Employment Proj ections Models ILO Employment Trends Port of Spain –November 2011
Obj ectives of training module j g • Introduction to employment projections done by the ILO Employment Trends Unit the ILO Employment Trends Unit • Getting an overview of the main principles of the methodology used methodology used • See a practical application (here: Mongolia) • Get to know requirements and data input Get to know requirements and data input
Overview • Background, objectives and data requirements • Sectoral structure and Input/Output table • Model set-up and solution • Preliminary results for Mongolia • Preliminary results at the Industry level • Some considerations regarding Trinidad
Objectives, background and data requirements
Obj Obj ectives/ practical applications of ti / ti l li ti f employment proj ection models • Planning for structural change, anticipating labour requirements • Produce scenarios/simulations (counterfactual analysis) P d i / i l i ( f l l i ) ▫ Produce alternate projections based on different assumptions ▫ Assess impact of exogenous economic shocks i f i h k ▫ Evaluate policy measures • Provide a consistent framework: � to analyse economic structure and linkages � assess underlying assumptions of economic forecasts • Assembling a database of economic and social data for b g b analysis
Background • Overview of different methodologies • ILO Employment Trends method ILO Employment Trends’ method • Landmark publications: ▫ Key Indicators of the Labour Market (KILM) ▫ Key Indicators of the Labour Market (KILM) ▫ Global Employment Trends (GET) • Employment Targeting Employment Targeting • Capacity Building
Available methodologies I g • Sectoral vs. aggregate models • Dynamic vs. static models • General equilibrium vs. statistical Ge e a equ b u s s a s ca models
Available methodologies II g • Use of different methodologies depends on ▫ Availability of data ▫ Availability of data ▫ Purpose of projection ▫ Numerical capacity at hand Numerical capacity at hand ▫ Timeframe to set up a projection ▫ Frequency of use
Available methodologies III g • Dynamic models typically most time-intensive: ▫ Use large amount of numerical capacity ▫ Use large amount of numerical capacity ▫ Require important set-up costs ▫ Require “confidence” in model Require confidence in model • But: ▫ Give dynamically consistent results ▫ Allow to integrate intertemporal effects (“ (“expectations”) i ”)
Available methodologies IV g • Sectoral models ▫ Are typically set up in a static context ▫ Are typically set up in a static context ▫ Allow comparisons “Before” – “After” ▫ Can be combined with GDP projections to get Can be combined with GDP projections to get sectoral projections ▫ Can be used to make occupational and skill forecasts
Available methodologies V g • General equilibrium models: ▫ Require setting up a fully specified model ▫ Require setting up a fully specified model, including behavioural equations ▫ Can be used both for dynamic (DSGE) and for b b y ( ) sectoral models (CGE) ▫ Are computationally intensive ▫ Are difficult to estimate • But: ▫ Allow for policy comparisons All f li i
Available methodologies VI g • What do I need? ▫ Time horizon of projection? ▫ Time horizon of projection? ▫ Analysis of different policy scenarios? ▫ Detail of labour market assessment Detail of labour market assessment (unemployment vs. sectoral labour demand) • What capacity do I have? ▫ Small team with short implementation period? ▫ Large, specialised team with input from external consultants? lt t ?
Dynamic models I y • All variables are endogenous: ▫ Consumption ▫ Consumption ▫ Labour demand and supply ▫ Investment Investment ▫ Trade • A behavioural rule needs to be defined ▫ Optimal decision process (maximization under constraints) ▫ Constant propensities (i.e. constant elasticities) i i (i l i i i )
Dynamic models II y • Most commonly used set-up: DSGE (Dynamic stochastic general equilibrium) stochastic general equilibrium) • Basic set-up ▫ Representative consumer: Maximizes utility by Representative consumer: Maximizes utility by choosing consumption conditional on a dynamic asset equation ▫ Firms choose labour and (sometimes) capital ▫ Models differ regarding (degree of) price and wage flexibility flexibility
Dynamic models III y • Recent developments ▫ Integrate labour market flows into DSGE ▫ Integrate labour market flows into DSGE modelling ▫ Accounts for employment adjustment costs at the p y j firm level… ▫ …for instance due to hiring and firing costs ▫ …and allows for different types of wage bargaining (at the firm or sectoral level, with persistence, etc.)
Dynamic models: An application I y pp • What will be the impact of increases in unemployment benefits on employment? unemployment benefits on employment? • Typical DSGE question because: ▫ Implies policy changes and individual reactions Implies policy changes and individual reactions ▫ Requires dynamic interactions and expectation effects ▫ Long-term effects that play through different transmission channels (wages, taxes, public deficits etc ) deficits, etc.)
Dynamic models: An application II y pp • Set-up of a DSGE model with unemployment benefits benefits 1.0 brium in %) 0.8 rate viation from equilib Unemployment 0.6 0.4 2 0.2 (dev 0.0 0 10 20 30 40 Quarters after shock No benefits Small benefits Large benefits
Background of the work on ILO’s Background of the work on ILO s employment proj ections • Cooperation between ILO Trends (Geneva) and Inforum (University of Maryland) to and Inforum (University of Maryland) to develop ‘interindustry macroeconomic models’’ • Employment projection models developed p y p j p for: Ukraine, Mongolia, Viet Nam, Philippines
U Use/ obj ectives of employment / bj ti f l t proj ection models • Economic development plans, strategic planning and employment targeting ▫ Planning for structural change, anticipating labour requirements ▫ Produce alternate projections based on different p j assumptions ▫ Set employment targets and measure progress towards reaching them reaching them • Policy responses to economic crises ▫ Assess impact of exogenous economic shocks A i t f i h k
Practical applications pp • Assembling a database of economic and social data for analysis as part of an LMIA system data for analysis as part of an LMIA system • Provide a consistent framework: � to analyse economic structure and linkages to analyse economic structure and linkages � assess underlying assumptions of economic forecasts • Produce scenarios/simulations (counterfactual analysis)
Model characteristics • Developed for limited resources environment/ limited data availability limited data availability • In Stata: accessible, user friendly • Scalable/ sustainable: • Scalable/ sustainable: � Capacity building: countries should be able to develop, maintain, improve the models p p
Model specification & features • Level of sophistication depends primarily on data availability/quality, time and resources available available • Every economy has its own structure/ characteristics/ data specificities • Limited capability to predict GDP and its components: Growth and expenditure patterns determined largely through exogenous determined largely through exogenous assumptions • Can be updated, scaled (upgraded) as more data p , ( pg ) becomes available
Model features • Limited capability to predict GDP and its components: Growth and expenditure patterns determined largely through exogenous determined largely through exogenous assumptions • Allow for scenario modelling • Can be updated, scaled (upgraded) as more data becomes available • Can be developed further into forecasting C b d l d f h i f i models, even dynamic general equilibrium models models
D Data considerations id i • Data availability � Sometimes, missing data imputed, taken from another country with similar economic/labour market structure structure • Data consistency and accuracy � Break in data series due to changes to base year, or conceptual definitions conceptual definitions � Different data from different sources within the country � Standard methodologies to adjust data, make d d h d l i dj d k estimates consistent with aggregates or with published data, etc.
Data Requirements Time series: Time series: 1. GDP by sector, current and constant prices (Supply) 2. GDP by expenditure, current and constant prices (D (Demand) d) 3. Gross output by sector 4. Employment by sector p y y 5. Total population and economically active population For one or more years: For one or more years: 6. Input-output table 7. Sectoral employment-occupation matrix
Key concepts for employment projections
Input – Output Tables: 3 maj or blocks bl bl k Supplier/ Buyer pp / y Industries Final Demand Vectors Output p ndustries C G I X M Intermediate Flows I Matrix Intermediate demand di d d e added ponents L comp K income value comp D Depreciation i i Indirect taxes Total Value Added Output
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