Strengthening the availability of sex disaggregated data on - - PDF document

Strengthening the availability of sex‐disaggregated data

• n employment in agriculture

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

Statistics Division Social Protection Division

Food and Agriculture Organization of the United Nations

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

LF SURVEYS 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% Country TOTAL persons engaged in farm work (HH members+hired employees) HHs members engaged in farm work hired employees AGRICULTURAL CENSUSES DIFFERENCE Employment in agriculture

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

Year Core (annual data) Module 1 labour force Module 2 Economy Module 3 Machinery, equipment Module 4 Production methods 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

19th 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

• n 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
• n ownership and management of plots, by hh members
• Will result in a revised “Intra‐holding responsibility and
• wnership” 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