Disziplinäre Grenzüberschreitungen - Gert G. Wagner zum 65. Geburtstag Marcel Erlinghagen, Karsten Hank & Michaela Kreyenfeld Zur Erklärung gesellschaftlicher Phänomene und individueller Verhaltensweisen werden seit einiger Zeit (wieder) zunehmend multi- und interdisziplinäre Perspektiven herangezogen. Auch wenn die Wissenschaftslandschaft nach wie vor stark durch disziplinäre Grenzziehungen geprägt ist, kann die komplexe Wirklichkeit globalisierter Gesellschaften und der in diesen agierenden Individuen nur durch disziplinäre Grenzüberschreitungen theoretisch und empirisch angemessen erfasst und analysiert werden. Ein beharrlicher Grenzgänger dieser Art, der in der Tradition der klassischen Nationalökonomie, die die Wirtschaftswissenschaft als Gesellschaftswissenschaft versteht und dabei neben den wirtschaftlichen und sozialen Rahmenbedingungen individuellen Handelns auch die Psychologie der Akteure mit in den Blick nimmt, ist Gert G. Wagner, der am 5. Januar 2018 seinen 65. Geburtstag feiert. Dieses Datum nehmen wir zum Anlass, durch die im vorliegenden Band versammelten sozial- und verhaltenswissenschaftlichen Beiträge, erstens, eine aktuelle Momentaufnahme jener von Gert Wagner seit Anfang der 1980er Jahre wesentlich mitgeprägten empirisch-quantitativen Forschung in Deutschland zu geben, die sich heute nicht mehr allein auf die Wirtschafts- und Sozialwissenschaften beschränkt, sondern auch wichtige Teile der Psychologie mit einbezieht. Damit soll, zweitens, einer der einflussreichsten und produktivsten Sozial- und Wirtschaftsforscher Deutschlands gewürdigt werden, der zudem in vielfältigen Funktionen als innovativer Wissenschaftsmanager (insbesondere als langjähriger Leiter der Längsschnittstudie 'Sozio-oekonomisches Panel') sowie als engagierter Politikberater immer wieder zentrale sozial- und wissenschaftspolitische Debatten mit angestoßen und beharrlich begleitet hat. Vor dem Hintergrund der interdisziplinären, kreativen und brückenbauenden Lebensleistung Gert Wagners sind 'Innovation' und 'Wissenstransfer' die Leitmotive des vorliegenden Bandes, die sich in den einzelnen Beiträgen in sehr unterschiedlichen Formen und Formaten - vom empirischen Fachaufsatz über Review-Artikel bis hin zum Essay - widerspiegeln. Die enorme Vielfalt des Wirkens von Gert Wagner kann in einem Band mit gerade einmal 15 Beiträgen natürlich nur im Ansatz reflektiert werden. Wir hoffen allerdings, dass es uns gelungen ist, eine zumindest halbwegs repräsentative Stichprobe gezogen zu haben: Das Sozio-oekonomische Panel (SOEP) ist - natürlich - das zentrale, von ihm in den Jahren 1989 bis 2011 am DIW Berlin geleitete, Projekt Gert Wagners. Während Stephen P. Jenkins und Timothy M. Smeeding die Besonderheiten des SOEP im Allgemeinen würdigen, arbeiten Bruce Headey und Ruud Muffels in ihrem Beitrag heraus, wie Gert Wagner mit dem SOEP u. a. die Erforschung von Lebenszufriedenheit befruchtet und weiter vorangetrieben hat. Exemplarische Analysen hierzu finden sich bei Sandra Gerstorf, Nilam Ram und Denis Gerstorf, die über ihre - gemeinsam mit Gert Wagner durchgeführte - Forschung zu Wohlbefinden und Lebenszufriedenheit am Lebensende berichten. Gert Wagner hat jedoch nicht nur die klassischen Fragebogeninhalte einer ursprünglich fast ausschließlich auf die Untersuchung sozio-ökonomischer Fragestellungen angelegten Panelstudie sukzessive erweitert. Er hat auch surveymethodisch die Grenzen des früher im Rahmen einer repräsentativen Bevölkerungsumfrage machbar erscheinenden verschoben, wie zum Beispiel das von Michaela Riediger ausführlich beschriebene Multi-Method Ambulatory Assessment Project eindrucksvoll belegt. Das SOEP hat die Welt (sozialwissenschaftlicher Längsschnittstudien) verändert - und ist selbst durch eine sich wandelnde Welt mitgeprägt worden. Eine besondere Rolle spielt hier natürlich die deutsche Wiedervereinigung. Wie deren Folgen für die Lebensbedingungen in Ost und West mit dem SOEP genau beschrieben und analysiert werden können, demonstriert Peter Krause in seiner Untersuchung. Dass das SOEP auch heute noch bestens dazu geeignet ist, klassische Themen der Ungleichheitsforschung - wie sie Gert Wagner immer beschäftigt haben - auf höchstem wissenschaftlichen Niveau zu untersuchen, zeigen insbesondere zwei der hier gesammelten Beiträge. Während Jan Goebel und Markus M. Grabka die Sensitivität von Armuts- und Ungleichheitsmessungen bei gerundeten Einkommensangaben betrachten, analysieren Richard Hauser, Richard V. Burkhauser, Kenneth A. Couch und Gulgun Bayaz-Ozturk auf Basis der Cross-National Equivalent Files des PSID und SOEP die wirtschaftlichen Folgen der Auflösung von Partnerschaften im deutsch-amerikanischen Vergleich. Wissenschaftliche Forschung (ob mit oder ohne SOEP) war und ist für Gert Wagner nie - und bei aller Neugierde (zum Beispiel ob alle Wähler rechtsextremer Parteien einen Schäferhund besitzen) - akademischer Selbstzweck, sondern im Mittelpunkt steht für ihn immer deren gesellschaftliche Relevanz. Nicht zufällig unterstreicht daher Hans-Jürgen Krupp in seinem Beitrag die Bedeutung einer wissenschaftlichen Fundierung von Politikberatung. Diese hat eine wichtige Basis unter anderem in der universitären Forschung und Lehre zur Sozialpolitik, mit deren (möglichem) Verschwinden sich Stephan Leibfried kritisch auseinandersetzt (an dieser Stelle sei daran erinnert, dass die Denomination von Gert Wagners erstem Lehrstuhl - an der Ruhr-Universität Bochum - 'Sozialpolitik und öffentliche Wirtschaft' lautete). Insbesondere ist eine wissenschaftlich fundierte Politikberatung jedoch auf qualitativ hochwertige Daten angewiesen, die nicht nur erhoben sondern einer breiten wissenschaftlichen Öffentlichkeit auch zugänglich gemacht werden müssen. Hierzu leisten, wie Reinhold Thiede und Tatjana Mika zeigen, Forschungsdatenzentren - für deren Einrichtung sich Gert Wagner als Vorsitzender des Rates für Sozial- und Wirtschaftsdaten stark gemacht hat - einen wichtigen Beitrag. 'Gute' Politikberatung darf, last but not least, aber auch den normativen Diskurs nicht scheuen (wie Gert Wagner u. a. als Vorsitzender der Kammer für soziale Ordnung der Evangelischen Kirche in Deutschland gezeigt hat). In diesem Sinne fragt etwa Axel Börsch-Supan in seinem Essay, ob die Rente gerecht sein kann. Die Liste der Themen, mit denen Gert Wagner sich bereits beschäftigt hat (und die neugierig auf seine Zukunftsthemen macht), lässt sich noch vielfältig fortsetzen, wie unter anderem die Beiträge von Reimund Schwarze zur Versicherung von Naturgefahren oder von C. Katharina Spieß zur Ökonomie frühkindlicher Bildung und Betreuung zeigen. Und schließlich wäre Gert Wagner nicht Gert Wagner, wenn er seiner privaten Sportbegeisterung nicht auch wissenschaftlich nachgegangen wäre. Neben dem Verhältnis von Sport und Doping, mit dem sich Nicolas Ziebarth in seinem Beitrag auseinandersetzt, geht es hier natürlich unvermeidlich auch um die - wie Jürgen Gerhards und Michael Mutz zeigen - schönste Nebensache der Welt: den Fußball (dem der Jubilar ein Semester seines Studiums widmete, um bei der Fußballweltmeisterschaft 1974 als freiwilliger Helfer mitzuwirken). Sucht man nach den Gemeinsamkeiten im vielfältigen Wirken Gert Wagners, dann findet sich neben wissenschaftlicher Exzellenz ('Innovation') und hoher gesellschaftlicher Relevanz ('Wissenstransfer') vor allem ein roter Faden: die frühe Förderung junger Wissenschaftlerinnen und Wissenschaftler (oft beginnend zu einem Zeitpunkt, an dem diese sich selbst noch nicht darüber im Klaren waren, dass sie diese Richtung einmal einschlagen würden). Einige der Autorinnen und Autoren, die zum vorliegenden Band beigetragen haben (und viele, die keinen Beitrag zu dieser Festschrift leisten konnten), hätten den langen und oft steinigen Weg zu akademischen Meriten mit Sicherheit weniger erfolgreich zurück gelegt, als es ihnen durch die verlässliche, stets konstruktive Begleitung des Grenzgängers Gert Wagner tatsächlich möglich gewesen ist. Dies gilt auch und insbesondere für die Herausgeberin und die Herausgeber dieses Bandes, die allesamt an der Ruhr-Universität Bochum bei Gert Wagner studieren und arbeiten durften und sich beim Jubilar mit dieser Festschrift und einem herzlichen 'Glück auf!' bedanken möchten. In Praise of Panel Surveys, a Sonder-Panel, and a Sonder-Panel-Papa Stephen P. Jenkins & Timothy M. Smeeding Introduction In this paper, we salute Gert Wagner and his work, focusing on his association with the Socio-Economic Panel (SOEP). To place Gert's contributions in context, we argue first that household panel surveys deserve to be praised for what they contribute to science and to public policy, forming a crucial component in a portfolio of different types of longitudinal data. Second, we show that the SOEP is a very successful example of a household panel survey, comparing its characteristics and innovations with those of its counterparts from other countries. Our case is that the SOEP is truly special (it is a Sonder-Panel) - and Gert Wagner has been responsible for much of this success. He is truly a Sonder-Panel-Papa. Why Praise Household Panel Surveys? Praise household surveys because they are a valuable source of longitudinal data, and longitudinal data are an important type of collection mechanism for addressing many social science issues relevant to policy. Longitudinal datasets are those in which the same set of individual units is tracked over time; we have movies on the same units rather than a series of snapshots on different samples of units as one does with repeated cross-section data. In principle, the movies may be created using surveys with retrospective recall questions, or using prospective data collection based on temporally-linked administrative registers, cohort studies, or - the focus of this paper - household panel surveys. The Value of Longitudinal Data Longitudinal data are valuable for three main reasons. They describe phenomena and relationships that are intrinsically longitudinal (and their correlates); they provide a better understanding of socioeconomic processes over the life course and behaviour and, thereby, they better inform policy. First, considering better description, longitudinal data enable us to distinguish gross change from net change. We can relate a fall in the poverty rate to increases in flows out of poverty or reductions in flows into poverty (Bane/Ellwood 1986). In addition, some phenomena of scientific and policy interest are inherently longitudinal. Examples include how long people remain poor (poverty persistence) or sick, the extent to which exits from unemployment are sustained or represent a 'low pay - no pay' cycle, the prevalence of residential mobility, and household formation and dissolution (marriage and divorce, births and deaths, and children leaving home or boomeranging back). We can look at not only events per se, but take spell-based perspectives, and assess how long spells last, how the chances of spell endings vary with elapsed duration, and with characteristics that change during the spell. Longitudinal data also provide information about the associations between current events and outcome experienced by individuals and their past history. We can study questions such as the relationships between current unemployment chances and past unemployment, children's development and life chances and their family background, income in old age and work-life history, current earnings and job tenure, labour market experience, and we can measure differences between current outcomes and past expectations ('surprises'). Second, concerning greater understanding, longitudinal data, by contrast with cross-sectional snapshot data, allow us to better align our models with underlying constituent processes. Rather than modelling changes in the unemployment rate directly, we model the chances of leaving work among people who have a job, and the chances of finding work for the people who do not currently have a job. The drivers of each process (and the people at risk of the events) differ, and should not be thought of as the same. Going further, one can understand not only transitions per se but also - with a spell-based perspective - how the chances of getting a job vary with how long the spell of unemployment has been, and how the chances vary with circumstances that change during the spell (e.g. the amount of unemployment benefits the person is eligible for). Empirical modelling and hence understanding is further enhanced by longitudinal data because they allow for the possibility of controlling for the effects on outcomes of not only observed characteristics such as age, sex, educational qualifications, but also the persistent characteristics of individuals that are unobserved (or intrinsically unobservable). Having repeated observations on individuals allows one to difference out time-constant factors of all kinds, observed and unobserved. Or one can exploit the fact that past histories of outcomes incorporate information about the realised effects of unobservables, and summarise their distribution. More generally, we can make better causal inferences from our empirical models using longitudinal data because there is a temporal ordering in the data of outcomes (later) and hypothesized drivers (earlier). Indeed, longitudinal data are the essential ingredient of the social experiment revolution in evidence-based policy analysis and impact evaluations, using methods such as randomised control trials as well as several types of quasi-experimental designs (including differences-in-differences based on before-after comparisons for the same individuals). Third, and a consequence of the two features just described, longitudinal data enable us to better inform policy. They enable better focus on the underlying processes, rather than on 'problem groups' at a point in time (such as 'the poor' or 'single parent families') that may be subject to a high degree of turnover in any case rather than being a fixed and unchanging population. The importance of this orientation is illustrated by David Ellwood, one-time advisor on welfare policy to President Clinton, who stated that: '[D]ynamic analysis gets us closer to treating causes, where static analysis often leads us towards treating symptoms. ... The obvious static solution to poverty is to give the poor more money. If instead, we ask what leads people into poverty, we are drawn to events and structures, and our focus shifts to looking for ways to ensure people escape poverty.' (Ellwood 1998: 49) The same point was picked up on by the UK's reform-minded New Labour government: 'In the past, analysis ... has focused on static, snapshot pictures of where people are at a particular point in time. Snapshot data can lead people to focus on the symptoms of the problem rather than addressing the underlying processes which lead people to have or be denied opportunities.' (HM Treasury 1999: 5) Longitudinal data contribute to policy design because generally they provide policy-relevant contextual information about key risks and potential intervention points relevant to policy focus and policy design and, specifically, they can be employed to evaluate the impacts of specific programmes. In short, they help us to understand not only the 'Whats' of social indicators (such as poverty rates) but also the much more difficult causal 'Whys' that help in successful policy design. Household Panel Surveys: Key Features, and Examples from Around the World The discussion above is about longitudinal data speaking generically, and there are multiple ways of collecting these. What are the particular features of household panel surveys such as the SOEP compared to other sources? Household panel surveys are prospective longitudinal designs. Data collection is undertaken in an initial year (call it t) with repeated follow-up data collection points typically at (approximately) annual intervals thereafter (years t+1, t+2, ...). The number of these has increased significantly over the last few decades. The pioneer and longest-running is the US Panel Study of Income Dynamics, which began in 1968 and celebrates its 50th anniversary in 2018. Major household panel surveys began in 1984 in Germany (Socio-Economic Panel; on-going), the Netherlands (Dutch Socio-Economic Panel, 1984-1997), and Sweden (Panel Study of Market and Nonmarket Activities, HUS, 1984-1998, and the Level of Living Surveys, from 1968 onwards). Over the following two decades, household panels began in Australia, Belgium, Canada, Korea, Luxembourg, the Lorraine region of France, Hungary, New Zealand, Switzerland, and Britain (the BHPS, 1991-2008). The BHPS has been superseded by Understanding Society - the UK Household Longitudinal Study, which not only incorporates the BHPS sample, but adds a new large sample of respondents (from 2009). There is the multi-country European Community Household Panel (ECHP) survey which used a cross-nationally harmonized instrument. In 1994, the first waves of surveys were fielded in twelfe member states, with some member states joining later. There were eight waves of fieldwork, with the final one in 2001. There are also a growing number of household panel surveys in other countries, including developing ones. Examples include the Korean Labour and Income Panel Survey (KLIPS), the KwaZulu-Natal Income Dynamics Study (KIDS), the Russia Longitudinal Monitoring Survey (RLMS), and the Indonesia Family Life Survey. A distinction can also be made between perpetual (or indefinite) life panels such as the SOEP for which data collection is intended to carry on indefinitely, with no final collection date set at the outset, and rotating panel surveys for which the number of data collection points is fixed at the outset by design, and there are typically new panels starting each year (e.g. panel I starts in year t, panel II starts in year t+1, etc.), so that for any given calendar year, there are data from multiple panels. Leading examples of rotating panels are the panel surveys used to contribute longitudinal data for EU-SILC (there are four annual data collection points per panel), European labour force surveys (five quarterly data collection points per panel), and the US Surveys of Income and Program Participation (interviews every four months over periods of 2½ to four years depending on the panel). Household Panel Surveys Compared to Retrospective Designs Both types of prospective panel survey can be contrasted with retrospective designs in which there is a single data collection point, with the data for previous periods collected by retrospective recall of respondents about their circumstances and characteristics now and in the past. Because it is difficult to reliably collect information about income amounts and some other detailed aspects of people's lives, retrospective designs have focused on topics for which this is less of an issue, e.g. less detailed information about a respondent's parents such as job type or occupation at the time the respondent was a teenager (for studies of social class mobility), or fertility histories for mothers of young children (as in many Demographic and Health Surveys). Otherwise, the most common form of retrospective data collection is within household panel surveys, to collect information about the period between the annual data collection points, e.g. monthly job histories, with recall reliability issues mitigated by the shorter recall period. Household Panel Surveys Compared to Cohort Surveys Household panel surveys can also be contrasted with cohort surveys which are also perpetual panel surveys. The key distinctions relate to features such as the population of interest, frequency of data collection, and the nature of the data collected. Household panel surveys are surveys of the private household population (individuals and their households), and are designed to maintain representativeness of the sampled population over an extended period. Representativeness is achieved by implementing particular 'following' rules for data collections after the initial one. Original panel members are followed even if the household splits (e.g. a husband and wife divorce and move to form two separate households) or is geographically mobile. Children who are members of respondent households become respondents in their own right when they reach a particular age (in the SOEP it is the year the child turns 17). The survey design mimics the way in which the population reproduces itself over time. By contrast, cohort surveys are more narrowly focused on individuals with a particular set of defining characteristics, and hence are designed to maintain representativeness of the sampled cohort (they are individual- rather than household-focused). The leading examples are birth cohort surveys, in which there is sampling of many (or all) individuals born round a particular date. For example, the UK has had birth cohort studies following individuals born in 1946, 1958, 1970, 1980, and 2000/1. In a cohort survey, each cohort member is followed over time and, although there may be some data collection about co-resident individuals on each occasion, the co-residents are not always followed. Interviews are typically less regular than for household panel surveys (often several years apart but not always thus) and cover much longer periods of individuals' lives. (The UK's 1958 birth cohort study recently interviewed individuals aged 55.) Other types of cohort surveys cover transitions from school to work and thereafter (e.g. the National Longitudinal Studies of Youth in the USA), or from work to retirement (such as the US Health and Retirement Study, and the English Longitudinal Study of Ageing, each focusing on individuals aged 50+). Data collection in cohort studies is relatively frequent initially when development is relatively rapid (early childhood in birth cohort surveys) and less frequent thereafter through the life course. (The UK's Millennium Cohort Survey which started in 2000/1 has collected data so far at ages nine months, three, five, seven, eleven, and 14.) The long-running nature of birth cohort surveys means that they focus on developmental and life course and intergenerational issues, and the topic focus varies between sweeps. By contrast, household panels with their annual data collection focus on topics for which short-term changes are more relevant, notably subjects such as labour market activity, incomes and other factors related to living standards, housing conditions, demographic change, and so on. High priority is given to repeated measurement of the same phenomena: the same topics are covered at each wave rather than changing from wave to wave as with cohort surveys. In addition, data collection refers to all individuals within the household by design (all of whom are followed over time), rather than one particular person and a varying degree of information about their household context. Household Panel Surveys Compared to Linked Administrative Data All the discussion so far has tacitly assumed that data collection is undertaken using a survey of the targeted respondents, whether the survey is done face to face or by other modes, such as telephone or web. Longitudinal datasets can also be compiled by temporal linkage of administrative register data. Administrative data have distinct advantages. They are typically based on very much larger samples and more comprehensive coverage than possible in surveys, participation is not a choice of the targeted individuals (reducing problems of unit non-response and loss to follow-up), and data are often viewed as being more accurate than respondent recall (e.g. income data included in the registers may come directly from employer payroll records, and penalties against tax avoidance may reduce incentives to under-report income). Also, the data are cheap to collect by comparison with surveys - they already exist as a by-product of the administrative process. However, the by-product nature of the data collection process also signals the main disadvantages of administrative register data. The scope of data collection is limited to the sponsoring agency's purposes, not the goals of researchers. The outcome variables in the longitudinal data may be rather limited in number and definition, and there may be few of the additional covariates that are routinely wanted for empirical modelling, e.g. income tax return data do not include information about a tax-payer's educational status because this is not relevant to assessing tax due. Similarly, no information about household composition may be collected. Precise details about pay may not exist for individuals earning below the social insurance liability threshold or above the maximum amount (as in the Integrated Employment Biographies from the German Institute for Employment Research (IAB)). Furthermore, payroll tax records do not capture non-covered earnings, which are reported on surveys, thus underestimating the variable of interest (Hoyakem et al. 2016). Major changes in a tax system may introduce non-comparabilities over time in coverage or variables collected. In countries with individual-based tax systems, it is usually impossible to link individuals with other household members. For this reason, longitudinal administrative data are most useful for individual-focused analyses and less useful for studies in which household context is important (which is of course the forte of household panel surveys). Finally, because of the very nature of much administrative data, there are concerns about privacy and confidentiality, so that researchers' access may be only under restrictive (or inconvenient) conditions, or the variables made available in the public use data may be censored or in banded form to reduce disclosure risks. This is not to say that administrative data are not valuable to panel surveys. Indeed the ability to link records for panel survey respondents to administrative data can help us understand the topic of panel attrition and its possible biases in much greater detail than by any other method (US National Academy of Sciences 2016). Household Panel Surveys: Conclusions On balance, it is impossible to say generally whether household survey data or linked administrative register data are best: it depends a lot on the national context and also the research question. At one extreme lie the Nordic countries with widespread use of administrative register data, characterized also by extensively linking across different types of registers. This means that it is possible to look at household context as well as individual circumstances per se, and a wide range of both outcomes and covariates. Also facilitating use are national cultures in which using a national identity (or social security) number for many purposes is widely accepted, and there are fewer concerns with personal privacy issues related to income and taxation than in most other countries. In other countries, the use of longitudinal administrative data is growing but not as developed. A notable example is the work of Chetty and colleagues linking US Internal Revenue Service records to derive income histories and to link individuals and their parents, and also exploiting detailed information about geographical location and correlates of intra-area mobility (see e.g. Chetty et al. 2014; 2016). The upshot is that there remains a substantial role for household panel surveys as a source of longitudinal data, particularly for research questions that require information about household context - including interactions among household members, whether concerning their living standards or demographic behaviour - and across multiple life domains (e.g. work, family, attitudes and beliefs, etc.). In addition, most countries use measures of household- or family-level income and resources when monitoring levels and trends in individual economic well-being and for assessing eligibility for social assistance and other income support programmes. Even where longitudinal administrative data are not available, administrative data may be used to supplement and enhance survey data collection. In some cases, one may be able to link administrative register data with survey respondents. (An example is the linking of test scores and other information in the English National Pupil Database with members of the UK birth cohort surveys.) This raises issues of informed consent to data linkage, and other linkage biases arising when statistical matching across registers is required. Another form of panel survey data supplementation is the matching of geocoded data about the areas in which respondents live rather than linking at the individual-level data. We return to this below with reference to the SOEP. A further important characteristic of household panel survey designs is that they have been implemented in very similar ways in a number of countries, and there is a core set of variables that is common to each of the surveys. Both features mean that production of cross-national harmonized data is relatively straightforward, at least in principle, though also dependent on securing the resources to make it happen. The notable success in this area is the Cross-National Equivalent File (CNEF), to which almost all national household panel surveys contribute data. Comparable cross-national panel data are available in the CNEF from eight countries, the contributing surveys being the PSID, SOEP, SLID, BHPS, HILDA, Swiss HPS, and KLIPS. See Frick et al. (2007) for a description of the CNEF. This picture of richness of cross-nationally comparative data is a marked contrast with that for longitudinal administrative register data, because countries differ so much in their social policy institutions and the systems used to administer them. Cross-nationally comparable data are also rare for birth cohorts because designs have differed, but exist for cohort surveys of elder people - precisely because comparability and harmonization were built in at the start. We are referring to the Survey of Health, Ageing, and Retirement in Europe (SHARE), modelled on the US Health and Retirement Study, which began with twelve participant countries and since expanded to include many more. We summarize the principal features of household panel survey designs for the collection of longitudinal data in Table 1, comparing their advantages and disadvantages relative to other data collection designs. The main advantages of household panels lie in their focus on individuals within their household context, the coverage of multiple life domains, and the relatively high frequency of data collection, enabling coverage of relatively frequent life events and exploitation of repeated measures modelling techniques. In the next section, we continue the story, but elaborating some details not covered so far. Focusing on the case of the SOEP, we demonstrate how it stands out as an exemplar of a good household panel survey.