Prepared by Brian Holtemeyer & James Thurlow

1. Women’s Inclusion in Structural Transformation
RIAPA’s WIST Indicator
Brian Holtemeyer & James Thurlow, IFPRI, Washington DC
Seminar on Gender Modeling | USAID-BRFS | August 5, 2021
and youth’s

2. 0.56
0.33
0.76
0.46
1.00
0.00
0.32
0.21
Low-value crops
High-value crops
Livestock
Forestry & fisheries
Agro-processing
Other industry
Low-value services
High-value services
0.45
1.00
0.00
0.14
0.26
0.09
0.26
0.05
Low-value crops
High-value crops
Livestock
Forestry & fisheries
Agro-processing
Other industry
Low-value services
High-value services
0.00
0.48
1.00
0.21
0.04
0.08
0.43
0.15
Low-value crops
High-value crops
Livestock
Forestry & fisheries
Agro-processing
Other industry
Low-value services
High-value services
0.88
1.00
0.74
0.81
0.13
0.04
0.24
0.00
Low-value crops
High-value crops
Livestock
Forestry & fisheries
Agro-processing
Other industry
Low-value services
High-value services
Uses | Prioritizing Growth Sectors (Tanzania)
Poverty
National
headcount rate
($1.90 a day)
Growth
Agri-food system
value added
(AgGDP+)
Jobs
Agri-food system
employment
(AgEMP+)
Diet quality
Food group spending
gap with healthy diet
(REDD-X)
Women’s inclusion
Labor productivity gains from
positive structural change
(WIST)
Final sector ranking
Outcomes treated as equally important
1.00
0.90
0.64
0.50
0.60
0.00
0.64
0.24
Low-value crops
High-value crops
Livestock
Forestry & fisheries
Agro-processing
Other industry
Low-value services
High-value services
0.74
0.63
0.58
0.42
0.40
0.38
0.13
0.04
High-value crops
Livestock
Low-value crops
Forestry & fisheries
Agro-processing
Low-value services
High-value services
Other industry
Equal weights
High-value crops
Livestock
Low-value crops
Forestry & fisheries
Agro-processing
Low-value services
High-value services
Other industry
Poverty Growth Employment
Diets Women
Model the same increase in national GDP driven by raising productivity
growth in targeted subsectors and compare impacts on different outcomes
Low value crops = cereals, roots Other industry = nonfood manufacturing, construction
High value crops = pulses, horticulture, oilseeds, tobacco, etc. Low value services = trade, transport, domestic work
Agro-processing = foods, beverages, tobacco, yarn, timber High value services = finance, business services, government
Provisional
Provisional

3. Indicator | Women’s Inclusion in Structural Transformation
Inclusive Growth vs. Pro-Poor Growth
• Inclusion means that poor should not only benefit
from, but actively contribute to growth process
• WIST focuses on employment outcomes
Absolute vs. Relative Outcomes
• For a policy to be “inclusive”, do women need to
benefit (absolute gains) or benefit more than men
(relative gains)?
• Relative interpretation allows for possible “pro-
poor recession” (i.e., everyone loses, but women
lose less)
• WIST takes absolute approach (i.e., prioritize what
benefits women the most, even if it doesn’t close
the gender gap)
Inclusion ≠ Empowerment
• WIST focuses on economic outcomes (i.e., work
that contributes to GDP)
• WIST includes some work within the home (e.g.,
subsistence farming, but not childcare)
• WIST does not consider working/workplace
conditions (i.e., decent employment)
• WIST does not explicitly consider decision-making
within the household (unlike WEIA)
WEIA Focuses on Agriculture
• Decisions about agricultural production
• Access to and decision-making power
about productive resources
• Control of use of income
• Leadership in the community
• Time allocation
WIST Looks Beyond Agriculture
• Employment conditions (productivity) & remuneration (wages,
profits, land rents, imputed value of unpaid family work, etc.)
• Includes both small-scale & commercial agriculture (crops,
livestock, forestry & fisheries)
• Employment in off-farm self-employed/informal businesses, as
well as formal sector enterprises (tracks all sectors – from
mining to health services)

4. • When we treat workers as a homogenous
group, we are less concerned about who moves
up the ladder (i.e., it’s about sectors, not people)
• When equity is a concern, we do care which
populations have upward economic mobility
(e.g., rural vs urban, women vs men, youth vs adults)
• Labor productivity (GDP per worker) is a standard measure of economic development
• An increase in labor productivity can be decomposed into two parts/drivers:
• Successful long-term development is strongly associated with positive structural change
• But within sector productivity gains are still important (e.g., rising ag productivity
necessary for income diversification)
• Workers can contribute to growth through both channels, but it is workers who move between
sectors drive long-term development
Indicator | Women’s Inclusion in Structural Transformation
WIST Focuses on Labor Productivity Gains Driven by Structural Change
Increase in average
economywide labor
productivity
Rising productivity
within each sector
Movement of labor
from low to high
productivity sectors
= +
1 2
Between-sector
component indicating
positive structural change
Within-sector component
indicating positive
technological improvement
Rising labor productivity
associated with economic
development
Isolating Women’s Contribution
Women’s contribution
to structural change
Men’s contribution
= WIST
WIST indicator is positive (i.e., structural transformation
process is “inclusive of women”) when women’s average
labor productivity (GDP per capita) is rising due to
positive structural change (i.e., women’s employment is
shifting towards sectors with higher average labor productivity)
WIST Definition “Climbing the ladder”

5. Model | WIST Module
Economywide Model
Job Allocation
Algorithm
Worker Sectoral
Employability Scores
How many jobs are created (lost) in
each sector? (no gender breakdown)
Who is most likely to find (lose)
a job within each sector?
How is employment in each sector
changing for men & women?
7.9%
0.4%
4.7%
0.9%
0.3%
5.4%
4.7%
1.1%
Low-value crops
High-value crops
Other agriculture
Agro-processing
Other industry
Low-value services
High-value services
Unemployment
Women Men
Agriculture
Industry
Services
Worker Sectoral Employability Scores
Women are more
likely than men to
work in low-value
crops & services
Use survey data to estimate characteristics of workers in each sector:
• Gender, education, age, work experience, household characteristics…
• Predict “sectoral employability scores” for all working-age people
Rank people based on their scores
• Who is first in line to be hired into or fired from a sector?
Gender intensity of sectoral employment (Tanzania)
Marginal effect of being a man or woman on the likelihood of employment in each sector
WIST Module

6. Model | Job Allocation Algorithm
Trade Manufacturing Agriculture Unemployment
Person most likely to get
next manufacturing job
+1
Person most likely to get
next trade sector job
Person most likely to get
next agriculture job
+1
BUT man gets higher
productivity job
Final assessment
How the algorithm tracks who moves between sectors (illustration)

7. 0.62
0.46
0.90
0.56
1.00
0.64
0.00
0.76
Low-value crops
High-value crops
Livestock
Forestry & fisheries
Agro-processing
Other industry
Low-value services
High-value services
Uses | Removing Gender Discrimination in Labor Markets (Tanzania)
Link to survey allows us to simulate changes in labor market conditions (e.g., discrimination), and to evaluate interventions to improve
women’s employability (e.g., skills training)
Simulation: Remove any gender-based factors influence hiring/firing, recalculate employability scores, rerun job allocation algorithm
Employability Scores Change Sector Rankings Change
Reduces bias
towards women in
low-value services
Reduces bias towards
men in other industry
& high-value services
0% 50% 100%
0% 50% 100%
First in line
Last in line
High-value
services
With discrimination Without discrimination
0% 50% 100%
0% 50% 100%
First in line
Last in line
Low-value
services
Women Men
Question:
Who is first in line to
get the next job
when it become
available?
With discrimination Without discrimination
0.56
0.33
0.76
0.46
1.00
0.00
0.32
0.21
Low-value crops
High-value crops
Livestock
Forestry & fisheries
Agro-processing
Other industry
Low-value services
High-value services
Question:
Which sectors
are most
effective at
raising WIST?

8. 0.26
0.07
1.00
0.22
0.99
0.13
0.00
0.47
Low-value crops
High-value crops
Livestock
Forestry & fisheries
Agro-processing
Other industry
Low-value services
High-value services
Way Forward
• What kinds of gender-intentional
interventions can be captured?
• In principle, we can consider anything affecting
employability scores (e.g., access to credit, markets,
rural services, work experience, etc.)
• Can also stylize findings from ex-post impact
assessments (e.g., programs that enhance women’s
education, skills, assets)
• Include rural-urban migration in addition to
intersectoral mobility
• Explore links between WEIA & WIST
• How does empowerment affect economic inclusion?
Vice versa?
• Consider non-gender dimensions of inclusion
in WIST module
Youth Inclusion in Structural Transformation (YIST ?)
0.56
0.33
0.76
0.46
1.00
0.00
0.32
0.21
Low-value crops
High-value crops
Livestock
Forestry & fisheries
Agro-processing
Other industry
Low-value services
High-value services
Women’s inclusion
Labor productivity gains from
positive structural change
(WIST)
Youth’s inclusion
Labor productivity gains from
positive structural change

9. Annex | RIAPA Data and Modeling System Countries with RIAPA Models
Rice
Wheat & barley
Maize
Other cereals
Sorghum & millet
Groundnuts
Other oilseeds
Sugarcane, beet
Milk
Cattle
Sheep & goats
Cotton & fibers
Pulses
Cassava
Potatoes
Sweet potatoes
Other roots & tubers
Leafy vegetables
Other vegetables
Bananas & plantains
Other fruits
Tea
Coffee
Nuts
Cocoa
Tobacco
Cut flowers
Rubber
Other crops
Poultry
Eggs
Other livestock
Vegetable fats & oils
Other foods
Animal feed
Beverages
Sugar refining
Forestry
Aquaculture
Capture fisheries
Rice milling
Wheat & barley milling
Maize milling
Meats
Fish & seafood
Dairy
Fruits & vegetables
Tobacco processing
Wood
Sorghum & millet processing
Other grain milling
Agriculture Processing
Coffee processing
Tea processing
Agri-Food Products in RIAPA
RIAPA
Dynamic
economywide
model
SAMs reconcile data from national
and external accounts, government
finances, and firm/household surveys
Investment tools track spending levels,
unit costs, and intermediate outcomes
(e.g., productivity gains, profit margins)
Survey-based microsimulation
modules estimate outcomes at
individual and household levels
Linked models track global and
biophysical drivers (e.g., animal
herds, crop yields, climate change)
RIAPA captures agri-food system
(e.g., agriculture, processing, trade,
transport, hotels, food services)