The TERA CGE models: analysing labour migration in diverse regional economies in the EU Euan Phimister (University of Aberdeen, UK)

SAM & General Equilibrium models Major Part of TERA project This Presentation and Next Complementary Aim - motivation, implementation, usefulness Structure Background Case Study Areas Modelling Approach SAMs and CGE Models Model Structure Using Models - labour migration.

Background TERA: Economic development in remote rural areas Aims: Role territorial factors which influence development review whether existing policies take account of factors propose new policy interventions. “The trends and choices that affect rural areas cannot be studied in isolation from what is going on in non-rural areas” (Saraceno, 1994)

Approach Regional/Local Modelling within region rural-urban linkages 6 Case Study areas. Reflect different Economic and Institutional Context Spatial Scale Rural-urban relationship

OECD Rural Classification

Case Study Differences Spatial Scale NUTS3 to NUTS 4/5 Population levels 110K – 400K Rural Pop Densities persons/km squared (Finland) –(Italy) Economic Size 0.5bn - 2bn euros/year GDP per capita. Developed (UK) – less developed (Latvia) Rural share of GDP – 5% (Greece) to 60%(Finland)

CZFINGRITALATUK Population GDP (m Euros) Rural Share (%) Urban Share (%) GDP Per Capita (Euros) Rural GDP Per Capita Urban GDP per Capita CASE Study Areas: Summary Statistics

Modelling Social Accounting Matrices (SAM) - all transactions given point in time SAM - basis for Computable Equilibrium Model (CGE) SAM Construction - each study area Existing secondary sources, e.g. national input-output tables Primary Data collection Survey of Households and Business survey, interviews with key informants

Computable Equilibrium Model (CGE) Behaviour of representative agents in economy Producers and Traders – maximise profits Consumers – maximise their well-being (have demand curves) Government collects taxes and makes transfers (tax rates and transfers are exogenously set) Model Closure rules – assumptions on how markets operate e.g. labour All transactions in “economy” accounted for. TERA-CGE Models IFPRI Standard CGE Model (Lofgren et al) ( ) Disaggregation of Accounts allows rural-urban analysis

Level of Disaggregation CZFINGRITALATUK Activities/Industries Of which Rural Commodities Factors of Productions109 6 Of which Rural Households Of which Rural Rest of World, 1 Government sector all case study areas

Production structure Urban/Rural Local/Regional

Factors and Households

CGE Model Estimation Each case study area Data - SAM plus other literature estimates Procedure - calibrate CGE models so each CGE replicates Case study SAM CGE Model Usefulness Full Picture of case-study economic transactions Controlled experiments – what if ? Example Simple Scenario – labour migration How different are the effects of large labour inflow/outflows in case study areas?

Case Study Areas Evidence Significant Growth Greek, Scottish Study areas Decline Finnish and Latvian areas Composition effect ? Finland - out migration of highly educated people Scotland- in migrants (skilled) but work in low skilled occupations Scenario % change in total labour supply all areas Scenario 2 a)-20% skilled labour category Czech R, Finland, Latvia b)+20% unskilled labour category Greece, Italy, UK Key Assumptions Each case study area separate labour market Urban-rural labour market integrated within case study area Capital fixed by sector, Government spending fixed

Scenario 1 +10% change in total labour supply all areas Aggregate level (GDP) broadly similar effects across case study areas +10% positive impact 5-8% (-10% approximately same negative effect) Components of GDP - Larger differences Rural-urban decomposition +10% positive impact Rural GDP effects 2-9% Mostly Rural same or less than Urban effect (except Italy) Largest differences GR, UK Rural-urban sectoral decomposition +10% positive impact Mostly Rural sectoral effect same or less than Urban effect (except Italy) Largest differences GR, UK

Scenario 1 +10% change in total labour supply all areas % Impact on Real Gross Domestic Product (GDP) CZFINGR ITLATUK Private Cons Investment Reg Exports Reg Imports GDP at Factor Cost

Scenario 1 +10% change in total labour supply all areas % Impact on Real Gross Domestic Product (GDP) at Factor Cost CZFINGRITLATUK Rural R-primary R-manufacturing R-services Urban U-manufacturing U-services

Scenario 2 a) -20% skilled labour category Czech R, Finland, Latvia b) +20% unskilled labour category Greece, Italy, UK Areas losing skilled labour Big differences in overall loss % Urban areas worst hit Broadly, impact by sector comparable Areas gaining unskilled labour Some differences in overall gains 2-4% No clear pattern whether Urban or rural areas gain most Differential sectoral impact by rural-urban Ratio Skilled: unskilled wages increases both losing & gaining areas

Scenario 2 a) -20% skilled labour category Czech R, Finland, Latvia b) +20% unskilled labour category Greece, Italy, UK % changes in GDP at Factor Cost -20% skilled +20% unskilled CZFINLATGRITUK Overall Rural R-primary R-manufacturing R-services Urban U-manufacturing U-services

Summary & Conclusions Modelling Approach SAMs and CGE CGE Models capture Case Study areas differences (?) Labour migration General – Rural GDP case study area differences Urban effect often bigger than Rural Skills mix-differential losses and gains Example CGE - What if? Further simulations – tailored to specific circumstances of each case study area Range simulations envisaged – Tourism, Transport, Agric Policy

Scenario 1 -10% change in total labour supply all areas % Impact on Real Gross Domestic Product (GDP) CZFINGR ITLATUK Private Cons Investment Reg Exports Reg Imports GDP at Factor Cost