Comments on social protection, transfers

and remittances indicators of the RuLIS

data platform

Miguel Niño-Zarazúa, UNU-WIDER

RuLis typology of Social Protection

• RuLis follows a typology of social protection adopted by the World Bank’s ASPIRE Project that includes public and private transfers

• It classifies various types of public interventions into two general groups:

– Social Insurance – Social Assistance

• Private transfers are also divided into two groups:

– Domestic remittance, income and support from charities– Remittances from abroad

Comments on RuLis typology

• The section on ‘public interventions’ could benefit from a more disaggregated classification, following either the same ASPIRE programme classification, or other existing typologies (e.g. Barrientos and Niño-Zarazúa 2010; 2016)

• This is important because countries have adopted very specific approaches in response to demographic priorities and financial and political economy considerations

• Disaggregated indicators could also help monitor progress made in coverage of specific programmes that target relevant vulnerable groups

Num

ber o

f P

rogr

amm

es

180

Cumulative flagship transfer programme starts by type

160

140

120

100

80

60

40

20

0

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

In Kind HD-CCT Employment Categorical-­‐pension Categorical-­‐Other

Cumulative flagship social assistance programmes by type

Source: [updated] from Barrientos, Niño-Zarazúa and Maitrot (2010)

Comments on data sources

• The ‘Notes on procedures and criteria for computing indicators obtained from household surveys’ states in pp. 8 that:

– “RuLIS aims at utilizing any available surveys from national and international sources for which micro data are available […] and has used as a starting point the methodology developed and adopted by the Rural Income Generating Activities (RIGA) project”.

• RIGA relies on data from the World Bank’ LSMS surveys that are of good quality; however, they have limited country coverage (only 41 countries). In fact, there is limited coverage of countries in the RuLisdatabase (only 22 countries, mainly with one single time point). Most populous countries still missing

• There are other data repositories that could contribute to increase the country coverage (e.g. Socio-Economic Database for Latin America and the Caribbean (SEDLAC), Luxemburg Income Study (LIS) database)

• At the moment, it is unclear the quality and level of representativeness of HH surveys

Comments on Indicators

1. Average annual per capita amount/transfer of i) international remittances, ii) private transfer, iii) Social Assistance and iv) Social Security are presented in current LCU and current international US$ (PPP)

• Comment: Why not also to present figures in constant international US$ (PPP)? This would be useful for cross-country time series analysis

2. Female primary decision makers on the use of public transfer, share of total population receiving public transfers (%)

• Comments: i) this indicator is driven by the design features and scale of dominant transfer programmes in a given country (e.g. CCTs in LA); it does not necessarily reflect gender empowerment; ii) Limited coverage at the moment. Only four countries in the database

Comments on Indicators

3. Households using vouchers for agricultural inputs, share of agricultural households (%)

• Comments: i) is the section on social protection the best place for this indicator? Shouldn’t be better to place it in the section on HH income?; ii) Very limited coverage at the moment, only three countries (single year point) with this indicator

4. Participation in private and public emergency programs, share of total population (%)

• Comments: i) these indicators (in particular private emergency programmes) are most likely to be mainly relevant for LICs. Shouldn’t they be better placed in the section on HH income? I would preferably not include emergency programmes as part of social protection systems

Comments on Indicators

5. Population with social assistance, share of total population (%)

• Comments:

i) Given that Social Assistance programmes are largely poverty-focused, shares of total population provide a partial picture of the actual coverage of the reference / targeted population. Furthermore, with the existing indicator, we could end up with a global inverted U-shape curve with LIC and HIC exhibiting low coverage but due to very different reasons!

ii) With mean incomes and quantile distributions, it would be possible to estimate % of population in poverty covered by SA, using domestic poverty lines OR/AND international definitions

Coverage

Income per capita

LICs HICsMICs

a

b

c

Comments on Indicators

6. Population with social insurance, share of total population (%)

• Comments:

i) As with Social Assistance, there is at the moment a very limited country coverage in the database

ii) If HH surveys are not nationally representative, these indicators would be biased

iii) There are some cases with odd statistics, for example, Armenia reports a coverage of 104% of social insurance?

Comments on Indicators

7. Relative incidence of i) international remittances; ii) private transfers; iii) social assistance and iv) social security to total income (%)

Note: the relative incidence measures the transfer amount received by a group as a share of total income of a group

% sample % sample % sample

Income quintile 1 18.7 253 37.2 132 37.3 40

Income quintile 2 14.7 175 29 219 2.7 14

Income quintile 3 12.3 81 15.8 142 5.2 23

Income quintile 4 15.2 61 12.7 205 13.5 21

Income quintile 5 18.3 25 8.4 264 4.1 26

Male headed HH 15.1 542 17.7 663 14 86

Female headed HH 31.3 53 19.5 299 7.8 38

National 16.4 595 18.3 962 12.7 124

Rural 12.1 372 0 0 6.5 65

Urban 24.7 223 0 0 15 59

Albania Bolivia GhanaReference group

A footnote on a parallel

(complementary) effort

Social Assistance, Politics and

Institutions (SAPI) database

• UNU-WIDER is currently developing a new database, ‘Social Assistance, Politics and Institutions’ (SAPI) database

• The SAPI provides a synthesis of longitudinal and cross-country comparableinformation on:

i. Social Assistance programmes in developing countries

ii. Country-level information on economic and social performance

iii. Political economy dimensions

Why is the SAPI important?

• There is a growing recognition about the importance of learning from, and better understand, the wide range of programme characteristics in terms of objectives, design features, reach and poverty effectiveness

• There are positive externalities from cross-country knowledge sharing. The fact that the experiences from one country can help others avoid placing resources on ineffective policies, underlies the critical role of documenting and making information on social assistance programmes widely available

The SAPI aims to contribute to this goal

Content of the SAPI

The SAPI will collect longitudinal annual data, starting from the 1990s, on the following dimensions:

• Programme type (e.g. CCT, old age pension, child allowance, workfare programmes, etc.)

• Programme design (scale, objectives, reach)

• Transfer design (amount, means of receipt, frequency, conditions)

• Programme budget and cost

• Programme implementation (centralisation, local discretion, horizontal or vertical coordination, government / agency / community / NGOs involvement

• Legal framework

• M&E Indicators and programme outcomes (on poverty reduction, human capital, asset accumulation, employment, etc.)

• Country level information (poverty, inequality, demographics, labour markets, macro indicators)

• Political Economy dimensions (political regimes, quality of government and institutions etc.)

www.wider.unu.eduHelsinki, Finland