1 A Recursive Dynamic Computable General Equilibrium Model For ...

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then an increase in the commodity price compared with the average industry price induces an increase in the production of that commodity ‘c’ in industry ‘i’ The output of commodity ‘c’ by industry ‘i’ is translated into commodity outputs via the market clearing equation: ⎡ MAKE( c, i) ⎤ x 0com( c) = ⎢ × q1( c, i) MAKE _ I( i) ⎥ (3) ⎣ ⎦ where the output of commodity ‘c’ is the sum of the industry ‘i’ output shares multiplied by their respective outputs of commodity ‘c’. Employing a revenue maximising CET structure, the output of commodity ‘c’ is divided between exports, ‘x4_c(c)’ and domestic demands, ‘x0com(c)’. Subsequently, a further CET function assigns exports between EU and non- EU export trade routes. 3. Final demands in ORANI-DYN - Private households, Tourism and NGOs In comparison with the standard ORANI model, ORANI-DYN includes two additional final demand accounts – inbound tourism subdivided by foreign and domestic tourists; and non-governmental organisation (NGO) demands for commodities. In addition, changes have been made to the private household nests to accommodate two modelling extensions: The first is the incorporation of multiple households, stratified by income groups. The second extension relates to the treatment of energy demands, in particular, the potential for substitutability between petroleum and biofuels ‘at the pump’. Employing concepts of weak homothetic separability, additional layers of nesting facilitate greater detail in modelling substitution possibilities in the final demand structure (compare Figure 5 with Figure 2). More specifically, in Figure 5, ORANI-DYN incorporates a split between composite energy and non-energy demands in the linear expenditure system nest. Subsequently, CES energy demands are disaggregated between non-biofuels and petroleum products, and biofuels and petroleum products. In each case these are further subdivided employing upper and lower CES Armington nests. Thus, the upper nest contains a domestic commodity and the composite import substitute; which in the lower nest is subdivided between EU and non-EU import routes. Indeed, the subdivision of EU and non-EU import trade constitutes an additional nest in the ORANI- DYN model compared with the standard ORANI. A discussion of the elasticity estimates in each of the nests is provided in section 19.1 in part I of this report. Whilst the CES elasticities are assumed constant over all ‘h’ private households (there are eight in total), the LES expenditure elasticities in the top nest are differentiated by 14
disposable income grouping. 11 Once aggregate household demands, ‘x3tot(h)’, are derived, these are aggregated over all ‘h’ to determine Spanish private household demand, ‘x3tot_h’. x3tot(h) x1ene_s(h) x3_s(negy1, h)…. x3_s(negyN, h) σ = 0.1 CES x3_s(nonbiopet1,h)… x3biopet(h) ..x3_s(nonbiopetN,h) σc CES CES σ Figure 5: The Private consumption nest for each household ‘h’ in ORANI-DYN. ‘Inbound tourism’, is defined as tourism expenditures within the territory of interest. In Figure 3 of part I of the report, the tourism account represents a new column addition in the ORANI-DYN database, labelled as account 7. The structure of ‘inbound’ tourism demands in the ORANI-DYN model is detailed in Figure 6. Since all of tourism expenditure is effectively a luxury or supernumerary expenditure, the usage of an LES function is not deemed appropriate. 12 In the ORANI-DYN model, the overall composition of commodity demands in inbound tourism is assumed to stay constant over time and changes in line with the fortunes of the tourism industry in Spain. Thus, in the top nest, a Leontief function is employed. Similarly, in the second nest, a Leontief function is also employed between domestic and foreign tourist expenditures on each composite 11 A discussion of the relevant expenditure elasticities and Frisch parameters is provided in section 19.1 in part I of this report 12 The LES function incorporates a subsistence and a supernumerary element to expenditure. 15 LES x3_s(biopet1,h) .x3_s(biopetN,h) x3(nonbiopetN,dom,h) x3mcomp_s(nonbiopetN,h) σc CES x3(nonbiopetN,meu,h) x3(nonbiopetN,mrow,h) σ σc x3(negyN,dom,h) x3mcomp_s(negyN,h) σc σc CES CES x3(necN,meu,h) x3(necN,mrow,h) CES x3(biopetN,dom,h) x3mcomp_s(biopetN,h) σc CES x3(biopetN,meu,h) x3(biopetN,mrow,h)
Page 1 and 2: A Recursive Dynamic Computable Gene
Page 3 and 4: Figure 5: The Private consumption n
Page 5 and 6: which the commodity is purchased. I
Page 7 and 8: 1.1.2 Private Final Demand In ORANI
Page 9 and 10: capital goods in industry ‘i’ i
Page 11 and 12: nesting to incorporate energy deman
Page 13: Note, that the values of the capita
Page 17 and 18: equal for each using industry ‘i
Page 19 and 20: modelled as sector-wide output cons
Page 21 and 22: Spain on (i) the biophysical charac
Page 23 and 24: the crops for which data on ‘gros
Page 25 and 26: ⎡ B ⎤ AREA = 1 − ⎢ ρ ⎣C
Page 27 and 28: qo(i,r) q1lnd(FCP) q1lnd(other agri
Page 29 and 30: Given that set-aside is mainly comp
Page 31 and 32: Price Pm Pfob Expenditure E1 E0 Q m
Page 33 and 34: household income is simply the addi
Page 35 and 36: G t = I/K (22) Relating changes in
Page 37 and 38: An additional extension of this dyn
Page 39 and 40: pick up) rather than a long term st
Page 41 and 42: A proportional link between househo
Page 43 and 44: (TRANSFER(“spend”)), less socia
Page 45 and 46: downward (negative) shock in the ex
Page 47 and 48: the data base and model structure a
Page 49 and 50: Harrison, W. J., and K. R. Pearson
Page 51 and 52: Appendix A: Nesting The choice of f
Page 53 and 54: The second condition is that the ag
Page 55 and 56: Appendix B: A Linearised representa
Page 57 and 58: Note that the absence of any price
Page 59 and 60: Rearrange (B14) in terms of v i,j,k
Page 61 and 62: t n ⎡ = y j, k − ⎢ f n, j, k

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