This document discusses different approaches to modeling the impacts of agricultural productivity growth and income distribution changes on poverty and inequality. It presents a hybrid approach that uses a global CGE model linked to detailed country models. The country models have representative households linked to fitted income distributions to estimate poverty impacts. This allows capturing both between-country and within-country sources of inequality. It also avoids having to directly age survey data over long-term time horizons.

1. Income Distribution
James Thurlow
IFPRI Washington DC
“Long-term scenario building for food and agriculture: A global overall model for
FAO”, Rome, 19 Feb 2016

2. Session Questions (1)
1. What is the differential impact of agricultural productivity growth
on aggregate incomes across countries?
2. Would fast agricultural productivity growth in LICs contribute to
reducing both between-country and within-country inequality?
3. Would doubling smallholder productivity end hunger and
poverty?

3. Session Questions (2)
4. Is smallholder agricultural productivity growth equivalent in
outcomes to redistributive policies, e.g., social protection?
5. How sensitive are diets to changes in income distribution?
6. Would climate smart agriculture speed up agricultural
productivity growth?
7. How does the saving/investment behavior of poor households
affect their food and nutrition security, and overall inequality?
8. What long-term productivity gains in agriculture could be
achieved through enhanced investments in human capital?

4. With fitted
income
distribution
Typology of Modeling Approaches
Proposed Hybrid
Approach
With and without
behavior
Linked to surveys
Typical Global
CGE Model
With
representative
households
Type 1
Type 3Type 2 Type 4

5. Type 1: Typical Global CGE Model
• Standard GTAP-based models with detailed
country disaggregation
• Advantages:
• Captures BC inequality (GTAP9 = 140 “regions”)
• Food systems (57 sectors with 18 in agriculture/food)
• Structural change (2-5 labor groups, land, capital)
• Limitations:
• Single aggregate household for each country
• No modeling of changes in WC income distributions
• No link to national poverty or malnourishment
• Modest scope for dietary change (driven by relative prices
and aggregate income elasticities only)
57
4/7
1
GTAP 8/9

6. Between vs. Within Country Inequality (1)
• Decompose inequality
Between countries (red)
+ Within countries (black)
• WB/Luxembourg data
7 post-tax household
income quantiles for 131
countries
• Sources of inequality
BC = 80%
WC = 20%
Source: Cohen (2014)

7. Between vs. Within Country Inequality (2)
Source: Cohen (2014)

8. Type 2: With Representative Households
• Global model with GTAP’s single household
disaggregated into subnational groups (RHs)
e.g., MIRAGE-HH, GLOBE?
• Advantages:
• Endogenous changes to WC income distributions and
consumption patterns
• Better capturing of dietary change (unique income
elasticities for different household groups)
• Limitations:
• Aggregated factor groups limits distributional channel
• Technically easy to address (MyGTAP) but data intensive
• Bound to GTAP’s representation of food systems in LICs
19
4
13-39
MIRAGE-HH

9. Comparison with IFPRI’s Malawi Model
• 58 sectors
• 33 agricultural and food processing sectors
• 15 factors of production
• 6 labor = rural and urban labor by education
• 4 crop land = small, medium and large-scale
smallholders + estate farms
• 5 capital stocks (incl. 3 livestock)
• 30 household groups
• Farm and nonfarm in rural and urban areas
• Small, medium and large-scale smallholders
• Per capita expenditure quintiles × 3 subnational
regions
19
4
13-39
MIRAGE-HH
58
15
30
IFPRI-Malawi

10. Type 3: Linked to Surveys (no behavior)
• Global models linked to microsimulation (MS) models via changes in
factor incomes and product prices
e.g., GIDDS, Hertel et al. (2003)
• Advantages:
• Retains information on survey households’ unique
income and expenditure patterns
• Captures within-group distributional changes
• Estimates changes in poverty and inequality
• Limitations:
• Aggregate household still means no endogenous changes to WC distributions
• Aggregated factor groups limits distributional channel
• Inconsistency between survey and model data (calibration + iteration)
• Long-term analysis means that surveys must be “aged”
57
4
1
1000s
GIDDS

11. Type 3: Linked to Surveys (with behavior)
• Non-behavioral MS models assume that households’ asset
endowments are exogenous (i.e., their claims on factors’ earnings)
• If unemployment falls, who earns the new workers’ incomes
• Behavioral models randomly or purposefully identify next employee
• May also identify which workers move between sectors
• Limitations:
• For dynamic analysis, do you need to age the behavioral parameters? (as well
as the survey)
• Focuses on labor employment, not land or capital allocations (maybe be
important for smallholders)

12. Type 4: With Fitted Income Distributions
• Model’s representative households are linked to an estimated
income distribution (e.g., lognormal, direct from survey, etc.)
e.g., MAMS
• Advantages:
• Can estimate poverty impacts (and nutrition impacts?)
• Simpler to implement than MS models
• Limitations:
• Works off aggregate income, and so how you earn your income doesn’t
influence the rate of poverty decline (less problematic with multiple RHs)
• For long-term analysis, you may still need to “age” the distributions (if
people move between RHs, must you adjust the fitted distribution?)

13. Proposed Hybrid Approach
• Global CGE model linked to a typology of country models via
changes in global prices and export demand
e.g., Kym Anderson’s agricultural distortions project
• Country models’ RHs then linked to fitted income distributions
• Advantages
• Global CGE captures BC inequality and global economic changes
• Country models capture WC inequality and agriculture/food system changes
• Avoid having to age surveys
• Can draw on existing and more detailed country models (e.g., Nexus)
• Could move towards incorporating RHs in global model using country SAMs

14. Nexus Project
Maize | Sorghum + millet | Rice | Other cereals | Pulses | Groundnuts | Other
oilseeds | Cassava | Other roots | Vegetables | Sugarcane | Tobacco | Cotton +
fibers | Fruits + nuts | Cocoa | Coffee + tea | Other crops | Cattle | Poultry |
Other livestock | Forestry | Fishing
Wholesale + retail trade | Transportation + storage | Accommodation + food
services | Information + communication | Finance + insurance | Real estate
activities | Business services | Public administration | Education | Health +
social work | Other services
Agricultural sectors (22)
Industrial sectors (25)
Coal | Crude oil | Natural gas | Other mining | Meat + fish + dairy | Fruits +
vegetables | Fats + oils | Grain milling | Sugar refining | Other foods | Beverages
| Tobacco | Textiles | Clothing | Leather + footwear | Wood + paper |
Petroleum | Chemicals | Non-metal minerals | Metals | Machinery + equipment
| Other manufacturing | Electricity + gas | Water + sewage | Construction
Rural farm expenditure quintiles | Rural nonfarm quintiles | Urban quintiles
Agricultural land | Capital | Rural and urban labor by educational attainment
Factors (8)
Households (15)
Service sectors (11)
15%
4%
8%
73%
14%
4%
20%
62%
Pop. GDP
Sub-Saharan Africa

15. Session Questions
1. What is the differential impact of agricultural productivity growth
on aggregate incomes across countries?
2. Would fast agricultural productivity growth in LICs contribute to
reducing both between-country and within-country inequality?
3. Would doubling smallholder productivity end hunger and
poverty?
4. Is smallholder agricultural productivity growth equivalent in
outcomes to redistributive policies, e.g., social protection?
5. How sensitive are diets to changes in income distribution?
6. Would climate smart agriculture speed up agricultural
productivity growth?
7. How does the saving/investment behavior of poor households
affect their food and nutrition security, and overall inequality?
8. What long-term productivity gains in agriculture could be
achieved through enhanced investments in human capital?

16. Between-Country Data
Total
GDP (%)
Total
pop. (%)
GDP per
capita (%)
Agric.
GDP (%)
Agric.
workers (%)
Ag. GDP per
worker ($)
World 100.0 100.0 10,460 100.0 100.0 1,349
Developed 69.5 19.5 37,284 25.6 2.5 18,220
Developing 30.5 80.5 3,962 74.4 97.5 924
East Asia 14.4 28.1 5,347 36.1 48.2 772
Central Asia 2.4 3.7 6,720 4.3 1.5 4,783
LAC 6.3 7.2 9,064 7.2 2.7 3,643
MENA 2.3 4.9 4,912 4.9 1.8 3,030
South Asia 3.1 23.6 1,374 13.3 27.0 694
SS Africa 2.1 13.0 1,696 8.5 16.2 701
National GDP Agricultural Productivity

17. Within-Country Data
Malnourish.
rate (%)
Malnourish.
people (%)
Poverty
rate (%)
Poor
people (%)
Rural
pop. (%)
Rural share
of poor (%)
World 10.9 100.0 12.7 100.0 47.4 n/a
Developed 1.2 1.8 0.0 0.0 19.4 n/a
Developing 12.9 98.2 15.8 100.0 54.2 74.6
East Asia 9.6 25.9 7.2 15.9 50.1 80.3
Central Asia 7.0 0.7 2.1 0.6 40.2 76.9
LAC 5.5 4.3 5.6 3.2 22.8 43.9
MENA 5.3 2.9 5.0 1.9 40.6 71.5
South Asia 15.7 35.4 18.8 34.8 68.2 71.9
SS Africa 23.2 27.7 42.7 43.6 63.8 75.5
Malnourishment Poverty