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Income Distribution

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Income Distribution

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Income Distribution

  1. 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. 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. 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. 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. 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. 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. 7. Between vs. Within Country Inequality (2) Source: Cohen (2014)
  8. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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

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