1. Evaluating the impact of trade liberalization on poverty with CGE/Micro-Simulation: areview of literature and an illustration with MIRAGE_HH (MIRAGE-Households) Antoine Bouet Carmen Estrades David Laborde Dakar, December 16th, 2011

2. Overview1. Motivations2. Data3. Model4. Illustrative results5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

3. 1. MOTIVATIONSINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

4. Poverty: across-countries heterogeneity Poverty headcount ratio at 1.24US$ a day (PPP) in % of pop. - 2007 Source: the World Bank908070605040302010 0 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

5. Within country heterogeneity: Rural vs. Urban Estimates of poverty headcount in urban/rural areas Source: the World Bank - 2005, 2006 and 20078070605040 Rural poverty Urban poverty302010 0 Cameroon Ecuador Bangladesh 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

6. Trade liberalization and Poverty Numerous evaluations of the impact of trade liberalization on poverty World Bank GEP 2002 and 2004 (Dominique Van der Mensbrugghe with the LINKAGE model) William Cline 2004 Institute for International Economics, with the HRT model (Harrison Rutherford Tarr) Bernard Decaluwe et al., Laval University, PAUPER system and the PEP network Poverty & the WTO, T.W.Hertel and L.A. Winters Ann Harrison, Globalization and Poverty, NBER UNECA, Regional Integration and Human Development, Mohamed Chemingui 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

7. Trade liberalization and Poverty Analytical framework detailed by Winters et al. (2003) Channels of trade on poverty: 1. Price and availability of goods 2. Factor prices, income and employment 3. Government transfers 4. Incentives for investment and innovation that affects long term growth 5. External shocks and in particular changes in terms of trade 6. Short run risks and adjustment costs 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

8. How to assess the poverty impact of trade scenarios? Hertel and Reimer (2002): distinction between four methodologies: Cross country regression analysis Partial equilibrium and /or cost of living approaches General equilibrium analysis Micro-macro synthesis which links a model with micro-level data. 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

9. Pros and Cons Cross country regression analysis Cannot offer a counterfactual analysis Cannot provide results on the impact of a policy shock on numerous economic variables. Partial equilibrium and /or cost of living approaches Income and interdependence effects omitted The cost of living analysis focuses on consumption effects General equilibrium analysis Micro-macro synthesis which links a model with micro-level data. 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

10. CGE analysis CGE analysis are undertaken under Unique representative household hypothesis: The average income and total income are endogenous while the moments of the distribution are exogenous Several representative households hypothesis How many representative agents? What are the criteria of selection? Use of Poverty Elasticities (GEP, 2002 and 2004; Cline, 2004; UNECA, 2011) Mechanical effect of trade liberalization on poverty Do not identify who come out and come in poverty 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

11. CGE-MS Analysis Top-down approach (Decaluwe et al., 2001) Idea: combination of theoretical consistency of a CGE model and the richness of information from a hhlds survey Implement few variables (Consumption prices, remuneration of productive factors) into an household survey Advantages: Modelling of household and labour market behaviour can be done separately from the economy-wide analysis. There is no need to reconcile household survey data with national accounts data. Functional forms: Sadoulet/ de Janvry vs. Dervis/de Melo /Robinson vs. Decaluwe No feedback effect: consistency between the micro- simulation and CGE results. If unemployment/employment or informal/formal sectors, selection problem 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

12. CGE-MS Analysis Non Parametric approach (Vos and Sanchez, 2010) Re-weighting techniques to get micro-macro consistency Random selection of individuals Hand of God criticism Evaluation of macroeconomic policy is somewhat arbitrary Cannot identify the losers and the winners, and the accompanying policies to be put in place This method is path dependant. In other words, it could make a difference, given the cumulative effects, whether in an assumed sequence one would first simulate, say, changes in employment by occupational category (O) rather than by sector of employment (S). Vos and Sanchez, 2010. 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

13. CGE-MS Analysis Behavioral microsimulation (Bourguignon et al., 2003; Lay, 2010) Combination of a CGE model and a behavioral model based on econometric techniques Lay, 2010, CGE model with modeling of both formal and informal sectors + econometric model with two stages: probit/logit +OLS Very detailed results but 1) Theoretical consistency ? In the CGE change in behavior comes from changes in relative prices In the behavioral model, it comes from individual characteristics (education, gender) 2) Validity of the estimation method? need for panel data 3) This method overemphasizes labor supply factors and neglects labor demand side factors.INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next steps

14. CGE-MS Analysis Savard (2003): top-down/bottom-up approach Micro macro iteration to solve the aggregation error. 1 Resolution of the CGE model 2 Implementation of macro variables (prices and employment) in a micro model 3 Calculation of new values for revenue variables by the micro model 4 Re injected in the macro model. Until convergence But convergence is not guaranteed And global procedure very demanding in terms of calculation time. Not feasible in a multi-country CGE 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

15. CGE-MS Analysis Integrated approach Integration of a complete household survey in a CGE model Modeling of the behavior of each household in terms of consumption and factor supply Cockburn (2006) 3,800 households in the case of Nepal Cockburn, Corong and Cororaton (2008): 24,000 households in the case of Philippines Much more detailed view of how the impacts of trade liberalization vary over the whole income distribution. Results are very sensitive to the choice of the poverty line. Very demanding in terms of calculation time Needs simplifying assumptions for the CGE Not feasible in a multi-country CGE 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

16. Developing an integrated framework in a dynamic global CGE Hertel and Winters (2006) combines an evaluation of the poverty impact of the DDA trade reform (multi country CGE) with 12 country case studies based on single country CGEs coupled with microsimulation. Results of the multi-country CGE (export and imports prices/export and import volumes) are implemented in national CGE/MS analysis Consistency issue: reaction of the country is already in the multi-country CGE Different approaches to evaluate poverty impact: are results comparable? 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

17. Developing an integrated framework in a dynamic global CGE World model in order to understand differentiated impact in various countries Integrated model to keep consistency Better method than linking heterogeneous CGE models Diversity of situations inside each country: Assuming a representative agent is very challenging for a micro-founded approach Modeling the behavior of various households in terms of demand (e.g. non homothetic) and supply (e.g. labor supply, savings) Dynamic issues and the role of adjustment costs: Inter sectoral mobility rural / urban mobility Domestic transfers and international remittance Savings / investments and liquidity constraint 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

18. 2. DATAINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

19. Framework to build a systematic and flexible treatment for a global model Raw household survey, all information Cleaning in STATA, Clustering analysis on relevant Thousands of HH dimensions 100-1000 HH All information formatted in an Excel workbook. detailed categories CGE model Aggregation should be changed easily (hierarchical1-100 HH broad category clustering analysis can guide the latter stage aggregation) 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

20. Clustering Analysis Tractability Household account broken down into a number of relatively homogeneous household groups reflecting the socioeconomic characteristics Decaluwe et al, 1999: location (e.g. rural vs. urban); asset ownership (particularly land ownership in the rural areas and human capital in urban areas); characteristics of the head or main earner, main employment status, main occupation, main branch of industry and educational attainment, gender Importance of capturing the household heterogeneity really modeled (preferences, endowments) Clustering analysis taking into account: income per capita of the household (in logarithm), consumption structure (share of each GTAP product in total consumption) and income structure (share of capital, labor, self-employed labor and transfers in total income of the household). 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

21. The intermediate stage: the Excel workbook Feed the model/data procedure Systematic treatment Can be easily filled by external collaborators (standardized platform) 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

22. Households survey in Excel workbook Household categories descriptions (from the clustering analysis), frequency, model mapping and flags Macro targets 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

23. Households survey in Excel workbook: HH resources HH expenditures 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

24. Data consistency Starting point: Household level: national household survey, information on income by sources and detailed consumption of different goods and services. SAM: GTAP 7 This information is checked with information from other sources: GDP, GDP per capita and GDP structure structure of population Aggregated saving rates poverty rates Automatic procedure, using iterative steps of cross entropy, to build a dataset consistent with the GTAP dataset 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

25. Data treatment: a standard and automatic procedureExcel workbook read by GAMS (including sets and mappings). Checks if allinformation is properly mappedGTAP 7 imported (GDX) Definition of minimal threshold (500 dollars for a household category) Rules for household expansion coefficients Treatment of final consumption by household Correction for trade margins Cross entropy to adjust expenditures structure to GTAP macro figures. Each household keeps his share in overall expenditures. Treatment of household income (production factor) Retreatment for farm income and dwelling (virtual rental payments) Cross entropy with different constraints depending on available information. GTAP Value Added data may be modified. Tax rate treatment Factor specific tax rate from GTAP Mapping of different taxes of the household survey (e.g. property tax) Computation of overall taxes based on income factor structure Homogenous Rescaling to maintain GTAP national tax level 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

26. Data treatment: a standard and automatic procedureTransfert treatment and SavingsReceived and PaidBetween Households (no bilateral matrix), Government and Rest ofthe WorldCross entropy to ensure that domestically Sum paid = Sum receivedunder constraint of No negative savings (minimal rate of savings of0.001 of disposable income). This constraint forces to replacenegative savings by intra household transfers. For each country included in the treatment, a summary report of the changes and results of cross entropy procedure is generated Final output: a GDX file with different mappings, disaggregated GTAP variables at the HH level: CVFM_HH, CFTRV_HH, CVDPA_HH, CVIPA_HH, CVDPM_HH, CVIPM_HH and other indicators: transferts matrix between institutions (Household categories, Government, Rest of the World) 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next steps INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

27. Data treatment: an illustration For the illustrative results, focus on two countries for which recent HH survey are available: Uruguay: Income and Expenditure Survey (IES) 2005-2006, carried out by the Statistics National Institute (INE) Pakistan: 2005-2006 Social & Living Standards Measurement Survey, carried out by the Federal Bureau of Statistics of the government Clustering analysis: 90 groups of households in Uruguay 142 in Pakistan Other countries have been processed (e.g. Brazil, Tanzania, Vietnam). Challenges: findings household survey that detailed the expenditures (preferences) and the income (factor endowments) sides. 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

28. Data treatment: an illustration Categorization of households in Uruguay Size = Percent of total households - Source : INE, 2005/06 5000 Montevideo urban capital income skilled male headed 4000Mean monthly income (current USD) Montevideo urban labor 3000 income skilled male headed 2000 Montevideo rural transfers income 1000 medium skilled female headed 0 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 -1000 Mean share of food in total expenditure 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

29. Data treatment: an illustration Categorization of households in Pakistan Size = Percent of total households - Source : FBS, 2005/06 700 600 Rural-Male-High educ.-Punjab-Farmers-Big landMean monthly income (current USD) Urban-Male-High educ.-Punjab-Other empl- 500 400 300 200 Rural-Male- 100 No educ.- Rest-Nofarmers- 0 Agric. and manuf 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 -100 Mean share of food in total expenditure 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next steps INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

30. 3. MODELINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

31. Traditional modeling inMIRAGEOne agent = public+private agentIt means that we suppose they getsame preferencesSavings of the representative agentfinance investmentNew calibration of the CES LESmodeled at the individual level 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

32. Traditional MIRAGE modeling: Main equations C(i,r,t,sim) =e= Pop_ag("Totpop",r,t)*(cmin(i,r)+a_C(i,r)*AUX(r,t,sim)*(P(r,t,sim)/PC(i,r,t,sim))**sigma_C(r)) P(r,t,sim)*AUX(r,t,sim) =e= sum(i$CO(i,r),PC(i,r,t,sim)*(C(i,r,t,sim)/Pop_ag("Totpop",r,t)- cmin(i,r))); BUDC(r,t,sim) =e= sum(i$CO(i,r),PC(i,r,t,sim)* C(i,r,t,sim)); DEMTOT(i,s,t,sim) =e= C(i,s,t,sim)$CO(i,s) + sum(j$ICO(i,j,s),IC(i,j,s,t,sim)) + (KG(i,s,t,sim))$KGO(i,s); REV(r,t,sim)+(PIBMVAL(t,sim)*SOLD(r,t,sim)) =e= sum(i,PVA(i,r,t,sim)*VA(i,r,t,sim))+ RECTAX(r,t,sim); BUDC(r,t,sim) =e= (1-epa(r))*REV(r,t,sim); epa(r)*REV(r,t,sim) =e= PINVTOT(r,t,sim)*INVTOT(r,t,sim); 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

33. Modeling in MIRAGEwith a public agentOne private agent with CES LES calibratedat the individual levelOne public agent with Cobb DouglasspreferencesSavings of the private agent financeinvestment and public deficitDifferent closures are proposed concerningthe public agent: - Deficit is constant and BUDGadapts to changes in fiscal receipts (publicdemand is reduced by libn) - Deficit is constant thanks toconstant tax receipts through a lump sumtax on the private agent - or another tax is changed(consumption tax, income tax) - 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next steps INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

34. MIRAGE modeling with public agent: Main equations CH(i,r,t,sim) =e= Pop_ag("Totpop",r,t)*(cmin(i,r)+a_C(i,r)*AUX(r,t,sim)*(P(r,t,sim)/PC(i,r,t,sim))**sigma_C(r)) PC(i,r,t,sim)*CG(i,r,t,sim) =e= alpha_G(i,r)*BUDG(r,t,sim) DEMTOT(i,s,t,sim) =e= CH(i,r,t,sim) + CG(i,r,t,sim) + sum(j$ICO(i,j,s),IC(i,j,s,t,sim)) + (KG(i,s,t,sim))$KGO(i,s); REV(r,t,sim)+(PIBMVAL(t,sim)*SOLD(r,t,sim)) =e= sum(i,PVA(i,r,t,sim)*VA(i,r,t,sim)) RECTAX(r,t,sim) =e= BUDG(r,t,sim) + PUBSOLD(r,t,sim)*sum(i,PVA(i,r,t,sim)*VA(i,r,t,sim)) epa(r)*REV(r,t,sim) + PUBSOLDO(r)*sum(i,PVA(i,r,t,sim)*VA(i,r,t,sim)) =e= PINVTOT(r,t,sim)*INVTOT(r,t,sim) 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

35. Modeling in MIRAGE with apublic agent, transfers andincome taxation and hhldsdisaggregationEach private agent receives transfers from the publicagentEach private agents income is taxed: new receipt forthe public agentAbout transfers one option is that they are constant inproportion of GDP (not neutral) - other options ? Constant in realterms ?... Different options may be proposed - the distribution of transfers isaffected ??Savings of all private agents finance investment andpublic deficitDifferent closures will be proposed - Deficit is constant and BUDGadapts to changes in fiscal receipts (public demand isreduced by libn) - Tax receipts are constant through alump sum tax on private agents - or lump sum tax on each household(lst(r)) such that public sold is constant in terms ofGDP - Another tax is changed (income tax!!) -- Redistribution policies 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next steps INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

36. Modeling in MIRAGE with a public agent, transfers and income taxation and hhlds disaggregation: main equations 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

37. Modeling in MIRAGE with a public agent, transfers and income taxation and hhlds disaggregation: main equations 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

38. Channels of income redistribution Public transfers to households Constant in nominal terms Constant in real terms Constant in % of households income Incomes taxes/consumption taxes/other taxes Inter-households transfers Lucas and Stark model (1985) of tempered altruism/ enlightened self interest Share of paid transfers in total income of the payer convex, then concave function of disposable incomeINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

39. 5. ILLUSTRATIVE RESULTSINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

40. First study, first results Design of the study Perfect competition in all sectors Dynamics : 2007 -2025 Liberalization shock: progressive elimination of all import duties throughout the world Implemented in 2011, linearly in ten years. 19 sectors, 23 countries/zones 5 countries with household breakdown Brazil, Pakistan, Tanzania, Uruguay, Vietnam 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

41. Geographic disaggregation 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

42. Sectoral disaggregation 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

43. Household disaggregation Current disaggregation Brazil: 13 representative households Pakistan: 25 representative households Tanzania: 35 representative households Uruguay: 39 representative households Vietnam: 33 representative households More disaggregation soon 80-100 households by country 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

44. Alternative closures Design of the central scenario: Public transfers to households are constant in real terms Public expenditures are constant in real terms Public deficit is constant in terms of GDP (no crowding-out effect on private investment) A lump-sum tax is perceived in order to compensate for the loss of public revenues and maintain the public deficit constant Sensitivity Analysis on: How do public transfers to hhlds adjust ? Either constant in real terms or in % of GDP How do public expenditures adjust ? Either constant in real terms or in % of GDP Compensation fiscal revenue 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

45. Using traditional MIRAGE: impact of FTL on real income (%) 2025 Scenario/Baseline 10 9 8 7 6 5 4 3 2 1 0 -1 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

46. Using traditional MIRAGE: impact of FTL on macroeconomic variables (%) 2025 Scenario/Baseline 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

47. Macroeconomic results From a sectoral point of view: Uruguay and Brazil: main force = large gains in access to foreign markets = terms of trade improvement Pakistan, Tanzania and Vietnam: main force = removal of domestic distortions = allocative efficiency : gains for consumers (final and intermediate) Uruguay: expansion of animal products; but also textile resulting in a large augmentation of the remuneration of land and unskilled labor Brazil: expansion of seeds and oilseeds, cattle and meat sectors (in general all agricultural sectors) Pakistan: expansion of textile and leather industries Vietnam: expansion of rice/textile/wearing/apparel/leather sectors Tanzania: cattle and meat sectors + textileINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next steps

48. Heterogeneous effects across households Using MIRAGE HH with households disaggregation Main results 1) At the hhlds level, in terms of real income: large heterogeneity of impacts 2) Divergences in gains and losses come mainly from the channel of factor prices (less from the channel of consumption structure) 3) If transfers are indexed on GDP or another way, it may significantly change the picture. 4) Impact on poverty is significant 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

49. Impact of full trade liberalization on households real income Brazil 2025: ln income in the baseline on x-axis; variation of real income Baseline/Reference on y-axis; bubbles are proportional to population 8 6 4 2 0-2 0 2 4 6 8 10 12 -2 -4 -6 -8 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next steps INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

50. Impact of full trade liberalization on households real income Pakistan 2025: ln income in the baseline on x-axis; variation of real income Baseline/Reference on y-azis; bubbles are proportional to population 12 10 8 6 4 2 0-1 0 1 2 3 4 5 6 7 8 -2 -4 -6 -8 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

51. Impact of full trade liberalization on households real income Tanzania 2025: ln income in the baseline on x-axis; variation of real income Baseline/Reference on y-azis; bubbles are proportional to population 10 8 6 4 2 0-2 0 2 4 6 8 10 -2 -4 -6 -8 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

52. Impact of full trade liberalization on households real income Uruguay 2025: ln income in the baseline on x-axis; variation of real income Baseline/Reference on y-azis; bubbles are proportional to population201510 5 0 2 3 4 5 6 7 8-5 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

53. Impact of full trade liberalization on households real income Vietnam 2025: ln income in the baseline on x-axis; variation of real income Baseline/Reference on y-azis; bubbles are proportional to population 30 20 10 0 -1 0 1 2 3 4 5 6 7 -10 -20 -30 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

54. Poverty analysis Side product of this approach: poverty analysis Micro accounting approach for poverty analysis Approach initially developed by Lofgren et al., 2002, and Agenor et al., 2003. CGE results (consumption prices/factor remunerations/public transfers/private transfers) implemented in the hhld survey with the strict correspondence CGE Representative Hhld / hhlds in the survey This method accounts for intra group real income variation Calculation of FGT indexes FGT0, FGT1 Calculation of Gini and Theil indexes 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

55. Impact of full trade liberalization on households Uruguay Brazil Vietnam Tanzania Pakistan Poverty indicators Base value 20.52 22.39 18.45 38.10 21.92Poverty headcount Percentage change -10.7 -1.6 -28.3 -3.8 -13.5 Base value 7.4 9.3 4.9 16.5 6.5 Poverty gap Percentage change -11.8 -1.8 -33.4 -4.4 -14.2 Extreme poverty Base value 2.6 8.5 12.1 26.5 4.0 headcount Percentage change -21.0 -6.2 -27.5 -2.8 -10.9 Base value 0.7 3.3 2.9 10.6 1.5Extreme poverty gap Percentage change -23.0 -36.9 -36.9 -3.6 -7.4 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

56. Impact of full trade liberalization on households Uruguay Brazil Vietnam Tanzania Pakistan Income distribution indicators Base value 0.456 0.596 0.426 0.591 0.645 Gini index Percentage change -0.424 -0.588 -0.588 -0.043 0.103 Base value 0.386 0.751 0.367 0.901 1.150 Theil index Percentage change -0.435 -0.423 -0.423 -0.070 0.273 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

57. Impact of full trade liberalization on households Uruguay Brazil Vietnam Tanzania Pakistan Poverty headcount by household head sex Base value 20.9 22.4 19.7 37.9 22.5 Male headed Percentage change -14.3 -1.7 -29.4 -3.3 -13.7 Base value 19.9 22.5 14.8 38.7 16.3 Female headed Percentage change -5.0 -1.3 -23.7 -5.2 -11.2 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

58. Impact of full trade liberalization on households Uruguay Brazil Vietnam Tanzania Pakistan Poverty headcount by household head education level Base value 28.3 31.8 27.6 40.2 30.3 Low education Percentage change -9.1 -1.3 -23.5 -4.0 -12.6 Base value 16.4 17.4 17.9 28.9 18.2Medium education Percentage change -14.4 -2.2 -32.7 -3.0 -15.9 Base value 1.4 3.5 5.4 24.5 6.9 High educated Percentage change -28.7 0.1 -43.1 -1.6 -15.9 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

59. Brazil dynamics of welfare variation at the household level Stock graph with open/low/high/close 2011/2025 percent- Simulation / Baseline6420 HH1 HH2 HH3 HH4 HH5 HH6 HH7 HH8 HH9 HH10 HH11 HH12 HH13-2-4-6-8INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

60. Pakistan dynamics of welfare variation at the household level Stock graph with open/low/high/close 2011/2025 percent- Simulation / Baseline 12 10 8 6 4 2 0 -2 -4 -6 -8INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

61. Tanzania dynamics of welfare variation at the household level Stock graph with open/low/high/close 2011/2025 percent- Simulation / Baseline86420-2-4-6 INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

62. Uruguay dynamics of welfare variation at the household level Stock graph with open/low/high/close 2011/2025 percent- Simulation / Baseline1816141210 8 6 4 2 0-2INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

63. Vietnam dynamics of welfare variation at the household level Stock graph with open/low/high/close 2011/2025 percent- Simulation / Baseline201510 5 0 -5-10-15-20-25INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

64. Brazil decomposition of the rate of variation in households welfare into consumption price effect and factor remuneration effect 2025 - Scenario/baseline10 8 6 4 2 welfare price effect 0 income effect HH1 HH2 HH3 HH4 HH5 HH6 HH7 HH8 HH9 HH10 HH11 HH12 HH13-2-4-6-8 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

65. Pakistan decomposition of the rate of variation in households welfare into consumption price effect and factor remuneration effect 2025 - Scenario/baseline 15 10 5 welfare price effect income effect 0 -5 -10 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

66. Tanzania decomposition of the rate of variation in households welfare into consumption price effect and factor remuneration effect 2025 - Scenario/baseline 8 6 4 2 welfare 0 price effect HH11 HH9 HH1 HH2 HH3 HH4 HH5 HH6 HH7 HH8 HH10 HH12 HH13 HH14 HH15 HH16 HH17 HH18 HH19 HH20 HH21 HH22 HH23 HH24 HH25 HH26 HH27 HH28 HH29 HH30 HH31 HH32 HH33 HH34 HH35 income effect -2 -4 -6 -8 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

67. Uruguay decomposition of the rate of variation in households welfare into consumption price effect and factor remuneration effect 2025 - Scenario/baseline30252015 welfare10 price effect income effect 5 0 HH10 HH12 HH13 HH14 HH15 HH16 HH17 HH18 HH19 HH20 HH21 HH22 HH23 HH24 HH25 HH26 HH27 HH28 HH29 HH30 HH31 HH32 HH33 HH34 HH35 HH36 HH37 HH38 HH39 HH1 HH2 HH3 HH4 HH5 HH6 HH7 HH8 HH9 HH11 -5-10 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next steps INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

68. Vietnam decomposition of the rate of variation in households welfare into consumption price effect and factor remuneration effect 2025 - Scenario/baseline201510 5 0 HH4 HH1 HH2 HH3 HH5 HH6 HH7 HH8 HH9 HH10 HH12 HH13 HH14 HH15 HH16 HH17 HH18 HH19 HH20 HH21 HH22 HH23 HH24 HH25 HH26 HH27 HH28 HH29 HH30 HH31 HH32 HH33 HH11 welfare -5 price effect income effect-10-15-20-25-30 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

69. Households real income Brazil 2025 Scenario/Baseline - % (Households are ranked in increasing 2025 income) Rule of indexation of public transfers matters6420 HH10 HH13 HH9 HH12 HH8 HH11 HH1 HH5 HH6 HH2 HH7 HH3 HH4 main sa1-2-4-6-8 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

70. Households real income Pakistan 2025 Scenario/Baseline - % (Households are ranked in increasing 2025 income) Rule of indexation of public transfers matters 12 10 8 6 4 main 2 sa1 0 -2 -4 -6 -8 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

71. Households real income Tanzania 2025 Scenario/Baseline - % (Households are ranked in increasing 2025 income) Rule of indexation of public transfers matters 8 6 4 2 main sa1 0 HH2 HH7 HH9 HH4 HH6 HH1 HH5 HH3 HH8 HH35 HH32 HH29 HH33 HH31 HH30 HH34 HH13 HH14 HH10 HH12 HH11 HH17 HH15 HH18 HH16 HH20 HH26 HH21 HH19 HH22 HH23 HH24 HH25 HH27 HH28 -2 -4 -6 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

72. Households real income Uruguay 2025 Scenario/Baseline - % (Households are ranked in increasing 2025 income) Rule of indexation of public transfers matters 18 16 14 12 10 main 8 sa1 6 4 2 0 HH2 HH1 HH3 HH4 HH6 HH5 HH8 HH7 HH9 HH15 HH18 HH10 HH11 HH13 HH12 HH14 HH19 HH16 HH17 HH20 HH29 HH22 HH21 HH23 HH24 HH27 HH25 HH30 HH26 HH28 HH31 HH33 HH32 HH35 HH36 HH34 HH37 HH39 HH38 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next stepsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

73. Households real income Vietnam 2025 Scenario/Baseline - % (Households are ranked in increasing 2025 income) Rule of indexation of public transfers matters25201510 5 0 main sa1 -5-10-15-20-25-30 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next steps INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

74. Concluding remarks Four main conclusions - Diversity of impact of trade liberalization at the households level within a country. - Positive impact on poverty ; ambiguous impact on inequality - Factor remuneration channel is much more important than commodities price channel. - Accompanying policies (transfers, indirect or direct taxes) are important and can amplify gains and losses or (totally) compensate for losses at the households level.INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next steps

75. 6. NEXT STEPSINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

76. Further developments Increasing the number of countries in our library Run more scenarios with larger number of households in each country (80-120) More sensitivity analysis, in particular concerning accompanying policiesINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next steps

77. Main challenges: dynamic issues Inter households transfers behavior Rural / Urban migration Dynamic evolution of endowments at the household level: Skilled vs Unskilled Labour supply Capital accumulation, investment decisions Modeling of households saving decisionsINTERNATIONAL FOOD POLICY RESEARCH INSTITUTE 1. Motivations 2. Data 3. Model 4. Illustrative results 5. Next steps