The Short-Run E ects of a Natural Disaster With Imperfect ... · The Short-Run E ects of a Natural Disaster With Imperfect Interregional Labour Mobility An Illustrative Application

The Short-Run Effects of a Natural Disaster

With Imperfect Interregional Labour Mobility

An Illustrative Application of a Prototype

Multi-Regional CGE Model of the New Zealand Economy

Nathaniel Robson1

Abstract

A multi-regional computable general equilibrium (CGE) model is used to sim-

ulate the short-run economic impact of a natural disaster that strikes the

central business district of Wellington, the capital city of New Zealand. A

key feature of the analysis is the inclusion of an endogenous interregional mi-

gration response to the loss of regional amenities — interpreted broadly as

regional characteristics that enhance utility over and above that derived from

consumption of market goods — along with feedbacks from regional real wage

relativities. The model economy describes the behaviour of twenty-five indus-

tries across five regions built upon bottom-up micro-foundations. As official,

statistically estimated regional input-output tables are not produced in New

Zealand, a maximum-entropy information-theoretic approach is used to derive

the multi-regional input-output database required for model calibration. The

natural disaster scenario assumes that capital of industries concentrated in

the Wellington central business district is rendered inoperative at least tem-

porarily but there are no fatalities. The time period considered is too short

for insurance claims to be paid out on and the government response is limited

to endogenous changes in the composition of its consumption expenditure.

The analysis of the simulation results considers the role that regional char-

acteristics and interdependencies play in generating the computed short-run

outcomes.

1The author has recently completed the requirements for a PhD degree in economics at Victoria

University of Wellington, Wellington, New Zealand. The present paper extends upon research undertaken

towards that qualification.

CONTENTS 2

Contents

1 Introduction 3

2 The JENNIFER Model 4

3 Model Implementation 23

4 An Illustrative Application 36

5 Conclusion 52

Appendices 53

A List of Model Variables 54

B List of Model Equations 57

C Additional Results 69

References 81

1 INTRODUCTION 3

1 Introduction

On February 22, 2011 a magnitude 6.3 earthquake struck the city of Christchurch, New

Zealand’s second largest city. Land and buildings already weakened by a magnitude 7.1

quake six months earlier sustained extensive damage and some buildings in the central

business district collapsed with tragic consequences. In total, 185 lives were lost in the

earthquake. The economic cost of the disaster has also been very high — preliminary

estimates indicate that GDP growth for 2011 was 1.5 percentage points lower due to the

earthquake and the cost of rebuilding the city will be at least $NZ15 billion (around 10 %

of GDP).2 Canterbury, the region in which the city of Christchurch is located, experienced

a decline in usually-resident population of around 5000 (about 1% of the regional total)

in the year to June 2011.3 Christchurch itself saw a larger fall in population with some

residents shifting to other parts of Canterbury while there were also population outflows

to other (particularly urban) areas and abroad.

Wellington is New Zealand’s third largest city and the country’s capital, with an

urban land area slightly smaller but a population slightly larger than Christchurch. With

a major fault line running through the centre of the central business district (CBD) and

hundreds of other minor faults in the urban area, the Wellington region is well-known for

its seismic activity. However, the last major quake to do serious damage to the CBD was

the magnitude 8.2 earthquake of 1855.4 In the wake of the Christchurch earthquake, it

is pertinent to consider the economic impact of a similar disaster occurring in Wellington

in the present day. One may wish to consider, for example, how the impact would be

different to that observed in Christchurch due to different regional characteristics and how

the public sector could respond to the disaster.

An applied tool that would be useful in investigating these kinds of issues is a multi-

regional CGE model of the New Zealand economy. A prototype model of this type has

2New Zealand Treasury estimates as reported in http://www.nzherald.co.nz/business/news/

article.cfm?c_id=3&objectid=10710515, accessed on September 1, 2012.3Statistics New Zealand estimates from http://www.stats.govt.nz/browse_for_stats/

population/estimates_and_projections/SubnationalPopulationEstimates_HOTPJun11/

Commentary.aspx, accessed on September 1, 2012.4See the entry in The Encyclopedia of New Zealand at http://www.teara.govt.nz/en/

historic-earthquakes/3 for details.

http://www.nzherald.co.nz/business/news/article.cfm?c_id=3&objectid=10710515

http://www.nzherald.co.nz/business/news/article.cfm?c_id=3&objectid=10710515

http://www.stats.govt.nz/browse_for_stats/population/estimates_and_projections/SubnationalPopulationEstimates_HOTPJun11/Commentary.aspx



http://www.teara.govt.nz/en/historic-earthquakes/3

http://www.teara.govt.nz/en/historic-earthquakes/3

2 THE JENNIFER MODEL 4

recently been developed as part of PhD research at Victoria University of Wellington,

New Zealand. The model, known as JENNIFER, is documented in the ensuing PhD

thesis, Robson (2012). This paper presents results from the application of the JENNIFER

model to a natural disaster scenario whereby some of the Wellington CBD capital stock is

rendered inoperative at least temporarily. As seen in the case of Christchurch, migration

may occur in the aftermath of the disaster. The concept of regional amenity is useful in

explaining these migration outflows: if regions have characteristics that enhance utility

over and above that derived from consumption of market goods, and households are able

to shift between regions, a deterioration of the utility-enhancing characteristics of a region

will lead to a net migration outflow. On the other hand, these migration outflows (and

the initial shock) will affect real wage relativities between regions, inducing a separate

migration response. The simulation results presented in this paper therefore consider the

impact not only of the damage to infrastructure, but also of migration flows resulting

from the loss of regional amenities and changes in real wage relativities.

A summary of the JENNIFER model is given in section 2. The third subsection

therein describes the procedure for simulating migration responses to changes in relative

amenities and real wage rates. Section 3 outlines how the model is implemented — the

key steps being database generation, calibration and closure. The simulation results of

the short-run natural disaster scenario are presented and discussed in section 4 before

concluding remarks in section 5.

2 The JENNIFER Model

The theoretical structure of the JENNIFER model is similar to that of other well-known

comparative-static CGE models in that it consists primarily of demand functions derived

from constrained optimisation and competitive equilibrium conditions. A key distinc-

tive feature of the model is that bottom-up microfoundations are used for the regional

modelling. That is, the aggregate economy is described as the sum of a set of interdepen-

dent regional economies. Households, firms, and endowments are assigned to regions and

agents face region-specific prices. Fundamental to this is the extension of the Armington

assumption — that domestic and imported varieties of otherwise identical products are


not perfect substitutes — to products from different regions within the domestic economy.

Extensions to the model core allow for distribution services to be used in the delivery of

products from sellers to buyers, and households to respond to changes in regional ameni-

ties and wage relativities by shifting between regions.

This section summarises the model structure. Due to its complexity, a scheme of

notation is adopted for expressing the model relationships in mathematical form. Multiple

index superscripts and subscripts are used in variable symbols to allow the model to be

written compactly. Table 1 lists the indices used and the corresponding set over which

they can be assumed to vary unless otherwise specified. Table 2 lists some other, less

commonly used sets of the model. Components used in variable symbols are set out in

table 3. The formation of parameter symbols follows analogously.

All economic activity is modelled as undertaken by representative agents, whether

those agents are optimising or are following some fixed behaviour rule. This implies an

assumption that the decisions made by all the people, firms, or organisations an agent

represents can be combined as though they make decisions as a single entity. That is,

they can be aggregated up to act as a single regional or national agent. In this model,

there are seven types of agent:

Households: one agent per region, representing the regional population. These agents

consume products and leisure, save, and supply labour to local industries. They are

also assumed to ultimately own the capital stock located in their region.

Industries: one agent per region for each industry. These agents demand inputs for

both current production and capital formation, although factors are only used for

the first of these. For simplicity it is assumed that each industry produces a single

unique product type.

Investor: one agent representing all industries collectively, who allocates the national

investment budget across industries and regions.

Government: one agent who decides the pattern of government consumption and tax-

ation


Exporters: one agent per product type (i.e. per industry) who purchases domestic prod-

ucts and sells them to the foreign agent.

Importers: one agent per product type (i.e. per industry) who purchases foreign prod-

ucts and sells them to domestic agents.

Foreign: one agent who demands exports from and supplies foreign products to the

domestic economy.

The next two subsections summarise the core CGE model. Extensions to the core model

to allow for margin usage of distribution services and endogenous labour mobility are then

detailed in subsequent subsections. Comprehensive lists of the core model variables and

equations can be found in appendices A and B respectively.

2.1 Behavioural Relationships

Table 4 summarises the behavioural responses of agents to changes in relative prices.

Equations (1) – (8) express in functional form the composition of input demand by in-

dustry agents for production. These demand functions are derived from optimisation

— expenditure minimisation subject to constant elasticity of substitution (CES) mixing

functions — within a nested production function structure as shown in figure 1. For

a given quantity of domestic product g, Qind(Q)(D)g,dom,j,r , industry j in region r chooses the

cost-minimising quantity demanded from each source region x. This optimising choice is

represented by equation (1) and the lowest branches of the decision nest of figure 1. The

source allocation of demand is sensitive to changes in product g prices from all regions

y, PQ,ind(Q)g,y,j,r , as determined by the elasticity of substitution σ

ind(Q)g,dom,j,r in the CES mixing

function. The special case when the substitution elasticity is zero — the Leontief case

— implies no substitution between source regions when prices of product g from those

sources diverge. A general and intuitive interpretation of a substitution elasticity σ over

a set of choices y ∈ SET is that if the price of one choice x rises by 1% relative to a

share-weighted index of all y prices, ceteris paribus, the demand for choice x will fall by

σ%.5

5This interpretation follows from the linearised form of the demand functions. See Dixon, Parmenter,

Powell & Wilcoxen (1992, p. 126) for details.


Index Default Set Elements

agent AGENTS Agents: household (hsh), industry (ind), investor

(inv), government (gov), exporter (exp), importer

(imp), foreign (for)

c CUR Currency denomination: domestic (d$) or foreign (f$)

g, h COM Products (= IND)

j, i IND Industries (user-specified)

o ORG Origins: domestic (dom), imported (imp)

p PPS Purposes: production for current usage ((Q)), capital

formation ((K))

r, x, y REG Regions (user-specified)

s SRC Sources (= REG ∪ imp)v VAL Valuations: current prices and quantities — nominal

(c$), base-year prices and current quantities — real

(b$), current prices and base-year quantities — for

Laspeyres calculations (bQ)

w TAXVAL Tax status of income measure: before tax (btax), after

tax (atax)

Table 1: Indices and Default Sets

Set Elements

NZREG All regions including the national ‘region’ (= REG ∪ NZ)ENDIND Industries for which investment is allocated endogenously to equalise

expected future rates of return (⊆ IND , user-specified)

TRDVAL Trade flow valuations: basic prices (bas), f.o.b. prices (fob), c.i.f.

prices (cif)

PINDEX Price index: Paasche (P), Laspeyres (L), Fisher (F), geometric

Laspeyres (G)

Table 2: Other Sets


Part Description

P Price

Q Product quantity

EXP Exports

IMP Imported products

FOR Foreign products

L Labour

K Capital (currently installed)

F Composite factor

Knew Newly formed capital

K future Future capital stock

U Utility

N Leisure

TIME Time endowment

t Tax rate

W Real wage rate

δ Depreciation rate

φ Nominal exchange rate

ϕ Real exchange rate

ψ Scaling factor

Ω economy-wide expected net rate of return

Ξi Price index of type i ∈ PINDEX

RA/Bx Ratio of variable Ax to Bx

X•x,y/z Share of •x,y in •z (percent)(D) Demand(S) Supply

[•] • (variable) evaluated at the benchmark equilibrium

•x |x a list of variables •x as x varies over its default set

f(•x |x) f is a function of the list of variables •x |x

Examples

Qhsh(D)g,s,r Demand for product g from source s by hsh in region r

L(S)r Supply of labour by hsh in region r

PQ,ind(K)g,s,j,r Price paid by indj,r for purchases of Qg,s for the purpose of capital

formation

Table 3: Variable Notation Scheme


Qind(Q)(D)g,x,j,r = Q

ind(Q)g,x,j,r (Q

ind(Q)(D)g,dom,j,r , P

Q,ind(Q)g,y,j,r |y ;σ

ind(Q)g,dom,j,r) (1)

Qind(Q)(D)g,dom,j,r = Q

ind(Q)g,dom,j,r(Q

ind(Q)(D)g,j,r , P

Q,ind(Q)g,dom,j,r , P

Q,ind(Q)g,imp,j,r ;σ

ind(Q)g,j,r ) (2)

Qind(Q)(D)g,imp,j,r = Q

ind(Q)g,imp,j,r(Q

ind(Q)(D)g,j,r , P

Q,ind(Q)g,dom,j,r , P

Q,ind(Q)g,imp,j,r ;σ

ind(Q)g,j,r ) (3)

Qind(Q)(D)g,j,r = Q

ind(Q)g,j,r (Q

ind(D)j,r , P

Q,ind(Q)h,j,r |h ;σ

Q,ind(Q)j,r ) (4)

Qind(D)j,r = Qind

j,r (Q(S)g,r , P

Q,indj,r , P F,ind

j,r ;σindj,r ) (5)

Lind(D)j,r = Lind

j,r (Find(D)j,r , PL,btax

r , PKj,r;σ

F,indj,r ) (6)

Kind(D)j,r = K ind


r , PKj,r;σ

F,indj,r ) (7)

Find(D)j,r = F ind

j,r (Q(S)g,r , P

Q,indj,r , P F,ind

j,r ;σindj,r ) (8)

Qind(K)(D)g,j,r = Q

ind(K)g,j,r (K

new(S)j,r , P

Q,ind(K)h,j,r |h ;σ

Q,ind(K)j,r ) (9)

XI,b$j,r/tot = XI,b$

j,r/tot

(NRORj,r

Ω,K

(S)j,r

Ib$; βj,r, δj,r

)(10)

Qhsh(D)g,x,r = Qhsh

g,x,r(Qhsh(D)g,dom,r, P

Q,hshg,y,r |y ;σhsh

g,dom,r) (11)

Qhsh(D)r = Qhsh

r (PQ,hshr , PL,atax

r ,ENDINC atax,c$r , SAV atax,c$

r ;σhshr ) (12)

Nhsh(D)r = Nhsh



r ;σhshr ) (13)

ENDINC atax,c$r = PL,atax

r · TIME r +∑j

PKj,r ·K

(S)j,r (14)

SAV atax,c$r =

1

100· APS atax

r · INC atax,c$r (15)

L(S)r = TIME r −Nhsh(D)

r (16)

Qgov(D)g,x = Qgov

g,x (Qgov(D)g,dom , PQ,gov

g,y |y ;σgovg,dom) (17)

Qexp(D)g,x = Qexp

g,x (Qexp(D)g , PQ,exp

g,y |y ;σexpg ) (18)

PEXP ,f$,forg =

(EXP for(D)

g

)−1/εEXPg

· ψEXPg (19)

Table 4: Behavioural relationships

Output

Materials / Factors

Product g 1,2,…

Origin o dom,imp

Source region x A,B,…

ind,

,

Q

rj

ind

rgQ

,

ind

rj ,

ind

rjQ

,

ind

rjF

,

ind,

,

F

rj

ind

rjL

,

ind

rjK

, Labour / Capital

ind

1 rjQ

,,

ind

2 rjQ

,,

ind

dom2 rj ,,,

ind

A2 rjQ

,,,

ind

B2 rjQ

,,,

ind

1 rj ,,

ind

dom1 rjQ

,,,

ind

imp1 rjQ

,,,

ind

2 rj ,,

ind

dom2 rjQ

,,,

ind

imp2 rjQ

,,,

ind

A1 rjQ

,,,

ind

B1 rjQ

,,,

ind

dom1 rj ,,,


Figure 1: Industry agents’ decision nest for current production

The quantity of domestic and imported product g demanded is determined one level

higher in the decision nest by equations (2) and (3). That is, the domestic/import mix for

a given quantity of product g, Qind(Q)(D)g,j,r , depends on the relative prices from those sources

and the sensitivity to price changes is determined by σind(Q)g,j,r . Having this domestic/import

mix choice separated from the domestic source region mix choice enables different degrees

of price sensitivity amongst domestic sources and between domestic and foreign sources.

One may wish to assume, for instance, that domestic varieties are more substitutable than

imported varieties.


The remainder of the decision nest for each industry j in each region r is described

analogously by the demand equations. The product mix of the agent’s intermediate input

Qind(D)j,r is given by the demand equations (4) which determine Q

ind(Q)(D)g,j,r for all products

g. The quantity of composite intermediate input demanded is in turn derived from cost

minimisation subject to a CES production function for Q(S)g,r (where g = j) using Q

ind(D)j,r

and composite factor Find(D)j,r as inputs. As shown in the decision nest and equations (6)

– (7), the composite factor input is a cost-minimising mix of labour Lind(D)j,r and capital

Kind(D)j,r .

Alongside the decision nest for inputs to current production, industry agents anal-

ogously choose the cost-minimising composition of inputs for capital formation. The

decision nest to form the new capital Knew(S)j,r is shown in figure 2. The equations that

correspond to all but the top level of the decision nest are a simple variation on equations

(1) – (3) and are therefore not shown in table 4.6 The top level of the decision nest has the

new capital formed as a combination of (composite) products, described by equation (9)

which is analogous to equation (4). Factor inputs are not directly used in the formation

of new capital.

The quantity of new capital formed by each industry agent is determined by endoge-

nous allocation of the national investment budget favouring those industries and regions

with relatively higher net rates of return to investment.7 Equation (10) represents the

implicit functional relationship between the proportion of the investment budget allocated

to industry j in region r (XI,b$j,r/tot) and the ratio of the net rate of return on capital specific

to that agent (NRORj,r) to the economy-wide average expected net rate of return (Ω).

For a given current capital stock (K(S)j,r ) relative to the real investment budget (Ib$) and

parameter values, higher net rates of return (relative to Ω) imply a larger share of the

investment budget.8

6The superscript Q’s are just replaced with superscript K’s so for example Qind(Q)(D)g,x,j,r becomes

Qind(K)(D)g,x,j,r . It is therefore possible for an industry agent to pay different prices for inputs depending

on their purpose and also have differing degrees of substitutability between them.7For some industries, such a behavioural response is not appropriate. This is particularly relevant

for industries whose capital formation is primarily funded by government. Industries that provide public

services such as education and health are therefore excluded from the set of industries for which investment

is endogenously allocated.8The parameters δj,r and βj,r are the depreciation rate and the marginal efficiency of capital respec-

tively. Larger current capital stocks imply higher shares of the investment budget as more replacement

investment is required. Equation (10) represents a relationship that can be analytically derived from the

investment allocation equation block listed in appendix B.

New capital

Product g 1,2,…

Origin o dom,imp


ind,

,

Q

rj

new

rjK

,

ind

1 rjQ

,,

ind

2 rjQ

,,

ind

dom2 rj ,,,

ind

A2 rjQ

,,,

ind

B2 rjQ

,,,

ind

1 rj ,,

ind

dom1 rjQ

,,,

ind

imp1 rjQ

,,,

ind

2 rj ,,

ind

dom2 rjQ

,,,

ind

imp2 rjQ

,,,

ind

A1 rjQ

,,,

ind

B1 rjQ

,,,

ind

dom1 rj ,,,


Figure 2: Industry agents’ decision nest for capital formation

Equations (11) – (13) encapsulate the decision nest for household agents shown in

figure 3. The household agent in region r chooses a utility-maximising combination of

product consumption (Qhsh(D)r ) and leisure (N

hsh(D)r ) at the top level of their decision

nest, and then the expenditure-minimising composition of consumption demand is chosen

analogous to the cost-minimising composition of intermediate input demand described

by equations (1) – (3). Equation (11) for example specifies the domestic source choice

of the household agent in region r for product g. By comparing this to equation (1),

it can be seen that household and industry agents can face different prices for the same

product from the same source and may have different degrees of substitutability between


sources. The utility-maximising choice at the top of the decision nest is made subject

to the constraint that the sum of consumption and leisure valued at current prices does

not exceed endowment income, defined in equation (14), net of saving, defined in equa-

tion (15). Endowment income is the sum of the time endowment (TIME r) valued at

the after-tax nominal wage rate (PL,ataxr ) and rental income from capital. The average

propensity to save (APS ataxr ) is exogenously specified such that equation (15) determines

household saving in region r as that proportion of household income (the sum of labour

and capital income). The household consumption / leisure choice leads via equation (16)

to an endogenous labour supply response to changes in the regional wage rate relative

to the price of consumption goods (i.e. the real wage rate). The time endowment is a

quantity proportional to the regional working age population so changes in labour supply

are reflected in changes in the labour force participation rate.

The government agent has a decision nest for the composition of its consumption

similar to that of each household agent — see figure 4. Equation (17) describes the do-

mestic source choice at the bottom of the decision nest, analogous to equation (11). The

top-level decision determines the product composition of a given level of real government

consumption expenditure, analogous to equation (4). Other aspects of government activ-

ity modelled include direct taxes on labour income, value-added taxes, commodity taxes,

import duties and export subsidies, and lump-sum transfers to households.

Since there is one exporter agent for each product g, each of these agents has a single

composition choice — the domestic source composition of their own product to be pack-

aged and delivered to ports.9 The exporter agent is assumed to face a downward sloping

demand curve for its product as specified by equation (19). The elasticity parameter εEXPg

captures the sensitivity of foreign export demand (EXP for(D)g ) to changes in the foreign

currency export price (PEXP ,f$,forg ). The scaling factor ψEXP

g represents the height of the

export demand curve and is useful for scenarios where there are exogenous changes in

foreign export demand.

9Following convention, it is assumed that exporters do not export products that have just been im-

ported. There must be some value added by at least one domestic industry before imported products are

returned to the international market.

Utility

Consumption / Leisure

Product g 1,2,…

Origin o dom,imp


hsh

dom2 r,,

hsh

A2 rQ ,,

hsh

B2 rQ ,,

hsh

A1 rQ ,,

hsh

B1 rQ ,,

hsh

dom1 r,,

hsh

1 r,

hsh

dom1 rQ ,,

hsh

imp1 rQ ,,

hsh

2 r,

hsh

dom2 rQ ,,

hsh

imp2 rQ ,,

hsh,Q

r

hsh

1 rQ ,

hsh

2 rQ ,

hsh

rU

hsh

r

hsh

rQ hsh

rN


Figure 3: Household agents’ decision nest

Once the exporter agent has assembled its composite product from domestic sources,

this is costlessly transformed into an export product and supplied to the foreign market.

That is, the exporter agent’s production function is:

EXP (S)g = Qexp(D)

g (20)

The importer agent similarly serves as a conduit between the domestic and foreign mar-

kets, producing imported products from foreign products according to the production

function:

IMP (S)g = FORimp(D)

g (21)

The foreign agent supplies products perfectly elastically to importers while demanding

exports according to equation (19) as discussed above.

Government Consumption

Product g 1,2,…

Origin o dom,imp


gov,Q

b$G

gov

1Q

gov

dom2,

gov

A2,Q gov

B2,Q

gov

1

gov

dom1,Q gov

imp1,Q

gov

2

gov

dom2,Q gov

imp2,Q

gov

A1,Q gov

B1,Q

gov

dom1,

gov

2Q


Figure 4: Government agent’s decision nest for consumption

2.2 Competitive General Equilibrium Conditions

General equilibrium is obtained by imposing market clearing and zero pure profits in

all economic activities. The equilibrium has the Walrasian property: once a numeraire

price is chosen, a unique vector of prices relative to the numeraire will satisfy the equa-

tion system provided one market clearing condition is dropped. The JENNIFER model

describes activity in markets for domestic and imported products, capital, labour, in-

vestment, exports, and foreign products. Market clearing conditions for these markets


are shown in table 5.10 Equation (24) specifies market clearing in the market for each

product g from source s, including imports.11 The breve marks ( ˘ ) over the Q’s indi-

cate undelivered quantities. Without the enhanced treatment of distribution services to

be discussed shortly, the distinction between delivered and undelivered products is sim-

ply that commodity tax is paid during delivery, which is otherwise costless. Equations

(25) and (26) ensure market clearing for factors.12 Labour and capital are region-specific

and capital is also industry-specific. Removing the subscript r from equation (26) would

make labour perfectly mobile between regions as well as industries. An additional mech-

anism is required if we wish labour to be mobile but regional wage differences to not

completely disappear in equilibrium. One such mechanism is the endogenous household

mobility algorithm discussed later. Equations (27) – (29) are simple conditions required

for completeness to support the model structure.

Equations (30) and (31) stipulate zero pure profits in current production and capital

formation while equations (32) and (33) do likewise for importing and exporting respec-

tively. By virtue of equations (21) and (32), it can be seen that the price received by

importers is equal to the domestic currency price they pay for foreign products (including

tariffs). The domestic currency price is converted from foreign currency using the nominal

exchange rate φ by:

PFOR,d$,impg = φ · PFOR,f$,imp

g (22)

A large number of other price equations are also required to support the decision nest

structure of the demand equations. For example, PQ,hshr , the purchase price of total

composite product Qhsh(D)r is defined as an expenditure-weighted sum of the purchase

prices of each product g, PQ,hshg,r by the equation:

PQ,hshr ·Qhsh(D)

r =∑g

PQ,hshg,r ·Qhsh(D)

g,r (23)

10The market clearing equation that is dropped is that for the saving market, which is identical to the

aggregate consistency condition I +NX ≡ S.11That is, when s = imp. The symbols IMP (S)

g and Q(S)g,imp refer to the same model variable.

12If there is any unemployment of labour or capital, an exogenous term can be added to the right hand

of the relevant equation.


Q(S)g,s =

∑r

Qhsh(D)g,s,r +

∑p

∑j

∑r

Qind(p)(D)g,s,j,r + Qgov(D)

g,s + Qexp(D)g,s (24)

K(S)j,r = K

ind(D)j,r (25)

L(S)r =

∑j

Lind(D)j,r (26)

Knew(S)j,r = I

ind(D)j,r (27)

EXP (S)g = EXP for(D)

g (28)

FOR(S)g = FORimp(D)

g (29)

PQg,r · Q(S)

g,r = PQ,indj,r ·Qind(D)

j,r + P F,indj,r · F ind(D)

j,r ∀g = j (30)

PK,newj,r ·Knew(S)

j,r =∑g

PQ,ind(K)g,j,r ·Qind(K)(D)

g,j,r (31)

P IMPg · IMP (S)

g = PFOR,d$,impg · FORimp(D)

g (32)

PEXPg · EXP (S)

g = PQ,expg ·Qexp(D)

g (33)

Table 5: Competitive general equilibrium conditions

All other purchase prices involved in the agents’ decision nests are similarly defined as

weighted averages of the prices one level down. The prices at the bottom of the decision

nests, such as PQ,hshg,s,r , are functions of the basic price received by the seller and all costs

involved in delivery, including taxes and margins. For this reason, these will be discussed

in the subsection on distribution services.

2.3 Other Equations

It is common practice and convenient here to assign the nominal exchange rate φ as the

numeraire for our simulations. The value of φ is fixed at an arbitrary level, essentially

making the nominal exchange rate exogenous to the model, by the equation:

φ = 1 (34)

Many other equations are included in the model to facilitate different closure assump-

tions and provide measures of economic activity for reporting. These equations typically


define new variables as functions of core model variables or other defined variables. A

comprehensive list can be found in appendix B. One important measure is regional GDP,

which is an extension of the national GDP concept to the reginal level. National GDP is

measured (using expenditures with valuation v) as:

GDPEXPv = Cv + Iv +Gv + TRDBALv,d$ (35)

For regional GDP, all purchases of out-of-region products are treated as imports and all

sales of products to out-of-region agents are treated as exports (including products to and

from the foreign sector), so region r’s GDP is calculated (at current prices) as:

GDPEXPc$r =

∑g

∑x

PQ,hshg,r,x ·Qhsh(D)

g,r,x +∑g

∑j

∑x

PQ,ind(K)g,r,j,x ·Qind(K)(D)

g,r,j,x

+∑g

PQ,govg,r ·Qgov(D)

g,r +∑g

PQ,expg,r ·Qexp(D)

g,r (36)

Real regional GDP can be calculated as above but with the prices variables evaluated at

their benchmark levels.

This completes the overview of the core model. We now look at the two model ex-

tensions that give special treatment to distribution services and allow partial mobility of

labour between regions.

2.4 Usage of Distribution Services

In recognition that distribution services have a special role in delivering products from

sellers to buyers, additional modelling is undertaken to treat these services more real-

istically than can be achieved in the core model.13 Without this special treatment, an

agent facing an increase in price of an out-of-region product will substitute away from

that product and towards distribution services involved in its delivery. A more realistic

model prediction would be that the distribution services also experience lower demand as

a result of the price change. This is achieved by treating distribution services as delivery

margins, demanded in proportion to the products they are used to deliver.

13The modelling approach is similar to that of the ORANI model of Australia (Dixon, Parmenter,

Sutton & Vincent 1982), subsequently extended for the multi-regional case in FEDERAL (Madden 1990).


A distinction is made between delivered and undelivered products, where a delivered

product is viewed as a package of distribution services and the undelivered product. The

demands at the lowest levels of the decision nests in the core model are therefore demands

for these product packages rather than just the undelivered products. With the set of

distribution services MAR specified as consisting of wholesale trade (WHOL), retail trade

(RETT), and transportation (TRAN), the additional structure shown in figure 5 is added

to the lowest level of each agent’s decision nest (for each product from each source). In

terms of model equations, adding this structure is achieved by the inclusion of demand

functions for each agent of the form:

Qagent(D)g,s = aQ,agentg,s · Q

agent(D)g,s

vQ,agentg,s

(37)

Qagent(D)m,y,g,s = aQ,agentm,y,g,s ·

Qagent(D)g,s

vQ,agentg,s

(38)

These equations represent the solution to a given agent’s optimisation problem where

expenditure is minimised in obtaining the quantity Qagent(D)g,s of delivered product, which is

a Leontief combination of the undelivered product and the distribution services m ∈ MAR

(from regions y ∈ REG) used in its delivery. The a’s and v’s are parameters of the Leontief

function, evaluated during model calibration as discussed in section 3.

The price paid by purchasers can now be related to the price received by sellers, taxes

(including goods and services tax), and delivery costs by:

PQ,agentg,s ·Qagent(D)

g,s = PQg,s · (1 + tQ,agentg + tGST ,agent

g ) · Qagent(D)g,s

+∑m

∑y

PQm,y ·Qagent(D)

m,y,g,s (39)

The market clearing conditions for distribution services need to be altered to add in the

margin demands Qagent(D)m,y,g,s as follows:14

Q(S)m,y =

∑agent

(Q

agent(D)h,y · 1h=m +

∑g

∑s

Qagent(D)m,y,g,s

)(41)

141 is the indicator function:

1condition =

1 if condition = true

0 if condition = false(40)

Delivered product g 1,2,…

from source s A,B,…,imp

Leontief mixing function 0, agent

sg

agent

sgQ ,

agent

sgQ ,

agent

sgQ ,,,ARETT

agent

sgQ ,,,BRETT

agent

sgQ ,,,ATRAN

agent

sgQ ,,,BTRAN

agent

sgQ ,,,AWHOL

agent

sgQ ,,,BWHOL

Transport margin from

region y A,B,…

Retail margin from

region y A,B,…

Wholesale margin from

region y A,B,…

Undelivered product g 1,2,…

from source s A,B,…,imp


Figure 5: Formation of delivered products

The first term on the right hand side is the non-margin demand for distribution service

m from region y. This allows transport for example to be used for purposes other than

delivering products, such as commuting and sightseeing.


2.5 Partial Labour Mobility

Jones & Whalley (1989, page 375) argue that neither perfect labour mobility nor complete

labour immobility is an appropriate assumption when evaluating the regional impacts of

policy changes. They then proceed to set up an interesting micro-foundation for partial

labour mobility between regions. The approach allows migration flows to be endogenously

determined based on regional income differences.

The model extension that allows a similar labour mobility response in JENNIFER

is yet to have formally developed micro-foundations but goes further than the Jones &

Whalley (1989) modelling in two respects. Firstly, it recognises that changes in relative

regional amenity may also lead to migration flows. Secondly, feedbacks from the migration

flows to the regional economies are incorporated. There are two aspects to these feedbacks

that complicate obtaining a model solution in a single run: households are assumed

to respond to real wage rate relativities but real wage rates relativities depend on the

availability of labour across regions, and households are not assumed identical across

regions so migration affects the regional composition of households (in terms of working

age persons per household, non-labour force per household, etc.)

The general approach taken to obtain a model solution that takes into account house-

hold migration responses and feedbacks is to solve the model once for a given shock

(including changes in regional amenities), use the solution to calculate the mobility re-

sponse of households to the resulting real wage rate differences, and then solve the model

again with an updated shock that takes the mobility response into account.

Given solution values (in angle brackets 〈〉) and benchmark equilibrium values (in

square brackets [ ]), the flows of households between regions due to changes in real wage

relativities are calculated by the formula:

HSH x→r

〈HSH x〉= max

θx,r100

(〈Wr〉〈Wx〉

− [Wr]

[Wx]

), 0

(42)

where HSH x→r is the flow of households from region x to region r

Wr is the pre-tax real wage rate in region r

θx,r is a parameter that represents the sensitivity of

households in region x to changes in the real wage rate

of region r relative to their own


In words, the proportion of households that move from region x to region r equals the

product of θx,r/100 and the increase in the real wage rate of r relative to x.15 This lends

a useful interpretation to θx,r: if θx,r = 10, a doubling of the real wage of r relative to x

ceteris paribus will cause 10% of the households in x to move to r. Further discussion of

the θx,r parameters is left until section 3.

Having obtained the household flows, the number of households is updated to take

those flows into account as follows (with ′ used to indicate updated values):

〈HSH r〉′ = 〈HSH r〉+∑

x∈REG

(HSH x→r − HSH r→x) (43)

The shock to HSH r for the second run is then such that the solution value is 〈HSH r〉′:

shock = 100× 〈HSH r〉′ − [HSH r]

[HSH r](44)

To the extent that average household characteristics differ across regions, inter-regional

migration may cause those characteristics to change. For example, if a region receives an

inflow of households from another region that has a relatively higher unemployment rate,

there is likely to be a change in the receiving region’s (and sending region’s) unemployment

rate. To account for this possibility, we assume that there is no bias in the self-selection of

households that move between regions. That is, the migration flows consist of households

with average characteristics of the source region.16 For our example of differing unem-

ployment rates across regions, the shock is adjusted so that the solution of the second

model run has:

〈UNEMP r〉′ = 〈UNEMP r〉+∑

x∈REG

〈CUNEMPx 〉 × HSH x→r

− 〈CUNEMPr 〉 ×

∑x∈REG

HSH r→x (45)

15Either pre-tax or post-tax real wage rates could be used. It is convenient to use the pre-tax rates

since they are normalised to one in the benchmark equilibrium. Post-tax rates would only need to be

used if simulations entailed direct tax rates on labour income that changed by different proportions across

regions.16A simpler approach is to assume instant assimilation of incoming households to the characteristics

of the receiving region. One could make the case for a quick improvement in unemployment outcomes

for households coming from regions with higher unemployment rates. It is more difficult to argue for

immediate assimilation of other characteristics however, particularly demographic characteristics.

3 MODEL IMPLEMENTATION 23

where UNEMP r is the number of unemployed persons in region r

CUNEMPr is the average unemployed per household in region r

The inflow of unemployed from all other regions is added to unemployment in region r

and the outflow of unemployed is subtracted. Similar adjustments can be made to other

demographic and labour market measures as needed. The effect of these adjustments will

see some regions converge in average household characteristics but others to diverge.

With some minor adjustments, equation (42) and the updating formulae such as (43)

can include household flows to and from abroad in response to changes in regions’ real

wage rates relative to the foreign real wage.

3 Model Implementation

JENNIFER is implemented in GAMS with a database for a 25-industry, 5-region version of

the model. The core component of the database, the multi-regional input-output (MRIO)

matrix, is generated from national input-output data and regional GDP estimates using

an information-theoretic procedure. The model database is used to establish a benchmark

equilibrium which is then used to calibrate the demand functions. As there are many more

variables in the model than equations, some need to be set exogenously. This facilitates

the comparative-static experiments of section 4. In this section, the above aspects of

model implementation are outlined to provide a context for those experiments.

New Zealand has two main islands within its jurisdiction, known as the North Island

and South Island. The three largest urban areas are Auckland (most populous city),

Wellington (the capital), and Canterbury. Auckland and Wellington are located at oppo-

site ends of the North Island and Canterbury is near the middle of the South Island. The

classification of regions and industries in the implemented model are shown in tables 6

and 7 respectively.


Label Description

AKL Auckland

WLG Wellington

ONI Other North Island

CAN Canterbury

OSI Other South Island

Table 6: Regions

3.1 Benchmark Equilibrium

The MRIO matrix is derived from a national input-output table and estimates of industry

contributions to regional GDP.17 The regional shares data is shown in table 8. Both the

national and multi-regional input-output data tables are very large and therefore omitted.

They are available in Robson (2012). A schematic view of the MRIO is shown in table 9.

McDougall (1999) demonstrates that biproportional allocation is a maximum entropy

solution to the matrix-filling problem. In other words, if we have estimates of the row

and column totals of a matrix M but no other information, then an entropy-maximising

solution is:

M(row , col) =M(row ,TOTAL)×M(TOTAL, col)

M(TOTAL,TOTAL)∀row , col (46)

Defining R(row) = M(row ,TOTAL)M(TOTAL,TOTAL)

and C(col) = M(TOTAL,col)M(TOTAL,TOTAL)

, this can be written as:

M(row , col) = R(row)× C(col)×M(TOTAL,TOTAL) ∀row , col

An entropy-maximising regional split of a cell of the national input-output table, IO(row , col),

can be obtained with regional shares of the flow to col (source shares) and regional shares

of the flow from row (destination shares).

17These items were adapted from data provided by Business and Economics Limited (BERL), an

economics consultancy based in Wellington, New Zealand.


Label Description

AGRI Agriculture

FOLO Forestry and logging

FISH Fishing

MINE Mining and quarrying

OIGA Oil and gas

PETR Refined petrol

FDBT Food, beverages, and tobacco

TWPM Textiles, wood, paper, and media

CHNM Chemicals and non-metallic minerals

METL Basic and fabricated metal

EQFO Equipment, furniture, and other manufacturing

UTIL Electricity, water, and waste services

CONS Construction

ACCR Accommodation, restaurants, and bars

CMIF Communications, insurance, and finance

PROP Real estate and equipment hire

RBUS Research and business services

GOVT Government administration

EDUC Education

HEAL Health

CUPE Cultural and personal services

OWND Ownership of owner-occupied dwellings

WHOL Wholesale trade

RETT Retail trade

TRAN Transport

Table 7: Industries


Regional Shares of Industry Output (%)

IndustryRegion

AKL WLG ONI CAN OSI

AGRI 4.4 1.0 58.8 6.1 29.7

FOLO 5.7 1.9 63.7 4.3 24.4

FISH 9.6 1.9 32.0 2.6 53.9

MINE 8.1 1.0 49.9 9.8 31.2

OIGA 0.6 9.4 90.0 0.0 0.0

PETR 0.0 0.0 100.0 0.0 0.0

FDBT 20.4 3.5 39.5 9.2 27.5

TWPM 30.9 6.8 37.1 11.1 14.0

CHNM 58.9 11.7 2.3 18.2 8.9

METL 38.5 6.1 31.1 13.4 11.0

EQFO 45.5 8.0 24.1 13.9 8.6

UTIL 24.6 17.2 39.8 6.8 11.6

CONS 30.8 9.8 34.9 10.8 13.7

ACCR 28.8 10.1 30.8 10.9 19.4

CMIF 45.2 20.4 16.9 11.2 6.4

PROP 37.7 9.7 28.9 12.2 11.5

RBUS 45.1 16.0 21.6 9.8 7.5

GOVT 22.7 35.7 25.5 8.7 7.5

EDUC 33.8 11.1 31.5 10.9 12.7

HEAL 28.8 11.1 33.8 12.5 13.8

CUPE 36.2 14.4 26.3 11.0 12.2

OWND 34.5 12.0 30.0 10.7 12.8

WHOL 51.6 9.4 18.6 11.7 8.7

RETT 32.3 9.8 33.0 11.1 13.8

TRAN 42.9 9.9 21.2 13.0 13.0

Table 8: Regional Shares of Industry Output (%)


MR

IOro

w/c

ol

Indust

ries

CO

NIN

VG

OV

EX

PT

OT

AL

AG

RI

...

TR

AN

AG

RI

...

TR

AN

AK

L...

OSI

AK

L...

OSI

AK

L...

OSI

AK

L...

OSI

AK

L...

OSI

AG

RI

AK

L..

....

....

....

....

....

....

....

....

....

... . .

. . .. . .

. . .. . .

. . .. . .

. . .. . .

. . .. . .

. . .. . .

. . .. . .

. . .. . .

. . .. . .

. . .. . .

OSI

....

....

....

....

....

....

....

....

....

....

. . .. . .

. . .. . .

. . .. . .

. . .. . .

. . .. . .

. . .. . .

. . .. . .

. . .. . .

. . .. . .

. . .. . .

. . .

TR

AN

AK

L..

....

....

....

....

....

....

....

....

....

... . .

. . .. . .

. . .. . .

. . .. . .

. . .. . .

. . .. . .

. . .. . .

. . .. . .

. . .. . .

. . .. . .

. . .. . .

OSI

....

....

....

....

....

....

....

....

....

....

IMP

-AG

RI

....

....

....

....

....

....

....

....

....

0. . .

. . .. . .

. . .. . .

. . .. . .

. . .. . .

. . .. . .

. . .. . .

. . .. . .

. . .. . .

. . .. . .

. . .. . .

IMP

-TR

AN

....

....

....

....

....

....

....

....

....

0..

TA

X..

....

....

....

....

....

....

....

....

....

..

LA

B..

....

....

....

0..

00

..0

..0

..0

00

..

CA

P..

....

....

....

0..

00

..0

..0

..0

00

..

TO

TA

L..

....

....

....

....

....

....

....

....

....

..

Tab

le9:

MR

IOD

ata

Mat

rix


For example, MRIO(g−x,CON−r) — the flow of product g from region x to the

household agent in region r — is estimated as:

MRIO(g−x,CON−r) = [XOUTPUTg,x/g ]× [XLABOUR

r/tot ]× IO(DOM−g,CON)

where [XOUTPUTg,x/g ] is region x’s share of output of g as shown in table 8, and [XLABOUR

r/tot ]

is region r’s employment share.

The other MRIO cells are derived in a similar fashion. This procedure adheres to

principles of information theory so as to not introduce unintended bias in the regional

pattern of trade. Formally, the entropy of the whole MRIO matrix is maximised subject

to the condition that each block adds up to the respective IO table cell.

In cases where we have additional information on regional sources and destinations,

this can be incorporated without disturbing the matrix balance through the use of a

RAS algorithm (which is just the same biproportional allocation technique but applied to

matrix balancing rather than matrix filling). For instance, we may want to assume some

industries are local — they only sell their product to agents in their local region (and

the government and exporters). This assumption is made in this implementation for the

industries listed in table 10.

Adjustments are required to the allocation of IO cell values over the relevant MRIO

blocks to reflect the local product assumption. For example, since the product/industry

EDUC is assumed local, the flows to MRIO column CON-r are specified as:

MRIO(EDUC−x,CON−r) = [XLABOURr/tot ]× IO(DOM− EDUC,CON)× 1

x=r (47)

These kinds of adjustments inevitably disturb the balance of the MRIO matrix. It is

almost certain that the totals of the domestic product rows will no longer equal the totals

of the respective industry columns and the MRIO matrix will not be consistent with

equilibrium. To enforce consistency and restrict the information gain to those parts of

the MRIO matrix that we directly manipulate, we seek a cross-entropy solution to re-

balancing the matrix. Performing a RAS algorithm on the unbalanced MRIO matrix

achieves this: the solution minimises the distance between the MRIO matrix and the

biproportional allocation.18

18The seminal treatment of the RAS method is Bacharach (1970) while McDougall (1999) links the

RAS to cross-entropy.


Local Industries

EDUC Education

HEAL Health

CUPE Cultural and personal services

OWND Ownership of owner-occupied dwellings

WHOL Wholesale trade

RETT Retail trade

Table 10: Local Industries

Once a balanced MRIO matrix has been obtained, it can be used to establish the

benchmark equilibrium. Benchmark prices are generally set at 1 so benchmark quantities

can be mapped straight from the MRIO matrix. For example, the benchmark quantity

of Qhsh(D)g,x,r is:

[Qhsh(D)g,x,r ] = MRIO(g−x,CON−r)

Other data is used to establish benchmark values for variables that are not functions

of prices and quantities, such as net rates of return on investment, population and labour

market measures, and tax rates. The population and labour market data will be useful for

understanding the simulation results of section 4 so these are shown in tables 11 and 12.19

For instance, the data can be used to calculate average persons per household, and it can

be observed that Auckland has a higher average (3.2) than Wellington (2.8) in terms of

this household characteristic.

To facilitate the endogenous household mobility response to changes in regional real

wage differences, values of the parameter θx,r in equation (42) were assumed as follows:

• between domestic regions x and r, θx,r = 3

• between domestic region x and the rest of the world (ROW), θx,ROW = 1

• between the ROW and domestic region r, θROW,r = 0.5

19These figures were obtained from the Statistics NZ website http://www.stats.govt.nz on

7/12/2010. Note that the population figures are estimates of usually-resident population based on 2006

regional boundaries. These figures differ slightly from the 2006 census night counts.

http://www.stats.govt.nz


Demographic and Labour Market Data (000s)

VariableRegion

AKL WLG ONI CAN OSI

Population 1371 466 1348 540 459

Households 434 167 482 200 172

Unemployment 37 13 36 11 9

Non-labour force 219 65 198 78 66

Table 11: Demographic and Labour Market Data (000s)

To allow for flows between ROW and the domestic economy, it was assumed there was

a pool of potential immigrants of 1 million people.

3.2 Calibration of Demand Functions

Having established a benchmark solution to the model, the parameters of the demand

functions are calibrated to values that are consistent with that solution. In the case of

optimisation with respect to Cobb-Douglas functions, it is well-known the distribution

(exponent) parameters are expenditure shares. For example, the demand functions for

product g by the household agent in region r are:

Qhsh(D)g,r = Qhsh(D)

r ·∏h

(aQ,hshg,r · PQ,hsh

h,r

aQ,hshh,r · PQ,hshg,r

)aQ,hshh,r

Given benchmark values [PQ,hshg,r ], [Q

hsh(D)g,r ], and [Q

hsh(D)r ], the above can be solved for

the distribution parameters:

aQ,hshg,r =[PQ,hshg,r ][Q

hsh(D)g,r ]

[Qhsh(D)r ]

Calibration for the general CES case is similar but more complicated since the distribution

parameters depend on the substitution elasticity.20

20See for example Dixon & Rimmer (2002, eq. 12.15, p. 127).


Employed Persons (000s) — [EMPpersons,j,r]

IndustryRegion

AKL WLG ONI CAN OSI

AGRI 8.8 4.2 69.8 18.0 26.9

FOLO 0.3 0.3 3.1 0.4 1.4

FISH 0.1 0.1 0.7 0.4 0.7

MINE 0.4 0.2 1.8 0.2 1.2

OIGA 0.0 0.0 0.2 0.0 0.0

PETR 0.0 0.0 6.9 0.0 0.0

FDBT 12.4 3.1 22.7 10.0 11.6

TWPM 24.9 7.1 20.7 9.9 8.0

CHNM 10.8 2.6 2.7 4.1 1.7

METL 10.3 2.0 8.2 3.4 3.0

EQFO 20.2 3.5 14.0 9.6 4.7

UTIL 2.6 1.0 3.8 1.2 1.1

CONS 45.4 15.9 50.3 19.6 18.2

ACCR 31.0 12.3 33.4 16.9 17.5

CMIF 40.4 16.6 17.2 11.2 6.3

PROP 19.1 6.0 16.1 7.6 5.7

RBUS 89.5 36.0 50.9 26.2 17.8

GOVT 20.1 22.9 22.9 8.7 6.6

EDUC 44.6 17.5 46.1 18.2 15.6

HEAL 54.5 21.5 65.0 27.8 24.0

CUPE 29.0 12.9 23.9 11.5 9.7

OWND 0.0 0.0 0.0 0.0 0.0

WHOL 45.5 8.9 19.4 13.5 7.6

RETT 59.2 21.9 62.9 28.2 23.9

TRAN 23.6 6.3 17.4 10.8 7.8

Employed FTEs (000s) — [EMP ftes,j,r]

IndustryRegion

AKL WLG ONI CAN OSI

AGRI 7.7 3.7 62.3 16.1 24.3

FOLO 0.3 0.3 3.0 0.4 1.3

FISH 0.1 0.1 0.6 0.3 0.7

MINE 0.4 0.2 1.8 0.2 1.2

OIGA 0.0 0.0 0.2 0.0 0.0

PETR 0.0 0.0 6.5 0.0 0.0

FDBT 11.6 2.8 21.5 9.4 11.0

TWPM 23.1 6.6 19.3 9.0 7.3

CHNM 10.3 2.5 2.6 3.9 1.6

METL 9.9 1.9 7.9 3.2 2.9

EQFO 19.3 3.4 13.3 9.2 4.4

UTIL 2.4 1.0 3.6 1.2 1.0

CONS 42.9 15.1 47.7 18.6 17.3

ACCR 24.9 9.7 26.3 13.4 14.1

CMIF 37.8 15.5 15.2 10.0 5.5

PROP 17.0 5.2 14.2 6.6 5.0

RBUS 81.1 32.8 45.0 23.2 15.7

GOVT 19.3 22.0 21.8 8.3 6.1

EDUC 38.1 14.9 38.9 15.2 13.1

HEAL 47.5 18.4 55.4 23.2 19.9

CUPE 24.7 11.1 20.2 9.7 8.2

OWND 0.0 0.0 0.0 0.0 0.0

WHOL 42.8 8.3 18.0 12.6 7.0

RETT 49.1 18.1 52.6 23.1 19.8

TRAN 22.2 6.0 16.1 10.1 7.3

Table 12: Employment Data (000s)


Substitution elasticity values were adopted based on international practice and New

Zealand estimates where available. The assumed elasticities are shown in table 13 where

~Σ is a vector of source substitution elasticities shown in table 14.21 This vector is scaled

up or down to reflect a short-run or long-run simulation mode, to reflect that agents are

more willing and able to substitute between regional varieties than between the domestic

and foreign varieties, and for sensitivity analysis. The particular scaling shown in table

13 represent a low level of relative price sensitivity in the short-run.

The calibration of the margin demand functions for distribution services — equation

(38) — ensures that assumptions made when formulating the benchmark equilibrium

regarding how the services are used to deliver products are reflected in the structure of

aQ,agentm,y,g,s . Specific assumptions made regarding distribution services in this implementation

include that retail services are only used for delivering products to local agents and that

transport services are only used as margins for delivering products between regions, not

within regions. Wholesale and retail services are associated with demands in destinations

while transport services are associated with supplies from sources. The vQ,agentg,s parameters

in equation (38) capture the commodity tax component of delivered products. For further

details on how the distribution services modelling is implemented in JENNIFER, see

Robson (2012, chapter 3).

3.3 Model Closure

As the model involves more variables than explaining equations, an additional set of

equations are required to fix the appropriate number of variables exogenously so that

the model becomes a square system. The closure choice is important because simulation

results can only be interpreted with respect to that choice. A typical short-run closure

for the model is shown in table 15, and this is the closure used for our simulations in

section 4. The closure is interpreted as short-run because the endowments of capital are

fixed — capital is not free to move between regions or industries to seek out the best rate

of return. The only response to a change in relative rates of return is a reallocation of

the investment budget. In the 25-industry implementation, the GOVT, EDUC, HEAL,

21These elasticities were adapted from data provided by Business and Economics Limited (BERL), an

economics consultancy based in Wellington, New Zealand.


Short-run Substitution Elasticities

Households

Level Choice between... Elasticity

1 consumption & leisure 0.5

2 product types 0.5

3 domestic & imported 13~Σ

4 regional sources 12~Σ

Government


1 product types 0.0



Exporters



Industries - current production


1 materials & factors 0.5

2= product types 0.0

2= labour & capital 0.5



Industries - capital formation


1 product types 0.0



Table 13: Short-run Substitution Elasticities

and OWND industries are excluded from the endogenous investment allocation module

as investment in these in industries is highly dependent on government policy. Other

variables that are decided primarily by government policy or the international market

are also naturally exogenous. Owing to the comparative-static nature of the model,

the aggregate level of saving, investment, and unemployment is not determined and so

need to be set exogenously. Finally, the number and composition of regional households

are exogenously specified. For scenarios where regional amenity changes, the number of

households is shocked to reflect the mobility response to these changes. The composition of

households can also be changed depending on the assumptions regarding the self-selection

of mobile households.


Source Substitution Elasticities

Industry Elasticity

AGRI 2.2

FOLO 2.8

FISH 2.2

MINE 2.8

OIGA 0.0

PETR 4.0

FDBT 2.5

TWPM 2.9

CHNM 1.9

METL 2.8

EQFO 2.9

Industry Elasticity

UTIL 2.7

CONS 1.5

ACCR 2.0

CMIF 1.9

PROP 1.9

RBUS 1.9

GOVT 1.5

EDUC 1.5

HEAL 1.5

CUPE 1.9

OWND 0.0

WHOL 2.0

RETT 2.0

TRAN 2.0

Table 14: Source Substitution Elasticities


Exogenous Items

number of households

composition of households

unemployment rates

capital stocks

depreciation rates

world import prices

export demands

propensities to save out of nominal income

direct labour income tax rate

commodity tax rates

regional investment share for industries j /∈ ENDIND

investment share of real GDP

real government consumption expenditure

Table 15: A Short-run Closure

4 AN ILLUSTRATIVE APPLICATION 36

4 An Illustrative Application

The 25-industry, 5-region implementation of JENNIFER as described above is used to

investigate a natural disaster scenario where a portion of the currently installed capital

stock of the Wellington region is rendered inoperative, at least for the (short-run) period of

the scenario. It is assumed that the Central Business District (CBD) of Wellington is the

area most affected, so industries concentrated within it are disproportionately affected —

hence the pattern of decline in the capital stock shown in table 4. While the disaster can

be thought of as an earthquake, other types of natural disaster or even a military attack

could have similar consequences. Aspects specific to an earthquake such as insurance pay-

outs or government spending in preparation for rebuilding are not simulated for simplicity.

Shock to WLG Capital Stock

Industry % change

UTIL -1

CONS -1

ACCR -5

CMIF -10

PROP -10

RBUS -10

GOVT -15

Industry % change

EDUC -10

HEAL -10

CUPE -5

OWND -5

WHOL -1

RETT -5

TRAN -10

Along with damage to the current capital stock, the natural disaster is assumed to

reduce Wellington’s regional amenity relative to that of other regions and abroad. House-

holds respond to the change in relative amenity with out-migration biased towards prox-

imate regions and urban areas. In the simulation results that follow, scenario A refers

to the outcome of a simulation of the capital shock only while scenario B includes the

migration response to the amenity change. The assumed change in regional households

can therefore be seen in column B of table 16. By comparing the results shown in tables

20 and 21, and the A and B columns of table 24 and 25, the significance of the outward

migration can be seen.


Scenario C adds in the endogenous migration response to the regional wage differences

calculated in scenario B, as shown in table 17. Due to its relatively higher real wage

rate, households from other regions and abroad flow into Wellington, as seen in table

18. This partially offsets the initial outflow due to the loss of regional amenity, so the

migration pattern is less pronounced — compare columns B and C in table 16. As

household characteristics such as the working age / non-working age composition differ

across regions, the migration flows cause changes in Wellington’s average characteristics.

To see the extent that the composition effects impact the results, scenario D repeats the

same shock to regional households as scenario C, but without the change in household

characteristics associated with the inward migration. Thus it can be seen from tables 22

and 23 that scenario C includes an increase in non-working age persons per household,

for example, while scenario D excludes this change.

The following discussion highlights a range of insights we can take from these simula-

tion results.

Aggregate supply shock At the national level, the loss of current capital has the

hallmarks of a classical aggregate supply shock — output falls and prices rise — in all the

scenarios (table 24). Household consumption and saving fall due to the loss of capital in-

come and investment falls in line with GDP by assumption. With no change in government

spending, the trade balance must decline more than proportionately for macroeconomic

balance. Domestic prices rise relative to foreign prices so there is some substitution to-

wards imported products but overall imports falls. A more than proportionate fall is

exports is therefore required. From the perspective of foreign borrowing requirements,

the decline in the government budget surplus (due to a fall in tax revenue) and household

saving will mean an increase in national debt is required to support investment and the

larger trade deficit.

Different impacts across regions While the shock has a relatively minor effect at the

national level, the regions experience quite different outcomes. For these scenarios where

the shock originates in one region only, the impact on that region stands in contrast to

all the others. In scenario A for example, real household consumption rises in Wellington

while it falls elsewhere (table 20). The sudden shortage of capital in Wellington drives


up rental rates (table 26) and the overall effect on nominal household income is positive.

Due to the assumption that nominal consumption moves in line with nominal income, and

since the rise in the regional consumption price index is less than the increase in nominal

income, real consumption must rise even though real income has fallen. This translates

to a substantial fall in real household saving in Wellington. The higher rental rates on

capital in Wellington lead to higher output prices for Wellington products, which increase

production costs and therefore output prices elsewhere. With no significant change in

real disposable income and higher consumption prices, real consumption falls in regions

outside Wellington.

Household outflows exacerbate the shock The negative aspects of the shock are

roughly doubled at the national level and tripled for Wellington when household out-

migration as shown in column B of table 16 is added to the scenario to take into account

the loss of regional amenity due to the disaster. Aggregate real GDP falls 0.8% in scenario

B compared to 0.4% in scenario A, while Wellington’s real GDP falls 6.1% rather than

1.9% (table 21). The fall in labour supply in Wellington means less regional income and

a higher wage rate. Faced with weak regional demand and higher factor costs, firms cut

back production more than in scenario A. Although the price of Wellington products is

much higher, the other regions are not adversely affected — with inflows of labour from

Wellington driving down their wage rates, the other regions see smaller rises in produc-

tion costs and do not have to decrease output as much. The fixed average consumption

propensity again means Wellington’s real consumption increases, and interestingly, more

than in scenario A, even with less regional households. Real consumption spending per

Wellington household increases significantly in scenario B but without a commensurate

increase in disposable income, this increase comes from a very large decrease in saving

per household (table 25). The other regions have the opposite outcome — with a similar

level of regional disposable income to that of scenario A but more households, income and

spending per household is lower in scenario B. These outcomes are reflected in the real

regional GDP per capita results, with Wellington actually seeing an increase in GDP per

capita.


... But sensitivity to regional real wage differences moderates the impact One

outcome of scenario B is that the real wage rate in Wellington increases by 25% while

those in other regions fall (table 17). It is unrealistic that such a gap between the regional

returns to labour remains in equilibrium, even in the short-run. Scenario C adjusts the

household outflow to take into account in-migration of households in response to the

higher real wage rate on offer in Wellington. The net outflow therefore is much lower

than in scenario B, as seen in table 16. In terms of the aggregate and key regional results,

scenario C is an intermediate case between scenarios A and B. The sign on most of the

results for scenario C is the same as for scenario B, but the magnitudes are lower. Gross

investment is one exception: while in scenario B investment in Wellington fell roughly in

line with other regions, in scenario C it saw a small increase in investment, against falls

elsewhere. This is partly the result of Wellington being allocated more of the investment

budget due to better rates of return outcomes and partly because overall the investment

budget is larger. In Wellington, most of those industries for which investment is allocated

endogenously see smaller falls in investment in scenario C (table 29) while the results for

Auckland were roughly the same between scenarios B and C (table 28). This is due to

lower capital construction costs in Wellington giving rise to higher net rates of return to

those industries in scenario C (table 27). Also, recall that the GOVT, EDUC, HEAL,

and OWND industries are not subject to endogenous investment allocation. Instead, their

share of the investment budget is set exogenous so they move in line with aggregate real

investment. In scenario C, aggregate investment falls less than in scenario B so investment

in these industries changes likewise. The lower capital construction costs can be traced

back to the dampened rise in the wage rate (due to the endogenous household migration

response) via the lower input prices to capital formation.

Composition effects are important for labour market outcomes It is noticeable

from table 22 that the household inflows resulting from a higher real wage in Welling-

ton change the average composition of households in that region, while the outflows from

Wellington due to the amenity loss affect the average composition of other regions, partic-

ularly those in the North Island. Wellington ends up with a higher number of persons per

household, especially of non-working age. With employment falling, there is also a shift


to non-labour force activity — the participation rate in Wellington falls 1.4 percentage

points to 77% (table 19). The way these composition effects are calculated is by assuming

that Wellington households are only sensitive to the amenity change while out-of-region

households are only sensitive to the relative real wage rate differences. We may wish to

consider the case where the higher real wage encourages some Wellington households to

remain when otherwise they would have left — then the composition effects would not be

as pronounced. For this purpose, the results of scenario D are included for comparison.

In this scenario the same net outflow of households is assumed as in scenario C (and the

capital shock) but the composition effects are calculated as though the net outflow is also

the gross flow — there are no households migrating to Wellington thereby affecting the

average household composition. Comparing the results for Wellington in tables 22 and

23, and the C and D columns of table 19, it can be seen that the composition changes

included in scenario C reduce employment by about one percentage point and the labour

force participation rate by about 0.7 percentage points. Regional real GDP per capita is

also reduced by 0.6 percentage points due to the composition effects. This comparison is

also useful as scenario C can also be interpreted as the outcome of biased self-selection

of migrating Wellington households. If those households leaving Wellington are typically

smaller and with less dependents than those staying (for example, young single profession-

als versus established families with school-aged children), the composition effects would

be similar to those of scenario C. Then a comparison with scenario D (where self-selection

is not biased) informs on the impact of that biased self-selection, an analysis of which

may be of interest to policymakers following the disaster.

Further results from these scenarios are available from the author on request. The

industry-by-region breakdown of gross output and employment effects, and changes to

various macro price indices are provided in appendix C for the interested reader.


Number of Households

Region Baseyear (000s)∆ on Baseyear

A B C D

AKL 434 0 5 1 1

WLG 167 0 -20 -7 -7

ONI 482 0 5 1 1

CAN 200 0 3 1 1

OSI 172 0 3 1 1

NZ 1454 0 -5 -4 -4

Table 16: Number of Households

Regional Real After-Tax Wage

Region% ∆ on Baseyear

A B C D

AKL -0.5 -2.4 -1.1 -1.1

WLG -1.6 25.2 9.0 7.3

ONI -0.6 -2.8 -1.3 -1.0

CAN -0.5 -2.9 -1.4 -1.3

OSI -0.7 -3.6 -1.9 -1.6

Table 17: Regional Real After-Tax Wage


Household Flows for Scenario C (000s)

SourceDestination

AKL WLG ONI CAN OSI ROW

AKL 3.729 0.110

WLG

ONI 0.048 4.197 0.138

CAN 0.028 1.757 0.008 0.060

OSI 0.065 1.566 0.048 0.041 0.066

ROW 1.260

Table 18: Household Flows for Scenario C

Labour Force Participation Rate

Region Baseyear (%)∆ on Baseyear

A B C D

AKL 74.2 0.0 0.1 0.1 0.1

WLG 78.4 -0.4 -1.3 -1.4 -0.7

ONI 75.7 0.1 0.2 0.2 0.1

CAN 77.5 0.0 0.1 0.1 0.1

OSI 77.8 0.1 0.2 0.2 0.1

NZ 76.0 0.0 0.0 0.0 0.0

Table 19: Labour Force Participation Rate


Key Results — Scenario A

Variable% ∆ on Baseyear

AKL WLG ONI CAN OSI NZ

Real GDP (expenditure) -0.3 -1.9 -0.2 -0.2 -0.2 -0.4

Real GDP per capita -0.3 -1.9 -0.2 -0.2 -0.2 -0.4

Gross output -0.2 -2.0 -0.2 -0.2 -0.2 -0.4

Gross investment -0.6 0.9 -0.7 -0.6 -0.7 -0.4

Disposable income 0.0 -3.8 0.0 0.0 0.0 -0.4

Private consumption -0.4 1.1 -0.6 -0.4 -0.6 -0.3

Private saving 1.5 -23.1 1.9 1.7 2.6 -1.0

Working age population 0.0 0.0 0.0 0.0 0.0 0.0

Non-working age population 0.0 0.0 0.0 0.0 0.0 0.0

Total population 0.0 0.0 0.0 0.0 0.0 0.0

Total households 0.0 0.0 0.0 0.0 0.0 0.0

Employment 0.1 -0.5 0.1 0.1 0.1 0.0

Unemployment 0.1 -0.5 0.1 0.1 0.1 0.0

Non-labour force -0.2 1.9 -0.2 -0.2 -0.3 0.0

Capital Stocks 0.0 -8.2 0.0 0.0 0.0 -1.1

Consumption price deflator 0.3 2.7 0.2 0.3 0.2 0.5

Real after-tax wage rate -0.5 -1.6 -0.6 -0.5 -0.7 -0.7

Output price deflator 0.2 3.8 0.1 0.2 0.0 0.5

Investment net rate of return -0.3 24.4 -0.7 -0.5 -0.8 2.3

Table 20: Key Results — Scenario A


Key Results — Scenario B



Real GDP (expenditure) -0.2 -6.1 0.1 0.0 0.2 -0.8

Real GDP per capita -1.2 6.7 -0.9 -1.3 -1.3 -0.5

Gross output -0.1 -5.9 -0.1 0.0 0.0 -0.7

Gross investment -0.7 -0.7 -0.9 -0.8 -0.9 -0.8



Private saving 4.6 -65.3 5.5 6.0 8.6 -2.2

Working age population 1.1 -12.0 1.1 1.3 1.5 -0.3

Non-working age population 0.9 -12.0 0.9 1.3 1.5 -0.3

Total population 1.0 -12.0 1.0 1.3 1.5 -0.3

Total households 1.2 -12.0 1.0 1.3 1.5 -0.3

Employment 1.2 -13.4 1.3 1.5 1.8 -0.4

Unemployment 1.2 -13.4 1.3 1.5 1.8 -0.4

Non-labour force 0.6 -6.8 0.4 0.7 0.7 -0.2

Capital Stocks 0.0 -8.2 0.0 0.0 0.0 -1.1


Real after-tax wage rate -2.4 25.2 -2.8 -2.9 -3.6 0.7

Output price deflator 0.3 13.3 0.0 0.0 -0.3 1.5


Table 21: Key Results — Scenario B


Key Results — Scenario C





Gross output -0.2 -3.7 -0.2 -0.2 -0.2 -0.6




Private saving 2.5 -41.1 3.1 3.5 5.1 -1.7





Employment 0.4 -6.2 0.4 0.6 0.7 -0.3

Unemployment 0.4 -6.3 0.4 0.6 0.7 -0.4

Non-labour force -0.3 1.5 -0.5 -0.2 -0.3 -0.1

Capital Stocks 0.0 -8.2 0.0 0.0 0.0 -1.1


Real after-tax wage rate -1.1 9.0 -1.3 -1.4 -1.9 0.0



Table 22: Key Results — Scenario C


Key Results — Scenario D





Gross output -0.2 -3.4 -0.3 -0.2 -0.2 -0.6




Private saving 2.5 -38.4 2.7 3.2 4.6 -1.6





Employment 0.4 -5.3 0.3 0.5 0.6 -0.3

Unemployment 0.4 -5.3 0.3 0.5 0.6 -0.3

Non-labour force 0.0 -1.4 -0.2 0.0 0.0 -0.2

Capital Stocks 0.0 -8.2 0.0 0.0 0.0 -1.1


Real after-tax wage rate -1.1 7.3 -1.0 -1.3 -1.6 -0.1



Table 23: Key Results — Scenario D


Real Macro Measures

Variable Baseyear ($m)% ∆ on Baseyear

A B C D

GDP (Expenditure) 156090 -0.4 -0.8 -0.6 -0.6

GDP (Income) 156090 -0.4 -0.6 -0.6 -0.6

Private Consumption 93331 -0.3 -0.2 -0.3 -0.3

Investment 38305 -0.4 -0.8 -0.6 -0.6

Government Consumption 28661 0.0 0.0 0.0 0.0

F.O.B. Exports 43290 -0.8 -2.0 -1.4 -1.4

C.I.F. Imports 47497 -0.3 -0.3 -0.3 -0.3

Trade Balance -4207 -5.5 -17.6 -11.1 -10.3

Government Balance 6900 -1.1 -2.5 -2.2 -2.1

Domestic Private Saving 27198 -1.0 -2.2 -1.7 -1.6

Domestic Saving 34098 -1.0 -2.3 -1.8 -1.7

Table 24: Macro Measures


Real Disposable Income Per Household

Region Baseyear ($)% ∆ on Baseyear

A B C D

AKL 95271 0.0 -0.6 -0.1 -0.1

WLG 88394 -3.8 2.4 -2.1 -1.6

ONI 75572 0.0 -0.4 0.1 0.0

CAN 64292 0.0 -0.6 -0.1 -0.1

OSI 88548 0.0 -0.6 -0.1 -0.1

NZ 82902 -0.4 -0.3 -0.3 -0.3

Real Spending Per Household


A B C D

AKL 73403 -0.4 -1.8 -0.8 -0.8

WLG 70464 1.1 18.4 7.2 7.0

ONI 57243 -0.6 -2.0 -0.9 -0.9

CAN 50335 -0.4 -2.0 -1.0 -0.9

OSI 70474 -0.6 -2.6 -1.3 -1.3

NZ 64195 -0.3 0.1 0.0 0.0

Real Saving Per Household


A B C D

AKL 21868 1.5 3.4 2.2 2.2

WLG 17930 -23.1 -60.5 -38.3 -35.5

ONI 18329 1.9 4.4 3.0 2.6

CAN 13957 1.7 4.7 3.1 2.8

OSI 18074 2.6 7.1 4.7 4.2

NZ 18707 -1.0 -1.9 -1.4 -1.3

Table 25: Real Income, Spending, and Saving Per Household


Regional Capital Rents Index


A B C D

AKL -0.1 0.3 0.0 0.0

WLG 14.7 16.4 15.2 15.1

ONI -0.4 -0.2 -0.4 -0.4

CAN -0.2 0.1 -0.1 -0.2

OSI -0.4 -0.3 -0.5 -0.5

NZ 1.5 1.9 1.6 1.5

Table 26: Regional Capital Rents Index

Regional Investment Price Index


A B C D

AKL 0.1 0.6 0.3 0.3

WLG 0.3 1.6 0.8 0.8

ONI 0.1 0.5 0.3 0.3

CAN 0.1 0.5 0.3 0.3

OSI 0.1 0.5 0.3 0.3

Table 27: Regional Investment Price Index


Investment By Industry — Region AKL

Industry Baseyear ($m)% ∆ on Baseyear

A B C D

AGRI 51 -0.9 -1.5 -1.2 -1.2

FOLO 7 -0.2 -0.4 -0.3 -0.3

FISH 4 -3.1 -4.6 -4.1 -4.0

MINE 15 -1.0 -1.8 -1.4 -1.3

OIGA 5 -0.8 -1.7 -1.2 -1.1

PETR 0 0.0 0.0 0.0 0.0

FDBT 195 -2.4 -2.7 -2.8 -2.7

TWPM 269 -0.9 -1.2 -1.1 -1.0

CHNM 377 -0.8 -1.3 -1.0 -1.0

METL 247 -0.6 -0.9 -0.7 -0.7

EQFO 228 -0.6 -0.8 -0.7 -0.7

UTIL 183 -4.2 -0.3 -2.9 -3.1

CONS 243 -0.1 -0.1 -0.1 -0.1

ACCR 96 -0.5 -0.6 -0.6 -0.6

CMIF 2048 0.4 1.2 0.7 0.7

PROP 681 0.0 0.1 0.0 0.0

RBUS 908 -0.1 0.6 0.2 0.1

GOVT 802 -0.4 -0.8 -0.6 -0.6

EDUC 444 -0.4 -0.8 -0.6 -0.6

HEAL 255 -0.4 -0.8 -0.6 -0.6

CUPE 308 -0.2 -0.5 -0.3 -0.3

OWND 4601 -0.4 -0.8 -0.6 -0.6

WHOL 495 -0.3 -0.5 -0.4 -0.4

RETT 344 -0.4 -0.7 -0.5 -0.5

TRAN 64 -46.4 -77.4 -61.7 -59.4

All 12867 -0.6 -0.7 -0.7 -0.7

Table 28: Investment By Industry — Region AKL


Investment By Industry — Region WLG


A B C D

AGRI 12 -2.3 -10.1 -5.4 -4.9

FOLO 2 -0.6 -2.4 -1.3 -1.2

FISH 1 -8.0 -32.1 -17.6 -16.2

MINE 2 -2.5 -11.2 -5.9 -5.4

OIGA 74 -0.8 1.0 -0.1 -0.3

PETR 0 0.0 0.0 0.0 0.0

FDBT 33 -5.4 -25.0 -13.3 -12.1

TWPM 60 -2.0 -11.5 -5.7 -5.1

CHNM 75 -1.4 -5.5 -3.1 -2.8

METL 39 -1.2 -6.2 -3.2 -2.9

EQFO 40 -1.3 -8.3 -4.1 -3.7

UTIL 128 -0.5 -8.4 -3.9 -3.4

CONS 77 -1.0 -1.2 -1.1 -1.0

ACCR 34 -0.4 -4.9 -2.2 -1.9

CMIF 923 -0.1 -2.3 -1.0 -0.8

PROP 175 -1.7 -2.9 -2.2 -2.1

RBUS 322 -4.0 -6.5 -5.0 -4.8

GOVT 1259 -0.4 -0.8 -0.6 -0.6

EDUC 145 -0.4 -0.8 -0.6 -0.6

HEAL 98 -0.4 -0.8 -0.6 -0.6

CUPE 122 -2.8 -1.6 -2.3 -2.4

OWND 1604 -0.4 -0.8 -0.6 -0.6

WHOL 90 -0.1 1.8 0.7 0.5

RETT 104 -1.1 2.6 0.4 0.2

TRAN 15 600.3 469.7 546.2 554.6

All 5434 0.9 -0.7 0.2 0.3

Table 29: Investment By Industry — Region WLG

5 CONCLUSION 52

5 Conclusion

By considering a set of scenarios concerning a natural disaster, we have seen the kind of

insights that may be obtained through the application of a multi-regional CGE model

to an economic impact analysis of such an event. The bottom-up regional modelling of

the implemented JENNIFER model not only allows for region-specific supply side shocks

as seen in our application, but also facilitates analysis of the impact of migration flows

in the wake of the disaster. Modelling household sensitivity to both relative regional

amenity and real wage rates helps build a detailed picture of inter-regional migration and

associated composition effects. With the JENNIFER model being prototype in nature,

there are a number of developments that would enable more useful results to be obtained

and improve the realism of the scenarios.

One development particularly relevant to supply-side shock scenarios would be a link

from the resources of the economy (and possibly quality-of-life measures) to regional

amenity along with an estimated functional relationship between changes in amenity and

household migration. This could potentially remove the need for running the no-mobility

intermediate simulation. Also useful for these scenarios and in general would be modelling

of local government including its relationship to central government, allowance for cross-

regional ownership of capital, and the regional distribution of employment by occupation.

These issues were not pursued due to time constraints. Analysis of the long-run effects of

our disaster scenario would be more complex, requiring a dynamic model of the economy

connected to a demographic growth model. Finally, the reliability of the scenario results

depends heavily on the quality of data used to drive the model. More work is required to

validate the assumed substitution elasticities and incorporate the available econometric

estimates of migration elasticities.

53

Appendices

A LIST OF MODEL VARIABLES 54

A List of Model Variables

This appendix lists the variables of the core model — measurement-type variables, where

the meaning is obvious from the variable name, are not included here.

Variable Description

APCwr average propensity to consume of hshr (out of w income)

APSwr average propensity to save of hshr (out of w income)

Cv aggregate private consumption expenditure (with valuation v)

CPI i consumption expenditure deflator index of type i ∈ PINDEX

CV r compensating variation for hshr

EMP• employment measures in IOunits

ENDINCw,vr w endowment income of hshr (with valuation v)

EV r equivalent variation for hshr

EXP for(D)g demand for exports of product g by for

EXP (S)g supply of exports of product g by expg

Find(D)j,r demand for composite factor by indj,r

FORimp(D)g foreign product g demanded by impg

FOR(S)g supply of foreign product g by for

Gv aggregate government consumption expenditure (with valuation v)

GDPDEF i GDP deflator index of type i ∈ PINDEX

GPI i government expenditure deflator index of type i ∈ PINDEX



GRORj,r gross rate of return on Kj,r

GVAv• gross value added measures (with valuation v)

Iind(D)j,r investment demand of indj,r

Iv aggregate investment expenditure (with valuation v)

IMP (S)g (≡ Q

(S)g,imp) supply of imported product g by impg

INCw,vr w income of hshr (with valuation v)

IPI i investment expenditure deflator index of type i ∈ PINDEX

Kind(D)j,r demand for capital by indj,r

K futurej,r future capital stock specific to indj,r

Knew(S)j,r new capital constructed by indj,r

K(S)j,r capital endowment specific to production by indj,r

Lind(D)j,r demand for labour by indj,r

L(S)r supply of labour by hshr

LCI i labour cost index of type i ∈ PINDEX

LST vr lump-sum transfers from government to hshr (with valuation v)

MPI i imports deflator index of type i ∈ PINDEX

Nhsh(D)r quantity of leisure demanded by hshr

NRORj,r net rate of return on Kj,r

NRORfuturej,r expected net rate of return on indj,r’s capital

OPI i output price index of type i ∈ PINDEX

PEXP ,c,forg price of export product g paid by for in c currency terms (the f.o.b.

export price)

PEXPg basic price of export product g

P F,indj,r price of composite factor paid by indj,r

PFOR,c,impg price of foreign product g paid by impg in c currency terms

PFORg basic price of foreign product g (the c.i.f. foreign currency price)

P IMPg (≡ PQ

g,imp) basic price of imported product g

PKj,r rental rate on Kj,r paid to hshr

PK,newj,r construction cost of Knew

j,r

PL,wr w wage rate paid to hshr

PQg,r basic price of product g from region r (≡ output price for indj,r

with g = j)

PQ,agent(p)• price of (composite) product paid by agent (for the purpose of p if

agent = ind)



Qagent(p)(D)• (composite) product demanded by agent (for the purpose of p if

agent = ind)

Qagent(p)(D)• demand for undelivered (composite) product by agent (for the pur-

pose of p if agent = ind)

Q(S)g,r output (supply) of product g in region r (by indj,r with g = j)

Q(S)g,s supply of undelivered product g from source s

SAV w,vr w saving of hshr (with valuation v)

SPN vr expenditure of hshr (with valuation v)

TIME r time endowment of hshr

TPI i net trade deflator index of type i ∈ PINDEX

tEXPg export tax rate on product g (negative for subsidy)

tFORg import tariff rate on foreign product g

tGST ,agentg effective rate of GST faced by agent on product g

tQ,agent(p)• commodity tax rate faced by agent (for the purpose of p if agent =

ind)

tL average labour income tax rate

Ur utility of hshr

Wwr real w income wage rate

XPI i exports deflator index of type i ∈ PINDEX

δj,r depreciation rate on Kj,r

φ nominal exchange rate (domestic $ per foreign $)

ϕ real exchange rate

ψEXPg co-efficient reflecting the height of the export demand curve for

product g

Ω economy-wide average expected net rate of return on capital

Ξi Price index of type i ∈ PINDEX

RA/Bx Ratio of variable Ax to Bx

X•x,y/z Share of •x,y in •z (percent)

B LIST OF MODEL EQUATIONS 57

B List of Model Equations

This appendix lists all the equations of the core model. Note that the form of the equa-

tions appear slightly different to those in the main body because these are closer to the

computer-representation, while the equations in the text were simplified for clarity.

B.0.1 Household Demand Functions

Qhsh(D)r = Qhsh



r ;σhshr ) (48)

Nhsh(D)r = Nhsh



r ;σhshr ) (49)

Qhsh(D)g,r = Qhsh

g,r (Qhsh(D)r , PQ,hsh

h,r |h ;σQ,hshr ) (50)

Qhsh(D)g,o,r = Qhsh

g,o,r(Qhsh(D)g,r , PQ,hsh

g,a,r |a∈ORG ;σhshg,r ) (51)

Qhsh(D)g,x,r = Qhsh

g,x,r(Qhsh(D)g,dom,r, P

Q,hshg,y,r |y ;σhsh

g,dom,r) (52)

B.0.2 Industry Demand Functions

Qind(D)j,r = Qind

j,r (Q(S)g,r , P

Q,indj,r , P F,ind

j,r ;σindj,r ) (53)

Find(D)j,r = F ind

j,r (Q(S)g,r , P

Q,indj,r , P F,ind

j,r ;σindj,r ) (54)

Lind(D)j,r = Lind


r , PKj,r;σ

F,indj,r ) (55)

Kind(D)j,r = K ind


r , PKj,r;σ

F,indj,r ) (56)

Qind(p)(D)g,j,r = Q

ind(p)g,j,r (Q

ind(D)j,r , P

Q,ind(p)h,j,r |h ;σ

Q,ind(p)j,r ) (57)

Qind(p)(D)g,o,j,r = Q

ind(p)g,o,j,r (Q

ind(p)(D)g,j,r , P

Q,ind(p)g,a,j,r |a∈ORG ;σ

ind(p)g,j,r ) (58)

Qind(p)(D)g,x,j,r = Q

ind(p)g,x,j,r (Q

ind(p)(D)g,dom,j,r , P

Q,ind(p)g,y,j,r |y ;σ

ind(p)g,dom,j,r) (59)

B.0.3 Government Demand Functions

Qgov(D)g = Qgov

g (Gb$, PQ,govh |h ;σQ,gov) (60)

Qgov(D)g,o = Qgov

g,o (Qgov(D)g , PQ,gov

g,a |a∈ORG ;σgovg ) (61)

Qgov(D)g,x = Qgov

g,x (Qgov(D)g,dom , PQ,gov

g,y |y ;σgovg,dom) (62)


B.0.4 Exporting and Importing

Qexp(D)g = EXP (S)

g (63)

Qexp(D)g,x = Qexp

g,x (Qexp(D)g , PQ,exp

g,y |y ;σexpg ) (64)

FORimp(D)g = IMP (S)

g (65)

PEXP ,f$,forg =

(EXP for(D)

g

)−1/εEXPg

· ψEXPg (66)

B.0.5 Investment Allocation

GRORj,r = 100×PKj,r

PK,newj,r

(67)

NRORj,r = GRORj,r − δj,r (68)

RGROR/NRORj,r =

GRORj,r

NRORj,r

(69)

RKnew/Kfuturej,r = 100×

Knew(S)j,r

K futurej,r

(70)

K futurej,r =

(1− δj,r

100

)K

(S)j,r +K

new(S)j,r (71)

NRORfuturej,r =

(K futurej,r

K(S)j,r

)−βj,r· NRORj,r (72)

NRORfuturej,r = Ω ∀j ∈ ENDIND (73)


B.0.6 Market Clearing Conditions

Q(S)g,s =

∑r

Qhsh(D)g,s,r +

∑p

∑j

∑r

Qind(p)(D)g,s,j,r + Qgov(D)

g,s + Qexp(D)g,s (74)

K(S)j,r = K

ind(D)j,r (75)

L(S)r =

∑j

Lind(D)j,r (76)

Knew(S)j,r = I

ind(D)j,r (77)

EXP (S)g = EXP for(D)

g (78)

FOR(S)g = FORimp(D)

g (79)

B.0.7 Zero Pure Profit Conditions

PQg,r · Q(S)

g,r = PQ,indj,r ·Qind(D)

j,r + P F,indj,r · F ind(D)

j,r ∀g = j (80)

PK,newj,r ·Knew(S)

j,r =∑g

PQ,ind(K)g,j,r ·Qind(K)(D)

g,j,r (81)

P IMPg · IMP (S)

g = PFOR,d$,impg · FORimp(D)

g (82)

PEXPg · EXP (S)

g = PQ,expg ·Qexp(D)

g (83)


B.0.8 Purchase Prices

PQ,hshr ·Qhsh(D)

r =∑g

PQ,hshg,r ·Qhsh(D)

g,r (84)

PQ,hshg,r ·Qhsh(D)

g,r =∑o

PQ,hshg,o,r ·Qhsh(D)

g,o,r (85)

PQ,hshg,dom,r ·Q

hsh(D)g,dom,r =

∑x

PQ,hshg,x,r ·Qhsh(D)

g,x,r (86)

PQ,hshg,s,r ·Qhsh(D)

g,s,r = PQg,s · (1 + tQ,hshg,r + tGST ,hsh

g ) · Qhsh(D)g,s,r (87)

PQ,indj,r ·Qind(D)

j,r =∑g

PQ,ind(Q)g,j,r ·Qind(Q)(D)

g,j,r (88)

P F,indj,r · F ind(D)

j,r = PL,btaxr · Lind(D)

j,r + PKj,r ·K

ind(D)j,r (89)

PQ,ind(p)g,j,r ·Qind(p)(D)

g,j,r =∑o

PQ,ind(p)g,o,j,r ·Qind(p)(D)

g,o,j,r (90)

PQ,ind(p)g,dom,j,r ·Q

ind(p)(D)g,dom,j,r =

∑x

PQ,ind(p)g,x,j,r ·Qind(p)(D)

g,x,j,r (91)

PQ,ind(p)g,s,j,r ·Qind(p)(D)

g,s,j,r = PQg,s · (1 + t

Q,ind(p)g,j,r ) · Qind(p)(D)

g,s,j,r (92)

PQ,govg ·Qgov(D)

g =∑o

PQ,govg,o ·Qgov(D)

g,o (93)

PQ,govg,dom ·Q

gov(D)g,dom =

∑x

PQ,govg,x ·Qgov(D)

g,x (94)

PQ,govg,s ·Qgov(D)

g,s = PQg,s · (1 + tQ,govg ) · Qgov(D)

g,s (95)

PQ,expg ·Qexp(D)

g =∑x

PQ,expg,x ·Qexp(D)

g,x (96)

PQ,expg,x ·Qexp(D)

g,x = PQg,x · (1 + tQ,expg + tGST ,exp

g ) · Qexp(D)g,x (97)

PEXPg = (1− tEXP

g ) · PEXP ,d$,forg (98)

PFOR,f$,impg = (1 + tFOR

g ) · PFORg (99)

PEXP ,d$,forg = φ · PEXP ,f$,for

g (100)

PFOR,d$,impg = φ · PFOR,f$,imp

g (101)

PL,ataxr = PL,btax

r (1− tL) (102)

φ = 1 (103)


B.0.9 Household Endowment Income, Expenditure, Income, and Saving

SPN c$r =

APCwr

100· INCw,c$

r ∀r ∈ NZREG (104)

SPN c$r = PQ,hsh

r ·Qhsh(D)r (105)

SPN b$r = [PQ,hsh

r ] ·Qhsh(D)r (106)

SPN bQr = PQ,hsh

r · [Qhsh(D)r ] (107)

SPN vNZ =

∑r

SPN vr (108)

INCw,vr = WAGESw,vr + KRENTS vr + LST v

r ∀r ∈ NZREG (109)

SAV w,vr = INCw,v

r − SPN vr ∀r ∈ NZREG (110)

APCwr = 100− APSwr ∀r ∈ NZREG (111)

ENDINCw,c$r = PL,w

r · TIME r +∑j

PKj,r ·K

(S)j,r + LST c$

r (112)

ENDINCw,b$r = [PL,w

r ] · TIME r +∑j

[PKj,r] ·K

(S)j,r + LSTb$

r (113)

ENDINCw,bQr = PL,w

r · [TIME r] +∑j

PKj,r · [K

(S)j,r ] + LSTbQ

r (114)

L(S)r = TIME r −Nhsh(D)

r (115)

B.0.10 Domestic Expenditure on GDP

Cv =∑r

SPN vr (116)

Ic$ =∑j

∑r

PK,newj,r · I ind(D)

j,r (117)

Ib$ =∑j

∑r

[PK,newj,r ] · I ind(D)

j,r (118)

IbQ =∑j

∑r

PK,newj,r · [I ind(D)

j,r ] (119)

Gc$ =∑g

PQ,govg ·Qgov(D)

g (120)

GbQ =∑g

PQ,govg · [Qgov(D)

g ] (121)


B.0.11 Trade Flows and the Trade Balance

EXPc$,d$,bas =∑g

PEXPg · EXP (S)

g (122)

EXPc$,f$,bas =1

φ· EXPc$,d$,bas (123)

EXPc$,c,fob =∑g

PEXP ,c,forg · EXP for(D)

g (124)

IMPc$,c,bas =∑g

PFOR,c,impg · FORimp(D)

g (125)

IMPc$,f$,cif =∑g

PFORg · FOR(S)

g (126)

IMPc$,d$,cif = φ · IMPc$,f$,cif (127)

EXPb$,d$,bas =∑g

[PEXPg ] · EXP (S)

g (128)

EXPb$,f$,bas =1

[φ]· EXPc$,d$,bas (129)

EXPb$,c,fob =∑g

[PEXP ,c,forg ] · EXP for(D)

g (130)

IMPb$,c,bas =∑g

[PFOR,c,impg ] · FORimp(D)

g (131)

IMPb$,f$,cif =∑g

[PFORg ] · FOR(S)

g (132)

IMPb$,d$,cif = [φ] · IMPc$,f$,cif (133)

TRDBALv,c = EXPv,c,fob − IMPc$,c,cif (134)


B.0.12 Government Revenue and the Fiscal Balance

INCTAX c$ = tL ·∑j

∑r

PL,btaxr · Lind(D)

j,r (135)

DUTY c$ =∑g

tFORg · φ · PFOR

g · FORimp(D)g (136)

EXPSUBc$ = −∑g

tEXPg · PEXP ,d$,for

g · EXP (S)g (137)

COMTAX c$ =∑g

∑s

∑p

∑j

∑r

(PQg,s · tQ,hshg,r · Qhsh(D)

g,s,r

+PQg,s · t

Q,ind(p)g,j,r · Qind(p)(D)

g,s,j,r + PQg,s · tQ,govg · Qgov(D)

g,s + PQg,r · tQ,expg · Qexp(D)

g,r

)(138)

GST c$ =∑g

∑s

∑r

(PQg,s · tGST ,hsh

g · Qhsh(D)g,s,r + PQ

g,r · tGST ,expg · Qexp(D)

g,r

)(139)

INCTAX b$ = tL ·∑j

∑r

[PL,btaxr ]L

ind(D)j,r (140)

DUTY b$ =∑g

tFORg · [φ] · [PFOR

g ] · FORimp(D)g (141)

EXPSUBb$ = −∑g

tEXPg · [PEXP ,d$,for

g ] · EXP (S)g (142)

COMTAX b$ =∑g

∑s

∑p

∑j

∑r

([PQg,s] · tQ,hshg,r · Qhsh(D)

g,s,r

+[PQg,s] · t

Q,ind(p)g,j,r · Qind(p)(D)

g,s,j,r + [PQg,s] · tQ,govg · Qgov(D)

g,s + [PQg,r] · tQ,expg · Qexp(D)

g,r

)(143)

GSTb$ =∑g

∑s

∑r

([PQg,s] · tGST ,hsh

g · Qhsh(D)g,s,r + [PQ

g,r] · tGST ,expg · Qexp(D)

g,r

)(144)

GOVREV v = INCTAX v + DUTY v + COMTAX v + GST v − EXPSUBv (145)

GOVBALv = GOVREV v −Gv −∑r

LST vr (146)


B.0.13 Labour Market Measures

EMP j,r = [PL,btaxr ] · Lind(D)

j,r (147)

EMP j,NZ =∑r

EMP j,r (148)

EMP r =∑j

EMP j,r ∀r ∈ NZREG (149)

B.0.14 Factor Incomes and Gross Value Added

WAGESw,c$j,r = PL,w

r · Lind(D)j,r (150)

WAGESw,b$j,r = [PL,w

r ] · Lind(D)j,r (151)

WAGESw,bQj,r = PL,wr · [Lind(D)

j,r ] (152)

WAGESw,vj,NZ =∑r

WAGESw,vj,r (153)

WAGESw,vr =∑j

WAGESw,vj,r ∀r ∈ NZREG (154)

KRENTS c$j,r = PK

j,r ·Kind(D)j,r (155)

KRENTSb$j,r = [PK

j,r] ·Kind(D)j,r (156)

KRENTSbQj,r = PK

j,r · [Kind(D)j,r ] (157)

KRENTS vj,NZ =∑r

KRENTS vj,r (158)

KRENTS vr =∑j

KRENTS vj,r ∀r ∈ NZREG (159)

GVAvj,r = WAGESbtax,v

j,r + KRENTS vj,r ∀r ∈ NZREG (160)

GVAvr = WAGESbtax,v

r + KRENTS vr ∀r ∈ NZREG (161)


B.0.15 Output and Investment

Ic$j,r = PK,new

j,r · I ind(D)j,r (162)

Ib$j,r = [PK,new

j,r ] · I ind(D)j,r (163)

IbQj,r = PK,newj,r · [I ind(D)

j,r ] (164)

Ivj,NZ =∑r

Ivj,r (165)

Ivr =∑j

Ivj,r ∀r ∈ NZREG (166)

OUTPUT c$j,r = PQ

g,r · Q(S)g,r ∀g = j (167)

OUTPUTb$j,r = [PQ

g,r] · Q(S)g,r ∀g = j (168)

OUTPUTbQj,r = PQ

g,r · [Q(S)g,r ] ∀g = j (169)

OUTPUT vj,NZ =

∑r

OUTPUT vj,r (170)

OUTPUT vr =

∑j

OUTPUT vj,r ∀r ∈ NZREG (171)

B.0.16 Capital Stocks and Net Returns

KSTOCK j,r = K(S)j,r (172)

KSTOCK j,NZ =∑r

K(S)j,r (173)

KSTOCK r =∑j

K(S)j,r ∀r ∈ NZREG (174)

NRTRN j,r = KRENTS c$j,r −

δj,r100· PK,new

j,r ·K(S)j,r (175)

NRTRN j,NZ =∑r

NRTRN j,r (176)

NRTRN r =∑j

NRTRN j,r ∀r ∈ NZREG (177)

NRORj,NZ = 100 · NRTRN j,NZ∑r P

K,newj,r ·K(S)

j,r

(178)

NRORr = 100 · NRTRN r∑j P

K,newj,r ·K(S)

j,r

∀r ∈ NZREG (179)


B.0.17 Measures of GDP and Domestic Saving

GDPEXPc$r =

∑g

∑x

PQ,hshg,r,x ·Qhsh(D)

g,r,x +∑g

∑j

∑x

PQ,ind(K)g,r,j,x ·Qind(K)(D)

g,r,j,x

+∑g

PQ,govg,r ·Qgov(D)

g,r +∑g

PQ,expg,r ·Qexp(D)

g,r (180)

GDPEXPb$r =

∑g

∑x

[PQ,hshg,r,x ] ·Qhsh(D)

g,r,x +∑g

∑j

∑x

[PQ,ind(K)g,r,j,x ] ·Qind(K)(D)

g,r,j,x

+∑g

[PQ,govg,r ] ·Qgov(D)

g,r +∑g

[PQ,expg,r ] ·Qexp(D)

g,r (181)

GDPEXPv = Cv + Iv +Gv + TRDBALv,d$ (182)

GDPINC v =∑r

INC btax,vr + GOVREV v −

∑r

LST vr (183)

GDPVAv = GVAv + GOVREV v − INCTAX v (184)

SAV v =∑r

SAV atax,vr + GOVBALv (185)

B.0.18 Price Indices

ΞF =√

ΞP · ΞL (186)

GDPDEFP =GDPEXPc$

GDPEXPb$(187)

GDPDEFL =GDPEXPbQ

[GDPEXPc$](188)

CPIP =Cc$

Cb$(189)

CPI L =CbQ

[Cc$](190)

IPIP =Ic$

Ib$(191)

IPI L =IbQ

[Ic$](192)


GPIP =Gc$

Gb$(193)

GPI L =GbQ

[Gc$](194)

XPIP,t =EXPc$,d$,t

EXPb$,d$,t∀t ∈ bas, fob (195)

XPI L,t =EXPbQ,d$,t

[EXPc$,d$,t]∀t ∈ bas, fob (196)

MPIP,t =IMPc$,d$,t

IMPb$,d$,t∀t ∈ bas, cif (197)

MPI L,t =IMPbQ,d$,t

[IMPc$,d$,t]∀t ∈ bas, cif (198)

TPIP =TRDBALc$,d$

TRDBALb$,d$(199)

TPI L =TRDBALbQ,d$

[TRDBALc$,d$](200)

CPIPr =SPN c$

r

SPN b$r

(201)

CPI Lr =SPN bQ

r

[SPN c$r ]

(202)

IPIPr =I c$r

I b$r

(203)

IPI Lr =I bQr[I c$r ]

(204)

IPIPj =I c$j

I b$j

(205)

IPI Lj =I bQj

[I c$j ]

(206)

OPIPr =OUTPUT c$

r

OUTPUTb$r

∀r ∈ NZREG (207)

OPI Lr =OUTPUTbQ

r

[OUTPUT c$r ]

∀r ∈ NZREG (208)

OPIPj =OUTPUT c$

j,NZ

OUTPUTb$j,NZ

(209)

OPI Lj =OUTPUTbQ

j,NZ

[OUTPUT c$j,NZ]

(210)


LCIP,wr =WAGESw,c$

r

WAGESw,b$r

∀r ∈ NZREG (211)

LCI L,wr =WAGESw,bQr

[WAGESw,c$r ]

∀r ∈ NZREG (212)

LCIP,wj =WAGESw,c$

j,NZ

WAGESw,b$j,NZ

(213)

LCI L,wj =WAGESw,bQj,NZ

[WAGESw,c$j,NZ]

(214)

B.0.19 Real Prices

Wwr =

LCI F,wr

CPI Fr∀r ∈ NZREG (215)

ϕ = φ · MPI F,cif

GDPDEFF(216)

B.0.20 Household Welfare

CV r =Ur − [Ur]

Ur·(ENDINC atax,c$

r − SAV atax,c$r

)(217)

EV r =Ur − [Ur]

[Ur]·(

[ENDINC atax,c$r ]− [SAV atax,c$

r ])

(218)

B.0.21 Selected Shares and Ratios

XI,b$j,r/tot = 100×

[PK,newj,r ] · I ind(D)

j,r

Ib$(219)

XGDP ,vC/tot = 100× Cv

GDPEXP(220)

XGDP ,vI/tot = 100× Iv

GDPEXP(221)

XGDP ,vG/tot = 100× Gv

GDPEXP(222)

XGDP ,vEXP/tot = 100× EXPv,d$,fob

GDPEXP(223)

XGDP ,vIMP/tot = 100× IMPv,d$,cif

GDPEXP(224)

RKSTOCK rEMPr

=KSTOCK r

EMP r

(225)

C ADDITIONAL RESULTS 69

C Additional Results


Output By Industry — Region AKL


A B C D

AGRI 737 -0.4 -0.5 -0.5 -0.5

FOLO 169 -0.5 -0.8 -0.8 -0.7

FISH 82 -0.5 -0.6 -0.6 -0.6

MINE 97 -0.3 -0.6 -0.5 -0.5

OIGA 15 -0.1 -0.2 -0.1 -0.1

PETR 0 0.0 0.0 0.0 0.0

FDBT 5648 -0.3 -0.1 -0.3 -0.3

TWPM 4032 -0.4 -0.3 -0.4 -0.4

CHNM 5346 -0.4 -0.5 -0.5 -0.5

METL 3117 -0.3 -0.4 -0.4 -0.4

EQFO 4836 -0.3 -0.3 -0.4 -0.3

UTIL 3099 -0.2 -0.1 -0.2 -0.2

CONS 9014 -0.4 -0.3 -0.4 -0.4

ACCR 1862 -0.2 0.1 -0.1 -0.1

CMIF 10852 0.1 0.4 0.1 0.1

PROP 5143 -0.1 -0.1 -0.1 -0.1

RBUS 11056 0.0 0.8 0.2 0.2

GOVT 3060 0.6 2.9 1.5 1.4

EDUC 2782 -0.1 0.3 0.0 0.0

HEAL 3613 0.0 0.6 0.2 0.2

CUPE 4261 -0.5 -0.8 -0.7 -0.7

OWND 4300 -0.2 -0.4 -0.3 -0.3

WHOL 10581 -0.3 -0.4 -0.4 -0.4

RETT 5809 -0.5 -0.7 -0.6 -0.6

TRAN 6639 -0.6 -0.9 -0.8 -0.7

All 106148 -0.2 -0.1 -0.2 -0.2

Table 30: Output By Industry — Region AKL


Output By Industry — Region WLG


A B C D

AGRI 169 -1.3 -8.9 -4.5 -4.0

FOLO 56 -1.6 -8.3 -4.4 -3.9

FISH 16 -1.3 -6.3 -3.4 -3.1

MINE 12 -1.0 -6.5 -3.3 -2.9

OIGA 232 -0.1 -0.2 -0.1 -0.1

PETR 0 0.0 0.0 0.0 0.0

FDBT 958 -0.9 -5.8 -2.9 -2.7

TWPM 893 -1.1 -8.8 -4.3 -3.8

CHNM 1059 -0.8 -5.4 -2.7 -2.4

METL 498 -0.9 -7.3 -3.5 -3.1

EQFO 850 -1.0 -9.5 -4.5 -4.0

UTIL 2173 -0.8 -2.3 -1.5 -1.4

CONS 2865 -0.8 -4.2 -2.2 -2.0

ACCR 649 -1.9 -8.7 -4.8 -4.3

CMIF 4891 -4.9 -9.0 -6.6 -6.4

PROP 1326 -6.8 -8.3 -7.4 -7.3

RBUS 3924 -3.9 -11.2 -6.9 -6.5

GOVT 4799 -1.4 -6.0 -3.3 -3.0

EDUC 910 -0.8 -8.0 -3.8 -3.4

HEAL 1393 -1.2 -6.5 -3.4 -3.1

CUPE 1691 -0.4 -1.9 -1.0 -0.9

OWND 1500 -2.7 -1.3 -2.1 -2.2

WHOL 1923 0.0 -1.4 -0.6 -0.5

RETT 1765 1.0 2.7 1.6 1.5

TRAN 1529 -2.1 -7.0 -4.2 -3.9

All 36082 -2.0 -5.9 -3.7 -3.4

Table 31: Output By Industry — Region WLG


Output By Industry — Region ONI


A B C D

AGRI 9933 -0.3 -0.3 -0.4 -0.4

FOLO 1898 -0.4 -0.6 -0.6 -0.6

FISH 273 -0.4 -0.6 -0.6 -0.6

MINE 597 -0.3 -0.5 -0.4 -0.4

OIGA 2232 -0.1 -0.2 -0.1 -0.1

PETR 3683 -0.3 -0.4 -0.4 -0.4

FDBT 10937 -0.2 0.0 -0.2 -0.3

TWPM 4846 -0.3 -0.2 -0.3 -0.4

CHNM 205 -0.3 -0.5 -0.5 -0.5

METL 2518 -0.3 -0.3 -0.3 -0.4

EQFO 2561 -0.2 -0.1 -0.3 -0.3

UTIL 5013 -0.2 -0.1 -0.2 -0.2

CONS 10225 -0.4 -0.3 -0.4 -0.4

ACCR 1985 -0.1 0.2 -0.1 -0.1

CMIF 4053 0.1 0.5 0.2 0.1

PROP 3949 0.0 0.0 -0.1 -0.1

RBUS 5308 0.0 0.9 0.3 0.2

GOVT 3430 0.6 3.0 1.5 1.4

EDUC 2590 0.0 0.4 0.1 0.0

HEAL 4240 0.0 0.7 0.3 0.2

CUPE 3090 -0.6 -1.0 -0.8 -0.8

OWND 3742 -0.3 -0.5 -0.4 -0.3

WHOL 3810 -0.4 -0.5 -0.5 -0.5

RETT 5932 -0.6 -0.9 -0.8 -0.8

TRAN 3283 -0.5 -0.8 -0.7 -0.7

All 100332 -0.2 -0.1 -0.2 -0.3

Table 32: Output By Industry — Region ONI


Output By Industry — Region CAN


A B C D

AGRI 1038 -0.4 -0.3 -0.4 -0.4

FOLO 127 -0.5 -0.6 -0.6 -0.6

FISH 22 -0.5 -0.5 -0.5 -0.6

MINE 117 -0.3 -0.5 -0.4 -0.4

OIGA 0 0.0 0.0 0.0 0.0

PETR 0 0.0 0.0 0.0 0.0

FDBT 2533 -0.3 0.0 -0.2 -0.3

TWPM 1453 -0.3 -0.2 -0.3 -0.3

CHNM 1657 -0.3 -0.4 -0.4 -0.4

METL 1083 -0.3 -0.3 -0.3 -0.3

EQFO 1474 -0.3 -0.1 -0.3 -0.3

UTIL 856 -0.2 -0.1 -0.2 -0.2

CONS 3180 -0.4 -0.2 -0.4 -0.4

ACCR 705 -0.2 0.3 0.0 -0.1

CMIF 2683 0.1 0.5 0.2 0.2

PROP 1664 0.0 0.0 -0.1 -0.1

RBUS 2414 0.0 1.0 0.4 0.3

GOVT 1166 0.6 3.0 1.6 1.4

EDUC 901 -0.1 0.5 0.1 0.1

HEAL 1567 0.0 0.7 0.3 0.3

CUPE 1288 -0.5 -0.9 -0.7 -0.7

OWND 1329 -0.2 -0.4 -0.3 -0.3

WHOL 2388 -0.3 -0.4 -0.4 -0.4

RETT 2005 -0.5 -0.8 -0.7 -0.7

TRAN 2006 -0.6 -0.6 -0.6 -0.6

All 33657 -0.2 0.0 -0.2 -0.2

Table 33: Output By Industry — Region CAN


Output By Industry — Region OSI


A B C D

AGRI 5006 -0.3 0.0 -0.2 -0.3

FOLO 728 -0.4 -0.4 -0.5 -0.5

FISH 460 -0.4 -0.4 -0.5 -0.5

MINE 374 -0.3 -0.3 -0.3 -0.3

OIGA 0 0.0 0.0 0.0 0.0

PETR 0 0.0 0.0 0.0 0.0

FDBT 7605 -0.2 0.1 -0.2 -0.2

TWPM 1832 -0.3 0.1 -0.2 -0.2

CHNM 811 -0.3 -0.3 -0.3 -0.4

METL 889 -0.2 -0.1 -0.2 -0.2

EQFO 912 -0.2 0.2 -0.1 -0.1

UTIL 1468 -0.2 0.0 -0.2 -0.2

CONS 4022 -0.3 -0.1 -0.3 -0.3

ACCR 1253 -0.1 0.5 0.1 0.0

CMIF 1548 0.1 0.6 0.3 0.2

PROP 1577 0.0 0.0 0.0 0.0

RBUS 1829 0.1 1.2 0.5 0.4

GOVT 1006 0.6 3.3 1.7 1.5

EDUC 1047 0.0 0.7 0.2 0.2

HEAL 1738 0.0 0.8 0.4 0.3

CUPE 1433 -0.6 -1.0 -0.8 -0.8

OWND 1601 -0.3 -0.6 -0.4 -0.4

WHOL 1786 -0.4 -0.4 -0.4 -0.4

RETT 2487 -0.6 -1.1 -0.9 -0.8

TRAN 2016 -0.5 -0.5 -0.6 -0.6

All 43428 -0.2 0.0 -0.2 -0.2

Table 34: Output By Industry — Region OSI


Employment By Industry — Region AKL (FTEs)

Industry Baseyear (000s)% ∆ on Baseyear

A B C D

AGRI 8 -0.3 0.4 -0.2 -0.2

FOLO 0 -0.4 0.2 -0.4 -0.3

FISH 0 -0.3 0.5 -0.2 -0.1

MINE 0 -0.3 0.3 -0.2 -0.2

OIGA 0 0.0 0.0 0.0 0.0

PETR 0 0.0 0.0 0.0 0.0

FDBT 12 -0.2 0.8 0.0 0.0

TWPM 23 -0.2 0.6 -0.1 0.0

CHNM 10 -0.2 0.4 -0.1 -0.1

METL 10 -0.2 0.5 -0.1 -0.1

EQFO 19 -0.2 0.6 -0.1 0.0

UTIL 2 -0.1 1.4 0.3 0.3

CONS 43 -0.3 0.7 0.0 0.0

ACCR 25 0.0 1.0 0.2 0.2

CMIF 38 0.7 2.4 1.2 1.2

PROP 17 0.4 1.4 0.6 0.7

RBUS 81 0.3 2.1 0.9 0.8

GOVT 19 0.9 4.2 2.1 2.0

EDUC 38 0.0 0.9 0.3 0.3

HEAL 47 0.2 1.4 0.6 0.6

CUPE 25 -0.3 0.1 -0.3 -0.2

OWND 0 0.0 0.0 0.0 0.0

WHOL 43 -0.1 0.7 0.1 0.1

RETT 49 -0.3 0.1 -0.3 -0.3

TRAN 22 -0.3 0.3 -0.3 -0.2

All 533 0.1 1.2 0.4 0.4

Table 35: Employment By Industry — Region AKL


Employment By Industry — Region WLG (FTEs)


A B C D

AGRI 4 -1.7 -18.7 -9.2 -8.1

FOLO 0 -2.1 -19.6 -9.8 -8.7

FISH 0 -1.7 -17.8 -8.8 -7.7

MINE 0 -1.7 -18.6 -9.1 -8.0

OIGA 0 -0.7 -12.7 -6.0 -5.2

PETR 0 0.0 0.0 0.0 0.0

FDBT 3 -1.3 -17.5 -8.4 -7.4

TWPM 7 -1.5 -19.3 -9.4 -8.2

CHNM 3 -1.2 -16.7 -8.0 -7.0

METL 2 -1.4 -18.5 -9.0 -7.8

EQFO 3 -1.4 -19.5 -9.4 -8.2

UTIL 1 -1.1 -14.8 -7.2 -6.3

CONS 15 -1.0 -15.2 -7.3 -6.3

ACCR 10 -1.5 -17.5 -8.5 -7.5

CMIF 16 -2.2 -16.5 -8.5 -7.6

PROP 5 -3.2 -16.7 -9.1 -8.2

RBUS 33 -2.1 -17.8 -9.0 -8.0

GOVT 22 0.2 -11.4 -4.9 -4.1

EDUC 15 0.0 -11.5 -5.1 -4.3

HEAL 18 0.6 -10.4 -4.2 -3.5

CUPE 11 1.1 -8.7 -3.3 -2.6

OWND 0 0.0 0.0 0.0 0.0

WHOL 8 0.1 -10.7 -4.7 -4.0

RETT 18 2.8 -3.2 0.1 0.5

TRAN 6 -0.8 -15.6 -7.3 -6.3

All 200 -0.5 -13.4 -6.2 -5.3

Table 36: Employment By Industry — Region WLG


Employment By Industry — Region ONI (FTEs)


A B C D

AGRI 62 -0.1 0.8 0.0 -0.1

FOLO 3 -0.2 0.5 -0.1 -0.2

FISH 1 -0.2 0.8 0.0 -0.1

MINE 2 -0.1 0.6 0.0 -0.1

OIGA 0 -0.1 0.6 0.0 -0.1

PETR 6 -0.1 0.9 0.1 0.0

FDBT 22 0.0 1.1 0.2 0.1

TWPM 19 -0.1 0.9 0.1 0.0

CHNM 3 -0.1 0.7 0.0 -0.1

METL 8 -0.1 0.8 0.1 -0.1

EQFO 13 0.0 1.0 0.2 0.0

UTIL 4 0.0 1.6 0.4 0.3

CONS 48 -0.2 1.0 0.1 0.0

ACCR 26 0.1 1.3 0.4 0.2

CMIF 15 0.8 2.6 1.4 1.2

PROP 14 0.5 1.7 0.8 0.6

RBUS 45 0.4 2.4 1.1 0.8

GOVT 22 1.0 4.5 2.3 2.0

EDUC 39 0.1 1.1 0.4 0.3

HEAL 55 0.3 1.6 0.7 0.5

CUPE 20 -0.3 0.1 -0.3 -0.4

OWND 0 0.0 0.0 0.0 0.0

WHOL 18 -0.1 0.8 0.1 0.0

RETT 53 -0.4 0.0 -0.4 -0.4

TRAN 16 -0.2 0.5 -0.1 -0.2

All 514 0.1 1.3 0.4 0.3

Table 37: Employment By Industry — Region ONI


Employment By Industry — Region CAN (FTEs)


A B C D

AGRI 16 -0.2 0.8 0.1 0.0

FOLO 0 -0.3 0.6 0.0 -0.1

FISH 0 -0.2 0.9 0.1 0.0

MINE 0 -0.2 0.7 0.1 0.0

OIGA 0 0.0 0.0 0.0 0.0

PETR 0 0.0 0.0 0.0 0.0

FDBT 9 -0.1 1.2 0.3 0.2

TWPM 9 -0.2 1.0 0.2 0.1

CHNM 4 -0.2 0.8 0.1 0.0

METL 3 -0.2 0.9 0.2 0.1

EQFO 9 -0.1 1.0 0.3 0.2

UTIL 1 -0.1 1.7 0.5 0.4

CONS 19 -0.2 1.0 0.2 0.1

ACCR 13 0.0 1.3 0.4 0.3

CMIF 10 0.7 2.7 1.4 1.3

PROP 7 0.4 1.8 0.9 0.8

RBUS 23 0.3 2.5 1.1 1.0

GOVT 8 0.9 4.5 2.4 2.1

EDUC 15 0.1 1.2 0.5 0.4

HEAL 23 0.2 1.7 0.8 0.7

CUPE 10 -0.3 0.3 -0.1 -0.2

OWND 0 0.0 0.0 0.0 0.0

WHOL 13 -0.1 1.0 0.3 0.2

RETT 23 -0.4 0.2 -0.2 -0.3

TRAN 10 -0.3 0.7 0.0 -0.1

All 227 0.1 1.5 0.6 0.5

Table 38: Employment By Industry — Region CAN


Employment By Industry — Region OSI (FTEs)


A B C D

AGRI 24 -0.1 1.4 0.5 0.4

FOLO 1 -0.2 1.2 0.4 0.2

FISH 1 -0.1 1.4 0.5 0.3

MINE 1 -0.1 1.3 0.5 0.3

OIGA 0 0.0 0.0 0.0 0.0

PETR 0 0.0 0.0 0.0 0.0

FDBT 11 0.0 1.8 0.7 0.5

TWPM 7 0.0 1.7 0.6 0.5

CHNM 2 -0.1 1.3 0.5 0.3

METL 3 0.0 1.5 0.5 0.4

EQFO 4 0.0 1.7 0.6 0.5

UTIL 1 0.0 2.2 0.8 0.7

CONS 17 -0.1 1.6 0.5 0.4

ACCR 14 0.1 1.9 0.8 0.6

CMIF 6 0.8 3.3 1.8 1.6

PROP 5 0.5 2.3 1.2 1.0

RBUS 16 0.5 3.1 1.5 1.3

GOVT 6 1.0 5.1 2.7 2.4

EDUC 13 0.1 1.5 0.7 0.6

HEAL 20 0.3 2.0 1.0 0.8

CUPE 8 -0.3 0.4 -0.1 -0.2

OWND 0 0.0 0.0 0.0 0.0

WHOL 7 0.0 1.3 0.5 0.3

RETT 20 -0.4 0.2 -0.3 -0.3

TRAN 7 -0.2 1.2 0.3 0.2

All 195 0.1 1.8 0.7 0.6

Table 39: Employment By Industry — Region OSI


Economy-wide Price Measures


A B C D

GDP Deflator 0.6 1.8 1.1 1.1

Consumer Price Index 0.5 1.3 0.9 0.8

Investment Price Index 0.2 0.7 0.4 0.4

Government Price Index 1.0 4.0 2.3 2.1

Export Price Index (F.O.B.) 0.2 0.5 0.4 0.3

Import Price Index (C.I.F.) 0.0 0.0 0.0 0.0

Trade Price Index -2.1 -4.9 -3.5 -3.4

Real Exchange Rate -0.6 -1.8 -1.1 -1.0

Nominal After-Tax Wage -0.2 2.0 0.8 0.7

Real After-Tax Wage -0.7 0.7 0.0 -0.1

Capital Rents Index 1.5 1.9 1.6 1.5

Output Price Index 0.5 1.5 1.0 0.9

Current Net Rate of Return (Average) 2.3 2.1 2.0 2.0

Expected Future Net Rate of Return 0.5 0.3 0.3 0.3

Table 40: Economy-wide Price Measures

REFERENCES 81

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http://researcharchive.vuw.ac.nz/handle/10063/2319

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