Using Quantitative Tools to Measure Gender Differences within Value Chains Máximo Torero Director Markets, Trade and Institutions Division International Food Policy Research Institute

Miguel Almánzar Senior Research Analyst Markets, Trade and Institutions Division International Food Policy Research Institute

A4NH Gender-Nutrition Methods Workshop Nairobi, Kenya December 7, 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.

Demand side effects

Sectoral linkages

Supply side effects

Food prices

National Level

Household Level

Food output

Nonfood output

Food consumption

Food expenditure

Non-food expenditure

Individual Level

Nutrient intake Child nutrition outcomes

Hou

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Drivers of “taste”: culture, location,

growth, globalization.

Intrahousehold inequality: gender bias, education, family

size, seasonality, religion, SCTs.

Public health factors: water, sanitation, health

services, education.

Food imports

Policy drivers of inequality: land policies, financial policies, infrastructure investments, education policies, empowerment policies for women

Policy drivers of nutrition: health, nutrition, social protection & education

Interacting socioeconomic factors [possible leakages]

Interhousehold inequality in assets, credit, access to public

goods & services

Health status

Mother’s nutrition outcomes

Health care expenditure

Female employment

National nutrition outcomes

Food production

Income from food production

Non-food income

Farm/nonfarm employment

Caring capacity & practices

Female energy expenditure

Women’s Employment – health/nutrition pathway

Demand side effects

Sectoral linkages

Supply side effects

Food prices

National Level

Household Level

Food output

Nonfood output

Food consumption

Food expenditure

Non-food expenditure

Individual Level

Nutrient intake Child nutrition outcomes

Hou

seho

ld a

sset

s an

d liv

elih

oods

Drivers of “taste”: culture, location,

growth, globalization.

Intrahousehold inequality: gender bias, education, family

size, seasonality, religion, SCTs.

Public health factors: water, sanitation, health

services, education.

Food imports

Policy drivers of inequality: land policies, financial policies, infrastructure investments, education policies, empowerment policies for women.

Policy drivers of nutrition: health, nutrition, social protection & education

Interacting socioeconomic factors [possible leakages]

Interhousehold inequality in assets, credit, access to public

goods & services

Health status

Mother’s nutrition outcomes

Health care expenditure

Female employment

National nutrition outcomes

Food production

Income from food production

Non-food income

Farm/nonfarm employment

Caring capacity & practices

Female energy expenditure

Women’s Employment – health/nutrition pathway

Demand side effects

Sectoral linkages

Supply side effects

Food prices

National Level

Household Level

Food output

Nonfood output

Food consumption

Food expenditure

Non-food expenditure

Individual Level

Nutrient intake Child nutrition outcomes

Hou

seho

ld a

sset

s an

d liv

elih

oods

Drivers of “taste”: culture, location,

growth, globalization.

Intrahousehold inequality: gender bias, education, family

size, seasonality, religion, SCTs.

Public health factors: water, sanitation, health

services, education.

Food imports

Policy drivers of inequality: land policies, financial policies, infrastructure investments, education policies, empowerment policies for women

Policy drivers of nutrition: health, nutrition, social protection & education

Interacting socioeconomic factors [possible leakages]

Interhousehold inequality in assets, credit, access to public

goods & services

Health status

Mother’s nutrition outcomes

Health care expenditure

Female employment

National nutrition outcomes

Food production

Income from food production

Non-food income

Farm/nonfarm employment

Caring capacity & practices

Female energy expenditure

Women’s Employment – health/nutrition pathway

• 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 vs. Age • Gender gap is 11% Δ can be decomposed in 4 elements: • Δ 0 – Unexplained by the model. Only

for being male wage is 30% higher. • Δ X - Explained by observables

(common support). The distribution of age for women and men that lies in the common support is such that it 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

Wake uptime

Sleep time Length ofday

Hoursworked

Leisurehours

Childcarehours

Householdchoreshours

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 𝑀𝑀𝑖𝑖

𝑀𝑀� is the percent of males in the value chain in occupation i (or node of the value chain); 𝐹𝐹𝑖𝑖 𝐹𝐹� is the percent of 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 women production workers would have to change jobs workers in order to have an equal distribution.

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=1, 2,…n, for each

observation. An truncated to equality (1) if the ratio is above one.

𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑜𝑜𝑖𝑖 = �𝑥𝑥𝑖𝑖𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓

𝑥𝑥𝑖𝑖𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓�

1

𝑅𝑅𝑖𝑖 𝑥𝑥𝑖𝑖𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 ≤ 𝑥𝑥𝑖𝑖𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓

𝑅𝑅𝑖𝑖 𝑥𝑥𝑖𝑖𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 > 𝑥𝑥𝑖𝑖𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓

6.4. Tool: Working conditions/Access to work

Equality Index

• 3 step: Calculate sub-index scores (for each category of variables j=1,2,..J)

𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔(𝒘𝒘)𝒄𝒄𝒄𝒄𝒄𝒄𝒔𝒔𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄 𝒋𝒋 = � 𝒘𝒘𝒔𝒔𝒔𝒔𝒄𝒄𝒘𝒘𝒄𝒄𝒔𝒔 × 𝑹𝑹𝒄𝒄𝒄𝒄𝒔𝒔𝒄𝒄𝒔𝒔𝒔𝒔𝒋𝒋

𝒔𝒔

– Weight: normalize the variables by equalizing their standard deviations.

• 4 step: Calculate final score

𝑬𝑬𝑬𝑬𝒔𝒔𝒄𝒄𝑬𝑬𝒔𝒔𝒄𝒄𝒄𝒄 𝑰𝑰𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔 =𝜮𝜮𝒋𝒋𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔 𝒘𝒘 𝒄𝒄𝒄𝒄𝒄𝒄𝒔𝒔𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄 𝒋𝒋

𝑱𝑱

• 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.

• An advantage of this measure is that it is comparable over time.

Step 1 and 2 Construct Ratios

•Variable 1: Wage

(hourly/weekly) 0.59 •Variable 2: Participation

(employment by gender)

0.028 •Variable 3:

Literacy 0.033

Step 3 Construct Sub indexes

•Category1, Variable 1: Wage (hourly/weekly) 0.59

•Category 2, Variable 2 and 3: Participation (employment by gender) and Literacy 0.516

Step 4 Final Score

• 0.555 •

55% Equality in access and working conditions

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 the CGIAR

Research Program on Policies, Institutions and Markets (PIM) [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 change) 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, ability, etc.) Job training

Working conditions/Access to work Equality Index

Extra: Gender wage gap (1) • Nopo (2008) derived the conditional cumulative

distribution functions of individuals’ characteristics X, conditional on being male and female respectively, denote their corresponding probability measures.

• The typical interpretation of the wage gap decomposition applies, but only over the common support. In this new construction, two new additive components have been included (out of common support), resulting in a four-element decomposition.

Extra: Gender wage gap (2) • The matching procedure in order to estimate these four components. It

resamples all females without replacement and match each observation to one synthetic male, with the same observable characteristics and with a wage obtained from averaging all males with exactly the same characteristics x

• As a result of the application of this one-to-many matching, it is generate a partition of the data set. In such a way, the estimation of the four components previously presented is reduced to computations of conditional expectations and empirical probabilities without the need to estimate the nonparametric earnings equations: 4 separate equations

Then adding and subtracting

“Traditional BO method”

differences in average characteristics

differences in average rewards to individual characteristics

“Extended method”

Considering the fact that the support of the distribution of characteristics for females, SF , is different than the support of the distribution of characteristics for males, SM, each integral is split over its respective domain into two parts: