CESSDA Data Catalogue OAI-PMH Repository
Not a member yet
32838 research outputs found
Sort by
KINMATRIX - Uncovering the Kinship Matrix
No full textThe data collected as part of the Horizon 2020-funded KINMATRIX project depicts families as egocentric networks of young adults and includes a wide range of relatives, including biological parents, grandparents, siblings (full and half), aunts, uncles, first cousins, and half- and step-relatives from complex family constellations. The survey thus extends the traditional concept of family to a comprehensive kinship matrix that reflects the diversity of today´s families and enables the analysis of family solidarity, intergenerational transmission and the role of the family as a safety network, social capital and financial support in different societies. The KINMATRIX survey was conducted as a self-administered online survey (CAWI) in 10 countries (Denmark, Finland, Germany, Italy, Netherlands, Norway, Poland, Sweden, United Kingdom, United States). Round 1 was conducted between December 2022 and March 2023 in the nine European countries and the United States. Round 2, which was only conducted in the USA, took place between December 2023 and January 2024. Respondents were selected through a quota sample from a Dynata online access panel. The target population of anchor respondents were Dynata panelists between the ages of 25 and 35 who were raised by at least one biological family member.1. Quota and filter questions: age; age groups and year of birth (recoded); gender; region; highest level of education; respondent’s subjective social status; employment status; raised by whom (e.g. by one or both of my biological parents, etc.). Additional demographic and personal questions: born in the survey country; country of birth; sexual orientation; partner status; living with a partner; number of children; birth years of children; satisfaction with life; self-assessment of health in general; agreement with the following statements: Parents and children should support each other mutually for a lifetime, you should get married if you permanently live with your partner, women should be more concerned about their family than about their career, men should participate in housework to the same extent as women, a child under 6 will suffer from having a working mother, Children often suffer because their fathers spend too much time at work, marriage is a lifelong union that should not be broken, couples should marry, at the latest, after a child is born; immigrants generally bad or good for the survey country’s economy; immigrants generally undermined or enrich country’s cultural life; immigrants make the country a worse or a better place to live; ethnicity (only in USA); Hispanic, Latino, or Spanish origin; belonging to a religion or denomination; religion or denomination; frequency of attending religious services. 2. Creating the family tree Name generator implemented in the form of a family tree; biological family: number of brothers; number of sisters; number of paternal uncles; number of paternal aunts; number of maternal uncles; number of maternal aunts; number of cousins from this uncle or aunt; how many brothers, sisters, uncles, aunts and cousins from this uncle or aunt are known. Attributes: age of this person; year of birth of this person, year of death of this person; kin gender; who was not born in the country; who does not live in the country; person lives in urban or rural area; who has completed university; person’s employment status; kin subjective social status; person has any long-term health problems; person who the anchor wants to invite to the survey; anchor wants to invite none of them to the survey. Relationships: who was important to the anchor; none of them was important; who helps with childcare; none of them helps with childcare; hours per week these persons helped with childcare; frequency of contact; time to get to this person; feeling of closeness to this person; person who gave money; none of them gave money; person who gave good advice; none of them gave good advice; person who comforted; none of the comforted; person the anchor could really count on; anchor could really count on none of them; amount of conflict, tension, or disagreement with this person; paternal grandparents separated, year of separation of paternal grandparents; maternal grandparents separated; year of separation of maternal grandparents. Separation and additional family members: parents are still together; parents separated before one of them died; anchor’ age when parents separated; anchor’s age guessed when parents separated; household in which the anchor lived after parents separated; number of new partners with whom the father cohabitated; number of new partners with whom the mother cohabitated; anchor’s age when parent repartnered with this partner; parent and this partner are still together; anchor’s age when parent and this partner separated; ever cohabitated with this partner; years of cohabitation between anchor and this partner; number of half-siblings from each of parent’s partner; ever cohabitated with this half-sibling; years of cohabitation between anchor and this half-sibling; number of step-siblings from each of parent’s partner; ever cohabitated with this step-sibling; years of cohabitation between anchor and this step-sibling; number of additional half-siblings from paternal side; number of additional half-siblings from maternal side; ever cohabitated with this additional half-sibling; years of cohabitation of anchor with this additional half-sibling. Additionally coded: survey country; anchor ID; anchor name status; total number of kin per anchor; total of “don’t knows” on any variable kin number; total of double “don’t knows” on any variable kin number; kin name status; kinship identification type; kinship category; kinship side; complex kinship incl. half-siblings and step-kin; kin living status; all born in the country; in which country was this person born (outside UK/ USA version/ UK/USA version); all live in the country; disrupted biography (early parental death or separation); early father or mother death, early father death; early mother death; total of “don’t knows” on the number of half-siblings; total of “don’t knows” on the number of cousins; total of double “don’t knows” on the number of cousins; total number of siblings per anchor; total of “don’t knows” on the number of siblings; total of double “don’t knows” on the number of siblings; total of “don’t knows” on the number of aunts and uncles; total of double “don’t knows” on the number of aunts and uncles; Interview date; interview duration in minutes; quality of answers; flag variables; mark variables (e.g. % of unknown or invalid names among all recorded kin); design weights; round in which anchor participated.Die im Rahmen des durch Horizon 2020 geförderten KINMATRIX-Projekts erhobenen Daten stellen Familien als egozentrierte Netzwerke junger Erwachsener dar und umfassen ein breites Spektrum an Verwandten, darunter biologische Eltern, Großeltern, Geschwister (voll und halb), Tanten, Onkel, Cousins und Cousinen ersten Grades sowie Halb- und Stiefverwandte aus komplexen Familienkonstellationen. Damit erweitert die Erhebung den traditionellen Familienbegriff hin zu einer umfassenden Verwandtschaftsmatrix, die die Vielfalt heutiger Familien abbildet und die Analyse von Familiensolidarität, intergenerationaler Weitergabe und der Rolle der Familie als Sicherheitsnetzwerk, soziales Kapital und finanzielle Unterstützung in verschiedenen Gesellschaften ermöglicht. Die KINMATRIX-Umfrage wurde als selbst-administrierte Online-Erhebung (CAWI) in 10 Ländern durchgeführt (Dänemark, Deutschland, Finnland, Italien, Niederlande, Norwegen, Polen, Schweden, Vereinigtes Königreich, Vereinigte Staaten). Runde 1 wurde zwischen Dezember 2022 und März 2023 in den neun europäischen Ländern und in den Vereinigten Staaten durchgeführt. Runde 2, die nur in den USA durchgeführt wurde, fand zwischen Dezember 2023 und Januar 2024 statt. Die Auswahl der Befragten erfolgte durch eine Quotenstichprobe aus einem Online-Access-Panel von Dynata. Die Zielpopulation der Ankerbefragten waren Dynata-Panelisten im Alter von 25 bis 35 Jahren, die von mindestens einem biologischen Familienmitglied aufgezogen wurden.1. Quoten- und Filterfragen: Alter; Altersgruppen und Geburtsjahr (umkodiert); Geschlecht; Region; höchster Bildungsabschluss; subjektiver sozialer Status der befragten Person; Beschäftigungsstatus; von wem aufgezogen (z. B. von einem oder beiden biologischen Elternteilen, usw.). Zusätzliche demografische und persönliche Fragen: im Erhebungsland geboren; Geburtsland; sexuelle Orientierung; Partnerstatus; mit einem Partner zusammenlebend; Anzahl der Kinder; Geburtsjahre der Kinder; Lebenszufriedenheit; Selbsteinschätzung der Gesundheit im Allgemeinen; Zustimmung zu den folgenden Aussagen: Eltern und Kinder sollten sich ein Leben lang gegenseitig unterstützen, man sollte heiraten, wenn man dauerhaft mit seinem Partner zusammenlebt, Frauen sollten sich mehr um ihre Familie als um ihre Karriere kümmern, Männer sollten sich in gleichem Maße wie Frauen an der Hausarbeit beteiligen, ein Kind unter 6 Jahren leidet darunter, dass die Mutter berufstätig ist, Kinder leiden oft darunter, dass ihre Väter zu viel Zeit bei der Arbeit verbringen, die Ehe ist ein lebenslanger Bund, der nicht gebrochen werden sollte, Paare sollten spätestens nach der Geburt eines Kindes heiraten; Einwanderer sind im Allgemeinen schlecht oder gut für die Wirtschaft des befragten Landes; Einwanderer untergraben oder bereichern im Allgemeinen das kulturelle Leben des Landes; Einwanderer machen das Land zu einem schlechteren oder besseren Ort zum Leben; ethnische Zugehörigkeit (nur in den USA); hispanische, lateinamerikanische oder spanische Herkunft; Zugehörigkeit zu einer Religion oder Konfession; Religion oder Konfession; Häufigkeit des Besuchs von Gottesdiensten. 2. Erstellung des Stammbaums Namensgenerator in Form eines Stammbaums; biologische Familie: Anzahl der Brüder; Anzahl der Schwestern; Anzahl der Onkel väterlicherseits; Anzahl der Tanten väterlicherseits; Anzahl der Onkel mütterlicherseits; Anzahl der Tanten mütterlicherseits; Anzahl der Cousins und Cousinen dieses Onkels oder dieser Tante; wie viele Brüder, Schwestern, Onkel, Tanten und Cousins dieses Onkels oder dieser Tante sind bekannt. Attribute: Alter dieser Person; Geburtsjahr dieser Person, Sterbejahr dieser Person; Geschlecht der Verwandten; wer wurde nicht im Land geboren; wer lebt nicht im Land; wer lebt in der Stadt oder auf dem Land; wer hat die Universität abgeschlossen; Beschäftigungsstatus der Person; subjektiver sozialer Status der Verwandten; Person mit langfristigen Gesundheitsproblemen; Person, die die befragte Person zur Umfrage einladen möchte; befragte Person möchte keine der Personen zur Umfrage einladen. Beziehungen: Wer war für die befragte Person wichtig; keiner von ihnen war wichtig; wer hilft bei der Kinderbetreuung; keiner von ihnen hilft bei der Kinderbetreuung; Stunden pro Woche, die diese Personen bei der Kinderbetreuung helfen; Häufigkeit des Kontakts; Zeit, um zu dieser Person zu gelangen; Gefühl der Nähe zu dieser Person; Person, die Geld gegeben hat; keiner von ihnen hat Geld gegeben; Person, die gute Ratschläge gegeben hat; keiner von ihnen hat gute Ratschläge gegeben; Person, die tröstete; keine von ihnen tröstete; Person, auf die befragte Person wirklich zählen konnte; befragte Person konnte auf keine von ihnen wirklich zählen; Ausmaß an Konflikten, Spannungen oder Meinungsverschiedenheiten mit dieser Person; Trennung der Großeltern väterlicherseits, Jahr der Trennung der Großeltern väterlicherseits; Trennung der Großeltern mütterlicherseits, Jahr der Trennung der Großeltern mütterlicherseits. Trennung und zusätzliche Familienmitglieder: Eltern sind noch zusammen; Eltern trennten sich, bevor einer von ihnen starb; Alter der befragen Person, als sich die Eltern trennten; geschätztes Alter, als sich die Eltern trennten; Haushalt, in dem die befragte Person nach der Trennung der Eltern lebte; Anzahl der neuen Partner, mit denen der Vater zusammenlebte; Anzahl der neuen Partner, mit denen die Mutter zusammenlebte; Alter der befragten Person, als ein Elternteil mit diesem Partner wieder eine Partnerschaft einging; Elternteil und dieser Partner sind noch zusammen; Alter der befragten Person, als sich Elternteil und dieser Partner trennten; jemals mit diesem Partner des Elternteils zusammengelebt; Jahre des Zusammenlebens mit diesem Partner; Anzahl der Halbgeschwister von jedem der Partner der Eltern; jemals mit diesem Halbgeschwister zusammengelebt; Jahre des Zusammenlebens mit diesem Halbgeschwister; Anzahl der Stiefgeschwister von jedem der Partner der Eltern; jemals mit diesem Stiefgeschwister zusammengelebt; Jahre des Zusammenlebens mit diesem Stiefgeschwister; Anzahl der zusätzlichen Halbgeschwister väterlicherseits; Anzahl der zusätzlichen Halbgeschwister mütterlicherseits; jemals mit diesem zusätzlichen Halbgeschwister zusammengelebt; Jahre des Zusammenlebens mit diesem zusätzlichen Halbgeschwister. Zusätzlich kodiert: Erhebungsland; Anker-ID; Status des Ankernamens; Gesamtzahl der Verwandten pro Anker; Gesamtzahl der „weiß nicht“ bei einer beliebigen Variable Verwandtenzahl; Gesamtzahl der doppelten „weiß nicht“ bei einer beliebigen Variable Verwandtenzahl; Status des Verwandtennamens; Identifikationstyp der Verwandtschaft; Verwandtschaftskategorie; Verwandtschaftsseite; komplexe Verwandtschaft einschl. Halbgeschwister und Stiefgeschwister; Wohnstatus der Verwandten; alle im Land geboren; in welchem Land ist diese Person geboren; Halbgeschwister und Stiefgeschwister; Wohnstatus der Verwandten; alle im Land geboren; in welchem Land wurde diese Person geboren (außerhalb des Vereinigten Königreichs/der USA); alle leben im Land; gestörte Biografie (früher Tod oder Trennung der Eltern); früher Tod von Vater oder Mutter, früher Tod des Vaters; früher Tod der Mutter; insgesamt „weiß nicht“ bei der Anzahl der Halbgeschwister; insgesamt „weiß nicht“ bei der Anzahl der Cousins; Summe der doppelten „weiß nicht“ zur Anzahl der Cousins und Cousinen; Gesamtzahl der Geschwister pro Anker; Summe der „weiß nicht“ zur Anzahl der Geschwister; Summe der doppelten „weiß nicht“ zur Anzahl der Geschwister; Summe der „weiß nicht“ zur Anzahl der Tanten und Onkel; Summe der doppelten „weiß nicht“ zur Anzahl der Tanten und Onkel; Interviewdatum; Interviewdauer in Minuten; Qualität der Antworten; Flaggenvariablen; Markierungsvariablen (z. z. B. % der unbekannten oder ungültigen Namen unter allen erfassten Verwandten); Design-Gewichte; Runde, an der der Anker teilgenommen hat
The Committee Membership Dataset (CMD)
No full textWe provide the Committee Membership Dataset (CMD), a major expansion of available legislative data. The CMD records all committee assignments for MPs in 14 countries (14,963 MPs in 260 parties) from 1989 to 2024, including positions held (e.g., member, chairperson), assignment dates, and committee policy areas. Harmonized MP and party identifiers allow linkage with other widely used datasets.We provide the Committee Membership Dataset (CMD), a major expansion of available legislative data. The CMD records all committee assignments for MPs in 14 countries (14,963 MPs in 260 parties) from 1989 to 2024, including positions held (e.g., member, chairperson), assignment dates, and committee policy areas. Harmonized MP and party identifiers allow linkage with other widely used datasets
Lapsesta aikuiseksi: 61-vuotiaiden elämänhistorian kalenteri 2020-2021
No full textElämänhistorian kalenteri 2020-2021 osa Lapsesta aikuiseksi -tutkimusta, joka on jatkunut samojen henkilöiden seurantana vuodesta 1968. Tämä tutkimusvaihe kartoittaa noin 61-vuotiaiden elämää sen keskeisillä alueilla, joita ovat perhe, työ, asuminen, terveys ja vapaa-aika. Vaiheen aineistonkeruu sisälsi elämäntilannekyselyn, psykologisen haastattelun, itsearviointilomakkeet, terveystarkastuksen sekä fyysisen aktiivisuuden seurannan. Haastattelun yhteydessä tutkittavat kertoivat elämästään 49 ikävuodesta eteenpäin elämänhistoriaa kuvaavan taulukon avulla. Kalenterin avulla selvitettiin vuosi vuodelta, millä paikkakunnilla vastaaja on asunut ja milloin on muuttanut. Avo- ja avioliitot sekä lasten ja lapsenlasten syntymät kirjattiin myös kalenteriin. Edelleen kysyttiin koulutuksesta, koko- ja osapäivätöistä, työttömyysjaksoista, uusista työtehtävistä sekä eläkkeestä. Ystävien ja läheisten sukulaisten kuolemantapauksista sekä muista omaa elämää kohdanneista ikävistä tapahtumista pyydettiin kertomaan. Lisäksi tiedusteltiin omista rikkomuksista sekä muista erityistä huolta aiheuttaneista asioista. Lopuksi kartoitettiin erityisiä iloisia elämäntapahtumia.The Life History Calendar 2020-2021 is part of the Jyväskylä Longitudinal Study of Personality and Social Development (JYLS), which has followed the same individuals since 1968. This research wave explores the lives of participants, now around 61 years old, across key areas such as family, work, housing, health and leisure. Data collection for this phase included a life situation questionnaire, a psychological interview, self-assessment forms, a health examination and monitoring of physical activity. During the interview, participants described their life history from the age of 49 onwards using a timeline chart. The calendar was used to examine, year by year, the places where the respondent had lived and the timing of any moves. Cohabiting and marital relationships, as well as the births of children and grandchildren, were also noted. Further questions covered education, full- and part-time employment, periods of unemployment, new job roles and retirement. Participants were asked to report the deaths of friends and close relatives, along with other adverse life events. In addition, they were asked about any personal offences and other matters that had caused particular concern. Finally, matters that had brought particular joy were documented
TraCiSS: School Teachers Survey-2025, Joint Dataset
No full textThe research project “Transversal Competences Assessment in Secondary Schools: An International Comparison of Teachers’ Attitudes and Teaching Practices” (TraCiSS) results from a collaboration between researchers at the University of Bern (ICER) and the St.Gallen University of Teacher Education (Institute of Educational Psychology) to explore shared research questions on assessing transversal competences in secondary schools. By combining resources and sharing knowledge and experience, we aim to improve the efficiency of our research and broaden its geographical reach through an international survey. The primary objective of the TraCiSS study is to compare teachers' teaching practices regarding the assessment of transversal skills across different countries. We focus on the following questions: 1. Future-oriented importance: How do teachers in the countries studied assess the importance of specific transversal competences for the future lives of their students in society? 2. Current teaching practice: Which transversal competences are currently promoted and assessed in the classroom by teachers in the countries studied? What methods are used? 3. Needs analysis: What needs do teachers in the countries studied have regarding the promotion and assessment of transversal competences? 4. International comparison: What country-specific differences and similarities can be recognized concerning future requirements, current teaching practices, and the needs of teachers? To answer these questions, the research team conducted a comprehensive online survey of school teachers in Switzerland and Ukraine, as well as the network of international partner schools of the Goethe-Institut’s exchange program 'SCHULWÄRTS!'. The study focuses on school-based assessment of transversal competences identified in all fields of activity and subject areas. In the countries where teachers are surveyed as part of the TraCiSS study, different terms are used for these competences in official educational documents, including "überfachliche Kompetenzen", “cross-cutting skills” and "transversal competences". As partners in the TraCiSS project, we have therefore agreed to use the internationally recognized term "transversal competences" in our study following the ESCO-Definition (European Skills, Competences, Qualifications, and Occupations): learned and proven abilities that are commonly seen as necessary or valuable for effective action in virtually any kind of work, learning, or life activity (are not exclusively related to any particular context). The term “transversal competences” is used in all the joint project documents and publications. The term used in the teacher survey questionnaire was appropriate for the sample (country), providing a short common explanation to ensure respondents understand it similarly
Labour Force Survey (LFS), 2008
No full textThe Labour Force Survey (LFS) is a large-scale, nationwide survey of households in Ireland conducted by the Central Statistics Office. It is designed to produce quarterly labour force estimates that include the official measure of employment and unemployment in the state (ILO basis). This survey replaced the Quarterly National Household Survey (QNHS) from Q3 2017. The QNHS began in September 1997, replacing the annual April Labour Force Survey (LFS). Each quarter the LFS produces data among others on: Numbers unemployed Persons in employment Labour force participation rates Inactive population (not in the labour force) Sectoral breakdown (Nace Rev. 2) of those in employment Breakdown of headline data by age, sex and region Persons in employment classified by occupation Seasonally adjusted headline series Data on family compositio
Surface Groups Trinidad and Tobago 1984-2023
No full textThis project develops a novel procedure for proxying economic activity with daytime satellite imagery across time periods and spatial units, for which reliable data on economic activity are otherwise not available. In developing this unique proxy, we apply machine-learning techniques to a historical time series of daytime satellite imagery from the Landsat program dating back to 1984. Compared to satellite data on night light intensity, another common economic proxy, our proxy more precisely predicts economic activity at smaller regional levels and over longer time horizons. Our procedure is generalizable to any region in the world, and it has great potential for analyzing historical economic developments, evaluating local policy reforms, and controlling for economic activity at highly disaggregated regional levels in econometric applications. Therefore, we produce our proxy for any region in the world and publish the data as georeferend TIF files in this repository. In our paper, we demonstrate our measure’s usefulness for the example of Germany, where East German data on economic activity are unavailable for detailed regional levels and historical time series
Surface Groups Mozambique 1984-2021
No full textThis project develops a novel procedure for proxying economic activity with daytime satellite imagery across time periods and spatial units, for which reliable data on economic activity are otherwise not available. In developing this unique proxy, we apply machine-learning techniques to a historical time series of daytime satellite imagery from the Landsat program dating back to 1984. Compared to satellite data on night light intensity, another common economic proxy, our proxy more precisely predicts economic activity at smaller regional levels and over longer time horizons. Our procedure is generalizable to any region in the world, and it has great potential for analyzing historical economic developments, evaluating local policy reforms, and controlling for economic activity at highly disaggregated regional levels in econometric applications. Therefore, we produce our proxy for any region in the world and publish the data as georeferend TIF files in this repository. In our paper, we demonstrate our measure’s usefulness for the example of Germany, where East German data on economic activity are unavailable for detailed regional levels and historical time series
Interviews on AV ethics with Swiss and international experts
No full textRapid advances in autonomous vehicles have outpaced ethical and legal assessment as well as social adaptation. While large-scale surveys of laypeople have provided insights into crash dilemmas, these studies often capture unreasoned intuitions and overlook the perspectives of those who actually design decision rules. This project focuses on examining the ethics of crash-dilemma programming as practiced and justified by technically informed stakeholders such as programmers and engineers working on self-driving cars. The study asks how these stakeholders conceptualize and justify trade-offs in crash situations, and which ethical principles and constraints - such as risk, bias, liability, and explainability - guide their choices. The research is situated within a theoretical framework that draws on normative ethics (consequentialism, deontology, virtue ethics), responsible research and innovation, and value-sensitive design. It is hypothesized that expert reasoning differs systematically from lay intuitions, that explicit principle-based reflection improves transparency and robustness, that awareness of bias and technical constraints increases demand for explainability and oversight, and that interdisciplinary exchange fosters safer and more accountable design choices. Methodologically, the project combines qualitative, semi-structured “active” interviews with experimental-ethics vignettes and think-aloud tasks, using purposive sampling of Swiss autonomous vehicle developers. Data will be analyzed thematically and comparatively, and findings will feed into iterative co-design workshops with ethicists to develop a practical governance framework. The expected outcome is a proactive and transparent framework for programming crash-situation algorithms. Such a framework aims to support safer deployment of autonomous vehicles, facilitate regulatory alignment, and strengthen public trust, thereby helping to realize their broader social benefits
Surface Groups Zambia 1984-2021
No full textThis project develops a novel procedure for proxying economic activity with daytime satellite imagery across time periods and spatial units, for which reliable data on economic activity are otherwise not available. In developing this unique proxy, we apply machine-learning techniques to a historical time series of daytime satellite imagery from the Landsat program dating back to 1984. Compared to satellite data on night light intensity, another common economic proxy, our proxy more precisely predicts economic activity at smaller regional levels and over longer time horizons. Our procedure is generalizable to any region in the world, and it has great potential for analyzing historical economic developments, evaluating local policy reforms, and controlling for economic activity at highly disaggregated regional levels in econometric applications. Therefore, we produce our proxy for any region in the world and publish the data as georeferend TIF files in this repository. In our paper, we demonstrate our measure’s usefulness for the example of Germany, where East German data on economic activity are unavailable for detailed regional levels and historical time series
Surface Groups Belize 1984-2023
No full textThis project develops a novel procedure for proxying economic activity with daytime satellite imagery across time periods and spatial units, for which reliable data on economic activity are otherwise not available. In developing this unique proxy, we apply machine-learning techniques to a historical time series of daytime satellite imagery from the Landsat program dating back to 1984. Compared to satellite data on night light intensity, another common economic proxy, our proxy more precisely predicts economic activity at smaller regional levels and over longer time horizons. Our procedure is generalizable to any region in the world, and it has great potential for analyzing historical economic developments, evaluating local policy reforms, and controlling for economic activity at highly disaggregated regional levels in econometric applications. Therefore, we produce our proxy for any region in the world and publish the data as georeferend TIF files in this repository. In our paper, we demonstrate our measure’s usefulness for the example of Germany, where East German data on economic activity are unavailable for detailed regional levels and historical time series