Strengthening the availability of sex ‐ disaggregated data on employment in agriculture Statistics Division Social Protection Division Food and Agriculture Organization of the United Nations Better Data to Better Monitor the Status of Women in Informal employment, unpaid work and work in rural areas and agriculture Joint ILO/Data 2X Round Table - Geneva, 1-2 October 2014 Introduction Challenges: • High incidence of unpaid and informal work, especially for women, who are more involved in such work • Multiple jobs and part ‐ time are common • Seasonality and peaks in demand • Time distribution is seldom surveyed, especially for women. Studies reveal that the female time ‐ use in agriculture varies widely depending on production stages. Scattered evidence (SOFA 2011) • Lack of information on working conditions and decent work • Individual data on access to productive assets, especially land are scarce
Outline • Data from existing sources: ⁻ agricultural censuses, labour force surveys, ⁻ ⁻ household surveys • Addressing limitations: FAO activities Agricultural Censuses The current World Census of Agriculture (round 2006 ‐ 15) suggests collecting data on two labour inputs: i. Labour provided by household (hh) members – q to the hh: Activity status (for hh members in working age) Employment status for main job (for economically active hh members) Time worked in the main job Time worked on the holding ii. Paid workers – q. to the holding: Number of employees on the holding, time worked and sex Form of payment for the employees Use of contractors for work on the holding, by type
Agricultural Censuses Review of ag census questionnaires in the 2006 ‐ 15 shows that: • Most censuses collect information only on whether the hh members worked on the holding. Time worked is less frequently collected (always in Europe) • Information on the ‘ sex of the hired employees ’ is frequently collected in Europe, and to a lower extent in Africa, Latin America & Carib, Asia and Pacific. Comparing ag censuses and labour force surveys Estimates from ag censuses tend to be higher than those from by LFS. Possible sources of discrepancies include: • the type of work • the reference period • agriculture is frequently also a secondary occupation AGRICULTURAL CENSUSES LF SURVEYS TOTAL persons engaged in farm HHs members DIFFERENCE Employment in Country work (HH engaged in farm hired employees agriculture members+hired work employees) Brazil (2006) 16,567,544 12,801,179 3,806,602 17,263,000 ‐ 4% Japan (2010) 2,605,736 NA 2,329,928 2,520,000 3% El Salvador (2006/7) 1,247,704 NA 1,247,704 435,900 65% Bulgaria (2010) 738,634 681,466 57,168 208,100 72% Czeck Rep. (2010) 186,100 45,551 140,549 151,200 19% Moldova Rep. (2011) 1,590,652 1,215,282 375,370 323,000 80%
Example: Moldova Total labour force shows high discrepancy (80%) between census and labour force surveys Lower discrepancy (20%) when comparing hh members working full time plus permanent employees with data from labour force survey Possible sources of discrepancy: reference period Labor force surveys Individuals considered employed in agriculture are those who worked at least 1 hour in the previous 7 days The 7 ‐ day period may bias the information, if the survey is not spread over the year Agricultural censuses • Employment status refers to one year: anyone who had a job at some time during the reference year is an employee • Data include permanent, seasonal, part ‐ time and casual workers. This may result in an overestimation of farm labor: double ‐ counting where laborers frequently work for more than one holding
Possible sources of discrepancy: the main occupation Labour force surveys Employment based on the main occupation: potential underestimation where agriculture is carried out also as secondary occupation Agricultural censuses The number of employees includes both those who work in agriculture as primary and secondary activity FAO activities Work in progress: • The Agricultural & Rural Integrated Surveys (AGRIS) project • The next World Census of Agriculture Programme; strengthening sex disaggregated data on access to land • Data on employment and decent work in agriculture and rural areas from household budget surveys
Agricultural & Rural Integrated Surveys (AGRIS) Work in progress • Multipurpose modular survey on agricultural holdings , collecting a minimum set of core data plus other relevant agricultural and rural data (based on the Global Strategy design) • Core data module includes questions on employment (status, sector) by sex • Collects also information on households, where relevant • Modular Structure: Core Module : every year collects data on current agricultural production integrated with economic and socio ‐ demographic statistics Modules on Specific Topics collects other structural data every two ‐ three years (also on sub ‐ samples) Agricultural & Rural Integrated Surveys (AGRIS) Example : core data collected every year, plus one specific module each year Core Module 1 Module 2 Module 3 Module 4 Year (annual labour force Economy Machinery, equipment Production methods data) 2015 X X 2016 X X 2017 X X 2018 X X 2019 X X 2020 X X 2021 X X 2022 X X 2023 X X
Agricultural & Rural Integrated Surveys (AGRIS) How does AGRIS operate? • Where an annual ag survey is in place: AGRIS adds a module to collecting the core data • Where only an ag census is in place: AGRIS promotes the introduction of a more frequent survey • Where an LSMS ‐ ISA is on ‐ going: AGRIS complements annual data with the minimum set of core data • Where no LSMS or annual ag survey are in place: AGRIS collects (at least) the minimum set of core data Revised Farm Labor theme for the next World Census of Agriculture Programme • Consistent with the ILO resolution adopted by the 19 th Int. Conf. of Labor Statisticians (2013). • New features: • Household members working on the holding are considered as ‘in employment’ only if the holding’s intended purpose is mostly sale. • Own consumption oriented work falls into the category of “own use production work” • Some concepts will be adapted to accommodate the one ‐ year reference period of censuses: Eg. 50% time cut ‐ off to consider a person within the labour force
Strengthening sex disaggregated data on access to land in ag censuses • Methodological work aimed at strengthening the availability of sex disaggregated data on land ownership and management in agricultural censuses • Work in cooperation with UNSD/UN Women and the EDGE initiative • Discuss the possibility to capture sex ‐ disaggregated data on ownership and management of plots, by hh members • Will result in a revised “Intra ‐ holding responsibility and ownership” theme for the WCA 2020 and operational guidelines for agricultural censuses and surveys Data from household budget surveys Work in progress Can complement information of Labour force surveys, especially for agriculture. Quality is variable, but hh budget survey data may: • capture time use, seasonality of employment, short term employment, incidence of part ‐ time, wage and income employment and proxy measures for informal employment • link with relevant variables, including sex, age, income, education. food security • offer insights on informal employment: eg the share of contributing family workers in total employed population
Open issues Most estimates of informal employment and work exclude agriculture ‐‐ while ag labour is largely informal. Need to distinguish between: • Subsistence vs non subsistence farming : location of production, paid vs family work, market vs own consumption • Formal vs informal employment : registration, paid vs family work • Multiple job holding : main activity, other activities by sector. All these types co ‐ exist in the same agricultural holding, and across income level Thank you for your attention piero.conforti@fao.org
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