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

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.

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.

• 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

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

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

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.

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 .

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.

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

-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%

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

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

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

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%

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

• 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

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%

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).

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

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

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.

Thanks!

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

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