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General Equilibrium Modelling of Trade Negotiations Outcomes

General Equilibrium Modelling of Trade Negotiations Outcomes. Stephen N. Karingi, Trade and International Negotiations Section, UNECA. Introduction. Policy makers need options in order to be able to make decisions.

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General Equilibrium Modelling of Trade Negotiations Outcomes

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  1. General Equilibrium Modelling of Trade Negotiations Outcomes Stephen N. Karingi, Trade and International Negotiations Section, UNECA

  2. Introduction • Policy makers need options in order to be able to make decisions. • These options must be derived in an objective manner, in order to give decision makers confidence. • If options are not derived in an objective and consistent way, technicians easily lose credibility.

  3. Introduction • Policy makers get comfort if they know: • That the methodology used to draw policy options is theoretically sound. • That the data used in generating the empirical results is verifiable. • That the assumptions used are realistic. • Different scenarios are offered.

  4. Why are economic models needed?

  5. Why are economic models needed? • Different policy questions: • What are prospective export markets? • What sectors are sensitive in a liberalisation scheme/schedule? • Is a country better off with a REC remaining as an FTA rather than customs union? • Has regional integration realised expected trade growth? • What are the potential benefits/costs from the EPAs and/or Doha Round for a country or sector? • How long will it take for incomes in a given REC to converge? • Does trade liberalisation help reduce poverty – MDG target? • Are trade policy options gender neutral? • What is the optimal point for trade and environment policies?

  6. Why are economic models needed? • “Without theory, practice is but routine born out of habit.” Louise Pasteur. • “(S)He who loves practice without theory is like the sailor who boards ship without a rudder and compass and never knows where he may cast.” Leonardo da Vinci. • “Being denied a sufficiently secure experimental base, economic theory has to adhere to the rules of logical discourse and must renounce the facility of internal inconsistency. A deductive structure that tolerates a contradiction, does so under the penalty of being useless, since any statement can be derived flawlessly and immediately from that contraction. In its mathematical form, economic theory is open to an efficient scrutiny of logical errors.” Gerard Debreu, Nobel Prize Winner, 1983.

  7. How do economic models help improve policymaking? • They provide a theoretically consistent framework for analyzing trade policy questions. • They provide a handle on complicated questions. • They help give greater intellectual support for a chosen trade policy. • The use of models can provide a common “language” for policy discourse or debate. • But models should complement rather than substitute for policy making.

  8. Models commonly used for trade policy analysis

  9. Models used for trade policy analysis • Trade flow analysis at HS-6 or more tariff line analysis. • Econometrics methods e.g. gravity modeling. • Partial equilibrium modeling e.g. WITS-SMART; ATPSM. • Computable general equilibrium model (single country). • CGE model (global) e.g. GTAP. • CGE models combined with micro-simulation model.

  10. Models used for trade policy analysis • Simulation models: they help answer “What if” types of questions (+ projections): • Partial Equilibrium Models e.g. WITS-SMART; ATPSM; TASTE. • General Equilibrium models e.g. GTAP; MIRAGE; LINKAGE. • Econometric Models : • gravity models (reduced form): can be used to establish whether certain economic variables have an effect on a variable of interest (Do RTAs or GSP increase trade?) • Macro-econometric models: tools for projections of aggregates but no information on the industrial structure of the economy + may lack micro-foundations. • Combination of simulation and econometric models. • Simulation (econometric) models are deterministic (stochastic)

  11. Partial equilibrium framework Price Impact of commodity x market on rest of the economy can be neglected DS Pw(1+t) Pw DD Commodity x

  12. A General Equilibrium Analysis spending on goods and services Savings Investments goods and services Households Firms FDI Factor services of production exports imports Factor incomes REST OF THE WORLD

  13. When is GE or PE analysis appropriate? • Nature of policy change • Does it cut across many markets or sectors? • Potential impact of change • Are there economy-wide impacts? • Constraints imposed by availability of data and resources: • PE data and models: free • CGE data: single country (SAM) could be free, multiple country (GTAP: from $ 360 to $ 4600) • CGE models: free (GTAP) but may need software for mathematical programming to run (LINKAGE, MIRAGE)

  14. A note about CGE and micro-simulation models • Top-down CGE models linked to micro-simulation. The micro-simulation component based on household survey data. • The micro-simulation component tends to focus on some of the following issues: • Poverty incidence (headcount ratios; poverty depth etc.); • Inequality measures (Lorenz curves on whose basis Gini coefficients are derived). • The micro-simulation allows the endogenous results of the CGE model to be fed exogenously to poverty and inequality equations.

  15. Basics of CGE Modelling

  16. Typologies of CGE Modeling Static: regions, sectors, factors, economic agents + set of economic behaviors & relationships Micro-Simulation Models: representative agents hypothesis “removed” Dynamic=Static features + explicit inter-temporal features

  17. The circular flow model of economic activity

  18. CGE models: basics • Computable  numerical solution (empirical data) • General  description of the whole economy • full economic cycle • all markets • Equilibrium  demand equals supply • prices are adjusted to achieve market equilibrium • general: on all markets simultaneously! • Model  solvable set of equations

  19. Partial equilibrium: an example • objective:  max • market clearance: , • production: • resource constraints:

  20. General equilibrium: an example • objective:  max • market clearance: , • production: • resource constraints: • income balance:

  21. Empirical calibration • Spend time on finding and organising your data: • The quality of the results depend on the quality of the data • Different sources can not always be quickly combined • Always carry out the benchmark check! • Much data available on internet, e.g. Statistical Offices;search and you will find

  22. CGE models: data (I) • Benchmark data • base year • (Social) Accounting Matrix, based on IO-table • often: values in the IO-table interpreted as quantities  all benchmark prices are 1 • Essentials of IO-table: • value of output equals total value of all inputs for each good • value of supply equals value of total demand for each good • total value of endowments equals total value of final expenditures (consumption) •  row total equals column total

  23. A basic input-output table

  24. CGE models: data (II) • Data describing the reactions of agents  oftendescribed in terms of CES functions: • substitution elasticities • income elasticities • output elasticities  Cobb-Douglas, linear and Leontieffunctions are special cases of a CES function

  25. Data and CES function calibration • Elasticities, benchmark quantities and prices determine the CES functions (technologies or preferences) (i) benchmark demand quantities  provide an anchor point for isoquants / indifference curves (ii) benchmark relative prices  fix the slope of the curve at that point (iii) elasticity of substitution  describes the curvature of the indifference curve

  26. L C L0 0 K0 K CES function calibration

  27. Input-Output economics & SAMs • Whether neoclassical, structuralist, neo-Keynesian, or Monetarist, a CGE modeler must respect accounting identities and equilibrium conditions. Hence, most applied work is based on a social accounting matrix to benchmark (calibrate) a model and to represent relevant accounting identities. • SAMs capture equilibrium conditions • Walras’ law applies

  28. Decision Making and Institutions • Linkages in SAMs are accounted for by modelling the decision-making process of the firm, the consumer, as well as other economic agents and institutions: production and demand structure • Trade results from that decision-making processes and their interaction with institutions: • Production- Exports + Imports=Consumption

  29. Closing the Model • Need to define a numéraire (Walras law allows to “drop” one market) • Assumption about the adjustment mechanism in factor and commodity markets • Macro closure • Macro accounting balance (govt expenditure and deficit; aggregate saving and investment; balance of trade and -real- exchange rate) • Macro adjustment mechanism (exogenously determined)

  30. Closing the Model • Johansen closure: investment is exogenous and consumption is the adjustment variable • Keynesian closure: nominal wage is fixed and employment is the adjustment variable (unemployment) • Kaldorian closure: wages could be less or equal to the marginal product of labor (exploitation of labor model) • Classical closure: prices and wages are the adjustment variables (constant employment) and investment becomes endogenous and adjusts to total savings available • Foreign borrowing (Robinson): trade balance is endogenous, current account and hence net capital inflows are the adjustment variable

  31. Beyond the Standard Model • Economies of scale, monopolistic competition and differentiated products • Institutional features of a particular economy (e.g. tax collection costs) • Specific features of a policy instrument • Increase effort on estimation of substitution elasticities • Dynamics to account for dynamic aspects (policy credibility; capital accumulation; FDI; knowledge accumulation and spillovers) and adjustment • Account for the extensive margin of trade (the “small-shares” issue)

  32. CGE Dynamic Models • Recursive: • solves annually • Current economic conditions (e.g. the availability of capital) are dependent on past outcomes but are unaffected by forward looking expectations • Linked with a macro econometric to include exogenously projected changes in demographic trends or in technology: baseline scenario • Impact of policy change is given with respect to the baseline scenario (sector specific TFP and real GDP growth are solved endogenously)

  33. CGE Dynamic Models • Forward looking: • Infinite lived consumer with financial market • No extensive baseline scenario: trade performance-productivity linkage + gvt investment on infrastructure and TFP linkage + investment in education and labor productivity linkage • Could account for transitionary disequilibrium states (true adjustment process?)

  34. Micro-Macro Models • Combination of a Micro Simulation model (based on Household surveys: fiscal and labor) and a CGE model • Ideal to assess the impact of macroeconomic (trade) policies and shocks on poverty/ inequality: MAMS (maquette for MDG simulation) • Two types of combination: • Fully-integrated: the household model built directly into the CGE : CGE model with heterogeneous agents (high complexity) • Sequential (top-down): CGE simulation results are passed on to an household model (macro and micro need not to be reconciled but possible lack of coherence)

  35. What is involved in a policy simulation?

  36. What is involved in a policy simulation? Economy before trade policy change Economy after trade policy change Policy change Difference between the two is attributed to policy change

  37. What is needed for a policy simulation? MODEL / Closure Inputs Outputs

  38. What are the inputs? • Baseline data: • trade flows • levels of protection • input-output structure: national income aggregates • Measure of responsiveness of economic agents to price changes (i.e. elasticities) • Policy - negotiating scenario • Sectors (Agriculture, NAMA, etc.) • Depth of liberalization

  39. What are the Outputs? • Configuration of the economy after policy change • Overall income gains/losses from policy change • Sources of income gain • Sectoral (agriculture vs. NAMA) • Policy instrument (market access or domestic support) • Winners or losers (at the country level) • Changes in pattern and volume of trade and income • “Story” to explain how inputs and model combine to determine the output/outcome

  40. Tracing Differences in Results • Deterministic – outcome is completely determined by choice of inputs and model (no “residuals”) MODEL Inputs + Outputs • Differences in simulation results = differences in choice of inputs and model/closure • “Story” must explain why the choice of inputs and model is appropriate/optimal for the policy question of interest

  41. Towards an “objective” look at trade liberalization CGE simulations

  42. Case 1: Global CGE Application on the likely development impacts of EPAs What to expect given the provisions on market access in the interim EPAs

  43. Possible result in SSA incomes for lack of sufficient asymmetry

  44. Due to focus on trade-rules, welfare gains for Africa limited

  45. Africa to specialise in primary products, hindering diversification

  46. Case for industry modernisation under the circumstances

  47. Regional integration as Africa’s development pillar under strain • Regional integration is to be a pillar of EPAs. • Intra-regional trade shrink significantly by as much as 18%. • Situation likely to be worsened by kind of specialization to occur.

  48. Social & economic expenditures strain likely due to fiscal loss

  49. Case 2: Global CGE Application on the Doha Round Potential implications for African countries of agriculture negotiations

  50. WTO: Positions and Prospects US moves on Ag. domestic support (12 – 18 billions US$ OTDS ?) Lamy’s Triangle EU moves on ag. tariff cuts (G20: 54% cuts) Advanced DC move on NAMA (Swiss 20 ?) Focus on Domestic Support, Market Access and NAMA

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