Expert consultation on methodology for an information system on rural livelihoods and Sustainable Development Goals indicators on smallholder productivity and income 7 - 8 December, FAO headquarters

1. RuLIS
Rural Livelihoods
Information System
An overview
The RuLIS Team

2. Motivation
• Information on rural income and livelihoods are sparse and scarce
 lack of a systematically organized data repository linking different aspects of rural
livelihoods in support of decision making for reducing rural poverty
• Increased demand, also with the 2030 Sustainable Development Agenda
 Need to design and implement polices that pursue the SDGs, and monitor progress,
notably 2.3.1 and 2.3.2 (income and productivity of smallholders) and targets 5a and
1.4 (access to land and rights to economic resources)
 information required on rural poverty, smallholders (productivity and incomes), social
protection, decent employment, migrations, sustainability, resilience; all sex-
disaggregated
• Household-level data not harmonized across-countries
• Household surveys under-utilized;
 increases the visibility of available surveys,
 reduces costs of using detailed data, by providing ready-to-use and customized
indicators
 guide the improvement of data availability and quality at national level

3. Building on past and ongoing activities
A number of projects aimed at gathering information on rural incomes and
livelihoods:
• The Rural Income generating activities (RIGA): research on computing
comparable income aggregates from LSMS-type surveys
• The Smallholder Data Portrait (SHDP): research on rural transformation
and smallholders and a set of indicators on small holder farmers
• The Rural Livelihoods Monitor (RLM): database on all aspects of rural
livelihoods, including income, consumption, employment, social
protection, gender, assets, infrastructure and markets
• The Gender and Land Rights Database (GLRD): Sex-disaggregated data
on land ownership
• The World Agricultures Watch (WAW): documenting structural change;
territorial indicators and typologies

4. RuLIS: vision
• Consistent information on rural incomes, livelihoods and
rural development from at least 70 countries, linked to
policy making
• Enhanced partnership with the World Bank and IFAD
(possibly more partners); synergies with other initiatives
• Four elements:
1. ready-made indicators, and related scropts and
methodologies
2. a facility to compute indicator and access bulk data
3. research products and materials (papers, briefs, info notes)
4. easy-access information and story-telling (maps, charts,
graphics)

5. Where do we stand?
• Methodology developed, under peer-review
• Pooling survey data and validation within FAO and with the World Bank
Meanwhile:
• Wide list (ca 250) of ready-made indicators computed for 26 surveys
and national-level sources, hosted on a temporary test IT platform,
partly validated.
• Set of template scripts and methods documented
• Collaboration with the Smallholder Dataportrait, in connection with
research on rural transformation
• Collaboration with WAW, the Gender and Land Rights Database

6. The data domains
1. Employment, health and education
2. Land and natural resources
3. Livestock
4. Infrastructure and services
5. Inputs and technology
6. Income, productivity and inequality
7. Social protection
8. Community characteristics
9. Household characteristics

7. National level Indicators
1. Employment, Health and Education
• Malnutrition, maternal mortality ratio, under-5 mortality rate, literacy
rate, improved sanitation facilities and water sources, employment in
agriculture, immunization, pregnancy, prevalence of undernourishment
2. Land and Natural Resources
• Per capita arable land
5. Inputs and Technology
• Agricultural area actually irrigated
6. Income, Productivity and Inequality
• Poverty indicators (poverty gaps, headcount ratios), value added in
agriculture
9. Household Characteristics
• Urban and rural population

8. Indicators from hh surveys
Both LSMS and other surveys
Processing to obtain indicators:
• The most resource- and time-consuming part: sequence from “themes”
to “countries”. Now one person to process one survey
• The RIGA Project scripts used as a starting point for computing
rural income
• Indicators on employment, social protection, community-level,
natural resources, access to technology, inputs and markets,
smallholders were developed ex-novo

9. Disaggregation of indicators: qualifiers
Category Qualifier Description
Farm holding size Non-farmers Hhs non participating in crop and or livestock
activities
Non-smallholder farms Farm size > median hectare; TLU > median TLU
Smallholder farms Farm size < median hectare; TLU < median TLU
Sex Male / Male headed household Depending on the type of underlying data
(Individual vs. household)Female / Female headed household
Income Quintile
Income Quintile 1
Income Quintile 2
Income Quintile 3
Income Quintile 4
Income Quintile 5
Participation in
agriculture
Income from agriculture greater than 30% Households involved in crop/livestock activities
Income from agriculture lower than 30%
No income from agriculture Hhs not involved in crop/livestock activities
Geographic area Urban
Rural

10. Income, productivity and inequality
• RIGA methodology consistent with ILO’s definition, plus deflation
• Gross Income = Revenues – Costs + (Stock Variation, when available)
• Income sources (and shares of):
 agricultural wage employment
 nonagricultural wage employment ,
 agricultural self-employment (mainly crops, livestock, smt fishery and forestry)
 non-agricultural self-employment,
 transfers, other income
• Value of production per hectare
• Concentration Index for crops and livestock

11. Employment, health and education
• Labour market, quantitative
 Employment related indicators
 Unemployment rate
• Labour markets, qualitative (employment statuses & precarious employment)
 Share of own-account and contributing family workers
 Seasonal workers
 Casual workers
• Income from employment
 Low pay rates
 Real wages
 Working poverty
• disaggregated by age
 adult, youth and children
• Education
 Literacy rates
 NEETs

12. Social Protection
• Classification of the ASPIRE project (World Bank): Atlas of Social
Protection Indicators of Resilience and Equity
• Level of benefit: average amount of the transfers
• Coverage of benefit: share of total population receiving transfers
• Incidence of benefit: relative incidence of transfers in the total income
• Help after shock: share of total population receiving private or public
help after shocks.
• Support to agriculture: share of rural population receiving free coupons
for (eg) seeds and fertilizers
• Decision making on the use of public transfers: female primary decision

13. Inputs and technology
• Access to technology: irrigation, machinery,
equipment
• Distance from markets
• Extension services and training
• Access to credit
• Agricultural inputs: fertilizers, pesticides

14. Land and Natural Resources
Access to agricultural land :
 average household farms size (ha): smallholders
 Gini coefficient of owned arable land (real number)
Gender-land inequalities, wherever possible:
 Distribution of land ownership (female/male agricultural landowners over
total agricultural landowners) (%)
 Incidence of land ownership (female/male agricultural landowners over
female/male adult population) (%)
 Household land area/value owned by men only, by women only, or jointly by
men and women as a share of total household owned land area/value (%)
Links to the Gender and Land Rights database

15. Community-level Information
• Community-level data to measure indicators on:
 Infrastructure and Services
 Inputs and Technologies
 Community Characteristics
• Social capital Indicators:
 Communities
 Groups/Organizations
 Group members
• Focus on:
 Agricultural Cooperatives,
 Farmers Groups,
 Women’s Groups
 Savings & Credit Groups
• Infrastructure and Services:
 roads, irrigation schemes, storage facilities, health and education facilities, and
microfinance in the community
 Main indicators are: the presence/absence and the distance to a given type of
facility/service

16. Qualifiers: smallholders
• Definition to be discussed here: need a harmonized criterion to
be proposed for the SDGs productivity and income indicators
• Temporarily: definition from the High Level Panel of Experts on
Food security and Nutrition (HLPE 2012) and the Smallholder
Dataportrait.
farm size at the weighted median hectare
farm size at the weighted median Tropical livestock units (TLU)
Engagement in agriculture and/or livestock activities

17. • An improved and validated methodology
• More, or less, or different indicators?
• Methodology for monitoring relevant SDGs
• Upscale: Data available for about 70 countries (90 surveys)
• An IT platform, including “customized” indicators, and a
dissemination/maps/easy access section
• Increased, deepened partnership
Way Forward

18. The RuLIS team (in alphabetical order)
Piero Conforti, AnaPaula De la O Campos, Giovanni Federighi, Panagiotis
Karfakis, Clara Aida Khalil, Evgeniya Koroleva, Erdgin Mane, Mira
Markova, Orsolya Mikecz, Svetlana Mladenovic, Gianluigi Nico, Giulia
Ponzini, Vanya Slavchevska, Alberto Zezza
Thank you
for your attention

19. Deflation of monetary values
• Survey periods from a few months to an entire year.
• Monetary values are nominal
• Lack of comparability of monetary values reported by
hh interviewed at different points in time
• All values are reported to the central point of the
survey

20. Outliers detection and imputation
• Considerable amount of outliers in the elementary data.
• Statistically robust approach to detect outliers and impute
values:
 the median is adopted as a measure of the central tendency
 the Median Absolute Deviation (MAD) is used as a measure of
variability
• This approach is developed for normal and lognormal:
 Normal for variables with a symmetric distribution (impoutmad)
 Log-Normal for asymmetric distributions (impoutlogmad)
• Observations detected as outliers are imputed using medians,
conditional on categorical variable(s)
• Imputation only once, at the lowest level

21. The test platform
• Variables and indicators
• The variable files are prepared in Stata
• The indicators file is prepared in (‘R) to ensure consistency
across countries
• Metadata
• Bulk download: 3 sets of files
• Next-steps: building customized indicators (self-service)
• A demonstration