Ben Gardiner and Fabio Manca

Transcription

1 Human Capital and Skills in a Regionally Integrated Model Ben Gardiner and Fabio Manca European Commission DG Joint Research Centre (IPTS) Regional Economic Modelling (REMO) The views expressed are purely those of the authors and may not in any circumstances be regarded as stating an official position of the European Commission. page 1

2 Contents Background to and purpose of RHOMOLO model Skills and training in Cohesion policy Need for more integrated modelling approach Outline of RHOMOLO model Measuring human capital/skills Supply, demand, interaction effects Expected impacts of increased human capital/skills expenditure Can we improve RHOMOLO in this area? Work undertaken by CEDEFOP on modeling skills supply and demand (SkillsNet group) Some applicability to scenario / impact analysis as well? page 2

3 Background to Cohesion Policy and Related Funds ( ) Three main objectives Convergence (Obj1) Regional competitiveness and employment (Obj2) Territorial cooperation (Obj3) Two main funds Cohesion Fund (CF) Structural Funds (SF) European Social Fund (ESF) European Regional Development Fund (ERDF) How the numbers add up CF + SF = 348bn (30% of EU budget c.f. 50% to agriculture) Regional comp and empl Territorial cooperation 250 ERDF bn ( ) ESF Convergence 50 CF 0 Funds Objectives page 3

4 Education and Training within Cohesion Policy ESF support Education and training System reforms HE networking Raising awareness LLL participation Supporting PG studies Workers and new skills Improved access to training Identify future occupational/skills requirements Transnational cooperation Improving human capital and educational systems ERDF support Educational infrastructure m ( ) per head of active population ( ) PT IT DE PL HU GR CZ FR RO UK BG SV IR SE BE ES NL LT EE SI DK CY AT FI LV MT LX % of ESF budget -> 'Improving Human Capital' PT HU GR CZ SI IR MT ES LT EE IT PL RO SV LV CY BG BE FR FI SE DE LX DK NL UK AT % of ESF budget -> 'Improving Human Capital' page 4

5 The Need for a New Tool to Model Cohesion Policy Impacts Our ambition: An economic computable general equilibrium model for the evaluation of European Cohesion policy (ECP) that covers the economy at a NUTS1/2 regional level with sufficient sectoral detail (waste, water, R&D incl. among 23 NACE sectors) Climate change Trade Investment Employment Macro-economic Models e.g. QUEST Multi-Country Regions RHOMOLO (Computable general equilibrium) Land use Energy e.g. POLES Modelling of: Research Policy Industrial structures EIT-KICs Education Policy Cohesion policy Transport Microeconomic foundations (R&D, Human Capital, People) Agriculture e.g. CAPRI page 5

6 Human capital in the RHOMOLO - SCGE framework Human capital affects different dimensions of the RHOMOLO model: the labour market conditions (relative wages and migration decisions) the households' time-investment decisions (whether to spend time receiving education, working or leisure) the technological capabilities of firms and regions to produce or implement technology. The main objective is to be able to analyze the effects of Cohesion Policies expenditures on the creation of new stock of human capital and, eventually, to check how the newly produced human capital impacts productivity differentials across European regions page 6

7 Empirical Human capital and Skills As we know, Human Capital is an Intangible Asset and for this reason it is usually proxied by either: Educational attainment levels (as shares of population) Years of education Skills are nested within human capital and are much harder to capture RHOMOLO computes the empirical proxy for human capital as: HK = P*6 + S*10 + T*14 P, S and T are share of labour with Primary, Secondary and Tertiary education over total region population page 7

8 The Law Motion for the accumulation of HK (SCGE-model) Human capital accumulates over time according to the following law: (1 δh ed, r ) LSH ed, r, t = AH ed, r ( LSH ed, r, t 1) ( sharehoustth, ed Cth, i, r, t k + CG i, r, t k + CGR i, r, t k ) δh ed, r SHE νh ed, r, t k th ed, r Where: δh ed, r is the depreciation rate of HK each period of time (if =1 HK depreciates in 1 period) sharehoustth ed C + th i r t k CG +,,,, i r t k CGR,, i, r, t k th is the share of investment in education by household and education type of regional and national governments ν ed, r, t k H ed r SHE, is the time devoted to acquire education page 8

9 Model Overview Labour Demand and Supply-Demand Interactions page 9

10 Human Capital and Technology diffusion In RHOMOLO regions lagging behind the technological level of the innovative leader (in terms of TFP levels) can potentially catch-up with it by technology transfers (spillovers): Human capital is fundamental in enhancing technology catching-up proxying for the region absorptive capacity--abramovitz (1986), Behnabib and Spiegel (1994,2005) RHOMOLO makes use of the empirical specification proposed by Behnabib and Spiegel (2005) in order to estimate the impact of human capital on the process of technology catch-up and convergence at the regional level TFP TFPr = b + HK HK * l β 1 ln r + β2 ln r + TFP r ε i Human capital plays a double-role here: It affects positively the productivity growth of each region productivity per se ( β > 0 1 ) It affects the absorptive capacity of the region, making it easier to implement the technology discovered in the leader region (l). The bigger the TFP gap and the higher the number of inventions which can be absorbed by the follower region At the same time, if 2 regions (r1 and r2) are at the same distance from the frontier BUT differ in their HK endowment, the one with better HK will catch-up relatively faster ( β > 0 2 ) page 10

11 Links, Skills and Productivity Improving access to employment Job Creation: Skills specific demand TFP Growth Cohesion policy Improving social inclusion Direct investments: educational policies Increasing Stock of Human Capital GDP Growth page 11

12 Improving the Modelling of Human Capital/Skills Definition of skills Similar in both modelling exercises (3 broad educational classifications) Supply of skills Lack of appropriate skills acting as a bottleneck for funding and preventing maximum benefits of CP Funding for training and skills is an explicit part of CP Migration effects Demand for skills Measuring attributes and capabilities that are not quite the same as educational attainment (a counterpart that helps define human capital) Use of SIC-SOC matrix as go-between for sector employment and worker qualifications Interaction between supply and demand (of labour) TFP effects on the supply-side of the model Work undertaken by CEDEFOP on modeling skills supply and demand Primarily a forecasting exercise looking at identifying mis-match Some applicability to scenario / impact analysis as well page 12