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. 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. 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. Why are economic models needed? Why are economic models needed? Different policy questions: 1. What are prospective export markets? 2. What sectors are sensitive in a liberalisation scheme/schedule? 3. Is a country better off with a REC remaining as an FTA rather than customs union? 4. Has regional integration realised expected trade growth? 5. What are the potential benefits/costs from the EPAs and/or Doha Round for a country or sector? 6. How long will it take for incomes in a given REC to converge? 7. Does trade liberalisation help reduce poverty – MDG target? 8. Are trade policy options gender neutral? 9. What is the optimal point for trade and environment policies? 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. 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. Models commonly used for trade policy analysis 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. 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) 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 A General Equilibrium Analysis spending on goods and services Savings Investments goods and services Households Firms Factor services of production Factor incomes REST OF THE WORLD exports imports FDI 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) 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. Basics of CGE Modelling 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 The circular flow model of economic activity 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 Partial equilibrium: an example 1 2 objective: U C1 C market clearance: Yi Ci i 1, 2 , i 1i i production: Yi Ai Li K resource constraints: L1 L2 L K1 K 2 K max General equilibrium: an example 1 2 objective: U C1 C market clearance: Yi Ci i 1, 2 , i max 1i i production: Yi Ai Li K resource constraints: L1 L2 L K1 K 2 K income balance: p1C1 p2C2 wL rK 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 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 A basic input-output table Agr. Indus. Serv. Cons. Inv. Gov. Exp Total Agr. 10 30 40 100 0 40 180 400 Indus. 20 60 50 300 200 30 340 1000 Serv. 40 60 10 200 0 20 170 500 Imp 60 120 20 400 100 100 Dep. 10 20 10 40 Wages 200 600 300 1100 Profits 60 70 240 Total 400 1000 500 110 1000 300 190 800 690 CGE models: data (II) Data describing the reactions of agents often described in terms of CES functions: substitution elasticities income elasticities output elasticities Cobb-Douglas, linear and Leontief functions are special cases of a CES function 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 CES function calibration L C L0 0 K0 K 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 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 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) 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 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 “smallshares” issue) 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) 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?) 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) What is involved in a policy simulation? What is involved in a policy simulation? Economy before Policy trade policy change change Economy after trade policy change Difference between the two is attributed to policy change What is needed for a policy simulation? Inputs MODEL / Closure Outputs 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 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 Tracing Differences in Results Deterministic – outcome is completely determined by choice of inputs and model (no “residuals”) Inputs + MODEL 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 Towards an “objective” look at trade liberalization CGE simulations 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 Possible result in SSA incomes for lack of sufficient asymmetry GDP Volume (% change) Asymmetry (40%) Asymmetry (20%) -0.12 -0.1 -0.08 -0.06 -0.04 -0.02 % change from baseline 0 0.02 0.04 0.06 Due to focus on trade-rules, welfare gains for Africa limited Welfare (million US$) 800 Asymmetry (40%) 700 600 500 400 300 Asymmetry (20%) 200 100 0 Africa to specialise in primary products, hindering diversification S S A to specialise in primary commodities % change in value added from baseline 12.0% 10.0% 8.0% 6.0% 4.0% 2.0% 0.0% Cereals Vegetables Oilseeds Sugar Cotton -2.0% -4.0% Asymmetry (20%) Asymmetry (40%) oCrops Livestock Case for industry modernisation under the circumstances Manufacturing sector a concern Other manufacturing Light manufacturing Agro-processing Natural resources -15.0% -10.0% -5.0% FTA Asymmetry (20%) 0.0% Asymmetry (40%) 5.0% 10.0% 15.0% Regional integration as Africa’s development pillar under strain Regional integration is to be a pillar of EPAs. Trade diversion (US$ million) Intra-regional trade shrink significantly by as much as 18%. FTA Situation likely to be worsened by kind of specialization to occur. -787 (2619) Asymmetry -532 ((20%) 1752) Asymmetry -415 ((40%) 1043) Social & economic expenditures strain likely due to fiscal loss Fiscal losses due to EPAs (US$ million) 3,529 2,103 1,038 FTA Asymmetry (20%) Asymmetry (40%) Case 2: Global CGE Application on the Doha Round Potential implications for African countries of agriculture negotiations 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 The Market Access Pillar The negotiations here are about tariff reduction formula – how ambitious? Tariff peaks dealt with if ambitious. Preference erosion occurs if too ambitious. Policy space for African countries could also be lost. A key issue is to find a balance between enhanced market access; flexibilities and policy space. Sensitive and special products provide the flexibilities. Special safeguard mechanisms Tariff Structure of the European Union (AVE) 300% Processed Cereal Grains Bovine and Pork Meat Mushrooms Garlic 250% Bananas Dairies Sugar 200% Prepared Vegetables Olive Oil 150% Rice 100% Source: Mario Jales, ICONE, Brazil HS TARIFF LINE 52 35 24 23 22 22 22 20 20 20 20 20 19 18 16 15 15 12 12 11 10 08 08 07 07 06 04 04 04 02 02 02 0% 02 50% 01 AVE% Starch Wine 10 11 20 1 21 20 0 62 20 1 73 40 6 41 50 0 30 60 0 29 70 0 69 71 0 08 71 0 42 80 0 43 81 0 02 90 0 12 90 2 9 10 4 0 07 11 00 04 12 21 02 12 10 09 12 22 13 14 00 03 15 10 10 15 00 15 16 90 02 17 90 02 18 11 06 20 31 01 20 10 07 20 91 09 22 60 01 22 10 08 23 90 06 70 Agricultural Tariff Structure: Switzerland, 2001 800% 700% Source, IFPRI 1465 908 1664 600% 500% 400% 300% 200% 100% 0% Bound Tariffs in % (AVE) Tariff Structure of the United States (AVE) 350% Tobbaco 300% Grapes Peanuts Sugar 200% Peanuts Products Dairies 150% 100% Source: Mario Jales, ICONE, Brazil HS CHAPTERS 52 51 41 24 23 22 21 21 20 20 20 20 19 18 18 17 16 15 12 12 10 09 08 08 07 07 07 06 04 04 04 04 04 02 0% 02 50% 01 AVE% 250% Market Access Pillar: Another Dilemma Market access versus the question of special products and sensitive products. How far should Africa go? Propose specific numbers. What combination of formula coefficients and precise percentages of special and sensitive products achieves Africa’s interests? Doha Round CGE Simulations Common results: Multilateral liberalization is beneficial at the global level There are potential gains for developing countries Developing countries own liberalization is an important source of their gains Removing subsidies may damage net food importer countries Doha Round CGE Simulations Results differ among studies on how gains are redistributed 1. What share of the benefits goes to developing countries? 2. What share comes from agriculture liberalization? From NAMA? The Proposed Cuts in Agriculture Market Access Tariff cap 150 Cut = 46.7% Tariff lines (in %) 130 Tariff cap Cut = 42.7% 100 Cut: 70% 80 75 Cut = 38% Cut = 64% 50 Cut = 57% 30 20 Cut =50% Cut =33.3% Tariff lines DEVELOPED COUNTRIES (in descending order) DEVELOPING COUNTRIES A Tiered Formula for reduction of OTDS subsidies Overall Trade Distorting Support (OTDS): amber box + de minimis + blue box Amounts of subsidies Cut = 80% 60 Cut = 70% 10 Cut = 55% 0 Country A Country B Country C Bands Thresholds (US$ billion) Cuts 3 > 60 EU 80% 2 10 – 60 US + Japan 70% 1 0 – 10 + All DC 55% Final Bound Total AMS – Amber Box: A Tiered Formula AMS (amber box) Amounts of subsidies Cut 3 = 70% 40 Cut 2 = 60% Bands Thresholds (US$ billion) Cuts 3 > 40 EU 70% 2 15 – 40 US + Japan 60% 1 0 – 15 + All DC 45 % 15 Cut 1 = 45% 0 Country A Country B Country C Motivation Not enough to look at the tariff structures outcome. Economic impacts results useful for following reasons: For instance, what should be the view on sensitive products? Are other groupings positions in Africa’s offensive interests? What are the trade-offs with respect to the pillars and our coalitions? Scenario 1 (DV: 0% DVG: 0%) Tariff band 0-20% Developed countries 65% Developing countries 20% 20-40% 75% 25% 40-60% 85% 28% Above 60% 90% 30% Scenario 2 (DV: 2% DVG: 20%) Tariff band 0-20% Developed countries 65% Developing countries 20% 20-40% 75% 25% 40-60% 85% 28% Above 60% 90% 30% Scenario 3 (DV: 2% DVG: 20%) Tariff band 0-20% Developed countries 20% Developing countries 15% 20-40% 30% 20% 40-60% 35% 25% Above 60% 42% 30% Further Assumptions Sensitive products: defined on basis of highest MFN rates. Domestic support pillar: Three bands which applied mainly to EU, US, Japan. Export subsidies: Total elimination in our simulations. The domestic support and export subsidies are same in each scenario. Welfare Impacts (US$ millions) Scenario I Scenario II Scenario III SSA 118.59 97.19 88.85 North Africa 210.97 35.98 -152.36 EU-25 136.94 270.13 347.64 USA 1639.47 1310.39 927.59 Cairns 1502.17 1204.23 966.11 Japan 859.69 824.22 146.75 ROW 702.50 -93.03 -120.04 Impacts on the value of GDP Scenario I Scenario II Scenario III SSA 0.29 0.26 0.26 North Africa 0.75 0.48 0.16 EU-25 -0.04 -0.03 0.02 USA 0.07 0.06 0.04 Cairns 0.23 0.19 0.18 Japan -0.13 -0.08 0.00 ROW 0.03 0.07 0.06 Trade balance (change $ mln) Scenario I Scenario II Scenario III SSA 106.54 82.69 68.44 North Africa -105.89 -58.45 -12.27 EU-25 -46.36 -393.44 -295.1 USA 573.36 326.08 182.79 Cairns 623.65 461.43 370.68 Japan -923.11 -870.51 -341.84 ROW -228.18 452.19 27.29 Value Added (% change) Scenario I Scenario II Scenario III SSA NAF SSA NAF SSA NAF Cereals 1.98 1.79 1.88 2.04 2.05 1.95 Veget. 1.77 2.49 0.96 1.59 -0.01 0.37 Sugar -0.31 2.05 -0.25 1.13 -0.16 0.12 Cotton 6.37 2.3 6.07 2.40 4.94 2.50 Cattle -0.25 0.59 -0.21 0.26 -0.15 -0.05 Milk -0.16 1.61 -0.13 0.94 -0.09 0.25 Fishing 0.05 0.03 0.07 0.01 0.03 0.01 VegOil -1.12 245 -0.78 139 -0.22 26.96 Price index of global merchandise exports Veg_Oil Scenario III Fishing Scenario II Scenario I Milk Cattle Cotton Sugar Vegetables Cereals 0 0.5 1 1.5 2 % change in price 2.5 3 3.5 4 Points on economic impacts Scenario I with no sensitive products and with deep cuts is the most ambitious. Scenario III shows the actual impact of both the sensitive products and lower ambition in the market access. Scenario III indicates the significance of trade-off between two important pillars. Cost of sensitive products and lower ambition Welfare implications of lower ambition 200.00 Percent change in welfare from scenario I 150.00 100.00 Impact of sensitive products Impact of lower ambition 50.00 0.00 SSA -50.00 -100.00 -150.00 -200.00 NAF EU25 USA Cairns Japan ROW Should market access be traded for domestic support? Approximate cost of lowering ambition in addition to sensitive products 80.00 EU25 60.00 40.00 20.00 0.00 -20.00 ROW SSA Cairns USA -40.00 -60.00 -80.00 -100.00 Japan NAF What it could mean for Africa Ambitious coefficients in agriculture remain the best result for Africa. Africa loses through sensitive products what it is supposed to gain from the ambitious tariffs cut. A trade-off by the advanced developing countries on market access that would reduce ambition may not be in Africa’s interest. www.uneca.org/atpc