Dynamic Inter-Regional Econometric IO Modelling Kurt Kratena Gerhard Streicher Michael Wueger

1. Dynamic Inter-Regional Econometric IO ModellingKurt KratenaGerhard StreicherMichael Wueger WIOD Conference: Industry-Level Analysis of Globalization and its Consequences Vienna, 26 – 28, May, 2010

2. Modelling Activities – Past and Present • Past: • National level: MultiMACI-IV, PROMETEUS • Regional level (9 provinces): MultiREG • Present: • „Local“ model (99 districts) ETMOS • Planned/Ongoing: • New national model DEIO – „Dynamic Econometric Input-Output Model“ • From this, derivation of „family of regional models“ • (essentially) identical model structure • Sub-Austrian level: • 9 provinces • 99 districts • Supra-Austrian level: • 27 EU members

3. General Characteristics of national Model DEIO • Data Base: • Supply-Use Tables; • 2-digit NACE(2003) and CPA-level; • Utilization of complete information provided by EUROSTAT (or national statistical offices); • Modelling Structure: • Quantity and price model • Dynamic models of private consumption and production • Intertemporal optimisation of households with durables and liquidity constraints • Cost function and factor demand with short term fixed input (K) in a ‚dynamic duality‘ model • Endogenous commodity structures: • AIDS for private consumption • Armington (CES) function for import demand • „frame shifting“ in intermediate demand

4. From national to regional • Idea: use essentially identical model structure at different geographical levels • One new element in model structure: • trade matrix (inter-regional linkages) • Data: decreasing data availability with increasing geographical detail • Some information on 9 province-level; • Very limited information on 35 NUTS3-level; • Almost no information on level of districts • When no information at regional level: • „simple“ breakdown of last available regional level • Trade matrix: increasing reliance on transport statistics

5. From national to regional • Example: sectoral output , value added, employment • National level: Full national account-information • 9 Province level: Regional analysis of primary statistics at the firm level (materials inputs, production statistics) • 99 districts: breakdown of province values using employment data • Assumption: within one province, sectors show identical stucture in all districts • Example: private consumption • Regionalisation based on official consumer survey • Utilization of information concerning demographic composition (age structure, educational characteristics) • Here, we do not employ the assumption of identical structures below the province level

6. Interregional Linkages • Private Consumption: • Commuting: • Regional re-distribution of regional value added (wages/profits) to regional income • Shopping: • Regional re-distribution of place of demand for consumption goods • Tourism: • Regional re-distribution of leisure expenditure • Data Base: official statistics (Commuters, Overnight Stays) and various official and commercial sources (Shopping Surveys) • Inter-regional Trade: • Balancing of regional demand and regional supply

7. Interregional Trade • Trade Matrix: • Trade survey (MultiREG, 2001) among firms with tradeable products and wholesalers; feasibility of new trade survey…… • Result: trade matrix at level of 9 provinces • Update using transport data; • Transport data also used for further disaggregation to district level • Not without problems (classifications – NSTR vs. CPA; modal split; statistical problems,….) • But: We have access to transport data of quite high quality: • „Verkehrsprognose 2025+“ – a projection of future traffic trends; • Collaboration of traffic scientists and economists; • One result: a very detailed description of current (2002, 2005) transport flows, involving a thorough overhaul of official transport statistics

8. Interregional Trade • Consistent trade matrix derived by balance of goods: • for each good, regional demand equals regional supply => balancing algorithm • Transport matrix as starting point for trade-matrix • row and column sums: from regional and national make – use matrices

9. Interregional Trade • Implementation in modelling framework • Ongoing work without definitive solution • Status q uo: • Changes in regional flows based on relative prices • Similar to modelling of imports: • Armington (CES) functions • Ensure consistency within model framework • New approaches arising from WIOD-environment ?! • Issue: „endogenous arrival“ of sectors in regions where they had no previous representation • Of minor concern in 9-province-model • But of major concern in model of 99 districts

10. Example: Export boom in Vienna • Increase in Viennese (900) exports of electrical equipment (CPA31)

11. Example: Price shock in Eisenstadt • All output prices -5% in Eisenstadt (101)

12. Interregional Trade • Future work: • Alternative specifications/modelling approaches • Gravity specification • Exploratory co-operation with TU institute – link of ETMOS with systems dynamics model of traffic flows • Test/implementation of New Economic Geography aspects • Separate treatment of trade in intermediates and consumption goods

13. Family of regional models – Appraisal and Applications • Regional data and quality: • National: full information from National Accounts • Province: disaggregation (to a sizable extent) based on primary statistics • District: breakdown based on employment; more information on CP and CG • Econometrics and estimation: • National: all coefficients econometrically derived from appropriate equations • Province: some coefficients based on provincial data; some (majority?) derived from national elasticities with calibration to provincial data; • District: all equations calibrated, based on provincial elasiticities • Applications: • National: base run (projections or forcasts), simulation runs • Provincial: limited forecasting abilities (in conjunction with national model); mainly simulation applications • District: VERY limited forecasting ability; simulation applications (transport and trade; location of sectors?)

14. Econometric Modelling Philosophy Philosophy of a dynamic EIO model: an alternative to static CGE models • Dynamic macro-modelling (DSGE): optimising agents with numerous institutional frictions: durables, liquidity constraints, technology lock-in, adjustment costs for investment • Keynesian closure with explicit modelling of labour markets: matching frictions, limited mobility and institutions (wage bargaining) • Calibration based on econometric estimation with panel data sets (EU 27) • Trade modelling beyond the Armington assumption

15. Modelling Block: Consumption Private Consumption: intertemporal optimisation • Chah, et.al. (1995): household maximes the expected value E0 of utility U in t = 0, chosing levels of K (durables), C (nondurables) with given financial wealth A: - j measures the part of durables that can be financed and lies between 0 and 1 (close to unity) • Lagrangean function of the maximization problem & first order conditions  • Relationship: marginal utility of C(UC)/marginal utility of K (UK) in t • Relationship: [Uc(t+1) - Uc(t)] / [UC(t) - UK(t)]

16. Modelling Block: Consumption Private Consumption: intertemporal optimisation • Chah, et.al. (1995): solution yields explicit equation for C that can be estimated econometrically: • Long-run (cointegrating) equation with error term Z • Short-run equation with interest rate rt and random shock h. Private Consumption: stock of K (durables) • Stock adjustment (household level) between bounds s and S (Eberly, 1994 and Caballero, 1993) • Approximated by stock adjustment with aggregate data:

17. Modelling Block: Production Cost function and factor demand with fixed K: ‘dynamic duality‘ • Cost functions with variable inputs xv and quasi fixed inputs xk: Shephard‘s lemma (demand for xv) and envelope condition (shadow price of xk), survey article by Galeotii (1996) • With or without explicit adjustment costs for xk • Adjustment of actual xk to optimal xk* in the spirit of Jorgenson‘s flexible accelerator model • Pindyck and Rotemberg (1983): Translog cost function with 2 quasi fixed inputs, L and K, and explicit adjustment costs  estimation of Euler equations • L, E, Mm, Md cost function with quasi fixed K and without explicit adjustment costs and including technical change • Factor demand with autonomous (bias), embodied and induced (Jin and Jorgenson,2008) technical change • (Implicit) investment demand as adjustment towards xk * with expectation formation mechanism • Mm is the link to trade modelling

18. Conclusions and Future Research • Trade matrix as a general consistent framework of modelling. • Consistency between: (i) Trade and IO/National Accounts Data (no problem in WIOD), (ii) Trade and Transport Data (monetary and physical) • Econometric estimation at an adequate regional level with recent  calibration (combining EIO and CGE philosophies) • Dynamic New Keynesian modelling in consumption and production: intertemporal optimisation with frictions, institutions, etc. (durable goods, liquidity constraints, adjustment costs for factor adjustment…) • Trade modelling beyond the Armington assumption: • Differentiating final (consumption) imports and intermediate imports • Testing economic geography assumptions (increasing returns) vs. Importance of transport costs • Testing new new trade theory (?)