Barrientos, Armando
(2020).
Social assistance in low and middle income countries 2000-2015.
[Data Collection]. Colchester, Essex:
UK Data Service.
10.5255/UKDA-SN-853810
Since the turn of the century low and middle income countries have introduced or expanded programmes providing direct transfers to families in poverty or extreme poverty as a means of strengthening their capacity to exit poverty. The rationale underpinning these programmes is that stabilising and enhancing family income through transfers in cash and in kind will enable programme participants to improve their nutrition, ensure investment in children's schooling and health, and help overcome economic and social exclusion.
The expansion of antipoverty transfer programmes has accelerated. Estimates suggest that around 1 billion people in developing countries reside with someone in receipt of a transfer. As would be expected, the spread of social assistance has been slower and more tentative in low income countries due to implementation and finance constraints and limited elite political support.
Antipoverty transfer programmes in developing countries show large variation in design, effectiveness, scale, and objectives. In most countries, there are several interventions running alongside one another with diverse priorities and designs, and often targeting different groups. In many countries social public assistance programmes work alongside social insurance programmes for formal sector workers and humanitarian or emergency assistance. Social assistance focuses on groups in poverty, provides medium term support, and is budget-financed.
The spread of social assistance in developing countries has revealed significant gaps in the knowledge, for example as regards their effectiveness, reach, and sustainability. Comparative analysis is essential to fill in these gaps and improve national, regional and global policy. For example, achieving a zero target for extreme poverty, as has been suggested in the context of the post-2015 international development agenda, would require effective and permanent institutions ensuring the benefits from economic growth reach the poorest. Social assistance is essential to achieving this goal.
This research project focuses on improving research infrastructure on social assistance, in terms of concepts, indicators and data. This is urgently needed to support comparative analysis of emerging social assistance institutions. The project will identify indicators to assess social assistance programmes and will collect information on these for 2000 to 2015 for all developing countries. The database will be made available online to researchers and policy makers globally.
As part of the project, the database will be analysed to examine patterns or configurations in social assistance programmes and institutions. Our interest is in identifying ideal types, broad features of social assistance programmes or institutions which enable reducing the large diversity of programmes and interventions to their core characteristics. These ideal types are social assistance regimes. Further analysis will test for potential combinations of political, demographic, economic and social factors linked to specific social assistance regimes. This analysis will allow us to examine what conditions can help explain the expansion of social assistance in developing countries; what factors influence the specific configuration of social assistance institutions in different countries and regions; and what conditions are needed for their effectiveness and sustainability. This research will throw light on the contribution of social assistance to the reduction of poverty and vulnerability and to economic and social development.
Data description (abstract)
The social assistance explorer contains a harmonised panel dataset of social assistance indicators spanning 2000-2015. It has been developed to support comparative research on emerging welfare institutions. Comparative analysis of social protection institutions in low and middle income countries is scarce. Yet social assistance accounts for most of the recent expansion of welfare institutions. The project collected data on programme design and objectives, institutionalisation, reach, and financial resources. Key indicators can be aggregated at country and region levels.
Data creators: |
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Sponsors: |
Economic and Social Research Council
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Grant reference: |
ES/N014561/1
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Topic classification: |
Social welfare policy and systems
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Keywords: |
SOCIAL ASSISTANCE, POVERTY, LOW INCOME, INCOME, SOCIAL PROTECTION, INSTITUTIONS, SOCIAL WELFARE, SOCIAL WELFARE ORGANIZATIONS, PENSIONS
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Project title: |
Improving research infrastructure on social assistance
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Alternative title: |
SALMIC
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Grant holders: |
Armando Barrientos
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Project dates: |
From | To |
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1 September 2016 | 28 February 2019 |
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Date published: |
26 Oct 2020 15:52
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Last modified: |
26 Oct 2020 15:53
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Temporal coverage: |
From | To |
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1 January 2000 | 31 December 2015 |
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Country: |
World Wide |
Spatial unit: |
Other |
Data collection method: |
The data collection included all countries defined as low and middle income in the 2016 version of the World Bank Country Classification. An inventory of potential social assistance programmes was developed for each country. The definition described above was then applied to identify social assistance programmes. For some countries with a large number of small or localised programmes, the data collection focused on nationwide, large-scale, and/or leading programmes. For example, some states in India have localised programmes. These were excluded from the data collection. In sub-Saharan Africa some programmes are very small in scale but they are significant in leading the expansion of social assistance. They were included. Where programmes consolidate pre-existing programmes, for example Brazil's Bolsa Família, the dataset includes Bolsa Família as well as its component programmes. Data were collected from a variety of sources: global and regional datasets (ASPIRE, ODI, CEPAL, ADB's SPI, IPC-PG); national government websites; programme agency reports; research papers; evaluation reports; policy documents; IFIs project documentation and reports; personal communication with programme agencies. The collection of the data was organised around a codebook, describing each of the variables and the specific coding of the information. The codebook was constructed after extensive consultation with specialist researchers. The codebook is available from the data webpage in the website. Specialist consultants supported data collection in had-to-reach areas. The data collected were checked against alternative sources of information where available. |
Observation unit: |
Other |
Kind of data: |
Numeric, Text |
Type of data: |
International macrodata |
Resource language: |
English |
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Data sourcing, processing and preparation: |
Data were collected from a variety of sources: global and regional datasets (ASPIRE, ODI, CEPAL, ADB's SPI, IPC-PG); national government websites; programme agency reports; research papers; evaluation reports; policy documents; IFIs project documentation and reports; personal communication with programme agencies.
Data on programme participants released by the respective agencies report the numbers of direct individual recipient or the numbers of participant households. For example, social pension programmes report the number of pensioners while conditional income transfers report the number of participant households. The dataset reports on the reach of programmes in terms of individuals and households. Where the raw data reported the number of individual recipients, it was multiplied by the average household size for the specific country to calculate the full reach of the programme. Where the raw data reported the number of households, it was multiplied by the average household size for the specific country to calculate the full reach of the programme.
Information on average household size is from: UNDESA. (2017). Household Size and composition around the world 2017 - Data Booklet (Data Booklet No. ST/ESA/SER.A/405). New York: United National Department of Economic and Social Affairs, Population Division.
All financial data - transfers values and programme budgets for example - are reported in domestic currency and in purchasing power parity. Purchasing poverty parity exchange rates enable consistent comparison across countries.
PPP exchange rates were taken from World Development Indicators (https://data.worldbank.org/indicator/PA.NUS.PPP; accessed 21/11/2017). Purchasing power parity conversion factor is the number of units of a country's currency required to buy the same amounts of goods and services in the domestic market as U.S. dollar would buy in the United States. For most economies PPP figures are extrapolated from the 2011 International Comparison Program (ICP) benchmark estimates or imputed using a statistical model based on the 2011 ICP.
Reporting on the financial resources associated with social assistance programmes shows significant variation across countries. The codebook included three data formats: (i) budgeted resources (bugt); (ii) programme expenditure (cost); and (iii) donor/government financing expenditure (dfinex/govfinex). One or more of these was reported for the majority of programmes. Where the amount reported referred to more than one year, it was allocated proportionally to each year.
A derived variable finres consolidates the information for each country/year by reporting only one data format where more than one was available. This was done by ordering the data (i), (ii), (iii) and selecting the first one available. The variable finresour indicates the data format selected.
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Rights owners: |
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Contact: |
Name | Email | Affiliation | ORCID (as URL) |
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Barrientos, Armando | armando.barrientos@manchester.ac.uk | Global Development Institute | http://0000-0003-0672-7094 |
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Notes on access: |
The Data Collection is available to any user without the requirement for registration for download/access. Commercial use of data is not permitted.
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Publisher: |
UK Data Service
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Last modified: |
26 Oct 2020 15:53
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