FLOORCASH is a collection of four datasets all of which refer to social cash transfers. The single datasets are designed to enable the pursuit of distinct research questions and differ, among others, with respect to case selection, indicators, and data sources.
Please refer to FLOORCASH - the collection of four datasets - as:
Katrin Weible, Tobias Böger, John Berten, Moritz von Gliszczynski, Lutz Leisering (2015): FLOORCASH. Research Project FLOOR (FloorCash), Bielefeld University, Germany, funded by Deutsche Forschungsgemeinschaft, www.floorcash.org.
For references to a single dataset please use the suggested citation as indicated in the introduction to the dataset below.
The FLOORCASH-Basic dataset covers all identifiable social cash transfer programmes in all 148 developing countries and emerging economies outside of Europe as of 2012/2013. All independent, internationally recognized countries outside Europe and North America are included (as well as a few disputed territories), except a few countries which have constantly been assigned very high Human Development Index (HDI) scores over decades (Australia, New Zealand, Japan). For a list of the countries covered please click here.
The dataset comprises 553 programmes and components of broader programmes, respectively, including borderline cases which might not be classified as social cash transfers. The selection of cases is restricted to those programmes which are administered at the national level (even if they are confined to selected regions of the country) and which are designed for national citizens. Public employment programmes (public works) are excluded, unless they imply a guaranteed payment even if work is not available within a certain period of time. For the definition of programmes covered see also "About social cash transfers".
The dataset includes both quantitative and qualitative indicators, covering a broad range of aspects, such as conditions of eligibility, benefits, implementation, institutional core features, evolution, and goals. For many indicators the data is provided in the form of text rather than in a computable form. Four kinds of sources fed into the dataset: 27 extant data collections on social security and social protection; 218 governmental documents and websites; 214 studies from the academic literature and policy papers; and 14 expert interviews.
Suggested citation: Katrin Weible, Tobias Böger, John Berten (2015): FLOORCASH-Basic. The basic dataset on social cash transfers in the global South, Version 2, Research Project FLOOR (FloorCash), Bielefeld University, Germany, funded by Deutsche Forschungsgemeinschaft, www.floorcash.org.
The FLOORCASH-SocCit dataset is based on FLOORCASH-Basic, but the data is provided in a computable form. The dataset covers all 282 social cash transfer programmes in the 148 countries of the global South (according to the definitions used for FLOORCASH-Basic), as of 2012/13. FLOORCASH-SocCit has been constructed in view of the sociological concept of social citizenship, focusing on entitlements to social cash transfers rather than welfare outcomes. FLOORCASH-SocCit emphasises three aspects: inclusion of social groups (with more refined data than the usual target groups), conditions of access to benefits, and institutionalization of the programmes. FLOORCASH-SocCit can be used for studies with different units of analysis (programmes, target categories, countries).
Suggested citation: Katrin Weible (2015): FLOORCASH-SocCit, The social citizenship dataset on social cash transfers in the global South, Version 2, Research Project FLOOR (FloorCash) Bielefeld University, Germany, funded by Deutsche Forschungsgemeinschaft, www.floorcash.org.
Please note: access to the dataset will be provided later.
The FLOORCASH-SocPen dataset collects institutional data on social pension programmes in 70 independent countries or autonomous territories across the global South. It provides time series data on the introduction and reform of non-contributory pension programmes. It also encompasses cross-sectional information on benefit levels, means-testing and qualifying ages as well as, where available, information on beneficiary numbers and expenditure from 2001 till 2011.
Suggested Citation: Tobias Böger (2013): FLOORCASH-SocPen. The social pension dataset on non-contributory old age pensions in the global South, Version 1, Research Project FLOOR (FloorCash), Bielefeld University, Germany, funded by Deutsche Forschungsgemeinschaft, www.floorcash.org.
The FLOORCASH-Discourse dataset is a collection of some 250 documents which are part of global discourses that have shaped global social policy, especially in view of social cash transfers. Four major discourses are covered: on poverty, on human rights, on development and on risk. Most of the documents contained in the dataset are policy papers and studies by global organisations published between 2000 and 2013, complemented by relevant articles in scientific journals and a variety of older documents. The documents were collected by checking references in an initial sample of papers on social cash transfers and through interviews with policy experts. Also part of this dataset is a 'hermeneutic unit' for use with Atlas.ti, which contains an analysis of the documents mentioned above, including analytic codes and research memos. The analysis focuses on the four discourses, on paradigm shifts in development policy and on the construction of models of social cash transfers.
Suggested citation: Moritz von Gliszczynski (2013): FLOORCASH-Discourse. The discourse dataset on social cash transfers in the global South, Version 1, Research Project FLOOR (FloorCash), Bielefeld University, Germany, funded by Deutsche Forschungsgemeinschaft, www.floorcash.org.
All FLOORCASH data has been constructed and assembled to be as accurate as possible and to the best of our knowledge. However, as with other complex large n datasets of this size, there will be errors and, due to the fast changing nature of the subject, outdated information. Researchers and legal entities involved in the creation of FLOORCASH shall not be liable for any loss suffered through the use of any of this information.