp026\u003c/li\u003e\n\u003cli\u003eOsiewalska, B., Matysiak, A., \u0026amp; Kurowska, A. (2024). Home-based work and childbearing. Population Studies, 78(3), 525\u0026ndash;545. https://doi.org/10.1080/00324728.2023.2287510\u003c/li\u003e\n\u003cli\u003ePerelli-Harris, B., Kreyenfeld, M. R., \u0026amp; Kubisch, K. (2010). Harmonized histories: Manual for the preparation of comparative fertility and union histories. Mimeo\u003c/li\u003e\n\u003cli\u003eSchmelzer, P. Hamjediers, M. (2020). SOEP-Core v35 - activity biography in the files PBIOSPE and ARTKALEN, SOEP Survey Papers, No. 877, Deutsches Institut f\u0026uuml;r Wirtschaftsforschung (DIW), Berlin\u003c/li\u003e\n\u003cli\u003eSchmitt, C. (2020). SOEP-Core v35-BIOBIRTH: A data set on the birth biography of male and female respondents (No. 875). SOEP Survey Papers.\u003c/li\u003e\n\u003cli\u003eTurek, K., Kalmijn, M., \u0026amp; Leopold, T. (2021). The Comparative Panel File: Harmonized Household Panel Surveys from Seven Countries. European Sociological Review, 37(3), 505\u0026ndash;523. https://doi.org/10.1093/esr/jcab006 \u003c/li\u003e\n\u003cli\u003eWatson, N. (2009, July). Disentangling overlapping seams: the experience of the HILDA survey. In HILDA Survey Research Conference, University of Melbourne (pp. 16-17).\u003c/li\u003e\n\u003cli\u003eWright, L. (2020). Producing working-life histories in the BHPS and UKHLS 2017-2020. [Data Collection]. Colchester, Essex: UK Data Service. 10.5255/UKDA-SN-854327 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"edf20a40-fba4-458e-8af1-42f8a39724ac","identifier":"10.13039/501100000781","name":"European Research Council","awardNumber":"866207","order_by":0},{"identity":"c8f015e6-dae7-47cb-a8f1-9a40ff299463","identifier":"10.13039/501100014434","name":"Narodowa Agencja Wymiany Akademickiej","awardNumber":"Polish Returns Programme 2019","order_by":1}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8376548/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8376548/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eReproducibility in social science is often hindered by inconsistent data preparation and limited transparency. The LabFam Individual Biographies (LIB) project addresses this challenge by providing open, cross-national harmonization of life-course histories from five long-running panels: Australia (HILDA), Germany (SOEP), Switzerland (SHP), the United Kingdom (BHPS/UKHLS), and the United States (PSID). LIB reconstructs spell-based data across three domains\u0026mdash;fertility (number and timing of births), partnership (timing of union formation/dissolution), and employment (employment spells and job characteristics)\u0026mdash;by integrating panel questionnaires, calendar modules, and retrospective components to recover events between waves and before survey entry. Outputs are organized as dated spells with explicit starts and ends, enabling duration and transition analyses within and across countries. We document variable definitions, harmonization and conflict-resolution procedures, and validation against internal survey indicators and external benchmarks. Implemented in R and released as open code, LIB supports complete reproduction, user customization, and linkage to complementary infrastructures (e.g., CPF/CNEF), thereby reducing setup costs and improving comparability.\u003c/p\u003e","manuscriptTitle":"LabFam Individual Biographies harmonised family and employment histories based on panel surveys","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-17 07:04:15","doi":"10.21203/rs.3.rs-8376548/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"77eaa80a-edfb-4620-9935-3c34cfb69e3e","owner":[],"postedDate":"December 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":59755280,"name":"Sociology"}],"tags":[],"updatedAt":"2025-12-17T07:04:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-17 07:04:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8376548","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8376548","identity":"rs-8376548","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}