Presented as part of the IFPRI Gender Methods Seminar Series, hosted by the IFPRI Gender Task Force. Presented by: Lucia Madrigal.

1. Using Quantitative Tools to Measure Gender Differences within Value Chains
Lucia Madrigal
Maximo Torero
MTID
IFPRI
August 27th, 2013

2. 1. Value chain overview
•Value chains are defined as a linked set of activities* that bring a product through the process of conception, production, delivery to final consumers
•However, multiple barriers affect people’s ability to participate and benefit
•The study of value chains is useful to identify bottlenecks that limit growth and in this way, support poverty reduction.
Map of Simple Value Chain
* Also can be called nodes or segments.

3. 2. Why focus on gender?
•Evidence of significant gender inequalities in access to assets, land, labor, credit, etc. (Deere and Leon, 2003; Doss 2005 among others).
•Also, gender discrimination in wages and employment conditions in rural markets (Maertens and Swinnen, 2012)
•FAO (2011) pointed out that reducing gender inequalities in access to productive resources and services could increase yields on women’s farms, which could result in an increase of agricultural output.

4. •Women and men cluster in different segments of the chain and have clearly gender-defined tasks, roles and responsibilities
•Wage differentials: Women earn between 70-80% of men’s wages
•Women are disproportionately temporary or casual workers: 70% of all temporary workers in processing
Source: USAID
3. Example in Bangladesh

5. 4. Example in Peru
Source: USAID
•Women make up 51 percent of employment along the chain
•Women and men cluster in different occupations
•Women are employed for specific tasks: peeling, cutting and de-leafing

6. 5. Goal
•Identifying key role of gender in value chains through quantitative tools
•Identifying gender imbalances
•Improving the design of policies and interventions that will lead to more equality and women’s participation in value chains

7. 6. Gender in Value Chains Toolkit
•Preliminary quantitative toolkit to answer gender-relevant questions, based on widely known strategies in gender and labor economics literature.
i)Gender wage gap;
ii)Time Use Analysis;
iii)Occupational segregation (Duncan Index); and
iv)Working conditions/access to work equality index.

8. 6.1. Tool: Gender wage gap
How is remuneration different for men and women?
How much of that difference is due to observable characteristics? And to unobservable characteristics?
Method of Non-parametric Oaxaca-Blinder (BO)decomposition
“Traditional method”
•The goal of BO decomposition is to estimate differences in mean wages, across two groups (males and females).
•Creates a counterfactual “What would the earnings for a male (female) with average individual characteristics be, in the case that he (she) is rewarded for his (her) characteristics in the same way as the average female (male) is rewarded?”
•Difference is divided in two components: one attributable to differences in the average observable characteristics of the individuals, and the other to differences in the average rewards that these observable characteristics have .

9. 6.1. Tool: Gender wage gap (Cont)
“Extension”
•Here use an extension of the BO decomposition that uses a non-parametric matching approach which :
1) Does not restrict analysis to comparable individuals.
 Females and males are matched when showing exactly the same combination of characteristics.
2) Does not make assumption of linearity.

10. 6.1. Tool: Gender wage gap (cont)
Equation:
Implementation: Create groups by gender:
(i)one of males whose observable characteristics cannot be matched to those of any female (ΔM),
(ii)one of females whose observable characteristics cannot be matched to those of any male (ΔF), and
(iii)one of matched characteristics of males and females (ΔX)
Δ = (ΔX +ΔM + ΔF) + Δ0
ΔX +ΔM + ΔF differences in observable characteristics;
Δ0 cannot be explained by those characteristics and could be attributable to differences in unobservable characteristics, possibly discrimination.
Nopo 2008. The Review of Economics and Statistics, May 2008, 90(2): 290–299

11. -15%
-10%
-5%
0%
5%
10%
15%
20%
25%
30%
35%
Δ
Δ0
ΔM
ΔF
ΔX
Gender Wage Gap Decompositions
6.1. Example: Gender wage gap
•Gender gap is 11%
Δ can be decomposed in 4 elements:
•Δ 0 – Unexplained by the model. Only for the fact of being male wage increased in 30%.
•Δ X - Explained by observables (common support). The distribution of age for women and men that lies in the common support is such that reduces the gender gap by Δ X .
•Δ M – Existence of men with ages that cannot be matched by any women reduces gender wage gap by Δ M .
•Δ F – Existence of women with unmatched age with men reduces gender wage gap by Δ F.
Improve: Include variables such as job characteristics and ethnicity and consider selection bias
Gender gap is 11%

12. 6.2. Tool: Time Use Analysis
How do men’s and women’s time expenditures differ throughout the value chain, especially for the major tasks in each node?
How does women’s burden in terms of time compare to men’s? How the time use has changed?
Method: t-test of difference of means, or linear regression
•Time use data is a useful instrument to provide a detailed account of the time devoted to different activities and tasks during a particular period of time, usually a day.
•This instrument not only describes the time that females and males dedicate to productive and unproductive activities, but also shows differences in job activities.
•Customized to each value chain that is being analyzed

13. 6.2. Example: Time Use Analysis
5.03
20.24
15.21
5.63
7.69
0.76
1.03
5.26
20.42
15.15
0.82
7.25
1.23
4.42
Males
Females
Significant differences in hours worked (typically outside the household) and household chores typically performed by women. Implies that women allocate a larger share of their time to activities not directly generating income than men.
t-test of difference of means, or linear regression
Formula:
Improve: include time allocation within value chain, tasks, occupations

14. 6.3. Tool: Occupation segregation: Duncan Index
How does participation (by occupation) differ between men and women?
Method: Duncan Index for occupational segregation
•Where Mi is the percent of males on total males in the value chain in occupation i (or node of the value chain); Fi is the percent of females on total females in the value chain in occupation i (or node of the value chain).
•The values range from 0 to 100 and measure the relative separation or integration of gender across occupations (or nodes) in the value chain.
•If the value equals 0% it means the occupations are distributed evenly between male and female. If the value is 100% it means the occupations are completely segregated.
•Formula:

=
-
=
n
i
i
i
F
F
M
M
D
1
2
1

15. 6.3. Example: Duncan Index
Node
Duncan Index
Production
0.98
Commercialization
0.85
•Implies very high occupational segregation, so very few women.
•98% of the male workers would have to be replaced for female workers in order to have an equal distribution.
Note: Benchmark is 25.86%

16. Is there unequal access to employment for males and females? Do working conditions differ by gender?
Method: Hausmann Global Gender Gap, 2012
•The index final value is bound between 0 (inequality) and 1 (equality) to facilitate comparisons and interpretation. It has two variable categories: 1) variables that characterize working conditions and 2) variables that describe access to work.
•This index follows the empirical methodology used by Hausmann et al. 2012 to calculate the Global Gender Gap Index (World Economic Forum).
Methodology in 4 steps:
•1 step: Calculate ratios by gender for each variable i in each observation.
•2 step: Truncate at equality (1) when necessary.
6.4. Tool: Working conditions/Access to work Equality Index

17. •3 step: Calculate sub-index scores (for each category of variables j=1,2)
–Weight: normalize the variables by equalizing their standard deviations.
•4 step: Calculate final score
•An un-weighted average for each sub-index is taken to create the overall Working conditions/Access to work Equality Index. Sub-indexes are for: i) variables that characterize working conditions, and ii) variables that describe access to work.
6.4. Tool: Working conditions/Access to work Equality Index

18. 6.4. Example: Working conditions/Access to work Equality Index
•Index is 55%, which implies roughly a 45% inequality in working conditions and access to work.
•Comparable over time.
Step 1 and 2 ratio
•1)
•Wage (hourly/weekly) 0.5936624
•2)
•Participation (employment by gender) 0.0282051
•Literacy 0.0333333
Step 3 subindex
•1)
•Wage (hourly/weekly) 0.5936624
•2)
•Participation (employment by gender)
•Literacy
• 0.51639217
Step 4 final score
•0.555027288
•55%

19. 7. Implementation of tools
Three elements needed:
1.Questionnaire modules customized to each value chain; unit identification, an employment and time use module. Two types of modules are recommended: one for the producer node and another for the commercialization node.
2.After data collection is complete, a Stata code is available to construct the desired indicators. Raw data to perform an example can be provided.
3.An excel file that shows a table and a graph (example).

20. 8. Integrating gender to value chains
•Indicators that could be used as a first step in the process to strengthen value chains (e.g. mapping gender roles)
•Also to track changes and performance, for example women’s and men’s shares in chain employment and income
Value chain analysis phases

21. 9. Relevance in practice
Gender-based Constraints
•Laws or customs that restrict women’s land ownership
•Bank policies that do not allow a married woman to obtain a loan without her husband’s signature
•Social norms limit women’s networking abilities
•Inequitable distribution of harvest income
Possible solutions
•Joint titling of land or concessions
•Promote joint accounts or accounts in women’s names and Increase women’s participation in producer associations
•Use multiple mediums for communicating price and marketing information (e.g. cell phones and radio)
•Create innovative payment incentives to ensure married women producers receive returns from their labor

22. 10. Value Chain Clearinghouse
•It is an initiative led by PIM CGIAR Research Program [IFPRI, CIAT, ILRI, IITA, World Agroforestry Centre, ICRISAT, Bioversity, and CIP].
•The purpose is to provide a comprehensive, easily accessible repository of research methods and best practices surrounding value chain performance that can be used by all the consortium research programs and partners.

23. Thanks!

24. Minimum
Desirable to further analysis
Hourly Wage (daily/weekly)
Age
Level of education or Literacy
Gender
Religion
Ethnicity (minority groups)
Marital status
Number of Children, children ages, health of children, gender of first born children
Registered employment (contract)
Payment in cash/kind
Benefits
Type of job (specific to the value chain)
Occupation (specific to the value chain)
Temporary work
Wage gap
Minimum
Desirable to further analysis
Relationship with head of the household
Gender
Occupation
Time wake up
Time goes to sleep
Activities: preparing food, transportation, working, leisure, and other activities specific to the tasks in the value chain.
Age
Ethnicity (minority groups)
Religion
Marital status
Household size
Time use
Data needed

25. Data needed (2)
Minimum
Desirable to further analysis
Employment total
Employment by gender
Occupation (specific to the value chain)
Type of job (specific to the value chain)
Duncan Index
Minimum
Desirable to further analysis
1) Working conditions
Wage (hourly/weekly)
2) Access to work
Participation (employment by gender)
Literacy or education level
1) Working conditions
Occupation (job activity)
Category (owner, worker, family worker)
Tenure
Temporary/Permanent
Contract
Physical Safety/risk of task performed
2) Access to work
Education level
Skilled, semi-skilled, non-skilled
Requirements for job (experience, abilities, etc)
Job-training
Working conditions/Access to work Equality Index