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
Presenter
Presentation Notes
Increased demand for information on rural income and livelihoods will be required in the coming years for the design and implementation of polices that pursue the 2030 Sustainable Development Agenda, and to monitor its progress. The Sustainable Development Goals (SDGs) that constitute the 2030 Agenda demand wide-ranging information on a number of domains such as rural poverty, food insecurity, rural livelihoods, smallholder productivity, social protection, decent rural employment, migrations, sustainability and resilience, among others. The need to access this information disaggregated by gender is also a key emerging demand. Specifically, monitoring progress towards SDG 2.3 requires the definition and estimation of indicators 2.3.1 (Volume of production per labour unit by classes of arming/pastoral/forestry enterprise size) and 2.3.2 (Average income of small-scale food producers, by sex and indigenous status). These indicators must be based on comparable and harmonized micro-data on rural income and livelihoods. Goal 2.3 calls for “By 2030, double the agricultural productivity and incomes of small-scale food producers, in particular women, indigenous peoples, family farmers, pastoralists and fishers, including through secure and equal access to land, other productive resources and inputs, knowledge, financial services, markets and opportunities for value addition and non-farm employment.” Source: United Nations, Economic and Social Council, Statistical Commission, Report of the Inter-Agency and Expert Group on Sustainable Development Goal Indicators E/CN.3/2016/2/Rev. 1
Building on past and ongoing activitiesA 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
Presenter
Presentation Notes
The focus of RIGA (http://www.fao.org/economic/riga/rural-income-generating-activities/en/) is on computing comparable income aggregates from LSMS-type surveys and improving the understanding and relative role of income sources in rural areas. It started as a research project which made its data available to others users upon request. Differently from the other two projects, RIGA does not produce indicators, but it makes available the constructed household variables. The SHDP (http://www.fao.org/economic/esa/esa-activities/esa-smallholders/dataportrait/farm-size/en/) is a research project focusing on rural transformation and smallholders, that is, farmers who may face specific structural constraints in managing their farms, arising from the limited scale of their activities and the context in which they operate. In this context, the SHDP analyzes households engaged in agricultural activities, identifies smallholders and it makes available through a public website a set of indicators on small holder farmers. The RLM (http://fenix.fao.org/demo/rlm/) aims at creating a database that gathers and harmonizes micro and macro level information on all aspects of rural livelihoods, including income and consumption, employment, social protection, gender, and the availability and accessibility of assets, infrastructure and markets. The project provides information by holding size (smallholders vs non-smallholders and non-farmers), sex (male vs female and male vs female headed households), income quintiles, participation in agriculture, and urban vs rural areas. The data are not yet in the public domain – the project was started in mid-2014.
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 data3. research products and materials (papers, briefs, info notes) 4. easy-access information and story-telling (maps, charts,
graphics)
Presenter
Presentation Notes
RuLIS il being built as an information product, freely available on the web, which will include four different sections plus a landing page. It is conceived as a portal including both data and a set of ordered research papers and materials.
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
The data domains
1. Employment, health and education2. Land and natural resources3. Livestock4. Infrastructure and services5. Inputs and technology6. Income, productivity and inequality7. Social protection8. Community characteristics9. Household characteristics
Presenter
Presentation Notes
These are for ready-made indicators
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
agriculture9. Household Characteristics
• Urban and rural population
Presenter
Presentation Notes
We should include here data on RURAL POVERTY Income, Productivity and Inequality Poverty gap at $1.90 a day (2011 PPP) (%) Poverty gap at $3.10 a day (2011 PPP) (%) Poverty gap at national poverty lines (%) – national, rural, urban Poverty headcount ratio at $1.90 a day (2011 PPP) (% of population) Poverty headcount ratio at $3.10 a day (2011 PPP) (% of population) Poverty headcount ratio at national poverty lines (% of population) – national, rural, urban Agriculture, value added (percent of GDP) Agriculture, value added per worker (constant 2000 US$) Agriculture, value added (annual % growth) Employment, Health and Education Malnutrition prevalence, weight for age (% of children under 5) – national, male, female Maternal mortality ratio (modeled estimate, per 100,000 live births) Mortality rate, under-5 (per 1,000 live births) – national, male, female Literacy rate, adult (% of people ages 15 and above) – national, male, female Improved sanitation facilities (% of population with access) – national, rural, urban Improved water source (% of population with access) – national, rural, urban Employment in agriculture (% of total employment) – national, male, female Immunization, DPT (% of children ages 12-23 months) Immunization, measles (% of children ages 12-23 months) Births attended by skilled health staff (% of total) Pregnant women receiving prenatal care (%) Community health workers (per 1,000 people) Mean years of schooling of adults (years) Prevalence of undernourishment (%) Household Characteristics Urban/Rural population (share of total population) – national, rural, urban Land and Natural Resources Per capita arable land (ha) Adjusted savings: net forest depletion (% of GNI) Inputs and Technology Agricultural area actually irrigated, share of arable land and permanent crops (percent)
Indicators from hh surveysBoth 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
Presenter
Presentation Notes
Let’s look at each of these modules
Disaggregation of indicators: qualifiersCategory 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 from agriculture greater than 30% Households involved in crop/livestock activitiesIncome from agriculture lower than 30%
No income from agriculture Hhs not involved in crop/livestock activities
Geographic area UrbanRural
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
Presenter
Presentation Notes
COMMENTS Slide 1 • The RIGA methodology is our starting point for income computation. As in RIGA, the methodology is consistent with ILO’s definition. Therefore, income receipts recur regularly, contribute to current economic well-being; and do not arise from a reduction in the net worth. However, the Canberra Group Handbook of Household Income statistics suggests for an inclusion of changes in the net value of household net worth. Ideally, we would like to construct net income, accounting for all costs including asset depreciation. Unfortunately, lack of data force us to stop to gross income. In some cases, however it is possible to account for some of these costs. An example is the possibility to account for livestock stock variation. • . • We would like to account for all costs including assets depreciation and construct a net income, but most of the time we do not have data so we are forced to compute gross income. In some cases, data allows to account for some change in value of assets (e.g. Livestock stock variation). Thus, the gross income is given by revenues minus costs plus stock variation component, whenever available. • Aggregate income is the sum of the following components: Major deviations occur when computing crop income and livestock income .
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
Presenter
Presentation Notes
Three main groups of indicators aimed at: Measuring and monitor labour markets quantitative aspects, including job creation (employment) and difficulties faced while entering the labour market (unemployment): employment in ag and non ag, rural ad urban, by sex and by age; unemployment (wide, excluding actively job searching; and strict, including job searching) Measuring unstable and insecure forms of employment (seasonal and casual workers (shares)), as well as job statuses more subject to a high degree of economic risk (own-account and contributing family workers – shares in total ag employment, qualifiers and age) 3) Measuring employment-related income with respect to adequate earning (low pay rate (less than 2/3 of the median wage) and real wages ) and contribution to household livelihoods (working poverty rate (ie share of worker leaving in poor (national poverty line) hh in total workers)) The set of Decent work indicators are disaggragted by -Areas (rural/urban) & Sectors (agriculture/non-agriculture) -Income quantile & small-holder/ non small-holder And age: adult, youths and children Overall, while rural and agriculture labour markets show a higher employment-to-population ratio than the urban one, quality job opportunities are less likely to be found in agriculture and rural areas. Compared to non-agricultural workers, agricultural ones -and especially own-account and contributing family workers- are more likely to partake in poorly remunerated jobs characterized by very low wages or even subsistence consumption; more generally, they are more likely to be excluded from decent working opportunities. Health: immunization, maternal mortality (macros) Education: Literacy, NEET (not in education, nor employed)
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
Presenter
Presentation Notes
ASPIRE: Four main categories of transfers: social insurance, social assistance, international remittances private domestic transfers Three categories are added: governmental support in agriculture, assistance after shocks decision making on public transfers.
Inputs and technology
• Access to technology: irrigation, machinery, equipment
• Distance from markets• Extension services and training • Access to credit • Agricultural inputs: fertilizers, pesticides
Land and Natural ResourcesAccess 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
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
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
Presenter
Presentation Notes
The resulting groups: Smallholder farms Non-smallholder farms Non-farmers
• 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
The RuLIS team (in alphabetical order)Piero Conforti, AnaPaula De la O Campos, Giovanni Federighi, PanagiotisKarfakis, Clara Aida Khalil, Evgeniya Koroleva, Erdgin Mane, MiraMarkova, Orsolya Mikecz, Svetlana Mladenovic, Gianluigi Nico, GiuliaPonzini, Vanya Slavchevska, Alberto Zezza
Thank you for your attention
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
Presenter
Presentation Notes
Households are interviewed over a survey period elapsing from a few months to an entire year. Monetary values are measured in nominal local currencies: changes in price levels over time may distort the reported figures. It is necessary to net out price fluctuations due to inflation. Once changes attributable to price movements are removed, inflation-adjusted indicators allow comparing income levels of hh interviewed at different points in time.
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
Presenter
Presentation Notes
In order to automatize the implementation of the approach described above, two Stata commands were constructed: impoutmad and impoutlogmad. In the first command, the observations are considered as outliers when their value is higher or lower than 3 x 1.4826 x MAD from the median. This choice is equivalent to 3 standard deviations for normally distributed variables, which means that the probability of classifying as outlier a true observation is only 0.27 percent. However, the commands allow changing the number of SD used to detect an outlier through the range option. The observations detected as outliers are then imputed using medians, computed conditional on categorical variable(s) (ie using the media of the reference group). Key aspect: When to impute!!!!
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)
