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Macro-economic Impact of Migrations in Madrid Region INTRODUCTION Centre of Migration Research, Warsaw, November 2007 Dr. Mahía & Dr. de Arce Professors Univ. Autónoma de Madrid WHY DO POLICY-MAKERS NEED MODELS? Reference paper: Piermartini, R. and Teh, R. (2005): “Demystifying Modelling Methods for Trade Policy”. WTO Discussion Papers, num. 10. September, 2005. • Economic models provide a theoretically consistent, rigorous and quantitative way of evaluating different policy analysis • A simulation of the model can confirm that judgment and provide estimate of the likely gains • Models can alert about effects in inter-related issues • But, what’s first? EXPONENTIAL GROWTH Advances in theoretical analysis Largeness and wideness Statistical collection Increase of Computational power STRENGTHS Reflects Inter-dependency of economic variables Computer based models allow us to track all of these interactions Simulation of several scenarios is available Simulations are performed in a transparent hypothesis frame They discipline thinking about how economies actually work Reference paper: Piermartini, R. and Teh, R. (2005): “Demystifying Modelling Methods for Trade Policy”. WTO Discussion Papers, num. 10. September, 2005. LIMITATIONS Aggregations can obscure important underlying relationships Data are not always “high quality” Responsiveness of supply and demand to price changes are not necessarily accurate Choices among scenarios and model specification can imply very different results Technical comprehension is only available for experts Reference paper: Piermartini, R. and Teh, R. (2005): “Demystifying Modelling Methods for Trade Policy”. WTO Discussion Papers, num. 10. September, 2005. CHOICE THE MODEL COMPARATIVE STATIC AND DYNAMIC ANALYSIS • Static: changes in policies and direct transmission to endogenous variables of model • Dynamic: final and intermediate process of change PARTIAL OR GENERAL EQUILIBRIUM ANALYSES • “Ceteris Paribus” criteria • Whole economy linkages KIND OF THECNICAL STRATEGIES FREQUENTLY USED IN INTERNATIONAL SIMULATIONS • Traditional Econometrical Multiequational Models • Optimization Models: Izquierdo, M., Jimeno, JF. and Rojas, JA (2007): “On the Aggregate Effects of Immigration in Spain”. Banco de España Working Papers. • Gravitational Models • Panel Data Models • Leontief Based Models (I-O Models): Vicens et al. (2005): “Impacto Macroeconómico de la Inmigración en la Comunidad de Madrid”. • Social Account Matrix Models Inmigration Model “Comunidad de Madrid 2005” STARTING POINT • More than 12% of Employed population are immigrants in Comunidad de Madrid • Majority of immigrants have come to CM in last five years • Estimation of economic aspects of immigration in Spanish Comunidad de Madrid in last five years Immigration CM Model Schema FORMAL ECONOMY TOTAL IMMIGRANTS ESTIMATION EMPLOYMENT & WAGES CONSUMPTION BASKET INFORMAL ECONOMY PADRÓN REGULARIZ. LAW DELPHI BRANCHES DESEGREGATION PUBLIC SOURCES ASSOCIATIONS EMBASSY C O E F. E M P L O Y M E N T ACADEMICALS ▲ INPUT PRODUCTION & DEMAND TIO Direct Effect Indirect Effect ▲ OUTPUT HEALTH EDUCATIONAL OTHER PUBLIC EXPENSES REMITTANCES BRANCHES DISTRIBUTION OF CONSUMPTION YIELD NEW DOMESTIC EMPLOYMENT GENERATION RATIO CONSUMPTION/INVESTMENT IMMIGRANTS CONSUMPTION STRUCTURE OTHER Main Outputs in CM Model 1) 2) 3) 4) 5) 6) 7) 8) Estimation of the Total Number of Immigrants in “Comunidad de Madrid” Estimation of wages in immigrant population (sector distribution and formal and informal differences) Simulation Model: An unrealistic Economy without Immigrants – Gosh I-O Model Productivity aspects in the whole economy Firms Margins derived from immigrants activities Indirect Effects in domestic employment derived from immigrants activities (production and consumption) Public sector issues about Immigration: revenues and expenditures. Vital Cycle considerations in Fiscal Balance Remittances of immigrants estimation: domestic financial sector implications and remittances destination considerations Ghosh Supply Model (I) Distribution Coefficients: delivers of sector “i” to sector “j” over total Outputs of sector “i”. Sells structure of each sector (interdependence between sectors) d ij xij Tot _ Outputi Ghosh Supply Model (and II) Followed from previous coefficients and in the same terms that Leontief Model, we can build one expression to impact from Value Added to Total Production: x' = VA(I-D)-1 +[(Tr+M+Taxes)*D](I-D)-1 Value Added and Employment Generation from Ghosh Production: Static vs. Dynamic coefficients of VA and Employment Coef .VA j VA jañoTIO Pr od j1añoTIO VAjt prod jt * Coef .VAj Coef .Emp jt Empl jt V . A. jt Empl jt V . A. jt * Coef .Empl jt I-O Ghosh Model Implementation: Production Effect CHANGES IN VALUE ADDED VECTOR ESTIMATION OF EFFECT ON TRANSFERS, IMPORTS & TAXES DIRECT PRODUCTION EFECT DISTRIBUTION BY BRANCHES (Matrix of Distribution Coefficients) TOTAL PRODUCTON EFECT Δ INTERMEDIATE PRODUCTION (Ghosh Inverse (I-D)-1) Δ PRODUCTION Δ ADDED VALUE COEFFICIENTS TRANSLATION Δ EMPLOYMENT Distribution Matrix D x' = VA(I-D)-1 +[(Tr+M+Taxes)*D] (I-D)-1 VAjt prod jt * Coef .VAj Empl jt V . A. jt * Coef .Empl jt Leontief I-O Model Implementation: Demand Efect Δ TOTAL PRODUCTION GROSS SALARIES TAXES YIELD SAVE Δ CONSUMPTION YIELD DIRECT DEMAND EFECT INDIRECT DEMAND EFECT Δ EMPLOYMENT DISTRIBUTION BY BRANCHES Δ INTERMEDIATE DEMAND (Leontief Inverse) TOTAL DEMAND EFECT: DIRECT + INDIRECTS Δ PRODUCTION / A.V Δ PRODUCTION / A.V. Δ EMPLOYMENT Δ PRODUCTION / A.V. Δ EMPLOYMENT MAIN RESULTS Salaries & Wages Margins & Benefits Production Taxes VA (Prod. Effect) VA (Induced Demand Effect) Total VA CM Total Industry Building Market Services 4.863.137 3,27% 7.334.185 4,93% 100.207 0,07% 12.297.529 8,27% 65.645 0,04% 44.717 0,03% 240 0,00% 110.602 0,07% 1.854.759 1,25% 987.676 0,66% 43.398 0,03% 2.885.833 1,94% 2.883.087 1,94% 6.297.398 4,24% 56.543 0,04% 9.237.027 6,21% Non Market Services 59.647 0,04% 4.394 0,00% 26 0,00% 64.067 0,04% 2.085.248 1,40% 9,68% 351.908 0,24% 0,31% 122.063 0,08% 2,02% 1.609.920 1,08% 7,30% 1.119 0,00% 0,04%